Functional Genomic Annotation of Genetic Risk Loci Highlights Inﬂammation and Epithelial Biology Networks in CKD

BASIC RESEARCH www.jasn.org

Nora Ledo, Yi-An Ko, Ae-Seo Deok Park, Hyun-Mi Kang, Sang-Youb Han, Peter Choi, and Katalin Susztak

Renal Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania

ABSTRACT Genome-wide association studies (GWASs) have identified multiple loci associated with the risk of CKD. Almost all risk variants are localized to the noncoding region of the genome; therefore, the role of these variants in CKD development is largely unknown. We hypothesized that polymorphisms alter transcription factor binding, thereby influencing the expression of nearby genes. Here, we examined the regulation of transcripts in the vicinity of CKD-associated polymorphisms in control and diseased human kidney samples and used systems biology approaches to identify potentially causal genes for prioritization. We interro- gated the expression and regulation of 226 transcripts in the vicinity of 44 single nucleotide polymorphisms using RNA sequencing and gene expression arrays from 95 microdissected control and diseased tubule samples and 51 glomerular samples. Gene expression analysis from 41 tubule samples served for external validation. 92 transcripts in the tubule compartment and 34 transcripts in glomeruli showed statistically significant correlation with eGFR. Many novel genes, including ACSM2A/2B, FAM47E, and PLXDC1, were identified. We observed that the expression of multiple genes in the vicinity of any single CKD risk allele correlated with renal function, potentially indicating that genetic variants influence multiple transcripts. Network analysis of GFR-correlating transcripts highlighted two major clusters; a positive correlation with epithelial and vascular functions and an inverse correlation with inflammatory gene cluster. In summary, our functional genomics analysis highlighted novel genes and critical pathways associated with kidney function for future analysis.

J Am Soc Nephrol 26: 692–714, 2015. doi: 10.1681/ASN.2014010028

Twenty million people suffer from CKD and ESRD in experiments to understand the genetics of a com- the United States. The risk of death signiﬁcantly plex trait such as CKD is the genome-wide associ- increases as kidney function (GFR) declines and it can ation study (GWAS).2 These studies compare be as high as 20% for patients with diabetes on 1 hemodialysis. Diseases of the kidney and urinary Received January 8, 2014. Accepted July 8, 2014. tract are ranked 12th in the mortality charts (www. Published online ahead of print. Publication date available at cdc.org), indicating their importance in public health. www.jasn.org. CKD is a typical gene environmental disease. Present address: Dr. Sang-Youb Han, Department of Internal Several environmental factors play important roles Medicine, Inje University, Ilsan-Paik Hospital, Goyang, Gyeonggi, inCKD development; diabetesandhypertension are South Korea. the two most important causes, accounting for close Correspondence: Dr. Katalin Susztak, Perelman School of to 75% of ESRD cases. In addition, CKD has a clear Medicine, University of Pennsylvania, 415 Curie Boulevard, 415 genetic component, because ,20% of patients with Clinical Research Building, Philadelphia, PA 19104. Email: diabetes or hypertension will actually develop kid- [email protected] ney disease. At present, one of the most powerful Copyright © 2015 by the American Society of Nephrology

692 ISSN : 1046-6673/2603-692 J Am Soc Nephrol 26: 692–714, 2015 www.jasn.org BASIC RESEARCH genetic variants in two groups of participants: people with the Reports from the ENCODE project indicate that the majority disease (patients) and similar people without the disease (con- (70%–80%) of the gene regulatory elements (promoters, en- trols). If a variant (single nucleotide polymorphism [SNP]) is hancers, and insulators) are within 250 kb of the gene.3 Using more frequent in people with the disease, the SNP is said to be these criteria, we identified 306 genes within 500 kb of 44 CKD associated with the disease. GWASs, however, have several SNPs. There was no gene within the 500-kb window around the limitations. First, GWASs became possible, because the ge- rs12437854 SNP; therefore, 43 loci were followed. We called netic information is inherited in fairly large blocks. Therefore, these transcripts CKD risk-associated transcripts (CRATs). we do not have to test the association with each of the close to 20 million genetic variations but can use fewer (about 1 mil- CRATs Are Enriched for Kidney-Specific Expression lion) SNPs representing the genetic variation of larger genetic We hypothesized that cells that express CRATs play an important regions (called haplotype or linkage disequilibrium block). role in controlling kidney function. Therefore, we determined Although haplotype blocks made GWAS convenient and fi- expression levels of all CRATsin control (normal) human kidney nancially feasible, they also mean that we do not know which samples (n=2) using comprehensive RNA sequencing analysis. of the many variants within a single haplotype block is func- We found that 41% of the CKD risk loci-associated transcripts tionally relevant. showed high expression (upper quartile) and that only 6% of Furthermore, .83% of the disease-associated SNPs are lo- CRAT transcripts were not detectable in human kidney tubule calized to the noncoding region of the genome3; therefore, it samples (Supplemental Figure 1). Overall, we found that a large is unclear how they induce illness. Recent reports from the percentage of the CKD SNP neighboring transcripts (94%; 287 Encyclopedia of DNA Elements (ENCODE) project indicate of 306) were expressed in the human kidney, indicating statisti- that most complex trait polymorphisms are localized to gene cally significant kidney-specific enrichment compared with 44 regulatory regions in target cell types.4 Disease-associated ge- randomly selected loci, where only 13% of the transcripts netic variants can alter binding sites for important transcrip- showed high expression and 16% of the nearby transcripts 2 tion factors and influence the expression of nearby genes.3,5–7 were not expressed in the kidney (P51.25310 9). Genetic variants can potentially alter steady-state expression Gene ontology analysis (david.abcc.ncifcrf.gov) to under- of genes, in which case they interfere with basal transcription stand the tissue specificity of CRATs indicated specificand factor binding or can alter the amplitude of transcript changes significant enrichment in the kidney and peripheral leukocytes after signal-dependent transcription factor binding. (P value=0.0082 and P value=0.0014, respectively). Next, we Here, we hypothesized that polymorphisms associated with compared absolute expression levels of CRATs by RNA se- renal disease will influence the expression of nearby transcript quencing in 16 different human organs using the Illumina levels in the kidney. We used genomics and systems biology Body Map database (www.ebi.ac.uk). The atlas confirmed approaches to investigate tissue-specific expression of tran- the statistically significant kidney-specific expression enrich- scripts and their correlation with kidney function. ment of CRATs (Supplemental Figure 2). For example, the atlas highlighted the high and kidney-specific expression of Uro- modulin (UMOD). In summary, expression of CRATs was en- RESULTS riched in the kidney and peripheral lymphocytes, potentially indicating the role of these cells in kidney disease development. CKD Risk-Associated Transcripts By manual literature search, we identified all GWASs reporting CRAT Expression in Normal and Diseased Human genetic association for CKD-related traits (Supplemental Table1). Kidney Glomerular Samples Many of these studies, however, used different parameters as We hypothesized that functionally important CRATs are not kidney disease indicators. We included SNPs associated with only expressed in relevant cell types (kidney and leukocytes) eGFR (on the basis of serum creatinine or cystatin C calcula- but that their expression level will change in CKD. To test this tions) or the presence of ESRD. Our analysis identified 10 pub- hypothesis, we analyzed gene expression levels in a large lications meeting these criteria.8–25 Most publications did not collection of microdissected human glomerular (n=51) and differentiate cases on the basis of disease etiology and included tubule (n=95) samples. Kidney samples were obtained from a cases with hypertensive and diabetic kidney disease. Coding diverse population (Supplemental Tables 3 and 4). Statistical polymorphisms and SNPs that did not reach genome-wide sig- analysis failed to detect ethnicity-driven gene expression dif- 2 nificance (P.5310 8) were excluded.26 Finally, 44 leading ferences (data not shown). SNPs meeting all of these criteria were used for further analysis Transcript profiling was performed for each individual sam- (Supplemental Table 2). Three SNPs associated only with dia- ple using Affymetrix U133v2 arrays. The data were processed betic CKD development were also analyzed separately; all other using established pipelines, and they contained probe set iden- SNPs were from studies including both diabetic and nondiabetic tifications for 226 transcripts from 306 originals CRATs. We cases. There were only two SNPs that reached genome-wide analyzed the expression levels of 226 CRATsin 51 microdissected significance in multiple studies (rs12917707 and rs9895661). glomerular samples (Supplemental Table 3). On the basis of the These two SNPs were counted only once. Chronic Kidney Disease Epidemiology Collaboration eGFR

