ACTAUNIVERSITATIS

UPSALIENSISUPPSALA

2016

Digital Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Science and Technology 1347

Mass Spectrometry-basedNeuroproteomics

Deciphering the Human Brain Proteome

SRAVANI MUSUNURI

ISSN 1651-6214ISBN 978-91-554-9485-8urn:nbn:se:uu:diva-277613

Dissertation presented at Uppsala University to be publicly examined in B42, BMC,Husargatan 3, Uppsala, Friday, 8 April 2016 at 10:15 for the degree of Doctor of Philosophy.The examination will be conducted in English. Faculty examiner: Professor Jean-CharlesSanchez (University of Geneva).

AbstractMusunuri, S. 2016. Mass Spectrometry-based Neuroproteomics. Deciphering the HumanBrain Proteome. Digital Comprehensive Summaries of Uppsala Dissertations from theFaculty of Science and Technology 1347. 70 pp. Uppsala: Acta Universitatis Upsaliensis.ISBN 978-91-554-9485-8.

Mammalian brain is challenging to study due to its heterogeneity and complexity. However,recent advances in molecular imaging, genomics and proteomics have contributed significantlyto achieve insights into molecular basis of brain function and pathogenesis of neurologicaldisorders. Efficient sample preparation is an integral part of a successful mass spectrometry(MS)-based proteomics. Apart from the identification, quantification of proteins is needed toinvestigate the alterations between proteome profiles from different sample sets. Therefore,this thesis investigates optimizing and application of the MS compatible sample preparationtechniques for the identification and quantification of proteins from brain tissue.

The central objective of this thesis was (i) to improve the extraction of proteins as well asmembrane proteins (MPs) from the brain tissue and (ii) to apply the optimized method alongwith the stable isotope dimethyl labeling (DML) and label-free (LF) MS approaches for therelative quantification of the brain proteome profiles during neurological conditions such asAlzheimer’s disease (AD) and traumatic brain injury (TBI). First study described in this thesisis focused on the qualitative aspects for the brain tissue sample preparation. The optimizedextraction buffers from first study containing n-octyl-β-glucopyranside or triton X-114 wereused in the further quantitative studies to extract the proteins from patient (AD or TBI) andcontrol human brain samples. Triton X-114 has additional advantage of separating MPs into amicellar phase. Therefore we also investigated the possibility to apply this in combination withDML quantitation approach for enrichment of low abundant MPs from AD brains.

AD and TBI causes severe socio-economic burden on the society and therefore there isa need to develop diagnostic markers to detect the early changes in the pathology of thedisease. Analytical tools and techniques applied and discussed in this thesis for neuroproteomicsapplications proved to be powerful and reliable for analyzing complex biological samples togenerate high-throughput screening and unbiased identification and quantitation of disease-specific proteins that are of great importance in understanding the disease pathology.

Keywords: Brain, Proteomics, Mass spectrometry, Alzheimer's disease, Traumatic braininjury, Membrane proteins, Sample preparation

Sravani Musunuri, , Department of Chemistry - BMC, Analytical Chemistry, Box 599,Uppsala University, SE-75124 Uppsala, Sweden.

© Sravani Musunuri 2016

ISSN 1651-6214ISBN 978-91-554-9485-8urn:nbn:se:uu:diva-277613 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-277613)

One who is always focused and devoted achieves knowledge. After attaining knowledge one attains peace.

Krishna, The Bhagavad Gita

To my family

List of Papers

This thesis is based on the following papers, which are referred to in the text by their Roman numerals.

I Shevchenko, G., Musunuri, S., Wetterhall, M., Bergquist, J. (2012) Comparison of Extraction Methods for the Comprehen-sive Analysis of Mouse Brain Proteome using Shotgun-based Mass Spectrometry. Journal of Proteome Research, 11(4): 2441-2451

II Musunuri, S., Wetterhall, M., Ingelsson, M., Lannfelt, L., Ar-temenko, K., Bergquist, J., Kultima, K., Shevchenko, G. (2014) Quantification of brain proteome in Alzheimer’s dis-ease using multiplexed mass spectrometry. Journal of Proteo-me Research, 13(4): 2056–2068

III Musunuri, S., Kultima, K., Richard, B., Ingelsson, M., Lannfelt, L., Bergquist, J., Shevchenko, G. (2015) Micellar ex-traction possesses a new advantage for the analysis of Alzhei-mer's disease brain proteome. Analytical and Bioanalytical Chemistry, 407(4): 1041-1057

IV Musunuri, S*., Khoonsari, P*., Mikus, M., Wetterhall, M., Häggmark, A., Lannfelt, L., Erlandsson, A., Bergquist, J., In-gelsson, M., Shevchenko, G., Nilsson, P., Kultima, K. In-creased levels of extracellular microvesicle markers and de-creased levels of exo-endocytic proteins in the Alzheimer’s disease brain. Submitted

V Musunuri, S., Hamdeh, S., Mi, J., Alafuzoff, I., Marklund, N., Bergquist, J., Shevchenko, G. Differential proteomic analysis for the evaluation of protein expression in fresh human cortical brain biopsies. Manuscript

*These authors contributed equally to the work

Reprints were made with permission from the respective publishers.

Papers not included in this thesis

I Musunuri, S., Shevchenko, G., Bergquist, J. (2012). Neuro-proteomic profiling of human brain tissue using multidimen-sional separation techniques and selective enrichment of membrane proteins. Electrophoresis, 33(24): 3779-3785

II Shevchenko, G., Konzer, A., Musunuri, S., Bergquist, J. (2015). Neuroproteomics Tools in Clinical Practice. Bio-chimica et Biophysica Acta - Proteins and Proteomics, 1854(7): 705-717

III Warnecke, A., Musunuri, S., Sandalova, T., Achour, A., Bergquist, J., Harris, A, R. Nitration of MOG at tyrosine 40 denies epitope presentation via H2-IAb and diminishes en-cephalitogenicity in an MHC-dependent manner. Submitted

Contents 

1. Introduction ............................................................................................... 11 

2. Neuroproteomics of brain tissue ............................................................... 14 

3. Neurological diseases and conditions ....................................................... 16 

4. Membrane proteins ................................................................................... 17 

5. Sample preparation ................................................................................... 19 5.1. Brain tissue collection and storage .................................................... 19 5.2. Additives in the lysis buffer for solubilizing MPs ............................. 20 5.3. Extraction .......................................................................................... 21 

5.3.1. Cloud Point Extraction .............................................................. 22 5.4. Protein purification/ Contaminant Removal ...................................... 23 5.5. Reduction, Alkylation and Digestion ................................................ 24 

5.5.1. On-filter digestion ...................................................................... 25 

6. Separation techniques ............................................................................... 27 6.1. Gel Electrophoresis ........................................................................... 27 6.2. Chromatography ................................................................................ 28 

7. Mass spectrometry .................................................................................... 30 7.1. Electrospray ionization ...................................................................... 30 7.2. Linear ion trap-Fourier transform ion cyclotron resonance mass spectrometry (LTQ-FTICR-MS) .............................................................. 31 7.3. Data Analysis .................................................................................... 32 

8. Mass spectrometry-based quantitative proteomics ................................... 34 8.1. Absolute vs. Relative quantitation ..................................................... 34 8.2. Label free quantification ................................................................... 35 8.3. Stable isotope labeling approaches for quantitation .......................... 36 

9. Validation by antibody-based assays ........................................................ 38 9.1. Western blot ...................................................................................... 38 9.2. Luminex ............................................................................................ 39 

10. Papers I-V ............................................................................................... 41 10.1. Aims ................................................................................................ 41 10.2. Brain tissue biopsies ........................................................................ 41 10.3. General workflow Papers I-V .......................................................... 42 10.4. Results and Discussion .................................................................... 43 10.5. Summary of Papers II-V.................................................................. 46 

11. Conclusions and future work .................................................................. 50 

12. Svensk Sammanfattning .......................................................................... 52 

13. Acknowledgments ................................................................................... 55 

14. References ............................................................................................... 59 

15. Appendix ................................................................................................. 64 

Abbreviations

AD Alzheimer’s disease

CID Collision-induced dissociation

DML Dimethyl labeling

ESI Electrospray ionization

FASP Filter-aided sample preparation

FT Fourier transform

ICR Ion cyclotron resonance

ICAT Isotope-coded affinity tag

ICPL Isotope-coded protein labeling

IEF Isoelectric focusing

IMPs Integral membrane proteins

iTRAQ Isotope tags for relative and absolute quantification

LC Liquid chromatography

LTQ Linear ion trap

MS Mass spectrometryMPs Membrane proteins NFT Neurofibrillary tanglesPMPs Pheriperal membrane proteinsPCR Polymerase chain reactionSDS-PAGE Sodium dodecyl sulfate polyacrylamide gelsSP Senile plaquesSILAC Stable isotope labeling with amino acids in cell culture TBI Traumatic brain injuryTMD Transmembrane domain2DE Two-dimensional gel electrophoresis

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1. Introduction

The completion of Human Genome Project in April 2003, have produced complete sequenced genomes (99%) within the limits of today’s technology and paved way for the Human Proteome Project (HPP). HPP aims to gener-ate the map of entire human proteins from the 20,300 protein-coding genes and characterize them to help elucidate biological and molecular functions and advance diagnostic treatment of diseases by using three experimental strategies: mass spectrometry, antibody capture and bioinformatics tools and knowledge bases1.

The word “proteome” was coined for the first time by Marc R. Wilkins in 1994 and derived from PROTEins expressed by a genOME2. The proteome refers to all the proteins produced by an organism, much like the genome is the entire set of genes. The genome is a static blueprint but the proteome is dynamic in nature, changing constantly in response to thousands of intra- and extracellular factors such as disease conditions, drugs, stress, develop-mental cue and environmental factors3.

Proteomics is the study of an organism’s complete complement of pro-teins expressed by the generic material of an organism. Currently, there has been a growing interest in applying proteomics to the clinical diagnosis of the disease. The aims of clinical proteomics are: to identify the absolute or relative changes in the protein levels that are likely to reflect the effects of the disease, to improve the accuracy of clinical diagnosis by identifying the biomarkers that are associated with pathophysiological mechanism, and to improve the drug therapy by discovering novel proteins which can act as drug targets4. Therefore, comprehensive proteome profiling is beneficial to identify and develop new prognostic and diagnostic biomarkers, to target drugs for therapeutic purpose.

Recently, several high-resolution separation techniques with state-of-art identification methodologies such as mass spectrometry and antibody-based approaches have been developed for proteomic applications. MS is common-ly used to identify intact proteins or peptide mixtures from biological sam-ples. Two types of approaches namely ‘bottom-up’ and ‘top-down’, are used in proteomics (Figure 1). In the bottom-up approach, proteins are converted into peptides by enzymatic or chemical digestion and analysis is done by using MS or tandem MS. Top-down approach studies intact proteins or large protein fragments by direct analysis with MS and MS/MS. Recently, anoth-er proteomic approach ‘middle-down’ has been proposed that could combine

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the benefits of top-down and bottom-up. The middle-down approach can be used to generate the peptides of larger length compared to the traditional trypsin digestion5. Top-down approach is not global like the bottom-up since it is mostly used for single protein analysis. Hence, the majority of proteo-mic studies undertake bottom-up approach which serves as a high through-put platform6.

Figure 1. Schematic representation of bottom-up and top-down approaches in prote-omics workflow

Global proteomics is a highly complex and challenging task due to large heterogeneity of proteins and lack of amplification techniques similar to polymerase chain reaction (PCR). Hence, the analysis of low abundant pro-teins, which usually serve as biomarker candidates, has become a very diffi-cult and demanding task. However, recent developments in the sample prep-aration techniques and advancement in the high resolution mass spectrome-ters made significant contributions for the comprehensive analysis of the proteome from complex biological samples.

The papers included in this thesis present results from the development of methods for the extraction of proteins from brain tissue, along with the in-vestigation of dimethyl labelling (DML) and label-free (LF) approaches for relative quantification of brain proteomes in disease vs. healthy individuals. Paper I describes the various extraction protocols, to investigate an opti-mized sample preparation method for brain tissue. Papers II-V utilizes the optimized extraction method from Paper I in combination with the stable-isotope labelling and label-free quantitation approach to compare changes in the protein expression profile of human brain samples taken from disease vs control patients. The papers included in the thesis contain brain tissue sam-ples from mouse (Paper I) and humans (Papers II-V). Papers II-IV deals

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with the Alzheimer’s disease (AD) brain samples and Paper V utilizes the traumatic brain injury (TBI) patient brain biopsies.

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2. Neuroproteomics of brain tissue

The mysteries of the nervous system can be unravelled by the application of the proteomic technology to neuroscience. Neuroproteomics is the branch of proteomics that focuses on the qualitative and quantitative aspects of tis-sue/organelle proteomes of the nervous system. The brain is the most fasci-nating, highly complex and mysterious organ of the nervous system, with a high degree of computation capability, enabling execution of wide spectrum of body activities, psychological processes and behavioral aspects. The hu-man brain is comprised of 60% fat, most of it localized in myelin. 25% of the total amount of cholesterol in body is present in brain, making it the fat-tiest organ in the body7, 8. Brain is only 2% of the body mass, but consumes 20% of the oxygen we breath and energy produced during resting state of the body9. Most of the energy is used to reverse the ion influxes that trigger syn-aptic and action potentials10.

The core component of the central nervous system (CNS), especially brain, is the neuron or nerve cell. Most of the activities controlled by brain are based on integrative neurotransmission among neural cells. It is estimat-ed that an average human brain has about 86 billion neurons and many more glial cells that support and protect the neurons. The most common glial cells are oligodendrocytes, astrocytes, microglia and ependymal cells. A single neuron is connected to several neurons junctions called as synapses and communicate by the release of chemicals called neurotransmitters from the axon terminal of the presynaptic cell into the synaptic cleft. Neurotransmit-ters are stored in uniformly sized organelles of 40-50 nm diameter called as synaptic vesicles. An action potential opens the voltage-gated Ca2+ channels, permitting an influx of Ca2+ ions into the cytosol. A rise in Ca2+ level in cytosol region near the synaptic vesicles triggers exocytosis to release neuro-transmitter into the synaptic cleft. The vesicles are recycled by endocytosis and the released neurotransmitters from synaptic cleft are removed by enzy-matic degradation, re-uptake into the presynaptic cell, or diffusion.

