proteomic screening of cerebrospinal fluid: candidate ... · pdf fileproteomic screening of...

Proteomic screening of cerebrospinal fluid:

Candidate proteomic biomarkers for

sample stability and experimental

autoimmune encephalomyelitis

Therese Rosenling

Paranymphs: Abdou Sar

Berend Hoekman

The work described in this thesis was performed in the research

group Analytical Biochemistry, Faculy of Mathematics and

Natural Sciences, University of Groningen, and within the

graduate school GUIDE. The studies were performed within the

framework of Top Institute Pharma project D4-102.

Sponsors:

The printing of this thesis was financed by TIPharma

Printed by Ipskamp Drukkers B.V., The Netherlands

Cover:

“Midnight-sun at Dundret” Therese Rosenling 2008

RIJKSUNIVERSITEIT GRONINGEN

Proteomic screening of cerebrospinal fluid:

Candidate proteomic biomarkers for

sample stability and experimental

autoimmune encephalomyelitis

Proefschrift

ter verkrijging van het doctoraat in de

Wiskunde en Natuurwetenschappen

aan de Rijksuniversiteit Groningen

op gezag van de

Rector Magnificus, dr. F. Zwarts,

in het openbaar te verdedigen op

maandag 20 december 2010

om 11.00 uur

door

Ann Therese Isabell Rosenling

geboren op 2 oktober 1980

te Malmberget, Zweden

Promotor: Prof. dr. R.P.H. Bischoff

Beoordelingscommissie: Prof. dr. E.M.J. Verpoorte

Prof. dr. S.S. Wijmenga

Prof. dr. J.M. van Dijl

Table of Contents

Scope of the thesis 1

Chapter 1: General Introduction

1. Introduction 5

2. Why use animal models 6

3. Description of the EAE model 8

4. Why proteomic biomarkers 9

5. Body fluids and tissues 11

5.1. Tissues 11

5.2. Blood samples 11

5.3. Cerebrospinal fluid (CSF) 12

5.4. Extracellular fluid (ECF) 13

6. Proteomic biomarker studies in EAE models 14

7. Connections between discriminatory proteins in

EAE and MScl 16

7.1. Inflammation/immunity 19

7.2. Exitotoxicity 20

7.3. Oxitative stress/iron homeostasis 20

7.4. Structure 21

7.5. Other 21

8. Concluding remarks 25

Chapter 2: The effect of pre-analytical factors on

stability of the proteome and selected metabolites in

cerebrospinal fluid (CSF)

1. Introduction 41

2. Material and Methods 42

2.1. Sample set 42

2.2. Design of study 43

2.3. Sample preparation (proteomic analysis) 43

2.4. Chip LC-MS(/MS) proteomic analysis 44

2.5. Data processing of chip LC-MS(/MS) data 46

2.6. MALDI-FT-ICR-MS and Orbitrap LC-MS/MS

proteomic analysis 48

2.7. GC-MS metabolomic analysis 49

2.8. LC-MS analysis of amino acids 50

3. Results and discussion 51

3.1. Effect of delayed time between sample

collection and freezing on the CSF

proteome and metabolome (Delayed Storage) 51

3.2. Effect of freeze/thaw cycles on the CSF

proteome 57

3.3. Effect of storage at 4˚C after digestion on

CSF proteome analysis 59

4. Conclusions 61

Chapter 3: The impact of delayed storage on the

proteome and metabolome of human cerebrospinal

fluid (CSF)

1. Introduction 70


2.1. Sample set 70

2.2. ChipLC-QTOF MS proteomic analysis 71

2.3. NanoLC Orbitrap-MS/MS shotgun proteomics

analysis 72

2.4. GC-MS metabolomic analysis 73

2.5. NMR metabolomic analysis 73

2.6. LC-MS/MS amino acid analysis 74

3. Results 74

3.1. Proteomic analysis 75

3.2. Metabolomic analysis 78

4. Discussion 79

Chapter 4: Profiling and identification of Cerebrospinal

Fluid (CSF) proteins in a Rat EAE Model of Multiple

Sclerosis

1. Introduction 89


2.1. Induction of acute EAE in the Lewis rat 90

2.2. CSF sampling 92

2.3. Sample preparation 92


2.5. ChipLC-QTOF MS data processing 94

2.6. Protein identification (QTOF data) 96

2.7. Nano-LC-ESI-Orbitrap MS/MS 96

2.8. Orbitrap data processing 97

3. Results 98

4. Discussion 106

Chapter 5: The effect of minocycline on the proteome

profile of cerebrospinal fluid from an acute animal

model of multiple sclerosis.

1. Introduction 121


2.1. Induction of acute EAE 122

2.2. CSF sampling 123

2.3. Sample preparation 123


2.5. ChipLC-QTOF MS data processing 125

3. Results 126

4. Discussion 134

Chapter 6: Summary and future perspectives

Summary and future perspectives 141

Samenvatting 143

Appendices

Appendix 1: Supplementary material Chapter 2 147

Appendix 2: Supplementary material Chapter 4 168

List of publications 173

Acknowledgements 175

1

Scope of the thesis This thesis handles the whole proteome/metabolome screening of cerebrospinal

fluid (CSF). The main focus is the unraveling of the importance of correct sample

handling by the study of stability markers in porcine and human CSF as well as

the exploration of disease markers connected to experimental autoimmune

encephalomyelitis (EAE), modeling the human central nervous system (CNS)

disorder multiple sclerosis (MScl).

Chapter 1 is an introduction to the EAE model. Here the model is

described, and the importance of animal models and biomarker studies is

discussed. The chapter gives an overview of current literature handling proteomic

biomarker studies performed on the EAE model and the translational possibility

to the human disease multiple sclerosis.

In Chapter 2 sample handling procedures are investigated. The proteome

of porcine CSF as well as the metabolome and free-amino acids are screened for

discriminatory differences in abundance after different sample handling

procedures. To mimic a possible clinic situation CSF was left at room temperature

for up to two hours without cellular elements removed. CSF samples were also

exposed to a number of freeze-thaw cycles to mimic the real situation of sample

handling in the laboratory. To imitate the auto-sampler environment, digested

CSF was left at 4 ˚C and analyzed after various time spans to investigate the

quantitative effect on peptides.

Chapter 3 describes a stability study on the proteome, metabolome and

free-amino acids of human CSF left at room temperature for up to two hours after

centrifugation and removal of cellular traces.

Chapter 4 portrays a proteomic biomarker study on CSF from an acute

EAE model in rats. The proteome was screened for disease related markers. The

study was performed on two different platforms with two different data

processing techniques applied on each of the both data sets.

In Chapter 5 a continuation of the study presented in chapter 4 is

described. Here the effect of the tetracycline derivate minocycline is tudied on

previous detected biomarker candidates. The thesis is summed up with

conclusions and future perspectives.

2

Chapter 1

3

General Introduction

The Experimental Autoimmune

Encephalomyelitis model as foundation for

proteomic biomarker studies: From rat to

human.

Therese Rosenling1, Amos Attali2 and Rainer Bischoff1

1Department of Analytical Biochemistry, Centre for Pharmacy University of

Groningen, Groningen, The Netherlands.

2Abbott Healthcare Products B.V., Weesp, the Netherlands

Manuscript in preparation

The EAE model as foundation for proteomic biomarker studies

4

Abstract

Multiple sclerosis (MScl) is defined by central nervous system (CNS)

inflammation, demyelination and axonal damage. Some of the disease

mechanisms are known but the cause of this complex disorder stays an enigma.

Experimental autoimmune encephalomyelitis (EAE) is an animal model

mimicking many aspects of MScl, developed in order to study the pathology in

more detail. This review aims to describe the EAE model and the proteomic

biomarker studies implemented so far. Further connections of discriminatory

proteins discovered in EAE to findings in MScl are described.

Chapter 1

5

1. Introduction

Multiple sclerosis is a CNS disorder characterized by neuroinflammation,

neurodegeneration and myelin degradation. Symptoms of disease range from

sensory changes and fatigue to motor dysfunctions and visual impairments.

Diagnosis is done by clinical examinations aided by cerebrospinal fluid (CSF)

analysis and magnetic resonance imaging (MRI) scans. For a definite MScl

diagnose evidence of relapses separated in time and space is a criterion. The cause

of the disease is yet to be surveyed; what is known is that both environmental

factors as well as genetic susceptibility have an influence. Multiple sclerosis has a

main prevalence of occurrence in northern Europe, northern America and eastern

Australia, leading to some of the theories connected to low sunlight, and high

hygiene. A few other hypotheses include infection by a virus, toxin exposure, sex

hormone relations, dietary habits and air pollution as possible causes (1). Multiple

sclerosis exhibits various disease courses described as relapsing-remitting (RR),

primary-progressive (PP), secondary-progressive (SP) and progressive-relapsing

(PR) as seen in Figure 1. Multiple sclerosis has been studied by means of

proteomics in order to reveal causes of the disease, increased understanding of

pathomechanisms and to discover biomarkers. The discovery of a quick and

simple method for diagnosis would be invaluable and the possibility to follow up

the disease progression and therapeutic response would be precious as well.

Another interesting opportunity of the discovery of proteomic biomarkers is the

development of personalized medicine.

Figure 1. Typical disease curves of different EAE models. Black solid line: MBP in Lewis

rat, dotted black line: MOG in dark agouti rat, grey solid line: PLP in SJL/J mouse, black

broken line: rhMOG in marmoset.

Neu

rolo

gic

al S

co

re

Time


6

The study of human subjects is limited. Since MScl affects the CNS, tissue

samples can be collected post mortem exclusively, making it impossible to follow

the disease course by means of proteomics in a longitudinal manner using tissue

samples. Blood samples are easily collected but the detection of disease-related

proteins might be hampered by the complexity of the blood proteome, the

masking effect by highly abundant proteins and the possible dilution effect,

especially if the sample is collected peripheral to the diseased area. Some of the

CNS-specific proteins might not even be present in the blood stream because of

the blood- brain barrier (BBB). The collection of CSF is a somewhat invasive

method but it is possible to collect samples in a longitudinal way and at any stage

of the disease. The proximity to the diseased tissue also makes this body fluid a

better choice than blood.

From animal models tissue samples can be collected at any time point and

during all stages of the disease, the biological variation is less than in a human

population and knock-outs can be created in order to study specific questions in

more detail. The animal model of MScl, experimental autoimmune

encephalomyelitis (EAE), has become an important tool for the understanding of

the human disease. This review aims to give an overview of proteomic EAE

studies, what questions need to be answered and what is already known. Further

this review will describe discriminatory proteins discovered in EAE and the

connection to observations done in the study of MScl patients.

2. Why use animal models

The use of animal models in research is an ongoing topic of discussion, sometimes

heated but always necessary. Over the past few decades, intensive discussions

between organizations opposing the use of animals and those supporting it have

led to increased attention towards the further implementation of Russell and

Burch’s so-called 3R’s – Replacement, Reduction and Refinement (1959). Indeed

where possible, alternative models are used in which hypotheses are tested in

silico, in vitro or ex vivo. This strategy is not only beneficial for the animals, but

also helps increase knowledge on a specific scientific subject (2). However, when

research areas address increasingly complex systems, such as the immune system

or the central nervous system, the use of animal models becomes increasingly

dictated.

Research using animal models can be separated into two distinct

categories: a) fundamental (or basic) research, in which new light is shed upon yet

unknown biological mechanisms involving complex systems and/or interactions

Chapter 1

7

thereof, and b) applied research, focused on understanding pathological

mechanisms to help us find and develop cures for human and animal diseases.

Much is written on the quality and/or validity of a model (3). Current

knowledge on pathological processes is limited, which imposes a reductionistic

approach by focusing on parts or elements of a disease one attempts to

understand and/or cure. Typical examples of such reductionism are so-called

disease models. In these types of animal models, one or several aspects of a

disease are mimicked and their validity can be categorized as follows: i) face

validity, which is the degree of similarity between an aspect of particular disease

and the parameter measured in the animal model; ii) predictive validity, which is

the degree of extrapolation of the outcome of an experimental procedure to the

human (or target) situation; and iii) construct validity, which is the degree of

similarity between the mechanisms modeled and the mechanism thought or

proven to play a role in the situation studied (i.e. a disease of interest or biological

process investigated) (4). Additionally, with an increase in reported results from

animal models and from clinical trials, comparative meta-analyses such as

performed by Perel et al. (5) will become valuable in determining the strength of

animal models to predict clinical outcome and further improve the validity of

disease models.

These disease models, used for both fundamental and applied research,

have often led to a better understanding of biological processes. As example, it is

worth noticing that the EAE (see also below) has enabled scientists to elucidate the

mechanisms involved in immune responses. This knowledge was then the basis

for the development of two commercialized drugs that alleviate MScl symptoms:

Copaxone ® (Teva Pharmaceuticals); and Tysabri ® (Biogen and Elan) (reviewed

in Steinman and Zamvil, 2005) (6).

In the late preclinical (i.e. non-clinical) stage of drug development,

evaluation of the safety aspects of a potential new drug as well as pharmacological

testing is mandatory. Institutions responsible for approving the commercialization

of new drugs, such as the Food and Drug Administration (FDA - US) and the

European Medicines Agency, require proof of efficacy and safety of a given

pharmaceutical prior to the first human exposure. Indeed, in a very early stage of

application of a novel drug, the FDA states in its Drug Review Process that

organizations applying for approval must show results from laboratory animal

testing prior to discussing their plans for clinical trials (7).

The use of animal models is therefore the price that society decides to pay

to ensure the safety of healthy volunteers and of patients participating in these

clinical trials required to bring a new drug to the medicine cabinets. In this view, it

is of importance that all animal testing is performed with the highest possible

respect and care for the animals involved. To warrant for such a ‘humane’

approach, performing experiments on animals is heavily regulated. Although


8

these regulations differ from country to country, local and international law (8) set

strict regulations to handling and care of laboratory animals as well as strict

definitions and limitations to the use of these animals, allowing only tests that are

essential in answering scientific or medical questions. The local implementation of

these regulations is often taken care of by ethical committees responsible to

evaluate the balance between the suffering and/or discomfort caused to the

animals studied versus the benefits for the target species (humans, but also in the

veterinary field).

It is therefore our responsibility as scientists that our understanding of the

disease models and their extrapolation to the human situation is such that the

chances increase that effects observed in animals reflect efficacy in human.

3. Description of the EAE model

During the late 19th century, in the early period of the search for a rabies vaccine,

suspensions of CNS material extracted from infected rabbits were injected as

prophylactic treatment for the disease. However, such interventions led to the

development of paralysis in some of the vaccinated patients (9). To understand the

link between these observed effects and the vaccination, several experiments were

performed in which CNS material was injected in the rabbit (10, 11). However,

these authors could not correlate the neurological symptoms observed with any

pathological changes in the nervous system. This correlation was first

demonstrated by Rivers who performed repeated administration of emulsified

rabbit brains in monkeys and described a type of ‚encephalomyelitis‛ (12, 13).

These series of experiments constitute the first steps towards the development of

the EAE as the protocols for induction became increasingly refined over the last 75

years (14, 15). Equally important, these first steps also led to the development of

the hypothesized immunological basis of Multiple Sclerosis’ pathology, as

similarities were observed with human demyelinating diseases (16).

The EAE has been instrumental in discovering and developing three of the

six currently approved therapies for MScl: Copaxone, Mitoxantrone and

Natalizumab. Both Copaxone and Natalizumab were discovered by developing

working hypotheses on the pathological processes involved in EAE (17, 18).

Mitoxantrone, however, was already discovered and used for cancer. It is when

this type of drug was hypothesized to be effective in MScl (19), that Mitoxantrone

was first tested in EAE and showed positive effects (20). This eventually led to its

use in reducing the frequency of relapses in Relapsing Remitting MScl and

slowing the progression of Secondary Progressive MScl.

Chapter 1

9

The use of the EAE in defining a therapeutic strategy for MScl has also

been heavily contested. Sriram & Steiner (21), elaborated on the differences in

pathology between EAE and MScl and linked this lack of correlation to the high

number of failures in clinical trials. Also, Steinman & Zamvil (2005) although

praising the EAE for its use in understanding neuroimmunological processes and

for being responsible for most of the current therapies for MScl, warn that the EAE

fails to detect potential infectious complications from novel therapies and lacks

predictability on toxicity.

An important aspect in the discussion of the validity of the EAE as model

for MScl is its heterogeneous nature. Currently, there exist several types of EAE

models: the antigen used (all from myelin origin) the species or even the strain in

which it is induced, influence the course of the disease (22) (see Fig 1). Depending

on the protocol, a different aspect of MS can be modeled. Indeed, several models

have been refined to mimic relapsing remitting MScl (23-25), secondary

progressive or primary progressive MScl (26). Next to exhibiting different disease

courses, these models also present different cellular and molecular aspects of

Multiple Sclerosis (14). Hence, there is not one EAE model, but rather a wide

range of variations on the theme, each representing one or more characteristic of

the pathologies observed in the different subtypes of MScl.

In this light, the difficulty arising by the differences in cellular and

molecular aspects of EAE models may not need to be impeding on our

understanding of the disease. On the contrary, by wisely combining studies

performed in different types of EAE models as Steinman and Zamvil advocate (6),

and adding studies from other models, a broader range of underlying MScl

pathological processes might come to light. As Gold et al. concluded ‚owing to the

complexities of human diseases, it is obvious that there is no single EAE model,

but rather a combination of different approaches that will finally help us to

develop new and more effective approaches‛.

It is undeniable that more studies are necessary to assess the degree of

extrapolation of EAE findings to the human situation. Also the application of

advanced technologies, such as Mass Spectrometry and the study of the EAE

metabolite and/or protein profile compared to the human situation will

undoubtedly lead to the discovery of potential new targets.

4. Why proteomic biomarkers

The set of all proteins expressed in a cell, tissue, body fluid or an organism is

called the proteome. The study of the proteins thereof in terms of quality,

quantity, function, structure, localization, modifications, activities and


10

identification is named ‚proteomics‛ (27, 28). Proteomics can also be defined as an

interpretation of genome coded information (29). The definition of a biomarker is

termed: ‚a characteristic that is objectively measured and evaluated as an

indicator of normal biologic processes, pathogenic processes or pharmacologic

responses to a therapeutic intervention‛ (30). In the case of proteomic biomarker

research, the proteins are the entities examined for changes according to different

disease states and drug interventions using material from patients or animal

disease models.

The proteome with the estimated 300 000 proteins (31) is by far more

complex than the genome that consists of maybe less than 30 000 genes (32). There

is truly no one-gene-one protein relationship and mRNA levels are not in direct

relationship with protein levels. The proteins exist in splice variations, conformal

changes and with post translational modifications leading to a large variation in

the proteome. The proteome influenced by metabolism, stress, drug interactions,

circadian rhythm, cell type, stimuli, micro and macro environment is highly

dynamic compared to the more static genome. Proteins play crucial roles in the

body with function as enzymes, transporters, structure creators, receptors, growth

factors and antibodies among many others. The proteome can be analyzed in a

range of different sources as e.g. cells, tissues, blood, cerebrospinal fluid, amniotic

fluid, synovial fluid, tears and urine (27, 30, 31, 33).

Multiple sclerosis, a highly complex disease with symptoms from various

parts of the body, requires several criteria to be fulfilled for a diagnosis to be

made. There is a limited ability to predict different courses of the disease as well as

to monitor treatment response. To make a diagnosis, time consuming neurological

examinations as well as expensive MRI scans are applied. CSF is also examined for

the presence of immunoglobulin gamma (IgG) bands, an increased IgG index and

an overall increased protein amount. Many times the definite diagnosis can be

made only after months or even years of examinations (34). Specific proteomic

biomarkers that in an early stage could be detected by a single analysis of a blood

sample, CSF or other biofluid could increase the certainty of diagnosis, saving

both time and money and enabling the initiation of an early drug treatment with a

better outcome for the patient. More than to serve as diagnostic and classification

markers, the discovery of disease-specific proteins can reveal the pathogenic

functions which in turn could lead to drug development since many

pharmacological drug targets are proteins (30, 31, 33).

There are many challenges to meet in the proteomic research; the great

diversity as well as the large dynamic range of proteins in tissues or bodyfluids

causes problems for the analytical methods applied. Therefore, several different

techniques are used in order to reduce the sample complexity; separations in more

than one dimension, depletions and analysis of selected protein groups as e.g.

phospho-proteins, glycoproteins and membrane proteins are some of the common

Chapter 1

11

techniques. Another challenge met by these complex analyzes include the need of

repeatability and reproducibility. Proteins and peptides are sensitive to different

sample handling techniques, storage conditions as temperature, sample vials and

number of freeze-thaw cycles affects the sample. Also the sampling technique and

how the sample is handled just after the sampling are crucial. CSF samples

contaminated with e.g. red or white blood cells are not useable and discarded

from studies. This calls for robust design and standardization of proteomic

biomarker studies in order to rule out the detection of differences caused by

biological differences, sample handling, adsorptions to the walls of the sample vial

and degradation of naturally present proteases (27, 35, 36). High-throughput

analysis strategies as often applied in biomarker studies call for the use of quick

automated techniques. The production of often huge data files makes the

processing of the data sets even a significant logistics problem. When properly

designed, proteomic biomarker studies could generate important and useful

information.

5. Body fluids and tissues

In the search for proteomic biomarkers in the EAE model there are a few options

of sample sources. Different tissues from the CNS as well as blood, CSF and ECF

can be collected from animals. In biomarker studies based on human subjects, CSF

and blood can be collected. Only in the case of post mortem subjects can tissue

samples also be used. Other body fluids such as urine, saliva (37) and lacrimal

fluid (38) can be collected as well, but are less useful in the case of EAE and MScl.

5.1. Tissues In the EAE model, all kinds of CNS connected tissue can be collected for extraction

of proteins for biomarker studies. The most used tissue is spinal cord (39-46), but

also cerebral micro vessels (47) as well as retina, optic nerve and brain tissue have

been harvested for this purpose (46). Also specific organelles or proteins can be

extracted from tissues for separate analysis as e.g. mitochondria (46), membrane or

cytosol proteins.

5.2. Blood samples An intact blood-brain barrier (BBB) complicates the detection of brain specific

proteins in the blood. Both EAE and MScl show a BBB impairment (48, 49) that

enables the passage of proteins between blood and brain, making it possible to

detect brain-specific proteins in blood samples. Blood, however, contains large

amounts of proteins among which the CNS-related proteins constitute only a


12

minor part of the total content. The distance to the diseased areas makes the

detection of markers specific to CNS disorders difficult, especially when applying

a screening approach of the total proteome. For targeted approaches, blood can be

a more useful compartment (50-52). Biomarkers that are detectable in serum are of

great use, since blood samples are easy to collect at the clinic. Blood is easily

collected from the tails of rats and mice and the samples can be collected in a

longitudinal fashion.

5.3. Cerebrospinal fluid (CSF) CSF is the most promising compartment for biomarker search in CNS disorders.

CSF is in close proximity to the diseased tissue and has around 400 times lower

protein concentration than blood. Because of the BBB, some brain-specific proteins

might be difficult to detect elsewhere than in CSF or CNS tissues. Because tissue

samples cannot be collected from patients until post-mortem, CSF enables

monitoring of the disease of living patients in a longitudinal fashion even though

the withdrawal of CSF is a quite invasive method that may introduce headache or

other complications for the patient. In the EAE model CSF has not been frequently

analyzed to date, probably because of the low sample volume and the laborious

method of sampling; previously, a stereotaxic surgery was needed to reach the

CSF (53), but with new techniques developed, the possibility to work with CSF has

increased (54).

In Table 1 and 2 characteristics of CSF from different species can be seen.

A rat of 200-300g has a total CSF volume of about 580 µL (55) (cisterna magna ~190

µL (54)). The maximum volume of CSF that can be collected from rat is around 120

µL with no visible contamination of blood (54). Since there is a very large

concentration difference between CSF and blood even a minor contamination will

have a huge impact. What has to be noted, however, is that CSF can be

contaminated with blood even though this might not be detectable by eye

(detectable by mass spectrometry). Since the BBB is disrupted during EAE and

MScl, there is an influx of blood proteins in the CSF and a blood contamination in

these samples cannot be excluded (49). CSF can be repeatedly collected also from

rats and mice (56, 57); for serial sampling, a maximum of 60 µL from rat and ~5 μL

from mouse can be safely taken each time at an interval of 2-3 months (57). The

low CSF volumes that can be collected from rat and mouse are partly

compensated by the higher protein concentration (5-10 times higher than in

humans [human approximately 400 ng/µL]).

Chapter 1

13

Table 1. CSF volume in different species (152).

Species mL

Horse 170-300

Human (total) 100-160

Human (ventricular) 16-27

Goat 25-30

Sheep 14-17

Dog (total) 7.8-24

Dog (intracranial) 5.5-9.5

Cat 4.0-4.9

Dogfish 2.0-2.7

Shark 2.0

Rabbit 1.4-2.3

Rat 0.28-0.3

Mouse 0.04

Table 2. Description of CSF production rate (153).

Species µL/min

Frog (R. pipiens) 0.3

Mouse 0.325

Chicken 1.4

Dogfish 2.0-4.0

Rat 2.1-5.4

Nurse shark 3.0

Lemon shark 4.0

Guinea pig 3.5

Rabbit 6-10

Cat 20-22

Monkey 29-41

Dog 31-66

Sheep 118

Goat 164

Human 350-400

5.4. Extracellular fluid (ECF) Just as CSF, extracellular fluid (ECF) from brain tissue can also be collected

repeatedly. The fluid is collected via cerebral microdialysis, with a probe inserted

in the brain of the animal. ECF also has the advantage of being close to the

diseased tissue. The mouse can keep the probe inserted when awake for several

days (58). Both smaller molecules like metabolites and neurotransmitters as well

as larger molecules like peptides and proteins can be sampled via microdialysis

(59). In humans the collection of ECF from brain tissue is implemented during

neurosurgery or to monitor the progress of stroke or trauma (60, 61) but because

of the invasiveness of this technique it is not implemented for biomarker research

in human subjects. Also in animal models this technique has still not been widely

used, since the uptake of proteins is still quite cumbersome (62).


14

6. Proteomic biomarker studies in EAE models

Since the first EAE model was introduced by Koritschoner and Schweinburg in

1925 (10), many studies based on the EAE model have been performed with the

intention of increasing the understanding of the pathological mechanisms of MScl

(14, 63). The rise of the proteomics era in the mid 90-ties (64) initiated several

studies with the goal to discover proteomic biomarkers for diagnosis and

increased knowledge in a range of diseases. Also numerous proteomic biomarker

studies targeting MScl have been implemented; proteomic biomarker studies

based on the EAE model, however, has still been moderate in number but have

seen an increase during recent years. While most EAE studies have been

hypothesis-driven attempts to discover biomarkers (39, 44-46, 50-52, 65), a few

have been non-biased discovery type (40-42, 47). In most of the studies, spinal

cord have been analyzed (39-46) and other tissues related to the CNS (46, 47, 50);

only one study has been based on CSF (52) and three on serum (50-52).

In the study by Ohgoh and colleagues (45), the transition between the

genomics and proteomics era is visible where comparison between messenger

ribonucleic acid (mRNA) and protein levels were done. The study was focused on

the involvement of excitatory amino acid transporters in EAE and revealed that

the EAAC1 (excitatory amino acid transporter 3) was increased in spinal cord

from Lewis rats with EAE compared to control rats. The GLAST and GLT-1

proteins (excitatory amino acid transporter 1 and 2 ,respectively) were, on the

other hand, decreased in the diseased rats compared to the controls. The effects

were suppressed when the rats were treated with the (AMPA) receptor antagonist

(NBQX), thereby revealing that glutamate toxicity might be one of the key factors

in the pathology of EAE.

Gene expression and protein levels were also compared in the study by Alt

et al.(47). Two breeds of mice with EAE (SJL/N and C57Bl/6) were analyzed for the

effect on the blood-brain-barrier (BBB) by changes of proteins in the cerebral

microvascular compartment. Six discriminatory proteins were discovered; four

that increased in the diseased mice compared to control and two that decreased.

However, only one of the proteins (fibroleukin precursor/fibrinogen like protein 2)

showed an overlapping result between the proteomic 2D gel analysis and the

gene-microarray results, showing the non-linear relationship between gene

expression levels and protein amounts.

Morgan et al. (44) also examined the effects on the BBB. Lumbar spinal cord

was analyzed for post translational modification (PTM) differences between EAE

rats and control animals on the tight junction protein, occludin. The protein was

dephosphorylated in the EAE group compared to the control group, where the

protein was found in a phosphorylated form. The de-phosphorylation was also

Chapter 1

15

shown to coincide with the outbreak of inflammation. Further, three other

proteins (albumin, transferrin and ceruloplasmin) were found at higher levels in

the EAE rats compared to control. PTMs were also the scope of the study by

Mikkat et al. in 2010 (43), where PTMs as well as single amino acid polymorphisms

(SAPs) and splice isoforms were analyzed by studying migration differences of

proteins from the spinal cord of one EAE susceptible (SJL/J) and one EAE resistant

(B10.S) mouse strain. The findings included 26 polymorphic proteins with altered

electrophoretic mobility and 14 single amino acid polymorphisms (SAPs).

Further PTM studies have been performed by Kidd and colleagues (51);

where citrullinated proteins were the target. Antibodies reactive against

citrullinated and native myelin basic protein (MBP) were detected in EAE mice,

while they were not detected in the controls. Also Grant et al. (39) have been

examining PTMs in the form of lysine and arginine methylation as well as arginine

citrullination and phosphorylation of proteins. Several modified proteins were

found to be both increased as well as decreased in the EAE rats compared to the

controls.

The whole proteome of the spinal cord of EAE rats was analyzed by

shotgun isobaric tag for relative and absolute quantitation (iTRAQ) analysis by the

group of Jain et al. (40) and Liu et al. (42) The proteome was screened for

discriminatory peptides as well as for altered proteolytic events. In the study by

Liu et al., 41 significantly discriminatory peptides between healthy animals and

EAE rats were identified. Later on, Jain et al. discovered 7 altered proteolytic

products in the same sample set.

Reactive oxygen species (ROS) toxicity was the focus of the study by Qi et

al. in 2006 (46). CNS derived tissue as well as mitochondrial isolates from the same

tissues were screened for nitrated proteins. The study revealed several nitrated

mitochondrial proteins, an increased ROS level in the tissue and ~80% decreased

adenosine triphosphate (ATP) synthesis in the EAE mice compared to control.

Linker et al. (41) screened for stage-specific markers in two different EAE

mice models (C57Bl/6 and CNTF-/-). Among the findings were five differently

regulated proteins discovered in the C57Bl/6 mouse strain and six discriminatory

proteins in the CNTF-/- strain. Glial fibrillary acidic protein (GFAP) were found at

increased levels in later stages of the EAE.

The interest of El Behi et al. (50) was to examine the effect of the

antihistamine pyrilamine and the platelet activating factor (PAFR) receptor

antagonist CV6209 on EAE. Lower levels of IgG’s were found in the treated mice

compared to untreated.

The possible involvement of the RAS system in EAE has been examined by

Stegbauer et al. in 2009 (65). Antigen presenting cells as well as spinal cord tissue

was analyzed in myelin myelin oligodendrocyte glycoprotein (MOG)-EAE mice.

Quantitative polymerase chain reaction (Q-PCR) revealed upregulation of renin,


16

ACE (angiotensin converting enzyme) and AT1R (Ang II 1 receptor). Treatment

with the renin inhibitor, Aliskiren, the ACE inhibitor, Enalapril, and the AT1R

antagonist, Losartan, ameliorated the course of the EAE. Enzyme-linked

immunosorbent assay (ELISA) analysis of chemokine (C-C motif) ligand 2 (CCL2)

and C-X-C motif chemokine 10 (CXCL10) show a decrease of these chemokine

ligands in macrophages after treatment with Losartan.

Villaroya et al. (52) found elevated tumor necrosis factor alpha (TNFα)

activity correlating with clinical scores of MBP-EAE rats in both serum and CSF.

An increased TNFα level was also detected in the spinal cord of EAE compared to

controls. Further findings included an increased concentration of both albumin

and IgG in the EAE rats, as well as an elevated cell count in both serum and CSF.

Several interesting proteins have been found discriminatory between EAE

animals and controls. The question is if they are translatable to the MScl disease in

real patients and further, how specific they are. In the following chapter, the

connections between possible biomarkers found in the EAE model and findings in

MScl patients are investigated.

7. Connections between discriminatory proteins in EAE and

MScl

EAE proteomic biomarker studies have revealed several new proteins with

discriminatory behavior between diseased and healthy animals. Many of these

proteins have also been connected to MScl in studies on tissues or body fluids

from human subjects. In this chapter, proteins that overlap between animal

models and human studies are discussed. In Table 3, proteins with increased

abundance in EAE compared to control are listed, Table 4 describes proteins with

the opposite behavior, namely a decrease in EAE affected animals compared to

controls. Table 5 shows proteins that decrease in EAE after drug treatment, and

Table 6 contains proteins with PTM differences between EAE and control. Table 7

describes proteins with a changed migration time during gel-electrophoresis in the

study by Mikkat et al. in 2010 (43).

Chapter 1

17

Table 3. Proteins that have been discovered at increased levels in EAE animals compared

to control. The proteins are listed with references that link them to EAE and MScl. In all

following tables (Table 3-7), the proteins are sorted by alphabetical order with overlapping

proteins between MScl- and EAE- based studies in the upper part and non-overlapping

EAE proteins in the lower part of the tables. In cases were no references were found to

MScl or EAE, the box is shaded in grey.

Protein (up in EAE)

AC nr. EAE ref. MScl ref.

α-1B-Glycoprotein Q9EPH1 Liu(42) Tumani(111)

α-2-macroglobulin P06238 Liu(42) Jensen(94) Gunnarsson(93)

Annexin V P48036 Linker(41) Elderfield(69)

Apolipoprotein D P23593 Liu(42) Reindl(139)

Apolipoprotein E P02650 Liu(42) Carlin(67) Hammack(72) Chiasserini(68) Fazekas(70)

Cathepsin B P00787 Liu(42) Bever(127, 128)

Ceruloplasmin P13635 Morgan(44) Liu(42)

Hunter(105)

Complement C3 P01026 Liu(42) Barnett(66) Hedegaard(73) Stoop(81, 82) Zhang(85) Padilla(77) Jongen(74) Gay(71)

Cystatin C P14841 Liu(42) Nakashima (138) Irani(137)

Exitatory amino acid transporter 3 EAAC1 (Human EAAT3)

P51907 Ohgoh(45) Werner(97) Vallejo-Illarramendi(96) Newcombe(95)

Fibronectin P04937 Liu(42) Sobel(79) van Horssen(84)

Filamin A IPI00409539 Liu(42) Lock(118)

Glial fibrillary acidic protein (GFAP) P03995 Linker(41) Norgren(122) Rosengren(123) Malmeström(121) Linker(41)

Hemopexin P20059 Liu(42) Hammack(72) Rithidech(114)

Heterogeneous nuclear ribonucleoprotein A2/B1 (ROA2)

O88569 Alt(47) Yukitake(130) Sueoka(129)

IgG (2A chain C) P20760 Villarroya(52) Liu(42)

MacPherson(76) Kabat(75) Tourtellotte(83)

Moesin O35763 Liu(42) Tajouri(110)


18

Myelin basic protein (MBP) P02688 Kidd(51) Berger(90) Egg(91) Vojdani(92)

Peptidyl-prolyl cis-trans isomerase B P24368 Liu(42) Ramanathan(132)

Mitochondrial heat shock protein 70 (mtHSP70) P38647 Qi(46) Witte(133)

Transferrin P12346 Morgan(44) Linker(41) Liu(42)

Weller(108) Zeman(112) Abo-Krysha(109) Tumani(111) Tajouri(110)

TNFα P16599 Villarroya(52) Philippe(78) Spuler(80)

Vimentin P31000 Liu(42) Holley(120) Selmaj(124)

Albumin P02770 Morgan(44) Linker(41) Villarroya(52) Liu(42)

α 1-Inhibitor III P14046 Liu(42)

α 2 HS glycoprotein: P24090 Liu(42)

α Internexin P46660 Linker(41)

Annexin III P14669 Liu(42)

Calreticulin precursor P14211 Linker(41)

Coactosin-like protein IPI00365323 Liu(42)

Contrapsin-like protease inhibitor 3 P05544 Liu(42)

Cytochrome C oxidase subunit IV P19783 Qi(46)

Cytosol aminopeptidase IPI00471530 Liu(42)

EF-hand containing IPI00200410 Liu(42)

protein 2

Eukaryotic initiation factor 4A-I IPI00369618 Liu(42)

Fibroleukin precursor/fibrinogen like protein 2 (FIBB) P14480 Alt(47)

GTP-GDP dissociation stimulator 1 Q9BUX6 Linker(41)

Hepatoma-derived growth Q8VHK7 Liu(42)

Factor

Guanine nucleotide-binding protein (GBLP) P25388 Alt(47)

L-plastin Q5XI38 Liu(42)

Lysozyme C2 Q05820 Liu(42)

Myosin heavy chain A (non-muscle) Q62812 Liu(42)

Chapter 1

19

Nucleophosmin P13084 Liu(42)

Phosphatase 2A inhibitor Q63945 Liu(42)

Proteasome activating complex Q63797 Liu(42)

Protein disulfide isomerase P04785 Liu(42) Linker(41)

Purine nucleotide phosphatase (PNPH) P23492 Alt(47)

Syntaxin-binding protein 1 O08599 Linker(41)

Talin 1 IPI00362014 Liu(42)

T-Kininogen 1 P01048 Liu(42)

40S Ribosomal protein SA P38983 Liu(42)

40S Ribosomal protein S18 P62271 Liu(42)

78 kDa Glucose regulated protein P06761 Liu(42)

7.1. Inflammation/immunity Inflammatory and immune responses, includes proteins involved in; the

complement system, acute phase proteins, vasodilation, antibodies, coagulation

and fibrinolysis. Among the discriminatory proteins discovered in EAE as well as

in MScl, the inflammatory/immune proteins; annexin V, apolipoprotein E,

complement C3, fibronectin, IgG and TNFα were increased in EAE compared to

controls. Also in the human studies these proteins showed a positive relationship

to MScl, different classes or stages of the disease (41, 42, 52, 66-85), reflecting the

inflammatory/immune part of the disease. The cytokines CCL2 and CXL10

decreased in macrophages of EAE mice after the treatment with the angiotensin II

receptor inhibitor Losartan (65), indicating a connection to the renin-angiotensin-

system (RAS). Also MScl studies have revealed proteins with connections to the

RAS system as interesting biomarker candidates (86-88), CCL2 and CXCL10 has

also been connected to to MScl in human subjects (89). Autoantibodies against

myelin basic protein (MBP) in native or citrullinated form have been found in EAE

animals (51), and in studies on human subjects, antibodies against MBP and MOG

were discriminatory between different sub-groups of the disease (90-92). Another

protein connected to inflammation, alpha 2 macroglobulin, was increased in the

spinal cord of EAE rats compared to control (42). The same protein was found to

have increased as a transformed version in plasma from PP-MScl and SP-MScl

patients compared to control detected by ELISA (93, 94).


20

7.2. Exitotoxicity The excitatory amino acid transporters GLAST (human EAAT1), GLT-1 (human

EAAT2) and EAAC1 (human EAAT3) were analyzed in EAE rats. EAAC1 were

found in elevated amounts in diseased animals compared to controls while

GLAST and GLT-1 were reduced in the same comparison (45). Studies on MScl

patients have also revealed an involvement of glutamate toxicity (95-97). Protein

amount as well as complementary deoxyribonucleic acid (cDNA) levels of EAAT1

and EAAT2 were increased in the optic nerve of MScl patients compared to

controls (96). In another study, protein levels of EAAT2 were decreased in

oligodendrocytes around MScl lesions when compared to control tissues (97).

Glutamate dehydrogenase (GD) was detected at lower levels in a knock-out EAE

mouse compared to control (41). GD was not detectable in active and silent

(chronic) MScl lesions but present in normal and non-MScl white matter (97).

These results show that glutamate toxicity might be a factor involved in the

demyelination and neurodegeneration in EAE as well as MScl. The protin

calcium/calmodulin dependent kinase II (CAMKII), a protein involved among

others in the regulation of glutaminergic synapses (98) was decreased in EAE

compared to control (42). It has been discovered that the Vδ2+ T cells that have

been detected in CSF of RR-MScl patients are dependent on CAMKII for the

transendothelial migration across the BBB to reach the CNS (99).

7.3. Oxidative stress/iron homeostasis Oxidative stress is a pathogenic mechanism that has been connected to the cause

of MScl (100-102). Ceruloplasmin, a copper-binding protein involved in the

protection against oxidative stress (103, 104), was found to be increased in spinal

cord of EAE rats compared to control (42, 44); the same observation was also made

in plasma from MScl patients (105). Transferrin, another protein involved in

protection against oxidative stress (106, 107) was detected at an elevated

abundance in rats and mice with EAE (41, 42, 44). An increased amount of

transferrin was also detected by ELISA in CSF from MScl patients (108). Another

study noted an increased amount of soluble transferrin receptor in serum from

MScl patients (109). Transferrin cDNA levels were also increased in a comparison

of MScl plaque tissue to normal tissue (110). A longitudinal study of patients with

clinically isolated syndromes (CIS) revealed a decrease of transferrin abundance in

the CSF of patients that converted to definite relapsing remitting MScl (RR-MScl)

compared to those that stayed diagnosed as CIS after two years (111). Further

more, the transferrin level was discriminatory between subclasses and stages of

MScl (112). Hemopexin also plays an important role in the protection against

oxidative stress by the heme-binding capacity (113). An increased amount of

Chapter 1

21

transferrin was found in the spinal cord of EAE diseased rats (42); the same has

also been observed for hemopexin in plasma from pediatric MScl (114). A possible

change in PTM’s of hemopexin in healthy and MScl patients was discovered by a

different location on 2D gels (72). The three proteins described in this section are

also acute phase proteins and could be listed together with the inflammatory

proteins (115).

7.4. Structure The intermediate filaments (IF) proteins, glial fibrillary acidic protein (GFAP),

vimentin and the neurofilament proteins, constitute part of the foundation of the

eukaryotic cytoskeleton (116) the filaments are connected via crosslinkers that

build up a complex network that enables cell signaling, and anchors

transmembrane receptors. One of these crosslinkers, filamin A, (117) has been

shown to increase in EAE rats at protein level (42) and chronic lesions from MScl

patients at gene level (118). Another cytoskeleton protein, moesin (119), was also

increased in EAE rats (42) as well as MScl patients on gene level (110, 119). Among

IF proteins, GFAP was shown to increase in spinal cord of late stage EAE as well

as SP-MScl compared to control (41, 119). GFAP in CSF was also increased on a

protein level in MScl patients compared to control (119-123). Vimentin was one of

the increased proteins in EAE rats (42). In MScl patients, corpora amylaceae

stained strongly for vimentin compared to control tissues (124); vimentin has also

been shown in another MScl study to be more expressed in diseased tissue

compared to healthy tissue (120). Neurofilaments have been shown to be

dephosphorylated in EAE (39); phosphorylation of neurofilaments have also

shown to be discriminatory in different CNS tissues from the of MScl patients

(125).

