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Biology and Biomarkers in Organ Failure - Paul Keown
 

Biology and Biomarkers in Organ Failure - Paul Keown

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Vital organ dysfunction is associated with profound changes in gene and protein expression that provide important insights into the cellular biology of these states, highlight potential targets for ...

Vital organ dysfunction is associated with profound changes in gene and protein expression that provide important insights into the cellular biology of these states, highlight potential targets for therapeutic intervention, and offer the opportunity for development of clinical biomarkers for diagnosis and therapy. Over 9000 genes are differentially expressed in kidney failure, of which the majority are down-regulated. Gene set enrichment analysis shows the mRNA processing and transport, protein transport, chaperone functions, the unfolded protein response and other key cellular functions are prominently inhibited while the complement system, liproprotein metabolism and other functions are up-regulated. Organ transplantation causes a rapid and highly dynamic perturbation of gene networks including chemotaxis and cell migration, inflammation and innate immunity and wound and tissue healing. Transcripts for many key cytokines and chemokines, which are respectively enhanced and reduced during renal failure and dialysis consistent with the complex inflammatory nature of this state, gradually normalize during the first months after organ transplantation. Even in healthy and well-functioning graft recipients, however, gene expression differs markedly from normal reflecting residual alterations in cell biology. The occurrence of acute graft rejection engenders a distinct alteration in gene and protein expression that reflects changes in the genes encoding cytoskeletal organization and biogenesis, signal transduction, immune system processes, cell motility and leukocyte activation. Alterations in the plasma proteome include increases in proteins that encompass processes related to inflammation, complement activation, blood coagulation and wound repair. These may be integrated to create sensitive and specific biomarkers of rejection or quiescence.

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    Biology and Biomarkers in Organ Failure - Paul Keown Biology and Biomarkers in Organ Failure - Paul Keown Presentation Transcript

