Integrative Systems Biology– Renal Diseases: a Road to a Holist View of Chronic Disease Mechanism

Integrative Systems biology– Renal Diseases: A road to a holist view of chronic disease mechanism

Matthias Kretzler Div. Nephrology / Internal Medicine Computational Medicine and Bioinformatics University of Michigan Medical School The challenge in chronic disease • Descriptive disease categorization with multiple pathogenetic mechanisms § Problems of ‘mixed bag’ diseases: • Unpredictable disease course and response to therapy • Nephrology as an ‘art of trial and error’

• Shift in our disease paradigms: § Mechanism based patient management • Deﬁne the disease process active in the individual patient – Base prognosis on speciﬁc disease process – Target therapy to interfere with the mechanism currently destroying endorgan function Molecular Nephrology approach

Clinical outcome research Genetics Molecular Pathology Molecular Epigenetics Phenotyping

Genomics Functional Clinical research Disease Genomics Biobanks Proteomics

Model systems

Animal models Molecular interaction In vitro tissue culture model systems Organ culture and development Tower of Babylon: Search for the universal language for the medicine of the 21st century

Pieter Bruegl: 1563. Kunsthistorisches Museum Wien Molecular Nephrology approach

Clinical outcome research Genetics Molecular Pathology Molecular Epigenetics Phenotyping

Genomics Functional Clinical research Disease Genomics Integrative Biobanks Proteomics Biology (Physiology)

Model systems

Animal models Molecular interaction of renal disease In vitro tissue culture model systems Organ culture and development Systems analysis view of renal disease

Harness the capabilities of genome wide analyses for an integrated view of regulatory networks activated in glomerular disease:

1. Develop cohorts for clinical and molecular phenotyping 2. Generate molecular map of renal disease 3. Integrate multi-level information 4. Develop strategies for outreach and clinical implementation The advantage of being a Nephrologist: Biopsy centered clinical and molecular phenotyping Research networks for molecular analysis of renal disease

Standardized protocol implemented: >3100 biopsies procured Biorepositories for molecular analysis of chronic renal disease

2560 renal biopsies for gene expression studies 24 European Centers

1165 Nephrotic Syndrome Patients in Registry 235 incipient Nephrotic syndrome Cohort 18 Centers in North-America

625 CKD patients in 5 Centers in Mid-West

180 Diabetic Nephropathy Cohort in Pima Interventional Trial 80 protocol biopsies available for molecular analysis Bioﬂuids/tissue/ digital Histology/ Cohorts/ BioBank clin. phenotype

NGS/RNAseq/ Bioinformatics Chipseq/ Core Microarray O’Brien

MiMI, Metscape, Kidney LRpath, Genomatix, Center Genepattern, Research Ingenuity Standardized / Base NCIBI semi- structured (core function 1) workﬂow (Core function 2)

Nephromine (Core function 3)

Worldwide Renal Research Base Integrative Biology for target identiﬁcation in CKD :

• Case Study 1: § The shared common pathway of CKD: Defining molecular context from genotype to phenotype • Case Study 2: § Identify molecular mechanism and non-invasive correlates of fibrosis in CKD • Case Study 3: § Molecular stratification of nephrotic syndrome Case Study 1: The shared common pathway of CKD: Defining molecular context from genotype to phenotype

Clinical outcome Genetics Environmental Exposures Epigenetics Molecular Pathology Genomics Functional Clinical research Genomics Integrative Proteomics Disease Biology Biobanks (Physiology)

Model systems

Animal models Molecular interaction In vitro tissue culture model systems Organ culture and development Disease Causality: Genetic variance can be linked to clinical phenotype • Genetic studies identify genetic variances associated with clinical phenotypes. • Genome Wide Association Studies (GWAS) gives lists of single nucleotide polymorphisms (SNPs) with genome wide signiﬁcant p- values Association studies project SNPs onto Phenotype

What’s the Biology in between? Rethinking the Problem: Systems Genetics Systems genetics concepts

Genetic variants affect regulatory and proteomic machinery of the cell, leading to disruption in a metabolic pathway resulting in clinical trait / renal disease phenotype.

B. Keller et al. Kidney Int, 2012 Integration SNPs – transcripts- clinical traits

B. Keller et al. Kidney Int, 2012 Systems Genetics in CKD Link polymorphism via regulatory networks to disease phenotypes 1. Integration of SNP with mRNA levels (eQTL Concept): Renal mRNA levels of CKDgen candidate genes 2. Systems Genetics Concept: CKD candidate genes are drivers in pathways associated with CKD. 3. Network concepts: CKD candidate genes, renal function analyses, and detection of shared transcript co-regulation => pathway enrichment analyses => CKD pathway network Glomerulus Tubulointerstitium Heatmap: CKD HT MN FG LN IA DN MC CKD MC DN MN LN IA HT FG -0.76 GFR-correlation F12 0.16 RGS14 of candidate LMAN2 genes 0.79 FAM63A ALMS1 GRK6 29 CKDgen PRR7 STC1 associated FNDC4 mRNAs: GCKR Sig. correlation SETDB1 FBXO22 with eGFR ? ATXN2 PRKAG2 PIP5K1B | r | ≥ 0.25 GP2 FDR ≤ 0.01 UMOD PRUNE DAB2 Red: Tub-int (17) NAT8 Blue: Glom (5) SLC7A9 DACH1 LASS2 Range of GFR ACSM5 correlation: TFDP2 VEGFA -0.76 to 0.788 ANXA9 NAT8B SLC34A1 Martini et al. JASN, 2014 Systems Genetics of CKD CKDGen-candidate markers

