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BASIC RESEARCH www.jasn.org Integrative Biology Identifies Shared Transcriptional Networks in CKD †‡ †‡ †‡ †‡ Sebastian Martini,* Viji Nair,* Benjamin J. Keller,§ Felix Eichinger,* Jennifer J. Hawkins,* †‡ | †† Ann Randolph,* Carsten A. Böger, Crystal A. Gadegbeku,¶ Caroline S. Fox,** ‡‡ †‡ Clemens D. Cohen, Matthias Kretzler,* the European Renal cDNA Bank, C-PROBE Cohort, and CKDGen Consortium Departments of *Internal Medicine, †Nephrology, and ‡Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan; §Department of Computer Science, Eastern Michigan University, Ypsilanti, Michigan; |Department of Internal Medicine II, University Hospital Regensburg, Regensburg, Germany; ¶Department of Medicine, Section of Nephrology and Kidney Transplantation, Temple University School of Medicine, Philadelphia, Pennsylvania; **Division of Intramural Research and Laboratory for Population and Metabolic Health, National Heart, Lung, and Blood Institute, Framingham, Massachusetts; ††Department of Endocrinology, Brigham and Women’s Hospital, Boston, Massachusetts; and ‡‡Institute of Physiology, University of Zürich, Zürich, Switzerland ABSTRACT A previous meta-analysis of genome-wide association data by the Cohorts for Heart and Aging Research in Genomic Epidemiology and CKDGen consortia identified 16 loci associated with eGFR. To define how each of these single-nucleotide polymorphisms (SNPs) could affect renal function, we integrated GFR- associated loci with regulatory pathways, producing a molecular map of CKD. In kidney biopsy specimens from 157 European subjects representing nine different CKDs, renal transcript levels for 18 genes in proximity to the SNPs significantly correlated with GFR. These 18 genes were mapped into their biologic context by testing coregulated transcripts for enriched pathways. A network of 97 pathways linked by shared genes was constructed and characterized. Of these pathways, 56 pathways were reported previously to be associated with CKD; 41 pathways without prior association with CKD were ranked on the basis of the number of candidate genes connected to the respective pathways. All pathways aggregated into a network of two main clusters comprising inflammation- and metabolism-related pathways, with the NRF2-mediated oxidative stress response pathway serving as the hub between the two clusters. In all, 78 pathways and 95% of the connections among those pathways were verified in an independent North American biopsy cohort. Disease-specific analyses showed that most pathways are shared between sets of three diseases, with closest interconnection between lupus nephritis, IgA nephritis, and diabetic ne- phropathy. Taken together, the network integrates candidate genes from genome-wide association stud- ies into their functional context, revealing interactions and defining established and novel biologic mechanisms of renal impairment in renal diseases. J Am Soc Nephrol 25: 2559–2572, 2014. doi: 10.1681/ASN.2013080906 CKD affects .13% of the Unites States population Received August 28, 2013. Accepted April 30, 2014. and is a major contributor to cardiovascular morbid- Published online ahead of print. Publication date available at 1,2 ity and mortality. Defining the pathophysiology of www.jasn.org. CKD is critical to identify predictors of the disease Correspondence: Dr. Matthias Kretzler, University of Michigan, course and therapeutic targets. Diverse mechanisms Department of Internal Medicine/Division of Nephrology, 1150 have been linked with the development and progres- West Medical Center Drive, 1560 MSRB II, Ann Arbor, MI 48109- sion of CKD using experimental models and renal 0676. Email: [email protected] – tissues based expression studies.3 6 Copyright © 2014 by the American Society of Nephrology J Am Soc Nephrol 25: 2559–2572, 2014 ISSN : 1046-6673/2511-2559 2559 BASIC RESEARCH www.jasn.org The CKDGen and CHARGE consortia were able to use background in transcriptional expression profiles of renal bi- genome-wide association studies (GWASs) to identify preex- opsies of 157 subjects (Figure 1, step 1). These biopsies were isting genetic risk factors for renal function decline.7,8 How- from subjects diagnosed as having one of nine different chronic ever, GWASs, just like renal tissue gene expression profiling renal diseases (FSGS, membranous GN [MGN], minimal studies, only capture one aspect of the underlying pathophys- change disease [MCD], diabetic nephropathy [DN], hyperten- iology of CKD. A combined effort, linking the knowledge of sive nephropathy [HTN], IgA nephritis [IgAN], lupus nephri- genetic and transcriptomic alterations with clinical phenotype tis [LN], and thin-membrane disease) or histologically unaf- information in CKD, is now feasible and offers the opportu- fected parts of tumor nephrectomies. Renal biopsies from 10 nity for an integrated understanding of the intrarenal drivers living kidney donors (LDs) served as controls to test for dis- of CKD. ease-specific regulation. Disease cohort sizes ranged from 4 to This study used a sequential strategy to construct and 30 patients, with LN (30 patients) and IgAN (24 patients) being interpret a network of CKD-associated pathways that com- the largest subcohorts. The mean age at the time of biopsy was bines distinct but complementary sources of data: GWAS 46617 years (mean6SD), sex ratio was 90:67 (men/women), candidate genes, renal biopsy-derived transcriptional profiles and the average eGFR was 70636 ml/min per 1.73 m2, with matching clinical information, and literature-derived covering a range from 44 to 101 ml/min per 1.73 m2 (Table knowledge of molecular pathways. 