    • Biology and Biomarkers in Organ Failure Dr. Paul Keown, 2013 University of British Columbia, PROOF Research Centre
    • The sequence of organ disease Baseline Risk Disease Presence Disease Progression “Recovered” Organ Function Earlier Intervention Organ Function (%) Improved Organ Function Recurrent Native Disease/Transplant Organ Failure End-stage Markers of Organ Failure Time (years) Transplantation/ Assist Devices • Biomarker panel opportunity Intervention point 2
    • Renal failure and uremia 3
    • 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. 4
    • 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. 5
    • 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. 6
    • 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 7
    • 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. 8
    • 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. 9
    • Changes in endosomal pathway 10
    • 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 11
    • 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 12
    • Vital organ failure and replacement 13
    • Site of action of therapeutics Samaniego M et al. Nat Clin Pract Neprol 2006;2: 688–699 14
    • 2 W1 W2 W3 W4 W8 W12 0 -1 -2 BL mean standardized log2(expression value) 1 1 0 -1 mean standardized log2(expression value) Surgical transplantation BL W1 W2 W3 W4 W8 W12 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 1. Defense response to infection 2. Embryonic growth and development 3. Innate immune response 4. Adaptive immunity 5. Other biological, cellular processes 15
    • Post-transplant rehabilitation 16
    • 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 17
    • Gene expression in quiescence -2 -4 -6 -8 mean standardized log2(expression value) 0 Category C: Below normal at W1, below normal for W2-W12 BL W1 W2 W3 W4 W12 Differential expression of 3773 probe-sets 18
    • B-cell gene expression in quiescence 19
    • Gene expression in rejection 1565484_x_at 206323_x_at 208120_x_at 211454_x_at 229120_s_at 218157_x_at 210686_x_at 204978_at 223578_x_at 215210_s_at 219100_at 227510_x_at 201043_s_at 214369_s_at 220305_at 200959_at 211787_s_at 240057_at 215760_s_at 210184_at 209286_at 216950_s_at 1555950_a_at 206130_s_at 227697_at 244752_at 238712_at 230735_at 236528_at 232555_at 212680_x_at 239021_at 210483_at 210569_s_at 217507_at 202180_s_at 1563509_at 219183_s_at 217728_at 205285_s_at 244556_at 202423_at 224807_at 211974_x_at 202910_s_at 203509_at 227490_at 200904_at 207643_s_at 211794_at 210754_s_at 210484_s_at 226872_at 220326_s_at 225673_at 223009_at 200805_at 201954_at 222244_s_at 226266_at 217475_s_at 211571_s_at 215646_s_at 208702_x_at 1569003_at 208488_s_at 203748_x_at 207266_x_at 209868_s_at 208919_s_at 202897_at 215990_s_at 219394_at 208018_s_at 203233_at 201473_at 223591_at 237544_at 237442_at 228793_at 209060_x_at 215415_s_at 207446_at 213596_at 242907_at 222435_s_at 222955_s_at 234640_x_at 205220_at 210563_x_at 200797_s_at 203471_s_at 1552264_a_at 211395_x_at 210992_x_at 1555797_a_at 205539_at 1558448_a_at 224254_x_at 1553186_x_at 211996_s_at 203239_s_at 210787_s_at 205921_s_at 1565599_at 211823_s_at 201651_s_at 1554691_a_at 210190_at 213505_s_at 1565717_s_at 200739_s_at 244356_at 202951_at 203624_at 208772_at 208922_s_at 201440_at 201729_s_at 201970_s_at 212036_s_at 220046_s_at 207127_s_at 226334_s_at 211797_s_at 1552542_s_at 235167_at 241774_at 1555420_a_at 233303_at 1555467_a_at 228216_at 216236_s_at 238320_at 216985_s_at 221695_s_at 207782_s_at 215236_s_at 200796_s_at 215832_x_at 224566_at 208811_s_at 228582_x_at 1568609_s_at 210191_s_at 1555852_at 211795_s_at 208885_at 212708_at 227396_at 201531_at 236155_at 221432_s_at 218380_at 1557924_s_at 211521_s_at 212974_at 203254_s_at 200709_at 201950_x_at 209083_at 211750_x_at 212639_x_at 211058_x_at 213646_x_at 201090_x_at 211072_x_at 202150_s_at 202216_x_at 211251_x_at 200852_x_at 37028_at 204166_at 210514_x_at 217436_x_at 212550_at 224909_s_at 217992_s_at 203591_s_at 1553297_a_at 201861_s_at 202510_s_at 202531_at Acute Rejection Normal No Rejection 20
    • Enriched ontology pathways 21
    • 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 22
    • T-cell surface recognition T-Cell APC Immunological quiescence CD3 Antigen recognition LFA-1 Synapse formation CD3 LFA-1 23
    • Biomarker selection, validation >80 Renal Allograft Recipients 66% NR 33% AR Training Cohort ~54,000 probe sets 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 No Rejection (0) Rejection (Banff ≥ 1) Whole blood Affymetrix microarrays Classification, Cross Validation Technical / Biological Validation 3-65 probe sets Test Cohort: Panel Performance INTERNALLY VALIDATED 10 GENE BIOMARKER PANEL 24