Proteomic analysis of the brain tissue has become an integral part of the neuroscience research providing new insights into the biological structures and functions of synapses, axons and dendrites. The field of brain prote-omics is challenging due to huge level of heterogeneity with complex cellu-lar and subcellular architecture of the brain and limited sample amounts. More than 1,000 disorders are associated with abnormalities in the neuronal activity and cellular dysfunctions, such as neurodegenerative diseases, psy-

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chiatric disorders, mental retardation and addiction11, 12. Many neurological and neuropsychiatric diseases are more complex, multigenic and multifacto-rial in nature that comprises interplay of many proteins, unlike inborn meta-bolic disorder which involves alterations in structure and function of a single protein. Therefore rather than focusing on a single protein, it will be benefi-cial to study the neuronal activity dependent alteration of global protein ex-pression. Neurodegenerative disorders such as Alzheimer’s disease, Parkin-son’s disease, amyotrophic lateral sclerosis, Huntington’s disease, Prion disease etc., are characterized by neuronal vulnerability with degeneration in the brain, and abnormal protein misfolding and aggregation.

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3. Neurological diseases and conditions

Alzheimer’s disease affects 20 million people worldwide and these numbers are estimated to be tripled by 205013. Therefore it is important to have a di-agnostic test that will detect AD before it causes irreversible damage. AD is a neurodegenerative disorder characterized clinically by progressive memory loss and pathologically by the presence of neurofibrillary tangles (NFTs) and senile plaques (SPs). The full etiopathology of AD still remains unclear and it poses an immense socio-medical burden. Several hypotheses claim amy-loid-β (Aβ), tau and oxygen free radicals as possible neurotoxic candidates in the pathology of SPs and NFTs; however their role as contributors to cog-nitive deficits in AD is not clear. Papers II-IV compares the proteome pro-file differences between the AD vs control brain tissues.

Traumatic brain injury is a form of brain injury that occurs due to sudden trauma causing damage to the brain. TBI can happen due to rapid and violent blow, jolt to the head or when external object pierces into the skull and en-ters brain. Neurotrauma in the form of TBI is affecting more individual an-nually than Alzheimer’s and Parkinson’s combined. Research is beginning to unravel the complexities of the injured CNS and has brought to light direct evidence for the involvement of protein processes in the neuronal cell death after the injury. Therefore, it is necessary to understand the mechanism of overproduction, accumulation or impaired clearance of the toxic proteins that initiates a cascade of events leading to brain cell death and dementia. Paper V discusses the changes in the protein levels in TBI brain. By apply-ing MS-based neuroproteomics to understand the molecular mechanisms that exacerbate AD and TBI, might generate diagnostic and therapeutic avenues to promote cell survival and functional recovery.

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4. Membrane proteins

Proteins are the work-horses of the cell and are made up of 20 different ami-no acids, varying in length, carrying multiple modifications and may have regions of quite opposite hydrophobic/hydrophilic properties. Membrane proteins (MPs) consist of hydrophobic transmembrane domains and hydro-philic extra- and intracellular domains. MPs are distributed throughout the different compartments of the nervous system and they occupy center-stage in the development processes, neurotransmission, axonal guidance, synaptic release and reuptake. MPs adopt the secondary structure by allowing exten-sive hydrogen bonding between backbone amides and carbonyls in order to shield the highly polar peptide bonds from the hydrophobic lipid bilayer. MPs are classified as peripheral membrane proteins (PMPs) and integral membrane proteins (IMPs) (Figure 2). PMPs are attached covalently to the polar lipid head groups or to the IMPs and can be extracted using high ionic strength or high pH buffers, whereas IMPs are located across the hydropho-bic core of the phospholipid bilayer and require harsh conditions for extrac-tion and solubilization.

Figure 2. Diagram of a plasma membrane illustrating phospholipid bilayer with different classes of membrane proteins

MPs act as receptors, enzymes, transporters, ion channels, drug targets and participate in various cellular processes including cell adhesion, cell-cell interaction, endocytosis, ion transport etc. Majority of the current drug tar-gets (~70%) are represented by MPs, which also act as epitopes for vaccine design3.

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Comprehensive analysis of the brain MPs, especially the transmembrane IMPs remains a challenge because of their high hydrophobic nature and low abundance. Several high-throughput proteomic platforms employ MS for the identification of the intact proteins or peptide mixtures from the biological samples. Despite the technical advancements in the MS instrumentation, studies of neurological disorders are confounded by the low abundance of many proteins (especially MPs) in the CNS. The recent advances in sample preparation technique using a phase separation phenomenon called cloud point extraction in combination with MS analysis have provided an option for enriching MPs in brain tissue (Paper III).

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5. Sample preparation

Mass spectrometry of proteolytically derived peptides has emerged as a powerful tool and aided in screening and discovery of protein markers in the complex neuronal samples. The proteins/peptides to be analyzed by MS should be free of contaminants to reduce unwanted interference and there-fore sample preparation plays a major role in dictating the final outcome of the analysis. There is no universal sample preparation technique that fits the needs of all sample types and it is practical to optimize the procedure. The saying goes “Garbage in, garbage out” holds true for proteomics as other scientific fields. Sample preparation protocols need to be prepared consider-ing several factors such as biological question to be addressed, the specifici-ty and the availability of the sample amount, the nature of the protein sub-set(s) to be investigated, availability of the instrumentation for data acquisi-tion and its characteristics, including the tool(s) for data processing14. Apart from these considerations, studies with the biological tissue poses challenges which arise due to the complex nature and high dynamic range of the prote-ome. Complexity of biological samples can be reduced by introducing vari-ous pre-fractionation techniques during sample preparation step, which de-termines the end result of the downstream proteomic analysis. The proteins detected by MS depend highly on the sample preparation and enrichment strategies. Starting from sample collection, storage, preparation, analysis until the final outcome, each step should be monitored with outmost vigi-lance to produce the proteomics data with minimum errors. Papers I-V con-tains sample preparation steps typically involving extraction, delipidation, cleaning and tryptic digestion of proteins into peptides. Analysis of tryptic peptides is achieved by using high resolution MS.

5.1. Brain tissue collection and storage Proteomic studies in the neurodegenerative disorders employ a wide range of biological samples such as tissues (brain, spinal cord), body fluids (blood, plasma, serum, cerebrospinal fluid, urine, saliva) and cell cultures. Apart from choosing the biological samples, it is of utmost importance to collect and store samples under the appropriate conditions to prevent the protein degradation. Human brain banking has become important in the neurosci-ence research to study biological processes, disease causing mechanisms as

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well to verify findings from animal model diseases. Therefore, there is al-ways a high demand for the good quality human brain tissue to study the proteome level alterations between the disease vs controls. The tissues col-lected should be frozen immediately or preserved by formalin fixation.

All the brain tissues for AD research in the Papers II-IV are collected by Uppsala Berzelii Technology Centre for Neurodiagnostics biobank at the Uppsala University hospital. The collection of brain tissues and the conduct-ed research had been approved by the Regional Ethical Review Board in Uppsala, Sweden. In Paper V, brain tissue biopsies from live TBI patients and normal pressure hydrocephalus (NPH) controls were collected during surgery. The collected brain tissues were placed in cryogenic vials or tubes and frozen immediately by using liquid nitrogen or dry ice and stored at -80 °C until further usage.

5.2. Additives in the lysis buffer for solubilizing MPs Membrane proteins as mentioned earlier are difficult to extract and solubil-ize because of their hydrophobic nature. Detergents are amphipathic in na-ture containing both hydrophobic and hydrophilic domains and are very ef-fective in the extraction and purification of the MPs. Micelles and other or-ganized amphiphilic assemblies are lipid-mimetic, which enables them to bind to the hydrophobic surfaces of TMDs. They can be classified into four groups, ionic (anionic/ cationic), zwitterionic, non- ionic and bile acid salts.

Ionic detergents are extremely efficient in solubilizing and denaturing the proteins15 by disrupting the protein-protein and intra-protein interactions6 but are incompatible with isoelectric focusing (IEF) of the two-dimensional gel electrophoresis (2DE) and interferes with the MS analysis by suppressing ionization by electrospray ionization (ESI)16. Non-ionic detergents are made up of either polyoxyethylene or glycosidic polar head groups and they main-ly disrupt the lipid-lipid and lipid-protein interactions6. Non-ionic detergents are considered as mild compared to the ionic detergents and the glycosidic detergents at low concentrations (0.01-0.1%) are fairly compatible with ESI16. Zwitterionic detergents have intermediate properties and hence they act as better solubilizing agents compared to non-ionic detergents but not as strong as ionic detergents17. Additionally they are generally compatible with trypsin digestion as well as ESI-MS16. Bile acid salts are also ionic deter-gents, but they are steroidal compounds with a polar and non-polar face and have less solubilizing and denaturing capability compared to linear ionic detergents17. Bile acid salts are compatible with in-solution trypsin digestion and can be easily removed from the samples by precipitating them at acidic pH prior to MS-analysis6.

Organic solvents such as methanol18, isopropanol19, acetonitrile6, organic acids like formic acid and trifluoroacetic acid (TFA) can be used for the

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extraction of the MPs. Organic solvents, especially methanol, acts by de-forming the membrane bilayers forming deep pockets in the membrane sur-face thus facilitating the denaturation and solubilization of MPs20, 21. Organic solvents aid the tryptic digestion, are compatible with LC-MS/MS and can also be easily removed by evaporation. High pH buffers like potassium phosphate are used in extraction of membrane proteins and the advantage is that they preserve the native topology and allow global mapping of hydro-philic domains by disrupting the plasma membrane without denaturing the lipid bilayer22.

5.3. Extraction Efficient extraction of proteins is the foremost and crucial step in the sample preparation to attain high quality results. Extraction involves isolating the protein of the target tissue or organelle by releasing proteins from cells through lysis, breaking protein-lipid interactions and solubilizing the pro-teins in a suitable buffer for further steps in sample preparation. Global ex-traction, simultaneous release and solubilization of all proteins from a highly complex biological sample poses a great challenge due to the large dynamic range and interfering contaminants during the sample processing steps. In-complete extraction of the proteins might be also due to the formation of protein-protein or protein-nucleic acid complexes. Therefore, to minimize the effects and to achieve higher efficiencies during proteins extraction, the protocol for each new tissue sample should be optimized (which is the major focus of Paper I).

For comprehensive proteome analysis, efficient cell disruption is a pre-requisite. Cell lysis can be achieved by mechanical disruption of the biologi-cal tissue/cells using a blender, an ultrasonication device (bath/ probe), a french press, a mortar and pestle, a glass bead agitator or by using detergent based lysis. Extraction conditions should be optimized depending on the characteristics of the biological tissue, the nature of protein to be studied, the subsequent assay or analytical steps used and the desired outcome. For ex-ample, harsh conditions by employing detergents are more preferable for the extraction of integral MPs compared to cytoplasmic proteins, but the same should be avoided if the topology of the protein or the protein-protein inter-actions should be studied4.

In Papers I-IV, we applied a combination of mechanical (blender) and detergent-based cell lysis to attain efficient protein extraction and solubiliza-tion from the brain tissue. In Paper V, detergent lysis buffer in combination with sonication probe and ultrasonic bath was employed to prevent losses from small tissue amount. Cell disruption causes the release of endogenous enzymes such as proteases and phosphatases which will degrade the proteins in the extracts resulting in reduction of the protein yield during isolation and

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purification. Protease inhibitor cocktail and ethylene diamine tetra acetic acid (EDTA) were added to the brain homogenates for inhibition of serine, cysteine, acid proteases, aminopeptidases and metalloproteases. Along with the protease inhibitors and detergents, other additives like organic acids, organic solvents, chaotropes can also be used in the lysis buffer in order to improve extraction and solubilization.

5.3.1. Cloud Point Extraction Brain MPs are usually under-represented in the CNS studies, despite of their biological and biomedical importance. Therefore, a surfactant- mediated temperature induced phase separation technique called as cloud point extrac-tion (CPE) can be used to pre-concentrate and separate the biomolecules based on their hydrophobicity. The cloud point is the temperature above which a non-ionic surfactant in the aqueous solution forms micelles and turns turbid/ cloudy and upon centrifugation, the solution separates into two phases: surfactant-rich phase of a small volumes and a dilute aqueous or surfactant-poor phase (Figure 3). The phase separation phenomenon on in-creasing temperature is reversible and usually observed in polyoxyethylene non-ionic surfactants. By increasing the temperature above cloud point, non-ionic micelles grow and phase separation occurs due to secondary associa-tion of small micelles into large micelle aggregates23. This association is due to decreased hydration of the ether linkages (head/micelle group). At room temperature, the non-ionic detergent is soluble in water because the ether linkages are highly hydrated and structured (i.e., less entropy). With an in-crease in temperature, the highly ordered structure of water around ethylene oxide chains breaks down due to unfavorable entropy contribution24. This leads to reduced hydration of ether linkages because the two micelles move close to each other and overlaps freeing some water molecules from hydra-tion sphere25. Thus the surfactant becomes less soluble in water. In the Tri-ton® series, the cloud point depends on the number of polyoxyethylene chain in a given series26. Therefore the longer the polyoxyethylene chain length, more ether linkages are hydrated, and higher is the cloud point tem-perature. Additives like inorganic salts can lower the cloud point tempera-ture, because the salts have greater affinity for water than the ether linkag-es27. For example, Triton X-100 has CPT 64 °C, but upon adding salts or higher ionic strength additives, it can be lowered24.