J Am Soc Nephrol 26: 692–714, 2015 Genetical Genomics of CKD 693 BASIC RESEARCH www.jasn.org determination, we had 27 samples with normal renal function (Figure 1D) correlated with eGFR. In normal nondisease human (eGFR.60 ml/min per 1.73 m2) and 24 samples with reduced kidney tissue, proteins coded by FAM47E (Figure 1E) and GFR (eGFR,60 ml/min per 1.73 m2).27 This eGFR cutoff was VEGFA (Figure 1G) were highly expressed in glomeruli. Immu- used as a CKD definition in the included GWASs. To match the nostaining studies (from the Human Protein Atlas) showed that transcript data with GWAS cases, we included samples from the protein encoded by PLXDC1 (also known as Tumor endo- patients with diabetic and hypertensive CKD. thelial marker 7 [TEM7]) exhibited glomerular endothelial- Linear correlation analysis identified the association of 34 specificexpressioninnormalhumankidneytissue(Figure1F). CRATs with eGFR (P,0.05) (Table 1). The correlation between MAGI2 seemed to have a podocyte-specific expression pattern the expression of seven CRATs and eGFR remained significant, (Figure 1H), potentially indicatingitsroleinthiscelltype.In- even after Benjamini–Hochberg-based multiple testing correc- terestingly, FAM47E, PLXDC1,andMAGI2 have not been iden- tion. The expression of multiple novel transcripts showed excel- tified in GWASs as potential causal genes in the vicinity of CKD lent correlation with kidney function. For example, expression risk loci. We also separately examined the expression and corre- levels of Family with sequence similarity 47, member E lation of diabetic CKD associated transcripts and their correla- (FAM47E) (Figure 1A), Plexin domain-containing 1 (PLXDC1) tion with glomerular gene expression (Supplemental Table 5). In (Figure 1B), Vascular endothelial growth factor A (VEGFA) (Fig- summary, the analysis highlighted that the expression of several ure 1C), and Membrane-associated guanylate kinase (MAGI2) CRATs in glomeruli correlates with renal function.

Table 1. Expression levels of 34 transcripts (CRATs) in glomeruli showed significant correlation with eGFR Gene Symbol Pearson R 95% Confidence Interval P Value (Two-Tailed) P Value (Corrected) 2 CTSS 20.501 20.68 to 20.26 1.8310 4 0.0426 2 FAM47E///STBD1 0.496 0.26 to 0.68 2.1310 4 0.0426 2 FYB 20.478 20.67 to 20.23 3.9310 4 0.0427 2 LTB 20.471 20.66 to 20.23 4.8310 4 0.0427 2 EHBP1L1 20.466 20.66 to 20.22 5.7310 4 0.0427 2 MFAP4 20.462 20.65 to 20.21 6.4310 4 0.0427 2 MICALL2 20.457 20.65 to 20.21 7.5310 4 0.0428 2 CTSK 20.428 20.63 to 20.17 1.7310 3 0.077 2 PLXDC1 20.417 20.62 to 20.16 2.3310 3 0.085 2 F12 20.390 20.60 to 20.13 4.6310 3 0.154 2 VEGFA 0.372 0.11 to 0.59 7.2310 3 0.222 2 PCOLCE 20.369 20.59 to 20.10 7.8310 3 0.222 2 MYCN 0.364 0.10 to 0.58 8.6310 3 0.231 GP2 0.356 0.09 to 0.58 0.010 0.235 SLC34A1 0.356 0.09 to 0.58 0.010 0.235 EFHD2 20.355 20.58 to 20.09 0.011 0.235 LST1 20.352 20.57 to 20.09 0.011 0.236 MICB 20.345 20.57 to 20.08 0.013 0.25 LRCH4 20.336 20.56 to 20.07 0.016 0.275 ANXA9 0.334 0.07 to 0.56 0.017 0.275 MAGI2 0.321 0.05 to 0.55 0.022 0.336 ACSM5 0.313 0.04 to 0.54 0.025 0.375 SLC22A3 20.308 20.54 to 20.04 0.028 0.387 PSRC1 20.304 20.54 to 20.03 0.030 0.398 UMOD 0.301 0.03 to 0.53 0.032 0.401 ALDH3A2 0.298 0.02 to 0.53 0.034 0.401 GATM 0.297 0.02 to 0.53 0.035 0.401 SORT1 0.294 0.02 to 0.53 0.036 0.401 AGMAT 0.293 0.02 to 0.53 0.037 0.401 DRAP1 0.293 0.02 to 0.53 0.037 0.401 CASP9 0.289 0.01 to 0.52 0.040 0.401 KDM5A 20.287 20.52 to 20.01 0.041 0.401 SLC6A13 0.286 0.01 to 0.52 0.042 0.401 HLA-C 20.284 20.52 to 20.01 0.044 0.406 Pearson product moment correlation coefficient (Pearson R) was used to measure the strength of association between gene expression and eGFR. Two-tailed test was used to determine the statistical significance. With Benjamini–Hochberg multiple testing correction, seven transcripts showed significant correlation with eGFR (P corrected,0.05). Gene symbols are official symbols approved by the Human Genome Organization Gene Nomenclature Committee (HGNC).

694 Journal of the American Society of Nephrology J Am Soc Nephrol 26: 692–714, 2015 www.jasn.org BASIC RESEARCH

We also looked for linear correlation between CRATsand renal function. Pearson correlation identified 92 transcripts with statistically significant (P,0.05) linear correlation with kidney function (Table 2). The correlation between the expression of 70 CRATs and eGFR remained significant, even after Benjamini–Hochberg-based multiple testing correction. More transcripts (58%) showed a positive correlation with renal function (i.e., their expression was decreased in samples with lower GFR), whereas 42% showed an inverse correlation. Renal function correlated with the expression of 25 CRATs both in glomeruli and tubules. Tubule-specific expression of solute carriers had the strongest correlation with renal function. For example, the levels of Solute carrier family 34, member 1 (SLC34A1), which codes a type II sodium/ phosphate cotransporter, and SLC7A9, which codes the light chain of an amino acid transporter (Figure 2, A and B), correlated strongly with eGFR (with R values of 0.61 and 0.59, respectively). Both transcripts encode proteins that are highly and specifically expressed in renal tubule epithelial cells (Figure 2, D and E). In addition to solute carriers, the expression of a meta- bolic enzyme, Acyl-CoA synthetase medium chain family member 5 (ACSM5), also highly correlated with renal function and showed high protein expression in tu- Figure 1. Correlation between CRAT expression in glomeruli and renal function. The bule epithelial cells (Figure 2, C and F). For y axis shows the relative normalized glomerular expressions of (A) FAM47E,(B)PLXDC1, external validation, we used a gene expres- (C) VEGFA,and(D)MAGI2.Thex axes show the eGFR for each sample. Each dot sion dataset containing genome-wide tran- represents one individual miscrodissected glomerular sample. The lines represent the scription profiling from 41 microdissected fitted linear correlation values. Immunohistochemistry shows the protein expression tubule samples. The clinical characteristics in human glomeruli ([E] FAM47E,[F]PLXDC1,[G]VEGFA,and[H]MAGI2). Scale bars, of these samples are described in Supple- 100 mm. Reprinted from www.proteinatlas.org. mental Table 7. Samples in this dataset were different from the primary dataset, and a slightly different method was used for mi- CRAT Expression in Normal and Diseased Human croarray probe labeling. Although this dataset was much Kidney Tubule Samples smaller with a narrower GFR range, we confirmed the signif- Next, we analyzed the expression levels of 226 CRATs in 95 mi- icant linear correlation of 51 transcripts, highlighting the im- crodissected human kidney tubule samples. Samples were obtained portance of these CRATs (Table 2). Next, we also specifically from patients with a wide range of kidney function (Supplemental examined the correlation of the diabetic CKD-associated Table 4): 56 samples with normal eGFR (eGFR.60 ml/min per polymorphisms (rs12437854, rs7583877, and rs1617640) 1.73 m2) and 39 samples with kidney disease (eGFR,60 ml/min and transcript changes only in diabetic kidney disease (Supple- per 1.73 m2). We performed a binary analysis by comparing the mental Table 5). The analysis highlighted that Procollagen expression levels of CRATs in control versus CKD samples. Using C-endopeptidase enhancer (PCOLCE) and Thyroid hormone re- statistical correction for multiple testing (Benjamini–Hochberg ceptor interactor 6 (TRIP6) in the vicinity of diabetic CKD SNPs corrected P value,0.05), 73 CRATs (from 226 CRATs) showed correlate with kidney function. In summary, the gene expression differential expression when CKD tubule samples were compared and kidney function correlation analysis underscored CRATs to controls (Supplemental Table 6). for future prioritization.