1. Filter candidate genes for GFR correlated mRNA expression

2. Detecon of co-regulated transcripts for every candidate gene

3. Detecon of enriched pathways among co-regulated transcripts Hypoxia Signaling

see also: Neusser, Am J Path 2010 - GFR co-regulated mRNAs Higgins J Clin Invest 2007 Ding, Nature Med 2006 - CKDGen associated transcript Systems Genetics of CKD CKDGen-candidate markers

1. Filter candidate genes for GFR correlated mRNA expression

2. Detecon of co-regulated transcripts for every candidate gene

3. Detecon of enriched pathways among co-regulated transcripts

4. Detecon CKD-associated pathway interdependencies Systems genetics of CKD

Martini et al. J Am Soc Nephrol, 2014

Pathway network associated with shared CKDgen co-regulated transcripts. Spring embedded algorithm. Node size reflects degree of connectivity, edge thickness increasing with more genes shared among two pathways. Node color reflects number of transcript associated with pathway: (multiple=red, few=green) Deﬁnition of highly interconnected nodes (clusters)

Metabolism Inﬂammation – Stress response

Xenobiotic Metabolism Signaling NRF2-mediated Oxidative Stress Response Aryl Hydrocarbon Receptor Signaling Cdc42 Signaling PPARgamma signaling NF-kappaB Signaling PXR/RXR Activation Dendritic Cell Maturation LPS/IL-1Mediated Inhibition of RXRFunction CD28 Signaling in T helper Cells… Tryptophan Metabolism… Lupus Nephritis pathways

Differentially regulated genes were enriched in pathways indicated by the blue color. Diabetic Nephropathy pathways eQTL – GWAS integration: Tissue compartment speciﬁc eQTLs Study 2: Identify molecular mechanism and non-invasive correlates of fibrosis in CKD

Clinical outcome Genetics Environmental Exposures Epigenetics Molecular Pathology Genomics Functional Clinical research Genomics Integrative Proteomics Disease Biology Biobanks (Physiology)

Model systems

Animal models Molecular interaction In vitro tissue culture model systems Organ culture and development Pima protocol Biopsy Cohort: Clinical Characteristics Clinical Characteristics at Biopsy F/M (no.) 29/11 Age (yrs.) 45.8 (±9.4)

ACR(mg/g) 154.1(±403.9) iGFR (ml/min) 144.2 (±45.4)

BMI (kg/m2) 35.3(±8.3)

HbA1c (%) 8.9(±2.1) Structure: Fractional Interstitial Area (FIA)

• Toluidine blue stained biopsies • 15 x 15 µm yellow grid, 225 intersections on 40x image • Grid intersections on interstitial tissue marked in red. • Interstitial area= outside of tubular and vascular structures • Repeated for a total of 10 grids for average fractional interstitial area

• FIA: 29.5% (±9.6) in Pima biopsies vs 11.9% (±2.8) in living kidney donor biopsies Linking gene expression and morphometry in early DN

1. Analysis Input RNA Tub.- FIA Interstitium

2. Variable association Pearson Correlation

3. Gene ranking/selection 651 FIA correlated Genes

Pathways & GO Networks Analysis 4. Downstream analysis Transcript Prediction/ Factor Biomarker Networks Transcript correlating with Fractional Interstitial Area Tubulo-interstitial transcript correlated with FIA: 651 genes: FDR ≤ 0.05, │r│>0.36

Glyoxalase II Molecular Concepts associated with FIA in early DKD Topological Mapping of enriched Gene Ontology Terms Biological Processes Cellular Compartment Immune system process Extracellular region Immune response Extracellular matrix (ECM) Response to stimulus Proteinaceous ECM Defense response Extracellular space

POS.COR. Response to stress and wounding Integral to plasma membrane

Organic acid metabolic process Mitochondrion

Cellular ketone metabolic process Cytoplasmic part

Oxoacid metabolic process Cytoplasm

Carboxylic acid metabolic process Mitochondrial lumen & matrix

NEG.COR. Cellular amino acid metabolic pro. Cell fraction

Amine metabolic process Mitochondrial membrane Integration into functional context: Morphogenomics networks § Correlation of intrarenal structural lesions with gene expression in early DKD: § 651 genes: (FDR ≤ 0.05 │r│= 0.36 - 0.68) § Display of three levels of evidence • Correlated mRNAs • Prior knowledge – Natural language processing: » Co-citation of genes in Pubmed abstract sentence • Unbiased sequence analysis: – Automated Promoter analysis deﬁning Transcription Factor binding sites

Integration with multi-level evidence of gene function to identify underlying regulatory mechanism § Gene (=Node) centred network displaying transcriptional dependencies as connections (=Edges) Transcriptional network RNA ≈ FIA in early DKD

IRF1

PLG

CAV1 VEGFA EGF

TP53

MMP9

PPARA CD44

IL8 POSS.CORR

NEG.CORR ICAM1 APOE

PCC FIA/mRNA: FDR ≤ 0.05: 651 genes;│r│= 0.36 - 0.68 FIA associated mRNA correlate with ACR 8 years after biopsy FIA-mRNAs: 5 of 651 ACR correlation at time 0 (biopsy)

442 of 651 ACR correlation 8.25 years after biopsy.