In Paper I, both n-octyl-β-D-glucoside and triton X-114 lysis buffers provided the highest total number of the proteins, as well as the number of membrane and transmembrane proteins. An additional advantage of non-ionic detergent a polyoxyethylene isooctylphenyl ether (Triton X-114) is low critical micellar temperature (CPT ~23 °C) at which the proteins are stable and not denatured. The hydrophobic compounds, e.g. MPs, initially present in the solution and bound to the micelles are extracted to the surfactant-rich

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phase. In Paper III, we have shown the possibility of using CPE method in combination with a stable isotope quantitative proteomic strategy for the separation and enrichment of highly hydrophobic MPs in the AD and control brains followed by nanoLC-MS/MS analysis.

Figure 3. Schematic overview of CPE procedure. Homogenization of brain tissue in presence of Triton X-114 lysis buffer. When temperature is increased to above the CPT the solution turns turbid. Centrifugation of the solution results in the separation of the micellar solution into a small, surfactant rich phase and a larger diluted aque-ous phase

5.4. Protein purification/ Contaminant Removal Lysis buffer for protein extraction contains salts, buffers and detergents as major constituents in order to maintain pH and ionic strength of the buffer as well for the solubilization of MPs. Brain is a fatty tissue with lipids as major constituents which are co-extracted along with the proteins during extrac-tion. The presence of contaminants such as nucleic acids, lipid-polysaccharides, cell debris etc., in the extracts, hampers the tryptic diges-tion and also interferes during the gel electrophoresis, reverse-phase separa-tions and mass spectrometry, thus reducing the efficiency of the analytical technique. Currently, there are several sample clean-up techniques available, of which dialysis28, ultrafiltration29, gel filtration30, precipitation31, 32, and solid phase extraction33 are majorly used in proteomic sample preparation to remove interfering contaminants. These clean-up techniques either alone or in combination, can be applied during the sample preparation procedure.

In bottom-up proteomics, precipitation using acetone is typically used, ei-ther alone or in combination with other organic solvents34. The standard precipitation involves the addition of cold organic solvent mixture to aque-

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ous sample mixtures and incubating in temperature of 4 to -20 °C. The choice of the protein purification techniques depends on the tissue character-istics, e.g. lipids are abundant in brain, so selective precipitation using organ-ic solvents called as delipidation step, is the common protein purification technique employed to efficiently remove lipids together with nucleic acids and detergents while retaining maximum protein recoveries35, 36. Five differ-ent delipidation techniques have been compared for recovery and post-precipitation analysis in-gel and in-solution digestion of brain tissue32. Of all the five delipidation techniques using organic solvent mixtures, the one with tri-n-butylphosphate/acetone/methanol (1:12:1) has shown high recovery of proteins and MPs. Therefore it has been used in all Papers I-V, for precipi-tating proteins and solubilizing lipids from the brain tissues.

5.5. Reduction, Alkylation and Digestion Shotgun proteomics involves the digestion of proteins to generate peptide fragments for effective protein characterization and identification by MS. Digestion can be performed either in-solution or in-gel pieces to generate a mixture of peptides, some undigested proteins and proteases37 which can be identified by LC-MS/MS analysis. In-solution digestion is simple and straightforward method and it requires less protein for digestion compared to 2D-gels. The recovery of peptides from gels is shown to be less efficient compared to in-solution digestion38, 39.

Protein pellet obtained after precipitation is usually re-suspended in a ly-sis buffer. Solubilizing protein precipitate can be challenging because the pellet is compacted by centrifugation and also dried to some extent. Most proteins are resistant to enzymatic proteolysis under non-denaturing condi-tions therefore employing lysis buffer with detergent-chaotrope mixture in addition to vigorous vortexing and sonication helps in solubilizing the hy-drophobic proteins by efficiently exposing pellet to buffer. The solubilized proteins are subjected to reduction and alkylation in order to disrupt the pro-tein tertiary structure groups prior to digestion by breaking the -S-S- linkage and by preventing the re-formation of disulfide bonds by covalent addition of carbamidomethyl. Several reducing agents such as dithiothreitol (DTT), dithioerythritol (DTE), tris(2-carboxyethyl) phosphine (TCEP), tribu-tylphosphine (TBP), 2-mercepatoethanol converts cysteine’s disulfide bond into cysteine’s free sulfhydryl groups. Alkylating agents such as iodoacetam-ide (IAA), iodoacetic acid reacts with free sulfhydryl groups of cysteine residues to form S-carboxyamidomethyl-cysteine, which cannot be re-oxidized to form disulfide bonds. The denatured, reduced and alkylated pro-teins are then digested into peptides either by enzymatic or chemical cleav-age of the proteins. Trypsin is the common choice for enzymatic digestion because it generates peptides of practical length and favorable charge suited

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for tandem MS sequencing by collision-induced dissociation (CID). Other proteases such as chymotrypsin, GluC, LysC, AspN for enzymatic digestion or cyanogen bromide for chemical cleavage can also be considered depend-ing on the desired outcome. In all Papers I-V, we have employed n-octyl-β-D-glucoside-Urea buffer for solubilizing the protein pellet and DTT and IAA for reduction and alkylation.

Trypsin is an immobilized protease which is typically used for the diges-tion of proteins into peptides. Trypsin cleaves proteins at the carboxyl side of the basic amino acids lysine and arginine. Native trypsin undergoes autolysis generating peptide fragments that could interfere with the MS analysis. Ad-dition of 10 mM calcium chloride to the native trypsin or usage of sequenc-ing grade modified trypsin is recommended to inhibit autolysis and to im-prove the digestion efficiency. Ca2+ is present in many biological samples naturally and it will be bound to the Ca-binding loop in trypsin, preventing autolysis40. Modified trypsin is produced by reductive methylation of the lysine residues producing highly active and stable trypsin which is resistant to autolysis. In order to maintain the optimal pH of 7.5-8.5 for tryptic diges-tion usually ammonium bicarbonate or triethyl ammonium bicarbonate buff-ers can be added prior to the digestion (maybe we can remove this para-graph).

5.5.1. On-filter digestion In order to solubilize and denature the protein pellets obtained in the delipi-dation step, a combination of detergent (1% n-octyl-β-D-glucoside) and cha-otrope (8M urea) have been employed in Papers I-V. Detergents are very effective in solubilizing the protein pellets; especially the hydrophobic MPs. Addition of chaotropic agents such as 8M urea or 6M guanidine hydrochlo-ride promotes the protein denaturation and solubility by disrupting the hy-drogen bonds within or between the molecules. However, the presence of detergents and chaotropes hinders the enzymatic digestion of proteins or interferes with the MS analysis. So detergents and chaotropes should be re-moved prior to the digestion. Therefore a desalting step or clean-up step is required to remove the unreacted reagents and other compounds such as chaotropes, detergents etc., which can interfere with trypsin digestion or the subsequent ESI-MS analysis.

Different techniques such as dialysis can be used for the removal of de-tergent. Dialysis procedure requires more time to remove the detergents re-sulting in the sample losses and degradation. To overcome these issues we considered an alternative fast, easy and reproducible filter-aided sample preparation (FASP). The method of using commercially available micro-centrifugal devices called as spin filters for protein clean-up and digestion was originally developed by Manza et al. in 200537 and the acronym FASP coined by Wisniewski, et al. in 200941.

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In Papers I-V we have applied on-filter digestion using 3kDa spin filters for sample clean-up followed by trypsin digestion of proteins into peptides (Figure 4). The on-filter “shotgun” based approach is rather straightforward and can be applied for large sample sets. The simplicity of using a common ultrafiltration device to act as a “proteomic reactor” for detergent removal, buffer exchange, chemical modification, and protein digestion is compelling42. The principle of using a spin filter is that it retains proteins while other interfering compounds are washed away. Proteins can then be converted into peptides by tryptic digestion which in turn can be eluted from the spin filters by centrifugation. Tryptic digestion is quenched either by adding acid or lyophilization.

Reduction (DTT)Alkylation (IAA)

3 kDa spin filter

Wash

Buffer exchange

37 oC, ~18 h

SpinDigestionTransfer solution

Discard

Add Trypsin

Elution of peptides

Figure 4. Steps in protein clean-up and digestion using 3 kDa spin filters

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6. Separation techniques

Several proteomic studies use MS instrument as a detector to identify large number of proteins/peptides derived from a complex biological sample. Even a single organelle such as synapse may contain huge number of distinct proteins11. Therefore direct infusion of the sample into mass spectrometer is not so optimal for analysis of thousands of proteins or peptides. Separation of the biomolecules is a pre-requisite step prior to the MS detection. A num-ber of separation techniques are available for the proteome analysis of which gel-based and gel-free approaches are common for the protein/peptide sepa-ration. The gel-based separation techniques involve sodium dodecyl sulfate polyacrylamide gels (SDS-PAGE) and 2D-GE to separate intact proteins or protein complexes followed by in-gel digestion of proteins into peptides.

Ion-exchange, affinity, size exclusion, reverse phase (RP) liquid chroma-tography (LC), capillary electrophoresis are common techniques used in gel-free proteomics. LC techniques work with both bottom-up and top-down approach, i.e. for the separation of proteolytic peptides or intact proteins. In Paper I, we employed SDS-PAGE for the visualization of the separated proteins, whereas RP-LC separation is used in Papers I-V to reduce the complexity of the proteolytic peptide mixture prior to the MS analysis.

6.1. Gel Electrophoresis SDS-PAGE separates denatured proteins depending on the charge and mo-lecular weight. Proteins with low molecular weight migrate faster compared to high molecular weight proteins. SDS denatures proteins by disrupting the 3D structure and imparts negative charge to the proteins in proportion to the mass enabling them to move towards the positive end of the gel. Visualiza-tion of proteins is achieved by staining, which can be processed prior to the in-gel digestion before MS identification or the specific gel-separated pro-teins can be detected by antibody-based techniques e.g. western blotting. In Paper I, visualization of mouse brain proteome with different lysis buffers was achieved by using SDS-PAGE. In Papers III-V, proteins were separat-ed using 1D gels and validation of a specific protein of interest was done by using western blot analysis.

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6.2. Chromatography Chromatography is a technique used to separate a mixture of compounds by distributing the components between two phases. The separation occurs based on the differing interactions of the compounds in a mixture with the stationary and mobile phases. Liquid chromatography has emerged as one of the powerful tools in analytical chemistry. In LC the sample must be dis-solved in a liquid and then transported by mobile phase into a stationary phase. In LC, the mobile phase flow occurs due to vacuum or gravity. Im-prove separation power can be achieved by using smaller size particles (<10 microns) in stationary phase of LC. However, smaller particles lead to more resistance in flow and hence high pressure is needed to create the desired solvent flow, which is achieved by using pumps. Therefore, the use of high pressures (up to 400 bar) to generate the required flow for LC in packed columns gave rise to HPLC technique. However, recent advances in instru-mentation and column technology were made by using smaller particles of 1.7 microns and to deliver mobile phase at 1000 bar gave rise to ultra-performance liquid chromatography technology (UPLC). This has tremen-dously improved the resolution, speed and sensitivity in LC. Apart from smaller size particles in stationary phase and high pressure, the flow rates are also crucial during the proteomic workflow. Standard HPLC operate at flow rates of mL/min and columns of diameter of few millimeters. At this setting, the proteins/peptides become too diluted in the column and most of the elut-ing proteins/peptides will be blown away from the entrance of ESI-MS. Therefore in neuroproteomics workflow, columns with smaller inner diame-ter (25-100 µm) were commonly employed to reduce the sample consump-tion. For successful coupling of HPLC with ESI, the flow rates should be low in the range of 150-300 nL/min to ionize the eluting peptides and a tip of 1 µm for generating smaller droplets of spray. This has given rise to nano-HPLC technique which is now a central separation technique in proteomics workflow.

Chromatographic techniques such as RP-LC is an essential tool for the separation and analysis of protein/peptides by coupling to MS, either offline or online. The stationary phase is non-polar and mobile phase is aqueous, to moderately polar. The sample is carried with the mobile phase and separates based on its interaction with hydrophobic stationary phase. Polypeptides are eluted from the stationary phase using gradient elution by increasing the concentration of organic solvent during separation. By increasing the con-centration of organic solvents in the mobile phase, the analytes with different affinity for stationary phase can be eluted sequentially. The column used in RP-LC is usually packed with silica particles attached with alkyl chains of varying lengths from C4-C18, of which C18 was commonly used for separa-tion of peptides. Trypsin is typically used in proteomics to cleave peptides at arginine and lysine, providing at least one arginine or lysine hydrophobic

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amino acid side chain for RP-LC. RP-LC has become a standard method in shotgun proteomics due to its high resolving power, good detection sensitivi-ty and its compatibility with ESI-MS. In Papers I-V, we employed a RP-nanoLC with flow rates of 200 nL/min for the separation of peptides, using a 15 cm long silica column emitter with inner diameter of 75 µm and outer diameter of 375 µm packed with fully end-capped Reprosil-Pur C18-AQ 3 μm resin.

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7. Mass spectrometry

MS has emerged into an indispensable tool for the identification of pro-teins/peptides, providing accurate mass measurements for peptides as well as amino acid sequence information. The basic components of MS are; an ion source to convert molecules into gas phase ions, a mass analyzer to separate the ionized analytes based on their mass-to-charge ratio (m/z), a detector to record the number of ions at specific m/z value, and vacuum pumps to pre-vent the collision of ions with residual gas and assist the ions uninhibited movement within the instrument. The introduction of two soft ionization techniques namely electrospray ionization43 and matrix-assisted laser desorp-tion/ionization (MALDI)44 for the analysis of proteins and peptides has greatly increased the MS application in the proteomics field. The heart of the MS instrumentation is the mass analyzer. Five different types of mass ana-lyzers, quadrupole (Q), ion trap (quadrupole ion trap, QIT; linear ion trap, LTQ), time-of-flight (TOF), fourier-transform ion cyclotron resonance (FTICR) and Orbitrap are commonly used45. These mass analyzers can be used alone or in combination depending on the need. In Papers I-V, a hybrid LTQ-FTICR (LTQ-FT Thermo Electron) Ultra MS, with ESI and LTQ cou-pled to FTICR, has been used for the comprehensive proteomic analysis of the brain tissue samples.

