Biology and Biomarkers in Organ Failure

Dr. Paul Keown, 2013

University of British Columbia, PROOF Research Centre

2

The sequence of organ disease

Transplantation/

Assist Devices

End-stage

Markers of

Organ Failure

Recurrent Native

Disease/Transplant

Organ Failure

“Recovered” Organ

Function

Baseline Risk

Disease Presence

Disease

Progression

Org

an F

unct

ion

(%)

Time (years)

Earlier

Intervention

• Biomarker panel

opportunity

Intervention point

Improved Organ

Function

3

Renal failure and uremia

4

Gene expression in uremia

Comparing the gene expression patterns with those

from healthy volunteers provides insight into the

biology of uremia as it manifests in the periphery.

Total of 12,933 transcripts representing 9,165 genes

are differentially expressed, with FC values ranging

from -5.3 to +6.8. Over 2/3 are down-regulated.

Differentially expressed genes and pathways reflect

many known biological processes comprising the

uremia syndrome, such as micro-inflammation and

bone remodeling.

5

Gene expression in uremia

(A) Contribution of variation to the dataset. In a multifactorial ANOVA model, the sources of variation in the dataset were estimated. The

presence or absence of uremia (“Uremia”) has the largest influence on the variation in the dataset, while “primary kidney disease” (PKD),

with the subgroups of normal, DM, GN, PCKD, other, and “no PKD”, has the least influence. The x-axis represents the factors in the ANOVA

model, the y-axis the F-ratio (signal to noise ratio) of the factors. The Average F Ratio is the average signal to noise ratio (mean square within

groups to mean square between groups) of all computed variables for each factor. “Error” is random within-group noise.

(B) Principal component analysis (PCA) with 36 probe sets identified in a 2-way ANOVA model which included PKD and Dialysis Type, but no

normals. The probe sets have a p-value for PKD <0.01 and for Dialysis Type >0.01. The balls are the centroids for each clinical group, the

endpoints of the vectors locate the samples of each group in the 3-dimensional space. DM tends to be separated from the other three

groups, mostly because of two samples.

6

Gene expression in uremia

Blue dots represent enriched probe sets of the gene set, blue circles represent probe sets of the gene set that are not

enriched, and grey dots represent all other probe sets on the array. X and Y axes are mean signal intensities in log2

scale. Source: http://www.broadinstitute.org/gsea/msigdb/index.jsp, MSigDB database v3.0 updated Sep 9, 2010.

7

Gene expression in uremia Principal gene pathways p Value Ratio

Transport: Clathrin-coated vesicle cycle 8.039E-23 60 / 71

Cytoskeleton remodeling: Cytoskeleton remodeling 3.226E-17 70 /102

Development: EPO-induced Jak-STAT pathway 2.658E-16 33 /35

Translation: Regulation of EIF4F activity 2.083E-15 43 /53

Chemotaxis: CXCR4 signaling pathway 2.445E-14 31 /34

Development: GM-CSF signaling 4.953E-14 40 /50

Immune response: T cell receptor signaling pathway 5.938E-14 41 /52

Immune response: IL-2 activation and signaling pathway 1.410E-13 39 /49

Oxidative phosphorylation 1.787E-13 66 /105

Immune response: Immunological synapse formation 2.407E-13 44 /59

Development: Flt3 signaling 2.595E-13 36 /44

Cell cycle: Influence of Ras and Rho proteins on G1/S Transition 1.552E-12 40 /53

Immune response: Role of DAP12 receptors in NK cells 4.346E-12 40 /54

Immune response: BCR pathway 4.346E-12 40 /54

Transcription: NF-kB signaling pathway 4.945E-12 32 /39

Development: EGFR signaling pathway 1.026E-11 44 /63

8

Biological features of uremia

Bone metabolism: PTH gene expression is enhanced. Wnt signaling pathway, represented by Casein kinase 1, Rac1, c-Fos, and p130. Smad2 and Smad4, TGFBR2 and other members of the TGF-beta and BMP pathways, among the most highly dysregulated probe sets in uremia.

Glucose intolerance: Insulin receptor gene (INSR) expression is increased but transcription of insulin receptor substrate 2 (IRS2) is reduced. This cytoplasmic signaling molecule mediates the effects of insulin, as a molecular adaptor. Mice lacking IRS2 have a diabetic phenotype.

Protein-calorie malnutrition; Transcription of Ghrelin and Leptin genes was not altered, but leptin receptor overlapping transcript (LEPROT) and transcript-like 1 (LEPROTL1) were increased, which may influence receptor expression and signaling . IGF receptor-1 expression was suppressed and post-receptor signaling down-regulated, which may influence protein synthesis, muscle and bone metabolism. AKTIP was down-regulated, and insulin resistance may promote muscle wasting by inhibition of PI3K/Akt leading to activation of caspase 3 and the ubiquitin-proteasome pathway.