All major network gene nodes retained FIA transcript in progressive DKD

555 / 651 (85.3%) FIA-mRNAs regulated (q<0.05) in progressive DKD (European indication biopsies, N=17) vs living kidney donor biopsy (N=31) Integration of human and murine transcriptomes in CKD Cross-species regulatory networks in Diabetic Glomerulopathy

Differentially Species Specific Human-Mouse Shared expressed Transcriptional Transcriptional HUMAN: glomerular genes Networks Networks

Type 2 diabetic, microalbuminuric vs 3213 Human- 4606 genes DBA/2J-STZ normoabluminuric nodes 143 nodes

MOUSE: Human- 3296 DBA/2J-STZ 4220 genes BKS-db/db nodes 97 nodes

4168 genes 3169 BKS-db/db nodes Human- BKS-eNOS(-/-) 3797 genes 2910 db/db BKS-eNOS(-/-)db/db nodes 162 nodes [each vs non-diabetic]

Hodgin et al, Diabetes, 2013 Human-mouse transcriptional networks: Shared pathways # genes observed in pathway Canonical pathway Human- Human- Human- DBA db/db eNOS-KO|db/db Cytokine receptor degradation signaling 27 17 21 (JAK-STAT pathway and regulation) Migration (VEGF signaling) 15 9 11 VEGFR1 and VEGFR2-mediated signaling 11 8 8 HIF-1-alpha transcription factor network 8 4 7 Angiopoietin receptor Tie2-mediated signaling 6 5 --- HGF receptor (c-met) signaling 6 4 --- Regulation of nuclear SMAD2/3 signaling --- 5 9 Regulation of Androgen receptor activity --- 6 5 IL6-mediated signaling events 8 --- 8 IL2 receptor beta chain in T cell activation 6 --- 7 EGFR1 10 ------Signaling events mediated by PTP1B 7 ------Alpha6Beta4Integrin --- 5 --- Endothelin pathway --- 5 --- C-MYB transcription factor network ------9 CDC42 signaling events ------7 Hitting targets in DN: Jak-Stat Pathway as key driver Early DN Progressive DN Control (LD+MCD): n=11 Control (LD+MCD): n=7 Early DN: n=22 Prog. DN: n=11

Cytoplasm Cytoplasm

Nucleus Nucleus Tubulointerstitium compartment Tubulointerstitium

Up-regulation Down-regulatation Differential regulation

Cytoplasm Cytoplasm

Nucleus Nucleus Glomerular compartmentGlomerular

Control (LD+MCD): n=12 Control (LD+MCD): n=8 Early DN: n=24 Prog. DN: n=7 Berthier et al., Diabetes. 2009 Jak2 in Human Progressive DN and non-diabetic Kidney Diseases

Berthier et al., Diabetes. 2009 Phase II trial of Jak 2 inhibition in diabetic nephropathy Baricitinib, oral JAK2 inhibitor, currently in Phase II for RA and Psoriasis => Repurposed in Diabetic Nephropathy

• RCT by Eli Lilly in DN • Primary outcome: • Change in urine ACR from baseline to 24 weeks • Study completed in Nov 2014 • Late braking trial report at ADA in June 2015

From target identiﬁcation to phase II completion in 42 months Methods Figure 1. Study Design

Treatment Period 24 weeks LY Dose Groups Group 1: eGFR 50 to 70 - 4 mg once daily eGFR 25 to <50 - 2.75 mg once daily Study Drug Group 2: eGFR 50 to 70 - 1.5 mg once daily Discontinued eGFR 25 to <50 - 1 mg once daily Patients who maintain response Randomization Group 3: eGFR 50 to 70 - 0.75 mg twice daily (30% decrease in UACR from (1.5 mg total daily dose) baseline) continue -- all other eGFR 25 to <50 - 0.5 mg twice daily patients discontinue from study (1 mg total daily dose)

Group 4: eGFR 50 to 70 - 0.75 mg once daily Screening eGFR 25 to <50 - 0.5 mg once daily Washout Washout 4 weeks 4 weeks 4 weeks maximum Placebo

–4 0 24 28 32 Weeks 24 8 12 16 20 25 Visits 1, 1a 34 5 6 7 8 10 2 9 11 12

Abbreviations: eGFR = estimated glomerular filtration rate; UACR = urinary albumin/creatinine ratio.