7.1. Electrospray ionization ESI is a technique which facilitates a soft ionization of large biomolecules such as proteins and peptides without in source fragmentation43. ESI em-ploys high electric field (3-5kV/cm) to produce a mist of droplets carrying excess charge at the surface. The droplet size is reduced in the ion source region due to the solvent evaporation by counter-flow of heated drying gas. As the droplets evaporate, the ions move close and due to Coulombic repul-sive forces between ions, the explosion of droplet occurs, resulting in smaller droplets. This process is repeated until solvent-free ions are formed that passes through the mass analyzer. In positive-ion mode, the droplets will carry positive charge forming [M+H]+ and [M+Na]+ adducts. Nanoelec-trospray (Papers I-V) with lower flow rates in the range of 1-1000 nL/min is an important development of this ionization method.

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7.2. Linear ion trap-Fourier transform ion cyclotron resonance mass spectrometry (LTQ-FTICR-MS) The LTQ-FT is a hybrid mass spectrometer consisting of LTQ and a FTICR, coupled to ESI nano scale-LC for the separation of complex mixtures. The linear ion trap works by using combination of direct current (DC) current and radio frequencies to select the ions of particular m/z. ICR mass analyzers use a strong magnetic field. The ions formed are trapped in a FTMS analyzer cell which is surrounded by the magnetic field, B. When the centripetal and centrifugal forces experienced by ions with a velocity v are equal, then it adopts a circular trajectory with a radius r perpendicular to the field (equa-tion 1).

zvB = (mv2)/r (1)

The angular velocity of the ions wc is given by equation 2

wc = v/r (2)

By substituting equation 2 into equation 1 and simplifying, we will get:

wc = zB/m (3)

The ions spiral rapidly around the magnetic field lines resulting in a cyclo-tron motion. The cyclotron frequency depends on the ions mass and charge but independent of velocity (equation 3) i.e. ions with higher m/z have low cyclotron frequency and ions with lower m/z have high cyclotron frequency. The m/z ratio for an ion can be determined by measuring its cyclotron fre-quency. Each ion adopt stable cyclotron orbit, however, they do not generate any detectable signal. Ions will be provided with extra energy through an oscillating electrical field. When the frequency of the field is same as cyclo-tron frequency then the ions absorb energy resulting in the increase in their velocity and orbital radius. The coherent motion of ions between the plates produces electric current, which is recorded as image current. In FTICR-MS, by applying a wide range of excitation energies, all ions in the trap can be excited simultaneously. The frequency domain spectrum is recorded as ion waveforms which can be transformed to mass spectrum using Fourier trans-formation, thus providing the information on m/z of all the ions in the trap.

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7.3. Data Analysis Protein identification in bottom-up approach is based on the analysis of pep-tides generated by tryptic digestion. The peptide ions are subjected to pre-cursor ion scan, which provides information on the m/z ion. A single peptide precursor ion is isolated and fragmented further by colliding with inert gas molecules. This process is called collision-induced dissociation and it gener-ates MS/MS spectra for the identification of the amino acid sequence of the peptide fragments. Depending on the fragmentation method used, different fragment ions are produced. During the CID fragmentation, if the charge is retained on the N-terminus then b-ions are generated; if the charge is on C-terminus then y-ions are produced. By identifying the b- and y-ions and cal-culating the masses of amino acids from the adjacent ions in the series, the amino acid sequence can be interpreted.

The main identification approaches include de novo sequencing and data-base searching. In de novo sequencing proteins can be identified by extract-ing the sequence information directly from the spectrum peaks derived from fragment ions without the assistance from sequence database. The database approach is more common in proteomics in which the experimental MS and MS/MS spectra can be matched against hypothetical peptide fragments gen-erated from an in-silico protein database to identify the proteins in the sam-ple. The database matching of peptide sequences in easier than de novo se-quencing because only an infinitesimal fraction of possible peptide amino acid sequences occur in nature. Therefore even the information presented by a peptide-fragment spectrum is not sufficient for predicting the complete amino acid sequence it can still have some information to uniquely match against a database. Several different algorithms such as Mascot, Sequest, Sonar ms/ms, ProteinProspector etc., are available to search sequence data-bases with tandem-MS-spectral data. Only limitation of this database search compared to de novo sequencing is that the organism sequenced genome should be already available in the database, so that all possible peptides are known. In Papers I-IV, protein identification is done by using Mascot search engine.

The usage of MS to analyze complex biological samples at large-scale gave produced highly complex data, that lead to development of computa-tional tools to analyze and statistically evaluate the LC-MS data. Several bioinformatics tools have been developed in recent years to aid in label-free and labelling quantitative analysis for comparative LC-MS. Several open source software such as MaxQuant, MapQuant, MZmine, OpenMs etc., and commercial software such as Decyder, SIEVE, ProteinLynx, Elucidator are available. The data processing steps in all the label-free pipelines usually include normalization, alignment, detection, quantification, peak matching, identification and statistical analysis46. In Papers II-III, for quantitation of DML data, MSQuant is used. In Paper IV, Decyder was used for label-free

33

quantification. In Paper V, MaxQuant was employed for both label-free and DML quantifications.

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8. Mass spectrometry-based quantitative proteomics

Detecting changes in the protein or peptide abundance in response to a dis-ease compared to a non-diseased state in the biological samples is the major goal in the quantitative clinical proteomics47. Quantitative information can be obtained by using several high-throughput methods of which 2DE is the most commonly used technology for monitoring changes in the expression of proteome profiles48. However, several issues such as reproducibility, dif-ficulty to separate and detect high molecular weight, high pI and hydropho-bic proteins make it not so attractive for the quantitative proteomic analysis. Recent developments in instrumentation have greatly improved the accuracy and sensitivity of the detection for MS-based quantitative proteomics. Cur-rently, two types of quantitative approaches, absolute and relative were ma-jorly used in the field of proteomics. Absolute quantitation determines the exact amount or mass concentration of protein in the samples whereas rela-tive quantitation compares the levels of proteins in different samples49.

8.1. Absolute vs. Relative quantitation Absolute quantitation involves spiking of known concentration of synthetic, isotope-labelled reference peptide(s) into the experimental sample and per-forming LC-MS/MS. Quantification is achieved by comparing the signal intensities of the target peptide in the experimental sample to the reference peptide50. Absolute quantitation is the choice when exact amounts or mass concentrations of a protein need to be determined or to detect precise levels of post-translational modifications for a few preselected target species. However, absolute quantitation requires costly reagents and assay develop-ment takes more time for each protein of interest. Therefore, relative quanti-tation is employed more often in proteomics when large sample sets are in-volved in order to find variations in the proteome profiles between different experimental conditions. Relative quantitation involves comparing the al-terations in the individual proteins/peptides between different experimental samples by employing either label-free or labeling strategies (Figure 5). Differential proteomic methods based on labeling strategies include in-vivo metabolic labeling and in-vitro enzymatic or chemical labeling.

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Figure 5. Schematic diagram showing the steps in label free and stable iso-tope dimethyl labelling approaches employed in Papers II-V

8.2. Label free quantification Label-free methods can be used for both absolute and relative quantitation, for clinical screening of large sample sets and biomarker discovery experi-ments without using isotope-labeled components. Most commonly employed label-free methods involve either spectral counting or measuring ion peak intensity. Signal intensity from the ESI has been found to correlate to the ion concentration. Therefore, the relative peptide levels between samples can be determined by measuring peak intensities from LC-MS51. Spectral counting method involves comparing the sum of MS/MS spectra from a peptide across multiple samples, which is shown to correlate with protein abun-dance51. Label-free quantitation is straightforward and inexpensive but the experiments need to be carefully controlled to account for any variations, since each sample is prepared and analyzed by LC-MS or LC-MS/MS sepa-rately. In Paper IV we applied label free quantification strategy to find the proteome level alterations in the brains of AD, controls and patients with other neurodegenerative disorders.

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8.3. Stable isotope labeling approaches for quantitation Stable isotope labeling for differential proteomics has gained popularity due to the advances in the MS and LC-MS technologies. Different labeling ap-proaches involve labeling of proteins/peptides with stable heavy isotopes (15N, 18O, 2H, 13C) either metabolically in cells or post metabolically by en-zymatic or chemical reactions. The light and heavy labeled peptide ions are chemically identical (except for 2H) and exhibit identical LC elution profiles but distinctively different MS spectra and are separated by the difference in their mass. The peak intensities of differentially labeled peptides are com-pared to determine the change in the abundance between the two sample sets.

Two of the most popular metabolic labeling approaches are 15N labeling and stable isotope labeling with amino acids in cell culture (SILAC)52. SILAC involves labeling of one or several amino acids (typically arginine or lysine) with heavy isotopes and adding them to the growth medium to incorporate the labels into proteins. SILAC is used to quantify in-vivo changes and the level of quantitation bias from processing errors is low because samples can be combined in the beginning of the sample preparation step. Previously, SILAC has been limited to cell cultures but in past few years its applicability has been expanded by labeling entire organisms53, 54, 55, 56. For samples that are not suited for metabolic labeling such as human body fluid, tissue57 and when the experimental time is limited, enzymatic or chemical labeling can be applied for quantitative proteomic analyses.

The enzymatic labeling approach involves the use of trypsin- or Glu-C-catalyzed incorporation of 18O atoms into C-terminus of the proteolytic pep-tides during or after protein digestion58, 59. Enzymatic labeling has disad-vantages such as incomplete labeling due to the slow back exchange of 18O and 16O and difference in the rate of incorporation of label for each peptide which complicates the data analysis60, 61. Another commonly used stable-isotope labeling strategy entails the incorporation of stable-isotope contain-ing tags into proteins or peptides through chemical reaction. Chemical label-ing strategy involves the usage of isotopic and isobaric tags for labeling, e.g. isotope-coded protein labeling (ICPL), isotope-coded affinity tag (ICAT), isotope tags for relative and absolute quantification (iTRAQ), tandem mass tags (TMT), stable-isotope dimethyl labeling (DML).

In Paper II, III & V, we have applied the DML approach for relative quan-tification of the brain proteome in AD or TBI. DML is a rapid and inexpen-sive chemical labeling strategy, which uses formaldehyde for methylation of primary amines of N-terminus and lysine residues of tryptic peptides via Schiff base formation and subsequent reduction by cyanoborohydride62, 63.

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Three isotopomeric labels namely light, medium and heavy containing for-maldehyde (CH2O), deuterated formaldehyde (CD2O) and 13C-labeled deu-terated formaldehyde (13CD2O) can be employed for DML. Sodium cyano-borohydride is added to light and medium labels, whereas deuterated sodium cyanoborohydride is added to the heavy labeled mixtures for reduction. The reaction of DML is demonstrated in Figure 6.

In MS-based quantitative proteomics, many options are available with their own set of advantages and disadvantages. The choice of method depends on the type and number of samples to be compared, the biological source and complexity of samples, analytical needs (e.g. precision, accuracy and abso-lute or relative quantification is needed), and the cost and time required for the analysis49. DML method has been applied in many studies but not for the quantitation in brain proteomics. We employed the DML strategy in Paper II & III to monitor the changes in protein levels in AD vs healthy control brain samples. The DML method fulfills many of our criteria for e.g. it re-quires less time, it is a straightforward and inexpensive approach compared to other labeling techniques. Quantitation is performed in MS mode, while protein identities are obtained in MS/MS mode.

Figure 6. Stable isotope dimethylation reaction; methylation of N-termini of lysines by formaldehyde and subsequent reduction by cyanoborohydride

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9. Validation by antibody-based assays

Highly sensitive antibody-based proteomics can be useful for the detection and validation of extremely low concentration of protein markers in biologi-cal samples. But in order to select the antibodies, prior knowledge about proteins of interest in relevance to disease mechanism is important. MS-based proteomics provides advantage of hypothesis-free biomarker discov-ery by the thorough assessment of thousands of proteins and peptides that are altering between disease vs control samples. Therefore, combining the best of both MS and antibody-based proteomic technology opens new ave-nues in biomarker research. Antibody-based assays include a variety of im-munodiffusion tests, membrane-based western blot assays, Enzyme Linked Immunosorbent Assays (ELISAs) and Luminex® bead-based technology. In order to validate the proteins found in MS analysis we have employed two different antibody-based immunoassays such as western blot (Papers III-V) and Luminex® (Paper IV).

9.1. Western blot Western blotting is a rapid and sensitive assay (1 ng of protein can be detect-ed) that allows the identification and characterization of proteins using tar-geted monoclonal or polyclonal antibodies against the specific protein64, 65. Proteins are extracted from biological sample and separated using gel elec-trophoresis based on their molecular weight followed by transfer onto a membrane (nitrocellulose or PVDF). By incubating the membrane with anti-bodies specific to a protein of interest, one can identify and quantify the pro-tein from a complex mixture. Multiplexed western blots using pre-labeled fluorescent antibodies can also be used to quantitatively characterize multi-ple proteins in a single experiment. Fluorescent western blots are less sensi-tive compared to traditional chemiluminiscent westerns66. Some of the limit-ing factors for immunoassays include low specificity, availability of pure antibodies and lack of antibodies for some proteins67-69.

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9.2. Luminex In recent years the technological limitations of traditional antibody assays such as western blots and ELISAs were overcome by introduction of new bead-based immunofluorescence technology known as xMAP® and xTAG®

(Luminex corporation, Austin, Texas, USA). xMAP® is used for detection of polypeptides, polysaccharides, oligo-nucleotides, antigens and antibodies whereas xTAG® is for the detection of nucleic acids (including viral ge-nomes). In Luminex® technology specially designed color-coded beads or microspheres were employed. By using 2 different fluorescent dyes (red and infrared) and mixing them at varying concentrations 500 distinct color coded bead sets can be created. Each of the individual bead sets are coated with target specific capture antibody (Figure 7). Multiple antibody-conjugated beads are then combined in a single well of 96-well plate to detect and quan-tify multiple targets simultaneously. The antibody-coated beads then capture the specific proteins of interest. A reported molecule (secondary antibody) labeled with different fluorescent dye is added to detect the presence of bound protein on the surface of the bead. The beads are analyzed by passing them through a flow cytometer which is equipped with two lasers. First laser (red) is to excite the beads to identify individual bead set and second laser (green) is to excite the fluorescent dye on secondary antibody to quantify the amount of protein bound to its surface. Luminex® offers advantage of multi-plex testing by allowing the multiple antibodies and antigens (proteins) to be tested simultaneously. So it requires less sample volumes and time to per-form the test compared to traditional immunoassays.