2). The validation cohort, although different in disease compo- The CKDGen GWAS identified genetic loci associated with sition, had comparable clinical characteristics (Table 2). eGFR. Intrarenal gene expression levels generated from an Tubulointerstitial and glomerular gene expression profiles independent population of subjects were tested for correlation were used to compute the correlation of log eGFR with the log- with eGFR. Genes with evidence for eGFR association from transformed steady-state expression levels of 29 candidates those two independent lines of evidence (genetic and tran- within each subject (Figure 1, step 2A). In the tubulointerstitial scriptomic) were evaluated for their molecular functional compartment, 18 of 29 candidate genes were found to signif- context in CKD using a coexpression strategy. For each gene in icantly correlate with renal function: VEGFA, ANXA9, NAT8B, the intersection, mRNAs with stringent coexpression in the SLC34A1, TFDP2, ACSM5, SLC7A9, LASS2, FBXO22, UMOD, renal tissues of subjects with CKD were identified. The PIP5K1B, NAT8, GP2, DAB2, ALMS1, LMAN2, PRUNE,and functional context of coexpressed gene sets was defined using F12 (false discovery rate [FDR] #0.01, |r|$0.25) (Figure 2A). prior biologic knowledge derived from comprehensive path- Tubulointerstitial gene expression profiles for these 18 tran- way databases, linking each gene from GWAS through gene scripts across all subjects with CKDs versus LD controls are expression and eGFR correlation to a set of molecular path- shown in Table 1. Four of these transcripts also passed the ways. Connections between these pathways indicate that they significance filter in the glomerular compartment (VEGFA, share at least one transcript correlated with one or more ANXA9, NAT8B,andSLC34A1), with only DACH1 specific GWAS-derived candidate genes, allowing the construction of a to the glomerular compartment. Therefore, CKDGen candi- network of interacting pathways in CKD. For each renal disease date genes were enriched for eGFR-correlated genes in the included in the CKD dataset, the disease-specificinterplayof tubulointerstitial compartment compared with a random gene CKD-related pathways was identified and used to define a set (z-score for enrichment compared with random datasets: transcript-based similarity matrix between the glomerular 3.86), and additional analysis focused on the tubulointerstitial diseases studied. gene expression datasets. The directionality of correla- The combination of genetic, molecular, and clinical datasets tion of transcript levels with eGFR was conserved across all in individuals with CKD allows for defining renal diseases on diseases. the basis of their shared and specificmolecularmechanism. Identification of CKD pathways and their interplay provides Gene Coexpression Pathways starting points for experimental studies to define the biologic The 18 CKDGen-associated and eGFR-correlated candidate mechanisms underlying chronic renal failure. genes were evaluated for their functional context using a coexpression strategy (Figure 1, step 2B). For 14 of 18 candi- date genes, the following number of transcripts correlated RESULTS with the expression of the candidate gene (FDR#0.01, |r|.0.5), providing a basis for the detection of enriched path- eGFR Correlation ways among coexpressed genes: SLC7A9 (1078), VEGFA A meta-analysis by the CKDGen and CHARGE consortia (988), ACSM5 (925), SLC34A1 (839), NAT8B (811), ANXA9 identified 16 loci associated with renal function.7 A follow-up (690), LASS2 (519), DAB2 (365), NAT8 (283), GP2 (174), study identified 13 additional loci for renal function and CKD TFDP2 (118), UMOD (83), LMAN2 (57), and F12 (22). The in the same regions.8 Forty candidate genes were located in resulting 14 coexpressed gene sets show significant enrichment proximity (660 kb) of 16 single-nucleotide polymorphisms of 147 unique canonical pathways. Of these pathways,
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  • Differential Expression Profile Prioritization of Positional Candidate Glaucoma Genes the GLC1C Locus

    Differential Expression Profile Prioritization of Positional Candidate Glaucoma Genes the GLC1C Locus

    LABORATORY SCIENCES Differential Expression Profile Prioritization of Positional Candidate Glaucoma Genes The GLC1C Locus Frank W. Rozsa, PhD; Kathleen M. Scott, BS; Hemant Pawar, PhD; John R. Samples, MD; Mary K. Wirtz, PhD; Julia E. Richards, PhD Objectives: To develop and apply a model for priori- est because of moderate expression and changes in tization of candidate glaucoma genes. expression. Transcription factor ZBTB38 emerges as an interesting candidate gene because of the overall expres- Methods: This Affymetrix GeneChip (Affymetrix, Santa sion level, differential expression, and function. Clara, Calif) study of gene expression in primary cul- ture human trabecular meshwork cells uses a positional Conclusions: Only1geneintheGLC1C interval fits our differential expression profile model for prioritization of model for differential expression under multiple glau- candidate genes within the GLC1C genetic inclusion in- coma risk conditions. The use of multiple prioritization terval. models resulted in filtering 7 candidate genes of higher interest out of the 41 known genes in the region. Results: Sixteen genes were expressed under all condi- tions within the GLC1C interval. TMEM22 was the only Clinical Relevance: This study identified a small sub- gene within the interval with differential expression in set of genes that are most likely to harbor mutations that the same direction under both conditions tested. Two cause glaucoma linked to GLC1C. genes, ATP1B3 and COPB2, are of interest in the con- text of a protein-misfolding model for candidate selec- tion. SLC25A36, PCCB, and FNDC6 are of lesser inter- Arch Ophthalmol. 2007;125:117-127 IGH PREVALENCE AND PO- identification of additional GLC1C fami- tential for severe out- lies7,18-20 who provide optimal samples for come combine to make screening candidate genes for muta- adult-onset primary tions.7,18,20 The existence of 2 distinct open-angle glaucoma GLC1C haplotypes suggests that muta- (POAG) a significant public health prob- tions will not be limited to rare descen- H1 lem.