J Am Soc Nephrol 26: 692–714, 2015 Genetical Genomics of CKD 695 BASIC RESEARCH www.jasn.org

Table 2. In tubules, expression levels of 92 transcripts (CRATs) showed signiﬁcant correlation with eGFR Gene Symbol Pearson R 95% Conﬁdence Interval P Value (Two-Tailed) P Value (Corrected) 2 2 SLC34A1a 0.610 0.47 to 0.72 5.3310 11 2.1310 8 2 2 SLC7A9a 0.588 0.44 to 0.71 3.6310 10 7310 8 2 2 ACSM5a 0.551 0.39 to 0.68 7.3310 9 9.6310 7 2 2 FYBa 20.531 20.66 to 20.37 3310 8 2.4310 6 2 2 ACSM2A///ACSM2Bb 0.526 0.36 to 0.66 4.3310 8 2.4310 6 2 2 NAT8Bb 0.518 0.35 to 0.65 7.4310 8 3.3310 6 2 2 ALDH3A2a 0.517 0.35 to 0.65 8.3310 8 3.3310 6 2 2 LTBa 20.517 20.65 to 20.35 8.3310 8 3.3310 6 2 2 LST1a 20.514 20.65 to 20.35 1310 7 3.6310 6 2 2 UMODa 0.504 0.34 to 0.64 1.9310 7 6.1310 6 2 2 ACAD10b 0.485 0.31 to 0.63 6.5310 7 1.8310 5 2 2 DNAJC16 0.478 0.31 to 0.62 9.7310 7 2.5310 5 2 2 GSTM4a 0.474 0.30 to 0.62 1.2310 6 3310 5 2 2 SLC6A13b 0.469 0.30 to 0.61 1.7310 6 3.3310 5 2 2 VEGFAa 0.468 0.29 to 0.61 1.7310 6 3.3310 5 2 2 CTSSa 20.468 20.61 to 20.29 1.8310 6 3.3310 5 2 2 ANXA9a 0.464 0.29 to 0.61 2.2310 6 3.9310 5 2 2 SLC6A12 0.454 0.28 to 0.60 3.8310 6 6310 5 2 2 FAM47E///STBD1a 0.444 0.27 to 0.59 6.5310 6 9.5310 5 2 2 SLC47A1a 0.444 0.27 to 0.59 6.5310 6 9.5310 5 2 2 NAT8///NAT8Bb 0.441 0.26 to 0.59 7.7310 6 1.1310 4 2 2 ALDH2b 0.440 0.26 to 0.59 8.1310 6 1.1310 4 2 2 CERS2 0.436 0.26 to 0.59 1310 5 1.2310 4 2 2 STC1b 0.431 0.25 to 0.58 1.3310 5 1.6310 4 2 2 APOMa 0.428 0.25 to 0.58 1.5310 5 1.8310 4 2 2 DAB2a 0.410 0.23 to 0.57 3.7310 5 3.9310 4 2 2 AGMATa 0.397 0.21 to 0.56 6.7310 5 6.9310 4 2 2 GATMa 0.393 0.21 to 0.55 8310 5 7.8310 4 2 2 SLC22A2 0.393 0.21 to 0.55 8.3310 5 7.9310 4 2 2 FAM89Bb 20.391 20.55 to 20.21 8.9310 5 8.1310 4 2 2 SLC30A4 0.382 0.20 to 0.54 1310 4 1.1310 3 2 2 MYCNa 0.376 0.19 to 0.54 2310 4 1.4310 3 2 2 AIF1b 20.352 20.52 to 20.16 5310 4 3.4310 3 2 2 TRIP6b 0.352 0.17 to 0.52 5310 4 3.5310 3 2 2 LARP4B 0.347 0.16 to 0.51 6310 4 4310 3 2 2 GPERa 0.347 0.16 to 0.51 6310 4 4310 3 2 2 LRCH4 20.342 20.51 to 20.15 7310 4 4.7310 3 2 2 SLC22A1 0.342 0.15 to 0.51 7310 4 4.7310 3 2 2 FAM193B 20.338 20.51 to 20.15 8310 4 5.4310 3 2 2 CTSKa 20.335 20.50 to 20.14 9310 4 5.9310 3 2 2 IGF2R 0.321 0.13 to 0.49 1.5310 3 9.5310 3 2 2 DDI2///RSC1A1 0.320 0.13 to 0.49 1.6310 3 9.9310 3 2 PLXDC1a 20.315 20.49 to 20.12 1.9310 3 0.012 2 TFDP2b 0.313 0.12 to 0.48 2310 3 0.012 2 BAG6 0.311 0.12 to 0.48 2.1310 3 0.013 2 EHBP1L1a 20.311 20.48 to 20.12 2.1310 3 0.013 2 CTSW 20.309 20.48 to 20.12 2.3310 3 0.013 2 ATXN2 0.309 0.12 to 0.48 2.3310 3 0.013 2 ERBB2b 0.308 0.11 to 0.48 2.4310 3 0.013 2 PHTF2b 20.307 20.48 to 20.11 2.5310 3 0.013 2 TBX2 0.305 0.11 to 0.48 2.7310 3 0.014 2 MICBa 20.303 20.48 to 20.11 2.8310 3 0.014 2 SIPA1 20.298 20.47 to 20.10 3.3310 3 0.017 2 PRUNE 0.298 0.10 to 0.47 3.4310 3 0.017 2 CCDC85Bb 20.287 20.46 to 20.09 4.9310 3 0.023 2 GRK6 20.287 20.46 to 20.09 4.9310 3 0.023