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Figure 7. Schematic diagram showing different steps in Luminex assay

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10. Papers I-V

10.1. Aims Paper I: To optimize the extraction of proteins from a highly complex sam-ple such as brain tissue using various lysis buffers in order to find the opti-mal method which is compatible with the downstream MS analysis. Paper II: To quantitatively compare the brain proteome in the temporal neocortex between Alzheimer’s disease patients and non-AD using differen-tial dimethyl labeling in combination with a shotgun based MS approach. Paper III: To apply the cloud-point extraction in combination with stable isotope DML and shotgun proteomics approach for quantification of both hydrophilic and hydrophobic MPs in the AD brain. Paper IV: To carry out a comprehensive proteomic profiling of post mortem AD brains and to compare the outcome with control brains as well as brains from other neurological diseases using MS-based LF quantitation. Paper V: To gain understanding of changes in protein expressions in TBI pathology and to identify novel potential protein biomarkers using LF and DML quantitation approaches.

10.2. Brain tissue biopsies In Paper I, mouse brain is used to determine the optimized extraction proto-col using different lysis buffers (detergents, organic solvents, high pH solu-tion, and organic acid). The lysis buffer that gave the highest number of pro-teins and MPs in Paper I was applied in later Papers II-V containing human brain samples. In Papers II-V, mass spectrometry based shotgun approach along with LF and DML techniques were applied to find the quantitative difference in the brain proteome levels between disease vs. control tissues.

In Papers II-IV, brains were collected from temporal neocortex area whereas in Paper V, brains were obtained from frontal cortex. The samples were collected in pre-frozen and pre-labeled 1.5 mL Eppendorf tubes and stored at −80 °C prior to analyses. The demographic information of brain

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tissue samples are presented in Table 1. Several factors such as age, sex, postmortem time should be considered while selecting the tissues for bi-omarker discovery.

In Papers II-IV, AD brains were selected based on CERAD and Braak staging. The CERAD and Braak staging reflects the severity of distribution of neuritic plaques and NFTs in different brain regions of AD. In Papers II-IV, all AD cases were neuropathologically diagnosed as CERAD C, Braak stages V or VI, meaning that there is high likelihood that dementia in pa-tients is due to AD and plaques and NFTs are found in neocortex regions (severe AD cases).

In case of brain or spinal cord injury, one of the tools that is considered to evaluate patient conscious level is the Glasgow Coma Scale (GCS). The "scores" are determined by assigning points to their various physical re-sponses such as visual ability, verbal responsiveness and motor skills. The GCS is scored between 3 and 15, in which higher score indicate better con-scious levels. In TBI, a score of 13 or higher correlates with a mild brain injury; 9-12 is a moderate injury and ≤ 8 indicate a severe brain injury. In Paper V, the motor component of the GCS of TBI patients during admission were in range of 3-5, showing the severity of the injury.

Table 1. Demographic data of patients and control samples from Papers II-V

Paper State No. Female Male Age average (Range) years

Postmortem time average (Range) years

Paper II (DML)

AD 10 7 3 76 (61-92) 26 (5-60) Control 5 1 4 84 (63-91) 30 (22-39)

Paper III (DML)

AD 8 5 3 79 (61-92) 19 (5-48) Control 4 2 2 82 (63-90) 30 (22-39)

Paper IV (LF)

AD 11 9 2 77 (61-92) 25 (5-60) Control 8 1 7 87 (63-93) 46 (22-101) OND 6 2 4 72 (61-88) 62 (7-120)

Paper V (LF& DML)

TBI 6 1 5 42 (17-73) NAControl 6 2 3 75 (64-80) NA

NA- not available

10.3. General workflow Papers I-V All papers included in this thesis utilize bottom-up proteomics on brain tis-sue (Figure 8). The workflow typically consists of extraction, delipidation, on-filter digestion, label-free or dimethyl labelling quantification, separation by RP-nanoLC, FTICR-MS analysis, data evaluation steps and validation with anti-body based assays. The changes in the workflow in each paper are highlighted with different colors. For extraction 50 mg mouse brain (in Pa-

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per I), 30 mg human brain (in Papers II-IV) and 10 mg human brain (in Paper V) were used. All the sample preparation steps and techniques in the workflow mentioned have been discussed in detail in the previous sections.

Figure 8. Workflow setup of Papers I-V included in this thesis, from sample prepa-ration to data analysis and validation. The steps highlighted in different color boxes (except blue) shows the changes in each paper.

10.4. Results and Discussion In Paper I, lysis buffers containing various detergents, organic solvents, and organic acids were evaluated in regards to protein yield and number of iden-tified proteins with emphasis on MPs. We compared different lysis buffers, with each buffer divided into two sets called as A and B. The set A, lysis buffers composition was identical for all protocols apart from the main ex-tracting constituent, that is, detergents, organic solvents and organic acids. The set B buffers were adapted from previous scientific publications but only the main extracting constituent was kept similar to the set A buffers. In terms of extraction of mouse brain, set A buffers gave a higher number of identified proteins compared to set B. Previously reported database investi-gations of the genomes in various organisms have shown that approximately 20-30 % of the genes encode the membrane proteins. We found that the de-tergent-based lysis buffers produce the high yield of proteins and high per-centage of MPs in the range of 25-30% compared to organic solvents and acid (14-23% MPs). Among the tested detergents, triton X-114 and n-octyl-β-D-glucoside provided the highest total number of identified proteins as

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well as the highest numbers and percentages of identified MPs (29%). Transmembrane proteins are usually under-represented in tissue proteomic studies due to solubilization issues. But in our study more than 50% of the identified MPs were assigned as transmembrane proteins using extraction protocols with either n-octyl-β-D-glucopyranoside or triton X-114 lysis buffers. This work provided the basis for the large-scale comparative brain proteome study of sixteen different extraction approaches with emphasis on the low abundant MPs. In Papers II, IV and V, n-octyl-β-D-glucoside de-tergent lysis buffer has been employed whereas Triton X-114 lysis buffer in Paper III to exploit its CPE properties to enrich MPs.

In Paper II, a total number of 827 unique proteins were identified out of which 227 proteins where found in at least 9 out of 10 patient pairs. A total of 69 proteins were found to be significantly (p < 0.05) increased (n = 37) or decreased (n = 32) in the AD brains compared to controls. These proteins play key roles in energy metabolism, cholesterol metabolism, glycolysis, inflammatory response, oxidative stress, synaptic functions, stress response, signal transduction, membrane trafficking, and cellular transport mecha-nisms. This study revealed 23 proteins that had not previously been implied in AD pathogenesis. Of these novel proteins, 16 are decreasing and 7 are increasing in levels in AD brains. Of the 23 novel proteins found, six pro-teins (2 ↑ and 4 ↓) are found in this study only (Paper II) and the other 18 proteins are found in either Paper III or IV or both. These six proteins are NAD-dependent deacetylase sirtuin-2 (SIRT2), plasmolipin (PLLP), neu-rotrimin (NTRI), synaptic vesicle glycoprotein 2A (SV2A), plasma mem-brane calcium-transporting ATPase 1 and 3 (AT2B1 and AT2B3). Thus, our findings provide new information on the proteome changes that occur in AD brain which might serve as novel potential biomarker candidates for AD diagnosis and progression.

The extraction and analysis of MPs pose difficulties because of their low abundance and high hydrophobicity. Therefore, in Paper III by exploiting the Triton X-114 phase separation properties and using stable isotope DML, we were able to enrich, identify, and quantify highly hydrophobic MPs in the detergent-rich micellar phase. A total of 1096 unique proteins were identi-fied and quantified, with 40.3 % (211/524) predicted as integral MPs found in the detergent phase, whereas only 10 % (80/798) proteins in the aqueous phase contained TMDs, confirming Triton X-114 enrichment for MPs. In this paper, 62 statistically significantly altering proteins were identified to be either increased or decreased in the AD brain. Of these 62 proteins, 19 pro-teins (31%) were identified in the detergent-rich fraction and 43 (69%) were found in the detergent-depleted aqueous phase. These proteins were involved with enzymatic function, transport, structural activity, apoptosis, altered synaptic function, metabolic process, glycolysis, signal transduction, cell cycle, endocytosis, stress response, and other functions. The hydrophilic and hydrophobic fractions showed no common significantly altering proteins,

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demonstrating the advantage of CPE phase separation for CNS tissue. The obtained results emphasize the need to study both the detergent-rich and detergent-depleted aqueous phases in order to cover a wider range of the proteome. This is especially important when any screening studies are planned on tissue to find potential biomarkers. We successfully verified our finding by Western blot analysis for four proteins, of which two are trans-membrane proteins-LAMP1 and AT2A2. In the detergent fraction we found ten significantly regulated highly hydrophobic transmembrane proteins con-taining from 2-14 putative TMD including transmembrane protein C9orf5, sarcoplasmic/endoplasmic reticulum calcium ATPase 2 and orphan sodium- and chloride-dependent neurotransmitter transporter NTT4 have not previ-ously been reported as related to AD

In Paper IV, label free shotgun mass spectrometry was used to relatively quantify the proteins in the brain samples from the subjects diagnosed with AD, non-neurological controls and OND brains. The complete experiment was performed twice (D1 and D2) including tissue dissection, sample prepa-ration, nanoLC-MS/MS and data analysis. The first experiment called D1 included 17 samples with eight ADs, four controls, and five ONDs. The second experiment called D2 contained a total of 21 samples (17 samples from D1+ 4 additional samples) with ten ADs, five controls, and six ONDs. A total of 7078 (1134 proteins) and 10213 (1480 proteins) protein specific peptides were identified and quantified in the D1 and D2 datasets, respec-tively. In order to translate the peptides information into proteins, the 50% coverage restriction was applied to the peptide list, resulting in 3768 (689 proteins) in the D1 and 4282 peptides (724 proteins) in the D2 dataset (549 overlapping proteins). Out of 864 total proteins, 163 proteins were signifi-cantly altering in levels (121 decreasing and 42 increasing) between AD and controls. . Several of these significantly altering proteins were found to be related to neuron development, transmission, and secretion processes. How-ever, when the same analysis was performed on 121 proteins with decreased levels in AD brains, the proteins were turned out to be enriched in more spe-cific terms related to generation of neurons, vesicle transmission (including exocytosis and endocytosis pathways), and more specifically, the SNARE complex. The enrichment analysis on the 42 proteins with increased expres-sion levels revealed proteins related to exosome vesicles, including typical exosome (e.g. CD9 antigen and annexins) and astrocyte protein makers (vi-mentin and glial fibrillary acidic protein).Out of these 163 proteins, 14 were found to be significantly altered between AD and ONDs but not between ONDs and controls. The 14 proteins that were altering between AD and ONDs are 1433T, 1433Z, ADDB, CALM, CATA, COF1, COR1A, GDIR1, IDH3A, KCRB, PRP39, SHLB2, SPTB2 and SYNJ1. Among the 163 pro-teins found in Paper IV, 50 were found to be significantly changing in lev-els (p<0.05) in both of the D1 and D2 datasets. In order to validate the mass spectrometry results, we performed antibody-based Luminex and western

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blot analysis and verified statistically significant changes of CD9, HSP72, TALDO, PI42A, and VAMP2 in AD compared to controls

In Paper V a comprehensive proteomic profiling of TBI patients was compared to NPH subjects using LF and DML quantitation approaches in order to reveal protein level alterations following TBI. A total of 555 pro-teins were found, out of which 544 and 246 proteins were identified and quantified in LF and DML approaches. Out of 555 proteins, 58 proteins dis-played significant quantitative changes (p<0.05) between TBI and controls in >80 % patient samples. Among the 58 significantly differentially ex-pressed proteins, 48 proteins were found by LF quantitation and 11 proteins using DML approach. Out of 48 significantly altering proteins in LF ap-proach, 20 were found in DML, and all 11 significantly changing proteins in DML were identified in LF approach also but they were not significant (p-value > 0.05 and found in 80% samples). TBI involves primary and sec-ondary insults characterized by a cascade of neurochemical events on the molecular level such as excitotoxic process triggered due to the excessive glutamate release from damaged cells, disrupted ionic hemostasis, metabolic changes and oxidative stress. These processes initiate secondary damage to the brain which may lead to further cell damage and neuronal degeneration. In Paper V, we showed several of the proteins that are involved with calci-um signaling, synaptic vesicle cycle and metabolic pathways such as glycol-ysis, tricarboxylic acid (TCA) cycle and oxidative phosphorylation were altering in levels in TBI patients.

10.5. Summary of Papers II-V The pathophysiology of AD is characterized by chronic, progressive neuro-degeneration. The precise etiology of AD is unclear and the characteristic features that are seen in AD involves synaptotoxicity and loss of neurophil, neurotransmitter disturbances, accumulation of extracellular Aβ plaques and intracellular NFTs, gliosis, chronic inflammatory reactions and oxidative stress70, 71. At later stages overt loss of neurons and associated brain atrophy can be seen in AD. The classical neuropathological hallmarks of AD brains are extracellular plaques and intracellular tangles.

In Papers II-IV, post-mortem AD brains were compared to control brains in order to find the alterations in the protein levels. In Paper V we used brain tissue biopsies from live TBI patients to compare with NPH subjects. We have employed quantitative stable isotope DML approach in Papers II & III, whereas LF quantitation approach in Paper IV. Both quantitation strategies have been applied in Paper V. The total number of proteins and significantly altering proteins in Papers I-V were shown in Table 2.