7.5. Other Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) a protein involved in

glycolysis, was increased in EAE; in the same study, a lower production of ATP

was shown in the diseased animals (46). In another study based on MScl patients,

GAPDH reactive antibodies were upregulated in diseased individuals compared

to control (126). The protease cathepsin B increased in EAE rats (42) as well as in

mononuclear cells of peripheral blood and cerebral white matter from MScl

patients compared to control (127, 128). Apolipoprotein D (Apo D), for example

involved in lipid transport increased in EAE rats (42); in MScl; an increased Apo D

CSF/serum index was found. The heterogeneous nuclear ribonucleoproteins A2/B1

(hnRNP, ROA2) were found at increased amounts in EAE compared to control

(47). Antibodies against this protein were found in the CSF of MScl patients (129,

130). Αlpha-1B-glycoprotein, a plasma protein with unknown function, was


22

detected at higher levels in EAE compared to control (42), and at lower levels in

CIS-RR-MScl compared to CIS-CIS (111). Peptidyl-cis-trans-isomerase B, a protein

involved in protein folding (131), increased in EAE at protein levels (42), while in

MScl patients this protein increased at the mRNA level by 22% compared to

controls (132). The mitochondrial stress protein 70 (mtHsp70) was found to be

nitrated in mitochondrial isolates from retina, brain and spinal cord of EAE, while

this was not observed in the control animals (46). In MScl lesions and particularly

in astrocytes and axons, there was an increased immunoreactivity against the

same protein (133). 2’,3’-Cyclic-nucleotide 3’-phosphodiesterase (CNPase), one of

the most abundant proteins in the myelin membrane (134) exhibited a shift in

migration time between EAE susceptible and EAE-resistant healthy mouse strains

(43). The same protein was recognized by IgG in serum and CSF from MScl

patients but not from controls (135, 136). Cystatin C, an inhibitor of cystein

proteases was found to increase in EAE animals compared to control (42). This

protein was also found to be discriminatory between MScl patients and controls

(137), but later studies have revealed that this difference might be caused by

improper storage of the samples (138).

Table 4. Proteins that have been discovered at decreased levels in EAE animals compared

to control. The proteins are listed with references that link them to EAE and MScl.

Protein (down in EAE)


Ca2+/Calmodulin kinase 2Alpha P11275 Liu(42) Poggi(99)

Exitatory amino acid transporter 1 (rat: GLAST, human: EAAT1)

P24942 Ohgoh(21) Werner(97), Vallejo-Illarramendi(96)

Exitatory amino acid transporter 2 (rat: GLT1, human: EAAT2)

P31596 Ohgoh(21), Liu(42)

Werner(97), Vallejo-Illarramendi(96) Newcombe(95), Pitt(140)

Glutamate dehydrogenase 1 P26443 Linker(41) Werner(97)

Enolase-phosphatase E1 Q8BGB7 Linker(41)

Guanine nucleotide-binding protein G(I)/G(S)/G(T) beta subunit 2/transducin beta chain 2 (GBB2)

P54312 Alt(47)

Tailless complex polypeptide 1B (TCP) P11983 Alt(47)

Tailless complex polypeptide 1B (TCP) P11983 Alt(47)

Chapter 1

23

Table 5. Proteins that have been detected at different levels in drug treated EAE animals

compared to non-treated EAE animals. The proteins are listed with references that link

them to EAE and MScl.

Protein (down with drug treatment)


Antihistamine treatment decreases IgG (pyrilamine and CV6209)

El Behi(50) Logothetis(141), Alonso(142)

Chemokine ligand 2 (CCL2) (decreases after treatment with Losartan)

Q7TMS1 Stegbauer(65) Szczucinski(89), Kawajiri(86), Wosik(88), Otterwald(87)

Chemokine ligand 10 (CXCL10) (decreases after treatment with Losartan)

P17515 Stegbauer(65) Szczucinski(89), Kawajiri(86), Wosik(88), Otterwald(87)

Table 6. Proteins that have been detected with discriminatory posttranslational

modifications in EAE animals compared to control. The proteins are listed with references

that link them to EAE and MScl.

Protein PTM AC nr. PTM EAE ref. MScl ref.

Glyceraldehyde 3-phosphatase dehydrogenase

P16858 Nitration Qi(46) Kolln(126)

Glial fibrillary acidic protein P47819 Citrullination Grant(39) Moscarello(143)

Hsp 70 (mitochondrial) P38647 Nitration Qi(46) Witte(133)

Myelin basic protein (MBP) P02688 Citrullination Kidd(51) De Seze(144), Kim(145), Tranquill(146, 147), Oguz(146), Moscarello(143)

Neurofilament heavy chain P16884 Phosphorylation Grant(39), Gresle(148)

Petzold(125)

Neurofilament light chain P19527 Phosphorylation Grant(39) Norgren(122)

Occludin Q6P6T5 Phosphorylation Morgan(44) Proia(149), Blecharz(150), Minager(151)

Ser/Thr kinase RIO3 IPI00364947 Citrullination Grant(39)

Centrosome-associated protein 350

IPI00207361 Arginine methylation

Grant(39)


24

Complement C1 s subcomplement

Q6P6T1 Lysine methylation

Grant (39)

Discs large homologue 7 IPI00766460 Lysine methylation

Grant(39)

Early endosome antigen 1 IPI00768104 Lysine methylation

Grant(39)

EGF-like domain-containing protein 4

IPI00365661 Arginine methylation

Grant(39)

Elongation factor 1-α 2 P62632 Lysine methylation

Grant(39)

Myosin 1E Q63356 Arginine methylation

Grant(39)

NADPH-ubiquinone oxidoreductase B14 subunit of complex I (NDUF6A)

Q9CQZ5

Nitration

Qi(46)

Nephrocystin 4 orthologue IPI00764323 Arginine methylation

Grant(39)

Phosphoglycerate mutase 2

P16290 Lysine methylation

Grant(39)

Plastin-3 Q63598 Lysine methylation

Grant(39)

PPAR-δ protein Q62879 Lysine methylation

Grant(39)

Protein phosphatase 1 regulatory subunit 3E

P0C7L8 Arginine methylation

Grant(39)

Ribophorin 1 Q6P7A7 Citrullination Grant(39)

ρ-associated protein kinase 1

Q63644 Lysine methylation

Grant(39)

R3H domain isoform 3 IPI00763599 Lysine methylation

Grant(39)

Transcription initiation factor TFIID, subunit 5

IPI00361559 Lysine methylation

Grant(39)

Chapter 1

25

Table 7. protein that have been detected with discriminatory migration times between

EAE animals and controls in Mikkat et al. (43). The proteins are listed with references that

link them to EAE and MScl.

Changed migration in B10.S and SJL/J AC nr. EAE ref. MScl ref.

Neurofilament heavy polypeptide P16884 Mikkat(43) Petzold(125),

Leucine-rich PPR motif-containing protein Q6PB66 Mikkat(43)

Advillin Q0VEI6 Mikkat(43)

Elongation factor G 1, mitochondrial Q8K0D5 Mikkat(43)

Acylamino-acid-releasing enzyme Q8R146 Mikkat(43)

Dipeptidyl-peptidase 3 Q99KK7 Mikkat(43)

Liver carboxylesterase N P23953 Mikkat(43)

Stress-70 protein (mitochondrial) P38647 Mikkat(43) Witte(133)

Annexin A6 P14824 Mikkat(43)

Dihydrolipoyllysine-residue acetyltransferase Q8BMF4 Mikkat(43)

component of pyruvate dehydrogenase compl

Putative hydroxypyruvate isomerase Q8R1F5 Mikkat(43)

Putative uncharacterized protein (Rap1gds1) Q3TA6 Mikkat(43)

Glutathione synthetase P51855 Mikkat(43)

2’,3’-Cyclic-nucleotide 3’-phosphodiesterase P16330 Mikkat(43) Lovato(105), Walsh(106)

3’ (2’), 5’-Bisphosphate nucleotidase 1 Q9Z0S1 Mikkat(43)

3-Mercaptopyruvate sulfurtransferase Q99J99 Mikkat(43)

EF-hand domain-containing protein 2 Q9D8Y0 Mikkat(43)

Enolase-phosphatase E1 Q8BGB7 Mikkat(43)

Glyoxalase domain-containing protein 4 Q9CPV4 Mikkat(43)

Carbonic anhydrase 2 P00920 Mikkat(43)

Glia maturation factor beta Q9CQI3 Mikkat(43)

Isocitrate dehydrogenase [NADP] cytoplasmic O88844 Mikkat(43)

Only in SJL

Apolipoprotein A-I-binding protein Q8K4Z3 Mikkat(43)

HD domain-containing protein 3 Q9D114 Mikkat(43)

Only in B10.S

Peflin Q8BFY6 Mikkat(43)

8. Concluding remarks

In this review, the EAE model has been described in terms of proteomic studies.

Discriminatory proteins discovered in the model have been listed together with

overlapping proteins discovered in Mscl studies. Many proteins show the same

behavior in the EAE model and MScl patients; some of them, however, do show

an opposite response. Reasons for opposite results may be due to the fact that

studies are done on different sample types, using different methods and different

targets. Multiple sclerosis is a highly polyfaceted disorder, which makes it

complicated to find clear and specific markers. There are many factors that can

affect the outcome in proteomic studies; storage temperature, number of freeze-


26

thaw cycles and sample vials as well as analysis technique and sample origin.

Samples obtained from animal models are far more homogenous than human

samples, which makes it easier to detect markers in an animal model. However,

this also gives a flaw in the sense of dynamic behavior of the expression of the

marker. In order to get reliable, robust, reproducible results which can be

translated between animal model and human subjects, these factors have to be

kept under strict control. It should be noted that the EAE model is a model with

face validity or mimicry of the real disease; therefore, the animal model will never

be an exact copy of the real situation in patients. Still, by careful, accurate

experimental designs, the combined use of the EAE model and human subjects

offers the possibility to reveal the enigma behind this devastating disease and in

the end give hope to affected people all over the world.

Chapter 1

27

References

1. Compston, A., and Coles, A. (2008) Multiple sclerosis. Lancet 372, 1502-1517

2. Manciocco, A., Chiarotti, F., Vitale, A., Calamandrei, G., Laviola, G., and Alleva, E. (2009) The

application of Russell and Burch 3R principle in rodent models of neurodegenerative disease: the case

of Parkinson's disease. Neurosci. Biobehav. Rev. 33, 18-32

3. Willner, P. (1986) Validation criteria for animal models of human

mental disorders: learned helplessness as a paradigm case. Prog. Neuropsychopharmacol. Biol. Psychiatry

10, 677-690

4. van der Staay, F. J., Arndt, S. S., and Nordquist, R. E. (2009) Evaluation of animal models of

neurobehavioral disorders. Behav. Brain Funct. 5, 11

5. Perel, P., Roberts, I., Sena, E., Wheble, P., Briscoe, C., Sandercock, P., Macleod, M., Mignini, L. E.,

Jayaram, P., and Khan, K. S. (2007) Comparison of treatment effects between animal experiments and

clinical trials: systematic review. BMJ 334, 197

6. Steinman, L., and Zamvil, S. S. (2005) Virtues and pitfalls of EAE for the development of therapies for

multiple sclerosis. Trends Immunol. 26, 565-571

7. http://www.fda.gov/drugd/resourcesforyou/consumers/

8. Council of Europe, European Convention for the Protection of Vertebrate Animals Used for

Experimental and Other Scientific Purposes, European Treaty Series - No 123, Strasbourg, 1986, Text

amended in 2005., In:

9. Balaguer, D. D. G. (1888) Un caso de rabia paralítica. Gaceta Médica Catalana 11, 45-57

10. Koritschoner, R., and Schweinburg, F. (1925) Klinische und experimentelle Beobachtungen über

Lähmungen nach Wutschutzimfung. Ztschr f Immunitätsforsch u Exper Therap 42, 271

11. Hurst, E. W. (1932) The effects of the injection of normal brain emulsion into rabbits, with special

reference to the aetiology of the paralytic accidents of antirabic treatment. J Hyg. 32, 33-44

12. Rivers, T. M., Sprunt, D. H., and Berry, G. P. (1933) Observations on attempts to produce acute

disseminated encephalomyelitis in monkeys. J Exp Med. 58, 39-53

13. Rivers, T. M., and Schwentker, F. F. (1935) Encephalomyelitis accompanied by myelin destruction

experimentally produced in monkeys. 61, 689 - 702. J Exp Med. 61, 689-702

14. Gold, R., Linington, C., and Lassmann, H. (2006) Understanding pathogenesis and therapy of multiple

sclerosis via animal models: 70 years of merits and culprits in experimental autoimmune

encephalomyelitis research. Brain 129, 1953-1971

15. Baxter, A. G. (2007) The origin and application of experimental autoimmune encephalomyelitis. Nat.

Rev. Immunol. 7, 904-912

16. Wolf, A., Kabat, E. A., and Bezer, A. E. (1947) The pathology of acute disseminated encephalomyelitis

produced experimentally in the rhesus monkey and its resemblance to human demyelinating disease.

J. Neuropath. Exp. Neurol. 6, 333-357


28

17. Teitelbaum, D., Meshorer, A., Arnon, R., and Sela, M. (1971) Suppression of experimental allergic

encephalomyelitis by a synthetic polypeptide. Eur J Immunol. 1, 242-248

18. Yednock, T. A., Cannon, C., Fritz, L. C., Sanchez-Madrid, F., Steinmann, L., and Karin, N. (1992)

Prevention of experimental autoimmune encephalomyelitis by antibodies against a4b1 integrin.

Nature. 356, 63-66

19. Ellison G.W, and Myers L.W. (1978) A review of systemic nonspecific immunosuppressive treatment

of multiple sclerosis . Neurology 28, 132-139

20. Ridge S.C, Sloboda A.E, and McReynolds R.A. (1985) Suppression of experimental allergic

encephalomyelitis by Mitoxantrone. Clin Immunol Immunopathol. 35, 35-42

21. Sriram, S., and Steiner, I. (2005) Experimental Allergic Encephalomyelitis: A misleading model of

Multiple Sclerosis. Ann Neurol. 58, 939-945

22. Joy, J. E., and Johnston R.B. (2001) Multiple Sclerosis - Current Status and Strategies for the future.

National Academy Press.

23. Brown, A. M., and McFarlin D.E. (1981) Relapsing experimental allergic encephalomyelitis in the SJL/J

mouse. Lab Invest 45, 278-284

24. Lublin, F. D., Maurer, P. H., Berry, R. G., and Tippett, D. (1981) Delayed, relapsing experimental

allergic encephalomyelitis in mice. J Immunol. 126, 819-922

25. Zamvil, S., Nelson, P., Trotter, J., Mitchell, D., Knobler, R., Fritz, R., and Steinman, L. (1985) T cell

clones specific for myelin basic protein induce chronic relapsing EAE and demyelination. Nature 317,

355-358

26. Tsunoda I, Kuang, L. Q., Theil, D. J., and Fujinami, R. S. (2000) Antibody associated with a novel

model for primary progressive multiple sclerosis: Induction of relapsing-remitting and progressive

forms of EAE in H2(s) mouse strains. Brain Path. 10, 402-418

27. Clarke, W., Zhang, Z., and Chan, D. W. (2003) The application of clinical proteomics to cancer and

other diseases. Clin. Chem. Lab Med 41, 1562-1570

28. Fields, S. (2001) Proteomics. Proteomics in genomeland. Science 291, 1221-1224

29. Aebersold, R., and Mann, M. (2003) Mass spectrometry-based proteomics. Nature 422, 198-207

30. Giovannoni, G. (2006) Multiple sclerosis cerebrospinal fluid biomarkers. Dis. Markers 22, 187-196

31. Pierce, J. D., Fakhari, M., Works, K. V., Pierce, J. T., and Clancy, R. L. (2007) Understanding

proteomics. Nurs. Health Sci. 9, 54-60

32. Claverie, J. M. (2001) Gene number. What if there are only 30,000 human genes? Science 291, 1255-1257

33. Kappos, L., Achtnichts, L., Dahlke, F., Kuhle, J., Naegelin, Y., Sandbrink, R., and Lindberg, R. L. (2005)

Genomics and proteomics: role in the management of multiple sclerosis. J Neurol 252 Suppl 3, iii21-

iii27

34. McDonald, W. I., Compston, A., Edan, G., Goodkin, D., Hartung, H. P., Lublin, F. D., McFarland, H.

F., Paty, D. W., Polman, C. H., Reingold, S. C., Sandberg-Wollheim, M., Sibley, W., Thompson, A., van

den, N. S., Weinshenker, B. Y., and Wolinsky, J. S. (2001) Recommended diagnostic criteria for

Chapter 1

29

multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann.

Neurol 50, 121-127

35. Ramstrom, M., and Bergquist, J. (2004) Miniaturized proteomics and peptidomics using capillary

liquid separation and high resolution mass spectrometry. FEBS Lett. 567, 92-95

36. Rosenling, T., Slim, C. L., Christin, C., Coulier, L., Shi, S., Stoop, M. P., Bosman, J., Suits, F.,

Horvatovich, P. L., Stockhofe-Zurwieden, N., Vreeken, R., Hankemeier, T., van Gool, A. J., Luider, T.

M., and Bischoff, R. (2009) The effect of preanalytical factors on stability of the proteome and selected

metabolites in cerebrospinal fluid (CSF). J Proteome. Res. 8, 5511-5522

37. Minagar, A., Adamashvili, I., Kelley, R. E., Gonzalez-Toledo, E., McLarty, J., and Smith, S. J. (2007)

Saliva soluble HLA as a potential marker of response to interferon-beta 1a in multiple sclerosis: a

preliminary study. J Neuroinflammation. 4, 16

38. Calais, G., Forzy, G., Crinquette, C., Mackowiak, A., de, S. J., Blanc, F., Lebrun, C., Heinzlef, O.,

Clavelou, P., Moreau, T., Hennache, B., Zephir, H., Verier, A., Neuville, V., Confavreux, C.,

Vermersch, P., and Hautecoeur, P. (2010) Tear analysis in clinically isolated syndrome as new

multiple sclerosis criterion. Mult. Scler. 16, 87-92

39. Grant, J. E., Hu, J., Liu, T., Jain, M. R., Elkabes, S., and Li, H. (2007) Post-translational modifications in

the rat lumbar spinal cord in experimental autoimmune encephalomyelitis. J Proteome. Res. 6, 2786-

2791

40. Jain, M. R., Bian, S., Liu, T., Hu, J., Elkabes, S., and Li, H. (2009) Altered proteolytic events in

experimental autoimmune encephalomyelitis discovered by iTRAQ shotgun proteomics analysis of

spinal cord. Proteome. Sci. 7, 25

41. Linker, R. A., Brechlin, P., Jesse, S., Steinacker, P., Lee, D. H., Asif, A. R., Jahn, O., Tumani, H., Gold,

R., and Otto, M. (2009) Proteome profiling in murine models of multiple sclerosis: identification of

stage specific markers and culprits for tissue damage. PLoS. One. 4, e7624

42. Liu, T., Donahue, K. C., Hu, J., Kurnellas, M. P., Grant, J. E., Li, H., and Elkabes, S. (2007) Identification

of differentially expressed proteins in experimental autoimmune encephalomyelitis (EAE) by

proteomic analysis of the spinal cord. J Proteome. Res. 6, 2565-2575

43. Mikkat, S., Lorenz, P., Scharf, C., Yu, X., Glocker, M. O., and Ibrahim, S. M. (2010) MS characterization

of qualitative protein polymorphisms in the spinal cords of inbred mouse strains. Proteomics 10, 1050-

1062

44. Morgan, L., Shah, B., Rivers, L. E., Barden, L., Groom, A. J., Chung, R., Higazi, D., Desmond, H.,

Smith, T., and Staddon, J. M. (2007) Inflammation and dephosphorylation of the tight junction protein

occludin in an experimental model of multiple sclerosis. Neuroscience 147, 664-673

45. Ohgoh, M., Hanada, T., Smith, T., Hashimoto, T., Ueno, M., Yamanishi, Y., Watanabe, M., and

Nishizawa, Y. (2002) Altered expression of glutamate transporters in experimental autoimmune

encephalomyelitis. J Neuroimmunol. 125, 170-178

46. Qi, X., Lewin, A. S., Sun, L., Hauswirth, W. W., and Guy, J. (2006) Mitochondrial protein nitration

primes neurodegeneration in experimental autoimmune encephalomyelitis. J Biol. Chem. 281, 31950-

31962

47. Alt, C., Duvefelt, K., Franzen, B., Yang, Y., and Engelhardt, B. (2005) Gene and protein expression

profiling of the microvascular compartment in experimental autoimmune encephalomyelitis in

C57Bl/6 and SJL mice. Brain Pathol. 15, 1-16


30

48. Minagar, A., and Alexander, J. S. (2003) Blood-brain barrier disruption in multiple sclerosis. Mult.

Scler. 9, 540-549

49. Wolburg-Buchholz, K., Mack, A. F., Steiner, E., Pfeiffer, F., Engelhardt, B., and Wolburg, H. (2009)

Loss of astrocyte polarity marks blood-brain barrier impairment during experimental autoimmune

encephalomyelitis. Acta Neuropathol. 118, 219-233

50. El Behi M., Zephir, H., Lefranc, D., Dutoit, V., Dussart, P., Devos, P., Dessaint, J. P., Vermersch, P., and

Prin, L. (2007) Changes in self-reactive IgG antibody repertoire after treatment of experimental

autoimmune encephalomyelitis with anti-allergic drugs. J Neuroimmunol. 182, 80-88

51. Kidd, B. A., Ho, P. P., Sharpe, O., Zhao, X., Tomooka, B. H., Kanter, J. L., Steinman, L., and Robinson,

W. H. (2008) Epitope spreading to citrullinated antigens in mouse models of autoimmune arthritis

and demyelination. Arthritis Res. Ther. 10, R119

52. Villarroya, H., Violleau, K., Ben Younes-Chennoufi, A., and Baumann, N. (1996) Myelin-induced

experimental allergic encephalomyelitis in Lewis rats: tumor necrosis factor alpha levels in serum and

cerebrospinal fluid immunohistochemical expression in glial cells and macrophages of optic nerve

and spinal cord. J Neuroimmunol. 64, 55-61

53. Consiglio, A. R., and Lucion, A. B. (2000) Technique for collecting cerebrospinal fluid in the cisterna

magna of non-anesthetized rats. Brain Res. Brain Res. Protoc. 5, 109-114

54. Nirogi, R., Kandikere, V., Mudigonda, K., Bhyrapuneni, G., Muddana, N., Saralaya, R., and Benade, V.

(2009) A simple and rapid method to collect the cerebrospinal fluid of rats and its application for the

assessment of drug penetration into the central nervous system. J Neurosci. Methods 178, 116-119

55. Lai, Y. L., Smith, P. M., Lamm, W. J., and Hildebrandt, J. (1983) Sampling and analysis of

cerebrospinal fluid for chronic studies in awake rats. Journal of Applied Physiology 54, 1754-1757

56. van den Berg, M. P., Romeijn, S. G., Verhoef, J. C., and Merkus, F. W. (2002) Serial cerebrospinal fluid

sampling in a rat model to study drug uptake from the nasal cavity. J Neurosci. Methods 116, 99-107

57. Liu, L., and Duff, K. (2008) A technique for serial collection of cerebrospinal fluid from the cisterna

magna in mouse. J Vis. Exp.

58. Boshi, G., Launay, N., Rips, R., and Scherrmann, J.-M. (1995) Brain microdialysis in the mouse. Journal

of Pharmacological and Toxicological Methods 33, 29-33

59. Afinowi, R., Tisdall, M., Keir, G., Smith, M., Kitchen, N., and Petzold, A. (2009) Improving the

recovery of S100B protein in cerebral microdialysis: implications for multimodal monitoring in

neurocritical care. J Neurosci. Methods 181, 95-99

60. Mazzeo, A. T., and Bullock, R. (2005) Effect of bacterial meningitis complicating severe head trauma

upon brain microdialysis and cerebral perfusion. Neurocrit. Care 2, 282-287

61. Tisdall, M. M., and Smith, M. (2006) Cerebral microdialysis: research technique or clinical tool. Br. J

Anaesth. 97, 18-25

62. Maurer, M. H. (2010) Proteomics of brain extracellular fluid (ECF) and cerebrospinal fluid (CSF). Mass

Spectrom. Rev. 29, 17-28

63. Linker, R. A., and Lee, D. H. (2009) Models of autoimmune demyelination in the central nervous

system: on the way to translational medicine. Exp. Transl. Stroke Med 1, 5

Chapter 1

31

64. James, P. (1997) Protein identification in the post-genome era: the rapid rise of proteomics. Q. Rev.

Biophys. 30, 279-331

65. Stegbauer, J., Lee, D. H., Seubert, S., Ellrichmann, G., Manzel, A., Kvakan, H., Muller, D. N., Gaupp,

S., Rump, L. C., Gold, R., and Linker, R. A. (2009) Role of the renin-angiotensin system in autoimmune

inflammation of the central nervous system. Proc. Natl. Acad. Sci. U. S. A 106, 14942-14947

66. Barnett, M. H., Parratt, J. D., Cho, E. S., and Prineas, J. W. (2009) Immunoglobulins and complement in

postmortem multiple sclerosis tissue. Ann. Neurol 65, 32-46

67. Carlin, C., Murray, L., Graham, D., Doyle, D., and Nicoll, J. (2000) Involvement of apolipoprotein E in

multiple sclerosis: absence of remyelination associated with possession of the APOE epsilon2 allele. J

Neuropathol. Exp. Neurol 59, 361-367

68. Chiasserini, D., Di, F. M., Candeliere, A., Susta, F., Orvietani, P. L., Calabresi, P., Binaglia, L., and

Sarchielli, P. (2008) CSF proteome analysis in multiple sclerosis patients by two-dimensional

electrophoresis. Eur. J Neurol 15, 998-1001

69. Elderfield, A. J., Newcombe, J., Bolton, C., and Flower, R. J. (1992) Lipocortins (annexins) 1, 2, 4 and 5

are increased in the central nervous system in multiple sclerosis. J Neuroimmunol. 39, 91-100

70. Fazekas, F., Strasser-Fuchs, S., Kollegger, H., Berger, T., Kristoferitsch, W., Schmidt, H., Enzinger, C.,

Schiefermeier, M., Schwarz, C., Kornek, B., Reindl, M., Huber, K., Grass, R., Wimmer, G., Vass, K.,

Pfeiffer, K. H., Hartung, H. P., and Schmidt, R. (2001) Apolipoprotein E epsilon 4 is associated with

rapid progression of multiple sclerosis. Neurology 57, 853-857

71. Gay, F. W. (2006) Early cellular events in multiple sclerosis. Intimations of an extrinsic myelinolytic

antigen. Clin. Neurol Neurosurg. 108, 234-240

72. Hammack, B. N., Fung, K. Y., Hunsucker, S. W., Duncan, M. W., Burgoon, M. P., Owens, G. P., and

Gilden, D. H. (2004) Proteomic analysis of multiple sclerosis cerebrospinal fluid. Mult. Scler. 10, 245-

260

73. Hedegaard, C. J., Chen, N., Sellebjerg, F., Sorensen, P. S., Leslie, R. G., Bendtzen, K., and Nielsen, C. H.

(2009) Autoantibodies to myelin basic protein (MBP) in healthy individuals and in patients with

multiple sclerosis: a role in regulating cytokine responses to MBP. Immunology 128, e451-e461

74. Jongen, P. J., Nijeholt, G., Lamers, K. J., Doesburg, W. H., Barkhof, F., Lemmens, W. A., Klasen, I. S.,

and Hommes, O. R. (2007) Cerebrospinal fluid IgM index correlates with cranial MRI lesion load in

patients with multiple sclerosis. Eur. Neurol 58, 90-95

75. KABAT, E. A., FREEDMAN, D. A., and . (1950) A study of the crystalline albumin, gamma globulin

and total protein in the cerebrospinal fluid of 100 cases of multiple sclerosis and in other diseases. Am.

J Med Sci. 219, 55-64

76. MacPherson, C. F., and Cosgrove, J. B. (1968) An unusual IgG globulin. Frequency of occurrence in

cerebrospinal fluid in multiple sclerosis. Arch Neurol 19, 503-509

77. Padilla-Docal, B., Dorta-Contreras, A. J., Fundora-Hernandez, H., Noris-Garcia, E., Bu-Coifiu-Fanego,

R., Gonzalez-Hernandez, M., and Rodriguez-Rey, A. (2007) C3c intrathecal synthesis evaluation in

patients with multiple sclerosis. Arq Neuropsiquiatr. 65, 800-802

78. Philippe, J., Debruyne, J., Leroux-Roels, G., Willems, A., and Dereuck, J. (1996) In vitro TNF-alpha, IL-

2 and IFN-gamma production as markers of relapses in multiple sclerosis. Clin. Neurol Neurosurg. 98,

286-290


32

79. Sobel, R. A., and Mitchell, M. E. (1989) Fibronectin in multiple sclerosis lesions. Am. J Pathol. 135, 161-

168

80. Spuler, S., Yousry, T., Scheller, A., Voltz, R., Holler, E., Hartmann, M., Wick, M., and Hohlfeld, R.

(1996) Multiple sclerosis: prospective analysis of TNF-alpha and 55 kDa TNF receptor in CSF and

serum in correlation with clinical and MRI activity. J Neuroimmunol. 66, 57-64

81. Stoop, M. P., Dekker, L. J., Titulaer, M. K., Burgers, P. C., Sillevis Smitt, P. A., Luider, T. M., and

Hintzen, R. Q. (2008) Multiple sclerosis-related proteins identified in cerebrospinal fluid by advanced

mass spectrometry. Proteomics 8, 1576-1585

82. Stoop, M. P., Dekker, L. J., Titulaer, M. K., Lamers, R. J., Burgers, P. C., Sillevis Smitt, P. A., van Gool,

A. J., Luider, T. M., and Hintzen, R. Q. (2009) Quantitative matrix-assisted laser desorption ionization-

fourier transform ion cyclotron resonance (MALDI-FT-ICR) peptide profiling and identification of

multiple-sclerosis-related proteins. J Proteome. Res. 8, 1404-1414

83. Tourtellotte, W. W., and Parker, J. A. (1967) Multiple sclerosis: brain immunoglobulin-G and albumin.

Nature 214, 683-686

84. van Horssen J., Bo, L., Vos, C. M., Virtanen, I., and De Vries, H. E. (2005) Basement membrane

proteins in multiple sclerosis-associated inflammatory cuffs: potential role in influx and transport of

leukocytes. J Neuropathol. Exp. Neurol 64, 722-729

85. Zhang, B., Jiang, Y., Yang, Y., Peng, F., and Hu, X. (2008) Correlation between serum thyroxine and

complements in patients with multiple sclerosis and neuromyelitis optica. Neuro. Endocrinol. Lett. 29,

256-260

86. Kawajiri, M., Mogi, M., Osoegawa, M., Matsuoka, T., Tsukuda, K., Kohara, K., Horiuchi, M., Miki, T.,

and Kira, J. I. (2008) Reduction of angiotensin II in the cerebrospinal fluid of patients with multiple

sclerosis. Mult. Scler. 14, 557-560

87. Ottervald, J., Franzen, B., Nilsson, K., Andersson, L. I., Khademi, M., Eriksson, B., Kjellstrom, S.,

Marko-Varga, G., Vegvari, A., Harris, R. A., Laurell, T., Miliotis, T., Matusevicius, D., Salter, H., Ferm,

M., and Olsson, T. (2010) Multiple sclerosis: Identification and clinical evaluation of novel CSF

biomarkers. J Proteomics

88. Wosik, K., Cayrol, R., Dodelet-Devillers, A., Berthelet, F., Bernard, M., Moumdjian, R., Bouthillier, A.,

Reudelhuber, T. L., and Prat, A. (2007) Angiotensin II controls occludin function and is required for

blood brain barrier maintenance: relevance to multiple sclerosis. J Neurosci. 27, 9032-9042

89. Szczucinski, A., and Losy, J. (2007) Chemokines and chemokine receptors in multiple sclerosis.

Potential targets for new therapies. Acta Neurol Scand. 115, 137-146

90. Berger, T., Rubner, P., Schautzer, F., Egg, R., Ulmer, H., Mayringer, I., Dilitz, E., Deisenhammer, F.,

and Reindl, M. (2003) Antimyelin antibodies as a predictor of clinically definite multiple sclerosis after

a first demyelinating event. N. Engl. J Med 349, 139-145

91. Egg, R., Reindl, M., Deisenhammer, F., Linington, C., and Berger, T. (2001) Anti-MOG and anti-MBP

antibody subclasses in multiple sclerosis. Mult. Scler. 7, 285-289

92. Vojdani, A., Vojdani, E., and Cooper, E. (2003) Antibodies to myelin basic protein, myelin

oligodendrocytes peptides, alpha-beta-crystallin, lymphocyte activation and cytokine production in

patients with multiple sclerosis. J Intern. Med 254, 363-374

Chapter 1

33

93. Gunnarsson, M., Sundstrom, P., Stigbrand, T., and Jensen, P. E. (2003) Native and transformed alpha2-

macroglobulin in plasma from patients with multiple sclerosis. Acta Neurol Scand. 108, 16-21

94. Jensen, P. E., Humle, J. S., Datta, P., and Sorensen, P. S. (2004) Significantly increased fractions of

transformed to total alpha2-macroglobulin concentrations in plasma from patients with multiple

sclerosis. Biochim. Biophys. Acta 1690, 203-207

95. Newcombe, J., Uddin, A., Dove, R., Patel, B., Turski, L., Nishizawa, Y., and Smith, T. (2008) Glutamate

receptor expression in multiple sclerosis lesions. Brain Pathol. 18, 52-61

96. Vallejo-Illarramendi, A., Domercq, M., Perez-Cerda, F., Ravid, R., and Matute, C. (2006) Increased

expression and function of glutamate transporters in multiple sclerosis. Neurobiol. Dis. 21, 154-164

97. Werner, P., Pitt, D., and Raine, C. S. (2001) Multiple sclerosis: altered glutamate homeostasis in lesions

correlates with oligodendrocyte and axonal damage. Ann. Neurol 50, 169-180

98. Lisman, J., Schulman, H., and Cline, H. (2002) The molecular basis of CaMKII function in synaptic and

behavioural memory. Nat. Rev. Neurosci. 3, 175-190

99. Poggi, A., Zocchi, M. R., Carosio, R., Ferrero, E., Angelini, D. F., Galgani, S., Caramia, M. D., Bernardi,

G., Borsellino, G., and Battistini, L. (2002) Transendothelial migratory pathways of V delta 1+TCR

gamma delta+ and V delta 2+TCR gamma delta+ T lymphocytes from healthy donors and multiple

sclerosis patients: involvement of phosphatidylinositol 3 kinase and calcium calmodulin-dependent

kinase II. J Immunol. 168, 6071-6077

100. Gonsette, R. E. (2008) Neurodegeneration in multiple sclerosis: the role of oxidative stress and

excitotoxicity. J Neurol Sci. 274, 48-53

101. LeVine, S. M., Lynch, S. G., Ou, C. N., Wulser, M. J., Tam, E., and Boo, N. (1999) Ferritin, transferrin

and iron concentrations in the cerebrospinal fluid of multiple sclerosis patients. Brain Res. 821, 511-515

102. LeVine, S. M., and Chakrabarty, A. (2004) The role of iron in the pathogenesis of experimental allergic

encephalomyelitis and multiple sclerosis. Ann. N. Y. Acad. Sci. 1012, 252-266

103. Rathore, K. I., Kerr, B. J., Redensek, A., Lopez-Vales, R., Jeong, S. Y., Ponka, P., and David, S. (2008)

Ceruloplasmin protects injured spinal cord from iron-mediated oxidative damage. J Neurosci. 28,

12736-12747

104. Tapryal, N., Mukhopadhyay, C., Das, D., Fox, P. L., and Mukhopadhyay, C. K. (2009) Reactive oxygen

species regulate ceruloplasmin by a novel mRNA decay mechanism involving its 3'-untranslated

region: implications in neurodegenerative diseases. J Biol. Chem. 284, 1873-1883

105. Hunter, M. I., Nlemadim, B. C., and Davidson, D. L. (1985) Lipid peroxidation products and

antioxidant proteins in plasma and cerebrospinal fluid from multiple sclerosis patients. Neurochem.

Res. 10, 1645-1652

106. Lok, C. N., and Loh, T. T. (1998) Regulation of transferrin function and expression: review and update.

Biol. Signals Recept. 7, 157-178

107. Stout, D. L. (1992) The role of transferrin in heme transport. Biochem. Biophys. Res. Commun. 189, 765-

770

108. Weller, M., List, U., Schabet, M., Melms, A., and Dichgans, J. (1999) Elevated CSF lactoferrin in

superficial siderosis of the central nervous system. J Neurol 246, 943-945


34

109. Abo-Krysha, N., and Rashed, L. (2008) The role of iron dysregulation in the pathogenesis of multiple

sclerosis: an Egyptian study. Mult. Scler. 14, 602-608

110. Tajouri, L., Mellick, A. S., Ashton, K. J., Tannenberg, A. E., Nagra, R. M., Tourtellotte, W. W., and

Griffiths, L. R. (2003) Quantitative and qualitative changes in gene expression patterns characterize

the activity of plaques in multiple sclerosis. Brain Res. Mol. Brain Res. 119, 170-183

111. Tumani, H., Lehmensiek, V., Rau, D., Guttmann, I., Tauscher, G., Mogel, H., Palm, C., Hirt, V.,

Suessmuth, S. D., Sapunova-Meier, I., Ludolph, A. C., and Brettschneider, J. (2009) CSF proteome

analysis in clinically isolated syndrome (CIS): candidate markers for conversion to definite multiple

sclerosis. Neurosci. Lett. 452, 214-217

112. Zeman, D., Adam, P., Kalistova, H., Sobek, O., Kelbich, P., Andel, J., and Andel, M. (2000) Transferrin

in patients with multiple sclerosis: a comparison among various subgroups of multiple sclerosis

patients. Acta Neurol Scand. 101, 89-94

113. Tolosano, E., and Altruda, F. (2002) Hemopexin: structure, function, and regulation. DNA Cell Biol. 21,

297-306

114. Rithidech, K. N., Honikel, L., Milazzo, M., Madigan, D., Troxell, R., and Krupp, L. B. (2009) Protein

expression profiles in pediatric multiple sclerosis: potential biomarkers. Mult. Scler. 15, 455-464

115. Gabay, C., and Kushner, I. (1999) Acute-phase proteins and other systemic responses to inflammation.

N. Engl. J Med 340, 448-454

116. Fuchs, E., and Weber, K. (1994) Intermediate filaments: structure, dynamics, function, and disease.

Annu. Rev. Biochem. 63, 345-382

117. van der Flier A., and Sonnenberg, A. (2001) Structural and functional aspects of filamins. Biochim.

Biophys. Acta 1538, 99-117

118. Lock, C., Hermans, G., Pedotti, R., Brendolan, A., Schadt, E., Garren, H., Langer-Gould, A., Strober, S.,

Cannella, B., Allard, J., Klonowski, P., Austin, A., Lad, N., Kaminski, N., Galli, S. J., Oksenberg, J. R.,

Raine, C. S., Heller, R., and Steinman, L. (2002) Gene-microarray analysis of multiple sclerosis lesions

yields new targets validated in autoimmune encephalomyelitis. Nat. Med 8, 500-508

119. Amieva, M. R., and Furthmayr, H. (1995) Subcellular localization of moesin in dynamic filopodia,

retraction fibers, and other structures involved in substrate exploration, attachment, and cell-cell

contacts. Exp. Cell Res. 219, 180-196

120. Holley, J. E., Gveric, D., Newcombe, J., Cuzner, M. L., and Gutowski, N. J. (2003) Astrocyte

characterization in the multiple sclerosis glial scar. Neuropathol. Appl. Neurobiol. 29, 434-444

121. Malmestrom, C., Haghighi, S., Rosengren, L., Andersen, O., and Lycke, J. (2003) Neurofilament light

protein and glial fibrillary acidic protein as biological markers in MS. Neurology 61, 1720-1725

122. Norgren, N., Sundstrom, P., Svenningsson, A., Rosengren, L., Stigbrand, T., and Gunnarsson, M.

(2004) Neurofilament and glial fibrillary acidic protein in multiple sclerosis. Neurology 63, 1586-1590

123. Rosengren, L. E., Lycke, J., and Andersen, O. (1995) Glial fibrillary acidic protein in CSF of multiple

sclerosis patients: relation to neurological deficit. J Neurol Sci. 133, 61-65

124. Selmaj, K., Pawlowska, Z., Walczak, A., Koziolkiewicz, W., Raine, C. S., and Cierniewski, C. S. (2008)

Corpora amylacea from multiple sclerosis brain tissue consists of aggregated neuronal cells. Acta

Biochim. Pol. 55, 43-49

Chapter 1

35

125. Petzold, A., Gveric, D., Groves, M., Schmierer, K., Grant, D., Chapman, M., Keir, G., Cuzner, L., and

Thompson, E. J. (2008) Phosphorylation and compactness of neurofilaments in multiple sclerosis:

indicators of axonal pathology. Exp. Neurol 213, 326-335

126. Kolln, J., Ren, H. M., Da, R. R., Zhang, Y., Spillner, E., Olek, M., Hermanowicz, N., Hilgenberg, L. G.,

Smith, M. A., van den, N. S., and Qin, Y. (2006) Triosephosphate isomerase- and glyceraldehyde-3-

phosphate dehydrogenase-reactive autoantibodies in the cerebrospinal fluid of patients with multiple

sclerosis. J Immunol. 177, 5652-5658

127. Bever, C. T., Jr., Panitch, H. S., and Johnson, K. P. (1994) Increased cathepsin B activity in peripheral

blood mononuclear cells of multiple sclerosis patients. Neurology 44, 745-748

128. Bever, C. T., Jr., and Garver, D. W. (1995) Increased cathepsin B activity in multiple sclerosis brain. J

Neurol Sci. 131, 71-73

129. Sueoka, E., Yukitake, M., Iwanaga, K., Sueoka, N., Aihara, T., and Kuroda, Y. (2004) Autoantibodies

against heterogeneous nuclear ribonucleoprotein B1 in CSF of MS patients. Ann. Neurol 56, 778-786

130. Yukitake, M., Sueoka, E., Sueoka-Aragane, N., Sato, A., Ohashi, H., Yakushiji, Y., Saito, M., Osame,

M., Izumo, S., and Kuroda, Y. (2008) Significantly increased antibody response to heterogeneous

nuclear ribonucleoproteins in cerebrospinal fluid of multiple sclerosis patients but not in patients with

human T-lymphotropic virus type I-associated myelopathy/tropical spastic paraparesis. J Neurovirol.