Note: Dose groups are described throughout the poster in terms of the highest eGFR group (eg, Group 1 is described as “4-mg dose group” although it includes patients treated with 4 mg [eGFR 50-70] and 2.75 mg [eGFR 25-<50]). Figure 3. UACR (24-Hour Urine)

Plot of Least Square Means +/- Standard Error for 24-hour UACR

3 months 6 months 4 wks washout

1.4

1.2

1.0

* * ** **

0.8 **

0.6 n-values: 26 19 23 22 22 24 17 23 23 19 26 20 24 22 21 Treatment Placebo 0.75mg QD 0.75mg BID 1.5mg QD 4mg QD Mixed model repeated measures analysis of log-transformed data with results back transformed. *p-value<0.05; **p-value<0.01 based on treatment difference compared to placebo.

♦ Reductions in 24-hour UACR were observed at 3 and 6 months (Figure 3). Figure Urine7. Urine IP-10 IP-10 (pg/mg in Creatinine) pg/mg Creatinine Plot of Least Square Means +/- Standard Error for IP10

2 weeks 1 month 3 months 6 months 1 wk washout 4 wks washout 1.8

1.6

1.4

1.2

1.0

0.8 * *

Fold change baseline Fold from 0.6

* 0.4

0.2 Treatment Placebo 0.75mg QD 0.75mg BID 1.5mg QD 4mg QD Mixed model repeated measures analysis of log-transformed data with results back transformed. *p-value<0.05; **p-value<0.01 based on treatment difference compared to placebo. Figure 8. PlasmaPlasma sTNF sTNF R2 R2 in (pg/ml) pg/mg Foldchange from baseline Analysis of diabetic endorgan damage across tissues, species and disciplines Deﬁning treatment response in Diabetic endorgan damage across

Species: • Human - Mouse

Endorgans: • Nerve – Kidney - Eye

Disciplines: • Neurology (Feldman) • Nephrology (Brosius, Pennathur) • Ophthalmology (Gardner) • Bioinformatics (Kretzler) • Computer Science (Jagadish) Predicton of CKD Progression Selecon of paents at risk Risk Predicon: § Use outcome to select predictor from genomic data set From tissue to urine: Non-invasive Biomarkers for CKD progression

Validation markers in independent cohorts

Validated markers Enrichment analysis: for testing in body signaling pathways ﬂuids & Therapeutic targets

Further validation in larger cohort Treatment targets A set of tissue Biomarkers based candidate markers Discovery and validation cohort for GFR prediction: intra-renal mRNA

Table 1a. Demographic characteristics of participating CKD patients from the discovery ERCB cohort. Disease type CKD patients eGFR Age Gender (n=164) (ml/min per 1.73 m2 ) (years) (male/female) SLE 30 63.7±29.4 34.7±13.3 7m/23f IgAN 24 75.9±37.9 36.4±14.6 18m/6f Affymetrix MGN 18 88.9±41.4 53.4±19.3 10m/8f FSGS 16 73.4±38.4 46.2±17.6 7m/9f HTN 20 43.9±25.1 57.2±12.1 15m/5f DN 17 44.3±24.9 58.3±10.7 12m/5f MCD 12 100.7±33.9 35.8±16.8 8m/4f The RPGN 21 46.6±31.5 58.5±14.1 12m/9f TMD 6 93.4±29.4 46.0±14.5 4m/2f European Total 164 66.4±37.2 46.9±17.6 93m/71f Renal Biopsy Table 1b. Demographic characteristics of the first validation group of CKD patients from ERCB. cDNA Disease type CKD patients eGFR Age Gender Bank (n=55) (ml/min per 1.73 m2 ) (years) (male/female) SLE 10 60.1±31.5 37.1±13.8 4m/6f (ERCB) IgAN 17 50.8±34.1 46.0±17.5 12m/5f MGN 4 54.4±32.4 58.7±23.6 2m/2f MPGN 1 40.6 65 1m FSGS 8 55.9±36.0 43.6±18.6 6m/2f TLDA HTN 1 33.2 57.1 1f DN 1 97.3 44.8 1m (qRT-PCR) MCD 4 86.0±44.5 44.4±19.3 4m RPGN 5 20.9±15.0 50.4±21.7 4m/1f Other 4 84.2±71.7 37.4±14.6 3m/1f Total 55 56.1±38.0 45.1±17.6 37m/18f

Table1c. Demographic characteristics of CKD patients from C-PROBE, the second validation cohort. Disease type CKD patients eGFR Age Gender (n=42) (ml/min per 1.73 m2 ) (years) (male/female) SLE 16 106.4 ± 60.1 31.3 ± 9.9 2m/14f IgAN 1 110.4 23 1f IgM 1 29.1 22 1m MGN 2 51.9 ± 21.9 53.0 ± 5.7 1m/1f MPGN 2 40.9 ± 11.6 59.0 ± 1.4 1m/1f FSGS 11 51.2 ± 21.0 41.9 ± 14.0 8m/3f HTN 1 146.5 29 1m DN 1 50.0 80.8 1f MCD 1 124.7 22 1f Other 6 43.5 ± 28.2 55.3 ± 18.9 2m/4f Total 42 76.3 ± 50.8 39.6 ± 15.7 16m/26f