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Table 2. Number of proteins and significantly altering proteins in Papers II-V in-cluded in this thesis, ↑ means increasing in levels and ↓ means decreasing in levels

Paper No. Total no. of unique proteins

Significantly altering proteins disease vs controls(p-value<0.05)

Paper II 827 69 (37 ↑, 32 ↓) (≥ 90% samples)Paper III 1096 62 (31 ↑, 31 ↓) (≥ 75% samples)Paper IV 864 163 (42 ↑,121 ↓) Paper V LF: 516

DML: 246LF: 48 (42 ↑, 6 ↓) (> 80% samples) DML: 11 (8 ↑, 3 ↓) (> 80% samples)

By combining the data obtained from all the AD studies (Papers II-IV), a total of 232 (86 ↑ and 146 ↓) statistically significant proteins were found. All the protein names and the direction of changes in levels are shown in Table 3 in Appendix. Our data demonstrated that the alterations in the protein levels compromise multiple cellular pathways. Of the 232 proteins, several of them (22%) are involved with metabolic processes. The proteins which are in-creasing are mainly involved in energy metabolism pathways, glycolysis etc. Our results are in agreement with previous studies, showing that the markers associated with inflammation, mitochondrial dysfunction and glycolysis are upregulated during AD72-74. The proteins which are decreasing in levels are found to be involved with transport processes, especially vesicle mediated transport, synaptic vesicle cycle, calcium signaling pathways etc. Most of these proteins are found in synapse, myelin sheath, cell junction etc. This shows that due to the neurodegeneration, loss of synapses and myelin sheath in AD brains, the proteins that are localized are also decreasing in the levels.

Out of these 232 proteins, 10 proteins were common in all the three stud-ies (Papers II-IV) (Figure 9A) and are found to be involved in synaptic transmission, exocytosis, neurogenesis, axon development etc. Of these 10 proteins, 3 are increasing in levels and 7 proteins are decreasing in levels in AD brains compared to controls. Proteins that are increasing are glial fibril-lary acidic protein (GFAP), apolipoprotein D (APOD) and serum albumin (ALBU) whereas heat shock protein 90 alpha (HSP90A), 14-3-3 protein beta/alpha (1433B), profilin-2 (PROF2), septin-5 (SEPT5), endophilin-A1 (SH3G2), synapsin-1 (SYN1) and guanine nucleotide-binding protein beta 2 (GBB2) were decreasing in levels in AD compared to controls. Out of these 10 proteins, 2 proteins GFAP and SYN1 are also found in Paper V (Figure 9B). But the direction of fold change in Paper V, for these 2 proteins is in opposite direction i.e. GFAP is decreasing in levels whereas SYN1 is in-creasing in levels in TBI brains compared to NPH controls.

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Figure 9. Venn diagrams showing number of statistically significantly altering pro-teins (A) in Papers II-IV and (B) in all Papers II-V

Increased levels of GFAP protein is often associated with reactive astro-cytes and referred as a marker for astrogliosis75. Astrocytes generally secrete various neurotrophic factors for neuronal survival. During trauma, disease, genetic disorders, or chemical insult to the brain, severe activation of astro-cytes occurs that leads to secretion of neurotoxic substances and initiates inflammatory response causing neuronal death. In Paper V, it has been ob-served that GFAP protein level is decreasing in TBI compared to controls. It is in agreement with other studies showing a decrease in number of GFAP-positive astrocytes within the first 24 hours followed by their increase again after one day, in human TBI brains indicating formation of reactive gliosis76. The histology of NPH patients that are included as controls demonstrates gliosis. Previous studies with NPH patients have shown, the elevated GFAP levels in cerebrospinal fluid (CSF) compared to the healthy controls77, 78.

We have showed in Papers II-IV the changes in protein levels related to exo-endocytic processes in the AD brains. Out of 10 proteins that are com-mon in all AD studies (Figure 9), 4 proteins decreasing in AD brains are related to exo-endocytic processes. For example, proteins such as SYN1, SEPT5, PROF2 regulate synaptic vesicle exocytic process and SH3G2 regu-late synaptic vesicle endocytic process. The disruption of exo-endosomal network might lead to the accumulation of amyloid plaques in the long run during the disease progression causing irreversible damage to neuronal net-work. A higher proportion of the statistically significant proteins had lower levels in AD compared to control due to the neurodegeneration process (Ta-ble 2). This can be clearly seen that many of the proteins with lower expres-sion in AD are involved with the generation of neurons, indicating a dimin-ished cell generation and development in the AD brain compared to the healthy controls. Furthermore, we found lower level of various proteins in-

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volved in exo-endocytic process leading to lower activity of the mentioned processes in AD compared to healthy brain. Apart from the commonly found proteins in all 3 AD papers, some proteins are found only in one of the pa-pers i.e., 28 proteins (20 ↑ and 8 ↓) found in Paper II, 34 proteins (17 ↑ and 17 ↓) in Paper III and 118 proteins (29 ↑ and 89 ↓) in Paper IV only (Fig-ure 9A).

By looking at the proteins identified and quantified using different quanti-tation approaches such as DML and LF in combination with MS-based shot-gun proteomics, we can conclude that the results might vary depending on the techniques employed. Even though LF approach showed more proteins identified and quantified, DML also showed some unique proteins that are not found using LF. Each of these quantitation techniques has their own merits and demerits and depending on the purpose of the study and sample availability, the selection should be made carefully considering several fac-tors. LF approach is simple and straightforward with no extra sample prepa-ration steps for labelling the peptides/proteins. However, using LF technique it is hard to compare samples from different LC-MS runs and several MS parameters, internal retention time and ionization standards should be strictly controlled. Therefore labelling methods can be used to overcome these limi-tations. However, some of the labeling techniques employ expensive rea-gents and require high sample concentrations. It might be beneficial to apply labelling techniques when large cohorts of samples need to be analyzed. Since the samples can be labeled as duplex or triplex using DML, it could reduce the instrument usage time by two or three times compared to analyz-ing each sample individually in LF LC-MS approach.

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11. Conclusions and future work

Optimizing the sample preparation of proteins at different stages such as extraction, delipidation and digestion is the bottleneck for the successful identification and quantification of proteins from a biological sample with minimum bias. Depending on the nature of tissue and its availability, it is important to optimize the proteomics workflow to dig deeper into the prote-ome and to gain as much information as possible from the precious clinical samples. In Paper I, we have optimized the extraction with different lysis buffers for the brain tissue. It is found, that both non-ionic detergents n-octyl-β-D-glucoside and triton X-114 lysis buffers provided the highest total number of the proteins, as well as the number of membrane and transmem-brane proteins (Paper I). Therefore, these lysis buffers containing n-octyl-β-D-glucoside and Triton X-114 were applied in further studies with the brain tissues in Papers II-V. An additional advantage of non-ionic detergent Tri-ton X-114 is temperature induced pre-fractionation separation called as CPE technique. The hydrophobic compounds, e.g. membrane proteins, initially present in the solution and bound to the micelles are extracted to the surfac-tant-rich phase. We have investigated in Paper III, the possibility of using a DML quantitative proteomic strategy in combination with CPE method for the separation and enrichment of highly hydrophobic MPs followed by nanoLC-MS/MS analysis.

It is important to remember that there is no universal technique which can satisfy all the criteria in the proteomics, and better techniques may be devel-oped in future. There is also considerable scope to improve the methodology further. One of the developments in sample preparation is to incorporate LC-MS compatible detergents such as RapiGest SF, PPS silent surfactant, Invi-trosol, ProteaseMAX. RapiGest SF and PPS are acid labile and are hydro-lyzed at low pH, and the hydrolysis products are compatible with LC-MS. Invitrosol elution profile does not overlap with the peptides elution profiles and ProteaseMAX surfactant degrades during proteolysis and the degrada-tion products do not interfere with LC-MS. There is also scope to improve digestion by utilizing trypsin-LysC to improve the sequence coverage and reduce missed cleavages. However, the developed methods and applications in this thesis have provided us with valuable information on brain proteome profiles, which can serve as potential leads for developing future therapies.

Till-to-date there is no drug treatment available that can inhibit or reverse the neurodegenerative process in AD. It is evident that AD starts decades

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prior to its clinical presentation. By using advanced brain imaging tech-niques and combining data on tau and Aβ levels could enhance AD diagno-sis. Nevertheless, there is a need for broader biomarker investigations that should lead to better understanding of early disease mechanisms. Despite of providing in-depth information on the proteome profile changes during AD, brain biopsies are not very useful for routine biomarker analysis. In order to find diagnostic biomarkers, CSF is the choice of sample owing to its direct contact with the brain and spinal cord, and is commonly used an indicator of the pathological state of the central nervous system. For that reason, we are investigating the possibility to perform proteomic studies on CSF using a large cohort of patients suffering from neurological disorders. TBI is shown as a risk factor for developing neurodegenerative disorders such as AD, sev-eral years after the treatment. Therefore, it is beneficial to analyze CSF sam-ples from patients that have suffered with TBI at regular time intervals to find early biomarkers even after patient’s recovery. This might provide po-tential leads for finding early clinical protein markers in the CSF. However, using the currently available analytical tools and proteomic techniques, we have shown the alterations in protein levels that are involved in different bio-chemical pathways in the brain tissue during AD and TBI pathology.

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12. Svensk Sammanfattning

Hjärnans mysterier fascinerar inte bara den intresserade allmänheten utan även det vetenskapliga kollegiet. Den fortlöpande forskningen inom neuro-vetenskap har gett oss viktiga insikter om hjärnans struktur, molekyler och funktioner. Hjärnan består ett komplext trådbundet nätverk uppbyggt av neuroner och varje neuron kommunicerar med andra genom små, elektriskt styrda kemiska signalterminaler som kallas synapser. Hjärnan kan med hjälp av dessa nervceller utföra en rad viktiga funktioner såsom tänkande, minne, inlärning, styra muskelrörelser, samt kontrollera och registrera våra sju sin-nen (synsinnet, luktsinnet, hörseln, smaksinnet, känseln, kroppssinnet och balansen). Hjärncellerna fungerar som små fabriker som behöver konsumera energi och syre för att fungera korrekt. Men dessa system börjar brytas ner då man drabbas av neurologiska sjukdomar och andra skadetillstånd. En sådan sjukdom är Alzheimers sjukdom (AD) som orsakar minnesförlust, störd tankefunktion och beteendeförändringar, skilda från de som uppstår vid normalt åldrande. I hjärnregioner hos personer som drabbats av AD, kan man påvisa proteinaggregationer, s.k. senila plack, i närheten av nervcellerna och fibrillära proteinknippen inuti cellerna. Proteinfragment som kallas Amyloid beta peptider deponeras som plack och ett annat protein som kallas Tau bygger upp fibrillära proteinknippen. Traumatisk hjärnskada (TBI) är en annan neurologisk sjukdom som uppstår då hjärnan blir skadad på grund av ett plötsligt slag mot huvudet. Vid TBI hindras bland annat blodflödet att nå hjärnvävnaden i den skadade regionen vilket leder till en störd energibalans och syrebrist. Mängden av flera av de proteiner som är inblandade i energi-balansen ändras också vid TBI, antingen som en direkt effekt av skadan eller som en indirekt effekt av energibristen. Proteiner är alltså avgörande funkt-ionella molekyler i alla organismer och de förändringar i nivåer vi kan upp-mäta under sjukdomstillstånd, stress och miljöförändringar etc. kan göra att vi på ett bättre sätt kan förstå mekanismerna bakom dessa tillstånd. Prote-omik är ett samlingsnamn för metodologi som möjliggör en samtidig studie av struktur, funktioner och förändringar hos en mängd proteiner på samma gång. Bottom-up proteomik (även kallad shotgun – hagelsvärmsproteomik) är metoder som möjliggör analys av proteiner genom att initialt klippa sön-der dem med enzymer (proteolys) till peptider som sedan separeras och ka-rakteriseras. Alternativet är s.k. top-down proteomik då intakta proteiner analyseras direkt.

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Proteiner och peptider kan med fördel studeras med hjälp av en teknik som kallas masspektrometri (MS). En masspektrometer mäter joniserade molekylers massa och laddning (m/z). Detta uppnås genom en rad olika steg. Det första och kanske mest utmanande steget är att omvandla de fasta eller i vätska lösta molekylerna till joner i gasfas. Andra steget involverar separat-ion av dessa joner i en massanalysator, som baserat på molekyljonens m/z-värde skiljer ut den från andra med hjälp av magnetiska och/eller elektriska fält. Sista steget är att detektera de separerade jonerna och bestämma deras massa, laddning och mängd. Fram till slutet av 70-talet, var MS-applikationer helt begränsade till analys av flyktiga och lågmolekylära (med låg molekylvikt) analyter. Detta berodde främst på svårigheten att jonisera stora biomolekyler, såsom proteiner. Men med uppfinningen av två nya joni-seringstekniker - elektro spray jonisering (ESI) och matris-assisterad laser-desorptionsjonisering (MALDI) kom också möjligheten för MS-baserade metoder att introduceras inom såväl proteomik som annan biomedicinsk forskning. John Fenn och Koichi Tanaka fick Nobelpriset i kemi 2002 för sina insatser i utvecklingen av ESI respektive LDI tekniken. Vid ESI, den joniseringsteknik som har använts i denna avhandling, skapas små laddade analytinnehållande vätskedroppar m.h.a ett starkt elektriskt fält genom att låta vätskan passera genom en nål under hög spänning (2-5 kV). Laddade partiklar genereras när lösningsmedlet avdunstar och dropparna krymper. Laddningen i dropparna repellerar varandra vilket resulterar i s.k. Coulomb explosioner som leder till flerladdade stabila joner. ESI kan användas för att koppla t.ex. vätskekromatografi (LC) till MS och användas som kontinuerlig joniseringsmetod för både små och stora biomolekyler, såsom peptider eller proteiner. LC-tekniken hjälper till att separera proteiner eller peptider base-rade på deras polaritet innan MS-analysen. Enda kravet för denna metod är att provet bör vara lösligt, analyterna stabila i lösning och helst helt fria från föroreningar såsom icke-flyktiga buffertar, detergenter eller salter. Därför optimeras provberedningsstegen för att producera ESI-MS-kompatibla pro-ver. I denna avhandling har fokus varit att optimera provberedningen för att kunna analysera proteiner från hjärnvävnad.