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Table 2. Continued

Gene Symbol Pearson R 95% Confidence Interval P Value (Two-Tailed) P Value (Corrected) 2 PIP5K1A 0.278 0.08 to 0.46 6.3310 3 0.029 2 LTBP3b 20.276 20.45 to 20.08 6.9310 3 0.032 2 CASP9b 0.273 0.08 to 0.45 7.5310 3 0.034 2 NSD1 0.272 0.07 to 0.45 7.7310 3 0.034 2 SOX11 20.271 20.45 to 20.07 7.8310 3 0.034 2 DUOX1 20.269 20.45 to 20.07 8.4310 0.036 2 AFF3 20.267 20.45 to 20.07 8.8310 3 0.036 2 NCR3 20.267 20.45 to 20.07 8.8310 3 0.036 2 FIBP 0.265 0.07 to 0.44 9.6310 3 0.039 GNAI3 20.263 20.44 to 20.07 0.010 0.040 FOSL1 0.261 0.06 to 0.44 0.011 0.042 MPPED2 0.259 0.06 to 0.44 0.011 0.044 MFAP4a 20.258 20.44 to 20.06 0.012 0.045 B9D1 20.254 20.43 to 20.06 0.013 0.049 SLC12A9 0.254 0.06 to 0.43 0.013 0.050 CFL1 20.250 20.43 to 20.05 0.015 0.055 CST8 20.248 20.43 to 20.05 0.015 0.057 DBN1a 20.242 20.42 to 20.04 0.018 0.067 CDC42SE1a 20.242 20.42 to 20.04 0.018 0.067 GP2a 0.233 0.03 to 0.42 0.023 0.079 MYH9b 20.233 20.42 to 20.03 0.023 0.079 IDI1 0.233 0.03 to 0.42 0.023 0.079 MLLT11b 20.233 20.42 to 20.03 0.023 0.079 DACH1 0.229 0.03 to 0.41 0.026 0.087 DDX1 0.226 0.03 to 0.41 0.028 0.093 ACSM1 20.225 20.41 to 20.03 0.028 0.094 PDLIM7b 20.225 20.41 to 20.02 0.029 0.094 RELA 20.224 20.41 to 20.02 0.029 0.096 SORT1 0.221 0.02 to 0.41 0.031 0.10 SLC28A2a 0.221 0.02 to 0.41 0.031 0.10 EPN2b 20.213 20.40 to 20.01 0.038 0.119 CELA2A///CELA2Ba 20.213 20.40 to 20.01 0.039 0.120 CUX2 20.212 20.40 to 20.01 0.040 0.122 PTPN12b 20.211 20.40 to 20.01 0.040 0.122 MICALL2a 20.206 20.39 to 2,0.004 0.046 0.137 MKKS 0.204 ,0.01 to 0.39 0.048 0.141 Pearson product moment correlation coefficient (Pearson R) was used to measure the strength of association between gene expression and eGFR. Two-tailed test was used to determine the statistical significance. Seventy transcripts showed significant correlation with eGFR (P corrected,0.05) after Benjamini–Hochberg- based multiple testing correction. Gene symbols are official symbols approved by the Human Genome Organization Gene Nomenclature Committee (HGNC). aThegeneexpressionsignificantly correlated with eGFR in the external validation microarray dataset containing 41 tubule samples and the correlation remained significant after multiple testing correction. bThe gene expression significantly correlated with eGFR in the external validation microarray dataset containing 41 tubule samples.

Transcript Levels around the UMOD Locus immunohistochemistry staining from samples used for the tran- We specifically further investigated expression changes of the scriptomic analysis, indicating the excellent correlation between UMOD transcript, because it is a potential causal gene underly- uromodulin protein expression and its transcript levels. ing the polymorphism of some of the best characterized CKD- Although UMOD has emerged as an important causal gene associated SNPs on chromosome 16 (rs12917707, rs4293393, and for CKD, unexpectedly, we found that three other nearby rs11864909). This gene encodes one of the most abundant pro- genes were also highly expressed in renal tubules, and their teins in human urine; Uromodulin or Tamm–Horsfall protein. expression strongly correlated with eGFR. To illustrate this Furthermore, functional studies seem to link UMOD expression observation, Figure 4, A–C shows the chromosome 16 locus, both as a biomarker and a causal gene for CKD development.28 including three leading SNPs (rs12917707, rs4293393, and We found that UMOD transcript levels showed a highly signifi- rs11864909) with polymorphisms that best correlate with 2 cant linear correlation with renal function (P51.9310 7)intu- CKD. Closest genes to these polymorphisms are UMOD and bule samples (Figure 3A). Figure 3, B–E, shows results of PDILT (Protein disulfide isomerase-like, testis expressed). The

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expression strongly correlated with renal function (Figure 3, F–J), potentially indicating a functional role for these transcripts. Similar results are shown for a chromosome 5region(Figure4,D–F). In summary, our observations showed that the expression of multiple genes around a single locus correlated with kidney function, potentially indicating that the regulation of these genes could be linked. Next, we examined whether in the prox- imity of a single SNP we could observe changes in expression of a single gene or multiple genes. We found that, on 23 of 43 examined CKD risk loci, multiple neighboring transcripts correlated with renal function (Supplemental Figure 3). For example, at the SLC47A1 locus (rs2453580), not only the SLC47A1 (multidrug extrusion protein) but also, the Aldehyde dehydrogenase 3 family, member A2 (ALDH3A2) correlated with 2 eGFR (P58310 8). We found that, around the rs267734 polymorphism on chromosome 1, both Ceramide synthase 2 (CERS2; 2 P51.01310 5) and Annexin A9 (ANXA9) 2 (P52.2310 6) transcripts correlated with eGFR in tubule samples. In addition, transcript level of Cathepsin S (CTSS) correlated with renal function both in tubules 2 and glomeruli (P51.773 10 6 and Figure 2. Correlation between CRAT expression in tubules and renal function. Ex- P5 3 24 pressions of (A) SLC34A1,(B)SLC7A9,and(C)ACSM5 correlate with eGFR in tubule 1.8 10 ,respectively).Onchromo- samples. The x axes represent eGFR (ml/min per 1.73 m2), whereas the y axes rep- some 5 at the rs11959928 polymorphism, resent the normalized gene expression values of the transcript. Each dot represents both Disabled homolog 2 (DAB2,aputa- transcript levels and eGFR values from a single kidney sample. The lines are the fitted tive mitogen-responsive phosphopro- correlation values. Immunohistochemistry shows tubular-specific expression of (D) tein) and FYN binding protein (FYB) SLC34A1,(E)SLC7A9,and(F)ACSM5. Scale bars, 100 mm. Reprinted from www. showed strong correlation with renal func- 2 2 proteinatlas.org. tion (P53.68310 5 and P53310 8,respectively) (Figure 4, D–F). On the basis of renal expression and expression level of one of the flanking genes PDILTwas nearly renal function association, we could prioritize potential target undetectable, but our RNA sequencing analysis confirmed and/or causal genes for CKD development for 39 of 44 high UMOD transcript levels in human kidney samples (Fig- examined loci. As mentioned earlier, there was no gene around ure 4B). Unfortunately, PDILT probes were absent from the rs12437854,andtheonlynearbygene(WDR72)around human U133 chips, and therefore, the correlation between rs491567 had no probe on the human U133 chips, albeit PDILT and renal function could not be analyzed. However, WDR72 is highly expressed in human kidney (Supplemental we observed that ACSM5 (Figure 2, C and F) and ACSM2A/B Table 2) by RNA sequencing analysis. No nearby transcript were highly expressed in human kidney tubule samples (Fig- showed association with renal function for three SNPs ure 4, A and B). Furthermore, we also validated the transcript (rs1394125, rs7805747, and rs4744712) (Supplemental Figure 3). expression of UMOD,Glycoprotein2(GP2), ACSM5, The correlation between these loci and kidney function would ACSM2A/B, ACSM1,andPDILT by quantitative real-time need to be re-evaluated. RT-PCR (QRT-PCR) (Supplemental Table 8) to confirm the Taken together, we identified 104 transcripts of 226 CRATs microarray results (Figure 4C). ACSM5 and ACSM2A/2B showing significant correlation with eGFR. We could highlight genes (ACSM family members) encode three genes in the genes for further prioritization for 39 of 44 loci (89%). Using a-fatty acid oxidation pathway. Interestingly, these transcripts UMOD, ACSM2A,andVEGFA genes as examples, we showed not only showed high expression in the kidney, but also, their that these expression changes likely correlate with protein