9

Biological features of uremia

Blood disorders: EPO receptor gene expression up-regulated, while down-stream signaling steps are repressed. Effect on platelet function reflected by changes in PKCeta, Rac1, ATP2A3 and GP-IB (platelet glycoprotein I beta) and “platelet aggregation” network genes.

Endosomal pathway; transcripts associated with the clathrin-coated vesicle endosomal pathway are markedly reduced consistent with a defect in phagocytosis.

Immune response; Gene expression associated with the complement pathway is increased, while key genes in the immune synapse and the T-cell receptor signaling pathway were reduced, including MHC-class II and the T-cell receptor alpha / beta heterodimer, the co-associated CD3 and CD4 molecules and a variety of downstream signaling components of the T-cell receptor pathway, the CD28 receptor pathway and the IL-2 response and signaling pathway.

10

Changes in endosomal pathway

11

Immunity and inflammation

A. Transcripts for many key cytokines are

elevated in chronic renal failure, HD and

PD (many peaking in PD), but expression

levels return towards normal after

transplantation

B. Transcripts for many key chemokines

are suppressed in chronic renal

failure, HD and PD (many reaching a

nadir in HD and PD), but expression

levels return towards normal after

transplantation

12

Principal pathways c-Myc & SP1

Blue wavy icons: generic binding proteins, yellow arrows: generic enzymes, green arrows: regulators. Blue

dots: under-represented, Red dots: over-represented. The complete legend can be found at:

http://www.genego.com/pdf/MC_legend.pdf

13

Vital organ failure and replacement

14

Site of action of therapeutics

Samaniego M et al. Nat Clin Pract Neprol 2006;2: 688–699

15

Surgical transplantation m

ea

n s

tan

da

rdiz

ed

lo

g2

(exp

ressio

n v

alu

e)

BL W1 W2 W3 W4 W8 W12

-10

12

1. Chemotaxis and cell migration

2. Inflammation and innate immunity

3. Adaptive immunity (T- and B-cell)

4. Wounding and tissue healing

5. Other biological, cellular processes

me

an

sta

nd

ard

ize

d lo

g2

(exp

ressio

n v

alu

e)

BL W1 W2 W3 W4 W8 W12

-2-1

01

1. Defense response to infection

2. Embryonic growth and development

3. Innate immune response

4. Adaptive immunity

5. Other biological, cellular processes

16

Post-transplant rehabilitation

17

Gene signatures in quiescence

1. Energy and transport regulation

2. Immune defense and antibodies

3. Nuclear transport and signaling

4. Control of intermediary metabolism

5. Other biological, cellular processes

18

Gene expression in quiescence

Differential expression of 3773 probe-sets

Category C: Below normal at W1, below normal for W2-W12

me

an

sta

nd

ard

ize

d lo

g2

(exp

ress

ion

va

lue

)

BL W1 W2 W3 W4 W12

-8-6

-4-2

0

19

B-cell gene expression in quiescence

20

Gene expression in rejection

Acute Rejection Normal No Rejection

202531_at202510_s_at201861_s_at1553297_a_at203591_s_at217992_s_at224909_s_at212550_at217436_x_at210514_x_at204166_at37028_at200852_x_at211251_x_at202216_x_at202150_s_at211072_x_at201090_x_at213646_x_at211058_x_at212639_x_at211750_x_at209083_at201950_x_at200709_at203254_s_at212974_at211521_s_at1557924_s_at218380_at221432_s_at236155_at201531_at227396_at212708_at208885_at211795_s_at1555852_at210191_s_at1568609_s_at228582_x_at208811_s_at224566_at215832_x_at200796_s_at215236_s_at207782_s_at221695_s_at216985_s_at238320_at216236_s_at228216_at1555467_a_at233303_at1555420_a_at241774_at235167_at1552542_s_at211797_s_at226334_s_at207127_s_at220046_s_at212036_s_at201970_s_at201729_s_at201440_at208922_s_at208772_at203624_at202951_at244356_at200739_s_at1565717_s_at213505_s_at210190_at1554691_a_at201651_s_at211823_s_at1565599_at205921_s_at210787_s_at203239_s_at211996_s_at1553186_x_at224254_x_at1558448_a_at205539_at1555797_a_at210992_x_at211395_x_at1552264_a_at203471_s_at200797_s_at210563_x_at205220_at234640_x_at222955_s_at222435_s_at242907_at213596_at207446_at215415_s_at209060_x_at228793_at237442_at237544_at223591_at201473_at203233_at208018_s_at219394_at215990_s_at202897_at208919_s_at209868_s_at207266_x_at203748_x_at208488_s_at1569003_at208702_x_at215646_s_at211571_s_at217475_s_at226266_at222244_s_at201954_at200805_at223009_at225673_at220326_s_at226872_at210484_s_at210754_s_at211794_at207643_s_at200904_at227490_at203509_at202910_s_at211974_x_at224807_at202423_at244556_at205285_s_at217728_at219183_s_at1563509_at202180_s_at217507_at210569_s_at210483_at239021_at212680_x_at232555_at236528_at230735_at238712_at244752_at227697_at206130_s_at1555950_a_at216950_s_at209286_at210184_at215760_s_at240057_at211787_s_at200959_at220305_at214369_s_at201043_s_at227510_x_at219100_at215210_s_at223578_x_at204978_at210686_x_at218157_x_at229120_s_at211454_x_at208120_x_at206323_x_at1565484_x_at