Proteiner bör extraheras från vävnaden i en vattenlösning innan analysen. För att extraktionen skall kunna lyckas så måste cellerna i vävnaden lyseras (spräckas) m.h.a av såväl mekaniska verktyg som buffertar innehållande olika salter så som fosfater, detergenter, organiska syror etc. Dessa buffertar kan delvis utformas för att anrika specifika proteiner, men hjälper också till att hålla de extraherade proteinerna i lösning. I Paper I har just olika buffer-tar testats för att extrahera proteiner från hjärnvävnad. På grund av protei-nernas heterogena natur och olika egenskaper, så skiljer deras löslighet sig åt. Membranproteiner (MPs) är t.ex. väldigt svåra att lösa i vattenhaltiga lösningsmedel. Detergenter kan ge en lipid-liknande miljö i vatten och hjäl-per på så sätt till att lösa upp hydrofoba MPs. Vi har också visat att lyse-ringsbuffertar innehållande vissa detergenter presterar bättre när det gäller att

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utvinna fler antal proteiner ur vävnaden. Hjärnan är en mycket fet vävnad (ca 50% av vikten består av fettmolekyler) och extraktion av vävnaden ger inte bara proteiner utan också lipider och andra cellulära komponenter. Därför är olika proteinreningssteg såsom delipidisering nödvändigt för att isolera pro-teinerna och för att avlägsna andra föroreningar innan analysen. Metoderna som utvärderades i Paper I på hjärnvävnad från mus tillämpades senare i Paper II-V, men då på humana hjärnprover. I Paper II-V, utfördes masspektrometri-baserad proteomik samt kvantifieringstekniker för att söka skillnaden i hjärnans proteom vid sjukdom jämfört med friska kontroller. I Paper II-IV studerades hjärnvävnad från obduktion av AD hjärnor och friska avlidna kontroller, medan vi i Paper V fick en unik möjlighet att jämföra hjärnvävnadsprover från levande TBI patienter med patienter med hydroce-falus som krävt hjärnkirurgiska ingrepp. Vi har använt kvantitativ stabil iso-topmärkning i Paper II & III, medan icke inmärkt kvantifiering genomfördes i Paper IV. Båda kvantifierings-strategierna tillämpades i Paper V. AD och TBI orsakar lidande inte bara för de drabbade patienterna utan även för deras familjer och innebär dessutom en betydande socioekonomisk börda för sam-hället som helhet. TBI har också visat sig vara en potentiell riskfaktor för att utveckla AD i senare skeden av livet. Dessutom uppvisar AD och TBI en likhet i patofysiologiska mekanismer och kan således ha potentiella orsaks-samband. Genom att identifiera i väsentlig grad förändrade proteinmönster i hjärnan, kan vi inte bara bättre förstå de bakomliggande skadorna men även få uppslag till hur läkemedelsbehandlingar bör utvecklas för att förbättra patientens hälsa och livskvalitet.

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13. Acknowledgments

I would like to express my sincere gratitude to my supervisors Prof. Jonas Bergquist and Dr. Ganna Shevchenko for the continuous support during my PhD studies. Jonas, thank you for always giving me inspiration and encour-agement during my PhD studies and in my personal life too. You always encouraged me to try new things not only in research but also in other as-pects of life. I will always cherish the India and Japan conference trips with you. One simply could not wish for a better or friendlier supervisor. Anna, you have been a tremendous advisor and mentor for me. Your training and guidance has helped me to be what I am in research field today. You held my hand through-out my thesis and I am really thankful to you for your car-ing nature and always standing by me during most difficult times. You have guided me with immense patience when I came here as a novice. You have taught me discipline, hard work and dedication not by words but by your deeds. Also thanks for always inviting me to your home. I enjoyed the time spent with you talking about research and personal things. I can proudly say whatever I achieved during my PhD studies and will achieve later also in my career is all because of both of your support and encouragement. Words alone cannot express my appreciation for the valuable guidance and inspira-tion you both provided me throughout my studies.

Thanks to Uppsala Berzelii Center for giving me opportunity to work with various amazing people and experts in the field.

My special thanks to Dr. Magnus Wetterhall for showing me a new path in my scientific career. I am grateful that you also introduced me to Berzelii center. It is a pleasure to work with you and I hope to continue this in future too. Your scientific knowledge, leadership qualities and enthusiasm inspired me in many ways. I am indebted for all the support and encouragement you provided me. I learned how to enjoy the work by looking at you. Thank you for giving me so much positive energy and motivation.

I would like to express my sincere thanks to Dr. Kim Kultima, for helping me with the bioinformatics work and all the guidance you provided during my PhD studies. Your dedication, hard work and critical thinking have in-spired me a lot. I always feel awestruck with your dedication to produce high

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quality work. I really enjoyed all the discussion and your presentation during Berzelii meetings.

I thank my collaborators Prof. Lars Lannfelt, Dr. Martin Ingelsson, Dr. Joakim Bergström, Dr. Anna Erlandsson from the Department of Public Health/Geriatrics, Uppsala University, for their tremendous contribution and support to the project. It’s been a great pleasure working with you. Thank you Martin, for providing great feedback that improved manuscripts a lot. Your comments and feedback helped me to improve my writing skills. It has been a great experience working with my collaborators Prof. Niklas Marklund and Sami Abu Hamdeh, Uppsala University for providing the TBI samples without which the work in Paper V in this thesis would not have been possible. Thanks to Prof. Peter Nilsson and group, KTH, Scilife, Stockholm for providing us with Luminex results for my MS data validation. I enjoyed the nice and productive meetings with you all and learned several new things. My special thanks to all my collaborators, Prof. Torsten Gordh, Uppsala University; Bernhard Clemens Richard, Georg-August-University, Germa-ny; Andreas Warnecke, Karolinska Hospital, Stockholm; Prof. Peter Derrick and Dr. Ariane Kahnt, University of Auckland, New Zealand; Gülce Sari, Okan University, Istanbul; for introducing me to various challenging and stimulating projects that are fun to work with. Ariane, I will always cherish the nice times we spent in Uppsala. It is a great pleasure to work with you and learn from you at the same time. Thank you for teaching me the label free data analysis. Special thanks to Prof. Peter Bergsten and group, Department of Medical and Cell Biology, BMC, Uppsala University for permitting me to use the Western blot instrumentation. Thank you, Bernhard and Levon for teaching me how to do western blot analysis.

Dr. Konstantin Artemenko, thank you very much for your collaboration, interesting ideas and discussions. Thanks for teaching me about FTICR and other MS instruments. You are so amazing in explaining the concepts clearly and always lending a helping hand. I always enjoyed the fika talks apart from the work and I find your enthusiasm for traveling inspiring. Also my special thanks to Dr. Anne Konzer for nice and productive discussions. I am really glad I came to know you. I would like to express my sincere thanks to Dr. Margareta Ramström, for all the training sessions with the Orbitrap and nice discussions. Thank you for putting up with all my questions and an-swering them patiently.

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I would also like to thank Dr. Rolf Danielsson and Dr. Jia Mi, for their sup-port during data evaluation. Thank you for helping with the statistics by clear-cut explanations. Jia, thanks for always sharing your knowledge in data analysis when I am lost with numbers. Thanks for teaching me MaxQuant and bioinformatics parts.

I would also like to express my special thanks to Dr. Jean Pettersson for his short and sweet, enjoyable discussions. You are one of the most energetic people I have met and thank you for arranging extra-curricular activities for the whole department. You are the reason, why I stayed in Sweden so far and thought about doing a PhD after my Master’s program. Your kind and encouraging words during my master’s studies gave me courage to pursue the PhD here.

I would also like to thank Dr. Kumari Ubhayasekera for the nice suggestions pertaining to both personal and professional matters. You bring a lot of posi-tive energy and smiles with you. I enjoyed our US and shopping trips a lot. Thanks for inviting to your home and for the delicious Thank you for always listening to me and giving me nice advice.

Dr. Maria Lönnberg, thank you for teaching me dot-it-spot-it. You are a great teacher and a nice person to be with. I would also like to thank Dr. Per Sjöberg and Dr. Marit Andersson for their help during the teaching. Dr. Ro-land Pettersson, Dr. Sara Lind, Dr. Denys Shevchenko, Dr. Ingela Lanekoff, Dr. Jeff, Simone Hagfeldt for providing guidance and advice whenever I need. You guys make analysen a joyful place to work. Thank you, Dr. Bo Ek and Erik Forsman for the technical assistance with lab. Bo, thanks for help-ing me with my projects. Erik, without you it would have been hard to man-age student labs. Thanks always for all the help.

Payam, thanks a lot for being a great friend. You are so good in clarifying all my questions regarding the statistics. Thanks a lot for all the times spent in discussions and making nice figures in manuscript. You are a wonderful human being and I am glad I met you. My special thanks go to Anne-Li and Ping, for making my days so much fun. I really enjoyed all our sleepless nights during the conference and very productive discussions (data not shown). We should plan our world travel soon. Anne-Li and Andreas, you both are so awesome in every aspect. I will never forget the love, care and warmth you shared with me. Ping thanks for arranging the fun and relaxing sleepovers at your place in Stockholm.

My PhD life would not have been so interesting without my office mates Julia, Marcus, Anne-Li. Usually ‘one does not simply walk into Mordor’, but our Mordor is such a fun place that after entering it ‘one simply does not

58

want to walk out’. Marcus thanks a lot for always listening to my pre-presentations and giving me great feedback. Apart from work, I enjoyed our chit-chat in office about several things for several hours. Thanks for keeping Mordor’ still a fun place to be.

Hilde, you are one of my best buddies. Talking with you always makes me happy. I am looking forward for our gardening adventures this year too. Neil, thanks for all the fun times and helping me with my move into a new home. Thank you Mårten, Malin, Claudia, Julia (Jr), Torgny, Barbro, David, Marcus, Amelie, Lena, Mikael, Katarina, Warunika, Alberto, Alexander for being joyful companions and our fun time fika talks.

I would also like to thank Fredrika Viktorsson, Eva Ohlsson, Lina Julin for the assistance during administrative work in addition to the fun fika time chats. I would like to thank Dr. Francoise for helping me with my mentor program in teachers training course. I would also like to thank Dr. Andreas Dahlin for helping to make nice figures in manuscript and Bo Fredriksson, IT department for helping me to fix my computer always.

I thank all my friends and colleagues at Analytical Chemistry, for amazing work environment and making my time fun and enjoyable at the department. I will always cherish the moments spent with all of you.

My friends outside the lab Sriram, Hari, Naresh, Nagarjuna, Huda & Javeed, Chandana & Maruthi, Pratyusha & Varun, Kanaka & Manoj, Spandana & Suri, Aravind, Roshan, Rohit, Venkat, Phani, Asawari & Devashish, Veena & Ranjeeth, Santosh, Ramesh, Sarita & Mahesh, Sreenu for all the movies, dinners, late night talks and our weekend get together. You guys have be-come my Uppsala family and without you all it would have been difficult for me to stay here for such a long time. Thanks for always reminding me to have little fun apart from my work. I want to express my heartfelt gratitude to my late uncle’s Srinivas (VVS Chowdary) and M. Bhaskar Rao who always supported, encouraged and cheered me up in all aspects of life. I miss you both and your memory will be eternal. Last but not least, I would like to thank my family, parents Rama Krishna Prasad and Manga Devi and my brother Vishnu for their constant support and encouragement throughout my life. Words are not enough to express my gratitude to you. Without your guidance and support all this would not have been possible. Your selfless time and care were sometimes all that kept me going.

59

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15. Appendix

Table 3. List of 232 significantly altering proteins in AD brains compared to con-trols, found in Papers II-IV included in this thesis. ↑ means increasing in levels and ↓ means decreasing in levels. Protein IDs and names highlighted in blue and red color shows their presence in hydrophilic and hydrophobic phases in Paper III

Found in Pa-pers

ID Description Change in levels

II, III, IV

1433B 14-3-3 protein beta/alpha ↓

II, III, IV

PROF2 Profilin-2 ↓

II, III, IV

HS90A Heat shock protein HSP 90-alpha ↓

II, III, IV

SH3G2 Endophilin-A1 ↓

II, III, IV

SYN1 Synapsin-1 ↓

II, III, IV

SEPT5 Septin-5 ↓

II, III, IV

ALBU Serum albumin ↑

II, III, IV

GFAP Glial fibrillary acidic protein ↑

II, III, IV

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

↓

II, III, IV

APOD Apolipoprotein D ↑

II, III BAP31 B-cell receptor-associated protein 31 ↓

II, III GDIA Rab GDP dissociation inhibitor alpha ↑

II, III KPYM Pyruvate kinase isozymes M1/M2 ↑

II, III 6PGD 6-phosphogluconate dehydrogenase, decarboxylating ↑

II, III SEPT2 Septin-2 ↑

II, III GSTP1 Glutathione S-transferase P ↑

II, III ENOA Alpha-enolase ↑

II, IV TPPP Tubulin polymerization-promoting protein ↓

II, IV NEUM Neuromodulin ↓

II, IV GDIR1 Rho GDP-dissociation inhibitor 1 ↓

II, IV PRDX6 Peroxiredoxin-6 ↑

II, IV CLH1 Clathrin heavy chain 1 ↓

65

II, IV PACN1 Protein kinase C and casein kinase substrate in neu-rons protein 1

↓

II, IV OPCM Opioid-binding protein/cell adhesion molecule ↓

II, IV SNG1 Synaptogyrin-1 ↓

II, IV GRP75 Stress-70 protein, mitochondrial ↓

II, IV CYC Cytochrome c ↓

II, IV STX1A Syntaxin-1A ↓

II, IV PRRT2 Proline-rich transmembrane protein 2 ↓

II, IV ACTN1 Alpha-actinin-1 ↓

II, IV 1433Z 14-3-3 protein zeta/delta ↓

II, IV CADM2 Cell adhesion molecule 2 ↓

II, IV CEND Cell cycle exit and neuronal differentiation protein 1 ↓

II, IV AP2A1 AP-2 complex subunit alpha-1 ↓

II, IV COF1 Cofilin-1 ↓

II, IV ANXA5 Annexin A5 ↑

II, IV HEM2 Delta-aminolevulinic acid dehydratase ↑

II, IV CAH2 Carbonic anhydrase 2 ↑

II, IV DHPR Dihydropteridine reductase ↑

II, IV FRIL Ferritin light chain ↑

II, IV SEPT3 Neuronal-specific septin-3 ↑

III, IV E41L3 Band 4.1-like protein 3 ↓

III, IV COR1A Coronin-1A ↓

III, IV 1433G 14-3-3 protein gamma ↓

III, IV MARE3 Microtubule-associated protein RP/EB family mem-ber 3

↓

III, IV PGAM1 Phosphoglycerate mutase 1 ↓

III, IV 1433T 14-3-3 protein theta ↓

III, IV SERC Phosphoserine aminotransferase ↑

III, IV HPT Haptoglobin ↑

III, IV VIME Vimentin ↑

III, IV NB5R3 NADH-cytochrome b5 reductase 3 ↑

III, IV AOFB Amine oxidase [flavin-containing] B ↑

II TBB3 Tubulin beta-3 chain ↓

II AT2B1 Plasma membrane calcium-transporting ATPase 1 ↓

II AT2B3 Plasma membrane calcium-transporting ATPase 3 ↓

II SV2A Synaptic vesicle glycoprotein 2A ↓

II BASP1 Brain acid soluble protein 1 ↓

II ODO2 Dihydrolipoyllysine-residue succinyltransferase com-ponent of 2-oxoglutarate dehydrogenase complex, mitochondrial