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Figure 3. UMOD and ACSM2A expressions correlate with renal function. The expressions of (A) UMOD and (F) ACSM2A correlate with eGFR in tubule samples. The x axes represent eGFR (ml/min per 1.73 m2), whereas the y axes represent the normalized gene expression values of the transcript. Each dot represents transcript levels and eGFR values from a single kidney sample. The lines are the fitted correlation value (Pcorr, P value after Benjamini-Hochberg multiple testing correction). Immunohistochemistry of the samples with low and high mRNA expression showed differences of (B–E) the UMOD and (G–J) the ACSM2A expression on protein level. Scale bars, 50 mm. levels. Our results also suggest that not only the closest gene 500-kb window that we used to identify CRATs. All 11 tran- but also, several genes in the close vicinity correlate highly with scripts were at least moderately expressed in human kidney tis- renal function, indicating their potential importance and their sue. Only one transcript CLTB (Clathrin, light chain B) showed potential coregulation. significant linear correlation with eGFR in glomerulus samples (P=0.016). Another transcript, CERS2 (also known as LASS2), Expressions Quantitative Trait Loci Highlight CKD showed variation in gene expression in lymphoblastiod tissue on Candidate Genes the basis of the rs267734 and rs267738 genotypes. Furthermore, Polymorphisms associated with kidney function can also directly CERS2 was differentially expressed in CKD and highly correlated control baseline transcript levels in disease-relevant types. To with eGFR (Table 2 and Supplemental Table 6), making it a examine whether CKD risk SNPs influence local transcript levels potential candidate gene for CKD development. (in cis; i.e., within 1-Mb distance), we examined multiple differ- Unfortunately, we did not have genomic DNA from all ent datasets where genotype and gene expression correlation analyzed kidney samples to examine genotype and gene expres- data were available. These datasets included the MuTHER (Mul- sion correlations, but we genotyped 21 control (eGFR.85 tiple Tissue Human Expression Resource) and other studies,29–33 ml/min per 1.73 m2) samples for the rs881858 polymorphism. where transcript levels were available from liver, adipose, and In the same samples, tubule-specific VEGFA transcript levels lymphoblastoid samples. Cis-expressions quantitative trait loci were determined by QRT-PCR. Tubule-specific VEGFA transcript (cis-eQTLs) often can be detected in multiple tissues. We found levels were lower in patients who were homozygous for the ma- that 4 SNPs from the previously identified 44 leading SNPs and jor allele on the rs881858 locus compared to heterozygous or 16 SNPs in their linkage disequilibrium (r2$0.8) acted as minor allele homozygous samples (Figure 5A). Glomerular or cis-eQTLs for 11 different transcripts (P,0.05) (Supplemental tubule-specific VEGFA transcript and protein expression level Table 9). Four of these transcripts (33%) were outside of the correlated with GFR (Figures 1C and 5, B–D). These results

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Figure 4. Tubule-speciﬁc transcript levels correlate with renal function near the UMOD locus (rs4293393, rs12917707, and rs11864909 polymorphisms) and the disabled homolog 2 (DAB2) locus (rs11959928). The x axes represent the genomic positions of each gene on (A and C) chromosomes 16 and (D and F) 5. The y axes represent the negative logarithms of the corrected P values (signiﬁcances) between the expressions of each gene and eGFR (ml/min per 1.73 m2). (A and D) Color coding represents the baseline expression of the

700 Journal of the American Society of Nephrology J Am Soc Nephrol 26: 692–714, 2015 www.jasn.org BASIC RESEARCH indicate that the rs881858 polymorphism likely influences critical regions in the genome with variations that are associated VEGF transcript levels and that VEGFA could be an important with kidney function. The second step is to identify transcripts CKD candidate gene. Additionally, we examined whether ge- that are regulated by SNPs. The working hypothesis in the field is netic polymorphism (rs6420094) on chromosome 5 around that causal polymorphisms alter transcription factor binding, SLC34A1 will influence transcript expression. We found that causing changes in transcript levels in target cell types and tubule-specific SLC34A1 expression was significantly higher in inducing diseaseinspecificorgans.Because there are hundreds of patients who were homozygous for the major allele on the genetic variants associated with disease development, analyzing rs6420094 locus compared with heterozygous or minor allele variants individually is a daunting task. Recently, two comple- homozygous samples (Supplemental Figure 4A). mentary methods have been developed and successfully applied Transcription factor binding analysis around 44 CKD risk- to identify genes that are targets of the polymorphisms. The first associated polymorphisms indicated that, at 42 loci, multiple method uses the transcript levels as quantitative traits to identify binding motifs are altered by the genetic variants (Supple- polymorphisms that influence their levels (eQTL).34 Toperform mental Table 10). This altered transcription factor binding such analysis, a large human tissue bank from target cell types is can potentially highlight the mechanism of CKD risk variant- necessary where both genetic polymorphisms and transcript associated disease development. levels are analyzed. The second (newer) method uses the cell type-specific cellular epigenome for regulatory element annota- CRATs Form Networks Highlighting the Role of tion and identifies target transcripts that are associated with Inflammation and Epithelial Biology genetic variants.35 A critical limitation of these methods is that Table 3 summarizes our results and provides comprehensive they only identify transcripts that are influenced by a basal tran- evaluation of the loci and transcripts studied here. Finally, we scription factor, because these datasets are generated from con- examined whether the 104 renal function-correlating CRATs trol healthy samples. However, it is possible that polymorphisms (either in tubule or glomerular samples) in the neighborhood control transcription factor binding sites for signal-dependent of 39 CKD risk loci show relatedness and can form a network. transcription factors. This would mean that the expression of The network analysis was performed separately on genes that aCKDcausinggeneisnotalteredatbaselinebutshowsdiffer- showed positive or negative correlation with kidney function. ences under stress conditions. Figure 7 summarizes the concept Genes showing negative correlation with kidney function underlying this work. Here, we performed the initial level of such (higher expression in CKD) clustered at the TNF-a, TGF-b1, analysis by examining the correlation between transcripts in the and NF-kB/RelA regulatory nodes (Figure 6A). Most members vicinity of CKD SNPs and GFR. of this cluster are known to play a role in immune function and On the basis of recent observations that close to 90% of target regulation of inflammation. The second cluster (transcripts with transcripts are within 250 kb of the polymorphism, we defined expression that inversely correlated with kidney function) cen- 306 CRATs. Most priorstudies focused onthetwo flanking genes, tered at VEGFA and EGF receptor 2 molecules (Figure 6B). As ignoring transcripts that are farther away.8,9 These 306 CRATs indicated by their name, these molecules play important roles in could be important for future studies as potential candidates for maintaining epithelial and endothelial functions. In summary, CKD development. We determined their baseline expression network analysis highlighted the relatedness of the regulated patterns using highly accurate RNA sequencing methods. Their genes and the potential role of epithelial cell biology and inflam- strong enrichment in the kidney supports their functional role. mation in CKD. However, it also highlighted that two separate cell types are likely important for CKD development: the kidney and peripheral leukocytes. This finding is supported by both network analysis DISCUSSION and tissue-specific gene expression analysis. Mechanistic studies shall determine the role of these cells in CKD development. Di- Understanding complex trait development is a formidable abetic and hypertensive renal disease are considered nonimmune- challenge. The first step is to understand the genetic architecture mediated renal diseases; however, this dogma might need to be of the disease. Initial GWASs have provided the first glimpse of revisited.

transcripts in human kidney on the basis of the RNA sequencing data. Red, high expression in the kidney; yellow, medium expression in the kidney; green, low expression in the kidney. (B and E) On the basis of the results of the Illumina Body Map (www.ebi.ac.uk), a heat map was generated from the FPKM values of the CRATs near these SNPs. High expression values (90th percentile) are marked red, and low expression values (,10th percentile) are marked blue. Expressions with FPKM values,0.1 are marked white. *Genes without probe set identifications on the Affymetrix arrays. QRT-PCR validation confirmed the significant correlation with eGFR of the following transcripts: (C) glycoprotein 2 (GP2), UMOD, ACSM5, ACSM2A,andACSM2B and (F) FYN binding protein (FYB)andDAB2. A shows a strong correlation between UMOD expression and eGFR, whereas the expressions of ACSM5 and -2A/2B also highly correlate with renal function. (D) At the rs11959928 locus, not only the transcript DAB2 but also, the FYB show high correlation with eGFR (PDILT, Protein disulfide isomerase-like, testis expressed; C9, Complement component 9).