21

Enriched ontology pathways

22

Signaling pathways over-expressed

Actin cytoskeleton

• actin cytoskeleton bundled at the site of MHC-peptide / TCR engagement,

• mediated by structural proteins like SLP-76, ADAP, CDC24EP, and LCP-2

•achieved through talin, pixallin, both increased in BCAR

JAK tyrosine kinase / STAT transcription factor

• responsible for immune cell development, proliferation and function

• important in T, B and NK cell activation

• increase in all 4 JAK family kinases, and in STAT 3, 5 (IL6R, IL2R) and 6 (IL4R)

Interferon signaling

• central role in rejection, T-cell toxicity, NK activity and MHC antigen expression

• increase in interferon-inducible guanylate binding protein (GBP),

• increase in interferon response factor 1, STAT-1

23

T-cell surface recognition

T-Cell

APC

CD3

LFA-1

CD3

LFA-1

Immunological

quiescence

Antigen

recognition

Synapse

formation

24

Biomarker selection, validation

66% NR 33% AR

>80 Renal

Allograft Recipients Training

Cohort

INTERNALLY VALIDATED 10 GENE BIOMARKER PANEL

Test Cohort: Panel

Performance

Normalization and pre-filtering;

Liberal to Restrictive

4-27,000 probe sets

Ranking and filtering;

False Discovery Rate <0.05

Fold Change >1.4

50-500 probe sets

Classification, Cross Validation

Technical / Biological Validation

~54,000 probe sets

Whole blood Affymetrix

microarrays

No Rejection

(0)

Rejection

(Banff ≥ 1) 3-65 probe sets

25

Biomarker selection, validation

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Classifier 1

False positive rate

Tru

e p

ositi

ve r

ate

AUC=0.9627

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Classifier 2

False positive rate

Tru

e p

ositi

ve r

ate

AUC=0.9668

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Classifier 3

False positive rate

Tru

e p

ositi

ve r

ate

AUC=0.9611

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Classifier 4

False positive rate

Tru

e p

ositi

ve r

ate

AUC=0.9182

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Classifier 5

False positive rate

Tru

e p

ositi

ve r

ate

AUC=0.9165

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Classifier 6

False positive rate

Tru

e p

ositi

ve r

ate

AUC=0.9293

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Classifier 7

False positive rate

Tru

e p

ositi

ve r

ate

AUC=0.9549

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Classifier 8

False positive rate

Tru

e p

ositi

ve r

ate

AUC=0.9132

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

Classifier 9

False positive rate

Tru

e p

ositi

ve r

ate

AUC=0.9326

26

Biomarker probe sets

FDR (LIMMA) (9 classifiers)

FD

R

0e

+0

01

e-0

42

e-0

43

e-0

44

e-0

45

e-0

46

e-0

4

CD

C4

2S

E1

RP

L3

8

TM

EF

F2

FK

SG

49

SL

C2

5A

16

22

94

20

_a

t

22

43

46

_a

t

CD

C4

2S

E1

MA

LA

T1

DF

FA

15

66

34

2_

at

GR

AM

D1

A

FA

M7

8B

LO

C1

00

13

31

09

RP

L2

7A

Probe-set Frequency (9 classifiers)