14, 130-135

131. Schonbrunner, E. R., and Schmid, F. X. (1992) Peptidyl-prolyl cis-trans isomerase improves the

efficiency of protein disulfide isomerase as a catalyst of protein folding. Proc. Natl. Acad. Sci. U. S. A

89, 4510-4513

132. Ramanathan, M., Weinstock-Guttman, B., Nguyen, L. T., Badgett, D., Miller, C., Patrick, K.,

Brownscheidle, C., and Jacobs, L. (2001) In vivo gene expression revealed by cDNA arrays: the pattern

in relapsing-remitting multiple sclerosis patients compared with normal subjects. J Neuroimmunol. 116,

213-219

133. Witte, M. E., Bo, L., Rodenburg, R. J., Belien, J. A., Musters, R., Hazes, T., Wintjes, L. T., Smeitink, J. A.,

Geurts, J. J., De Vries, H. E., van, d., V, and van, H. J. (2009) Enhanced number and activity of

mitochondria in multiple sclerosis lesions. J Pathol. 219, 193-204

134. Myllykoski, M., and Kursula, P. (2010) Expression, purification, and initial characterization of

different domains of recombinant mouse 2',3'-cyclic nucleotide 3'-phosphodiesterase, an enigmatic

enzyme from the myelin sheath. BMC Res. Notes 3, 12

135. Lovato, L., Cianti, R., Gini, B., Marconi, S., Bianchi, L., Armini, A., Anghileri, E., Locatelli, F., Paoletti,

F., Franciotta, D., Bini, L., and Bonetti, B. (2008) Transketolase and 2',3'-cyclic-nucleotide 3'-

phosphodiesterase type I isoforms are specifically recognized by IgG autoantibodies in multiple

sclerosis patients. Mol. Cell Proteomics 7, 2337-2349

136. Walsh, M. J., and Murray, J. M. (1998) Dual implication of 2',3'-cyclic nucleotide 3' phosphodiesterase

as major autoantigen and C3 complement-binding protein in the pathogenesis of multiple sclerosis. J

Clin. Invest 101, 1923-1931

137. Irani, D. N., Anderson, C., Gundry, R., Cotter, R., Moore, S., Kerr, D. A., McArthur, J. C., Sacktor, N.,

Pardo, C. A., Jones, M., Calabresi, P. A., and Nath, A. (2006) Cleavage of cystatin C in the

cerebrospinal fluid of patients with multiple sclerosis. Ann. Neurol 59, 237-247


36

138. Nakashima, I., Fujinoki, M., Fujihara, K., Kawamura, T., Nishimura, T., Nakamura, M., and Itoyama,

Y. (2007) Alteration of cystatin C in the cerebrospinal fluid of multiple sclerosis. Ann. Neurol 62, 197-

200

139. Reindl, M., Knipping, G., Wicher, I., Dilitz, E., Egg, R., Deisenhammer, F., and Berger, T. (2001)

Increased intrathecal production of apolipoprotein D in multiple sclerosis. J Neuroimmunol. 119, 327-

332

140. Pitt, D., Nagelmeier, I. E., Wilson, H. C., and Raine, C. S. (2003) Glutamate uptake by

oligodendrocytes: Implications for excitotoxicity in multiple sclerosis. Neurology 61, 1113-1120

141. Logothetis, L., Mylonas, I. A., Baloyannis, S., Pashalidou, M., Orologas, A., Zafeiropoulos, A., Kosta,

V., and Theoharides, T. C. (2005) A pilot, open label, clinical trial using hydroxyzine in multiple

sclerosis. Int. J Immunopathol. Pharmacol. 18, 771-778

142. Alonso, A., Jick, S. S., and Hernan, M. A. (2006) Allergy, histamine 1 receptor blockers, and the risk of

multiple sclerosis. Neurology 66, 572-575

143. Moscarello, M. A., Mastronardi, F. G., and Wood, D. D. (2007) The role of citrullinated proteins

suggests a novel mechanism in the pathogenesis of multiple sclerosis. Neurochem. Res. 32, 251-256

144. De Seze J., Dubucquoi, S., Lefranc, D., Virecoulon, F., Nuez, I., Dutoit, V., Vermersch, P., and Prin, L.

(2001) IgG reactivity against citrullinated myelin basic protein in multiple sclerosis. J Neuroimmunol.

117, 149-155

145. Kim, J. K., Mastronardi, F. G., Wood, D. D., Lubman, D. M., Zand, R., and Moscarello, M. A. (2003)

Multiple sclerosis: an important role for post-translational modifications of myelin basic protein in

pathogenesis. Mol. Cell Proteomics 2, 453-462

146. Oguz, K. K., Kurne, A., Aksu, A. O., Karabulut, E., Serdaroglu, A., Teber, S., Haspolat, S., Senbil, N.,

Kurul, S., and Anlar, B. (2009) Assessment of citrullinated myelin by 1H-MR spectroscopy in early-

onset multiple sclerosis. AJNR Am. J Neuroradiol. 30, 716-721

147. Tranquill, L. R., Cao, L., Ling, N. C., Kalbacher, H., Martin, R. M., and Whitaker, J. N. (2000) Enhanced

T cell responsiveness to citrulline-containing myelin basic protein in multiple sclerosis patients. Mult.

Scler. 6, 220-225

148. Gresle, M. M., Shaw, G., Jarrott, B., Alexandrou, E. N., Friedhuber, A., Kilpatrick, T. J., and

Butzkueven, H. (2008) Validation of a novel biomarker for acute axonal injury in experimental

autoimmune encephalomyelitis. J Neurosci. Res. 86, 3548-3555

149. Proia, P., Schiera, G., Salemi, G., Ragonese, P., Savettieri, G., and Di, L., I. (2009) Neuronal and BBB

damage induced by sera from patients with secondary progressive multiple sclerosis. Int. J Mol. Med

24, 743-747

150. Blecharz, K. G., Haghikia, A., Stasiolek, M., Kruse, N., Drenckhahn, D., Gold, R., Roewer, N., Chan,

A., and Forster, C. Y. (2010) Glucocorticoid effects on endothelial barrier function in the murine brain

endothelial cell line cEND incubated with sera from patients with multiple sclerosis. Mult. Scler. 16,

293-302

151. Minagar, A., Ostanin, D., Long, A. C., Jennings, M., Kelley, R. E., Sasaki, M., and Alexander, J. S.

(2003) Serum from patients with multiple sclerosis downregulates occludin and VE-cadherin

expression in cultured endothelial cells. Mult. Scler. 9, 235-23.

Chapter 1

37

152. Table 26 from Spinal drug and delivery, page 216, Tony L. Yaksh – 1999, Elsevier sciences B.V. ISBN: 0-

444-82901-6 Chapter 8, Spinal cerebrospinal fluid chemistry and physiology, A.A. Artru, (seattle, WA,

USA), pp 177-238).

153. Table 27 from Spinal drug and delivery, page 216, Tony L. Yaksh – 1999, Elsevier sciences B.V. ISBN: 0-

444-82901-6 Chapter 8, Spinal cerebrospinal fluid chemistry and physiology, A.A. Artru, (seattle, WA,

USA), pp 177-238)

38

Chapter 2

39

The effect of pre-analytical factors on

stability of the proteome and selected

metabolites in cerebrospinal fluid (CSF)

Therese Rosenling1, Christiaan L. Slim1, Christin Christin1, Leon Coulier2, Shanna

Shi5, Marcel P. Stoop3, Jan Bosman1, Frank Suits6, Peter L. Horvatovich1, Norbert

Stockhofe-Zurwieden7, Rob Vreeken4, Thomas Hankemeier4, Alain J. van Gool8,

Theo M. Luider3, Rainer Bischoff1

1Analytical Biochemistry, Department of Pharmacy, University of

Groningen, Groningen, The Netherlands

2TNO, Quality of Life, Zeist, The Netherlands

3 Department of Neurology, Erasmus University Medical Center,

Rotterdam, The Netherlands

4Analytical BioSciences, Leiden/Amsterdam Centre for Drug Research,

Leiden University, Leiden, The Netherlands

5Netherlands Metabolomics Centre, Leiden/Amsterdam Centre for Drug

Research, Leiden University, Leiden, The Netherlands

6IBM TJ Watson Research Centre, Yorktown Heights, NY, USA

7Animal Sciences Group, Wageningen UR, Division Infectious Diseases,

Lelystad, The Netherlands

8Schering-Plough, Oss, The Netherlands

Rosenling et al. J Proteome Res. 2009 Dec;8(12):5511-22.

The effect of pre-analytical factors on the proteome and metabolites of CSF

40

Abstract

In order to standardize the use of cerebrospinal fluid (CSF) for biomarker research,

a set of stability studies have been performed on porcine samples to investigate

the influence of common sample handling procedures on proteins, peptides,

metabolites and free amino acids. This study focuses at the effect on proteins and

peptides, analyzed by applying label-free quantitation using microfluidics nano-

scale liquid chromatography coupled to quadrupole time-of-flight mass

spectrometry (chipLC-MS) as well as matrix assisted laser desorption ionization

Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FT-ICR-

MS) and Orbitrap LC-MS/MS to trypsin-digested CSF samples. The factors

assessed were a 30 or 120 min time delay at room temperature before storage at -

80 °C after the collection of CSF in order to mimic potential delays in the clinic

(delayed storage), storage at 4 °C after trypsin digestion to mimic the time that

samples remain in the cooled auto-sampler of the analyzer, and repeated freeze-

thaw cycles to mimic storage and handling procedures in the laboratory. The

delayed storage factor was also analyzed by gas chromatography mass

spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS)

for changes on metabolites and free amino acids, respectively. Our results show

that repeated freeze/thawing introduced changes in transthyretin peptide levels.

The trypsin digested samples left at 4 °C in the autosampler showed a time-

dependent decrease of peak areas for peptides from prostaglandin D-synthase and

serotransferrin. Delayed storage of CSF lead to changes in prostaglandin D-

synthase derived peptides as well as to increased levels of certain amino acids and

metabolites. The changes of metabolites, amino acids and proteins in the delayed

storage study appear to be related to remaining white blood cells. Our

recommendations are to centrifuge CSF samples immediately after collection to

remove white blood cells, aliquot and then snap freeze the supernatant in liquid

nitrogen for storage at -80 °C. Preferably samples should not be left in the

autosampler for more than 24 hours and freeze/thaw cycles should be avoided if

at all possible.

Chapter 2

41

1. Introduction

In the search of molecular biomarkers for disorders of the central nervous system

(CNS), such as Alzheimer’s Disease (AD) or Multiple Sclerosis (MScl),

cerebrospinal fluid (CSF) is the body fluid of choice because of its proximity to the

diseased tissue and its relative ease of sampling (1-9). In order to obtain reliable

results and reduce the risk of detecting false biomarker candidates it is important

to handle samples properly and to control for the effect of pre-analytical

parameters on proteins or metabolites. Earlier a cleavage product of cystatin C

was reported as a possible biomarker for MScl, but later studies revealed that this

cleavage product was not linked to the disease but rather to the storage conditions

(10-12). This observation emphasizes that keeping control over factors such as

storage conditions, number of freeze/thaw cycles and the collection of CSF itself is

critical.

Studies on urine and plasma or serum have shown that internal (protease

activity, blood contamination and protein concentration) as well as external factors

(storage, handling, and analysis method) crucially affect the proteome profile (13-

17). There are a few reports on the stability of single proteins in CSF connected to

AD18-21 as well as on the effect of storage temperature (4 °C or 23 °C) on the

proteome profile of CSF by analyzing intact proteins and peptides with surface-

enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-

TOF-MS) (20, 22).

Two studies on canine CSF report the effect of storage temperature on

protein concentration, glucose level, pH and enzyme activity (23) as well as the

effect of storage temperature, with or without addition of stabilizing agent, on the

cell count (24). Furthermore, guidelines have been published concerning pre-

analytical factors that should be examined (e.g. cytological examination and

glucose concentration measurement) leading to recommendations for proteomic

CSF analysis (25-27).

Many of the studies evaluating the influence of pre-analytical factors on

protein composition in body fluids have been performed by SELDI-TOF-MS (15,

16, 22, 28) a method that suffers from rather poor concentration sensitivity and

that may be prone to mass spectrometric artifacts (29). More sensitive approaches

using enrichment of proteins and peptides on magnetic bead separators or

separation by Liquid Chromatography (LC) followed by Matrix-Assisted Laser

Desorption Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF-MS) have

also indicated that conditions of sample handling and preparation are critical (14,

30-36).

In a study conducted by Anesi et al. the level of amino acids (Glu, Gly, Asp

and Tau) was analyzed with respect to different storage conditions (-20 °C and -80


42

°C) after a number of pre-treatments (deproteinization, neutralization, no

treatment) (37). This study indicated changes in the amino acid concentrations in

samples that were not treated prior to long term storage. Another study by Levine

et al. reported the effect of storage at room temperature (for 72 hours) on CSF

metabolites (31). In this study the samples had been stored at -70 °C for 30 months

prior to analysis by NMR. NMR analysis revealed changes in metabolite

concentration with both increasing and decreasing concentration levels.

In this paper we describe a study into the effects of pre-analytical factors

on the porcine CSF proteome and metabolome using a variety of techniques

comprising LC-MS, GC-MS and MALDI-FT-ICR-MS.

2. Material and Methods

2.1. Sample set Porcine CSF was collected at the Animal Sciences Group, Wageningen University,

Division of Infectious Diseases, Lelystad, The Netherlands. Five conventional pigs

(ca. 100 kg weight, males) were euthanized and CSF was sampled from the

cerebromedullary cistern of the subarachnoid space in the cervical region directly

after euthanasia. Euthanasia was performed by intravenous injection of T61®

(Embutramid) (pig 3 [P3] and pig 7 [P7]) or pentobarbital (pig 2 [P2], pig 4 [P4]

and pig 5 [P5]) followed by exsanguination. Samples (~10 mL) were taken under

mild suction (using a syringe with a 22 G needle), transferred to a 50 mL Falcon

tube and immediately transported to the laboratory where contamination with red

blood cells (RBC) and white blood cells (WBC) was assessed with an F-800 cell

counter (Sysmex). The CSF samples from P2 contained 40 RBC/µL and 40

WBC/µL, P4 contained 40 RBC/µL and 60 WBC/µL and P5 had 120 RBC/µL and 40

WBC/µL before centrifugation (10 min, 1500 g). RBC or WBC were undetectable

after centrifugation. The CSF from P3 and P7 showed no contamination with RBC

but both contained 100 WBC/µL (samples were not centrifuged).

Aliquots of samples (1.5 mL) were directly snap-frozen in liquid nitrogen

(T0). Further aliquots were left for 30 min (T30) or 2 h at room temperature (T120)

before being frozen in liquid nitrogen and stored at -80 °C. The aliquots were

stored in 2 mL Nalgene Cryo-100-025u sterile tubes (part # N.Alg 5000-0020). For

studying the effect of WBC contamination on stability at room temperature

between CSF collection and freezing, samples T0, T30 and T120 of P3, P7 (not

centrifuged) and P2, P4 and P5 (centrifuged) were compared. For the freeze/thaw

study sample T0 from P3 was used and for evaluating stability of trypsin-digested

CSF at 4 °C T0 from P2 and P4 was used. The total protein concentration of the

samples was measured using the Micro BCA™ Assay (Pierce, Rockford, IL, USA).

For proteomics analysis CSF was depleted of the six most abundant serum

Chapter 2

43

proteins and digested with trypsin (see section 2.3). The mean protein

concentration in the non depleted samples was 870 ng/µL and in the depleted

samples it was 50 ng/μL (Relative Standard Deviation (RSD): < 10% measured in

triplicate).

A quality control (QC) sample was prepared to control for analytical

variability during the chipLC-MS analyses. This sample consisted of pooled,

depleted, trypsin-digested porcine CSF spiked with digested horse heart

cytochrome C (Fluka, part # 30396) (final concentration 100 fmol/μL). Prior to the

study all porcine CSF samples were exposed to two freeze/thaw cycles

(sampling/storage and depletion/storage). After this the samples were exposed to

at most two further freeze/thaw cycles before analysis (aliquoting and storage

between digestion and analysis).

After depletion the CSF was stored, and all reactions were carried out in 1.5 or 2

mL reaction tubes (Greiner Bio-One, part # 616 201/623 201). After digestion the

samples were transferred to plastic inserts (Waters, Milford, MA, USA, part #

WAT094170) inside glass vials with screw caps (Agilent Technologies, Santa Clara,

CA USA, part # 5187-0716 + 5182-0717).

2.2. Design of study In this study three pre-analytical parameters were assessed: 1. Delayed storage

after sample collection; porcine CSF was frozen at three different time points after

sampling (0, 30, 120 min at room temperature without centrifugation or after

centrifugation). The proteome profile was first assessed on the chipLC-MS

platform and subsequently by MALDI-FT-ICR-MS in a separate laboratory.

Furthermore the metabolome profile was assessed using two different analytical

platforms in two separate laboratories (see sections 2.4 – 2.8). 2. Number of

freeze/thaw cycles of depleted CSF; the proteome profile of depleted porcine CSF

exposed to different numbers of freeze/thaw cycles (control, 1×, 2×, 3×, 4×, 5× and

10× cycles) was assessed by chipLC-MS. 3. Storage of depleted/digested CSF at 4

°C; after depletion and digestion CSF was left at 4 °C for up to one month (control,

1 day, 1 week and 1 month) and the effect was assessed by chipLC-MS.

2.3. Sample preparation (proteomic analysis) For proteomic analysis all CSF samples were depleted of 6 highly abundant

proteins; albumin, IgG (immunoglobulin G), IgA (immunoglobulin A),

haptoglobin, transferrin and α-1-Antitrypsin by immuno-affinity chromatography.

Prior to depletion 200 μL of porcine CSF was concentrated 20 times on an

Ultrafree® -0.5 centrifugal filter device (5 kDa cut-off, Millipore, Billerica, MA,

USA) by centrifugation for 20 min at 12000 rpm (11290 g) at 4 °C. After

centrifugation ~10 μL of concentrated CSF (retentate of > 5 kDa) remained on the


44

filter. This retentate was diluted in Buffer A (Agilent, part # 5185-5987) to a final

volume of 135 μL at 4 °C, transferred to a 0.22 μm cellulose acetate spin filter

(Agilent, part # 5185-5990) and centrifuged for 20 min at 13000 rpm (13250 g) (4

°C) to remove particulates. The filtrate was transferred to a sample vial (Agilent,

part # 5182-0716 + blue screw cap 5182-0717) with a 300 µL insert (Waters, part #

WAT094170) and 110 μL were injected on a Multiple Affinity Removal column,

Hu-6 (Human), 4.6 × 50 mm, (Agilent, part # 5185-5984) using an ÄKTA FPLC

system (Amersham Biosciences, Uppsala, Sweden) with detection at 280 nm. The

system was connected to a cooled autosampler (4 °C) and fraction collector. The

following elution scheme was used; 0-10 min, 100% buffer A (0.25 mL/min); 10-

13.5 min 100% buffer B (part # 5185-5988, Agilent) (1 mL/min); 13.5-20 min 100%

buffer A (0.25 mL/min). The flow-through fraction (depleted CSF collected

between 2.8-5.3 min) of a total volume of ~0.6 mL was collected. The depleted

protein fraction (containing the 6 depleted proteins) was also collected between

11.6-13.2 min in a total volume of ~1.6 mL. Both fractions were snap frozen in

liquid nitrogen and stored at -80 °C until further use.

The digestion of CSF for proteomics analysis was done according to the

following procedure; 30 μL CSF and 30 μL 0.1% RapiGest™ (in 50 mM

ammonium bicarbonate) (Waters) were added to the same sample tube (Greiner

Bio-One, Alphen aan den Rijn, The Netherlands, part # 623201). The sample was

reduced by adding 0.6 μL 1,4-dithiotreitol (DTT) (0.5 M) followed by incubation at

60 °C for 30 min After cooling to room temperature the sample was alkylated with

3μL iodoacetamide (IAM) (0.3 M) in the dark for 30 min at room temperature and

0.6 μL sequencing grade modified trypsin (Promega, Madison, WI, USA part #

V5111) (125 ng/µL) was added to give a trypsin to protein ratio of 1:20 (w/w). The

sample was incubated for ~16 h at 37 °C with slight vortexing (450 rpm) in a

thermomixer comfort (Eppendorf). Thereafter 6 μL hydrochloric acid (0.5 M) was

added to stop the digestion followed by incubation for 30 min at 37 °C to

hydrolyze the RapiGest™. The sample was centrifuged at 13000 rpm (13250 g) for

10 min at 4 °C to remove the insoluble part of the hydrolyzed RapiGest™ and any

particulates that might block the LC-chip. 60 μL of the liquid phase was

transferred to six screw cap vials (Agilent) with 300 μL inserts (Waters) in 10 μL

aliquots for chipLC-MS analysis.

2.4. ChipLC-MS(/MS) proteomic analysis A quadrupole-time-of-flight (QTOF) mass spectrometer (Agilent 6510) with a

liquid chromatography-chip electrospray ionization (LC-chip ESI) interface was

coupled to a nano LC system (Agilent 1200) with an 8 μL injection loop. The

instrument was operated under the MassHunter Data Acquisition software

(version B.01.02/B.01.03; Build 1.3.157.0; Agilent Technologies, Santa Clara, USA).

To compare peptide profiles, samples were analyzed by LC-MS and the

Chapter 2

45

discriminatory peptides were identified and assigned to proteins by targeted LC-

MS/MS. For the freeze-thaw and the 4 °C storage studies an LC-chip with a 40 nL

trap column was used (Protein ID chip #2; G4240-62002; Agilent Technologies;

separating column: 150 mm × 75 µm Zorbax 300SB-C18, 5 µm; trap column: 40 nL

Zorbax 300SB-C18, 5 µm). For the delayed storage study an LC-chip with a 160 nL

trap column was used (Protein ID chip #3; G4240-63001 SPQ110: Agilent

Technologies; separating column: 150 mm × 75 µm Zorbax 300SB-C18, 5 µm; trap

column: 160 nL Zorbax 300SB-C18, 5 µm). For LC separations the following

solutions were used; A: ultra-pure water (conductivity 18.2 MΩ, Elga Labwater,

Ede, The Netherlands) with 0.1% formic acid (98-100%, pro analysis, Merck,

Darmstadt Germany); B: acetonitrile (HPLC-S gradient grade, Biosolve,

Valkenswaard, The Netherlands) with 0.1% formic acid. 8 µL depleted, digested

sample (section 2.3.) was injected on the trap column at a flow rate of 3 μL/min

(3% B). After ~3 min the trapped peptides were transferred to the separation

column at a flow rate of 250 nL/min. For the freeze/thaw and the 4 °C storage

studies peptides were eluted from the separation column using the following

program: 80 min linear gradient from 3 to 50% B, 5 min linear gradient from 50 to

70% B, 20 min linear gradient from 70 to 3% B. For the delayed storage study the

gradient program was set up as follows; 80 min linear gradient from 3 to 40% B; 10

min linear gradient from 40 to 50% B; 10 min linear gradient from 50 to 3% B.

The MS settings were the following; mass range: 100-2000 m/z (delayed storage

50-2000 m/z), acquisition rate: 1 spectrum/sec (delayed storage 0.9 spectra/sec),

data storage: profile, fragmentor: 175 V, skimmer: 65 V, OCT 1 RF Vpp: 750 V. The

spray voltage was ~1800 V, drying gas (N2) temp: 325 °C, drying gas flow: 6 L/min.

Mass correction was done during analysis using internal standards (methyl

stearate m/z: 299.294457 and HP-1221 m/z: 1221.990637) continuously evaporating

from a wetted wick inside the spray chamber.

For protein identification, selected peptide ions were fragmented by

collision-induced dissociation. The MS/MS settings used were the following;

fragmentor: 175 V, skimmer: 65 V, OCT 1 RF Vpp: 750 V, precursor ion selection:

medium (4 m/z), mass range: 200-2000 m/z, acquisition rate MS: 4 scans/sec,

acquisition rate MS/MS: 3 scans/sec, collision energy 4 V/100 Da, offset: 2 V.

Collision energies for targeted ions; (4 °C storage) m/z 369.188, z = 2: 16 V, m/z

681.858, z = 2: 27 V, (delayed storage) m/z 638.655, z = 3: 25 V, m/z 921.443, z = 4: 30

V, m/z 957.479, z = 2: 35 V. In the freeze-thaw study fragmentation spectra were

obtained by auto-MS/MS; precursor setting: maximum 3 precursors/cycle,

absolute threshold: 1000, relative threshold: 0.01% of the most intense peak, active

exclusion enabled after 2 selections, release of active exclusion after 0.15 min,

precursors sorted by abundance only. The MS/MS files were stored in centroid

mode with a threshold of; MS: 300/0.01(absolute/relative threshold), MS/MS:

100/0.01.


46

In order to assess the reproducibility of the analytical platform and exclude

method-related changes in peak areas, each sample was analyzed five times in a

randomized order with blank runs and QC samples between every 5th or 10th

sample injection (depending on the total number of samples analyzed in one

batch). Each sample was divided over 6 vials. Only one injection was done from

each vial to avoid changes caused by changing surface to volume ratios Each

sample was left in the autosampler for no more than 24 h at 4 °C. The QC samples

were injected in order to calculate the repeatability of the method over the course

of the analysis in terms of mass accuracy, retention time and peak area. Selected

cytochrome C peptides were smoothed (Gaussian function: width 15 points (1.08 s

between each data point) and integrated for calculation of the relative standard

deviation (RSD) with respect to peak area and retention time. The peak area RSD

was within +/- 20% and the retention time shifts were less than +/- 0.5% (7 sec.) for

the selected peaks. Mass accuracy was calculated as a mean of five measurements

of each selected ion and compared to the theoretical mass of the originating

peptides and was below +/- 7 ppm. Overall these data confirm the performance of

the chipLC-MS system as reported before.39

Prior to starting the analyses the linear range of the chipLC-MS system was

determined in order to define the maximal amount of digested CSF that could be

injected without column overloading (extensive peak broadening) or saturation of

the detector of the mass spectrometer (ion signal does no longer increase with

increasing injected amount) (data not shown). The injection volume was 8 μL

(~200 ng [4 pmol]) for the CSF stability samples and 5 µL for the QC samples (~125

ng [2.5 pmol], 500 fmol cytochrome C) (assuming a mean protein mass of 50 kDa).

Repeatability of the analysis is depicted in Figs. 1, 4 and 5 in the form of box and

whisker plots.

2.5. Data processing of chipLC-MS(/MS) data The raw LC-MS data were pre-processed using the Agilent MassHunter

Qualitative Analysis software (version B.01.02/B.01.03; Build 1.3.157.0;) (Agilent).

The data was analyzed both manually and with two in-house developed data

processing pipelines (40-42). Using the manual method conditions were compared

to each other by overlaying chromatograms (total ion chromatograms, [TIC] and

base peak chromatograms, [BPC]) in the MassHunter software. In regions with

visible differences between the conditions, extracted ion chromatograms (EIC’s)

were compared for peaks that were multiply-charged (trypsin digested proteins

analyzed on this instrument give rise to at least doubly charged ions). Peaks that

appeared to be discriminating were smoothed (Gaussian function: width 15 points

(1.08 s between each data point)) and integrated. Peak areas were compared using

a two-tailed Student’s t-test (Microsoft Office Excel 2007) and box plots were

created in Origin 7.0.

Chapter 2

47

For more extensive data analysis, two different data processing workflows

were used. An in-house data processing method developed in Matlab40 and a

data processing approach developed in C++ (42). For both approaches, raw data

was converted to MzData.XML. in profile mode. These files were further

converted to ASCII format with the following parameter settings; peak intensity: >

300 counts, mass range: 200-1600 m/z (no multiply charged ions were found

outside this range), retention time range: 3-80 min (elution range of the peptides).

The Matlab workflow consists of the following steps; meshing the raw data in the

m/z dimension to 1 amu mass resolution using Gaussian smoothing followed by

peak picking based on MN-rules with M = 3 and N = 8 (43). One dimensional peak

matching was performed using a sliding window along a given m/z trace (1 min

width and 0.1 min interval step) to create an aligned peak matrix containing the

intensities of the matched peaks from the different samples where the row

corresponds to the peak number and the column to the sample number.

In the C++ programmed data processing workflow the data was meshed to

an evenly spaced grid with a Gaussian algorithm of 0.01 amu mass resolution.

From these ‚meshed‛ files, the 10 000 most intense peaks were picked using a

slope-based geometric peak picking algorithm. In order to compare

chromatograms, peaks were time-aligned using warp2D, which uses the

overlapping peak volume of the extracted peaks as a two-dimensional benefit

function to drive the optimization procedure to obtain optimal retention time

correction. Peaks from different LC-MS runs were matched after alignment

resulting in one aligned matrix containing the average height, the average

retention time and the average m/z value for each peak. Columns correspond to

the individual samples (chromatograms) and rows to the matched peaks.

Using the aligned peak matrices from both workflows, a double cross-

validated Nearest Shrunken Centroid (NSC) algorithm (44) was applied to classify

samples according to the evaluated pre-analytical conditions and to detect peaks

that changed due to changing pre-analytical parameters. EIC’s were subsequently

extracted for these peaks from the raw data focusing on those peaks that occurred

at the highest shrinkage and lowest cross validation error. All ions in the list of

selected peaks were assessed with respect to their charge state. Only ions with a

charge of +2 or higher were further analyzed as described above for the manual

method. Class separation based on discriminatory peaks selected by the NSC

algortithm was visualized after Principal Component Analysis (PCA).

For each peak that by univariate statistical analysis (two-tailed Student’s t-

test) proved to be discriminatory between the sample groups, based on its p-value

being below 0.05 (confirmed in a second, independent set of analyses),

fragmentation spectra were manually extracted from the obtained MS/MS data

files, each selected spectrum was converted to the Mz.Data.XML format and

exported to the PhenyxOnline data base search tool (Geneva Bioinformatics,


48

version 2.5/2.6; for details about parameters see Figure S.1 in Supporting

Information). The databases searched were uniprot_sprot (56.5_25_Nov_2008) and

uniprot_sprot_rev (56.5_25_Nov_2008) (the later database is a reverse version of

uniprot_sprot and serves to determine the false positive rate). Taxonomy:

mammalian, scoring model: ESI-QTOF (QTOF), parent charge: +2, +3, +4 (trust the

charge). The search was done in two subsequent rounds. The following search

parameters were common for both rounds: peptide/AC score: ≥ 5, peptide length:

≥ 5, p-value < 0.0001, enzyme: trypsin (KR), missed cleavage: ≤ 1, parent tolerance:

10 ppm. The following search parameters differed between round 1 and round 2

of the search. Round 1: amino acid modifications: Cys_CAM (carboxy

methylation, fixed), Oxidation_M (oxidation of methionine, variable, ≤ 2); Round

2: Cys_CAM (fixed), Oxidation_M (variable, ≤ 2), Oxidation_HW (oxidation of

histidine and tryptophan, variable, ≤ 2), DEAMID (deamidation, variable, ≤ 2),

PHOS (phosphorylation, variable, ≤ 2). The cleavage mode was set to ‘normal’ for

round 1 and to ‘half cleaved’ for round 2. The collision spectrum from each ion

was manually evaluated, only fragments with intensities clearly above the

background noise (50-100 counts) were considered.

2.6. MALDI-FT-ICR-MS and Orbitrap LC-MS/MS proteomic

analysis The samples were depleted and digested as described in section 2.3. A matrix

solution was prepared by dissolving 10 mg 2.5-dihydroxybenzoic acid (DHB) in 1

mL 0.1% TFA/water. All samples were desalted by loading them on C18 material

and washing the samples with 0.1% TFA/water. Subsequently the samples were

eluted in 10 µL 50% acetonitrile / 0.1% TFA in water. From each sample, 0.5 µL of

eluate was added to 0.5 µL of the matrix solution on a MALDI target plate

(600/384 AnchorChipTM with transponder plate, Bruker Daltonics, Germany). All

samples were manually spotted in duplicate. After drying at room temperature

the samples were measured manually on an APEX IV Qe 9.6 Tesla MALDI-FT-ICR

mass spectrometer (Bruker Daltonics, USA) equipped with the first version of the

vacuum Combisource, and a 20 Hz nitrogen laser with an irradiation area of ~200

µm. Multishot accumulation was used as recommended in the literature (45-47).

Ten laser shots were accumulated in the storage hexapole, transferred to the FT-

ICR cell, and scanned for 0.78 seconds. Fifty scans were summed for each mass

spectrum. The acquisition mass range was m/z 800 to 4000. The mass spectra were

subsequently apodized and zerofilled twice. An external mass calibration using

Pepmix standard (Bruker Daltonics, Germany) was applied using a quadratic

equation. All samples were measured until the highest peak in the spectrum had

attained a pre-determined intensity of 107counts. For each spectrum, all peak

intensities were normalized relative to this peak. The mass spectrometer was

Chapter 2

49

operated under Xmass version 7.0.8 and the data analysis was done using the

DataAnalysis version 3.4 (both from Bruker Daltonics).

LC-MS/MS measurements, for identification of the peptide peaks that by

visual inspection showed differential abundance by MALDI-FT-ICR-MS, were

carried out on an Ultimate 3000 nano LC system (Dionex, Germany) online

coupled to a hybrid linear ion trap /Orbitrap mass spectrometer (LTQ Orbitrap

XL; Thermo Fisher Scientific, Bremen, Germany) (Orbitrap LCMS/MS). 5 µL digest

was injected onto a C18 trap column (C18 PepMap, 300 µm ID × 5 mm, 5µm

particle size, 100 Å pore size; Dionex, Amsterdam, The Netherlands) and desalted

for 10 min using a flow rate of 20 µL /min 0.1% TFA. The trap column was then

switched online to the analytical column (PepMap C18, 300 μm ID × 5 mm, 3 μm

particle and 100 Å pore size; Dionex, The Netherlands) and peptides were eluted

with the following binary gradient: 0% - 25% solvent B for 120 min and 25% - 50%

solvent B for further 60 min, where solvent A consists of 2% acetonitrile and 0.1%

formic acid in water and solvent B consists of 80% acetonitrile and 0.08% formic

acid in water. Column flow rate was set to 300 nL/min For MS detection a data

dependent acquisition method was used starting with a high resolution survey

scan from 400 – 1800 m/z. This was detected in the Orbitrap (target value of

automatic gain control (AGC) 106, resolution 30,000 at 400 m/z; lock mass was set

to 445.120025 u (protonated (Si(CH3)2O)6). Based on this survey scan the 5 most

intense ions were consecutively isolated (AGC target set to 104 ions) and

fragmented by collision induced dissociation (CID) applying 35% normalized

collision energy in the linear ion trap. Precursors that had been selected for MS/MS

were excluded for further CID for 3 minutes. Peptides were identified using the

Bioworks 3.2 software package (Thermo Fisher Scientific, Germany) and its’

Sequest feature adhering to the HUPO criteria with XC scores of 1.8, 2.2 and 3.1

for singly, doubly and triply charged ions, respectively, in the uniprot_sprot

(56.5_25_Nov_2008, taxonomy: mammalia) database. The cut-off for mass

differences with the theoretical mass of the identified peptides was set at 2 ppm.

For identification of the peptides the Sequest feature of the Xcalibur software

package was used (Thermo Scientific).

2.7. GC-MS metabolomic analysis A non-targeted GC-MS method including a derivatization step, that has frequently

been applied for metabolomics studies (48), was used to analyze various classes of

(polar) metabolites simultaneously (e.g. amino acids, organic acids, fatty acids,

sugars). In short lyophilized CSF samples (100 μL) were derivatized using a

solution of ethoxyamine hydrochloride in pyridine as the oximation reagent

followed by silylation with N-methyl-N-(trimethylsilyl)trifluoroacetamide

principally as described earlier (48) 1 μL aliquots of the derivatized samples were

injected in splitless mode on an HP5-MS 30 mm × 0.25 mm × 0.25 mm capillary


50

column (Agilent Technologies, Palo Alto, CA) using a temperature gradient from

70 °C to 320 °C at a rate of 5 °C/min. The GC-MS analysis was performed using an

Agilent 6890 gas chromatograph coupled to an Agilent 5973 quadrupole mass

spectrometer. Detection was carried out using MS detection in electron impact

ionization (70 eV) and full scan monitoring mode (m/z 15-800). During the

different steps in the sample work-up, i.e. lyophilization, derivatization and

injection, different (deuterated) internal standards (leucine-d3, glutamic acid-d3,

phenylalanine-d5, glucose-d7, alanine-d4, cholic acid-d4, dicyclohexylphthalate,

Sigma-Aldrich Chemie B.V. (Zwijndrecht, the Netherlands), typical purity of

standards > 97% atom D) were added at a level corresponding to the concentration

of analytes in the QC sample. The samples were derivatized in duplicate,

(excepted for t=0) and each derivatized sample was injected twice in a random

manner.

Data-pre-processing was carried out by automated peak integration

followed by a manual check. This resulted in a target list of 79 peaks (both known

and unknown metabolites) characterized by a combination of retention time and

m/z ratio. All peak areas were subsequently normalized using the internal

standard dicyclohexylphthalate (DCHP). These normalized target lists were used

for further statistical analysis. Identities were assigned based on the presence of

library searching of the identical mass spectra in the TNO database.

Multivariate data-analysis (PCA and PCDA) was performed in the Matlab

environment (version 6.5.1 Release 13, The Mathworks, 2003) using in-house

written routines and the PLS toolbox (version 3.0.2, Eigenvector Research, 2003).

Student’s t-test was applied to all amino acids and known metabolites. Metabolites

that showed a significant difference in both samples (p < 0.05) were considered as

discriminatory and some of them were represented as box plots. The analytical

error was within +/-20% based on replicated injections of the samples.

2.8. LC-MS analysis of amino acids Briefly, 10 µL of internal standard (13C15N-labeled amino acids; Asp_C13N15,

Glu_C13_N15, Asn_C13N15, Gln_C13N15, Gly_C13N15, Thr_C13_N15, Ala_C13_N15,

Arg_C13N15, Tyr_C13N15, Val_C13N15, Met_C13N15, Trp_C13N15, Phe_C13N15,

ILe_C13N15, Leu_C13N15, Lys_C13N15, Pro_C13_N15, Cambridge Isotope Laboratories,

Inc; Andover, MA, USA) with a concentration range of 70 ng/mL – 930 ng/mL for

different amino acids were added to 10 µL of CSF followed by addition of 100 µL

of methanol. The mixture was vortexed for 10 s and centrifuged at 10.000 rpm

(9408 g) for 10 min at 10 °C. The supernatant was dried under N2 and the residue

was dissolved in 80 μL borate buffer (pH 8.5). After 10 s vortexing, 20 μL of AQC

reagent (Waters part #: 186003836) was added and the mixture was vortexed

immediately. The sample was heated 10 min at 55 °C. Each sample (except P3: T0)

was prepared in duplicate. After cooling 1 μL of the reaction mixture was injected

Chapter 2

51

in duplicate on a UPLC-MS/MS system comprising an ACQUITY UPLCTM system

with autosampler (Waters Chromatography B.V., Etten-Leur, The Netherlands)

coupled to a Quattro Premier Xe tandem quadrupole mass spectrometer (Waters

Corporation) and analyzed in positive-ion electrospray mode. The instrument was

operated under the MassLynx data acquisition software (version 4.1; Waters). The

samples were analyzed on an AccQ-TagTM Ultra 2.1 × 100 mm (1.7 μm particle

size) column (Waters) and a binary gradient system of water/eluent A (10:1, v/v)

(AccQ Tag, Waters) and 100% eluent B (Accq Tag, Waters). Analytes were eluted

by ramping the percentage of eluent B from 0.1 to 90% in approx. 9.5 min using a

combination of both linear and convex profiles. The flow-rate was 0.7 mL/min, the

column temperature was maintained at 60 °C and the temperature of the

autosampler tray was set to 10 °C. After each injection the injection needle was

washed with 200 μL strong wash solvent (95% ACN), and 600 μL weak wash

solvent (5% ACN). All analytes were monitored by Selective Reaction Monitoring

(SRM) using nominal mass resolution (FWHM 0.7 amu). Next to the

derivatisation reagent all amino acids were selectively monitored via the transition

from the protonated molecule of the AccQ-Tag derivative to the common

fragment at m/z 171. Collision energy and collision gas (Ar) pressure were 22 eV

and 2.5 mbar, respectively. The complete chromatogram was divided into 6 time

windows, restricting the number of SRM transitions to follow and allowing

quantitative information to be gathered in each segment. Acquired data was

evaluated using MassLynx software (version 4.1; Waters). Quantification and pre-

data analysis was done using LC-QuanLynx (Waters) and Microsoft Office Excel

2003 (Microsoft, Redmond, WA, USA), respectively. Data was analyzed by

Student’s t-test (T0 vs. T30, T30 vs. T120 and T0 vs. T120) and amino acids that

showed significant differences between the sample groups (p < 0.05) in samples

from P3 and P7 were considered as discriminating. The data from P3 and P7 were

combined and box plots were created for the discriminatory peaks (Origin 7.0).

PCA plots were created to visualize classifications based on the discriminatory

amino acids. The repeatability of the measurement of significantly discriminatory

amino acids was +/-21 % based on the replicated analysis of each sample.

3. Results and Discussion

3.1. Effect of delay time between sample collection and freezing

on the CSF proteome and metabolome (Delayed Storage) In the clinical situation it may not always be possible to treat samples taken from

patients immediately by centrifugation in a refrigerated centrifuge and snap

freezing. It is therefore important to record changes that may occur during this

time period in order to discriminate them from disease-related changes. In order


52

to follow proteome and metabolome profiles of CSF, samples were left at room

temp (30 and 120 minutes) directly after sampling without centrifugation and after

centrifugation before being frozen and stored at -80 °C. The CSF proteome was

analyzed by chipLC-MS and followed up by MALDI-FT-ICR-MS. Non-centrifuged

samples were also analyzed for differences in metabolite profiles by LC-MS

(amino acid analysis) and GC-MS.

Non-centrifuged porcine CSF that was left at room temperature after

sampling showed differences in terms of peak areas of certain peptides, although

the sample was free of red blood cells, a criterion that is often applied to CSF for

acceptance or rejection in proteomics (47, 49)

Figure 1A depicts a discriminatory region in the base peak chromatograms

(BPC’s) from a sample (P7), where visible changes were detected as a result of

increasing the time delay between sampling and freezing (chipLC-MS method).