↓

II QCR1 Cytochrome b-c1 complex subunit 1, mitochondrial ↓

66

II NTRI Neurotrimin ↓

II THIL Acetyl-CoA acetyltransferase, mitochondrial ↑

II ALDOC Fructose-bisphosphate aldolase C ↑

II ANXA6 Annexin A6 ↑

II NCAM2 Neural cell adhesion molecule 2 ↑

II PRDX2 Peroxiredoxin-2 ↑

II PGK1 Phosphoglycerate kinase 1 ↑

II MDHC Malate dehydrogenase, cytoplasmic ↑

II DPYL2 Dihydropyrimidinase-related protein 2 ↑

II HSP71 Heat shock 70 kDa protein 1A/1B ↑

II PRDX1 Peroxiredoxin-1 ↑

II ALDH2 Aldehyde dehydrogenase, mitochondrial ↑

II LDHB L-lactate dehydrogenase B chain ↑

II CATD Cathepsin D ↑

II AATC Aspartate aminotransferase, cytoplasmic ↑

II GDIB Rab GDP dissociation inhibitor beta ↑

II PLLP Plasmolipin ↑

II G3P Glyceraldehyde-3-phosphate dehydrogenase ↑

II FRIH Ferritin heavy chain ↑

II TKT Transketolase ↑

II SIRT2 NAD-dependent deacetylase sirtuin-2 ↑

III OLA1 Obg-like ATPase 1 ↓

III TBB2C Tubulin beta-2C chain ↓

III ODPA Pyruvate dehydrogenase E1 component subunit alpha, somatic form, mitochondrial

↓

III DTD1 D-tyrosyl-tRNA(Tyr) deacylase 1 ↓

III ENPL Endoplasmin ↓

III AP2M1 AP-2 complex subunit mu ↓

III CYFP2 Cytoplasmic FMR1-interacting protein 2 ↓

III CALX Calnexin ↓

III SEPT8 Septin-8 ↓

III TERA Transitional endoplasmic reticulum ATPase ↑

III HBA Hemoglobin subunit alpha ↑

III PEBP1 Phosphatidylethanolamine-binding protein ↑

III NCAM1 Neural cell adhesion molecule 1 ↑

III DPYL3 Dihydropyrimidinase-related protein 3 ↑

III AL9A1 4-trimethylaminobutyraldehyde dehydrogenase ↑

III DDAH1 N(G),N(G)-dimethylarginine dimethylaminohydrolase 1

↑

III NFL Neurofilament light polypeptide ↑

67

III SYWC Tryptophanyl-tRNA synthetase, cytoplasmic ↑

III ESTD S-formylglutathione hydrolase ↑

III SIRBL Signal-regulatory protein beta-1 isoform 3 ↓

III NCDN Neurochondrin ↓

III AT2A2 Sarcoplasmic/endoplasmic reticulum calcium ATPase 2

↓

III GBRA1 Gamma-aminobutyric acid receptor subunit alpha-1 ↓

III VATH V-type proton ATPase subunit H ↓

III PHB Prohibitin ↓

III PHB2 Prohibitin-2 ↓

III GRIA2 Glutamate receptor 2 ↓

III S6A17 Orphan sodium- and chloride-dependent neurotrans-mitter transporter NTT4

↑

III RAC1 Ras-related C3 botulinum toxin substrate 1 ↑

III RAB5C Ras-related protein Rab-5C ↑

III CI005 Transmembrane protein C9orf5 ↑

III BASI Basigin ↑

III SORT Sortilin ↑

III LAMP1 Lysosome-associated membrane glycoprotein 1 ↑

IV TBB4B Tubulin beta-4B chain ↓

IV STXB1 Syntaxin-binding protein 1 ↓

IV 1433F 14-3-3 protein eta ↓

IV CALM Calmodulin ↓

IV AT5F1 ATP synthase F ↓

IV UCHL1 Ubiquitin carboxyl-terminal hydrolase isozyme L1 ↓

IV EAA2 Excitatory amino acid transporter 2 ↓

IV LETM1 LETM1 and EF-hand domain-containing protein 1, mitochondrial

↓

IV AMPH Amphiphysin ↓

IV MOES Moesin ↓

IV SYUA Alpha-synuclein ↓

IV SNP25 Synaptosomal-associated protein 25 ↓

IV IDHP Isocitrate dehydrogenase [NADP], mitochondrial ↓

IV ICAM5 Intercellular adhesion molecule 5 ↓

IV HSP72 Heat shock-related 70 kDa protein 2 ↓

IV REEP5 Receptor expression-enhancing protein 5 ↓

IV VATB2 V-type proton ATPase subunit B, brain isoform ↓

IV FPPS Farnesyl pyrophosphate synthase ↓

IV NPTN Neuroplastin ↓

IV VATD V-type proton ATPase subunit D ↓

IV IGKC Ig kappa chain C region ↓

68

IV PADI2 Protein-arginine deiminase type-2 ↓

IV PI42A Phosphatidylinositol 5-phosphate 4-kinase type-2 alpha

↓

IV SEPT6 Septin-6 ↓

IV PRDX3 Thioredoxin-dependent peroxide reductase, mito-chondrial

↓

IV SYNJ1 Synaptojanin-1 ↓

IV ECHA Trifunctional enzyme subunit alpha, mitochondrial ↓

IV SPTN1 Spectrin alpha chain, non-erythrocytic 1 ↓

IV ATPA ATP synthase subunit alpha, mitochondrial ↓

IV PRP39 Pre-mRNA-processing factor 39 ↓

IV SCRN1 Secernin-1 ↓

IV SDHA Succinate dehydrogenase [ubiquinone] flavoprotein subunit, mitochondrial

↓

IV NDUS3 NADH dehydrogenase [ubiquinone] iron-sulfur pro-tein 3, mitochondrial

↓

IV GBB5 Guanine nucleotide-binding protein subunit beta-5 ↓

IV AP2A2 AP-2 complex subunit alpha-2 ↓

IV RAB2A Ras-related protein Rab-2A ↓

IV WASF1 Wiskott-Aldrich syndrome protein family member 1 ↓

IV STX1B Syntaxin-1B ↓

IV SHPS1 Tyrosine-protein phosphatase non-receptor type sub-strate 1

↓

IV HPLN2 Hyaluronan and proteoglycan link protein 2 ↓

IV TBB2A Tubulin beta-2A chain ↓

IV RHOB Rho-related GTP-binding protein RhoB ↓

IV PLPP Pyridoxal phosphate phosphatase ↓

IV MAP1B Microtubule-associated protein 1B ↓

IV CX7A2 Cytochrome c oxidase subunit 7A2, mitochondrial ↓

IV NEGR1 Neuronal growth regulator 1 ↓

IV VATG2 V-type proton ATPase subunit G 2 ↓

IV SYT1 Synaptotagmin-1 ↓

IV CC168 Coiled-coil domain-containing protein 168 ↓

IV VDAC1 Voltage-dependent anion-selective channel protein 1 ↓

IV GBG12 Guanine nucleotide-binding protein G ↓

IV AT2B2 Plasma membrane calcium-transporting ATPase 2 ↓

IV PPIA Peptidyl-prolyl cis-trans isomerase A ↓

IV FGFR3 Fibroblast growth factor receptor 3 ↓

IV ESIP1 Epithelial-stromal interaction protein 1 ↓

IV NDUAC NADH dehydrogenase [ubiquinone] 1 alpha subcom-plex subunit 12

↓

69

IV SNAB Beta-soluble NSF attachment protein ↓

IV PTGDS Prostaglandin-H2 D-isomerase ↓

IV SNAA Alpha-soluble NSF attachment protein ↓

IV GPM6B Neuronal membrane glycoprotein M6-b ↓

IV PLCB1 1-phosphatidylinositol 4,5-bisphosphate phosphodi-esterase beta-1

↓

IV VAPB Vesicle-associated membrane protein-associated protein B/C

↓

IV SSDH Succinate-semialdehyde dehydrogenase, mitochondrial

↓

IV VAMP2 Vesicle-associated membrane protein 2 ↓

IV IDH3A Isocitrate dehydrogenase [NAD] subunit alpha, mitochondrial

↓

IV CRYM Ketimine reductase mu-crystallin ↓

IV VPP1 V-type proton ATPase 116 kDa subunit a isoform 1 ↓

IV SAHH Adenosylhomocysteinase ↓

IV VISL1 Visinin-like protein 1 ↓

IV TPIS Triosephosphate isomerase ↓

IV ADDB Beta-adducin ↓

IV SHLB2 Endophilin-B2 ↓

IV EZRI Ezrin ↓

IV NDUS1 NADH-ubiquinone oxidoreductase 75 kDa subunit, mitochondrial

↓

IV SPTB2 Spectrin beta chain, non-erythrocytic 1 ↓

IV RAB3A Ras-related protein Rab-3A ↓

IV LY6H Lymphocyte antigen 6H ↓

IV ACON Aconitate hydratase, mitochondrial ↓

IV TMOD2 Tropomodulin-2 ↓

IV ODP2 Dihydrolipoyllysine-residue acetyltransferase compo-nent of pyruvate dehydrogenase complex, mitochon-drial

↓

IV CDC37 Hsp90 co-chaperone Cdc37 ↓

IV NRCAM Neuronal cell adhesion molecule ↓

IV QCR8 Cytochrome b-c1 complex subunit 8 ↓

IV GSTO1 Glutathione S-transferase omega-1 ↓

IV DPP6 Dipeptidyl aminopeptidase-like protein 6 ↓

IV SBP1 Selenium-binding protein 1 ↓

IV VATC1 V-type proton ATPase subunit C 1 ↓

IV ODPB Pyruvate dehydrogenase E1 component subunit beta, mitochondrial

↓

IV TENR Tenascin-R ↓

IV PRDX5 Peroxiredoxin-5, mitochondrial ↓

70

IV SH3G3 Endophilin-A3 ↓

IV CSPG2 Versican core protein ↑

IV QCR2 Cytochrome b-c1 complex subunit 2, mitochondrial ↑

IV AMPL Cytosol aminopeptidase ↑

IV HECAM Hepatocyte cell adhesion molecule ↑

IV TALDO Transaldolase ↑

IV MPCP Phosphate carrier protein, mitochondrial ↑

IV GBP5 Guanylate-binding protein 5 ↑

IV VAT1 Synaptic vesicle membrane protein VAT-1 homolog ↑

IV CATA Catalase ↑

IV PIMT Protein-L-isoaspartate ↑

IV SIR2 NAD-dependent protein deacetylase sirtuin-2 ↑

IV HSP7C Heat shock cognate 71 kDa protein ↑

IV NCEH1 Neutral cholesterol ester hydrolase 1 ↑

IV CXA1 Gap junction alpha-1 protein ↑

IV G6PI Glucose-6-phosphate isomerase ↑

IV ERO1A ERO1-like protein alpha ↑

IV SCOT1 Succinyl-CoA:3-ketoacid coenzyme A transferase 1, mitochondrial

↑

IV ANXA1 Annexin A1 ↑

IV GLNA Glutamine synthetase ↑

IV SATT Neutral amino acid transporter A ↑

IV F213A Redox-regulatory protein FAM213A ↑

IV CTL1 Choline transporter-like protein 1 ↑

IV CD9 CD9 antigen ↑

IV HNRPK Heterogeneous nuclear ribonucleoprotein K ↑

IV CAP1 Adenylyl cyclase-associated protein 1 ↑

IV KCRB Creatine kinase B-type ↑

IV PAK1 Serine/threonine-protein kinase PAK 1 ↑

Acta Universitatis UpsaliensisDigital Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Science and Technology 1347

Editor: The Dean of the Faculty of Science and Technology

A doctoral dissertation from the Faculty of Science andTechnology, Uppsala University, is usually a summary of anumber of papers. A few copies of the complete dissertationare kept at major Swedish research libraries, while thesummary alone is distributed internationally throughthe series Digital Comprehensive Summaries of UppsalaDissertations from the Faculty of Science and Technology.(Prior to January, 2005, the series was published under thetitle “Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Science and Technology”.)

Distribution: publications.uu.seurn:nbn:se:uu:diva-277613

ACTAUNIVERSITATIS

UPSALIENSISUPPSALA

2016