    • Biomarker selection, validation 0.0 0.2 0.4 0.6 0.8 1.0 1.0 0.8 0.6 0.2 AUC=0.9611 0.0 0.0 0.0 0.2 AUC=0.9668 0.4 True positive rate 0.8 0.6 0.4 True positive rate 0.8 0.6 0.4 AUC=0.9627 0.2 True positive rate Classifier 3 1.0 Classifier 2 1.0 Classifier 1 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 Classifier 5 Classifier 6 0.2 0.4 0.6 0.8 1.0 0.8 0.6 0.2 AUC=0.9293 0.0 0.0 0.2 AUC=0.9165 0.4 True positive rate 0.8 0.6 0.4 True positive rate 0.8 0.6 0.4 True positive rate 0.0 0.2 AUC=0.9182 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 Classifier 8 Classifier 9 0.4 0.6 False positive rate 0.8 1.0 0.8 0.6 0.2 AUC=0.9326 0.0 0.0 0.2 AUC=0.9132 0.4 True positive rate 0.8 0.6 0.4 True positive rate 0.8 0.6 True positive rate 0.4 0.0 0.2 AUC=0.9549 0.2 1.0 1.0 Classifier 7 0.8 False positive rate 1.0 False positive rate 1.0 False positive rate 0.0 1.0 1.0 Classifier 4 0.8 False positive rate 1.0 False positive rate 1.0 False positive rate 0.0 0.2 0.4 0.6 False positive rate 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 False positive rate 25
    • CDC42SE1 ZNF575 1566342_at SLAMF6 FKSG49 TMEFF2 PTPRA 8162 /// LOC613037 /// LOC728888 /// NPIPL3 224346_at SLC25A16 RPL38 0.0 0.2 1e-04 0.4 2e-04 frequency 3e-04 0.6 4e-04 0.8 5e-04 1.0 6e-04 FDR (LIMMA) (9 classifiers) DFFA 229420_at CDC42SE1 RPL27A LOC100133109 FAM78B GRAMD1A 1566342_at DFFA MALAT1 CDC42SE1 224346_at 229420_at SLC25A16 FKSG49 TMEFF2 RPL38 CDC42SE1 0e+00 FDR Biomarker probe sets Probe-set Frequency (9 classifiers) 26
    • -5 0 5 DFFA MALAT1 CDC42SE1 --- --- SLC25A16 FKSG49 TMEFF2 RPL38 CDC42SE1 START 0.0 0.00 0.2 0.05 0.4 0.15 0.6 AUC 0.10 0.8 1.0 0.20 Vital organ failure Linear Discriminant Score 27
    • Plasma proteome in uremia Protein Lipopolysaccharide-binding protein precursor Vasorin precursor Ceruloplasmin precursor Hepatocyte growth factor precursor Peptidase inhibitor 16 precursor Complement factor D Complement component C2 Mannose binding protein C precursor Protein z-dependent protease inhibitor Complement component 9 precursor Beta-2 microglobulin Complement c1s subcomponent Coagulation factor IX precursor Function LPS-TRL4 binding TGF-b binding protein, kidney, vessels acute phase reactant, copper transport inflammation, remodeling protease inhibitors, Serpins alternate pathway, complement system classical path, complement system complement system Serpin, coagulation system, factor Xa, XIa complement system MHC, renal disease classical pathway, complement coagulation system http://www.acponline.org/about_acp/chapters/az/mtg06_blair.pdf 28
    • Molecular structure of HLA 29
    • B2-microglobulin dynamics Keown, Kidney International 2013 30
    • Performance of proteomic biomarkers Discriminant Var. 2 5 2.5 Sensitivity: 82% Specificity: 67% 0 -2.5 -5 -3 -2 -1 0 1 2 Discriminant Var. 1 Acute Rejection 3 4 No Rejection 31
    • Patterns of antibody reactivity PRA cI 98% PRA cII 0% 32
    • Chromosome 6: structure & organization Gene content and type Length (bps): Known Protein-coding Genes: Novel Protein-coding Genes: Pseudogene Genes: 171 Mb 1,021 53 733 miRNA Genes: 81 rRNA Genes: 26 snRNA Genes: 111 snoRNA Genes: 73 Misc RNA Genes: 67 SNPs: 1,8 M 33
    • Chromosome 6: disease associations Societal costs: Hundreds of Billions of $ Over 120 major disease associations recognized so far. • • • • • • • • • • • • • • • • • 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 * • • • • • • • • • • • • • • • • • 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 * * Diseases of global importance and multi-billion dollar impact 34
    • Chromosome 6: the immunopeptidome Mining the HLA 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 35
    • Chromosome 6: autoimmunity Rheumatoid disease 36
    • Ch6 Consortium: organization GENOME CANADA STEERING COMMITTEE: Genome Canada University Liaisons Project Leads Core Leads Other representatives GENOME BC PROJECT LEADERSHIP Clinomics Genomics Economics SCIENTIFIC ADVISORY BOARD: Biobanking Immunobiology Proteomics Metabolomics 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) Advanced diagnostics and therapeutics Streamlined translation and application Reduced healthcare burden 37
    • 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 38
    • The PROOF Centre team: Management Team Computation Operations 39