Age and eGFR are presented as means ± standard deviation. eGFR was calculated using MDRD equation.

eGFR Predictor panel

(predicted vs. observed) vs. (predicted

2 2 R

Number of markers Intra-renal EGF correlation with eGFR in European CKD/DKD

Training Cohort: Test Cohort: TILDA qRT-PCR 2C Affymetrix U 133 10 10 10

8 ) r=0.66, p<0.0001 8

2 r=0.81, p<0.0001 og

(L 6 6

FR 4 eG 4 2

2 0 2 4681010 12 12 -20-15 -10-50 ERCB tissue EGF mRNA(Log ) by array 2 ERCB Tissue EGF mRNA(Log ) by TLDA training cohort (n=164) 2 validation cohort 1 (n=55) 2A 2B 8 8 r=0·77, p<0·0001 ) 2 7

6 Gene symbol

NNMT (predicted vs. observed) vs. (predicted

2 2 EGF R Predicted eGFR (Log Predicted eGFR 5 TMSB10 TIMP1 TUBA1A ANXA1 4 2 345678 Number of markers Observed eGFR (Log2) 2C 10 10 10

8 8 r=0·81, p<0·0001 ) r=0·66, p<0·0001 8 r=0·42, p=0·006 2

og 6 (L 6 6

FR 4 eG 4 4 2

2 0 2 2 46810 12 -20-15 -10-50246810 12

ERCB tissue EGF mRNA (Log ) by a rra y 2 ERCB Tissue EGF mRNA(Log2) by TLDA C-PROB E Ti ssue EGF mRNA(Log 2) by a rra y training cohor t (n=164) validation cohort 1 (n=55) validation cohort 2 (n=42) EGF expression in normal human tissue 2D 2E

6 o ti ra

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m 0 Intens 2 Log

0 -2 li x a s s y r t n n s y u ex ll lla i e ar i ng ti in r rt u ip lv s ra e orte du d e Live lood Sk Lu m Co e ryT He Colo creasTe Ovar o lC Me lP Kidn Retina n eb Uterus al a lM la a eB Prostate Gl n n al a il n Pa e n n ap Re e Re Whol rR Re P Whol e er rR n t e er n t In Ou In Ou Data derived from BioGPS portal 2F Wu et al., Genome Biology, 2009 I II