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only correlates with kidney function, but in other tissues, CERS2 levels strongly influenced by a nearby polymorphism, making this gene a very strong CKD candidate. Our analysis highlighted a large number of novel genes lo- cated in the vicinity of CKD-related GWAS hits; these genes can be the target of additional analysis. A critically important observation of the work is that the expression of more than one gene correlated with eGFR on a single genetic locus. We illustrated this observation on the chromosome 16 locus, where not only UMOD but also, a cluster of ACSM genes (ACSM2A/B and -5) showed association with eGFR. Because ACSM2A/B and -5 are part of the same a-fatty oxidation pathway, the examination of this pathway warrants additional scrutiny. This interesting coregula- tory pattern was present for most of the CKD GWAS SNPs, potentially indicating that a single polymorphism can control the expression of multiple genes. These observations could indicate that a SNP may not just regulate a single gene but may cause the differential regulation of an entire gene cluster. Our analysis emphasized the importance of smallexpression differencesin many genes in CKD, but these genes do not seem to be independent but instead, form organized clusters and pathways. We identified two major clusters. One of them centered at Figure 5. The expression of VEGFA correlates with renal function. The expression of epithelial and VEGF signaling. These genes VEGFA is significantly lower (*P=0.025) in samples homozygous for A alleles (A/A; show a linear correlation with kidney func- n=7) at the rs881585 locus compared with samples with minor alleles (A/G; n=7 or tion, likely indicating the relationship be- . 2 G/G; n=7) at this locus. (A) Only control samples (eGFR 85 ml/min per 1.73 m ) tween epithelial and vascular integrity in were used for the analysis. (B) Microarray-based transcript levels of VEGFA correlate 2 26 progressive nephropathy. The second cluster with renal function in tubule samples (R =0.219, P51.7310 ). (C) QRT-PCR–based 2 VEGFA transcript levels (R2=0.228, P57.8310 4)confirm its correlation with kidney highlightedTNF andTGF-b1; these genes are fl function. (D) VEGFA protein expression (by immunohistochemistry) correlates with known to play important roles in in amma- transcript levels. Counterstained with hematoxylin. Scale bars, 50 mm. tion and fibrosis. Expressions of these transcripts showed an inverse correlation with renal function, indicating an increased ex- The highlight of our work is the identification of novel genes pression of these genes in CKD. Functional experiments support in the vicinity of CKD-associated SNPs that show strong our findings. Increased inflammation and destruction of func- correlation with kidney function; thereby, they are potential tioning epithelial cells are cornerstones of fibrosis develop- candidates for CKD development (for example, FAM47E, ment.39,40 PLXDC1, ACSM2A/B, ACSM5,andMAGI2). PLXDC1 (previously Alimitationofthestudy isthatitisfromasinglecenter,andwe known as tumor endothelial marker 7) is primarily associated with did not have genetic and genomic information from the same angiogenesis in the cancer field, including kidney cancers.36 Re- kidney samples to directly correlate genetic variation and gene cently, its increased expression in diabetic retinopathy has been expression. Furthermore, as with every human study, the work reported.37 We found that the MAGI2 expression correlates with mostly highlights an association and cannot fully establish renalfunctioninglomeruli.AlthoughMAGI2 is expressed in the causality. Changes of transcript levels do not fully indicate that brain, MAGI2 expression is enriched in podocytes.38 Given the they are functionally relevant. However, even if some of the critical role of podocytes in kidney disease development, this gene identified genes are not causally linked to CKD development, the could be an important candidate. The expression of CERS2 not expression of these transcripts correlates with kidney function

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Table 3. Summary of the CRATs correlating with eGFR CKD Risk- CRATs Correlating Position of the SNP No. of Total Location Genes within Associated Position with eGFR in T Relative to Each Correlating (Chromosome) 6250 kb Locus and G Samples Correlating CRAT CRATs (P,0.05) rs267734 1 150951477 CTSSb,e CTSS (T and G)f Upstream 8 CTSKb,e CTSK (Tf and G) Upstream ARNTb,e CERS2 (T)f Upstream SETDB1b,e ANXA9 (Tf and G) Downstream CERS2a,e PRUNE (Tf) Downstream ANXA9a,e MLLT11 (T) Downstream FAM63Aa,e CDC42SE1 (T) Downstream PRUNEa,e PIP5K1A (T)f Downstream MLLT11b,e BNIPLc C1orf56b,e GABPB2b SEMA6Cb,e CDC42SE1a,e LYSMD1b SCNM1b,e TMOD4c VPS72a,e PIP5K1Aa,e TNFAIP8L2c,e rs1933182 1 109999588 SARSa,e PSRC1 (G) Upstream 4 CELSR2b,e SORT1 (T and G) Upstream PSRCc,e GNAI3 (Tf) Downstream MYBPHLc GSTM4 (Tf) Downstream SORT1a,e PSMA5a,e SYPL2a ATXN7L2b CYB561D1b AMIGO1b GPR61c GNAI3a,e AMPD2b,e GSTM2a,e GSTM4a,e GSTM1a,e GNAT2c,e rs12124078 1 15869899 FHAD1c EFHD2 (G) Upstream 8 EFHD2a,e CELA2A (T) Upstream CTRCd,e CELA2B (T) Upstream CELA2Ad,e CASP9 (Tf and G) Upstream CELA2Bc,e DNAJC16 (Tf) Intronic CASP9a,e AGMAT (Tf and G) Downstream DNAJC16a,e DDI2 (Tf) Downstream AGMATa,e RSC1A1 (Tf) Downstream DDI2b,e RSC1A1b,e SLC25A34b TMEM82a FBLIM1a rs16864170 2 5907880 SOX11c,e SOX11 (Tf) Upstream 1

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Table 3. Continued

CKD Risk- CRATs Correlating Position of the SNP No. of Total Location Genes within Associated Position with eGFR in T Relative to Each Correlating (Chromosome) 6250 kb Locus and G Samples Correlating CRAT CRATs (P,0.05) rs6431731 2 15863002 DDX1a,e DDX1 (T) Upstream 2 MYCNb,e MYCN (Tf and G) Downstream rs10206899 2 73900900 ALMS1b,e NAT8 (Tf) Upstream 2 NAT8a,e NAT8B (Tf) Downstream NAT8Ba,e TPRKBa,e DUSP11a,e C2orf78c STAMBPb,e ACTG2c,e rs7583877 2 100460654 AFF3c,e AFF3 (Tf) Intronic 1 rs347685 3 141807137 ATP1B3a,e TFDP2 (Tf) Intronic 1 TFDP2b,e GK5b XRN1b rs17319721 4 77368847 SCARB2a,e FAM47E (Tf and Gf) Upstream 2 rs13146355 77412140 FAM47Eb,e STBD1 (Tf and Gf) Upstream STBD1b,e CCDC158c SHROOM3b rs6420094 5 176817636 NSD1b,e NSD1 (Tf) Upstream 7 RAB24a SLC34A1 (Tf and G) Intronic PRELID1a F12 (G) Downstream MXD3c,e GRK6 (Tf) Downstream LMAN2a,e DBN1 (T) Downstream RGS14a,e PDLIM7 (T) Downstream SLC34A1a,e FAM193B (Tf) Downstream PFN3b F12b,e GRK6b,e PRR7c,e DBN1a,e PDLIM7a,e DOK3c,e DDX41a,e FAM193Bb,e TMED9a,e B4GALT7a,e rs11959928 5 39397132 FYBb,e FYB (Tf and Gf) Upstream 2 C9c,e DAB2 (Tf) Intronic DAB2a,e rs881858 6 43806609 POLHb,e VEGFA (Tf and G) Upstream 1 GTPBP2a,e MAD2L1BPa,e RSPH9c MRPS18Aa,e VEGFAa,e C6orf223c rs2279463 6 160668389 IGF2Ra,e IGF2R (Tf) Upstream 4 SLC22A1c,e SLC22A1 (Tf) Upstream