Three peptide-related peaks increased strongly in CSF that was left for 30 min or

120 min at room temperature. Figure 1B shows the extracted ion chromatograms

(EIC’s, detection window +/- 50 ppm) corresponding to these discriminatory peaks

related to two peptides (m/z: 638.655 [z: 3, Mw: 1912.943], m/z: 957.479 [z: 2, Mw:

1912.943], m/z: 921.443 [z: 4, Mw: 3681.743]). Univariate statistical comparison of

the peak areas from 5 repetitive analyses (Figure 1C) showed that the observed

differences are highly significant with p- values below 0.001 (T0 vs. T30 and T0 vs.

T120) indicating that these peptides may serve as stability markers especially in

already existing CSF sample collections. Differences in peak areas between T30

and T120 are not statistically significant (p > 0.05) indicating that the changes occur

rather rapidly following CSF sampling. Multivariate statistical analysis by PCA

showed that classification of all sample groups (T0, T30, T120) is possible based on

these discriminatory peaks (Figures S.6 - S.8, Supporting Information). In order to

assure that the observed differences were not animal-specific, the discriminatory

peaks were confirmed in CSF from another animal (P3) supporting our findings

(data not shown). The discriminatory ions were subsequently identified as two

tryptic peptide fragments of prostaglandin-D synthase (PTGDS_PIG, Q29095, no

hit in reverse data base) by LC-MS/MS analysis (see Figures S.2-S.5 in Supporting

Information for details). MALDI-FT-ICR-MS pointed to another peptide

originating from prostaglandin-D synthase (m/z: 1350.705 [z: 1, Mw: 1349.697])

that changed with increasing delay time. However, instead of increasing with time

delay, this peptide decreased in intensity (Supporting Information S.19 - S.22).

While both analyses show that prostaglandin D-synthase-derived peptides change

in abundance upon Delayed Storage, further analyses are needed to explain the

changes of these peptides in terms of structural alterations of the enzyme.

To assess the stability of the observed peptides in cell-free porcine CSF,

samples were centrifuged prior to incubation at room temperature for 30 or 120

min, snap-freezing and storage at -80 °C. Interestingly, no significant change in

Chapter 2

53

peak area of the respective peptides was observed between the groups indicating

that changes might be related to remaining WBCs that may release proteases upon

Delayed Storage.

Figure 1. A. Base peak chromatograms of the chipLC-MS proteomic analysis of depleted,

trypsin-digested, non-centrifuged porcine CSF with delay times of 0, 30 or 120 min at

room temperature (T0, T30, T120) between CSF collection and freezing in liquid nitrogen.

The encircled regions show peaks at ~39 min and ~46 min retention time that change with

increasing delay time. B. Extracted ion chromatograms (detection window +/- 50ppm) of

three peaks, derived from two peptides, that change with increasing delay time (see panel

A). Peaks at m/z = 638.655 (Mw: 1912.943) and 957.479 (Mw: 1912.943) have the same

retention time and represent different charge states of the same peptide that was assigned

to prostaglandin D-synthase by MS/MS. The peak at m/z = 921.443 (Mw: 3681.743) was

also assigned to prostaglandin D-synthase by MS/MS. C. Univariate statistical analysis of

peak areas of peptides that change significantly with respect to delay time. Analysis is

based on two-tailed student’s t-tests of 5 repetitions of the LC-MS analyses. Data are

represented as box plots with p-values (T0 vs. T30 and T0 vs. T120, respectively). Peak

areas between T30 and T120 were not significantly different (p > 0.05).


54

Our findings indicate that changes in the proteome profile are introduced

in CSF when left at room temperature after collection. These changes are most

notable within the first 30 min after sampling when CSF is not centrifuged. While

changes are clearly reduced or absent when removing cells through centrifugation

right after CSF collection, it is still recommended to snap-freeze CSF after the

centrifugation step and to store it at -80 °C in aliquots to avoid freeze-thaw cycles

(see below). According to our results prostaglandin D-synthase is particularly

sensitive. The detected peptides after trypsin digestion may serve as indicators of

sample quality in retrospective sample collections. Similarly, changes in

prostaglandin D-synthase in CSF as a possible biomarker for disease-related

processes must be considered with care unless sample handling steps are

rigorously controlled in order to avoid false positives.

Amino acid analysis (LC-MS) of non-centrifuged CSF indicated a trend to

elevated concentrations with increasing time delay. Ten (Thr, Glu, Gly, His, Ser,

Ala, Phe, Tyr, Ile and Asn) of the detected amino acids (Met, Trp, Leu, Val, Pro,

Asp, Arg, Lys, Thr, Glu, Gly, His, Ser, Ala, Phe, Tyr and Asn) showed a

significantly increased concentration when compared by univariate statistics (only

metabolites that were significantly different in both of the samples were

considered) (Figure 2). The rest of the amino acids also showed a tendency to

increase in level with increased incubation time but either they were only

significantly discriminatory in the samples from one of the pigs (Met, Leu, Val,

Asp, Arg, and Lys) or the difference was not significant in any of the samples (Pro,

Trp). PCA analysis based on the discriminatory amino acids showed a time-

dependent trend (Figure S.25 in Supporting Information), since groups T0 and

T120 are clearly separated from each other with group T30 forming an

intermediate state. This is reflected in box plots (Figure 2), where the standard

deviation is generally higher in T30 samples than in T0 and T120.

Figure 2. Amino acids in non-centrifuged porcine CSF that show an increase in

concentration with delay times of 0, 30 or 120 min at room temperature (T0, T30, T120)

Chapter 2

55

between sampling and snap-freezing as assessed by LC-MS amino acid analysis. Only

amino acids with significant differences between the sample groups in both P3 and P7 are

shown. The figure show combined data from P3 and P7. Symbols mark significant p-

values according to students t-test (T0 vs. T30 and T0 vs. T120) * =p < 0.05, ■ =p < 0.01,

♦ =p < 0.005, ● =p < 0.001, ◘ =p < 0.0005, ◙ =p < 0.0001

PCA analysis of the GC-MS data considering all detected metabolites

shows also a tendency of evolving profiles between T0, T30 and T120 (Figure S.24

in Supporting Information). The GC-MS metabolite analysis detected in total 57

known and 22 unknown metabolites (only the identified metabolites were

considered in the statistical analysis). Out of the known metabolites 11 were amino

acids (Leu, Ile, Lys, Val, Gln, Met, Ala, Phe, Ser, Thr, Pro). All metabolites except

eight (sucrose, 2,3,4-trihydroxybutanoic acid, 1-monostearinglycerol, lactic acid,

uric acid, 1-monopalmitinglycerol, C9:0 fatty acid and proline) showed a

significant increase in concentration over time. All 49 metabolites that presented

significant differences between the time groups are listed in Table S.23. in

Supporting Information. In total 24 metabolites showed a significant increase (p <

0.05) in concentration between T0 and T30 while 46 metabolites were significantly

increased after 120 min (T0 versus T120) (see Figure S.23 in Supporting

Information). This indicates that metabolic processes are ongoing during Delayed

Storage and that rapid centrifugation and snap freezing are indispensable for

reliable metabolite analysis in CSF. Figure 3 shows the eleven amino acids that

were significantly different between time groups determined by GC-MS analysis.

Seven of them showed increased levels in LC-MS and GC-MS analyses (Ala, Ser,

Phe, Thr, Ile and Gly) and one (Pro) was unchanged in both methods. There were

four amino acids, detected by both methods, that showed a significant difference

by GC-MS only (Leu, Lys, Val and Met).These four amino acids were increased in

only one of the samples in the LC-MS analysis and were thus not considered

significant. There was no discrepancy between GC-MS and LC-MS analysis, since

no amino acid showed an opposite trend.


56

Figure 3. Amino acids showing a significant increase in concentration in non-centrifuged

porcine CSF with increased delay time when analyzed by GC-MS metabolomic analysis.

The amino acids were significantly different between the groups in both sample P3 and P7

and the figure shows a combination of the results from both samples. The symbols mark

significant p-values according to students t-test (T0 vs. T30 and T0 vs. T120) * =p < 0.05,

♦ =p < 0.005, ◘ =p < 0.0005 ◙ =p < 0.0001.

One of the compounds that increased upon delayed storage (GC-MS

analysis) was glucose, a metabolite that is routinely measured in CSF (25) (Figure

S.23, Supporting Information). It is thus important to realize that sample handling

after CSF collection may affect glucose measurements and therefore the

interpretation of routine laboratory analyses. Centrifugation after CSF collection

followed by snap freezing and storage at -80 °C is therefore also recommended for

metabolite analysis. In the NMR study by Levine et al. glucose, alanine, myo-

inositol and acetate showed no change over 72 hours at room temperature (31).

Acetate was not found in our analysis but the three other metabolites were

detected and showed an increased level after 120 minutes at room temperature.

They also showed a decreased citrate level, while in our study this metabolite

(citric acid) increased. These variances might depend on the differences in the

experimental designs. Levine et al. assessed samples that were stored frozen for 30

months prior to incubation at room temperature, which may have affected the

Chapter 2

57

outcome. The fact that the incubation time was significantly longer (72 hours

compared to maximally 2 hours in our study) may have also affected the results.

Some metabolites may decrease in concentration because of adsorption to the tube

walls (50) while others might increase due to enzymatic processes. Furthermore

Levine et al. revealed that lactate, creatine, glutamine and creatinine were found to

increase. While lactate and creatine were not detected in our GC-MS analysis, both

glutamine and creatinine showed the same result in our analysis as in the NMR

study.

3.2. Effect of freeze/thaw cycles on the CSF proteome Freeze/thaw cycles are often unavoidable when analyzing biological samples.

Freeze/thawing is known to affect biological samples and notably to induce

conformational changes in proteins that may ultimately lead to aggregation or

degradation (51-53). In order to assess the influence of freeze/thawing on the

proteome profile, depleted non-centrifuged porcine CSF was exposed to 1×, 2×, 3×,

4×, 5× or 10× freeze/thaw cycles and compared to the starting material. A freeze-

thaw cycle was comprised of 30 min thawing on ice and 2 min freezing in liquid

nitrogen. All samples were left on ice throughout the study until the overnight

trypsin digestion (the control sample was thus left on ice throughout the entire

process of freeze/thawing (~ 5.5 h until digestion) and the 5 × freeze/thaw sample

was left on ice ~ 2.5 h). The experiment was performed twice on two separate

dates on CSF from the same animal (P3, T0). Only peaks that showed the same

pattern in both the experiments were considered as discriminatory. The

chromatograms of freeze/thaw-treated samples did not show differences when

inspected visually at the TIC/BPC level. However, detailed statistical analysis after

data processing (see section 2.4) revealed four tryptic peptide peaks out of 41

peaks on the list that changed significantly in peak area when comparing the

starting material with a sample having undergone 10 freeze/thaw cycles. PCA

analysis based on the discriminatory peaks showed a clear discrepancy between

the control group and the 10× freeze/thaw group (Figure S.14 in Supporting

Information). Figure 4A depicts the EIC’s (detection window +/- 50 ppm) of the

four peptide peaks, that were identified by LC-MS/MS as tryptic fragments of

transthyretin (TTY_PIG, P50390, no hit in reverse data base) (m/z: 428.234 [z: 2,

Mw: 854.453], m/z: 464.267 [z: 2, Mw: 926.519], m/z: 555.265 [z: 3, Mw: 1662.7732],

m/z: 681.838 [z: 2, Mw: 1361.661]) indicating that transthyretin is particularly

sensitive to freeze/thawing (Supporting Information, Figures S.9 - S.13). Univariate

statistical analysis of the peak areas (Figure 4B) resulted in highly significant

differences. Analysis of these peptides for an increasing number of freeze/thaw

cycles showed that 3 of the 4 peptides were already increased after a single

freeze/thaw cycle while one peptide (m/z: 428.234) increased only significantly

after 10 freeze/thaw cycles (Figure 4C). These results emphasize that the number


58

of freeze-thaw cycles should be controlled when comparing different sets of CSF

samples.

Figure 4. (previous page) A. Extracted ion chromatograms (detection window +/-

50ppm) of four peptide peaks that increase after 10× freeze/thaw cycles. All peaks originate

from different tryptic peptides of transthyretin detected by the chipLC-MS proteomic

analysis. B. Univariate statistical analysis of peak areas of the peptides (see Figure 5A)

Chapter 2

59

based on two-tailed students t-tests of 5 repetitions of the chipLC-MS runs. The same

experiment was performed twice on CSF from the same pig confirming the results. Data

are represented as box plots with p-values (control vs. 10× freeze/thaw cycles). C.

Univariate statistical analysis of peak areas of the peptides that change significantly with

respect to the number of freeze/thaw cycles (see Figure 5A). Analysis is based on two-

tailed students t-tests of 5 repetitions of the chipLC-MS runs of depleted, trypsin-digested,

porcine CSF. The same experiment was performed twice confirming the results. Data are

represented as box plots with p-values (control vs. 1× freeze/thaw cycle, control vs. 5×

freeze/thaw cycles, control vs. 10× freeze/thaw cycles, respectively). ●: p< 0.05, ♦ p< 0.005,

■: p< 0.0001, n.s: non significant with respect to the control CSF sample.

Transthyretin is a homo-tetrameric protein present in plasma and CSF that

is known to be potentially unstable and prone to fibril formation leading to

amyloidosis causing plaque formation in Alzheimer’s disease (54). Mutants of

transthyretin are presently being screened with respect to the risk of developing

Familial Amyloidotic Polyneuropathy (55). This propensity for aggregation might

explain the sensitivity of transthyretin to repeated freeze/thawing. The strong

increase in tryptic peptides after freeze/thaw cycles indicates that cleavage sites

become more accessible possibly due to a change in conformation. This finding

underlines that transthyretin must be considered with caution as biomarker

candidate due to its structural instability and that the number of freeze/thaw

cycles should be carefully controlled, since changes in transthyretin levels may

simply be due to freeze-thawing rather than to biological processes related to the

disease of interest.

3.3. Effect of storage at 4 °C after digestion on CSF proteome

analysis Earlier results from our group have shown that peptide concentrations may

change during storage in the autosampler of the LC-MS system at 4 °C. We

therefore investigated whether this is also the case for depleted, trypsin-digested

CSF, since different samples might stay for various time periods in the

autosampler prior to injection, which may lead to differences in peptide profiles.

While randomization of the order of injection can alleviate this problem to some

extent, it will lead to an increased variability of the results making observation of

minor changes in peptide concentrations related to disease difficult. For the

discovery of peptide peaks that are susceptible to this effect, we compared freshly

prepared (depleted, trypsin-digested) porcine CSF (P4) with the same samples

stored for 1 month at 4 °C. PCA analysis based on the discriminatory peaks

showed a distinct difference between the control group and the 1 month sample


60

group (Supporting Information, Figure S.18.). Figure 5A displays EIC’s (detection

window +/- 50ppm) of two peptide peaks out of 45 peaks that decrease

significantly after storage of the digest at 4 °C for one month (m/z: 681.858 [z: 2,

Mw: 1361.701], m/z: 369.188 [z: 2, Mw: 736.361]). One of the peptides was derived

again from prostaglandin D-synthase (369.188) (PTGDS_PIG Q29095, no hit in

reverse data base) while the other belongs to serotransferrin (681.858) (TRFE_PIG

P09571, no hit in reverse data base) (Figures S.15 – S.17 in Supporting

Information). Comparison of profiles after 1 day or 1 week showed that the

serotransferrin peptide (m/z = 681.858) decreased already significantly after 1

week at 4 °C (Figure 5B). A repetition of the experiment on samples from two

different animals (P2/P4) confirmed these results. Both peptides showed a

significant decrease in peak area after one week or even after a single day (data

not shown) in the autosampler at 4 °C. Although major changes in the overall

peptide profile were not observed, the results show that samples should not be left

for more than 1 day at 4 °C in the autosampler to avoid effects that might

confound disease-related changes.

Figure 5. (previous page) A. Extracted ion chromatograms (detection window +/-

50ppm) of two discriminatory peptide peaks that decrease upon storage of a tryptic digest

of depleted porcine CSF at 4 °C. The peaks originate from tryptic peptides of

serotransferrin (m/z: 681.858, Mw: 1361.701) and prostaglandin D-synthase (m/z:

Chapter 2

61

369.188, Mw: 736.361)) detected by the chipLC-MS proteomic analysis B. Univariate

statistical analysis of peak areas of peptides that change significantly upon storage of a

tryptic digest of depleted porcine CSF at 4 °C (see Figure 5A). Analysis is based on two-

tailed students t-tests of 5 repetitions of the chipLC-MS runs of depleted, trypsin-digested,

porcine CSF. Data are represented as box plots with p-values for cases that were

significantly different (control vs. 1 month and control vs. 1 week).

4. Conclusions

In this study we have evaluated a number of pre-analytical factors related to

sample handling of CSF to assess their effect on the protein, peptide and

metabolite profiles using different analytical platforms. The time delay between

CSF collection, centrifugation and snap-freezing proved to affect notably

prostaglandin D-synthase as revealed by a significant increase of two peptides.

Metabolite analysis showed that the level of a number of metabolites, including

free amino acids, increased during delayed storage. These changes indicate that

remaining enzymatic activity in CSF after collection may lead to altered metabolite

and protein levels, emphasizing that CSF should be centrifuged and snap frozen

as soon as possible after collection for proteomics or metabolomics studies. The

prostaglandin D-synthase-derived peptides and the identified metabolites may be

used to assess consistent sample handling notably when considering analysis of

existing CSF sample collections retrospectively.

The addition of stabilizing agents or specific pretreatments (protease

inhibitors, deproteinization) was not assessed in this study. The addition of

stabilizing agents alters the samples by introducing additional compounds, which

lead to new peaks in the mass spectra and chromatogram. It was our intention to

handle CSF in a way to avoid difficulties with subsequent analyses. In this study,

we wanted to assess how CSF is affected during a possible delay of sample

handling in the clinic.

CSF samples should ideally be centrifuged immediately after collection

followed by storage at -80 °C in order to remove all cellular elements and to avoid

protease activation and other kinds of degradation of macromolecules. Unless

sample handling in the clinic is carefully controlled, we recommend studies

targeting certain proteins (e.g. prostaglandin D-synthase, transthyretin) or

metabolites to be cautious with interpretations. We have indications that even

minor contamination of CSF with white blood cells accelerates the observed

changes and that it is not sufficient to assess that CSF is free of erythrocytes. Chow

et al. (56) and Steele et al. (57) pointed out that the major part of the WBC’s lysed

when CSF samples were exposed to room temperature for 22 hours and that this


62

process continues at 4 °C even if the rate is slower. This indicates that leaving cell-

containing CSF samples on ice is not recommended.

We have also shown that widely used sample handling routines such as

freeze/thawing or keeping digested CSF in an autosampler at 4 °C prior to LC-MS

analysis can induce changes in proteome-derived peptide profiles. In order to

avoid the introduction of unnecessary freeze/thaw cycles, samples should be

aliquoted after centrifugation in volumes that avoid freeze-thawing. CSF samples

that have been exposed to repeated freeze/thaw cycles are not ideal for the

assessment of transthyretin, since peptides of this protein showed an increased

level with increasing numbers of freeze/thaw cycles. Samples remaining for

extensive time periods (> 24 hours) in a cooled autosampler are not reliable when

it comes to the quantitative analysis of for example, serotransferrin and

prostaglandin D-synthase.

Several factors affect the stability of proteins, peptides and metabolites in

CSF. We focused on the effect of delay time between CSF sampling and freezing,

the number of freeze/thaw cycles and the stability of trypsin-digested CSF in the

autosampler. Other factors that might be interesting to assess are the material of

the sample tubes, long term storage at different temperatures and the effect of

adding stabilizing agents.

Acknowledgment

The authors would like to thank Bas Muilwijk for the GC-MS analysis and Adrie

Dane for the targeted amino acid LC-MS data analysis support. This study was

financially supported by and performed within the framework of the Top Institute

Pharma project D4-102. The work was further supported by the BioRange 2.2.3

project from the Netherlands Proteomics and Bioinformatics Centers (Christin).

Supporting Information

Data base search criteria and results, MS/MS spectra, amino acid sequences of

identified proteins and sequence coverage, PCA plots, lists of discriminatory

metabolites in GC-MS analysis.

Chapter 2

63

References

1. Belogurov, A. A., Jr.; Kurkova, I. N.; Friboulet, A.; Thomas, D.; Misikov, V. K.; Zakharova, M. Y.;

Suchkov, S. V.; Kotov, S. V.; Alehin, A. I.; Avalle, B.; Souslova, E. A.; Morse, H. C., III; Gabibov, A. G.;

Ponomarenko, N. A. Recognition and Degradation of Myelin Basic Protein Peptides by Serum

Autoantibodies: Novel Biomarker for Multiple Sclerosis. J. Immunol. 2008, 180 (2), 1258-1267.

2. Dekker, L. J.; Burgers, P. C.; Kros, J. M.; Smitt, P. A.; Luider, T. M. Peptide Profiling of Cerebrospinal

Fluid by Mass Spectrometry. Expert. Rev. Proteomics. 2006, 3 (3), 297-309.

3. Lutz, N. W.; Viola, A.; Malikova, I.; Confort-Gouny, S.; Ranjeva, J. P.; Pelletier, J.; Cozzone, P. J. A

Branched-Chain Organic Acid Linked to Multiple Sclerosis: First Identification by NMR Spectroscopy

of CSF. Biochem. Biophys. Res. Commun. 2007, 354 (1), 160-164.

4. Myint, K. T.; Aoshima, K.; Tanaka, S.; Nakamura, T.; Oda, Y. Quantitative Profiling of Polar Cationic

Metabolites in Human Cerebrospinal Fluid by Reversed-Phase Nanoliquid Chromatography/Mass

Spectrometry. Anal. Chem. 2009, 81 (3), 1121-1129.

5. Noben, J. P.; Dumont, D.; Kwasnikowska, N.; Verhaert, P.; Somers, V.; Hupperts, R.; Stinissen, P.;

Robben, J. Lumbar Cerebrospinal Fluid Proteome in Multiple Sclerosis: Characterization by

Ultrafiltration, Liquid Chromatography, and Mass Spectrometry. J. Proteome. Res. 2006, 5 (7), 1647-

1657.

6. Rejdak, K.; Petzold, A.; Stelmasiak, Z.; Giovannoni, G. Cerebrospinal Fluid Brain Specific Proteins in

Relation to Nitric Oxide Metabolites During Relapse of Multiple Sclerosis. Mult. Scler. 2008, 14 (1), 59-

66.

7. Stoop, M. P.; Dekker, L. J.; Titulaer, M. K.; Burgers, P. C.; Sillevis Smitt, P. A.; Luider, T. M.; Hintzen,

R. Q. Multiple Sclerosis-Related Proteins Identified in Cerebrospinal Fluid by Advanced Mass

Spectrometry. Proteomics. 2008, 8 (8), 1576-1585.

8. Walter, A.; Korth, U.; Hilgert, M.; Hartmann, J.; Weichel, O.; Hilgert, M.; Fassbender, K.; Schmitt, A.;

Klein, J. Glycerophosphocholine Is Elevated in Cerebrospinal Fluid of Alzheimer Patients. Neurobiol.

Aging 2004, 25 (10), 1299-1303.

9. Zhang, J.; Goodlett, D. R.; Montine, T. J. Proteomic Biomarker Discovery in Cerebrospinal Fluid for

Neurodegenerative Diseases. J. Alzheimers. Dis. 2005, 8 (4), 377-386.

10. Del Boccio, P.; Pieragostino, D.; Lugaresi, A.; Di, I. M.; Pavone, B.; Travaglini, D.; D'Aguanno, S.;

Bernardini, S.; Sacchetta, P.; Federici, G.; Di, I. C.; Gambi, D.; Urbani, A. Cleavage of Cystatin C Is Not

Associated With Multiple Sclerosis. Ann. Neurol. 2007, 62 (2), 201-204.

11. Hansson, S. F.; Simonsen, A. H.; Zetterberg, H.; Andersen, O.; Haghighi, S.; Fagerberg, I.; Andreasson,

U.; Westman-Brinkmalm, A.; Wallin, A.; Ruetschi, U.; Blennow, K. Cystatin C in Cerebrospinal Fluid

and Multiple Sclerosis. Ann. Neurol. 2007, 62 (2), 193-196.

12. Irani, D. N.; Anderson, C.; Gundry, R.; Cotter, R.; Moore, S.; Kerr, D. A.; McArthur, J. C.; Sacktor, N.;

Pardo, C. A.; Jones, M.; Calabresi, P. A.; Nath, A. Cleavage of Cystatin C in the Cerebrospinal Fluid of

Patients With Multiple Sclerosis. Ann. Neurol. 2006, 59 (2), 237-247.

13. Chaigneau, C.; Cabioch, T.; Beaumont, K.; Betsou, F. Serum Biobank Certification and the

Establishment of Quality Controls for Biological Fluids: Examples of Serum Biomarker Stability After

Temperature Variation. Clin. Chem. Lab Med. 2007, 45 (10), 1390-1395.


64

14. Hsieh, S. Y.; Chen, R. K.; Pan, Y. H.; Lee, H. L. Systematical Evaluation of the Effects of Sample

Collection Procedures on Low-Molecular-Weight Serum/Plasma Proteome Profiling. Proteomics. 2006,

6 (10), 3189-3198.

15. Papale, M.; Pedicillo, M. C.; Thatcher, B. J.; Di, P. S.; Lo, M. L.; Bufo, P.; Rocchetti, M. T.; Centra, M.;

Ranieri, E.; Gesualdo, L. Urine Profiling by SELDI-TOF/MS: Monitoring of the Critical Steps in Sample

Collection, Handling and Analysis. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 2007, 856 (1-2),

205-213.

16. Schaub, S.; Wilkins, J.; Weiler, T.; Sangster, K.; Rush, D.; Nickerson, P. Urine Protein Profiling With

Surface-Enhanced Laser-Desorption/Ionization Time-of-Flight Mass Spectrometry. Kidney Int. 2004, 65

(1), 323-332.

17. West-Nielsen, M.; Hogdall, E. V.; Marchiori, E.; Hogdall, C. K.; Schou, C.; Heegaard, N. H. Sample

Handling for Mass Spectrometric Proteomic Investigations of Human Sera. Anal. Chem. 2005, 77 (16),

5114-5123.

18. Kaiser, E.; Schonknecht, P.; Thomann, P. A.; Hunt, A.; Schroder, J. Influence of Delayed CSF Storage

on Concentrations of Phospho-Tau Protein (181), Total Tau Protein and Beta-Amyloid (1-42). Neurosci.

Lett. 2007, 417 (2), 193-195.

19. Schoonenboom, N. S.; Mulder, C.; Vanderstichele, H.; Van Elk, E. J.; Kok, A.; Van Kamp, G. J.;

Scheltens, P.; Blankenstein, M. A. Effects of Processing and Storage Conditions on Amyloid Beta (1-42)

and Tau Concentrations in Cerebrospinal Fluid: Implications for Use in Clinical Practice. Clin. Chem.

2005, 51 (1), 189-195.

20. Bibl, M.; Esselmann, H.; Otto, M.; Lewczuk, P.; Cepek, L.; Ruther, E.; Kornhuber, J.; Wiltfang, J.

Cerebrospinal Fluid Amyloid Beta Peptide Patterns in Alzheimer's Disease Patients and

Nondemented Controls Depend on Sample Pretreatment: Indication of Carrier-Mediated Epitope

Masking of Amyloid Beta Peptides. Electrophoresis 2004, 25 (17), 2912-2918.

21. Ramont, L.; Thoannes, H.; Volondat, A.; Chastang, F.; Millet, M. C.; Maquart, F. X. Effects of

Hemolysis and Storage Condition on Neuron-Specific Enolase (NSE) in Cerebrospinal Fluid and

Serum: Implications in Clinical Practice. Clin. Chem. Lab Med 2005, 43 (11), 1215-1217.

22. Ranganathan, S.; Polshyna, A.; Nicholl, G.; Lyons-Weiler, J.; Browser, R. Assessment of protein

stability in cerebrospinal fluid using surface-enhanced laser desorption/ionization time-of-flight mass

spectrometry protein profiling. Clin. Proteomics 2006, 2 (1-2), 91-101.

23. Rosato, P. M.; Gama, F. G. V.; Santana, A. E. Physical-chemical analysis of the cerebrospinal fluid of

healthy dogs submitted to different storage periods and temperatures. Cienc Rural 2006, 36 (6), 1806-

1810.

24. Fry, M. M.; Vernau, W.; Kass, P. H.; Vernau, K. M. Effects of time, initial composition and stabilizing

agents on the results of canine cerebrospinal fluid analysis. Vet. Clin. Path 2006, 35 (1), 72-77.

25. Deisenhammer, F.; Bartos, A.; Egg, R.; Gilhus, N. E.; Giovannoni, G.; Rauer, S.; Sellebjerg, F.

Guidelines on Routine Cerebrospinal Fluid Analysis. Report From an EFNS Task Force. Eur. J. Neurol.

2006, 13 (9), 913-922.

26. Hale, J. E.; Gelfanova, V.; You, J. S.; Knierman, M. D.; Dean, R. A. Proteomics of Cerebrospinal Fluid:

Methods for Sample Processing. Methods Mol. Biol. 2008, 425 (2), 53-66.

27. Jerrard, D. A.; Hanna, J. R.; Schindelheim, G. L. Cerebrospinal fluid. J Emerg Med 2001, 21 (2), 171-178.

Chapter 2

65

28. Timms, J. F.; rslan-Low, E.; Gentry-Maharaj, A.; Luo, Z.; T'Jampens, D.; Podust, V. N.; Ford, J.; Fung,

E. T.; Gammerman, A.; Jacobs, I.; Menon, U. Preanalytic Influence of Sample Handling on SELDI-TOF

Serum Protein Profiles. Clin. Chem. 2007, 53 (4), 645-656.

29. Ekblad, L.; Baldetorp, B.; Ferno, M.; Olsson, H.; Bratt, C. In-Source Decay Causes Artifacts in SELDI-

TOF MS Spectra. J. Proteome. Res. 2007, 6 (4), 1609-1614.

30. Aristoteli, L. P.; Molloy, M. P.; Baker, M. S. Evaluation of Endogenous Plasma Peptide Extraction

Methods for Mass Spectrometric Biomarker Discovery. J. Proteome. Res. 2007, 6 (2), 571-581.

31. Levine, J.; Panchalingam, K.; McClure, R. J.; Gershon, S.; Pettegrew, J. W. Stability of CSF Metabolites

Measured by Proton NMR. J. Neural Transm. 2000, 107 (7), 843-848.

32. Pan, S.; Wang, Y.; Quinn, J. F.; Peskind, E. R.; Waichunas, D.; Wimberger, J. T.; Jin, J.; Li, J. G.; Zhu, D.;

Pan, C.; Zhang, J. Identification of Glycoproteins in Human Cerebrospinal Fluid With a

Complementary Proteomic Approach. J. Proteome. Res. 2006, 5 (10), 2769-2779.

33. Strawn, J. R.; Ekhator, N. N.; Geracioti, T. D., Jr. In-Use Stability of Monoamine Metabolites in Human

Cerebrospinal Fluid. J. Chromatogr. B Biomed. Sci. Appl. 2001, 760 (2), 301-306.

34. Tammen, H.; Schulte, I.; Hess, R.; Menzel, C.; Kellmann, M.; Mohring, T.; Schulz-Knappe, P.

Peptidomic Analysis of Human Blood Specimens: Comparison Between Plasma Specimens and

Serum by Differential Peptide Display. Proteomics 2005, 5 (13), 3414-3422.

35. Villanueva, J.; Philip, J.; Entenberg, D.; Chaparro, C. A.; Tanwar, M. K.; Holland, E. C.; Tempst, P.

Serum Peptide Profiling by Magnetic Particle-Assisted, Automated Sample Processing and MALDI-

TOF Mass Spectrometry. Anal. Chem. 2004, 76 (6), 1560-1570.

36. Villanueva, J.; Philip, J.; Chaparro, C. A.; Li, Y.; Toledo-Crow, R.; DeNoyer, L.; Fleisher, M.; Robbins,

R. J.; Tempst, P. Correcting Common Errors in Identifying Cancer-Specific Serum Peptide Signatures.

J. Proteome. Res. 2005, 4 (4), 1060-1072.

37. Anesi, A.; Rondanelli, M.; d'Eril, G. M. Stability of Neuroactive Amino Acids in Cerebrospinal Fluid

Under Various Conditions of Processing and Storage. Clin. Chem. 1998, 44 (11), 2359-2360.

38. van Midwoud, P. M.; Rieux, L.; Bischoff, R.; Verpoorte, E.; Niederlander, H. A. Improvement of

Recovery and Repeatability in Liquid Chromatography-Mass Spectrometry Analysis of Peptides. J.

Proteome. Res. 2007, 6 (2), 781-791.

39. Horvatovich, P.; Govorukhina, N. I.; Reijmers, T. H.; van der Zee, A. G.; Suits, F.; Bischoff, R. Chip-

LC-MS for Label-Free Profiling of Human Serum. Electrophoresis 2007, 28 (23), 4493-4505.

40. Christin, C.; Smilde, A. K.; Hoefsloot, H. C.; Suits, F.; Bischoff, R.; Horvatovich, P. L. Optimized Time

Alignment Algorithm for LC-MS Data: Correlation Optimized Warping Using Component Detection

Algorithm-Selected Mass Chromatograms. Anal. Chem. 2008, 80 (18), 7012-7021.

41. Kemperman, R. F.; Horvatovich, P. L.; Hoekman, B.; Reijmers, T. H.; Muskiet, F. A.; Bischoff, R.

Comparative Urine Analysis by Liquid Chromatography-Mass Spectrometry and Multivariate

Statistics: Method Development, Evaluation, and Application to Proteinuria. J Proteome. Res. 2007, 6

(1), 194-206.

42. Suits, F.; Lepre, J.; Du, P.; Bischoff, R.; Horvatovich, P. Two-Dimensional Method for Time Aligning

Liquid Chromatography-Mass Spectrometry Data. Anal. Chem. 2008, 80 (9), 3095-3104.


66

43. Radulovic, D.; Jelveh, S.; Ryu, S.; Hamilton, T. G.; Foss, E.; Mao, Y.; Emili, A. Informatics Platform for

Global Proteomic Profiling and Biomarker Discovery Using Liquid Chromatography-Tandem Mass

Spectrometry. Mol. Cell Proteomics 2004, 3 (10), 984-997.

44. Tibshirani, R.; Hastie, T.; Narasimhan, B.; Chu, G. Diagnosis of Multiple Cancer Types by Shrunken

Centroids of Gene Expression. Proc. Natl. Acad. Sci. U. S. A 2002, 99 (10), 6567-6572.

45. Mize, T. H.; Amster, I. J. Broad-Band Ion Accumulation With an Internal Source MALDI-FTICR-MS.

Anal. Chem. 2000, 72 (24), 5886-5891.

46. Moyer, S. C.; Budnik, B. A.; Pittman, J. L.; Costello, C. E.; O'Connor, P. B. Attomole Peptide Analysis

by High-Pressure Matrix-Assisted Laser Desorption/Ionization Fourier Transform Mass Spectrometry.

Anal. Chem. 2003, 75 (23), 6449-6454.

47. O'Connor, P. B.; Costello, C. E. Application of Multishot Acquisition in Fourier Transform Mass

Spectrometry. Anal. Chem. 2000, 72 (20), 5125-5130.

48. Koek, M. M.; Muilwijk, B.; van der Werf, M. J.; Hankemeier, T. Microbial Metabolomics With Gas

Chromatography/Mass Spectrometry. Anal. Chem. 2006, 78 (4), 1272-1281.

49. You, J. S.; Gelfanova, V.; Knierman, M. D.; Witzmann, F. A.; Wang, M.; Hale, J. E. The Impact of Blood

Contamination on the Proteome of Cerebrospinal Fluid. Proteomics 2005, 5 (1), 290-296.

50. Kraut, A.; Marcellin, M.; Adrait, A.; Kuhn, L.; Louwagie, M.; Kieffer-Jaquinod, S.; Lebert, D.;

Masselon, C. D.; Dupuis, A.; Bruley, C.; Jaquinod, M.; Garin, J.; Gallagher-Gambarelli, M. Peptide

Storage: Are You Getting the Best Return on Your Investment? Defining Optimal Storage Conditions

for Proteomics Samples. J Proteome. Res. 2009, 8 (7), 3778-3785.

51. Bhatnagar, B. S.; Bogner, R. H.; Pikal, M. J. Protein Stability During Freezing: Separation of Stresses

and Mechanisms of Protein Stabilization. Pharm. Dev. Technol. 2007, 12 (5), 505-523.

52. Dong, A.; Prestrelski, S. J.; Allison, S. D.; Carpenter, J. F. Infrared Spectroscopic Studies of

Lyophilization- and Temperature-Induced Protein Aggregation. J. Pharm. Sci. 1995, 84 (4), 415-424.

53. Klibanov, A. M.; Schefiliti, J. A. On the Relationship Between Conformation and Stability in Solid

Pharmaceutical Protein Formulations. Biotechnol. Lett. 2004, 26 (14), 1103-1106.

54. Sekijima, Y.; Kelly, J. W.; Ikeda, S. Pathogenesis of and Therapeutic Strategies to Ameliorate the

Transthyretin Amyloidoses. Curr. Pharm. Des 2008, 14 (30), 3219-3230.

55. Bergen, H. R., III; Zeldenrust, S. R.; Naylor, S. An on-Line Assay for Clinical Detection of

Amyloidogenic Transthyretin Variants Directly From Serum. Amyloid. 2003, 10 (3), 190-197.

56. Chow, G.; James, W.; Schmidley, M. D. Lysis of erythrocytes and leukocytes in traumatic lumbar

punctures. Arch Neurol 1984, 41 (10), 1084-1085.

57. Steele, R. W.; Marmer, D. J.; O'Brien, M. D.; Tyson, S. T.; Steele, C. R. Leukocyte Survival in

Cerebrospinal Fluid. J Clin. Microbiol. 1986, 23 (5), 965-966.

67

Chapter 3

68

The impact of delayed storage on the

proteome and metabolome of human

cerebrospinal fluid (CSF)

Therese Rosenling1, Marcel P. Stoop2, Agnieszka Smolinska3, Bas Muilwijk4,

Leon Coulier4, Shanna Shi5, Adrie Dane5, Christin Christin1, Frank Suits6,

Peter L. Horvatovich1, Sybren Wijmenga3, Lutgarde Buydens3, Rob

Vreeken7, Thomas Hankemeier7, Alain J. van Gool8, Theo M. Luider2, Rainer

Bischoff1

1Analytical Biochemistry, Department of Pharmacy, University of

Groningen, Groningen, The Netherlands

2Department of Neurology, Erasmus University Medical Center,


3Biophysical Chemistry, Radboud University Nijmegen, Nijmegen, The

Netherlands

4TNO, Quality of Life, Zeist, The Netherlands

5Netherlands Metabolomics Centre, Leiden/Amsterdam Centre for Drug

Research, Leiden University, Leiden, The Netherlands

6IBM TJ Watson Research Centre, Yorktown Heights, NY, USA

7Analytical BioSciences, Leiden/Amsterdam Centre for Drug Research,

Leiden University, Leiden, The Netherlands

8MSD, Oss, The Netherlands


Chapter 3

69

Abstract

Cerebrospinal fluid (CSF) is in close contact with the diseased areas in

neurological disorders and is therefore an important source of material in the

search for molecular biomarkers. CSF is withdrawn from patients in a clinical

setting where sample handling might not always be adequate in view of

proteomic and metabolomic studies. We therefore initiated a combined

proteomics and metabolomics study of the impact of time delay between sampling

and freezing of CSF samples.

We left CSF for 0, 30 and 120 min at room temperature after sample

collection and subsequent centrifugation/removal of cell pellet. We analyzed the

CSF samples at five separate laboratories using five different analytical platforms:

Proteome analysis with nanoLC Orbitrap-MS/MS and chipLC QTOF-MS after

tryptic digestion; metabolome analysis with NMR and GC-MS; and amino acid

analysis with LC-MS.

Our results show that changes in the metabolome and proteome of human

CSF left at room temperature after centrifugation are minor compared to the

biological variation between the samples. The delayed storage at room

temperature resulted in few but statistically significant changes in the proteome

and metabolome. We detected two non-identified peptides as well as one

metabolite; 2,3,4-trihydrobutanoic acid, that changed significantly.

With the applied analysis strategies the proteome/metabolome profile of

centrifuged, human CSF with all cells removed proved to be rather stable even

when stored at room temperature for up to two hours. This gives the laboratory

personnel at the collection site time to aliquot samples before freezing and storage

at -80 °C.

The effect of delayed storage on the proteome of human CSF

70

1. Introduction

The conditions during the journey of a biological sample from the clinical

collection site to the analytical research laboratory may not always be adequate for

subsequent proteomics and metabolomics analyses, especially in cases where the

sample collection was not originally performed with these large-scale analyses in

mind. In order to detect reliable molecular biomarker candidates it is imperative to

handle biological fluids according to standardized procedures and to evaluate the

effect of pre-analytical parameters on the final result to avoid artifacts (1, 2).

Earlier studies on urine, plasma and cerebrospinal fluid (CSF) have shown that

sample handling can affect the stability of proteins as well as metabolites (3-11).

Sample handling according to standardized procedures is also important when

trying to compare results between different laboratories (12-14). In the search for

molecular biomarkers related to disorders of the central nervous system, CSF is

the most promising bio-fluid, because of the proximity to the affected tissue (13,

15-20).

This study complements our previous stability study where we showed

that a number of proteins and metabolites changed over a time period of two

hours at room temperature in porcine CSF samples that contained low levels of

remaining white blood cells (21). In this study we have analyzed a set of human

CSF samples in order to assess protein and metabolite stability at room

temperature after a low-speed centrifugation step to remove cells. To cover a wide

range of proteins and metabolites, the results from a number of analytical

platforms comprising LC-MS, GC-MS and NMR have been combined. To our

knowledge this is the first stability study on CSF of this scale that has been

reported.


2.1. Sample set Six human CSF samples were obtained from the Department of Neurology at the

Erasmus University Medical Center (Rotterdam, The Netherlands). The CSF

samples were collected as part of routine clinical examinations of the patients with

various symptoms (Table 1). All samples were withdrawn via lumbar puncture

between the 3rd and 4th lumbar vertebrae using a Spinocan needle (0.90 × 88 mm).

The Medical Ethical Committee of the Erasmus University Medical Center in

Rotterdam, The Netherlands, approved the study protocol and all patients gave

their consent. Samples were centrifuged (10 min at 956 g) immediately after

collection (maximum time between sample collection and centrifugation was

approximately 5 minutes) to remove cells. Aliquots were directly snap-frozen in

Chapter 3

71

liquid nitrogen or left at room temperature for 30 and 120 min before snap

freezing and storage at -80 °C. Routine CSF diagnostics including total protein and

albumin concentration measurements as well as intrathecal cell count were

performed and absence of hemoglobin and apolipoprotein B100 was assured to

eliminate the possibility that samples were contaminated with blood. Samples H2

- H6 were analyzed by all platforms. Sample H1 was analyzed by the chipLC

QTOF-MS and the nanoLC Orbitrap-MS/MS platforms only. Protein digestion for

proteomic analysis was performed as previously described (21). Before analysis on

each platform the samples were exposed to two freeze-thaw cycles. Since this

factor was kept constant for all samples it had no effect on sample quality.