III IV EGF, top up-stream regulator of GFR slope correlated genes

Table X: Top 10 upstream regulators of eGFR slope correlated genes. Upstream Molecule p-value of Target molecules in dataset Regulator Type overlap EGF growth 2.09E-12 ACTN1,APOA1,B4GALT5,CCL20,CCND2,CD44,CDC42EP1,CLDN3,CLU,CTSD,DUSP6,ELF3,HIF1A,ICAM1,ID2,IER3,ITGB3, factor LCN2,LTF,MYC,MYCN,NCAN,NOS2,NRP1,PBK,PPIB,RRM2,SERPINA3,SLC37A1,SNAP25,SOX4,SPP1,SYP,TFPI2,THBS1, VCAN,VIM TP53 transcription 4.83E-11 ACTN1,ALOX5,ANLN,ANTXR1,ANXA1,APAF1,APOA1,ASF1B,ATL3,CCL2,CCND2,CD44,CLU,COL4A1,CTSD,CXCL1,DLGAP1, regulator EIF4G3,ELF4,FHL1,FHL2,GDF15,GLIPR1,HIF1A,HS3ST1,ICAM1,ID2,IER3,ILK,KITLG,KRT18,MCM7,MET,MYC,MYOF,NOS2, NRP1,PBK,PDLIM1,PFN1,PMEPA1,PPFIBP1,PPIC,PTPRE,RRM2,SGPL1,SIVA1,SOD2,SPHK1,SPP1,TFPI2,TGFB2,THBS1, THBS2,TMSB10/TMSB4X,TNFSF9,TOP2A,TPX2,TSC22D3,UBE2C,VCAN,VIM,XRCC5,ZMAT3, ZYX IL1B cytokine 3.45E-10 ADAMTS1,ANTXR1,ANXA1,CCL2,CCL20,CD44,CD74,CFB,CXCL1,CXCL6,ELF3,FAM129A,G0S2,GDF15,HIF1A,HMGA1, HSD11B1,ICAM1,IER3,ITGB3,ITGB8,LAMC2,LCN2,LHB,MARCKSL1,MMP7,MYC,NFKBIZ,NOS2,NRP1,OSM,PIGR,PSMB10, SERPINA3,SNAP25,SOD2,SOX9,SPP1,TAC1,TFPI2,THBS1,TSC22D3,UGCG,VCAN,VIM,ZYX TGFB1 growth 1.36E-09 ACTN1,ALOX5,CCL2,CCL20,CCND2,CD207,CD44,CFL1,CLU,COL4A1,COL4A2,CTSD,CXCL1,DARC,DYRK2,ELF3,ELF4, factor FCER1A,FHL1,FNDC3B,FOSL2,GDF15,GGT6,GNG7,HIF1A,HMGA1,ICAM1,ID2,IER3,ILK,ITGB3,ITGB6,KCNG1,KDELR3, KDM5B,KITLG,KLF9,KRT18,LAMC1,LAMC2,LCN2,LOXL1,LOXL2,MET,MMP7,MYC,MYCN,MYOF,NCAN,NNMT,NOS2,NRP1, OSM,PDLIM7,PKIG,PMEPA1,PNMT,SERPINA3,SOD2,SOX4,SOX9,SPARCL1,SPHK1,SPP1,SYP,TGFB2,THBS1,TOP2A, TSC22D3,TUBA1A,VCAN,VIM,ZYX IL6 cytokine 1.28E-08 ADAMTS1,ANXA1,APOA1,ARL4C,CCL2,CCL20,CD74,CLU,CXCL1,CXCL6,DUSP6,HIF1A,ICAM1,ID2,ITGB3,JAK1,KRT18, LCN2,LTF,MET,MPO,MYC,NOS2,PSMB10,REG1A,RRM2,SENP2,SERPINA3,SOD2,SPP1,STS,SYP,TAC1,THBS1,TOP2A, UBE2C,VIM,XRCC5 TNF cytokine 2.54E-08 ALOX5,ANXA1,APAF1,APOA1,CCL2,CCL20,CCND2,CD44,CFB,CLCF1,CLU,CXCL1,CXCL6,DUSP6,ELF3,FOSL2,G0S2, GDF15,HIF1A,HSD11B1,ICAM1,IDE,IER3,IFNAR2,ITGB3,ITGB6,JAK1,KITLG,LAMC1,LAMC2,LCN2,LGALS12,LIPE,MARCKSL1, MET,MMD,MMP7,MPO,MYC,NCAN,NFKBIZ,NNMT,NOS2,NRP1,OSM,PIGR,PSMB10,RRM2,SERPINA3,SOAT1,SOD2,SOX4, SOX9,SPHK1,SPP1,TAC1,TAPBP,TFPI2,TGFB2,THBS1,THBS2,TNFSF9,TSC22D3,UGCG,VIM,ZYX CSF2 cytokine 9.96E-08 ALOX5,ANLN,ANXA1,APAF1,CCL2,CD63,CD74,CLCF1,CXCL1,DUSP6,GDF15,ICAM1,ID2,IER3,ITGB3,MET,MMD,MPO,MYC, NOS2,NRP1,OSM,RRM2,SOD2,SPP1,THBS1,TOP2A,TPX2,UBE2C CEBPA transcription 1.13E-07 ANXA1,ARL4C,CCL20,CCND2,FHL1,G0S2,GLIPR1,HSD11B1,ICAM1,ID2,IER3,LCN2,LTF,MALT1,MPO,MYC,MYF5,NRP1, regulator PTPRE,SOD2,SPP1,TAC1,TGFB2,TRIB1,TSC22D3,VCAN NFkB complex 1.48E-07 CCL2,CCL20,CCND2,CD44,CD74,CFB,CLCF1,CLU,CXCL1,CXCL6,ELF3,G0S2,GDF15,HGFAC,HIF1A,ICAM1,IER3,IFNAR2, ITGB8,LCN2,MYC,NFKBIZ,NOS2,SENP2,SERPINA3,SOAT1,SOD2,SPP1,TAC1,TFPI2,TRIM38,VIM,XRCC5 ERBB2 kinase 3.46E-07 CCL20,CCND2,CLDN3,COL4A1,DHH,DUSP18,DUSP6,ELF3,ETV6,HIF1A,ID2,ILK,ITGB3,KCNN4,KDM5B,LAMC2,LCN2,MYC, MYCN,NNMT,NOS2,NRP1,PMEPA1,RRM2,SERPINA3,SOX4,SPARCL1,SPINT1,TAPBP,THBS1,TOP2A,TUBA1A,UBE2C, VCAN,VIM

From tissue to urine: EGF and renal function in CKD

Urinary EGF protein correlates signiﬁcantly with intrarenal EGF mRNA in matching samples

C-PROBE (n=34) NEPTUNE (n=100)

Urinary EGF/Cr. Protein (Log2) Urinary EGF protein signiﬁcantly correlates with GFR

C-PROBE (n=349) NEPTUNE (n=196)

Urinary EGF/Cr. Protein (Log2) Glass slides are scanned using Aperio ScanScope OS

Kidney bx slide

Annotator Reader #1 Reader #2

Scoring process on Scoring process on annotated images annotated images

Data analysis Barisoni et al. 2013 Urinary EGF protein and tubular damage

Figure 5. NEPTUNE: Interstitial Fibrosis / Tubular Atrophy

A B 100 100 r=-0.76, p< 0.001 r=-0.77, p< 0.001 80 80

osis 60 60 ophy tr rA al Fibr ti 40 40 tersti Tubula In 20 20

-2 2 4 6 -2 246 Urinary EGF/Cr. Protein (Log2) -20 -20 Urinary EGF/Cr. Protein (Log2)