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Table 3. Continued

CKD Risk- CRATs Correlating Position of the SNP No. of Total Location Genes within Associated Position with eGFR in T Relative to Each Correlating (Chromosome) 6250 kb Locus and G Samples Correlating CRAT CRATs (P,0.05) SLC22A2a,e SLC22A2 (Tf) Intronic SLC22A3b,e SLC22A3 (G) Downstream rs3828890 6 31440669 HLA-Ca,e HLA-C (G) Upstream 8 HLA-Ba,e MICB (Tf and G) Downstream MICAb,e LST1 (Tf and G) Downstream MICBc,e NCR3 (Tf) Downstream DDX39Ba,e AIF1 (Tf) Downstream ATP6V1G2b,e BAG6 (Tf) Downstream LTAc,e APOM (Tf) Downstream NFKBIL1a,e LTB (Tf and Gf) Downstream LST1b,e NCR3c,e AIF1a,e PRRC2Aa,e BAG6a,e C6orf47a,e GPANK1b CSNK2Ba,e LY6G5Bb ABHD16Aa LY6G5Cb,e APOMa,e LY6G6Fd LY6G6Cc,e DDAH2a,e C6orf25c,e LTBb,e TNFc,e rs6465825 7 77416439 PTPN12a,e PTPN12 (T) Upstream 3 RSBN1Lb PHTF2 (Tf) Downstream TMEM60a MAGI2 (G) Downstream PHTF2b,e MAGI2b,e rs7805747 7 151407801 RHEBa,e 0 PRKAG2a,e rs10277115 7 1285195 C7orf50a GPER (Tf) Upstream 2 GPR146b MICALL2 (Gf) Downstream GPERb,e ZFAND2Aa UNCXc MICALL2b,e INTS1a,e rs1617640 7 100317298 TSC22D4a,e LRCH4 (Tf and G) Upstream 4 NYAP1c PCOLCE (G) Upstream AGFG2b,e SLC12A9 (T) Downstream SAP25b,e TRIP6 (Tf) Downstream LRCH4a,e FBXO24c,e PCOLCEb,e MOSPD3a,e TFR2c,e

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Table 3. Continued

CKD Risk- CRATs Correlating Position of the SNP No. of Total Location Genes within Associated Position with eGFR in T Relative to Each Correlating (Chromosome) 6250 kb Locus and G Samples Correlating CRAT CRATs (P,0.05) ACTL6Bd,e GNB2a,e GIGYF1b POP7a,e EPOb,e ZANd EPHB4a,e SLC12A9a,e TRIP6a,e SRRTa,e UFSP1b ACHEc,e rs10109414 8 23751151 NKX3-1c,e STC1 (Tf) Upstream 1 rs1731274 23766319 NKX2-6d STC1a,e rs4744712 9 71434707 PIP5K1Bb,e 0 FAM122Ab PRKACGd,e FXNb,e rs10794720 10 1156165 LARP4Bb,e LARPB4 (Tf) Upstream 2 GTPBP4a,e IDI1 (T) Upstream IDI2b,e IDI1a,e WDR37b,e ADARB2c,e rs4014195 11 65506822 SCYL1a LTBP3 (Tf) Upstream 11 LTBP3a,e FAM89B (Tf) Upstream SSSCA1a,e EHBP1L1 (Tf and Gf) Upstream FAM89Ba,e SIPA1 (Tf) Upstream EHBP1L1b,e RELA (T) Upstream KCNK7c,e CFL1 (T) Downstream MAP3K11a,e CCDC85B (Tf) Downstream PCNXL3b FOSL1 (Tf) Downstream SIPA1b,e CTSW (Tf) Downstream RELAa,e FIBP (Tf) Downstream KAT5a,e DRAP1 (G) Downstream RNASEH2Ca AP5B1b OVOL1b,e SNX32c CFL1a,e MUS81a,e EFEMP2a,e CCDC85Ba,e FOSL1b,e CTSWc,e FIBPa,e C11orf68a,e TSGA10IPd

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Table 3. Continued

CKD Risk- CRATs Correlating Position of the SNP No. of Total Location Genes within Associated Position with eGFR in T Relative to Each Correlating (Chromosome) 6250 kb Locus and G Samples Correlating CRAT CRATs (P,0.05) SART1a,e DRAP1a,e rs3925584 11 30760335 MPPED2b,e MPPED2 (Tf) Upstream 1 DCDC5c DCDC1c rs653178 12 112007756 CUX2c,e CUX2 (T) Upstream 4 FAM109Ab ATXN2 (Tf) Intronic SH2B3b,e ACAD10 (Tf) Downstream ATXN2b,e ALDH2 (Tf) Downstream BRAPb,e ACAD10a,e ALDH2a,e rs10774021 12 349298 IQSEC3b,e SLC6A12 (Tf) Upstream 3 SLC6A12a,e SLC6A13 (Tf and G) Intronic SLC6A13a,e KDM5A (G) Downstream KDM5Ab,e CCDC77b B4GALNT3b rs626277 13 72347696 DACH1b,e DACH1 (T) Intronic 1 rs491567 15 53946593 WDR72a 0 rs1394125 15 76158983 SNUPNa,e 0 IMP3a,e SNX33b CSPG4c,e ODF3L1c UBE2Q2a NRG4c C15orf27c rs2453533 15 45641225 DUOX1c,e DUOX1 (Tf) Upstream 4 DUOXA2c SLC28A2 (T) Upstream DUOXA1d GATM (Tf and G) Downstream SHFc SLC30A4 (Tf) Downstream SLC28A2b,e GATMa,e SPATA5L1b,e C15orf48b SLC30A4b,e BLOC1S6a rs12437854 15 94141833 No gene in 0 ,250 kb distance rs12917707 16 20367690 GP2b,e GP2 (T and G) Upstream 6 rs4293393 20364588 UMODa,e UMOD (Tf and G) Upstream rs11864909 20400839 PDILTd ACSM5 (Tf and G) Downstream ACSM5a,e ACSM2A (Tf) Downstream ACSM2Aa,e ACSM2B (Tf) Downstream ACSM2Ba,e ACSM1 (T) Downstream ACSM1c,e rs9895661 17 59456589 BCAS3b,e TBX2 (Tf) Downstream 1 TBX2a,e C17orf82c TBX4d,e NACA2b,e