Table 1. Description of CSF samples used for the stability study.

Sample H1 was only analyzed with respect to proteomics.

Sam

ple

Ag

e

Gen

de

r

Dia

gn

os

is

Pro

tein

co

nc.

(ng

/ul)

Alb

um

in c

on

c.

(ng

/ul)

# C

ell

s/μ

L (

aft

er

cen

trif

ug

.)

H1 49 M Migraine 0.415 0.193 0

H2 56 M Idiopathic intracranial hypertension 0.472 0.247 0

H3 69 F Headache 0.395 0.236 0

H4 48 M Idiopathic intracranial hypertension 0.436 0.241 0

H5 29 F Clinical isolated syndrome (Neuromyelitis optica) 0.387 0.226 0

H6 38 F Epilepsy 0.381 0.184 0

2.2. ChipLC QTOF MS proteomic analysis Half a microliter digested CSF samples (H1-H6: T0, T30, T120) were randomly

injected in quintuplicate with 0.5 µL digested QC samples (pooled CSF spiked

with cytochrome C; Fluka, part # 30396, end concentration: 375 fmol/uL) and

blanks injected between every 10th sample for LC-MS analysis on an Agilent

chipLC QTOF-MS system as previously described (21). Enrichment and separation

was done using an LC chip (G4240-63001 SPQ110, Agilent Technologies

[separation column: 150 mm × 75 µm Zorbax 300SB-C18, 5 µm; trap column: 160

nL Zorbax 300SB-C18, 5 µm]). The LC separations were carried out as described

earlier (21) using the following gradient: 80 min linear gradient from 3 to 40% B;

10 min linear gradient from 40 to 50% B; 10 min linear gradient from 50 to 3% B.

MS analysis was performed under the following conditions; mass range: 200-2000

m/z in profile mode, acquisition rate: 1 spectrum/s, fragmentor voltage: 175 V,

skimmer voltage: 65 V, OCT 1 RF Vpp: 750 V. The spray voltage was ~1800 V and


72

the drying gas (N2) was 6 L/min at a temperature of 325 ºC. Mass correction was

done for each spectrum using internal standards (methyl stearate m/z: 299.294457

and HP-1221 m/z: 1221.990637) evaporating from a wetted wick inside the spray

chamber. Reproducibility was monitored on selected cytochrome C peaks in the

QC samples; Mass difference between theoretical and the measured values was

within +/- 4 ppm. The selected peaks showed a peak area RSD of less than +/- 20%

and a retention time (RT) RSD of less than 2%.

Data was processed using a pipeline developed in C++ (22) as previously

described (21). MzData.XML data were converted to ASCII format over a mass

range of 200 to 1600 m/z (outside this range no multiply charged ions were

detected), a retention time range of 3 to 80 min (peptide elution range) and with an

intensity threshold of 300 counts. A double cross validated Nearest Shrunken

Centroid (NSC) (23) algorithm was applied to the complete peak matrix; the NSC

comparison gives for a certain shrinkage value a cross validation error between 0

and 1, where 1 imply that both classes are assigned to the wrong class, 0.5 is a

random class assignation and 0 means that both classes are correctly assigned.

NSC selected features were compared by univariate statistical analysis in terms of

Student’s t-test with Bonferroni correction for multiple comparisons and ANOVA

(Microsoft Excel 2007 and SPSS 16.0). The features were considered significantly

different with a p-value below 0.05 (T0 vs. T120 and T0 vs. T30). Each

discriminatory feature was analyzed by targeted tandem MS for identification.

Principal component analysis (PCA) (24) was applied to the complete peak matrix

(10 000 peaks) as well as on the NSC-selected features (MatLab, R2009a). For

visualization box and whisker plots were created in Origin 7.0.

2.3. NanoLC Orbitrap-MS/MS shotgun proteomics analysis Digested CSF samples (H1-H6: T0, T30, T120) were injected one time each in

random order and analyzed by MS/MS (shotgun approach) on an Ultimate 3000

nano LC system (Dionex, Germany) online coupled to a hybrid linear ion

trap/Orbitrap mass spectrometer (LTQ Orbitrap XL; Thermo Fisher Scientific,

Bremen, Germany) as previously described (21).

Data files were analyzed and pre-processed using the Progenesis LC-MS

software package (Nonlinear Dynamics, United Kingdom); retention times were

aligned and the intensities of the ions were normalized. To assess inter-patient

variability all identified peaks were analyzed by PCA. All identified peaks were

also analyzed by NSC (23) for classification. Peptides were analyzed for

differential abundance between the sample-groups by performing ANOVA, p-

values below 0.01 were considered significant.

All MS/MS spectra were searched against the UniProt/SwissProt database

(version 57.6, August 20, 2009, taxonomy: Homo sapiens 20070 sequences) using

the Mascot (version 2.2.06). Search parameters were; parent ion tolerance: 2 ppm,

Chapter 3

73

amino acid modifications: carbamidomethylation of cysteine (fixed) and oxidation

of methionine (variable). The results were further on linked to the original data

(Progenesis).

2.4. GC-MS metabolomic analysis CSF samples (H2-H6: T0, T30, T120) were treated with an oximation reagent

followed by silylation prior to GC-MS analysis (21, 25). Each sample was injected

twice in random order and analyzed on an Agilent 6890 gas chromatograph

coupled to an Agilent 5973 quadrupole mass spectrometer as described earlier

(21).

Peaks were characterized by retention time and m/z ratio and identified by

comparison with a spectral data base (TNO) (21). All detected metabolites were

analyzed by PCA. A two tailed Student’s t-test with Bonferroni correction for

multiple comparisons as well as ANOVA and Kruskal-Wallis test (Microsoft Excel

2007 and SPSS 16.0) was applied to all known metabolites (T0 vs. T30 and T0 vs.

T120); metabolites with a p-value below 0.05 were considered significantly

affected by storage time.

2.5. NMR metabolomic analysis Samples (H2-H6: T0, T30, T120) were randomized prior to sample preparation and

analysis. Fifty microliters of CSF were diluted in 200 µL of D2O (99.99 % D).

Twenty-five µL of 8.8 mM TSP-d4 (3-(Trimethylsilyl)propionic acid-d4 sodium salt,

99 % D) stock solution in D2O were added to 250 µL CSF to a final concentration of

0.8 mM TSP as internal standard and as chemical shift reference (δ0.00). The TSP-

d4 stock solution was prepared from dry TSP-d4. The pH was adjusted (7.0 – 7.1)

by adding phosphate buffer (9.7 µL of a 1 M stock solution) to a final

concentration of 35 mM (26). Finally the sample (284.7 µL) was transferred to a

SHIGEMI microcell NMR tube for measurements, (each sample was analyzed

once).

1D 1H NMR spectra were acquired on an 800 MHz Inova (Varian) system

equipped with a 5 mm triple-resonance, Z-gradient HCN cold-probe. Suppression

of water was achieved using WATERGATE (delay: 85 s) (27). For each spectrum

256 scans of 18 000 data points were accumulated with a spectral width of 9000

Hz. The acquisition time for each scan was 2 s. An 8 s relaxation delay was

employed between scans. Prior to spectral analysis, all acquired Free Induction

Decays (FIDs) were zero-filled to 32 000 data points, multiplied with a 0.3 Hz line

broadening function, Fourier transformed and manually phased. Calibration of

the chemical shift scale was done on the external reference standard TSP-d4 by

using ACD/SpecManager software (Advanced Chemistry Development Inc.,


74

Toronto, Canada). Spectra were transformed to MatLab, version 7.6 (R2008b)

(Mathworks, Natick, MA) for further analysis.

NMR spectral data was preprocessed by baseline correction using the

Asymmetric Least Squares method (28) and alignment with the Correlation

Optimized Warping (COW) method (29). Each spectrum was divided (along the

chemical shift axis) in equally sized bins (0.04 ppm) and each data point was

averaged over each bin. The areas of the bins were summed to provide an integral

so that the intensities of the peaks in such defined spectral regions were extracted.

Each NMR spectrum was reduced to 210 variables, calculated by integrating

regions of equal width (0.04 ppm) corresponding to the regions of δ0.7-9. To

remove effects of variation in water resonance suppression, spectral regions

between δ4.4-5.4 were removed. All spectra thus reduced were normalized to unit

area.

The data was further processed by vast scaling, in order to determine

group-specific scaling factors (30, 31). To visualize possible systematic variation,

grouping, trends and outliers, PCA was applied to the entire data set.

2.6. LC-MS/MS amino acid analysis Fifteen CSF samples (H2-H6: T0, T30, T120) were prepared in triplicate as

previously described (21). One microliter of each reaction mixture was injected in

duplicate on an ACQUITY UPLCTM system (Waters Chromatography B.V., Etten-

Leur, The Netherlands) coupled to a Quattro Premier Xe tandem quadrupole mass

spectrometer (Waters Corporation) operated under the MassLynx data acquisition

software (version 4.1; Waters). Quantification and pre-analysis of the data was

done using LC-QuanLynx (Waters) and Microsoft Excel 2003, respectively. The

complete data set was examined by PCA. The data was further statistically

analyzed by two-tailed Student’s t-test with Bonferroni correction and ANOVA

(Microsoft Excel 2007, SPSS 16.0) (T0 vs. T30 and T0 vs. T120), amino acids with p-

values below 0.05 were considered discriminatory.

3. Results

Centrifuged human CSF samples were left at room temperature for 30 and 120

min before being snap-frozen in liquid nitrogen and stored at -80 °C. Metabolome

and proteome profiles were compared to control samples that were frozen

immediately after centrifugation and subsequent removal of the cell pellet.

Proteome profiles were obtained by chipLC QTOF-MS and nanoLC Orbitrap-

MS/MS shotgun analysis after tryptic digestion. The metabolome was analyzed by

Chapter 3

75

GC-MS, NMR and LC-MS (amino acid analysis). The analyses were performed in

five separate laboratories.

3.1. Proteomic analysis The Orbitrap-MS/MS shotgun analysis resulted in a list of 55421 peaks out of

which 5780 peptides were identified. All identified peptides from the Orbitrap-

MS/MS data and the 10000 most intense QTOF-MS peaks (complete peak matrix)

were used for unsupervised multivariate statistical analysis (PCA). No trend with

respect to time left at room temperature was visible (Fig. 1A and 2A). PCA shows

that biological variation is more prominent than the effect of time delay between

sampling and freezing, since data points clustered according to the individual

patients rather than according to time points (Fig. 1B-D and 2B-D).

Both the ANOVA comparison of all peaks with respect to time groups as

well as NSC analysis of the Orbitrap MS/MS data showed only random differences

between the time groups (e.g. NSC analyses provided the lowest average cross

validation error of 0.5). This lead to the conclusion that that there was no

significant discrimination between the samples stored at -80 °C immediately after

centrifugation and samples left at room temperature for 30 or 120 min prior to

being frozen and stored.


76

Fig. 1. Multivariate statistical analysis (PCA) of the 10.000 most intense peaks

selected from chipLC QTOF-MS proteomic data (quintuplicate sample analysis).

No separation based on time between sampling and freezing (T0 [▼]/T30 [٭]/T120 [O]) is

visible, while data from individual samples cluster together indicating that the inter-

individual differences are larger than those related to time. (A) All samples. (B) Samples

H2 (٭) and H5 (■). (C) Samples H1 (+) and H6 (▲). (D) Samples H3 (x) and H4 (♦).

Fig. 2. Multivariate statistical analysis (PCA) of 5780 identified peaks from

nanoLC Orbitrap-MS/MS proteomic data (single sample analyses).

No separation based on time between sampling and freezing (T0 [▼]/T30

T120 [O]) is visible, while data from individual samples cluster together/[٭]

indicating that the inter-individual differences are larger than those related to time. (A)

All samples, (B) Samples H2 [٭]) and H5 (■). (C) Samples H1 (+) and

H6 (▲). (D) Samples H3 (x) and H4 (♦)

NSC analysis of the QTOF-MS data revealed that differences between T0

and T120 were only random, with a double cross validation error of 0.5.

Comparison of T0 versus T30 by NSC reached a minimal average cross validation

error of 0.34 and resulted in 11 selected peaks. PCA on the NSC-selected peaks (T0

vs. T30) from the QTOF data showed no clear discrimination but a weak tendency

Chapter 3

77

of clustering according to time groups (Fig. 3A). Further analysis of these peaks by

univariate statistical analysis resulted in two significantly discriminatory peaks

(m/z: 656.335, z: 2, RT: 41 min. and 736.383, z: 3, RT: 59 min.) (Fig. 3B and 3C).

Peak areas (combined from all samples) for the two peaks detected at m/z: 656.335

and 736.383 were significantly decreased after the samples were left for 30 minutes

at room temperature compared to the control samples (p < 0.00005 and p <

0.0000005 respectively with Bonferroni-corrected t-test). These peaks were further

decreased after 120 min (p < 1E-6 and p < 5E-9 respectively). However the double-

cross validated NSC algorithm provided the lowest cross-validation error in

combination of 5 most selective variables which shows that the discriminatory

power of these peaks is highly affected by samples left out in the inner loop

during the double cross validation process. Two-way ANOVA of the two peaks

showed that both the discrimination between the time left at room temperature as

well as biological variation (p < 1E-15) and the interaction between the two

variables were highly significant (p < 1E-10 for both peaks). Attempts to identify

the peaks by targeted MS/MS failed.


78

Fig. 3. (A) Multivariate statistical analysis by PCA based on 11 NSC-selected

peaks derived from chipLC QTOF-MS proteomics data (T0 [▼] vs. T30 [٭]). (B and

C) Univariate statistical analysis of two peaks that decreased significantly with

respect to delay time between CSF sampling and freezing at room temperature.

Data are represented as box and whisker plots with significant p-values marked (p < 5E-5)

(T0 vs. T30 and T0 vs. T120). (A) PCA of 11 NSC selected peaks for T0 vs. T30. (B) Peak

detected at m/z: 656.335 that decreased significantly after 30 and 120 min at room

temperature. (C) Peak at m/z 736.383 that decreased significantly after 30 and 120 min at

room temperature. The statistical analysis was based on two-tailed Students t-tests with

Bonferroni correction of the combined data from five repetitive analyses of six human CSF

samples (H1-H6).

Fig. 4. Statistical analysis of metabolomics data derived from human CSF (H2-

H6). (A,B,C), Multivariate statistical analysis by PCA based on all detected

metabolites. (D,) Univariate statistical analysis (Kruskal-Wallis non-parametric

ANOVA) of combined data from duplicate analyses of five human CSF samples

(H2-H6) by GC-MS. The analysis is visualized by box and whisker plots with

significant p-value (T0 vs. T120) marked (p < 5E-3).

(A) GC-MS (90 metabolites, duplicate sample analysis). (B) NMR (51 metabolites, single

analysis) and (C) LC-MS targeting the 19 natural amino acids (sextuplicate analysis).

Lower panels; (D) 2,3,4-trihydroxybutanoic acid was significantly increased (Kruskal-

Wallis) after storage at room temperature for 120 min (p < 5E-3).

Chapter 3

79

3.2. Metabolomic analysis GC-MS analysis quantified 88 metabolites, of which 67 were assigned to known

compounds based on spectral libraries. This analysis was complemented by

targeted LC-MS of 19 natural amino acids. NMR analysis identified and quantified

51 metabolites. Thirteen of the metabolites were detected by all three methods, 24

were detected by GC-MS and NMR, 14 by GC-MS and LC-MS and 16 by NMR

and LC-MS. In total 93 unique identified metabolites were quantified. PCA of the

data from the different analytical platforms showed that clustering occurs

primarily according to the individual patients rather than to the time points when

all data are considered (Fig. 4A-C). Statistical analysis revealed that the

concentration of 2,3,4-trihydrobutanoic acid (erythronic acid, threonic acid),

detected by GC-MS (Fig. 4D) increased in all samples with increased time left at

room temperature. In sample H2 the increase of this metabolite was extremely

high after 120 minutes. Non-parametric ANOVA (Kruskal-Wallis) showed that the

discrimination between T0 and T120 was clearly significant (p < 5E-3), Bonferroni

corrected Student’s t-test as well as one-way ANOVA also resulted in highly

significant group separation when the samples providing outlier value was left

out (p < 5E-5 for both t-test and ANOVA).

4. Discussion

We have previously shown that the stability of proteins and metabolites in porcine

CSF samples was affected when residual white blood cells were not completely

removed (21). Here we present a detailed study, performed on different analysis

platforms, into the stability of the proteome and metabolome of cell-free, human

CSF when leaving samples at room temperature for up to 2 h to mimic the clinical

situation.

Unsupervised multivariate statistical analysis (PCA) of the data showed

that patient-to-patient variation is most prominent overriding variation due to

delay time as the data points cluster in groups of individual samples rather than in

time-related groups. After variable selection based on the pre-classification of

samples according to delay time, we found that only two peptides and one

metabolite changed significantly over time. Our results demonstrate that the

measured proteome and metabolome of human CSF samples, using the applied

techniques, is quite stable when left at room temperature as long as all cells have

been removed first.

Another study on the stability of the proteome in CSF at room temperature

pointed in the same direction with the detection of only two polypeptides that

changed after storage (32). These samples were, however contaminated with blood


80

since both polypeptides were derived from hemoglobin. Another study showed

that blood contamination decreases the stability of the CSF proteome (11)

corroborating our earlier results (21). One explanation to the decreased level of the

two non-identified peaks in the proteome analysis is the possible adsorption to the

vial surface via e.g. hydrophobic or van der Waals interactions (21, 33, 34).

Metabolomics revealed increased levels of 2,3,4-trihydroxybutanoic acid after

storage at room temperature. This increase may be caused by oxidative

degradation of ascorbic acid (35-37) because here the ascorbic acid levels were

slightly decreased with increased time at room temperature. This decrease,

however, was too small to be a significant factor. Interestingly, 2,3,4-

trihydroxybutanoic acid decreased in CSF containing white blood cells (21), which

might be due to further metabolism of the acid by enzymes released from white

blood cells. The concentration of 2,3,4-trihydroxybutanoic acid was too low for

NMR detection.

The stability of metabolites and proteins measured with the most common

analytical profiling methods is an important factor to take into consideration when

handling biofluids and designing biomarker studies. A previous study from our

team shows that the biological variation of some proteins and peptides have large

variability (sometimes exceeding 100%), which requires high discriminating

power for compounds considered as biomarker candidates (38).

In conclusion, we report a study of combined analysis methods into the

stability of CSF using five different analytical platforms. The results agree that

there are minor or no significant changes in the metabolome and proteome of cell-

free CSF that was left for different time spans at room temperature as compared to

control samples that were frozen immediately after centrifugation. Earlier studies

showed that blood or white blood cell contamination reduces CSF stability

considerably, emphasizing the importance of the initial centrifugation step.

From our study we draw the conclusion that with the analysis techniques

used in this study biological variation between individuals is far more

pronounced than variation introduced by delay time at room temperature. This

gives laboratory personnel sufficient time to aliquot CSF samples before being

frozen and stored for use in similar studies. Aliquoting samples in small volumes

reduce the number of freeze-thaw cycles at later time points, which we have

shown to be beneficial (21). We can, however, only draw conclusions about the

analytes detected with our analysis techniques, and it remains possible that other

techniques may find other metabolites and peptides that are more affected by this

treatment.

Chapter 3

81

Acknowledgements

The authors would like to thank Rogier Hintzen from the Department of

Neurology, Erasmus University Medical Center, Rotterdam, The Netherlands for

providing the CSF samples. The study was performed within the framework of

the Top Institute Pharma project number D4-102. The work was also supported by

the project BioRange 2.2.3 from the Netherlands Proteomics and The Netherlands

Bioinformatics Center.


82

References

1. Hansson SF, Simonsen AH, Zetterberg H, Andersen O, Haghighi S, Fagerberg I et al.

Cystatin C in cerebrospinal fluid and multiple sclerosis. Ann Neurol 2007;62:193-6.

2. Irani DN, Anderson C, Gundry R, Cotter R, Moore S, Kerr DA et al. Cleavage of

cystatin C in the cerebrospinal fluid of patients with multiple sclerosis. Ann Neurol

2006;59:237-47.

3. Anesi A, Rondanelli M, d'Eril GM. Stability of neuroactive amino acids in

cerebrospinal fluid under various conditions of processing and storage. Clin Chem

1998;44:2359-60.

4. Kaiser E, Schonknecht P, Thomann PA, Hunt A, Schroder J. Influence of delayed

CSF storage on concentrations of phospho-tau protein (181), total tau protein and

beta-amyloid (1-42). Neurosci Lett 2007;417:193-5.

5. Kraut A, Marcellin M, Adrait A, Kuhn L, Louwagie M, Kieffer-Jaquinod S et al.

Peptide storage: are you getting the best return on your investment? Defining

optimal storage conditions for proteomics samples. J Proteome Res 2009;8:3778-85.

6. Levine J, Panchalingam K, McClure RJ, Gershon S, Pettegrew JW. Stability of CSF

metabolites measured by proton NMR. J Neural Transm 2000;107:843-8.

7. Schaub S, Wilkins J, Weiler T, Sangster K, Rush D, Nickerson P. Urine protein

profiling with surface-enhanced laser-desorption/ionization time-of-flight mass

spectrometry. Kidney Int 2004;65:323-32.

8. Teahan O, Gamble S, Holmes E, Waxman J, Nicholson JK, Bevan C, Keun HC.

Impact of analytical bias in metabonomic studies of human blood serum and

plasma. Anal Chem 2006;78:4307-18.

9. West-Nielsen M, Hogdall EV, Marchiori E, Hogdall CK, Schou C, Heegaard NH.

Sample handling for mass spectrometric proteomic investigations of human sera.

Anal Chem 2005;77:5114-23.

10. Wuolikainen A, Hedenstrom M, Moritz T, Marklund SL, Antti H, Andersen PM.

Optimization of procedures for collecting and storing of CSF for studying the

metabolome in ALS. Amyotroph Lateral Scler 2009;10:229-36.

11. You JS, Gelfanova V, Knierman MD, Witzmann FA, Wang M, Hale JE. The impact of

blood contamination on the proteome of cerebrospinal fluid. Proteomics 2005;5:290-

6.

Chapter 3

83

12. Diamandis EP. Mass spectrometry as a diagnostic and a cancer biomarker discovery

tool: opportunities and potential limitations. Mol Cell Proteomics 2004;3:367-78.

13. Giovannoni G. Multiple sclerosis cerebrospinal fluid biomarkers. Dis Markers

2006;22:187-96.

14. Villanueva J, Philip J, Chaparro CA, Li Y, Toledo-Crow R, DeNoyer L et al.

Correcting common errors in identifying cancer-specific serum peptide signatures. J

Proteome Res 2005;4:1060-72.

15. Dekker LJ, Burgers PC, Kros JM, Smitt PA, Luider TM. Peptide profiling of

cerebrospinal fluid by mass spectrometry. Expert Rev Proteomics 2006;3:297-309.

16. Lutz NW, Viola A, Malikova I, Confort-Gouny S, Ranjeva JP, Pelletier J, Cozzone PJ.

A branched-chain organic acid linked to multiple sclerosis: first identification by

NMR spectroscopy of CSF. Biochem Biophys Res Commun 2007;354:160-4.

17. Myint KT, Aoshima K, Tanaka S, Nakamura T, Oda Y. Quantitative profiling of

polar cationic metabolites in human cerebrospinal fluid by reversed-phase

nanoliquid chromatography/mass spectrometry. Anal Chem 2009;81:1121-9.

18. Noben JP, Dumont D, Kwasnikowska N, Verhaert P, Somers V, Hupperts R et al.

Lumbar cerebrospinal fluid proteome in multiple sclerosis: characterization by

ultrafiltration, liquid chromatography, and mass spectrometry. J Proteome Res

2006;5:1647-57.

19. Stoop MP, Dekker LJ, Titulaer MK, Lamers RJ, Burgers PC, Sillevis Smitt PA et al.

Quantitative matrix-assisted laser desorption ionization-fourier transform ion

cyclotron resonance (MALDI-FT-ICR) peptide profiling and identification of

multiple-sclerosis-related proteins. J Proteome Res 2009;8:1404-14.

20. Zhang J, Goodlett DR, Montine TJ. Proteomic biomarker discovery in cerebrospinal

fluid for neurodegenerative diseases. J Alzheimers Dis 2005;8:377-86.

21. Rosenling T, Slim CL, Christin C, Coulier L, Shi S, Stoop MP et al. The effect of

preanalytical factors on stability of the proteome and selected metabolites in

cerebrospinal fluid (CSF). J Proteome Res 2009;8:5511-22.

22. Suits F, Lepre J, Du P, Bischoff R, Horvatovich P. Two-dimensional method for time

aligning liquid chromatography-mass spectrometry data. Anal Chem 2008;80:3095-

104.

23. Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by

shrunken centroids of gene expression. Proc Natl Acad Sci U S A 2002;99:6567-72.


84

24. Wold S, Esbensen K, Geladi P. Principal Component Analysis. Chemometr Intell Lab

1987;2:37-52.

25. Koek MM, Muilwijk B, van der Werf MJ, Hankemeier T. Microbial metabolomics

with gas chromatography/mass spectrometry. Anal Chem 2006;78:1272-81.

26. Cunniffe JG, Whitby-Strevens S, Wilcox MH. Effect of pH changes in cerebrospinal

fluid specimens on bacterial survival and antigen test results. J Clin Pathol

1996;49:249-53.

27. Piotto M, Saudek V, Sklenar V. Gradient-tailored excitation for single-quantum

NMR spectroscopy of aqueous solutions. J Biomol NMR 1992;2:661-5.

28. Eilers PH. A perfect smoother. Anal Chem 2003;75:3631-6.

29. Tomasi G, van den Berg F, Andersson C. Correlation optimized warping and

dynamic time warping as preprocessing methods for chromatographic data. J

Chemometr 2004;18:231-41.

30. Keun HC, Ebbels TMD, Antti H, Bollard ME, Beckonert O, Holmes E et al. Improved

analysis of multivariate data by variable stability scaling: application to NMR-based

metabolic profiling. Anal Chim Acta 2003;490:265-76.

31. van den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, van der Werf MJ.

Centering, scaling, and transformations: improving the biological information

content of metabolomics data. BMC Genomics 2006;7:142-56.

32. Berven FS, Kroksveen AC, Berle M, Rajalahti T, Flikka K, Arneberg R et al. Pre-

analytical influence on the low molecular weight cerebrospinal fluid proteome.

Proteom Clin Appl 2007;1:699-711.

33. van Midwoud PM, Rieux L, Bischoff R, Verpoorte E, Niederlander HA.

Improvement of recovery and repeatability in liquid chromatography-mass

spectrometry analysis of peptides. J Proteome Res 2007;6:781-91.

34. Burke CJ, Steadman BL, Volkin DB, Tsai P-K, Bruner MW, Middaugh CR. The

adsorption of proteins to pharmaceutical container surfaces. Int J Pharm 1992;86:89-

93.

35. Deutsch JC. Ascorbic acid oxidation by hydrogen peroxide. Anal Biochem

1998;255:1-7.

36. Mystkowski EM, Lasocka D. Factors preventing oxidation of ascorbic acid in blood

serum. Biochem J 1939;33:1460-4.

37. Kuellmer V. Vitamins: Ascorbic acid. In: Francis FJ, ed. Wiley encyclopedia of food

science and technology. West Sussex: Wiley Interscience; 1999:2449-67pp.

Chapter 3

85

38. Stoop MP, Coulier L, Rosenling T, Shi S, Smolinska AM, Buydens L et al.

Quantitative proteomics and metabolomics analysis of normal human cerebrospinal

fluid samples. Mol Cell Proteomics, 2010 [Epub ahead of print].

86

Chapter 4

87

Profiling and identification of

Cerebrospinal Fluid (CSF) proteins in a Rat

EAE Model of Multiple Sclerosis

Therese Rosenling1, Marcel Stoop2, Amos Attali3, Hans van Aken3, Ernst

Suidgeest3, Christin Christin1, Christoph Stingl2, Frank Suits4, Peter

Horvatovich1, Rogier Hintzen2, Tinka Tuinstra3, Rainer Bischoff1, Theo

Luider2

1Department of Analytical Biochemistry, Centre for Pharmacy,

University of Groningen, Groningen, The Netherlands



3 Abbott Healthcare Products B.V., Weesp, the Netherlands 4IBM TJ Watson Research Centre, Yorktown Heights, NY, USA


Proteome profiling in a rat EAE model

88

Abstract

The experimental autoimmune encephalomyelitis (EAE) model resembles certain

aspects of multiple sclerosis (MScl), with common features such as motor

dysfunction, axonal degradation and infiltration of T-cells during the affected

period. In MScl the main cause of the disease yet to be discovered and there is still

no specific molecular biomarker in use for early diagnosis and patient

classification. We here chose to study the EAE rat model to discover novel MScl

related proteomic biomarker candidates using a screening approach with the goal

to translate them into the human situation.

EAE was induced in male Lewis rats by injection of myelin basic protein

(MBP) together with complete Freund’s adjuvant (CFA). An inflammatory control

group was injected with CFA alone and a non-treated group served as healthy

control. CSF was collected from the cisterna magna at day 10 (disease onset) and 14

(peak of symptoms) after treatment and analyzed by bottom-up proteomics. The

analysis was based on Orbitrap LC-MS and QTOF LC-MS platforms in two

independent laboratories. The obtained data were processed in a crossover design

by a combination of two different approaches including our in-house developed

data processing pipeline, the Progenesis pipeline and the nearest shrunken

centroid classifier (NSC) as well as principal component analysis (PCA). By

combining results, we discovered 41 proteins significantly increasing in EAE

animals compared to the control groups, 28 of these have not been discovered as

discriminatory in the EAE model before. Among the discriminatory proteins are

Vitamin D binding protein, lysozyme C1, fetuin B, T-kininogen, serum

paraoxonase/arylesterase 1, glutathione peroxidase 3 and afamin. To our

knowledge, this is the first study of its kind focusing on a screening approach of

rat CSF from the EAE model.

Chapter 4

89

1. Introduction

Multiple Sclerosis (MScl) is an autoimmune, demyelinating, neurodegenerative

disorder of the central nervous system (CNS) (1). Presently there is no rapid and

specific diagnostic test that excludes other diseases or that can distinguish the

different sub-classes of MScl from each other. Diagnosis is currently based on

clinical evaluation of the patient together with evidence of lesions by magnetic

resonance imaging (MRI) as well as the presence of non-specific oligoclonal IgG

bands in CSF (2, 3).

The discovery of reliable molecular biomarkers to diagnose and classify

MScl is of great importance to initiate in an early phase the most appropriate

therapy in order to at least slow disease progression and to improve the quality of

life of MScl patients. An additional value of MScl-specific molecular biomarkers is

the possibility of gaining further insight into the disease mechanism and to

monitor the effect of new drugs that are developed for MScl.

Although the cause of MScl remains elusive, studies based on the

experimental autoimmune encephalomyelitis (EAE) rat model have revealed

many pathogenic processes. The EAE model mimics several aspects of the disease

such as, axonal and neuronal degradation, motor dysfunction in the lower limbs

(4-6), demyelination, infiltration of CD4+/CD8+ T-cells, activation of microglia and

macrophages, blood-brain-barrier (BBB) rupture and release of inflammatory

cytokines (e.g. TNF-α and IL1-β) (4-8). However, the EAE model still remains a

model and it does not cover all clinical aspects of MScl. Most proteomic studies

based on the EAE model have been hypothesis-driven (8-16), while only a few

large-scale whole proteome screening studies have been performed to date on

affected tissue such as spinal cord an lesions (17-20). Until now, no studies are

described that look into the proteome of rodent EAE models to identify and

quantify EAE related proteins in CSF.

MScl lesions are usually found in the periventricular white matter and at

the surface of the spinal cord (21) which are both in contact with cerebrospinal

fluid (CSF). This makes CSF an ideal compartment to discover biomarkers. In the

clinic CSF is collected from patients with relative ease albeit not as readily as

blood. CSF has been used for MScl research in humans for many years (22). The

EAE model, on the contrary, has not been studied extensively (8) from CSF. This is

probably due to the relatively small volume of CSF that can be withdrawn from

rodents (for rats < 60 µL (unpublished results) and mice < 15 µL (23)) without

blood contamination of hemoglobin and apolipoprotein B100. It is obvious, that

analysis of such small volumes requires sensitive, miniaturized techniques and

highly skilled operators.


90

Here we report a comprehensive whole proteome analysis of CSF from

MBP-EAE Lewis rats using two LC-MS/MS platforms in two independent

laboratories. Samples were taken at the onset of disease symptoms (day 10) and at

the apex of symptoms (day 14) to detect early-indicator, disease-specific proteins

and link them to hallmarks of MScl disease and pathogenesis.


2.1. Induction of acute EAE in the Lewis rat At the start of the study (day 0) EAE was induced in 30 male Lewis rats (Harlan

Laboratories B.V.) with a starting weight of 175 – 200 g, by injection of 100 μL

saline-based emulsion containing 20 μg guinea pig myelin basic protein (MBP),

500 μg Mycobacterium tuberculosis type 37HRa (Difco) and 50 μL Complete

Freunds’ Adjuvant (CFA) (EAE groups; E and F). Also at day 0, 30 additional male

Lewis rats received the same saline-based emulsion as above but without added

MBP (inflammatory control groups; C and D). The emulsion was injected

subcutaneously in the left hind paw under isoflurane anesthesia. Another 30 rats

were kept as untreated controls undergoing anesthesia only (healthy control

groups; A and B). The animal groups and samples included in the experimental

design of the present study are summarized in Table 1.

Chapter 4

91

Table 1. Samples included in the study. Sample data that were analyzed by

Orbitrap only are marked O, sample data analyzed by QTOF only are marked Q.

Group E was divided in two groups according to the TIC area Ehigh (high TIC) and

Elow (low TIC). Samples marked were not included because of blood contamination

(samples 62, 78, 81). Sample 80 was excluded because the rat did not show visual

signs of disease (considered as outlier).

Samples analyzed

Healt

hy

co

ntr

ol

da

y

10

Healt

hy

co

ntr

ol

da

y

14

Infl

am

mato

ry c

on

tro

l (C

FA

on

ly)

da

y 1

0

Infl

am

mato

ry c

on

tro

l (C

FA

on

ly)

da

y 1

4

EA

E (

CF

A +

MB

P)

da

y 1

0

EA

E (

CF

A +

MB

P)

da

y 1

4

Group A Group B Group C Group D Group E Group F

1 16 31 46 O 61 76

2 17 32 47 62 (blood) 77 O

3 18 33 48 O 63 Q 78 (blood)

4 19 34 49 64 (Ehigh) 79

5 20 35 50 65 (Ehigh) 80 (outlier)

6 21 O 36 51 66 81 (blood)

7 22 37 52 67 82

8 23 O 38 53 68 O 83

9 24 39 54 69 (Ehigh) 84

10 25 40 55 70 85

11 26 41 56 71 (Ehigh) 86

12 O 27 42 57 72 87 O

13 28 43 58 73 (Ehigh) 88

14 29 44 59 74 89

15 30 45 60 75 O 90


92

The animals were randomized for treatment and individually marked.

Three animals were kept in each cage (type III cages) where food and water were

available ad libitum. Disease symptoms and weights of all animals were recorded

daily. The following scores for motor dysfunction were used; 0: no pathological

signs, 1: paralysis of the tip of the tail, 2: no curling reflex/no strength at tail basis,

3: slightly impaired walking, 4: unstable walk, 5: half hind limb paralysis, 6:

complete hind limb paralysis, 7: midriff paralysis, 8: half fore limb paralysis, 9:

moribund, 10: death due to EAE.

From each of the 3 groups (EAE, inflammatory control, non-treated

control) 15 animals were sacrificed at day 10 (disease onset) (groups A, C and E)

and another 15 rats were sacrificed at day 14 (disease peak) (groups B, D and F).

CSF was also collected and pooled from 10 healthy animals to be used as quality

control (QC) sample during LC-MS analysis, for protein concentration

measurements, and for the set-up of protocols and analysis methods. All animal

experiments described in this study were approved by the local Ethical Committee

for Animal Experiments.

2.2. CSF Sampling At day 10 and 14, the animals were euthanized using CO2/O2, the head of the rats

were fixed using a holder to reveal the Arachnoid membrane and a skin incision

was made followed by a horizontal incision in the musculus trapezius pars

descendens. A maximum volume of 60 µL CSF was obtained by direct insertion of

an insulin syringe needle (Myjector, 29 G × 1/2" - 0.33 × 12 mm, 0.3 mL = 30 units)

via the arachnoid membrane into the cisterna magna. Within 20 minutes after

collection, each sample was centrifuged at a force of 2000 g for 10 minutes at 4 °C.

After centrifugation, the supernatant was aliquoted in five tubes of ~10 µL each

and stored (up to 6 months) at -80 °C until further analysis. Samples that

according to visual control as well as to the detection of hemoglobin-derived

peptides (analysis by Orbitrap LC-MS/MS) were considered to be contaminated

with blood and were subsequently excluded from the study. The samples

analyzed by the two platforms are listed in Table 1.

2.3. Sample preparation Protein digestion was performed as follows: 10 µL CSF and 10 µL 0.1%

RapiGest™ (Waters) dissolved in 50 mM ammonium bicarbonate were added to a

sample tube. The sample was reduced by adding 0.5 μL 1,4-dithiotreitol (DTT) (0.5

M) followed by incubation at 60 °C for 30 min. After cooling to room temperature

the sample was alkylated with 1 μL iodoacetamide (IAM) (0.3 M) in the dark for

30 min at room temperature. To the sample 1 μL sequencing grade modified

Chapter 4

93

trypsin (Promega, Madison, WI, USA, part # V5111) (1 µg/µL) (enzyme to protein

ratio; 1:10-50) was added and the sample was incubated for ~16 h at 37 °C under

slight agitation (450 rpm). Thereafter, 3 μL hydrochloric acid (0.5 M) was added

followed by incubation for 30 min at 37 °C. The sample was centrifuged at 13250 g

for 10 min at 4 °C to remove precipitated, hydrolyzed RapiGest™. The samples

were transferred to sample vials and analyzed in random order. Each of the

samples was exposed to three freeze-thaw cycles prior to LC-MS analysis.

2.4. ChipLC-QTOF MS proteomic analysis The sample order was randomized with quality controls (cytochrome C spiked

into the pooled CSF sample after digestion at 200 fmol/µL) and blanks injected

after every tenth sample. The sample injection volumes were normalized

according to the TIC area; a first injection of 0.5 μL of samples from all group

revealed a TIC area that was 5 times higher in group E and F compared to group

A-D. To normalize the TIC area a five times lower volume was injected of group E

and F (0.2 μL) compared to group A-D (1 μL). Peptides were separated on a

reverse phase LC-chip (Protein ID chip #3; G4240-63001 SPQ110: Agilent

Technologies; separating column: 150 mm × 75 µm Zorbax 300SB-C18, 5 µm; trap

column: 160 nL Zorbax 300SB-C18, 5 µm) coupled to a nano LC system (Agilent

1200) with a 40 μL injection loop. Ions were generated by electrospray ionization

(ESI) and transmitted to a quadrupole-time-of-flight (QTOF) mass spectrometer

(Agilent 6510). Instrumentation was operated under the MassHunter Data

Acquisition software (version B.01.03; Build 1.3.157.0; Agilent Technologies, Santa

Clara, USA).

For LC separation the following eluents were used; A: ultra-pure water

(conductivity 18.2 MΩ, Elga Labwater, Ede, The Netherlands) with 0.1% formic

acid (98-100%, pro analysis, Merck, Darmstadt, Germany); B: acetonitrile (HPLC-S

gradient grade, Biosolve, Valkenswaard, The Netherlands) with 0.1% formic acid.

The samples were injected on the trap column at a flow rate of 3 μL/min (3% B).

After 10 min the sample was transferred to the separation column at a flow rate of

250 nL/min and the peptides were eluted using the following gradient: 100 min

linear gradient from 3 to 50% B; 5 min linear gradient from 50 to 70% B; 4 min

linear gradient from 70 to 3%.

The MS analysis was done in the 2 GHz extended dynamic range mode

under the following conditions; mass range: 275-2000 m/z, acquisition rate: 1

spectrum/sec, data storage: profile and centroid mode, fragmentor: 175 V,

skimmer: 65 V, OCT 1 RF Vpp: 750 V, spray voltage: ~1900 V, drying gas temp: 325

ºC, drying gas flow (N2): 6 L/min. Mass correction was done during analysis using

internal standards; m/z: 371.31559 (originating from a ubiquitous background ion

(Dioctyl adipate, DOA, plasticizer) and m/z: 1221.990637 (HP-1221 calibration


94

standard, continuously evaporating from a wetted wick inside the spray

chamber).

Repeatability of the LC-MS analyses was assessed in terms of mass

accuracy, retention time and peak area based on four selected peptide peaks

originating from a cytochrome C digest spiked into the QC sample. The peaks

were smoothed (Gaussian function width; 15 points, [15 sec]) and integrated to

calculate the relative standard deviation (RSD) of the peak area (< +/- 30%) and the

retention time (< +/- 0.3% or 5 sec). Mass accuracy calculated as the mean of five

measurements from each selected cytochrome C peak, was within +/- 5 ppm of the

expected value.

For protein identification, samples were analyzed by auto MS/MS in the 4

GHz mode using the following settings; fragmentor: 175 V, skimmer: 65 V, OCT 1

RF Vpp: 750 V, precursor ion selection: medium (4 m/z), mass range: 200-2000 m/z,

acquisition rate for both MS and MS/MS: spectrum/sec, isolation width: ~ 4 amu,

ramped collision energy 3.7 V/100 Da, offset: 2.5 V, precursor setting: maximum 3

precursors/cycle, absolute threshold: 1000, relative threshold: 0.01% of the most

intense peak, active exclusion enabled after 2 selections, release of active exclusion

after 0.5 min, precursors sorted by abundance only. The MS/MS files were stored

in centroid mode. The LC separation was identical except for a slightly modified

enrichment and gradient elution program. After injection, peptides were enriched

for 15 min on the trap column before being transferred to the separation column.

Elution was done using the following gradient: 97 min linear gradient from 3 to

60% B; 15 min linear gradient from 60 to 3% B.