C D

8 Interstitial Fibrosis 8 Tubular Atrophy ) ) 6 6 r.(Log2 r.(Log2 4 4 /C /C GF GF 2 2 yE yE

0 0 urinar urinar -2 -2

al ld te al ld te m Mi ra m Mi ra ni vere ni vere Absent Mi Se Absent Mi Se Mode to Mode to

vere vere Se Se te te ra ra

Mode Mode Association with disease progression

Slope of eGFR decline %/yr (mixed effects model)

Time to renal event (ESRD or 40% reduction of baseline eGFR) A B 5 5 r=0.58, p < 0.0001 r=0.78, p < 0.0001

0 0 51015 246 e% e% op op sl

sl -5 -5 Figrue 4 Urinary EGF predicts GFR slope% FR FR eG -10 eG -10

-15 -15 EGF mRNA (Log2 intensity) u-EGF/Cr. (Log2) A B C-PROBE C C-PROBE Albumin/Cr 5 A B 5 5 r=0.58, p < 0.0001 20 20 ) C r=0.65, p<0.0001 5 D ) r=0.29, p<0.50001 ) ) )

(% r=0.55, p<0.0001 r=0.27, p=0.0003 (% r=0.65, p<0.0001 ) r=0.29, p<0.0001 (% 0 (%

(% 10 10 51015 (% pe pe ope -20-15 -10-5510 ope

l -20-15 -10-5510 l e% lo lo ope l

-20-15 -10-5510 ope -20-15 -10-5510 op l Rs Rs Rs Rs sl -5 -5 -5 Rs -40-20 20 -40-20 20 Rs GF GF GF GF FR -5 -5 GF

GF -10 -10 de de de de eG de by ACR (og2) ACR by te

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te -10 -10 ic ic ic ic te ic by u-EGF/Cr. (Log2) u-EGF/Cr. by -10 -10

ic -20 -20 Pred Pred Pred Pred

-15 Pred -15 Pred -15 -15 -15 -30 -30 Intrarenal EGF mRNA (log ) Observed eGFR slope (%) Observed eGFR slope (%) 2 Observed eGFR slope (%) ObservedObserved eGFR sl opeeGFR (%) slope (%) Observed eGFR slope (%) (n=34) (n=344) (n=344)

Correlation between ACR predicted eGFR slope versus the observed slope is r=0.25 Cohort HR (95% CI)

C-PROBE 0.27 (0.13-0.54) NEPTUNE 0.29 (0.15-0.58) A PKU-IgAN 0.53 (0.37-0.69)

0.0 0.5 1.0 1.5 uEGF: Hazard RatioAdjusted for HR of uEGF/Crtime to event in glomerular disease

Cohort HR (95% CI)

C-PROBE 0.27 (0.13-0.54) B NEPTUNE 0.29 (0.15-0.58) PKU-IgAN 0.53 (0.37-0.69)

0.0 0.5 1.0 1.5

Adjusted HR of uEGF/Cr Multivariable-adjusted hazard ratios urinary EGF/Cr for outcomes. HRs adjusted by age, gender, eGFR and ACR and obtained by independent Cox regression models in each study cohort.

Cohort

C-PROBE

NEPTUNE C PKU-IgAN

0.0 0.5 1.0 1.5

Adjusted HR of uEGF/Cr Case Study 3: Molecular Subgroups for targeted treatment of FSGS/MCD

Clinical outcome Genetics Environmental Exposures Epigenetics Molecular Pathology Genomics Functional Clinical research Genomics Integrative Proteomics Disease Biology Biobanks (Physiology)

Model systems

Animal models Molecular interaction In vitro tissue culture model systems Organ culture and development Molecular Phenotype - Clinical Outcome

Precision Medicine approach: Deﬁne functional disease group => associate with outcome => predictors of group Tubulointerstitial gene expression PCA FSGS and MCD patients

FSGS PCA: Overlap of MCD MCD and FSGS, but FSGS subgroup

PCA of normalized Affymetrix ST2.1 based gene expresssion data set of 68 FSGS and 51 MCD patients FSGS-MCD cluster - tubulointersal mRNA data

CR*

Cluster 1 Cluster 2 Cluster 3

GFR* 108mL/min/m2 79 42

UPCR 0.9 1.25 1.7

*=signiﬁcant differences among clusters ERCB Validation in independent ERCB cohort Cluster 1 Cluster 2 Cluster 3

GFR 104 94 69 Concordant expression in 196/202 genes between ERCB and NEPTUNE cluster 3 vs 1+2 Replicaon in second NEPTUNE cohort

Cluster 1 Cluster 2 Cluster 3 Proportion without Complete Remission

1.00 0.75 0.50 0.25 0.00 0 Time to Complete Proteinuria Remission adjusted for FSGS, Baseline age, eGFR and FSGS,UPCR for age, eGFR Baseline adjusted Kaplan Meier SurvivalKaplan Estimates 100 per molecularsubgroup cluster =3 cluster =1 cluster analysis time analysis 200 cluster =2 cluster 300 1 400 3 2 What transcript are responsible for molecular subgroups?