fre

qu

en

cy

0.0

0.2

0.4

0.6

0.8

1.0

CD

C4

2S

E1

22

94

20

_a

t

DF

FA

RP

L3

8

SL

C2

5A

16

22

43

46

_a

t

LO

C1

00

13

22

47

/// L

OC

34

81

62

/// L

OC

61

30

37

/// L

OC

72

88

88

/// N

PIP

L3

PT

PR

A

TM

EF

F2

FK

SG

49

SL

AM

F6

15

66

34

2_

at

ZN

F5

75

CD

C4

2S

E1

27

Vital organ failure

-5 0 5

0.0

00

.05

0.1

00

.15

0.2

0

Linear Discriminant ScoreA

UC

ST

AR

T

CD

C4

2S

E1

RP

L3

8

TM

EF

F2

FK

SG

49

SL

C2

5A

16 ---

---

CD

C4

2S

E1

MA

LA

T1

DF

FA

0.0

0.2

0.4

0.6

0.8

1.0

28

Plasma proteome in uremia

http://www.acponline.org/about_acp/chapters/az/mtg06_blair.pdf

Protein Function

Lipopolysaccharide-binding protein precursor LPS-TRL4 binding

Vasorin precursor TGF-b binding protein, kidney, vessels

Ceruloplasmin precursor acute phase reactant, copper transport

Hepatocyte growth factor precursor inflammation, remodeling

Peptidase inhibitor 16 precursor protease inhibitors, Serpins

Complement factor D alternate pathway, complement system

Complement component C2 classical path, complement system

Mannose binding protein C precursor complement system

Protein z-dependent protease inhibitor Serpin, coagulation system, factor Xa, XIa

Complement component 9 precursor complement system

Beta-2 microglobulin MHC, renal disease

Complement c1s subcomponent classical pathway, complement

Coagulation factor IX precursor coagulation system

29

Molecular structure of HLA

30

B2-microglobulin dynamics

Keown, Kidney International 2013

31

Performance of proteomic biomarkers

Sensitivity: 82% Specificity: 67%

-5

-2.5

0

2.5

5

-3 -2 -1 0 1 2 3 4

Discriminant Var. 1

Disc

rimin

ant V

ar. 2

Acute Rejection No Rejection

32

Patterns of antibody reactivity

PRA cI 98%

PRA cII 0%

33

Chromosome 6: structure & organization

Gene content and type

Length (bps): 171 Mb

Known Protein-coding Genes: 1,021

Novel Protein-coding Genes: 53

Pseudogene Genes: 733

miRNA Genes: 81

rRNA Genes: 26

snRNA Genes: 111

snoRNA Genes: 73

Misc RNA Genes: 67

SNPs: 1,8 M

34

• Narcolepsy *

• Nephritis *

• Neuroblastoma *

• Parkinson disease *

• Pemphigus vulgaris *

• Polycystic kidney disease *

• Porphyria

• Primary ciliary dyskinesia

• Psoriasis *

• Retinitis pigmentosa

• Rheumatoid arthritis *

• Schizophrenia *

• Spinocerebellar ataxia

• Sudden infant death syndrome

• Systemic lupus erythematosus *

• Tourette syndrome

• Viral resistance and response *

• Alzheimer’s disease *

• Ankylosing spondylitis *

• Autism *

• Behcet’s disease *

• Bipolar disorder *

• Celiac disease *

• CHAR syndrome

• Complement deficiency

• Crohn’s disease *

• Diabetes mellitus type 1 *

• Ehlers-Danlos syndrome

• Epilepsy *

• Fanconi anemia

• Hashimoto’s thyroiditis *

• Macular degeneration *

• Maple syrup urine disease

• Multiple sclerosis *

Chromosome 6: disease associations

Societal costs: Hundreds of Billions of $

Over 120 major disease associations recognized so far.

* Diseases of global importance and multi-billion dollar impact

35

Mining the HLA immunopeptidome

Chromosome 6: the immunopeptidome

Blood is used for affinity purification of

soluble MHC/peptide complexes. Peptides

are isolated from the associated heavy

chains and sequenced using tandem MS and

in silico analysis. Sequences are mined to

identify biomarkers and immunotherapy

targets for diagnosis, monitoring and

treatment.

Raychaudhuri S, Nature Genetics 2013 Hickman H D, PNAS 2010

36

Chromosome 6: autoimmunity

Rheumatoid disease

37

Ch6 Consortium: organization

GENOME CANADA STEERING COMMITTEE: Genome Canada University Liaisons Project Leads Core Leads Other representatives

SCIENTIFIC ADVISORY BOARD: Clinomics Biobanking Immunobiology Genomics Proteomics Metabolomics Economics Ethics & Law Bioinformatics

CLINOMICS and BIOLIBRARY CORE (Autoimmunity, alloimmunity, inflammatory

and degenerative disorders)

GENOMICS CORE

PROTEOMICS CORE

BIOLOGICS CORE

BIOINFORMATICS and KNOWLDEGE NETWORK (Informaticians, Cell biologists, Clinicians, Clinical Scientists

Decision makers, Policy makers)

GENOME BC

PROJECT LEADERSHIP

Advanced diagnostics and therapeutics

Streamlined translation and application

Reduced healthcare burden

38

Chromosome 6 Consortium

Networks of Centres of Excellence of Canada

Immunity & Infection Research Centre

University of

Victoria-Genome BC

Proteomics Centre

PROOF Centre of / Centre d’

EXCELLENCE

39

The PROOF Centre team:

Management Team Computation

Operations