2.5. ChipLC-QTOF MS data processing Raw data were visually examined and exported to MzData.XML files in centroid

mode using the Agilent MassHunter Qualitative Analysis software (Agilent

version B.01.03; Build 1.3.157.0). Files were converted to ASCII format. In order to

limit file size, thresholds were used for the conversion (conversion of data was

limited to the time and mass range corresponding to eluting peptides and data

were converted with intensity filtering to reduce background noise) according to

following parameter settings; peak intensity: > 400 counts, mass range: 275-1600

m/z, retention time range: 22-95 min. The ASCII files were pre-processed using an

in-house data processing workflow developed in C++ (24, 25) producing a

common peak matrix of 5336 peaks (based on selection of 10 000 most intensive

peaks from each data file, this number is selected based on the quality of the data

and the noise threshold). The raw peak matrix was analyzed by principal

component analysis (PCA) to visualize the overall variability prior to feature

selection and analysis with double cross validated Nearest Shrunken Centroid

(NSC) algorithm (26) (MatLab R2009a, Mathworks, Natick, MA, USA).

Chapter 4

95

The NSC classifier was applied on the generated peak matrix in order to

find the most discriminatory peptide peaks in the comparisons; A vs. E, C vs. E, B

vs. F and D vs. F. Double cross-validation was implemented by counting

classification error rate in function of the shrinkage in the inner loop and to

measure the classification performance in the outer loop. When the lowest

classification error rate was recorded for broad continuous range of shrinkage, the

optimal shrinkage value was selected manually in order to generate the peak list.

The NSC-selected features were subsequently visualized by PCA.

NSC-selected peaks were subsequently visualized using extracted ion

chromatograms (EICs) with the MassHunter software. EICs for selected m/z

values were smoothed and integrated, and the generated peak areas were

exported in Excel format. Peak areas from both the peak matrix as well as the

chromatograms were compared between groups using one way ANOVA with

Bonferroni correction (SPSS Inc., Chicago, Il, USA). Peak area differences with a p-

value below 0.01 were considered significant.

For processing of the same raw data with a different workflow, the data

files were converted to MzXML format using the open source msconvert parser in

the proteowizard pipeline (27). The generated files were pre-processed in terms of

feature extraction, retention time alignment and matching the same peaks across

multiple chromatograms using the Progenesis LC-MS software package (version

2.5, Nonlinear Dynamics, Newcastle-upon-Tyne, United Kingdom) generating a

raw peak matrix. The resulting peak matrix was exported in Excel format

(Microsoft Office Excel 2007, Microsoft Corporation, Redmond, WA, USA) and the

10 000 most intensive peaks were selected for further analysis. The NSC classifier

was applied and the same procedure as described above was applied on the data

set.

Discriminatory features were searched for matching m/z, RT and charge

states (Δ RT: max ± 1 min, Δ m/z: max ± 10 ppm) in an Excel file containing all

identified ions from the auto MS/MS analysis (see following section). After having

assigned a discriminatory peptide peak to a given protein, all other identified

peptides from that protein were examined to evaluate whether they followed the

same pattern. Only proteins where at least 80 % of all identified peptides had a p-

value below 0.01 when compared by ANOVA with Bonferroni correction (A vs. E,

C vs. E, B vs. F and D vs. F) were considered discriminatory. The peak areas for all

discriminatory peptides belonging to the same protein were summed within the

sample groups and an average was calculated. A protein with a difference of at

least a factor of two for proteins that increased in the diseased groups (E and F)

was considered as significantly discriminatory. For decreasing proteins a factor of

ten times was considered as significant (since group E and F was injected at five

times lower volume compared to group A-D, a protein decreasing ten times are


96

decreasing only two times at native relative comparison [raw samples without any

normalization]).

2.6. Protein identification (QTOF data) For protein identification one sample from group F and one sample from group A

were analyzed by data-dependent MS/MS, the generated data files were converted

to the .mgf format and exported to the Phenyx data base search tool (version 2.6,

Geneva Bioinformatics, Geneva, Switzerland,). MS/MS spectra were searched

against the uniprot_sprot and uniprot_sprot_rev databases (version: 20090608,

Taxonomy: Rattus norvegicus). The search parameters were set as follows: scoring

model: ESI-QTOF (QTOF), parent charge: +2, +3, +4, peptide/AC score: ≥ 5, peptide

length: ≥ 4, p-value < 0.001, enzyme: trypsin, missed cleavage: 2, parent tolerance:

10 ppm, amino acid modifications: carbamidomethylation of cysteine (fixed, all),

oxidation of methionine (variable, <4), cleavage mode: normal, false positive rate

upon searching the reverse database < 2%. Only proteins that were identified with

at least two separate peptides were considered as true positives.

2.7. Nano-LC-ESI-Orbitrap MS/MS Digested rat CSF samples were analyzed by LC-MS/MS using an Ultimate 3000

nano LC system (Dionex, Germering, Germany) online coupled to a hybrid linear

ion trap / Orbitrap mass spectrometer (LTQ Orbitrap XL; Thermo Fisher Scientific,

Bremen, Germany). Five microliter digest were loaded onto a C18 trap column

(C18 PepMap, 300µm ID x 5mm, 5µm particle size, 100 Å pore size; Dionex, The

Netherlands) and desalted for 10 minutes using a flow rate of 20 µL /min. The trap

column was switched online to the analytical column (PepMap C18, 75 μm ID x

150 mm, 3 μm particle and 100 Å pore size; Dionex, The Netherlands) and

peptides were eluted with the following binary gradient: 0% - 25% solvent B for

120 min and 25% - 50% solvent B for further 60 minutes, where solvent A

consisted of 2% acetonitrile and 0.1% formic acid in ultra pure water and solvent B

consisted of 80% acetonitrile and 0.08% formic acid in water. The column flow rate

was set to 300 nL/min. For MS detection a data dependent acquisition method was

used: a high resolution survey scan from 400 – 1800 m/z was performed in the

Orbitrap (automatic gain control (AGC) 106, resolution 30,000 at 400 m/z; lock

mass set to 445.120025 m/z [protonated (Si(CH3)2O)6])(28). Based on this survey

scan the 5 most intense ions were consecutively isolated (AGC target set to 104

ions) and fragmented by collision-activated dissociation (CAD) applying 35%

normalized collision energy in the linear ion trap. Once a precursor had been

selected, it was excluded for 3 minutes.

Chapter 4

97

2.8. Orbitrap data processing The raw data was preprocessed using the Progenesis LC-MS software package

(version 2.5) (no normalization was applied). Peptides were identified and

assigned to proteins by exporting features, for which MS/MS spectra were

recorded, using in the Bioworks software package (version 3.2; Thermo Fisher

Scientific, Germany, peak picking by Extract_msn, default settings). The resulting

.mgf file was submitted to Mascot (version 2, Matrix Science, London, United

Kingdom) for identification to interrogate the UniProt-database (version 57.0,

rattus taxonomy: 7114 sequences). Only ions with charge states between +2 and +7

were considered and only proteins with at least two unique peptides (with a

Mascot sore > 25) assigned to them were accepted as true identifications.

Modifications: carbamidomethylation of cysteine (+57.021 m/z) was set as fixed

and oxidation of methionine (+15.996 m/z) as variable modification allowing a

maximum of 2 missed cleavages. Mass tolerance for precursor ions was set to 10

ppm and for fragment ions at 0.5 Da. The cut-off for mass differences between the

measured and the theoretical mass of the identified peptides was set at 2 ppm. The

Mascot search results were imported back into the Progenesis software to link the

identified peptides to the detected abundances of these peptides. Subsequently the

data were exported in Excel format.

The peak matrix containing only the identified features was analyzed by

one-way ANOVA with Bonferroni post hoc test (A vs. E, C vs. E, B vs. F and D vs.

F) (SPSS). All identified proteins with 80 % of the identified peptides having a p-

value below 0.01 in the statistical test were kept for further analysis. The sum of all

peptides originating from the same protein was calculated for each sample and the

average of this sum was calculated for each group of animals and compared

between diseased groups and controls (A vs. E, C vs. E, B vs. F and D vs. F). If the

average sum in the diseased groups was increased at least 10 times or decreased

more than 2 times, the protein was considered as significantly discriminatory

(since the overall protein abundance was increased by five times in the diseased

groups, we considered that proteins had to increase a factor of two more than this

overall increase to be significantly discriminatory).

For further analysis of the Orbitrap data using a second data processing

procedure a normalization (29) was applied in the Progenesis software (to

compensate for the large TIC differences and detect differences between groups

that were not caused by an overall increase in protein abundance). The resulting

peak matrix was further processed in Microsoft Office Excel 2007. All non-

identified peaks were filtered out and the peak matrix was imported into MatLab

(version R2009a) for NSC and PCA analysis. The NSC analysis was done pair wise

(A vs. E, C vs. E, B vs. F and D vs. F) for selection of discriminatory peaks between

the diseased and control groups. PCA was applied to the NSC-selected features as

well as to the complete peak matrix to visualize group separations. The NSC-


98

selected peaks were linked to the identified protein in the Excel peak matrix and

listed for comparison of results between workflows and platforms.

3. Results

Trypsin-digested CSF samples from Lewis rats with induced EAE were compared

to CSF from healthy as well as inflammatory control animals collected at day 10 or

day 14 after injection of the disease-inducing agent (see Table 1 for details). The

weight of the rats was monitored daily from the day of injection until euthanasia

at day 10 or 14 (Figure 1). The average weight of animals belonging to the healthy

(group B) or inflammatory control (group D) increased continuously from day 1 to

day 14, while animals in the EAE group (group F) showed a marked decrease

starting on day 11. The weight pattern of the rats from groups A, C and E was

similar until day 10 (data not shown). The weight decrease in the EAE group

(group F) was paralleled by an increase in neurological scores (Table 2). None of

the other animals (group A-E) showed an increase in neurological score.

Figure 1. Average weight of rats in groups B, D and F (groups A, C and E showed no

difference in the weight pattern between each other). A diverging weight pattern can be

seen for group F from day 11 onwards. Rat groups; A: healthy control day 10, B: healthy

control day 14, C: inflammatory control (CFA) day 10, D: inflammatory control (CFA)

day 14, E: EAE day 10, F: EAE day 14.

Chapter 4

99

Table 2. Neurological scores of the samples included in the analysis from animals in group

F monitored at each day (Only group F showed increased neurological scores).

Explanation; 0: no pathological signs, 1: paralysis of the tip of the tail, 2: no curling

reflex/no strength at tail basis, 3: slightly impaired walking, 4: unstable walk, 5: half hind

limb paralysis, 6: complete hind limb paralysis, 7: midriff paralysis, 8: half for-limb

paralysis, 9: moribund, 10: death due to EAE.

Sam

ple

nr.

Sco

re d

ay 9

Sco

re d

ay 1

0

Sco

re d

ay 1

1

Sco

re d

ay 1

2

Sco

re d

ay 1

3

Sco

re d

ay 1

4

76 0 0 2 4 5 4

77 0 0 0 1 3 4

79 0 0 0 1 3 4

80 0 0 0 0 0 0

82 0 0 0 3 4 5

84 0 0 2 4 4 4

85 0 0 0 1 3 4

86 0 0 0 3 6 6

87 0 0 1 3 4 3

88 0 0 1 4 4 4

89 0 0 2 4 5 5

90 0 0 1 3 6 6

Visual inspection of the LC-MS data revealed that the total protein

concentration (measured as the total ion chromatogram or the UV-trace) was

significantly higher (~5 times) for all animals in group F and for about half of the

animals in group E. Differences were highly significant as shown by box and

whisker plots of the area under curve of the total ion chromatograms (TIC) based

on the chipLC-QTOF analyses (Figure 2A). In order to not overload the trap and

separating columns on the microfluidics device and to make optimal use of the

dynamic range of the LC-MS system, samples from groups E and F were injected a

second time at a 5 times lower volume (0.2 µL) compared to group A-D (1 µL)

giving comparable areas for the TIC (Figure 2B). Interestingly, variation was very

high in group E (EAE animals at the general onset of disease, day 10). This is most

probably due to variable inception of disease depending on the individual animal.

Variability at day 14, when disease symptoms have reached their maximum, was

again rather small (Figure 2 A, 2B).

In the LC-Orbitrap system all samples were injected at the same volume (5

µL), with no problems of overloading, because a trap column with larger volume

capacity was used for acquisition with Orbitrap MS. In order to, in the PCA and

NSC analyses, rule out detections of differences caused by an overall increase of


100

protein abundance in the group E and F analyzed by the Orbitrap, the data was

normalized at data level (29). For peak area comparisons the raw data without

normalization was used. Combining the QTOF and Orbitrap methods we

identified 233 proteins with at least 2 unique peptides each.

Figure 2. A. Area under the curve of the Total Ion Chromatograms (TICs) from analysis of

0.5 µL trypsin-digested cerebrospinal fluid (CSF) from groups A-F. Univariate statistical

analysis (Student’s t-test) revealed significant differences between the sample groups (A

vs. E: p < 0.05; C vs. E: p < 0.05; B vs. F: p < 0.0001; D vs. F: p < 0.0001). B. TIC areas

after taking the difference in TIC area into account by injecting 1 µL of trypsin-digested

CSF for groups A-D and 0.2 µL for groups E and F (Univariate statistical analysis

revealed no significant difference when the groups were compared with each other).

Since protein concentration (measured as Total Ion Chromatogram, TIC) in

group E was highly variable, we compared this group as a whole but also divided

it into a ‚high‛ TIC group (Ehigh) (squares) and a ‚low‛ TIC group (Elow) (stars)

(Table 1). Figure 3A visualizes the PCA of the normalized Orbitrap data showing

that groups E and F deviate from the control groups A-D even though the total ion

chromatograms were normalized. Figure 3B show the corresponding PCA of raw

QTOF data (normalized through injection of different volumes at sample level)

confirming that groups E and F differ significantly from the control groups in their

digested protein profile. In both the Orbitrap and the QTOF analysis group Ehigh is

more separated from group A-D than Elow.

In order to select individual peptide peaks that show statistically

significant differences in abundance between the different groups of animals, we

applied the NSC classifier to the QTOF-MS and the Orbitrap-MS peak matrices

using a double cross validation scheme (30, 31). For all the comparisons the NSC

recorded the lowest classifications error rates for wide continuous ranges of

Chapter 4

101

shrinkage values which means that there was a very clear separation of the

groups. Figures 4A-D show principal component analyses of identified NSC-

selected peaks for groups A vs. E (upper panels) and C vs. E (lower panels) based

on the data from Orbitrap (left panels) and QTOF LC-MS analyses (right panels).

The E group is divided in Ehigh (squares) and Elow (stars) (see Table 1). In the plots

it is visible that the Ehigh group is more separated from the control groups than the

Elow group, indicating differences in the protein profile between healthy controls

and EAE group in part of the animals at day 10. This indication can be seen as an

asymptomatic onset of the disease of some of the animals already at day 10.

Figures 5A-D show principal component analyses of identified NSC-selected

peaks for the comparison of groups B vs. F (upper panels) and D vs. F (lower

panels) for the both Orbitrap- (left panels) and QTOF-derived data (right panels).

The PCA plots of these comparisons show a clear to moderate tendency to group

separation indicating that the trypsin-digested CSF protein profiles are different

between healthy and inflammatory control groups and the EAE group at the

height of disease symptoms (day 14).

Figure 3. A. PCA based on all identified peptide ions (6195 peaks) of groups A-D (●),

group Elow (*), Ehigh (□) and group F (∆) from the preprocessed, normalized Orbitrap LC-

MS data. B. PCA based on the complete peak matrix (5336 peaks) of groups A-D (●),

group Elow (*), Ehigh (□) and group F (∆) from the in-house preprocessed QTOF LC-MS

data.

For a protein to be considered as discriminatory we have used highly

stringent criteria. Proteins identified with only one peptide were discarded from

further analysis and at least 80 % of the detected peptides belonging to one protein

had to be significantly discriminatory (p < 0.01) by ANOVA comparison with

Bonferroni correction between diseased animals and control (A vs. E, C vs. E, B vs.


102

F and D vs. F). Because of the large differences in total amount of protein in the

EAE diseased animals (groups E and F) compared to the control groups, only

proteins that exceeded the increase of total protein amount (five times) by at least

twofold. To determine the fold increase the average of the sum of all peptides

from a protein in one group was compared it to the identical protein in another

group. For proteins that decreased in the diseased groups compared to control a

factor of two was considered as discriminatory. The protein level in samples from

group E and F was already present at a factor of five lower than in the raw, native

samples for QTOF data (because of the normalized injection). This means that in

the QTOF data a two fold increase and a tenfold decrease was considered as

discriminatory. In the Orbitrap data that was not normalized at either sample or

data level but instead compared at a native relative level the increase had to be a

factor of ten in group E and F compared to control, while the decrease had to be

twofold to be considered as significant.

Figure 4. PCA of NSC-selected peaks from the Orbitrap (A and C) and QTOF data (B and

D). A. A (▼) vs. E: Elow (*) Ehigh (□) Orbitrap data: PCA based on 3 identified NSC-selected

peaks at shrinkage 8. B. A vs. E QTOF data: PCA based on 70 identified NSC-selected

Chapter 4

103

peaks at shrinkage 4. C. C (▼) vs. E: Elow (*) Ehigh (□) Orbitrap data: PCA based on 1950

identified NSC-selected peaks at shrinkage 1. D. C vs. E QTOF data: PCA based on 17

identified NSC-selected peaks at shrinkage 7.

Figure 5. PCA of NSC-selected peaks from the Orbitrap (A and C) and QTOF data (B and

D). A. B (▼) vs. F(*) Orbitrap data: PCA based on 96 identified NSC-selected peaks at

shrinkage 4. B. B vs. F QTOF data: PCA based on 23 identified NSC-selected peaks at

shrinkage 7. C. D (▼) vs. F (*) Orbitrap data: PCA based on 583 identified NSC-selected

peaks at shrinkage 2. D. D vs. F QTOF data: PCA based on 31 identified NSC-selected

peaks at shrinkage 7.

Figure 6 A-D shows box and whisker plots of the average sum of the peak

areas of all detected peptides from four different proteins. The serum protein

paraoxonase/arylesterase 1 (P55159) (average of 4 peptides) is clearly increased in

the Ehigh group (84 times increased at native comparison level [no normalization]

in Orbitrap data) and decreases relative to group Ehigh to an 8 fold increase in

group F relative to healthy and inflammatory control groups (see Figure 6A and

Table 3). This indicates that animals react differently to induction of EAE in time


104

and that the onset of disease, as detected by proteomics, is not synchronized

exactly on Day 10. It is noteworthy that none of the animals in group E had

neurological symptoms (the neurological score was zero). The fact that this protein

was increasing strongly at the onset of the disease while decreasing to a lower

level at the climax of the disease course, indicates that this protein is elevated as a

result of an acute early disease process or the start of disruption of the blood brain

barrier. Lysozyme C1 (P00697) (average of 4 peptides) was dramatically increased

in group F (11 times increased in the QTOF data [normalized at sample level])

while it remained almost at the level of the control groups in group E (see Figure

6B, Table 3). This protein showed the same behavior in the Orbitrap data as well

but did not fulfill our criteria to be considered as significantly discriminatory.

Lysozyme C1 here show a very disease specific pattern with a clear increase only

at the apex of the disease, this indicates that this protein might be involved in

processes that are more related to CSF membrane disrupture. Complement C3

(P01026) (figure based on QTOF data, normalized at sample level, average of 11

peptides) showed yet another pattern in that it was increased in group Ehigh as well

as the F group (3 times increased in both groups compared to control in QTOF

analysis, see Figure 6C, Table 3). This protein was 14 times increased in the F vs. B

comparison and 26 times increased in the Ehigh vs. A comparison in the Orbitrap

analysis (non-normalized). The pattern of an increased level already at the onset of

the disease and with a maintained high level at the apex makes it an early onset

candidate marker as well as a marker for one of the pathological processes in the

developing disease. Vitamin D binding protein (P04276) (figure based on QTOF

data, normalized at sample level, average of 8 peptides) also increased

significantly already in the Ehigh group (3 times increased in QTOF analysis and 21

times increased in Orbitrap analysis), in group F the increase was also significant

but lower (2 times increased in QTOF analysis and 7 times increased in Orbitrap

analysis) compared to control (Figure 6D, Table 3). Also this protein has a

stronger effect in the early stage of the disease making it a potential early

candidate marker.

There were a few proteins, apolipoprotein E (P02650), angiotensinogen

(P01015) and prostaglandin D synthase (PTGDS, P22057), of which the

concentration did not follow the TIC. We assume that these proteins in this case

are genuine CSF-derived proteins, which are not affected by a change in

permeability of the blood-brain barrier (BBB). Figure 7 shows the sum of the peak

areas of 4 peptides originating from PTGDS detected in the QTOF analysis. In

Figure 7A the raw data from the QTOF analysis (TIC normalized at sample level,

five times less injected of group E and F compared to group A-D) showing a

significantly decreasing pattern of this protein in the diseased animals compared

to control (p < 0.01) the decrease of the diseased groups compared to the control

groups were however less than 4 times. Figure 7B is based on a QTOF analysis

Chapter 4

105

were all samples were injected at the same volume (native relative comparison),

here the protein is stable in all groups, indicating clearly that differences between

groups are non-significant (p > 0.05).

Figure 6. Boxplots of the average sum of all peptides from 4 discriminatory proteins. In the

figures group E is divided in two groups (Ehigh and Elow) based on their TIC area; Ehigh: high

TIC, Elow: low TIC. All values originates from raw data from the analysis (no

normalization at data level). A. Serum paraoxonase/arylestarase 1 (average of 4 peptides),

this protein was detected by the Orbitrap analysis only. B. Lysozyme C1 (average of 4

peptides) the protein was only significantly discriminatory in the QTOF analysis but

showed the same behavior in the Orbitrap analysis. C. Complement C3 (average of 10

peptides), significantly discriminatory in both Orbitrap and QTOF analysis. D. Vitamin

D binding protein (average of 8 peptides) also this protein was significantly discriminatory

in both QTOF and Orbitrap analysis.

In Table 3 all discriminatory proteins that increased more than 2 times (10

times in non-normalized data) in the diseased groups Ehigh and/or F compared to

groups A and B respectively from both analysis platforms are listed with the


106

relative change in abundance between groups F and B and Ehigh and A,

respectively. No protein showed a significantly decrease in abundance in the

disease groups (E and F). The upper part of the table shows 14 proteins that were

found to be discriminatory in both data sets, the middle part shows 23 proteins

that were only detected by the Orbitrap, and the lower part shows 4 proteins that

were discriminatory only in the QTOF data set, although these four proteins were

also detected in the Orbitrap analysis. While Orbitrap data showed the same

tendency as the QTOF data, the results did not fulfill our stringent criteria and the

proteins were therefore not considered as significantly different (see Table S.1.,

supplementary material for the identification information for each peptide and

protein detected by the QTOF method given in Table 3). In summary, based on

our data we can discriminate 41 proteins that increase in EAE compared to healthy

and inflammatory control groups, of which 28 are not described as discriminatory

in EAE before.

Figure 7. Boxplots of the average sum of 4 peptides originating from prostaglandin D

synthase detected in the QTOF analysis. Group E is divided in two groups (Ehigh and Elow)

based on their TIC area; Ehigh: high TIC, Elow: low TIC. A. Raw values from the QTOF LC-

MS analysis (normalization of TIC’s at injection level; samples from group E and F

injected at 5 times lower volume than group A-D). B. raw values from QTOF LC-MS

analysis where all samples were injected at the same volume (0.5 µL each).

4. Discussion

In human subjects, the large biological variation complicates the detection of

biomarkers, especially when using a screening approach of the whole proteome.

Working with an animal model makes it possible to collect both tissue samples

and CSF; a better control of the disease course in the animal model enables

Chapter 4

107

collection of samples at a specific stage of the disease, which makes it possible to

get a strong response also when screening complex samples. Candidate markers

detected in the EAE model can then be evaluated in human subjects in a targeted

fashion.

Here we have described an interlaboratory proteomic biomarker study

based on CSF from an acute EAE model mimicking certain aspects of MScl. The

goal was to find proteins connected to the disease. We have analyzed CSF samples

using two different mass spectrometry based proteomics platforms at two

independent laboratories. We have also applied two separate data processing

workflows on both of the data sets.

Our study shows that induction of EAE in rats leads to a significant on

average five times increase in the total protein concentration in cerebrospinal fluid

(CSF). Despite this general change in protein concentration, we identified 41

proteins that increased at least by a further factor of 2 clearly discriminating EAE

animals from control animals. The fact that we have also found proteins that do

not change in concentration between the groups of animals demonstrates that the

observed increase in protein concentration is not simply due to a decreased

volume of CSF or a generic overall response of all proteins in groups E and F, but

that the identified proteins increase in response is due to the induction of EAE in a

disease controlled active regulated way.

There was a longitudinal component in our study, as the animals in groups E and

F were both injected with MBP + CFA but in group E, CSF was collected on day

10, at the onset of EAE while CSF from group F was collected at the climax of

disease symptoms on day 14. Figure 1 and Table 2 show that the animals in group

F started to lose weight and showed elevated neurological scores from day 11

onwards, some of the animals in group F started off with a higher neurological

score at day 11 while some of the animals still had no score at this day. The

separation of the animals in group E might be a sign of which animals will

develop scores earlier (Ehigh) than other animals (Elow), all rats in group E were

asymptomatic at the day of sacrifice (day 10) despite the fact that the Ehigh group

had clearly elevated levels of proteins related to the animals in group F at the apex

of disease (see Figure 6). This indicates that protein patterns may be used as early

indicators of disease onset prior to the appearance of clinical symptoms. The time

difference in response to induction of EAE in an inbred strain of rats held under

the same conditions must be taken into account when evaluating the effect of

pharmacological interventions with newly developed drug candidates at an early

stage of onset of the disease.

This study was interlaboratory in the means of the same sample set being

analyzed at two separate university sites using different analysis platforms. We

also combined two different data processing pipelines on the two data sets each in

order to cover as much of the variation as possible. The results from the different


108

data processing strategies were partly overlapping showing reproducibility

between the analysis strategies but the combination of the techniques also yielded

an increased coverage of discriminatory features.

Taking both analyses together, we identified 233 proteins from which 41

proteins were increased in group F versus the healthy control group B and the

inflammatory control group D (all collected on day 14) and group Ehigh versus the

healthy control group C and the inflammatory control group A (all collected on

day 10). Out of these 41 proteins, 28 have not been described as discriminatory in

other EAE studies (see highlighted proteins in Table 3). Fifteen proteins were

shown to be discriminatory on both LC-MS/MS platforms while another 23

proteins were only observed on the Orbitrap. Four proteins fulfilled our criteria

for discriminatory proteins based on the QTOF data but not based on the Orbitrap

data, although they were identified on this instrument. They showed, however,

the same tendency of increasing concentration on the Orbitrap as well. This shows

that analysis of trypsin-digested CSF samples from an acute rat EAE model

resulted in a considerable overlap with respect to discriminatory proteins in an

interlaboratory proteomics analysis.

Table 3. Discriminatory proteins listed with name, accession number, number of peptides,

the quotient of F/B and Ehigh/A (non-normalized peak areas). The values are based on the

average of the sum of all peptides identified as originating from that protein. The first part

of the table consists of proteins that were discriminatory in both the Orbitrap and QTOF

data, while the mid part contains proteins that were detected as discriminator by the

Orbitrap analysis only and the lower part contains proteins that were discriminatory in

the QTOF data only. The orbitrap data originates from an equal sample injection of all

samples from all groups (no-normalization) and the data from the QTOF analysis

originates from a normalization of the TIC at sample level (group E and F injected at five

times lower volume than group A-D). Proteins that have not been described as

discriminatory in the EAE model before are marked in bold. All proteins are present in

plasma as well.

Discriminatory proteins Orbitrap and QTOF

AC #

Orbitrap

QTOF

# o

f

pe

pti

de

s

F/B

Eh

igh/A

# o

f p

ep

tid

es

F/B

)

Eh

igh/A

Afamin P36953 28 6 13 3 3 2

Alpha-1-inhibitor 3 P14046 52 16 29 2 5 5

Chapter 4

109

Alpha-1-macroglobulin Q63041 63 6 22 8 5 8

Ceruloplasmin P13635 37 9 19 7 4 4

Complement C3 P01026 94 14 26 10 3 3

Fetuin-B Q9QX79

19 8 15 4 3 2

Haptoglobin P06866 21 13 35 4 3 4

Hemopexin P20059 28 8 21 12 3 5

Ig gamma-2A chain C region P20760 9 13 17 4 3 4

Murinoglobulin-1 Q03626 54 14 28 10 4 5

Murinoglobulin-2 Q6IE52 6 17 44 3 3 4

T-kininogen 1 P01048 19 21 34 6 10 10

T-kininogen 2 P08932 18 30 38 7 11 11

Vitamin D-binding protein P04276 24 7 21 6 2 3

Discriminatory proteins Orbitrap only

Alpha-1-antiproteinase P17475 22 7 11

Apolipoprotein A-I P04639 11 6 26

Apolipoprotein M P14630 2 12 51

Complement C1q subcomponent subunit A P31720 5 6 12

Complement C4 P08649 61 8 16

Fibrinogen alpha chain P06399 13 30 96

Fibrinogen beta chain P14480 19 6 32

Fibrinogen gamma chain P02680 18 1 19

Glutathione peroxidase 3 P23764 2 17 77

Ig gamma-1 chain C region P20759 6 14 28

Ig gamma-2B chain C region P20761 4 12 32

Ig gamma-2C chain C region P20762 7 21 25

Ig kappa chain C region B allele P01835 6 10 13

Ig lambda-2 chain C region P20767 4 9 11

Insulin-like growth factor-binding protein complex acid labile chain

P35859 3 12 34

Inter-alpha-trypsin inhibitor heavy chain H3 Q63416 19 11 13

Kininogen-1 P08934 7 8 18

Plasminogen Q01177 32 5 10

Protein Z-dependent protease inhibitor Q62975 2 10 20

Serine protease inhibitor A3K P05545 13 7 12

Serine protease inhibitor A3M Q63556 9 13 17

Serum paraoxonase/arylesterase 1 P55159 4 8 84

Zinc-alpha-2-glycoprotein Q63678 5 13 18

Discriminatory proteins QTOF only

Lysozyme C1 P00697 4 11 1


110

Alpha 1 acid glycoprotein P02764 8 2 4

Serine protease inhibitor A3N P09006 4 2 2

Alpha-2-HS-glycoprotein (Fetuin A) P24090 6 2 3

Among the 41 proteins that discriminated diseased from control groups, all

are well-known, highly, moderate and low abundant plasma proteins which

indicates that elevation may be partially the result of a change in the function of

the blood-brain barrier (BBB). Since most of these proteins are also present in CSF

under normal circumstances, discrimination between an increased intrathecal

production and an increased infiltration, or both is not possible in this case.

Several of the discriminatory proteins belong to the class I acute phase

proteins; e.g. alpha 1 inhibitor 3, fibrinogen, plasminogen, ceruloplasmin,

hemopexin, haptoglobin, alpha 2 macroglobulin, alpha 1 antiproteinase, alpha 1

acid glycoprotein and the components of the complement system. These proteins

are activated during inflammation (32-34), and up regulation of these proteins

reflects the inflammatory processes that are ongoing in the EAE model as well as

in MScl. Only a few of these proteins (haptoglobin, hemopexin and alpha 1 acid

glycoprotein) were discriminatory in both the QTOF and the Orbitrap analysis

when the inflammatory controls (C and D) where compared to the healthy

controls (A and B). This shows that that the elevation of most of the proteins is not

just due to a general inflammatory response to complete Freund’s adjuvant.

Complement C3, involved in the activation of the complement system has been

connected to both EAE and MScl in previous studies (20, 35-37), complement C1

has been discovered as discriminatory in the sense of PTM polymorphisms

between EAE rats and controls (10). In this study Complement C3 were

significantly increased in CSF from EAE rats in both the Orbitrap analysis and the

QTOF analysis. Complement C1 was detected in the Orbitrap analysis only and

was significantly increased in group Ehigh compared to group A. Alpha 1 inhibitor

3 is a protease inhibitor that was also found increased in the EAE model before

(20) supporting our results. T-kininogen is another protein that is involved in

inflammatory cascades and an important mediator of the inflammatory response

(38). Earlier published data have shown that an elevated level of the kinin B1

receptor correlates with increased disease severity in MScl patients (39).

Hemopexin and haptoglobin are heme-binding proteins, which have the ability to

effectively inhibit the toxic effect of free heme by forming complexes (40), Heme is

an iron containing porphyrin that exerts toxic effects on cells, DNA and organelles

through the formation of reactive oxygen species (ROS). ROS are involved in the

pathogenesis of several vascular diseases (41) and there are indications that ROS

are involved in neurodegeneration and BBB disruption, which contribute to the

Chapter 4

111

formation and persistence of lesions in MScl (40, 42, 43). Oxidative stress has also

been connected to neurodegeneration and apoptosis in the EAE model. We found

both hemopexin and haptoglobin at elevated levels in CSF of EAE compared to

control. Hemopexin was also detected at elevated levels in spinal cord of EAE rats

(20) and have been connected to MScl in earlier studies (44, 45). This connection

between heme toxicity and MScl/EAE indicates that the increased levels of heme-

binding proteins in our study could be related to the pathogenesis of EAE and

ultimately MScl. Ceruloplamin is another protein involved in iron metabolism,

this protein was also elevated in EAE compared to control in this study and the

same behavior was observed in two earlier EAE studies (13, 20), also in MScl this

protein has been found at elevated levels (46).

It is interesting to note that lysozyme C1 is strongly increased at the full-

scale EAE (group F) (see Figure 6B). The primary role of lysozymes is to offer

protection against bacterial infections through specific cleavage of the bacterial

peptidoglycan cell wall (47). Previous studies revealed increased lysozyme levels

in patients with MScl and other neurological disorders (48, 49) as well as in

rheumatoid arthritis, another autoimmune disease (50). In EAE lysozyme C2 has

been found at increased levels in the spinal cord of EAE rats compared to control

(20). An increase in lysozyme C1 may be due to the presence of mycobacteria in

complete Freund’s adjuvant but it is again noteworthy that the increase in CSF is

not observed in the inflammatory control groups.

CSF of MScl patients is normally screened for an increase in

immunoglobulin G (IgG) as well as for the presence of IgG bands upon isoelectric

focusing (2). In agreement with this, we found that a number of immunoglobulins

were elevated in CSF of EAE rats relative to inflammatory controls (see Table 3)

IgG’s have also been detected at elevated levels in EAE compared to control in two

previous studies (8, 20). A recent study into sequencing the complementarity

determining regions (CDRs) of immunoglobulins opens new possibilities to study

the antibody repertoire in more detail with ramifications for MScl and other

autoimmune diseases (51, 52).

Another interesting protein that was found at elevated level is vitamin D

binding protein. This protein has not been described as discriminatory in other

EAE studies but elevated levels of this protein have previously been shown in CSF

from patients with secondary progressive MScl compared to controls and patients

with relapsing-remitting MScl (53) as well as in blood samples from pediatric MScl

patients (45). Vitamin D is involved in the calcium homeostasis (54) and deficiency

of this vitamin is one of the theories that is advanced in conjunction with the

development of MScl (55); there are evidences that connect a reduced vitamin D

level to MScl (56, 57). Another protein involved in the calcium homeostasis is the

alpha 2 HS glycoprotein (also known as fetuin A) (58). This protein was found


112

elevated in EAE both in this study and a previous one (20) and has been found

both at elevated and reduced levels connected to MScl (53, 59).

A recent study from our laboratories shows that some proteins in CSF

show a very high biological variability, for example, haptoglobin, indicating that

they are not well suited as disease-related biomarkers. The vitamin D binding

protein on the contrary show low biological variation between healthy human

subjects and therefore serves as a better biomarker candidate (60).

This interlaboratory study of CSF from an animal model of certain aspects

of MScl, the rat EAE model, resulted in identification of a set of 41 proteins that

discriminate EAE rats from control groups. To our knowledge, this is the first

study of its kind focusing on rat CSF from the EAE model.

Part of the proteins detected in this study has been connected to EAE in

previous studies supporting our data. However, our study revealed 28 additional

proteins to be discriminatory between EAE and control (Table 3, highlighted). The

fact that many of the proteins have also been connected to MScl before shows that

it is possible to translate findings from the animal model to the human situation

and that our findings may have a clinical interest in form of early disease

biomarkers. In summary, we show a range of proteins that merit further study in

the context of MScl (early diagnosis, prognosis, response to therapy). We are

currently pursuing work focusing on analyzing these proteins in CSF in response

to drug treatment in the EAE rat model.

Acknowledgements

This study was performed within the framework of Top Institute Pharma project

D4-102. The work was also supported by the project BioRange 2.2.3 from the

Netherlands Proteomics, The Netherlands Bioinformatics Center and Dutch

Multiple Sclerosis Foundation. We thank Balaji Srinivasan (University of

Groningen) for preparation of the CSF samples.

Chapter 4

113

Reference List


2. McDonald, W. I., Compston, A., Edan, G., Goodkin, D., Hartung, H. P., Lublin, F. D., McFarland, H.

F., Paty, D. W., Polman, C. H., Reingold, S. C., Sandberg-Wollheim, M., Sibley, W., Thompson, A., van

den, N. S., Weinshenker, B. Y., and Wolinsky, J. S. (2001) Recommended diagnostic criteria for

multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann.

Neurol 50, 121-127

3. Polman, C. H., Reingold, S. C., Edan, G., Filippi, M., Hartung, H. P., Kappos, L., Lublin, F. D., Metz, L.

M., McFarland, H. F., O'Connor, P. W., Sandberg-Wollheim, M., Thompson, A. J., Weinshenker, B. G.,

and Wolinsky, J. S. (2005) Diagnostic criteria for multiple sclerosis: 2005 revisions to the "McDonald

Criteria". Ann. Neurol 58, 840-846

4. Herz, J., Zipp, F., and Siffrin, V. (2009) Neurodegeneration in autoimmune CNS inflammation. Exp.

Neurol

5. Soulika, A. M., Lee, E., McCauley, E., Miers, L., Bannerman, P., and Pleasure, D. (2009) Initiation and

progression of axonopathy in experimental autoimmune encephalomyelitis. J Neurosci. 29, 14965-

14979

6. Vogt, J., Paul, F., Aktas, O., Muller-Wielsch, K., Dorr, J., Dorr, S., Bharathi, B. S., Glumm, R., Schmitz,

C., Steinbusch, H., Raine, C. S., Tsokos, M., Nitsch, R., and Zipp, F. (2009) Lower motor neuron loss in

multiple sclerosis and experimental autoimmune encephalomyelitis. Ann. Neurol 66, 310-322

7. Trapp, B. D., Peterson, J., Ransohoff, R. M., Rudick, R., Mork, S., and Bo, L. (1998) Axonal transection

in the lesions of multiple sclerosis. N. Engl. J Med 338, 278-285

8. Villarroya, H., Violleau, K., Ben Younes-Chennoufi, A., and Baumann, N. (1996) Myelin-induced

experimental allergic encephalomyelitis in Lewis rats: tumor necrosis factor alpha levels in serum and

cerebrospinal fluid immunohistochemical expression in glial cells and macrophages of optic nerve

and spinal cord. J Neuroimmunol. 64, 55-61

9. El Behi M., Zephir, H., Lefranc, D., Dutoit, V., Dussart, P., Devos, P., Dessaint, J. P., Vermersch, P., and

Prin, L. (2007) Changes in self-reactive IgG antibody repertoire after treatment of experimental

autoimmune encephalomyelitis with anti-allergic drugs. J Neuroimmunol. 182, 80-88

10. Grant, J. E., Hu, J., Liu, T., Jain, M. R., Elkabes, S., and Li, H. (2007) Post-translational modifications in

the rat lumbar spinal cord in experimental autoimmune encephalomyelitis. J Proteome. Res. 6, 2786-

2791

11. Kidd, B. A., Ho, P. P., Sharpe, O., Zhao, X., Tomooka, B. H., Kanter, J. L., Steinman, L., and Robinson,

W. H. (2008) Epitope spreading to citrullinated antigens in mouse models of autoimmune arthritis

and demyelination. Arthritis Res. Ther. 10, R119

12. Mikkat, S., Lorenz, P., Scharf, C., Yu, X., Glocker, M. O., and Ibrahim, S. M. (2010) MS characterization

of qualitative protein polymorphisms in the spinal cords of inbred mouse strains. Proteomics 10, 1050-

1062

13. Morgan, L., Shah, B., Rivers, L. E., Barden, L., Groom, A. J., Chung, R., Higazi, D., Desmond, H.,

Smith, T., and Staddon, J. M. (2007) Inflammation and dephosphorylation of the tight junction protein

occludin in an experimental model of multiple sclerosis. Neuroscience 147, 664-673


114

14. Ohgoh, M., Hanada, T., Smith, T., Hashimoto, T., Ueno, M., Yamanishi, Y., Watanabe, M., and

Nishizawa, Y. (2002) Altered expression of glutamate transporters in experimental autoimmune

encephalomyelitis. J Neuroimmunol. 125, 170-178

15. Qi, X., Lewin, A. S., Sun, L., Hauswirth, W. W., and Guy, J. (2006) Mitochondrial protein nitration

primes neurodegeneration in experimental autoimmune encephalomyelitis. J Biol. Chem. 281, 31950-

31962

16. Stegbauer, J., Lee, D. H., Seubert, S., Ellrichmann, G., Manzel, A., Kvakan, H., Muller, D. N., Gaupp,

S., Rump, L. C., Gold, R., and Linker, R. A. (2009) Role of the renin-angiotensin system in autoimmune

inflammation of the central nervous system. Proc. Natl. Acad. Sci. U. S. A 106, 14942-14947

17. Alt, C., Duvefelt, K., Franzen, B., Yang, Y., and Engelhardt, B. (2005) Gene and protein expression

profiling of the microvascular compartment in experimental autoimmune encephalomyelitis in

C57Bl/6 and SJL mice. Brain Pathol. 15, 1-16

18. Jain, M. R., Bian, S., Liu, T., Hu, J., Elkabes, S., and Li, H. (2009) Altered proteolytic events in

experimental autoimmune encephalomyelitis discovered by iTRAQ shotgun proteomics analysis of

spinal cord. Proteome. Sci. 7, 25

19. Linker, R. A., Brechlin, P., Jesse, S., Steinacker, P., Lee, D. H., Asif, A. R., Jahn, O., Tumani, H., Gold,

R., and Otto, M. (2009) Proteome profiling in murine models of multiple sclerosis: identification of

stage specific markers and culprits for tissue damage. PLoS. One. 4, e7624

20. Liu, T., Donahue, K. C., Hu, J., Kurnellas, M. P., Grant, J. E., Li, H., and Elkabes, S. (2007) Identification

of differentially expressed proteins in experimental autoimmune encephalomyelitis (EAE) by

proteomic analysis of the spinal cord. J Proteome. Res. 6, 2565-2575

21. Tumani, H., Hartung, H. P., Hemmer, B., Teunissen, C., Deisenhammer, F., Giovannoni, G., and Zettl,

U. K. (2009) Cerebrospinal fluid biomarkers in multiple sclerosis. Neurobiol. Dis. 35, 117-127

22. Giovannoni, G. (2006) Multiple sclerosis cerebrospinal fluid biomarkers. Dis. Markers 22, 187-196

23. Liu, L., and Duff, K. (2010) A Technique for Serial Collection of Cerebrospinal Fluid from the Cisterna

Magna in Mouse, In: Journal of Visualized Experiments





25. Suits, F., Lepre, J., Du, P., Bischoff, R., and Horvatovich, P. (2008) Two-dimensional method for time

aligning liquid chromatography-mass spectrometry data. Anal. Chem. 80, 3095-3104

26. Tibshirani, R., Hastie, T., Narasimhan, B., and Chu, G. (2002) Diagnosis of multiple cancer types by

shrunken centroids of gene expression. Proc. Natl. Acad. Sci. U. S. A 99, 6567-6572

27. Kessner, D., Chambers, M., Burke, R., Agus, D., and Mallick, P. (2008) ProteoWizard: open source

software for rapid proteomics tools development. Bioinformatics. 24, 2534-2536

28. Olsen, J. V., de Godoy, L. M., Li, G., Macek, B., Mortensen, P., Pesch, R., Makarov, A., Lange, O.,

Horning, S., and Mann, M. (2005) Parts per million mass accuracy on an Orbitrap mass spectrometer

via lock mass injection into a C-trap. Mol. Cell Proteomics 4, 2010-2021

29. (2010) In:

Chapter 4

115

30. Smit, S., van Breemen, M. J., Hoefsloot, H. C., Smilde, A. K., Aerts, J. M., and de Koster, C. G. (2007)

Assessing the statistical validity of proteomics based biomarkers. Anal. Chim. Acta 592, 210-217

31. Smit, S., Hoefsloot, H. C., and Smilde, A. K. (2008) Statistical data processing in clinical proteomics. J

Chromatogr. B Analyt. Technol. Biomed. Life Sci. 866, 77-88

32. Tolosano, E., and Altruda, F. (2002) Hemopexin: structure, function, and regulation. DNA Cell Biol. 21,

297-306

33. Poli, V. (1998) The role of C/EBP isoforms in the control of inflammatory and native immunity

functions. J Biol. Chem. 273, 29279-29282

34. Bjork, P., Gudmundsson, T., and Ohlsson, K. (1994) Acute phase protein response and

polymorphonuclear leukocyte cathepsin g release after slow interleukin-1 stimulation in the rat.