Cluster 3 Transcriponal Networks in Cluster 3 vs 1+2

FONT II Clinical Trail in multi-drug resistant FSGS: • > 16 weeks of Adalimumab TNFa (24 mg/m2, max 40) • Outcome: 4/16 (25%) with 50% reduction in UPC with preserved eGFR • No clinical or laboratory features that predicted response

309 probesets q<0.01 plus FC>2, co-expression interacon network (IPA 8.5) Correlates of subgroups in urine and ssue

+ + Molecularly deﬁned subgroup – renal tissue

Identify a surrogate marker for Kaplan Meier Survival Estimates adjusted for FSGS, Baseline age, eGFR and UPCR

patients with worse clinical 0.00

outcome: 0.25

1. Reﬂects renal tissue mRNA 0.50

2. Urine (“liquid biopsy”) 0.75 1.00 Proportionwithout Complete Remission 3. Histology 0 100 200 300 400 analysis time

cluster = 1 cluster = 2 cluster = 3 Associaon ssue transcripts encoded proteins in matching urine samples Means Urine FC 3 vs 1+2 1 + 2 3 MMP7 CCL22 CCL5 CCL2 Transcripts of TIMP1 Matching urinary 13/24 of the urine CXCL10 proteins are biomarkers CCL4 concodant measured are TNF regulation in also differentially CXCL1 Cluster 3 vs. expressed in the TIMP2 Cluster 1+2 tubulointerstitium IL8 (p<0.05) (q<5) PDGFA VEGFA Associaon ssue transcripts encoded proteins in matching urine samples

Means Histology FC 3 vs 1+2 1 + 2 3 MMP7 0.56 CCL22 0.62 CCL5 0.58 CCL2 0.51 Transcripts of TIMP1 0.59 13/24 of the urine CXCL10 0.36 Pearson biomarkers CCL4 0.34 Correlation measured are TNF 0.49 with also differentially CXCL1 0.20 Interstitial expressed in the TIMP2 0.59 Fibrosis/ IL8 tubulointerstitium 0.46 Tubular PDGFA (q<5) 0.36 Atrophy VEGFA -0.53 Scores Precision Medicine for Nephrotic Syndrome

(mod. National Research75 Council, 2011) Sharing Knowledge in the Informational Commons • Nephromine: Kidney speciﬁc web based search engine: Nephromine 4.0

Combined data base and systems biology search engine for standardized analysis by the renal research community Empower Participatory Research

From predeﬁned analyses to domain expert driven knowledge discovery Translational Research: Data Integration & Analysis

The Challenge: A solution: • Data Integration & Analysis • tranSMART Open-source solution for sharing, integration, standardization and analysis of heterogeneous data from collaborative translational studies supported by an open-data biomedical research community

Athey and Omenn, 2009

Overview at http://lanyrd.com/2014/transmart/sdfqkf/ tranSMART in renal disease

Implemented for NEPTUNE consortium 09/2014: § Data exploration – Combining diverse data sets and studies with my unique background § Hypothesis generation – Leveraging the unpredicted observations for disruptive insights § Develop ancillary study concepts – Using live cohorts to test hypotheses, and enrich core data set with ancillary data in the process Translational Medicine in Renal Disease

Molecular mechanism emerging: § Prognostic and predictive biomarkers § Integration along all steps of the Genotype- Phenotype continuum using data sets rapidly becoming available ⇒ Novel Therapeutic Targets

Identiﬁed intervention points to be tested: § Molecular characterized cohorts in place to test identiﬁed targets across diseases and continents Team Science in Renal Research Nephrology: Heather Ascani, Celine Berthier, Jeni Chapman, Rachel Dull, Sean Eddy, Felix Eichinger, Bradley Godfrey, Jennifer Harder, Jennifer Hawkins, Wenjun Ju, Colleen Kincaid-Beal, Claudiu Komorowsky, Alex Klim, Chrysta Lienczewski, Scott Launius, Tina Mainieri, Laura Mariani, Sebastian Martini, Viji Nair, Ross Peterson, Ann Randolph, Tyler Schlientz, Michelle Smith, Shahaan Smith, Denise Taylor-Moon, Ellen Woodard F. Brosius, H.Zhang Pediatric Nephrology: Matt Sampson, John Troost, Emily Herreshoff, Debby Gipson Nephro-Pathology: Jeff Hodgin

Bioinformatics: University of Munich/Zurich: Funding support: Yuanfan Guang Clemens D. Cohen NIH: NIDDK, NCATS/ORDR, Brian Athey Matthias Neusser NIGMS, NIAMS, Gil Omenn Detlef Schlöndorff EU FP V-VII, H.V. Jagadish NIDDK: NephCure Foundation, JDRF, J. Patel Rob Nelson Eli Lilly, Roche, School of Information Jennifer Weil Boehringer-Ingelheim, B. Mirel U. Minnesota: Astra-Zeneca and Abbvie. Statistics M. Mauer and team K. Shedden Genomatix Inc, Ann Arbor: P. Song Andreas Klingenhoff Thomas Werner Maria Pia Rastaldi, Milan, Italy

… and our patients and their families