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Table 3. Continued

CKD Risk- CRATs Correlating Position of the SNP No. of Total Location Genes within Associated Position with eGFR in T Relative to Each Correlating (Chromosome) 6250 kb Locus and G Samples Correlating CRAT CRATs (P,0.05) rs2453580 17 19438321 EPN2b,e EPN2 (T) Upstream 4 B9D1a,e MFAP4 (Tf and Gf) Upstream MAPK7b,e SLC47A1 (Tf) Intronic MFAP4a,e ALDH3A2 (Tf and G) Downstream RNF112c SLC47A1a,e ALDH3A2a,e ALDH3A1c,e SLC47A2a ULK2b,e rs11078903 17 37631924 FBXL20b ERBB2 (Tf) Downstream 1 MED1b,e CDK12b,e NEUROD2d,e PPP1R1Bb STARD3a,e PNMTc,e PGAP3a,e ERBB2a,e TCAPc,e rs7208487 17 37543449 PLXDC1c,e PLXDC1 (Tf and G) Upstream 1 CACNB1c,e ARL5Cd RPL19a,e STAC2b FBXL20b MED1b,e CDK12b,e NEUROD2d,e PPP1R1Bb STARD3a,e rs12460876 19 33356891 ANKRD27b,e SLC7A9 (Tf) Intronic 1 RGS9BPc NUDT19b,e TDRD12c,e SLC7A9a,e CEP89b C19orf40c,e RHPN2a GPATCH1b,e rs911119 20 23612737 NAPBb CST8 (T) Upstream 1 rs13038305 23610262 CSTL1d CST11c,e CST8c,e CST9Ld CST9c CST3a,e CST4d,e CST1d,e CST2d,e CST5c,e

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Table 3. Continued

CKD Risk- CRATs Correlating Position of the SNP No. of Total Location Genes within Associated Position with eGFR in T Relative to Each Correlating (Chromosome) 6250 kb Locus and G Samples Correlating CRAT CRATs (P,0.05) rs6040055 20 10633313 MKKSa,e MKKS (T) Upstream 1 SLX4IPc JAG1a,e rs4821469 22 36616445 APOL3a,e MYH9 (T) Downstream 1 APOL4b APOL2a,e APOL1a,e MYH9a,e TXN2a,e The table shows the list of genes within the 250-kb region of the CKD-associated SNPs. Baseline expression of the transcripts in human kidneys (by RNA sequencing) are shown. T, tubule; G, glomerular. aHigh expression. bMedium expression. cLow expression. dNo expression. eGenes with available probe set identifications on the microarray chip. fThe correlation with eGFR in T and G samples remained significant (P,0.05) after Benjamini-Hochberg multiple testing correction. in a large collection of human kidney samples. Therefore, these RNA quality and quantity were determined using the Laboratory-on- genes could be important potential candidate biomarkers for Chip Total RNA PicoKit Agilent BioAnalyzer. Only samples without renal function decline. evidence of degradation were further used (RNA integrity number.6). In summary, this study is one of the first studies to perform a comprehensive functional genomic analysis of Microarray Procedure CKD-associated GWAS hits. These results highlight multiple Purified total RNAs from 95 tubule samples were amplified using the new CKD risk-associated candidate genes, that were not Ovation Pico WTA System V2 (NuGEN) and labeled with the Encore originally considered by GWAS experiments. Future candidate Biotin Module (NuGEN) according to the manufacturer’s protocol. molecular and cell biology experiments will be needed to The purified total RNAs from 51 glomerular samples and 41 tubule understand the functional role of these CRATs. samples used for validation were amplified using the Two-Cycle Target Labeling Kit (Affymetrix) as per the manufacturer’s protocol. Transcript levels were analyzed using Affymetrix U133A arrays. CONCISE METHODS Microarray Data Processing Human Kidney Samples After hybridization and scanning, raw data files were imported into Kidney samples were obtained from routine surgical nephrectomies GeneSpring GX software, version 12.6 (Agilent Technologies). Raw and leftover portions of diagnostic kidney biopsies. Only the normal, expression levels were summarized using the RMA16 algorithm. non-neoplasmatic part of the tissue was used for further investigation. Normalized values were generated after log transformation and base- Samples were deidentified, and corresponding clinical information line transformation. GeneSpring GX software then was used for was collected by an individual who was not involved in the research statistical analysis. We used Benjamini–Hochberg multiple testing protocol.41,42 The study was approved by the institutional review correction with a P value,0.05. In the case of genes with more probe boards (IRBs) of the Albert Einstein College of Medicine and Mon- set identifications, the results with the lowest P values are represented. tefiore Medical Center (IRB 2002–202) and the University of Penn- Statistical analyses for the patient demographics and the linear cor- sylvania (IRB 815796). Glomerular sclerosis and interstitial fibrosis relation tests between the gene expression arrays and eGFR were were evaluated using periodic acid–Schiff-stained kidney sections by performed using Prism 6 software (GraphPad). two independent nephropathologists. Network Analyses Tissue Handling and Microdissection Transcripts with expression levels showing significant linear correlation The kidney tissue was immediately placed and stored in RNAlater with eGFR were exported to Ingenuity Network Analysis software (Ambion) according to the manufacturer’s instruction. The tissue (Ingenuity Systems). This software determines the top canonical was manually microdissected under a microscope in RNAlater for pathways by using a ratio (calculated by dividing the number of genes glomerular and tubular compartment. Dissected tissue was homog- in a given pathway that meet cutoff criteria by the total number of genes enized, and RNA was prepared using RNAeasy mini columns that constitute that pathway) and then scoring the pathways using a (Qiagen, Valencia, CA) according to the manufacturer’sinstructions. Fischer exact test (P value,0.05).

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Figure 6. Kidney function-correlating CRATs form tight networks. (A) CRATs showing negative correlation with eGFR (green with P corrected,0.05) clustered around TNF and TGF-b. (B) CRATs showing positive correlation with eGFR (red with P corrected,0.05) centered around VEGFA and ERBB2 [erythroblastic leukemia viral oncogene homolog 2 (EGFR2, epidermal growth factor receptor 2)] (Ingenuity Systems).

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After quantile normalization, we determined the transcripts as no expression, which had zero FPKM values. The rest of the genome (FPKM values.0) was divided in three equal groups: transcripts with high, medium, and low expression in the kidney. We used these four groups (high, medium, low, and no expression) to de- scribe the baseline expression of the CRATs in the kidney.

QRT-PCR Two-hundred ﬁfty ng RNA was reverse tran- scribed using the cDNA Archival Kit (Life Technology) and QRT-PCR was run in the ViiA 7 System (Life Technology) machine using SYBRGreen Master Mix and gene-speciﬁcprimers. The data were normalized and analyzed using the ΔΔCT method.

Immunohistochemistry Immunohistochemistry was performed on parafﬁn- embedded sections with the following antibodies: UMOD (AAH35975; Sigma-Aldrich), VEGFA (Ab46154), and ACSM2A (Ab181865). We used the Vectastain MOM or anti-rabbit Elite ABC Peroxidase Kit and3,39diaminobenzidine for visualizations. Antibody speciﬁcity was evaluated separately; secondary antibodies alone showed no positive staining. Figure 7. Schematic representation of the experimental design. GWASs examine the relationship between genetic variants (SNP) and disease state (CKD). The eQTL examines the relationship between transcript levels and genetic variation in control samples. Here, we ACKNOWLEDGMENTS investigated the relationship between transcript levels around CKD risk variants and kidney function by examining the contribution of genetic and environmental factors. The work was supported by National Institutes of Health Grants DK087635 (to K.S.) and DK076077 (to K.S.). Part of the work was presented at the Annual RNA Sequencing Analyses Meeting of the American Society of Nephrology (November 5–10, 2013, RNA sequencing was carried out on microdissected kidney tubules. Atlanta, GA). Total RNAwas isolated using the miRNeasy Kit (Qiagen) according to the manufacturer’s protocol. An additional DNase1 digestion step was performed to ensure that the samples were not contaminated DISCLOSURES with genomic DNA. RNA purity was assessed using the Agilent 2100 The laboratory of K.S. received research support from Boehringer Bioanalyzer. Each RNA sample had an A260:A280 ratio.1.8, an RNA Ingelheim. integrity number.9, and an A260:A230 ratio.2.2. Single-end 100-bp RNA sequencing was carried out an Illumina HiSeq2000 machine. REFERENCES RNA sequencing reads were aligned to the human genome

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