Mediators. Inflamm. 3, 425-431

35. Barnett, M. H., Parratt, J. D., Cho, E. S., and Prineas, J. W. (2009) Immunoglobulins and complement in

postmortem multiple sclerosis tissue. Ann. Neurol 65, 32-46

36. Hedegaard, C. J., Chen, N., Sellebjerg, F., Sorensen, P. S., Leslie, R. G., Bendtzen, K., and Nielsen, C. H.

(2009) Autoantibodies to myelin basic protein (MBP) in healthy individuals and in patients with

multiple sclerosis: a role in regulating cytokine responses to MBP. Immunology 128, e451-e461

37. Stoop, M. P., Dekker, L. J., Titulaer, M. K., Burgers, P. C., Sillevis Smitt, P. A., Luider, T. M., and

Hintzen, R. Q. (2008) Multiple sclerosis-related proteins identified in cerebrospinal fluid by advanced

mass spectrometry. Proteomics 8, 1576-1585

38. McLean, P. G., Perretti, M., and Ahluwalia, A. (2000) Kinin B(1) receptors and the cardiovascular

system: regulation of expression and function. Cardiovasc. Res. 48, 194-210

39. Prat, A., Biernacki, K., Saroli, T., Orav, J. E., Guttmann, C. R., Weiner, H. L., Khoury, S. J., and Antel, J.

P. (2005) Kinin B1 receptor expression on multiple sclerosis mononuclear cells: correlation with

magnetic resonance imaging T2-weighted lesion volume and clinical disability. Arch Neurol 62, 795-

800

40. Schreibelt, G., Kooij, G., Reijerkerk, A., van, D. R., Gringhuis, S. I., van der, P. S., Weksler, B. B.,

Romero, I. A., Couraud, P. O., Piontek, J., Blasig, I. E., Dijkstra, C. D., Ronken, E., and de Vries, H. E.

(2007) Reactive oxygen species alter brain endothelial tight junction dynamics via RhoA, PI3 kinase,

and PKB signaling. FASEB J 21, 3666-3676

41. Kumar, S., and Bandyopadhyay, U. (2005) Free heme toxicity and its detoxification systems in human.

Toxicol. Lett. 157, 175-188

42. Gonsette, R. E. (2008) Oxidative stress and excitotoxicity: a therapeutic issue in multiple sclerosis?

Mult. Scler. 14, 22-34

43. Gonsette, R. E. (2008) Neurodegeneration in multiple sclerosis: the role of oxidative stress and

excitotoxicity. J Neurol Sci. 274, 48-53

44. Hammack, B. N., Fung, K. Y., Hunsucker, S. W., Duncan, M. W., Burgoon, M. P., Owens, G. P., and

Gilden, D. H. (2004) Proteomic analysis of multiple sclerosis cerebrospinal fluid. Mult. Scler. 10, 245-

260

45. Rithidech, K. N., Honikel, L., Milazzo, M., Madigan, D., Troxell, R., and Krupp, L. B. (2009) Protein

expression profiles in pediatric multiple sclerosis: potential biomarkers. Mult. Scler. 15, 455-464


116

46. Hunter, M. I., Nlemadim, B. C., and Davidson, D. L. (1985) Lipid peroxidation products and

antioxidant proteins in plasma and cerebrospinal fluid from multiple sclerosis patients. Neurochem.

Res. 10, 1645-1652

47. Van Herreweghe, J. M., Vanderkelen, L., Callewaert, L., Aertsen, A., Compernolle, G., Declerck, P. J.,

and Michiels, C. W. (2010) Lysozyme inhibitor conferring bacterial tolerance to invertebrate type

lysozyme. Cell Mol. Life Sci.

48. Firth, G., Rees, J., and McKeran, R. O. (1985) The value of the measurement of cerebrospinal fluid

levels of lysozyme in the diagnosis of neurological disease. J Neurol Neurosurg. Psychiatry 48, 709-712

49. Hansen, N. E., Karle, H., Jensen, A., and Bock, E. (1977) Lysozyme activity in cerebrospinal fluid.

Studies in inflammatory and non-inflammatory CNS disorders. Acta Neurol Scand. 55, 418-424

50. Torsteinsdottir, I., Hakansson, L., Hallgren, R., Gudbjornsson, B., Arvidson, N. G., and Venge, P.

(1999) Serum lysozyme: a potential marker of monocyte/macrophage activity in rheumatoid arthritis.

Rheumatology. (Oxford) 38, 1249-1254

51. Obermeier, B., Mentele, R., Malotka, J., Kellermann, J., Kumpfel, T., Wekerle, H., Lottspeich, F.,

Hohlfeld, R., and Dornmair, K. (2008) Matching of oligoclonal immunoglobulin transcriptomes and

proteomes of cerebrospinal fluid in multiple sclerosis. Nat. Med 14, 688-693

52. de Costa D., Broodman, I., Vanduijn, M. M., Stingl, C., Dekker, L. J., Burgers, P. C., Hoogsteden, H. C.,

Sillevis Smitt, P. A., van Klaveren, R. J., and Luider, T. M. (2010) Sequencing and quantifying IgG

fragments and antigen-binding regions by mass spectrometry. J Proteome. Res. 9, 2937-2945

53. Ottervald, J., Franzen, B., Nilsson, K., Andersson, L. I., Khademi, M., Eriksson, B., Kjellstrom, S.,

Marko-Varga, G., Vegvari, A., Harris, R. A., Laurell, T., Miliotis, T., Matusevicius, D., Salter, H., Ferm,

M., and Olsson, T. (2010) Multiple sclerosis: Identification and clinical evaluation of novel CSF

biomarkers. J Proteomics

54. Huotari, A., and Herzig, K. H. (2008) Vitamin D and living in northern latitudes--an endemic risk area

for vitamin D deficiency. Int. J Circumpolar. Health 67, 164-178

55. Ascherio, A., Munger, K. L., and Simon, K. C. (2010) Vitamin D and multiple sclerosis. Lancet Neurol 9,

599-612

56. Correale, J., Ysrraelit, M. C., and Gaitan, M. I. (2009) Immunomodulatory effects of Vitamin D in

multiple sclerosis. Brain 132, 1146-1160

57. Smolders, J., Menheere, P., Kessels, A., Damoiseaux, J., and Hupperts, R. (2008) Association of vitamin

D metabolite levels with relapse rate and disability in multiple sclerosis. Mult. Scler. 14, 1220-1224

58. Schafer, C., Heiss, A., Schwarz, A., Westenfeld, R., Ketteler, M., Floege, J., Muller-Esterl, W., Schinke,

T., and Jahnen-Dechent, W. (2003) The serum protein alpha 2-Heremans-Schmid glycoprotein/fetuin-

A is a systemically acting inhibitor of ectopic calcification. J Clin. Invest 112, 357-366

59. Tumani, H., Lehmensiek, V., Rau, D., Guttmann, I., Tauscher, G., Mogel, H., Palm, C., Hirt, V.,

Suessmuth, S. D., Sapunova-Meier, I., Ludolph, A. C., and Brettschneider, J. (2009) CSF proteome

analysis in clinically isolated syndrome (CIS): candidate markers for conversion to definite multiple

sclerosis. Neurosci. Lett. 452, 214-217

60. Stoop, M. P., Coulier, L., Rosenling, T., Shi, S., Smolinska, A. M., Buydens, L., Ampt, K., Stingl, C.,

Dane, A., Muilwijk, B., Luitwieler, R. L., Sillevis Smitt, P. A., Hintzen, R. Q., Bischoff, R., Wijmenga, S.

Chapter 4

117

S., Hankemeier, T., van Gool, A. J., and Luider, T. M. (2010) Quantitative proteomics and

metabolomics analysis of normal human cerebrospinal fluid samples. Mol. Cell Proteomics

118

Chapter 5

119

The effect of minocycline on the proteome

profile of cerebrospinal fluid from an acute

animal model of multiple sclerosis.

Therese Rosenling1, Amos Attali2, Tinka Tuinstra2, Theo Luider3, Rainer

Bischoff1

1Department of Analytical Biochemistry, Centre for Pharmacy,

University of Groningen, Groningen, The Netherlands

2 Abbott Healthcare Products B.V., Weesp, the Netherlands




The effect of minocycline on the proteome of a rat EAE model

120

Abstract

The experimental autoimmune encephalomyelitis (EAE) is an induced disorder of

the central nervous system (CNS) in animals used to model the human CNS

disorder multiple sclerosis (MScl). Minocycline, a tetracycline antibiotic, has

exhibited a modulatory effect of disease symptoms as well as an inhibitory effect

of MMP activity and production in EAE mice. In MScl patients, minoclycline was

shown to reduce the numbers of lesions detected by MRI.

In an earlier study performed by this group, an acute EAE rat model was

used for the screening of disease related proteomic biomarkers in cerebrospinal

fluid (CSF). By the combination of two different bottom-up mass spectrometry

based proteomic platforms (Orbitrap LC-MS and chipLC-QTOF MS) in two

independent laboratories a set of 41 discriminatory proteins were discovered to be

highly discriminatory between the EAE animals compared to healthy and

inflammatory control groups. Among these proteins 18 were detected as

discriminatory on the chipLC-QTOF MS system. In the present study described

here the intention was to elucidate the effect of minocycline treatment on the level

of the earlier discovered discriminatory proteins.

EAE was induced in 60 rats by intraperitoneal injection of myelin basic

protein (MBP) together with complete Freund’s adjuvant (CFA); half of the

animals were treated with minocycline and the other half was treated with

vehicle. CSF was collected from 30 of the rats at day 10 after induction and from

the other 30 rats CSF was collected at day 14 after induction. As control 60 rats

were kept that were injected with CFA only and treated in the same way as the

rats with induced EAE (half of them treated with minocycline, CSF collected at

day 10 and day 14). The pattern of increasing protein levels in EAE compared to

control was significantly decreased in animals treated with minocycline for 8 of

the studied proteins (alpha 1 inhibitor 3, alpha 2 HS glycoprotein (Fetuin A),

complement C3, haptoglobin, IgG 2A chain C, lysozyme C1, murinoglobulin 1,

serine protease inhibitor A3N and T-kininogen 1). Further, an effect of

minocycline on the protein level in control animals was recorded.

Chapter 5

121

1. Introduction

Multiple sclerosis (MScl), a disorder of the central nervous system (CNS), is

characterized by inflammation, demyleination and neurodegeneration that

commonly lead to e.g. motor dysfunctions, visual and sensory impairments

usually with a relapsing-remitting pattern that accumulates over time (1).

Experimental autoimmune encephalomyelitis (EAE) induced in rodents is an

animal model induced for the study of disease processes related to MScl. Different

pathological patterns are seen in MScl and the disease is classified according to the

disease development in; relapsing-remitting (RR), primary progressive (PP) and

secondary progressive (SP) forms (1). The EAE model demonstrates either a

chronic, relapsing-remitting or an acute disease progression depending on what

animal strain and inducing agent is used to evoke the disease in the model (2-6).

Minocycline is an antibiotic that belongs to the tetracycline derivates. The

molecule has lipophilic characteristics and therefore easily cross the blood-brain

barrier and can thus be used to target the central nervous system. Minocycline has

shown protection against neuroinflammation and cell death in animal models of

central nervous system trauma and various neurological disorders (7). In a mouse

EAE model a delay or reduction of disease progression, a reduced production of

MMP-9 and transmigration of T-lymphocytes was observed (8). Another study on

the EAE model revealed that the combination of glatiramer acetate and

minocycline resulted in a reduction of inflammation, axonal loss and

demyelination. In the same study a decreased level of IF-γ and an increased level

of IL-5 were recorded (9). Also in human subjects the treatment with glatiramer

acetate in combination with minocycline was proven to give a lower risk of relapse

and that minocycline alone reduce number of lesions (10, 11). There are evidences

that the neuroprotective effects of minocycline may be exerted by an effect on

proinflammatory cytokines like TNF-α and IL-β. Minocycline has also shown

effect on other inflammatory mediators as chemokines, reactive oxygen species

(ROS) and nitric oxide (NO) (7).

We have in a previous study screened CSF from an acute EAE model in rat

in order to discriminate proteomic biomarkers that can be connected to the

disease. By the combination of two different mass spectrometry based proteomics

methods (Orbitrap LC-MS and chipLC QTOF MS) we detected 41 proteins that

were highly significantly increasing in CSF of EAE rats compared to control

groups, 18 of the discriminatory proteins were detected by the QTOF platform. In

the present study we have induced acute EAE in rats that were treated with

minocycline or vehicle. The aim was to study the effect of minocycline on the

previously detected proteome biomarker candidates in terms of quantitative

differences between rats treated or not treated with minocycle applying the same

chipLC-QTOF MS platform as earlier.


122

2. Material and methods

2.1. Induction of acute EAE To induce EAE, 60 male Lewis rats (Harlan Laboratories B.V.) with an average

starting weight of 200 g was injected subcutaneously in the left hind paw (under

isoflurane anesthesia) with 100 μL saline-based emulsion containing 20 μg guinea

pig myelin basic protein (MBP), 500 μg Mycobacterium tuberculosis type 37HRa

(Difco) and 50 μL Complete Freunds’ Adjuvant (CFA) (groups; B, D, F and H).

Another 60 rats were kept as inflammatory control groups by the injection of the

same emulsion but without the MBP added (groups; A, C, E and G). Sixty of the

rats were treated with minocycline at onset of the study (day 0), 50 mg/kg

bodyweight was injected intraperitoneal in the belly (groups; C, D, G and H). The

rest of the animals were treated with vehicle (A, B, E and F). Each of the animals

were marked and randomized for treatment. The animals were kept in type III

cages three by three in random order, food and water was available ad libitum.

Weight and disese symptoms were recorded on a daily basis. The motor

dysfunction was scored according to following criteria; 0: no pathological signs, 1:

paralysis of the tip of the tail, 2: no curling reflex/no strength at tail basis, 3:

slightly impaired walking, 4: unstable walk, 5: half hind limb paralysis, 6:

complete hind limb paralysis, 7: midriff paralysis, 8: half for-limb paralysis, 9:

moribund, 10: death due to EAE. In Table 1 the animal groups and the samples

included in this study are described. These animal experiments were approved by

the local Ethical Committee for Animal Experiments.

Table 1 Samples included in the study, samples marked with an L were collected at too

low volume and were not included. Samples marked with B were contaminated with blood

and were not included. Samples marked with H had a high TIC value compared to the rest

of the samples within the group. MC= minocycline, V= vehicle.

Samples analyzed

Day 10 Day 14

Co

ntr

ol

+ V

EA

E

+ V

Co

ntr

ol

+ M

C

EA

E

+ M

C

Co

ntr

ol

+ V

EA

E

+ V

Co

ntr

ol

+ M

C

EA

E

+ M

C

Group A Group B Group C Group D Group E Group F Group G Group H

31 L 16 B 7 22 19 4 L 10 1 L

14 17 B 8 23 20 5 11 2

15 18 9 24 21 6 12 3 H

46 34 H 25 31 43 37 28 40 H

47 35 H 26 32 44 L 38 29 L 41

48 36 27 33 45 39 B 30 42 H

61 49 67 64 55 52 L 70 58 B

62 50 H 68 65 L 56 53 71 59 B

Chapter 5

123

63 51 H 69 66 L 57 54 72 60

73 79 L 85 82 76 91 94 L 88

74 80 86 83 77 92 95 89

75 81 87 84 78 93 B 96 90 H

106 118 109 112 115 L 100 103 97 L

107 119 110 113 116 101 104 98

108 L 120 111 114 117 102 105 99

2.2. CSF Sampling Half of the animals were euthanized at day 10 (groups A-D) and the rest of the

animals (group E-H) were euthanized at day 14 using CO2/O2. For the CSF

sampling procedure the head of each rat was held in a fixed position using a

holder. The arachnoid membrane was revealed by a skin incision followed by an

incision in the musculus trapezius pars descendens. The CSF was collected from the

cisterna magna using an insulin syringe needle (Myjector, 29 G × 1/2" - 0.33 × 12

mm, 0.3 mL = 30 units); a maximum of 60 µL was collected from each animal. Each

sample was centrifuged at 2000 g for 10 minutes at 4 °C within 20 minutes after

the collection. The supernatant was aliquot in five tubes of ~10 µL each and stored

at -80 °C until the analysis. Samples that were visually contaminated with blood

were discarded from the study, some of the samples were obtained at too low

volume to be analyzed (see Table 1).

2.3. Sample preparation The protein digestion was done in a random order according to following

procedure; 10 μL CSF was added to a tubes containing 10 μL 0.1% RapiGest™

(Waters) dissolved in 50 mM ammonium bicarbonate. The proteins in the CSF

were reduced by the addition of 0.5 μL 1,4-dithiotreitol (DTT) (0.5 M) incubated at

60 °C for 30 min. The sample were let to cool down to room temperature, they

were subsequently alkylated using 1 μL iodoacetamide (IAM) (0.3 M) incubated in

the dark for 30 min at room temperature. One micro liter sequencing grade

modified trypsin (Promega, Madison, WI, USA, part # V5111) (1 µg/µL) was

added to the samples and incubated for ~16 h at 37 °C under agitation (450 rpm).

After the digestion, 3 μL hydrochloric acid (0.5 M) was added and the digests

were incubated for 30 min at 37 °C. The samples were centrifuged at 13250 g for 10

min at 4 °C to remove RapiGest™ particles. The samples were transferred to

sample vials, and kept in at -80 °C. Each sample was exposed to two freeze-thaw

cycles prior to the LC-MS analysis.

2.4. ChipLC-QTOF MS proteomic analysis The digested CSF samples were analyzed in a random order, after every tenth

sample a blank and a quality control (cytochrome C spiked into pooled CSF


124

sample after digestion at 200 fmol/µL) was injected to check the technical quality

of the analysis. Samples from group A, C, D, E and G were injected at a volume of

1 μL and samples from group B, F and H were injected at a volume of 0.2 μL. This

was done to normalize the total ion chromatograms (TIC) of the samples

(approximately five times higher in group B, F and H compared to the rest of the

groups, determined by a pre-analysis of samples from all groups at the same

volume) and thereby avoid overloading the trap column.

The peptide separation was done on a reverse phase LC-chip (Protein ID

chip #3; G4240-63001 SPQ110: Agilent Technologies; separating column: 150 mm ×

75 µm Zorbax 300SB-C18, 5 µm; trap column: 160 nL Zorbax 300SB-C18, 5 µm)

coupled to a nano LC system (Agilent 1200) with a 40 μL injection loop. An

electrospray ionization (ESI) source was used to generate ions. For the detection of

the ions a quadrupole-time-of-flight (QTOF) mass spectrometer (Agilent 6510) was

used. The MassHunter Data Acquisition software was used for the operation

(version B.02.00; Agilent Technologies, Santa Clara, USA). The LC separation was

done by using following eluents; A: ultra-pure water (conductivity 18.2 MΩ,

Sartorius Stedim, Nieuwegein, The Netherlands) with 0.1% formic acid (98-100%,

pro analysis, Merck, Darmstadt, Germany); B: acetonitrile (HPLC-S gradient

grade, Biosolve, Valkenswaard, The Netherlands) with 0.1% formic acid.

The samples were de-salted and enriched on the trap column for 10

minutes at a flow rate of 3 μL/min (3% B). The samples were then transferred to

the separation column at a flow rate of 250 nL/min. For the elution of the peptides,

following gradient was used: 100 min linear gradient from 3 to 50% B; 5 min linear

gradient from 50 to 70% B; 4 min linear gradient from 70 to 3%.

MS analysis was done in the 2 GHz extended dynamic range mode under

the following conditions; mass range: 275-2000 m/z, acquisition rate: 1

spectrum/sec, data storage: profile and centroid mode, fragmentor: 175 V,

skimmer: 65 V, OCT 1 RF Vpp: 750 V, spray voltage: ~1900 V, drying gas temp: 325

ºC, drying gas flow (N2): 6 L/min. Mass correction was done during analysis using

internal standards; m/z: 371.31559 (originating from a ubiquitous background ion

(Dioctyl adipate, DOA, plasticizer) and m/z: 1221.990637 (HP-1221 calibration

standard, evaporating from a wetted wick inside the spray chamber).

To assess the repeatability of the LC-MS analysis the relative standard

deviation (RSD) was calculated for the mass accuracy, retention time and peak

area based on selected cytochrome C peaks in the QC samples. The peaks were

first smoothed (Gaussian function width; 15 points, [15 sec]) and subsequently

integrated; the peak area RSD was within +/- 25%, the retention time deviation

was less than +/- 0.3% (or 5 sec) and the mass accuracy (calculated as the mean of

five measurements from each selected cytochrome C peak), was within +/- 9 ppm

of the theoretical value value.

Chapter 5

125

2.5. QTOF-MS data processing Acquired raw data was processed by the use of Agilent MassHunter Qualitative

Analysis software (Agilent version B.02.00). The files were exported to

MzData.XML format in centroid mode, the files were subsequently converted to

ASCII format and pre-processed using an in-house data processing workflow

developed in C++ (12, 13) producing a common peak matrix of 3883 peaks (based

on selection of 20 000 most intensive peaks from each data file). To visualize the

variability of the data, the raw peak matrix was analyzed by principal component

analysis (PCA) (MatLab R2009a, Mathworks, Natick, MA, USA).

The peak matrix was searched for features matching the mass to charge

ratio (m/z), retention time (RT) and charge state of previously discovered

discriminatory peptides between EAE and control animals. A feature was

considered a match with a Δ m/z value of ± 10 ppm and a RT shift within ± 1 min,

the charge state had to be the same. In Table 2 the proteins from which peptides

were studied are listed. Peak areas were compared in a pair-wise manner between

groups of animals using one-way ANOVA with Bonferroni post hoc test (SPSS

Inc., Chicago, Il, USA). Peak area differences with a p-value below 0.05 were

considered significant, only proteins with the major part of the peptides (at least

60 %) having a p-value below 0.05 in the ANOVA analysis were considered as

discriminatory. At least 2 peptides from each protein had to be detected. Each

discriminatory peptide was visualized by histograms of the average value for that

peptide within each group (Microsoft Excel, Microsoft Corporation, Redmond,

WA, USA).

Table 2. Discriminatory proteins (EAE vs. control) discovered by chipLC QTOF MS in

previous study. Peptides from these proteins were investigate in present study.

Discriminatory proteins discovered in previous study AC #

Afamin P36953

Alpha-1-inhibitor 3 P14046

Alpha-1-macroglobulin Q63041

Alpha-2-HS-glycoprotein (Fetuin A) P24090

Ceruloplasmin P13635

Complement C3 P01026

Fetuin-B Q9QX79

Haptoglobin P06866

Hemopexin P20059

Ig gamma-2A chain C region P20760

Murinoglobulin-1 Q03626


126

Murinoglobulin-2 Q6IE52

T-kininogen 1 P01048

T-kininogen 2 P08932

Vitamin D-binding protein P04276

Lysozyme C1 P00697

Alpha 1 acid glycoprotein P02764

Serine protease inhibitor A3N P09006

3. Results

Experimental autoimmune encephalomyelitis was induced in 60 rats (groups B, D,

F, H) from which 30 were treated with minocycline (groups D and H) and 30 were

non-treated (groups B and F). An additional 60 rats were kept as inflammatory

control (groups A, C, E, G; injected with CFA only), 30 of the rats were treated

with minocycline (groups C and G) and 30 were non-treated (groups A and E).

CSF was collected and analyzed by bottom-up proteomics using a chipLC-QTOF

MS platform. The study was also designed to study the longitudinal effect by

collecting CSF samples both at onset of disease on day 10 (groups A-D) and at the

climax of the disease on day 14 (groups E-H). The aim of the study was to examine

the effect of minocycline treatment on a set of previously discovered

discriminatory proteins between diseased and control animals.

The proteins that were studied for a quantitative effect of minocycline are

listed in Table 2. The disease developed in the same way as described before; At

day 11 the disease scores started to increase in the EAE rats (group F and H) as

seen in Table 3. None of the rats without EAE or rats with EAE sacrificed at day

10 showed any increased disease scores (data not shown). The weight of the rats

increased continuously in all groups except in group F where the weight pattern

started to decrease from day 11 (Figure 1). In group H a slight decrease of the

average weight is seen at day 14. The lower disease score and the continous

increase in weight of the EAE rats treated with minocycline (group H) show that

minocycline has an effect on the symptoms of disease in the animals.

The complete peak matrix containing 3883 features were analyzed by PCA

to discover global differences related to treatment (Figure 2). Samples from group

B, F and H were injected at a five times lower volume (0.2 µL) compared to the

rest of the groups (A, C, D, E, G) (1 µL). To compensate for a significant elevation

in TIC area of analyzed CSF from the animals with EAE. This was done to avoid

overloading of the trap column in the LC-system. This also prevent detection of

differences only caused by an overall increase in protein concentration. In the

figure it is visible that groups A, C, D and E are clustered together. Group A, C

Chapter 5

127

and E are all control groups, while group D is a diseased group treated with

minocycline, this result show that the minocycline treatment of the EAE rats

produces a proteomic profile similar to the control rats. Group B that constitutes of

diseased but non-treated animals clusters partly together with the control groups

while a few samples deviates from this pattern. Group F (non-treated EAE day 14)

deviates the most from the control groups, group H (minocycline treated EAE day

14) is partly deviating but are closer to the control groups than F showing that the

minocycline treatment affects the protein profile towards a closer connection to

the control situation. An interesting observation is that day 14 control animals

treated with minocycline (group G) deviates clearly from the other control groups.

Table 3. Disease scores of animals from group F and H, recorded daily (here only shown

from day 10 onwards). The score was 0 until day 10 and started to increase at day 11.

Animals of group F have higher scores than group H. Rats from group A, E and G did not

show any elevated disease scores.

Sam

ple

gro

up

Sam

ple

nr.

Sco

re d

ay 1

0

Sco

re d

ay 1

1

Sco

re d

ay 1

2

Sco

re d

ay 1

3

Sco

re d

ay 1

4

F 4 0.0 1.0 3.0 3.0 3.0

5 0.0 0.0 1.0 2.0 2.5

6 0.0 0.5 2.5 2.5 3.0

37 0.0 1.0 3.0 3.0 2.5

38 0.0 0.0 0.5 1.0 2.0

39 0.0 0.5 2.0 3.0 3.0

52 0.0 0.0 1.0 2.5 3.0

53 0.0 0.0 1.0 2.5 3.0

54 0.0 0.0 1.0 3.0 3.0

91 0.0 0.0 0.0 0.5 1.0

92 0.0 1.0 2.5 3.0 1.0

93 0.5 2.5 3.0 3.0 2.5

100 0.0 0.0 2.0 2.5 3.0

101 0.0 0.0 1.0 2.5 3.0

102 0.0 0.0 0.0 0.0 0.0

H 1 0.0 0.0 1.0 2.5 3.0

2 0.0 0.5 2.0 3.0 3.0

3 0.0 0.0 0.0 0.0 0.0

40 0.0 0.0 0.0 0.0 0.5

41 0.0 0.0 0.0 0.0 0.0

42 0.0 0.0 0.0 0.5 1.0


128

58 0.0 0.0 0.0 0.0 0.0

59 0.0 0.0 0.0 0.0 0.0

60 0.0 0.0 0.0 0.0 0.0

88 0.0 0.0 0.0 0.0 0.0

89 0.0 0.0 0.0 0.0 0.0

90 0.0 0.0 0.0 0.5 1.0

97 0.0 0.0 0.0 0.0 0.0

98 0.0 0.0 0.0 0.0 0.0

99 0.0 0.0 0.0 0.0 0.0

In our previous study we discovered that the average protein level was

increased in all animals of the EAE affected group sacrificed at day 14, EAE

animals sacrificed at day 10 showed up a large variation in average protein

concentration (as measured by TIC area) with some of the animals having a TIC

correlated to the control groups while others had a TIC elevated approximately 5

times just as in the EAE animals day 14. The elevated TIC in the day 10 animals

was interpreted as a no symptomatic early disease onset marker. In this study we

recorded the same behavior of the TIC area in the EAE day 10 and 14 as

previously, the groups were however not divided in ‚low‛ and ‚high‛ TIC groups

because this would result in too small groups basing statistical calculations on (see

Table 1). What was also observed was that the minocycline treated EAE animals

of day 14 showed up a pattern similar to EAE day 10 with a large spread of the

TIC in a high and a low group. The minocycline treated day 10 EAE animals

showed a TIC comparable to control animals. This can be interpreted as delay of

disease onset or decreased severity of the disease. Since no animals were kept

alive and further monitored after day 14 it is not possible to distinguish between

these two possibilities.

Chapter 5

129

Figure 1. Average body weight of rats displayed as mean value of all animals in the same

group at the particular day. Left panel; average body weight of animals sacrificed at day 10

(groups A-D). All groups show a steady increase in body weight. Right panel; average

weight of animals sacrificed at day 14 (groups E-H) group E and G show a steady increase

while group F decrease in weigh from day 11, group H show a slight decrease at day 14.

Figure 2. Global principal component analysis (PCA) on the complete peak matrix (3883

features). Group A, C, E and D are clustered together while group F (non treated EAE,

day 14) and G (minocycline treated control group, day 14) deviates from this cluster.

Group b (non treated EAE, day 10) clusters partly together with control groups and group

H (minocycline treated EAE, day 14) are closer to the control groups than group F.

Explanation of symbols representing each group is present in the legend, group codes are

explained in Table 1.


130

Table 4. List of all discriminatory peptides plotted in the figures. Information included is

m/z value, retention time (RT), charge state and peptides equence.

ID m/z RT charge sequence

Alpha 1 inhibitor 3 708.378 73.6 3 K/NLHPLNELFPLAYIEDPK/M

Alpha 1 inhibitor 3 559.934 40.2 3 K/ISLCHGNPTFSSETK/S

Alpha 1 inhibitor 3 561.288 40.8 2 R/SSGSLLNNAMK/G

Alpha 1 inhibitor 3 431.739 37.4 2 R/YNVPLEK/Q

alpha 2 HS glycoprot 487.265 38.0 2 -/APQGAGLGFR/E

alpha 2 HS glycoprot 712.696 54.9 3 R/HAFSPVASVESASGEVLHSPK/V

alpha 2 HS glycoprot 616.309 42.6 3 K/VGQPGDAGAAGPVAPLCPGR/V

alpha 2 HS glycoprot 386.230 39.2 2 R/CPILIR/F

alpha 2 HS glycoprot 739.370 57.2 3 R/HAFSPVASVESASGEVLHSPK/V

Complement C3 922.942 55.8 2 R/SDVDEDIIPEEDIISR/S

Complement C3 590.668 59.3 3 K/VHQFFNVGLIQPGSVK/V

Complement C3 467.007 52.4 4 R/VELKPGDNLNVNFHLR/T

Complement C3 413.903 35.4 3 K/TVLTGATGHLNR/V

Complement C3 294.169 35.0 3 R/DHVLGLAR/S

Haptoglobin 460.280 47.3 3 K/GAVSPVGVQPILNK/H

Haptoglobin 645.865 57.0 2 K/DIAPTLTLYVGK/N

Immunoglobulin G 2A chain C 379.555 41.7 3 R/SVSELPIVHR/D

Immunoglobulin G 2A chain C 615.336 42.9 2 K/VNSGAFPAPIEK/S

Lysozyme C1 412.224 42.0 2 R/DLSGYIR/N

Lysozyme C1 458.742 47.2 2 R/AWVAWQR/H

Lysozyme C1 730.328 54.9 3 R/NYNPGDQSTDYGIFQINSR/Y

murinoglobulin 1 408.924 43.4 3 K/VLIVEPEGIKK/E

murinoglobulin 1 441.777 36.8 2 K/NLQPAIVK/V

murinoglobulin 1 451.761 40.4 2 K/QQPAFALK/V

murinoglobulin 1 578.321 42.6 2 R/SSGSLFNNAMK/G

murinoglobulin 1 644.837 42.2 2 R/VTASPQSLCGLR/A

murinoglobulin 1 685.864 35.5 2 K/VQTVPLTCNNPK/G

PTGDS 972.485 60.3 2 K/DQGLTEEDIVFLPQPDK/C

PTGDS 421.219 34.1 2 R/MATLYSR/A

PTGDS 371.714 42.8 2 K/FITFSK/D

PTGDS 486.227 31.3 3 G/HDTVQPNFQQDK/F

T-Kininogen 1 420.220 42.8 3 K/KDGAETLYSFK/Y

T-Kininogen 1 464.560 33.9 3 K/SAHSQVVAGMNYK/I

T-Kininogen 1 565.765 46.5 2 K/DGAETLYSFK/Y

T-Kininogen 1 901.932 54.0 2 K/YNAELESGNQFVLYR/V

Chapter 5

131

When studying the quantitative effect of minocycline on the previously

detected 18 discriminatory proteins, 8 of them showed a significant effect of the

minocycline treatment (alpha 1 inhibitor 3 [P14046], alpha 2 HS glycoprotein

[P24090], complement C3 [P01026], haptoglobin [P06866], IgG 2A chain C

[P20760], lysozyme C1 [P00697], murinoglobulin 1 [Q03626] and T-kininogen

1[P01048]). The major part of the peptides from these proteins had a p-value below

0.05 for EAE vs. control (F vs. E) and EAE vs. EAE with minocycline (F vs. H)

when analyzed by one-way ANOVA with Bonferroni post-hoc test. In Figure 3

and 4 the average peak area of the significantly (p < 0.05) discriminatory peptides

in each animal group from the eight proteins can be seen. The general picture is

that the peptide levels are homogenous among group A-E while a clear elevation

is seen in group F. In group G and H the level is decreased compared to group F

either at the same level as group A-E or moderate to strongly elevated compared

to these groups. As can be seen in the global PCA where there is a separation of

group G from the other control groups, some of the peptides are elevated in group

G compared to the control groups. The minocycline seems to have an effect on

control animals at day 14 while this cannot be seen in the animals collected at day

10. In group B (non treated EAE day 10) a slight elevation can be observed in some

of the proteins. In the figures the peptides of the proteins are marked with their

m/z without the decimals included. In Table 4 all discriminatory peptides are

listed with m/z, RT, charge state and peptide sequence.

In Figure 5 the average of 4 prostaglandin D synthase (PTGDS, P22057)

derived peptides are plotted. This protein was not discriminatory in our previous

study and also here it showed a somewhat stable level between EAE and control

animals (p > 0.05), what can be noted is that in our previous study the EAE treated

animals showed a lower peak area of these peptides compared to controls but here

only the day 10 samples show this behavior. Also in this protein the minocycline

show an effect on the control animals at day 14.


132

Figure 3. Average peak area of discriminatory peptides derived from 4 proteins (alpha 1

inhibitor 3 [P14046], alpha 2 HS glycoprotein [P24090], complement C3 [P01026] and

haptoglobin [P06866]), the peptides are significantly discriminatory (p < 0.05) between

group F (non-treated EAE day 14) and E (non-treated control group day 14) as well as

between group F and H (minocycline treated EAE day 14). An increase of the protein level

can be seen in group F compared to all other groups. Group G (minocycline treated control

day 14) and H are generally increased compared to group A – E but decreased compared to

group F. Peptides are marked with their m/z value without decimals (described in Table 4)

Statistics are based on one way ANOVA with Bonferroni post hoc test.

Chapter 5

133

Figure 4. Average peak area of discriminatory peptides derived from 4 proteins (IgG 2A

chain C [P20760], lysozyme C1 [P00697], murinoglobulin 1 [Q03626] and T-kininogen

1[P01048]), the peptides are significantly discriminatory (p < 0.05) between group F (non-

treated EAE day 14) and E (non-treated control group day 14) as well as between group F

and H (minocycline treated EAE day 14). An increase of the protein level can be seen in

group F compared to all other groups. Group G (minocycline treated control day 14) and

H are generally increased compared to group A-E but decreased compared to group F.

Peptides are marked with their m/z value without decimals (described in Table 4)

Statistics are based on one-way ANOVA with Bonferroni post hoc test.


134

Figure 5. Average peak area of 4 peptides derived from prostaglandin D synthase

(PTGDS). The peptide levels are stable between the groups A, C, D, E, F and H. A slight

decrease is seen in group B (non-treated EAE day 10) and a clear elevation is seen in group

G (minocycline treated control, day14). The peptides are marked with their m/z values

without decimals (see Table 4).

4. Discussion

This manuscript describes the study of the quantitative effect of minocycline on

tryptic peptides derived from 18 proteins that were discovered as discriminatory

between EAE animals and control animals in a previous study. Eight out of

eighteen proteins were discriminatory between EAE animals treated with

minocycline and EAE animals that received no treatment.

Minocycline is a tetracycline analog currently in use for the treatment of

acne and rheumatoid arthritis, it has been used for several years, the adverse

effects are well described and the drug is considered relatively safe (14, 15). Apart

from the antibiotic effect, minocycline show additional properties that can be

useful as therapeutics in other applications (16). Minocycline possess highly

lipophilic characteristics that enable passage through the blood-brain barrier and

therefore can reach the CNS (17). The molecule has gained an interest as a possible

treatment in neurological disorders (17) and has proven to be effective in studies

on the EAE model and MScl patients (8, 11, 18-20).

Minocycline is considered as neuroprotective and has proven to decrease

inflammation, lower the axonal degeneration and reduce demyelination (20). The

modulation of cytokines and glutamate toxicity, obstruction of T-cell proliferation

Chapter 5

135

and migration as well as an inhibitory effect of the activation of microglia are

proposed mechanisms behind this protection (8, 21-24). In this study many of the

proteins that were affected by minocycline are connected to inflammation, the

regulation of pro-inflammatory cytokines could be a reason for the decreased level

of these proteins in the minocycline treated animals.

Minocycline has also shown effect on matrix metalloproteinase (MMP)

activity and production (8). MMP’s have previously shown an ability to degrade

membranes surrounding blood vessels and thereby causing blood-brain barrier

(BBB) disruption (25). Other studies have shown a connection between BBB

disruption and MMP’s (26-28). Matrix metalloproteinases have also been

connected to the pathogensis of MScl in several studies (29-31).

In our previous study most of the proteins that increased in the EAE

animals compared to control are known to be present in blood. The increased

levels of these proteins may have been caused by a disruption of the BBB. The

proteins show the same behavior in the present study with a significant increase in

the EAE animals sacrificed at day 14 (group F). The decreased protein level in the

minocycline treated EAE animals (group H) may have been caused by the

protection of the BBB via the suppression of MMP’s.

One interesting and puzzling observation made in this study was the

increased level of some of the proteins in the control animals treated with

minocycline (group G, sacrificed at day 14) compared to the untreated control

animals (group E). The minocycline treated control animals sacrificed at day 10

did not show this increased protein levels. The rats in group G showed no

increased neurological scores nor a decreased weight pattern but the increased

protein levels might be a sign of a non-symptomatic adverse late effect. It is well

known that minocycline treatment can lead to negative side-effects (15). What has

to be noted is that the control animals in this study were also treated with CFA

(without MBP), they were thus not completely healthy. The minocycline effect on

these animals sacrificed at day 14 may have been caused by the fact that these

animals are not healthy. This has to be investigated further in healthy control

animals to determine if this effect is only observed in CFA treated animals or if the

same will be recorded in completely healthy animals as well.

In summary our results show that EAE pathogenesis is attenuated both at

a level of clinical symptoms as well as at the proteomic level in the CSF of acute

EAE rats. The symptoms are delayed or decreased. Further there seems to be a

non-symptomatic side-effect of minocycline on control animals sampled at a later

stage. Our results correlates with earlier studies showing anti-inflammatory as

well as BBB defensive functions behind the protective effect of minocycline on the

EAE model.


136

Acknowledgements

This study was performed within the framework of Top Institute Pharma project

D4-102.

Chapter 5

137

Reference List


2. Aharoni, R., Eilam, R., Stock, A., Vainshtein, A., Shezen, E., Gal, H., Friedman, N., and Arnon, R.

(2010) Glatiramer acetate reduces Th-17 inflammation and induces regulatory T-cells in the CNS of

mice with relapsing-remitting or chronic EAE. J Neuroimmunol.

3. Floris, S., Blezer, E. L., Schreibelt, G., Dopp, E., van der Pol, S. M., Schadee-Eestermans, I. L., Nicolay,

K., Dijkstra, C. D., and de Vries, H. E. (2004) Blood-brain barrier permeability and monocyte

infiltration in experimental allergic encephalomyelitis: a quantitative MRI study. Brain 127, 616-627

4. Gold, R., Linington, C., and Lassmann, H. (2006) Understanding pathogenesis and therapy of multiple

sclerosis via animal models: 70 years of merits and culprits in experimental autoimmune

encephalomyelitis research. Brain 129, 1953-1971

5. Rasmussen, S., Wang, Y., Kivisakk, P., Bronson, R. T., Meyer, M., Imitola, J., and Khoury, S. J. (2007)

Persistent activation of microglia is associated with neuronal dysfunction of callosal projecting

pathways and multiple sclerosis-like lesions in relapsing--remitting experimental autoimmune

encephalomyelitis. Brain 130, 2816-2829

6. Soulika, A. M., Lee, E., McCauley, E., Miers, L., Bannerman, P., and Pleasure, D. (2009) Initiation and

progression of axonopathy in experimental autoimmune encephalomyelitis. J Neurosci. 29, 14965-

14979

7. Stirling, D. P., Koochesfahani, K. M., Steeves, J. D., and Tetzlaff, W. (2005) Minocycline as a

neuroprotective agent. Neuroscientist. 11, 308-322

8. Brundula, V., Rewcastle, N. B., Metz, L. M., Bernard, C. C., and Yong, V. W. (2002) Targeting

leukocyte MMPs and transmigration: minocycline as a potential therapy for multiple sclerosis. Brain

125, 1297-1308

9. Giuliani, F., Metz, L. M., Wilson, T., Fan, Y., Bar-Or, A., and Yong, V. W. (2005) Additive effect of the

combination of glatiramer acetate and minocycline in a model of MS. J Neuroimmunol. 158, 213-221

10. Metz, L. M., Zhang, Y., Yeung, M., Patry, D. G., Bell, R. B., Stoian, C. A., Yong, V. W., Patten, S. B.,

Duquette, P., Antel, J. P., and Mitchell, J. R. (2004) Minocycline reduces gadolinium-enhancing

magnetic resonance imaging lesions in multiple sclerosis. Ann. Neurol 55, 756

11. Metz, L. M., Li, D., Traboulsee, A., Myles, M. L., Duquette, P., Godin, J., Constantin, M., and Yong, V.

W. (2009) Glatiramer acetate in combination with minocycline in patients with relapsing--remitting

multiple sclerosis: results of a Canadian, multicenter, double-blind, placebo-controlled trial. Mult.

Scler. 15, 1183-1194





13. Suits, F., Lepre, J., Du, P., Bischoff, R., and Horvatovich, P. (2008) Two-dimensional method for time

aligning liquid chromatography-mass spectrometry data. Anal. Chem. 80, 3095-3104

14. Alarcon, G. S. (2000) Tetracyclines for the treatment of rheumatoid arthritis. Expert. Opin. Investig.

Drugs 9, 1491-1498


138

15. Feldman, S., Careccia, R. E., Barham, K. L., and Hancox, J. (2004) Diagnosis and treatment of acne. Am.

Fam. Physician 69, 2123-2130

16. Nelson, M. L. (1998) Chemical and biological dynamics of tetracyclines. Adv. Dent. Res. 12, 5-11

17. Kim, H. S., and Suh, Y. H. (2009) Minocycline and neurodegenerative diseases. Behav. Brain Res. 196,

168-179

18. Metz, L. M., Zhang, Y., Yeung, M., Patry, D. G., Bell, R. B., Stoian, C. A., Yong, V. W., Patten, S. B.,

Duquette, P., Antel, J. P., and Mitchell, J. R. (2004) Minocycline reduces gadolinium-enhancing

magnetic resonance imaging lesions in multiple sclerosis. Ann. Neurol 55, 756

19. Zabad, R. K., Metz, L. M., Todoruk, T. R., Zhang, Y., Mitchell, J. R., Yeung, M., Patry, D. G., Bell, R. B.,

and Yong, V. W. (2007) The clinical response to minocycline in multiple sclerosis is accompanied by

beneficial immune changes: a pilot study. Mult. Scler. 13, 517-526

20. Luccarini, I., Ballerini, C., Biagioli, T., Biamonte, F., Bellucci, A., Rosi, M. C., Grossi, C., Massacesi, L.,

and Casamenti, F. (2008) Combined treatment with atorvastatin and minocycline suppresses severity

of EAE. Exp. Neurol 211, 214-226

21. Kloppenburg, M., Verweij, C. L., Miltenburg, A. M., Verhoeven, A. J., Daha, M. R., Dijkmans, B. A.,

and Breedveld, F. C. (1995) The influence of tetracyclines on T cell activation. Clin. Exp. Immunol. 102,

635-641

22. Lee, S. M., Yune, T. Y., Kim, S. J., Park, D. W., Lee, Y. K., Kim, Y. C., Oh, Y. J., Markelonis, G. J., and

Oh, T. H. (2003) Minocycline reduces cell death and improves functional recovery after traumatic

spinal cord injury in the rat. J Neurotrauma 20, 1017-1027

23. Yrjanheikki, J., Keinanen, R., Pellikka, M., Hokfelt, T., and Koistinaho, J. (1998) Tetracyclines inhibit

microglial activation and are neuroprotective in global brain ischemia. Proc. Natl. Acad. Sci. U. S. A 95,

15769-15774

24. Yrjanheikki, J., Tikka, T., Keinanen, R., Goldsteins, G., Chan, P. H., and Koistinaho, J. (1999) A

tetracycline derivative, minocycline, reduces inflammation and protects against focal cerebral

ischemia with a wide therapeutic window. Proc. Natl. Acad. Sci. U. S. A 96, 13496-13500

25. Rosenberg, G. A., Kornfeld, M., Estrada, E., Kelley, R. O., Liotta, L. A., and Stetler-Stevenson, W. G.

(1992) TIMP-2 reduces proteolytic opening of blood-brain barrier by type IV collagenase. Brain Res.

576, 203-207

26. Barr, T. L., Latour, L. L., Lee, K. Y., Schaewe, T. J., Luby, M., Chang, G. S., El-Zammar, Z., Alam, S.,

Hallenbeck, J. M., Kidwell, C. S., and Warach, S. (2010) Blood-brain barrier disruption in humans is

independently associated with increased matrix metalloproteinase-9. Stroke 41, e123-e128

27. Lee, M. A., Palace, J., Stabler, G., Ford, J., Gearing, A., and Miller, K. (1999) Serum gelatinase B, TIMP-

1 and TIMP-2 levels in multiple sclerosis. A longitudinal clinical and MRI study. Brain 122 ( Pt 2), 191-

197

28. Waubant, E., Goodkin, D. E., Gee, L., Bacchetti, P., Sloan, R., Stewart, T., Andersson, P. B., Stabler, G.,

and Miller, K. (1999) Serum MMP-9 and TIMP-1 levels are related to MRI activity in relapsing

multiple sclerosis. Neurology 53, 1397-1401

29. Fainardi, E., Castellazzi, M., Bellini, T., Manfrinato, M. C., Baldi, E., Casetta, I., Paolino, E., Granieri, E.,

and Dallocchio, F. (2006) Cerebrospinal fluid and serum levels and intrathecal production of active

Chapter 5

139

matrix metalloproteinase-9 (MMP-9) as markers of disease activity in patients with multiple sclerosis.

Mult. Scler. 12, 294-301

30. La, R. A., Cittadella, R., De Marco, E. V., Valentino, P., Andreoli, V., Trecroci, F., Latorre, V.,

Gambardella, A., and Quattrone, A. (2010) Single nucleotide polymorphism in the MMP-9 gene is

associated with susceptibility to develop multiple sclerosis in an Italian case-control study. J

Neuroimmunol.

31. Sastre-Garriga, J., Comabella, M., Brieva, L., Rovira, A., Tintore, M., and Montalban, X. (2004)

Decreased MMP-9 production in primary progressive multiple sclerosis patients. Mult. Scler. 10, 376-

380

140

Chapter 6

Chapter 6

141

Summary and future perspectives In the search for diagnostic markers, therapeutic response indicators and to

unravel pathological mechanisms, numerous proteomic biomarker studies have

been initiated. But despite extensive research there are few proteomic biomarkers

that have made it to the stage where they are implicated in the clinic. Proteomic

biomarker studies suffer from poor reproducibility which in part is caused by the

lack of standardization of analysis methods and sample handling. The sensitive

nature of biomolecules and the complexity of biological samples cause challenges

for the researcher. Sample collection procedure, time span between sample

collection and storage, contamination, additives, storage time, storage

temperature, number of freeze-thaw cycles, digestion protocol, analysis strategy

and data processing method are some of the procedures that affects the quality of

the results.

This thesis focus on the screening of cerebrospinal fluid (CSF), in search for

molecular biomarkers related to sample stability. CSF is the most suitable

compartment to analyze when searching for biomarkers related to central nervous

system (CNS) disorders. In this thesis CSF from an rat model of multiple sclerosis

(MScl), the acute experimental autoimmune encephalomyelitis (EAE), was

analyzed in order to search for biomarkers related to the disease.

Chapter 2 deals with the study on the effect of different sample handling

procedures on proteins, metabolites and free amino acids in porcine CSF. Three

sample handling procedures were investigated; samples were left at room

temperature directly after sampling, up to two hours. The room temperature

introduced quantitative changes of prostaglandin D synthase derived peptides,

and a number of metabolites and free amino acids. The second condition analyzed

was the effect of freeze-thaw cycles on the proteome profile; this treatment

introduced changes in the amount of transthyretin derived peptides. The third

condition investigated was the storage of digested CSF samples at 4 °C. After

storage at this temperature reduced amounts of prostaglandin D-synthase and

serotransferrin derived peptides were detected.

In Chapter 3 the study of human CSF samples left at room temperature

after centrifugation are presented. The CSF samples were analyzed by the means

of proteomics, metabolomics and free amino acids. The results were homogenous

across the platforms, showing that biological variation was the main variable in

the data sets. The only discriminatory features beween samples left at room

temperature compared to controls were two non identified ions and 2,3,4-

trihydrobutanoic acid.

Summary and future perspectives

142

Chapter 4 and 5 describes proteomic biomarker studies applied on CSF

from an acute EAE model in rat. In chapter 1 the model is described and previous

proteomic biomarker studies on the EAE model are reviewed. In chapter 4 CSF

from the EAE model was screened for discriminatory biomarkers connected to the

disease by the combination of two different mass spectrometry based proteomic

platforms. A number of proteins were discovered at increased levels in EAE rats

compared to controls. The combination of different analysis as well as data

processing strategies resulted in increased coverage but also showed overlapping

results. The combination of different techniques at different levels is an interesting

aspect that needs more attention.

Chapter 5 presents an extension of the EAE study described in chapter 4.

Described here, is the investigation of the effect of minocycline on the previously

detected discriminatory proteins. In this study only the QTOF platform was

applied. The results show that 8 out of the 18 discriminatory proteins detected in

chapter 4 were significantly changed by the minocycline treatment. These proteins

show a reproducible behavior between the first and the second study as being

increased in EAE rats compared to control animals. An additional discovery was

that minocycline also affected the peptide level of control animals.

Conclusively, different sample handling procedures introduce quantitative

changes of different biomolecules; this ought to be taken into account when

designing biomarker studies. Key to reproducible results are the standardization

of sample handling and sample analysis procedures, which can be done on several

levels. It would be interesting to further investigate the effect of long time storage

at different temperatures as well as freeze-thaw cycles with the addition of

stabilizing agents. A full comparison of different proteomic platforms and data

processing pipelines would also be interesting, to further address the

reproducibility question.

In the EAE studies we have discovered candidate markers that are related

to the disease. An interesting question is if these results are translatable to human

subjects and if the same pattern would be detected in blood samples as well. The

collection of CSF is relatively easily done but is still of a quite invasive nature and

the detection of biomarkers in blood would be more optimal. The fact that

minocycline treatment of EAE rats affected a number of proteins show that this

drug has a possible effect on inflammation and/or blood-brain barrier function but

the increased protein level in control animals could be a sign of an adverse effect

that calls for attention. Finally, I have through the work on this thesis come to the

understanding that the discovery of specific, useful and fully reproducible

molecular biomarkers is an art and requires full attention on the small details,

nothing can be neglected, everything influences the end results.

Chapter 6

143

Samenvatting In de zoektocht naar diagnostische markers, therapeutische reactie-indicatoren,

om pathologische mechanismen te ontrafelen, zijn talrijke studies naar proteomic

biomarker gestart. Desondanks uitgebreid onderzoek zijn er weinig proteomic

biomarkers die het stadium bereikt hebben dat zij in een kliniek kunnen worden

gebruikt.

Studies naar proteomic biomarkers lijden aan slechte

reproduceerbaarheid. Dit wordt voor een deel veroorzaakt door een gebrek aan

normalisatie van analysemethodes en monsterbehandeling. De gevoelige aard van

biomoleculen en de ingewikkeldheid van biologische monsters zijn een uitdaging

voor de onderzoeker. De procedure van de monsterinzameling, de tijdspanne

tussen monsterinzameling en opslag, verontreiniging, additieven, opslagtijd,

opslagtemperatuur, het aantal freeze-thaw cycli, digest-protocol, analysestrategie

en gegevens, zijn een deel van de procedures die de kwaliteit van de resultaten

beïnvloedt.

Deze dissertatie is gefocust op het onderzoek van hersenvloeistof (CSF), in

het zoeken naar biomarkers voor monster-stabiliteit. CSF is de meest geschikte

vloeistof om te analyseren in het zoeken naar biomarkers voor ziektes in het

centrale zenuwstelsel (CNS). In deze these werd CSF van een ratten-model van

multiple sclerose (MScl), de experimentele auto-immune encefalomyelitis (EAE),

geanalyseerd om ziekte gerelateerde biomarkers te vinden.

Hoofdstuk 2 behandelt de studie over het effect van verschillende

monsterbehandelingsprocedures op proteïnen, metabolites en vrije aminozuren in

varkens CSF. Drie monsterbehandelingsprocedures werden onderzocht; monsters

werden na afname op kamertemperatuur gelaten, tot maximaal twee uur. De op

kamertemperatuur geïntroduceerde kwantitatieve veranderingen bevatten

peptiden van prostaglandine D-synthase, een aantal metabolites en vrije

aminozuren. De tweede analysevoorwaarde was het effect van freeze-thaw cycli

op het proteomeprofiel; dit geintroduceerde veranderingen in de hoeveelheid van

transthyretin afgeleide peptiden. De derde onderzochte voorwaarde bedroeg de

Samenvatting

144

opslag van digested CSF monsters op 4 °C. Dit resulteerde in een vermindert

niveau van prostaglandine D-synthase en serotransferrin afgeleide peptiden.

In Hoofdstuk 3 zijn de effecten van kamertemperatuur op menselijke CSF

monsters geanalyseerd. De CSF monsters werden geanalyseerd door proteomics,

metabolomics en vrije aminozuren analyse. De resultaten waren homogeen over

de platforms en lieten zien dat de biologische variatie de voornaamste variabele in

de data was. De enige discriminerende eigenschappen tussen de monsters op

kamertemperatuur en de controles, waren twee niet-geïdentificeerde ionen en

trihydrobutanoic zuur 2.3.4.

Hoofdstuk 4 en 5 beschrijft proteomic biomarkerstudies die op CSF van

een acuut EAE ratten model worden toegepast. In hoofdstuk 1 wordt het model

beschreven en de vorige proteomic biomarkerstudies van het model EAE worden

opnieuw in ogenschouw genomen. In hoofdstuk 4 wordt CSF van het EAE model

onderzocht op discriminerende ziekte-biomarkers, door de combinatie van twee

verschillende gebaseerde proteomic platforms van massaspectrometrie. Een antaal

proteïnen werden ontdekt op verhoogde niveaus bij EAE ratten in vergelijking

met de controles. De combinatie van verschillende analyse- en

dataverwerkingsstrategieën resulteerden in een verhoogde dekking maar toonden

ook overlappende resultaten. De combinatie van technieken op verschillende

niveaus is een interessant aspect dat meer aandacht vergt.

Hoofdstuk 5 bevat een uitbreiding van de EAE studie, die in hoofdstuk 4

wordt beschreven. Hierin wordt het onderzoek van het effect van minocycline op

de eerder ontdekte discriminerende proteïnen beschreven. In deze studie werd

slechts het QTOF platform toegepast. De resultaten tonen aan dat 8 van 18 van de

discriminerende proteïnen gevonden in hoofdstuk 4 door de minocycline

behandeling werden veranderd. Deze proteïnen vertonen een reproduceerbaar

gedrag tussen de eerste en de tweede studie; ze zijn gestegen bij EAE ratten in

vergelijking met controledieren. Een extra ontdekking was dat minocycline ook

het peptide niveau van controledieren beïnvloedde.

Concluderend; verschillende steekproef-behandelingsprocedures

introduceren kwantitatieve veranderingen van verschillende biomoleculen; dit

zou in acht genomen moeten worden bij het ontwerpen van biomarker studies. De

Chapter 6

145

sleutel tot reproduceerbare resultaten is de normalisatie van monsterbehandeling

en analyseprocedures, die op verscheidene niveaus kan worden gedaan. Het zou

interessant zijn om verder onderzoek te doen naar het effect van stabiliserende

agents op langtermijn-opslag bij verschillende temperaturen evenals freeze-thaw

cycli. Een volledige vergelijking van verschillende proteomic platforms en

gegevens zijn ook interessant zijn om de reproduceerbaarheid verder te

verbeteren.

In de EAE studies hebben wij kandidaatmarkers ontdekt die met MScl

verwant zijn. Een interessante vraag is of deze resultaten ook waargenomen

kunnen worden bij patiënten, daarnaast rijst de vraag of hetzelfde patroon

eveneens in bloedmonsters zal worden ontdekt. Het vergaren van CSF wordt vrij

gemakkelijk gedaan maar is nogal ingrijpend van aard en de opsporing van

biomarkers in bloed zou beter zijn. Het feit dat minocycline een aantal proteïnen

van EAE ratten beïnvloedt, toont aan dat deze stof een mogelijk effect heeft op

ontsteking van de blood-brain barrièrefunctie. Maar het verhoogde eiwitniveau in

controledieren is een teken van een ongunstig effect dat aandacht vereist. Tot slot

ben ik door het werk aangaande deze dissertatie tot besef gekomen dat het

ontdekken van specifieke, nuttige en volledig reproduceerbare moleculaire

biomarkers een kunst is en volledige aandacht vereist op de kleine details, niets

kan worden veronachtzaamd, alles beïnvloedt het eindresultaat.

Samenvatting

146

Appendices

Supplementary material Chapter 2 Supplementary material Chapter 4

Appendix 1

147

Appendix 1

Supplementary material Chapter 2

Figure S.1. Search parameters used in PhenyxOnline for all the peptides identified by

chipLC-MS proteomic analysis.


148

Figure S. 2. The sequences that are colored highlight parts of porcine prostaglandin-D-

synthase (PTGDS_PIG, Q29095) that have been assigned to discriminatory peptides

(chipLC-MS analyses: dark bold; MALDI-FT-ICR-MS: light bold) in the delayed storage

study.

Figure S.3. Identification information for PTGDS_PIG peptide (m/z: 638.654) discovered

by chipLC-MS/MS in the delayed storage study. A. Peptide sequence with charge, mass,

delta m/z, score, p-value, position and number of missed cleavages for the precursor ion

shown in PhenyxOnline. B. Raw MS/MS spectrum. C. Collision spectrum with fragment

denomination. D. Sequence coverage, intensity and delta m/z for product ions as shown in

PhenyxOnline.

Appendix 1

149




showed in PhenyxOnline. B. Raw MS/MS spectrum. C. Collision spectrum with fragment


PhenyxOnline.


150




showed in PhenyxOnline. B. Raw MS/MS spectrum. C. Collision spectrum with fragment


PhenyxOnline.

Appendix 1

151

S.6. PCA plot of 6 discriminatory peaks (T0 vs. T30) selected by the NSC algorithm (C++

workflow) in the delayed storage study (proteomic analysis by chipLC-MS). The groups

are clearly separated from each other showing that the profiles are highly discriminatory.


152

S.7. PCA plot of 31 discriminatory peaks (T0 vs. T120) selected by the NSC algorithm

(C++ workflow) in the delayed storage study (proteomic analysis by chipLC-MS). The

groups are clearly separated from each other showing that the profiles are highly

discriminatory.

Appendix 1

153

S.8. PCA plot of 172 discriminatory peaks (T30 vs. T120) selected by the NSC algorithm

(C++ workflow) in the delayed storage study (proteomic analysis by chipLC-MS). The

groups are clearly separated from each other showing that the profiles are highly

discriminatory.

Figure S.9. The sequences that are colored highlight parts of porcine transthyretin

(TTY_PIG, P50390) that have been assigned to discriminatory peptides (chipLC-MS

analyses: bold, grey) in the freeze/thaw study.


154

Figure S.10. Identification information for TTY_PIG peptide (m/z: 464.266) discovered by

chipLC-MS/MS in the freeze/thaw study. A. Peptide sequence with charge, mass, delta

m/z, score, p-value, position and number of missed cleavages for the precursor ion showed

in PhenyxOnline. B. Raw MS/MS spectrum. C. Collision spectrum with fragment


PhenyxOnline.

Appendix 1

155






PhenyxOnline.


156






PhenyxOnline.

Appendix 1

157






PhenyxOnline.


158

S.14. PCA plot of 41 discriminatory peaks (control vs. 10x) selected by the NSC algorithm

(C++ workflow) in the freeze/thaw study (proteomic analysis by chipLC-MS). The groups

are clearly separated from each other showing that class separation based on these peaks is

possible.

Appendix 1

159

Figure S.15. Amino acid sequence of porcine transthyretin (PTGDS_PIG, Q29095)

and serotransferrin (TRFE_PIG, P09571). The sequence parts in bold letters are the

sequences covered by the discriminatory peptides found by chipLC-MS upon

storage of trypsin-digested CSF at 4 °C.


160


by chipLC-MS/MS in the 4 °C study. A. Peptide sequence with charge, mass, delta m/z,

score, p-value, position and number of missed cleavages for the precursor ion showed in

PhenyxOnline. B. Raw MS/MS spectrum. C. Collision spectrum with fragment


PhenyxOnline.

Appendix 1

161

Figure S.17. Identification information for TRFE_PIG peptide (m/z: 681.858) discovered

by chipLC-MS/MS in the 4 °C study. A. Peptide sequence with charge, mass, delta m/z,

score, p-value, position and number of missed cleavages for the precursor ion showed in

PhenyxOnline. B. Raw MS/MS spectrum. C. Collision spectrum with fragment


PhenyxOnline.


162

Figure S.18. PCA plot of 45 discriminatory peaks (control vs. 1 month) selected by the

NSC algorithm (C++ workfklow) in the 4 °C storage study (trypsin-digested CSF samples

stored at 4 °C, proteomic analysis by chipLC-MS). Class separation is noticeable although

not as pronounced as for the freeze-thaw or the delayed storage studies.

Appendix 1

163

Figure S.19. MALDI-FT-ICR-MS spectra of depleted, trypsin-digested, porcine CSF with

delay times of 0, 30 or 120 min at room temperature (T0, T30, T120) before snap freezing

and storage at -80 °C.

Figure S.20. Orbitrap MSMS fragments (b- and y- series) from ion detected as

discriminatory in the MALDI-FT-ICR-MS analysis and identified as a PTGDS peptide

(see Figure S.2. for full PTGDS sequence).


164

Figure S.21. Orbitrap MSMS spectra from the ion detected as discriminatory upon

delayed storage in the MALDI-FT-ICR-MS analysis and identified as a PTGDS peptide

(see Figure S.2. for full PTGDS sequence).

Figure S.22. Identification results from Orbitrap analysis of ion m/z: 1350.705 detected as

discriminatory between the sample groups in the delayed storage study by MALDI-FT-

ICR-MS.

Appendix 1

165

Figure S.23. List of discriminatory metabolites identified in the delayed storage study by

GC-MS. The metabolites are sorted with the lowest p-values (T0 vs. T120) on the top.


166

Figure S.24. PCA plot of all 79 detected peaks (0 vs. 30 vs. 120) in the delayed storage

study (metabolomic analysis by GC-MS). The 0 and 120 min-clusters are clearly separated

from each other, with the 30 min cluster overlapping both the 0 and 120 min-clusters. This

can also be seen in the box-plots (Figure 3), where the 30 min-group shows a larger

standard deviation than the 0 and 120 min-groups.

Appendix 1

167

Figure S.25. PCA plot of 9 discriminatory peaks (0 vs. 30 vs. 120) in the delayed storage

study (amino acid analysis by LC-MS). The 0 and 120 min-clusters are clearly separated

from each other, with the 30 min cluster overlapping with both the 0 and 120 min-clusters.

This can also be seen in the box-plots (Figure 2), where the 30 min-group shows a larger

standard deviation than the 0 and 120 min-groups.

Supplementary material chapter 4 Appendix 2

168

Appendix 2


Table S.1. Identification information for the discriminatory proteins detected by the QTOF method

AC

nr.

ID

# p

ep

tid

es

Seq

ue

nce

co

vera

ge

(%)

Pep

tid

e

seq

ue

nce

m/z

Ch

arg

e

RT

(m

in)

Ph

en

yx

sco

re

P36953 Afamin 3 6 IANDAIQDMLCDMK 819,372 2 62,6 11,90

AAPQLPMEELVSLSK 806,936 2 66,3 10,69

FLVNLVK 416,769 2 53,9 6,13

P14046 Alpha 1 inhibitor 3 8 9 NLHPLNELFPLAYIEDPK 708,377 3 73,9 11,59

LTAQPAPSPEDLALSMGTIK 1020,534 2 61,6 14,29

GDPIPNEQVLIK 661,872 2 48,6 8,74

YNVPLEK 431,736 2 36,8 6,56

ALMAYAFALAGNQEK 533,275 3 62,1 10,87

ISLCHGNPTFSSETK 559,934 3 39,9 9,47

QQNSYGGFSSTQDTVVALDALSK 806,057 3 59,4 16,34

LVDIKGDPIPNEQVLIK 631,033 3 55,3 10,84

P02764 Alpha 1 acid glycoprotein 2 8 IFAHLIVLK 351,900 3 55,4 9,19

GLSFYAK 393,216 2 40,9 6,12

Q63041 Alpha 1 macroglobulin 8 7 GVVSFPIR 437,757 2 50,0 6,60

LSPQSIYNLLPQK 500,953 3 62,3 12,45

DTVVKPVIVEPEGIEK 584,667 3 48,4 12,95


169

LADLPGNYITK 602,830 2 47,4 8,15

QLNYQHSDGSYSTFGDR 658,955 3 39,6 10,00

QDLNDNDAYSVFQSIGLK 676,329 3 65,3 10,83

AEQGAYLGPLPYK 703,866 2 49,3 7,51

YVVLVPSELYAGVPEK 881,986 2 63,0 10,15

P24090 Alpha 2 HS glycoprotein 6 25 CPILIR 386,232 2 38,7 7,79

APQGAGLGFR 487,269 2 37,5 8,15

LGGEEVSVACK 574,785 2 33,1 9,46

VGQPGDAGAAGPVAPLCPGR 616,315 3 42,6 14,34

HAFSPVASVESASGEVLHSPK 712,696 3 54,9 16,34

ELACDDPETEHVALIAVDYLNK 839,073 3 62,7 11,02

P07151 Beta 2 microglobulin 2 19 TPQIQVYSR 546,296 2 37,1 9,92

KIPNIEMSDLSFSK 804,909 2 55,6 12,02

P13635 Ceruloplasmin 7 9 PYTFHAHGVTYTK 381,197 4 36,5 8,22

DCNKPSPDDDIQDR 558,905 3 28,0 12,66

DIFTGLIGPMK 596,324 2 64,8 8,22

ADDKLFPGQQYLYVLR 642,673 3 65,1 13,66

DIASGLIGPLILCK 735,417 2 69,2 10,58

EMGPTYADPVCLSK 784,360 2 47,1 10,69

ANEPSPGEGDSNCVTR 845,358 2 27,9 11,98

P01026 Complement C3 10 8 DHVLGLAR 294,174 3 34,3 7,78

KGYTQQLAFK 395,218 3 38,5 7,23

GKGQGTLSVVTVYHAK 411,985 4 40,5 7,88

TVLTGATGHLNR 413,899 3 34,8 10,13

VELKPGDNLNVNFHLR 467,007 4 52,3 7,80

LEEPYLTK 496,770 2 39,1 5,13

LITQGESCLK 574,803 2 34,6 7,07

VHQFFNVGLIQPGSVK 590,660 3 59,3 11,05


170

SGIPIVTSPYQIHFTK 596,661 3 58,6 9,40

SDVDEDIIPEEDIISR 922,941 2 55,8 11,68

Q9QX79 Fetuin B 4 20 LVVLPFPGK 485,309 2 57,6 6,85

DGYMLTLNR 541,770 2 47,5 8,72

KIHSMCPDCPHPVDLSAPSVLEAATESLAK 652,925 5 64,7 9,42

IHSMCPDCPHPVDLSAPSVLEAATESLAK 783,874 4 66,5 12,67

P06866 Haptoglobin 4 14 GAVSPVGVQPILNK 460,267 3 48,3 8,16

GSFPWQAK 460,737 2 46,5 5,68

ATDLKDWVQETMAK 545,940 3 58,4 11,15

DIAPTLTLYVGK 645,873 2 56,9 8,88

P20059 Hemopexin 12 35 GPDSVFLIK 488,279 2 52,0 6,05

WKNPVTSVDAAFR 497,597 3 53,8 11,39

GATYAFSGSHYWR 501,567 3 47,9 10,84

VDGALCLEK 502,759 2 37,6 6,82

YYCFQGNK 540,237 2 34,3 7,82

CNADPGLSALLSDHR 542,596 3 51,9 13,73

WFWDFATR 564,767 2 69,9 8,76

RLWWLDLK 565,329 2 65,4 6,95

SGAQATWAELSWPHEK 599,956 3 55,5 11,29

GECQSEGVLFFQGNRK 619,295 3 49,0 8,55

ELGSPPGISLDTIDAAFSCPGSSK 802,723 3 66,9 17,79

LFQEEFPGIPYPPDAAVECHR 824,726 3 62,6 14,39

P20760 IgG 2A chain C 4 14 SVSELPIVHR 379,553 3 41,2 6,42

TKDVLTITLTPK 443,939 3 50,4 9,85

DVLTITLTPK 550,831 2 54,0 8,94

VNSGAFPAPIEK 615,330 2 42,4 9,64

P00697 Lysozyme C1 4 38 DLSGYIR 412,221 2 41,0 6,19

AWVAWQR 458,744 2 48,0 6,08


171

NYNPGDQSTDYGIFQINSR 730,336 3 54,0 15,97

NACGIPCSALLQDDITQAIQCAK 849,741 3 66,0 12,50

Q03626 Murinoglobulin 1 10 8 VLIVEPEGIKK 408,925 3 43,2 7,18

NLQPAIVK 441,774 2 36,3 6,37

QQPAFALK 451,760 2 39,8 6,24

SSGSLFNNAMK 578,281 2 43,0 10,31

DMGLTAFTNLK 605,811 2 58,0 9,82

YMVLVPSQLYTETPEK 633,326 3 60,4 11,63

VTASPQSLCGLR 644,837 2 41,9 10,38

VQTVPLTCNNPK 685,858 2 35,0 10,97

QSGVKEEHSFTVMEFVLPR 555,784 4 62,4 9,15

QLSFSLSAEPIQGPYK 882,960 2 58,8 12,13

Q6IE52 Murinoglobulin 2 3 3 MLIYTILPDGEVIADSVK 659,693 3 67,2 12,44

HAEAHHTAYAVYSLSK 446,974 4 36,2 10,46

GEAFTLK 383,211 2 37,8 5,14

P09006 Serine protease inh. A3N 4 13 GFGHLLQR 309,845 3 40,7 8,53

MQQVEASLQPETLR 543,947 3 45,7 9,53

AVLDVAETGTEAAAATGVK 591,979 3 50,0 15,76

DSLRPSMIDELYLPK 592,983 3 60,5 12,54

P01048 T-Kininogen 1 6 17 KDGAETLYSFK 420,217 3 42,0 8,34

SAHSQVVAGMNYK 464,563 3 33,0 11,58

LLNSCEYK 513,751 2 31,0 5,82

DGAETLYSFK 565,773 2 46,0 8,40

FSVATQICNITPGKGPK 606,658 3 49,0 11,51

YNAELESGNQFVLYR 901,941 2 54,0 11,86

P08932 T-kininogen 2 7 29 SAHSQVVAGMNYK 464,564 3 33,3 11,58


172

DGAETLYSFK 565,783 2 46,1 8,40

FSVATQICNITPGKGPK 606,660 3 49,5 11,51

HLGQSLNCNANVYMRPWENK 608,547 4 50,2 9,67

KTEEDLCVGCFQPIPMDSSDLKPVLK 752,371 4 61,3 14,25

FSVATQICNITPGK 768,395 2 51,2 11,25

TEEDLCVGCFQPIPMDSSDLKPVLK 960,131 3 63,4 12,21

P22057 Prostaglandin D-synthase 4 22 HDTVQPNFQQDK 486,233 3 31,3 10,24

MATLYSR 421,216 2 33,2 7,5

DQGLTEEDIVFLPQPDK 972,486 2 60,8 13,89

FITFSK 371,712 2 42 5,7

P04276 Vitamin D binding protein 6 19 QLTSFIEK 483,265 2 44,1 7,92

VCSQYAAYGK 573,766 2 29,8 6,73

KFPSSTFEQVSQLVK 575,644 3 58,3 11,25

SCESDAPFPVHPGTSECCTK 755,978 3 40,6 12,12

MPNASPEELADMVAK 801,878 2 56,8 13,39

VPTANLEDVLPLAEDLTEILSR 803,435 3 93,5 15,08

Appendix 2

173

List of publications Articles

Rosenling, T., Slim, C.L., Christin. C., Coulier, L., Shi, S., Stoop, M.P., Bosman, J.,

Suits. F., Horvatovich, P.L., Stockhofe-Zurweiden, N., Vreeken, R., Hankemeier,

T., van Gool, A.J., Luider, T.M., Bischoff, R. The effect of pre-analytical factors on

stability of the proteome and selected metabolites in cerebrospinal fluid (CSF). J

Proteome Res. 2009 Dec;8(12):5511-22.

Stoop, M.P., Coulier, L., Rosenling, T., Shi, S., Smolinska, A.M., Buydens, L.,

Ampt, K., Stingl, C., Dane, A., Muilwijk, B., Luitwieler, R.L., Sillevis Smitt, P.A.,

Hintzen, R.Q., Bischoff, R., Wijmenga, S.S., Hankemeier, T., van Gool, A.J., Luider,

T.M. Quantitative proteomics and metabolomics analysis of normal human

cerebrospinal fluid samples. Mol Cell Proteomics. 2010 Sep;9(9):2063-75.

Oral presentations

Rosenling, T., Professional use of CSF for proteomic/metabolomic biomarker

Search in CNS disorders. TI Pharma spring meeting, Utrecht, The Netherlands,

April 22, 2008

Rosenling, T. Search of proteomic biomarkers in CSF related to Multiple

Scleroseis with the use of a rat EAE model. Annual Meeting of the NWO/CW

Studiegroep Analytische Scheikunde, Lunteren, The Netherlands, November 2-3,

2009

Poster presentations

Rosenling, T., Stoop, M.P., Attali, A., Christin, C., Suits, F., Horvatovich, P.,

Tuinstra, T., Luider, T.M., Bischoff, R. Proteins in cerebrospinal fluid (CSF) as

biomarker candidates for experimental autoimmune encephalomyelitis (EAE) in

rats. ISPPP conference, Bologna, Italy, September 4-8, 2010

Rosenling, T., Tuinstra, T., Attali, A., Luider, T., Bischoff, R. Search for multiple

sclerosis related proteomic biomarkers in the CSF of the rat EAE model. EuPa

conference, Stockholm, Sweden, June 15-17, 2009

http://www.ncbi.nlm.nih.gov/pubmed/20811074



Appendix 2

174

Rosenling, T., Slim, C., Bosman, J., Christin, C., Gool, A. van, Horvatovich, P.,

Bischoff, R. Protein stability in cerebrospinal fluid (CSF): Analysis of the proteome

profile by chip-LC-MS. Keystone Symposia: Multiple Sclerosis, Santa Fe, New

Mexico, USA, January 22-25, 2009

Rosenling, T., Slim, C., Bosman, J., Christin, C., Gool, A. van,

Horvatovich, P., Bischoff, R. Protein stability in cerebrospinal fluid (CSF): Analysis

of the proteome profile by chip-LC-MS. Keystone Symposia: Multiple Sclerosis,

Santa Fe, New Mexico, USA, January 22-25, 2009

Awards

Poster prize winner at the TI Pharma spring meeting 2010, Utrecht, The

Netherlands. (Travel grant of € 2500 to visit the FIP Pharmaceutical Sciences

World Conference in New Orleans, LA, USA)

Appendix 2

175

Acknowledgements Looking back to the last four years, I have lots of great memories, moments of joy

but also moments of hard work. These memories were created together with the

people around me. I have not made the journey alone, I did it togetherwith my

friends, family and colleagues. I would herewith like to express my

acknowledgements to those.

To start with, I would like to thank Prof. dr. Rainer Bischoff, my promotor and

supervisor, for guiding me into becoming a researcher. You always stayed

positive, enthousiastic and full of ideas, and in this way, inspiring me. Thank you

also, for showing me appreciation for my work.

Thanks also to Prof. dr. Sabeth Verpoorte for being a good support foremost

during my first years. Especially I would like to thank you for accepting me as a

student in your group in 2006 and in this way opening the way to my new life in

Groningen. I also would like to thank you for being part of the reading committee.

Next, I would like to thank Prof. dr. Peter Bergsten, you were the driving force

behind getting me to Groningen in the first place. I am very greatful for the fact

that you took me under your wings in the beginning of 2006. You inspired me a

lot with your open and positive attitude.

Thanks to Prof. dr. Sybren Wijmenga and Prof. dr. Jan Maarten van Dijl, who were

also part of the reading committee, for taking the time to evaluating my thesis.

Thanks to the people in the TIPharma consortium. Dr. Alain van Gool, thank you

for being a great project leader, encouraging me and giving me appreciation, Dr.

Theo Luider, you were also a good project leader, keeping a positive attitude. Also

thanks to all the other colleagues in the TIP consortium; Dr. Robert-Jan Lamers,

Shanna Shi, Agnieszka Smolinska, Dr. Marek Noga, Dr. Macel Stoop, Dr. Amos

Attali, Dr, Lionel Blanchet, Dr. Leon Coulier, Dr. Tinka Tuinstra, Prof. dr. Thomas

Hankemeier, Prof. dr. Sybren Wijmenga and Prof. dr. Lutgarde Buydens, you

were all great collaborators and we had many good discussions together.

Thanks to all colleagues at the Analytical Biochemistry group. I have spent many

hours together with you and I have enjoyed your company. I feel very blessed to

have had such great people around me at my work, I always felt at home in the

group, everyone have been so helpful and friendly, and there was no competition

Appendix 2

176

between us. Laurette and Lorenza, my office mates and friends, thanks for all fun

and the support, keep up the spirit, soon it’s your time! Thanks Christin and Peter

for your help with the dataprocessing and for being great friends. Nicolas,

Krisztina, Jos, Jan and Natalia, thanks for the help with work in the lab. Eslam,

Julien, Ishtiaq, and Vikram thank you for the nice lunches, and friendship. Thank

you Andries, Hjalmar, Margot and Annie for help with mass spectrometry-related

issues. Thank you to my super-students; Christiaan and Balaji, I really enjoyed to

be your supervisor. I am also happy to have learned to know the new members of

the group; Karin, Hanna and Sara, you are great as well. Jolanda, thank you for all

help with everything between heaven and earth. Also thanks to Theo and Ramses,

my office-mates during my first year.

I would like to thank all members of the Pharmaceutical Analysis group; Byeong-

Ui, L-J, Paul, Patty, Harm, Laurent, Corry, Maurits and Heiko, you also helped to

spread the good feeling at work and I enjoyed chatting with you in the corridor or

at the borrels, Maria; you are a happiness injection that spread good feelings,

thanks for being a good friend.

Thanks to my paranymphs. Abdou, my homie, you became my best friend from

day one, and you still are. Berend, thanks for your help with all the computer

issues and for being my friend. Thanks both of you for joining me as paranymphs,

by my side at the defence.

Thank you also Arnold, Alice, Wiktor, Yuli and Valentina, you meant a lot to me

during my time in Groningen, you are great.

Jimmy, tack för att du flyttade med mig till Holland och tack för tiden vi hade

tillsammans. Tack också till mina vänner i Sverige, för allt kul vi haft tillsammans

på somrar och jullov; Björn, Jonas, Jenny & Joel, Jenkan och Ebbe.

Bedankt naar de Voortman familie voor alle steun en interesse dat jullie heb gehad

voor mijn werk, dank jullie wel voor nemen me in als een deel van jullie familie. Ik

voel me t’huis bij jullie. Bedankt ook Theo, Meine en Patrick voor leuke tijden in

de achterhoek.

Tack till alla släktingar som visat intresse för mitt liv i Holland och mina

komlicerade experiment, för att nämna några, Anne-Maj, Gunna och Åke, Karl-

Erik och Ulla, Maggan och Tony, Maja, Gun-Britt och Jan, tack.

Appendix 2

177

Kära mor och far, under min tid i Groningen, har vi varit långt från varandra men

ni finns alltid i mitt hjärta och jag tänker på er. Jag är glad att jag har en så fin

familj. Tack för att jag har fått en uppväxt som gjort mig till en stark och

självständig människa, och tack för att ni har gett mig friheten att tänka själv och

välja min egen väg, jag har aldrig känt mig tvingad in i något. Tack för att ni gjort

långa resor hit för att hälsa på mig. Och tack för att ni alltid funnits där för mig i

goda som svåra tider. Man kan alltid räkna med er. Tobias, Beatrice och Robert, ni

är de bästa syskonen man kan ha, tack för fina stunder tillsammans, tack för era

besök och tack för att ni finns till. Tack till hela familjen för all kärlek och omtanke.

Woutis, my dear, I am very greatful that you came my way and I am very happy

to share my life with you. You have been a great support in good and bad times,

you give me so much love and appreciation, I know it was not always easy to

handle all the stress my work brought, especially the last years. I will make it up

to you. I really appreciate all you have done for me. Together with you I can look

into the future. I hope we will stay together for the rest of out lives. Jag älskar dig!

Concludingly I would like to thank everyone that crossed my road in one or

another way during the last four years, no one is forgotten you made a difference

and you are all appreciated.

proteomic screening of cerebrospinal fluid: candidate ... · pdf fileproteomic screening of...

Documents