Twenty million people suffer from CKD and ESRD in the United States. The risk of death significantly increases as kidney function (GFR) declines and it can be as high as 20% for patients with diabetes on hemodialysis.1 Diseases of the kidney and urinary tract are ranked 12th in the mortality charts (www.cdc.org), indicating their importance in public health.
CKD is a typical gene environmental disease. Several environmental factors play important roles in CKD development; diabetes and hypertension are the two most important causes, accounting for close to 75% of ESRD cases. In addition, CKD has a clear genetic component, because <20% of patients with diabetes or hypertension will actually develop kidney disease. At present, one of the most powerful experiments to understand the genetics of a complex trait such as CKD is the genome-wide association study (GWAS).2 These studies compare genetic variants in two groups of participants: people with the disease (patients) and similar people without the disease (controls). If a variant (single nucleotide polymorphism [SNP]) is more frequent in people with the disease, the SNP is said to be associated with the disease. GWASs, however, have several limitations. First, GWASs became possible, because the genetic information is inherited in fairly large blocks. Therefore, we do not have to test the association with each of the close to 20 million genetic variations but can use fewer (about 1 million) SNPs representing the genetic variation of larger genetic regions (called haplotype or linkage disequilibrium block). Although haplotype blocks made GWAS convenient and financially feasible, they also mean that we do not know which of the many variants within a single haplotype block is functionally relevant.
Furthermore, >83% of the disease-associated SNPs are localized to the noncoding region of the genome3; therefore, it is unclear how they induce illness. Recent reports from the Encyclopedia of DNA Elements (ENCODE) project indicate that most complex trait polymorphisms are localized to gene regulatory regions in target cell types.4 Disease-associated genetic variants can alter binding sites for important transcription factors and influence the expression of nearby genes.3,5–7 Genetic variants can potentially alter steady-state expression of genes, in which case they interfere with basal transcription factor binding or can alter the amplitude of transcript changes after signal-dependent transcription factor binding.
Here, we hypothesized that polymorphisms associated with renal disease will influence the expression of nearby transcript levels in the kidney. We used genomics and systems biology approaches to investigate tissue-specific expression of transcripts and their correlation with kidney function.
Results
CKD Risk-Associated Transcripts
By manual literature search, we identified all GWASs reporting genetic association for CKD-related traits (Supplemental Table 1). Many of these studies, however, used different parameters as kidney disease indicators. We included SNPs associated with eGFR (on the basis of serum creatinine or cystatin C calculations) or the presence of ESRD. Our analysis identified 10 publications meeting these criteria.8–25 Most publications did not differentiate cases on the basis of disease etiology and included cases with hypertensive and diabetic kidney disease. Coding polymorphisms and SNPs that did not reach genome-wide significance (P>5×10−8) were excluded.26 Finally, 44 leading SNPs meeting all of these criteria were used for further analysis (Supplemental Table 2). Three SNPs associated only with diabetic CKD development were also analyzed separately; all other SNPs were from studies including both diabetic and nondiabetic cases. There were only two SNPs that reached genome-wide significance in multiple studies (rs12917707 and rs9895661). These two SNPs were counted only once.
Reports from the ENCODE project indicate that the majority (70%–80%) of the gene regulatory elements (promoters, enhancers, and insulators) are within 250 kb of the gene.3 Using these criteria, we identified 306 genes within 500 kb of 44 CKD SNPs. There was no gene within the 500-kb window around the rs12437854 SNP; therefore, 43 loci were followed. We called these transcripts CKD risk-associated transcripts (CRATs).
CRATs Are Enriched for Kidney-Specific Expression
We hypothesized that cells that express CRATs play an important role in controlling kidney function. Therefore, we determined expression levels of all CRATs in control (normal) human kidney samples (n=2) using comprehensive RNA sequencing analysis. We found that 41% of the CKD risk loci-associated transcripts showed high expression (upper quartile) and that only 6% of CRAT transcripts were not detectable in human kidney tubule samples (Supplemental Figure 1). Overall, we found that a large percentage of the CKD SNP neighboring transcripts (94%; 287 of 306) were expressed in the human kidney, indicating statistically significant kidney-specific enrichment compared with 44 randomly selected loci, where only 13% of the transcripts showed high expression and 16% of the nearby transcripts were not expressed in the kidney (P=1.25×10−9).
Gene ontology analysis (david.abcc.ncifcrf.gov) to understand the tissue specificity of CRATs indicated specific and significant enrichment in the kidney and peripheral leukocytes (P value=0.0082 and P value=0.0014, respectively). Next, we compared absolute expression levels of CRATs by RNA sequencing in 16 different human organs using the Illumina Body Map database (www.ebi.ac.uk). The atlas confirmed the statistically significant kidney-specific expression enrichment of CRATs (Supplemental Figure 2). For example, the atlas highlighted the high and kidney-specific expression of Uromodulin (UMOD). In summary, expression of CRATs was enriched in the kidney and peripheral lymphocytes, potentially indicating the role of these cells in kidney disease development.
CRAT Expression in Normal and Diseased Human Kidney Glomerular Samples
We hypothesized that functionally important CRATs are not only expressed in relevant cell types (kidney and leukocytes) but that their expression level will change in CKD. To test this hypothesis, we analyzed gene expression levels in a large collection of microdissected human glomerular (n=51) and tubule (n=95) samples. Kidney samples were obtained from a diverse population (Supplemental Tables 3 and 4). Statistical analysis failed to detect ethnicity-driven gene expression differences (data not shown).
Transcript profiling was performed for each individual sample using Affymetrix U133v2 arrays. The data were processed using established pipelines, and they contained probe set identifications for 226 transcripts from 306 originals CRATs. We analyzed the expression levels of 226 CRATs in 51 microdissected glomerular samples (Supplemental Table 3). On the basis of the Chronic Kidney Disease Epidemiology Collaboration eGFR determination, we had 27 samples with normal renal function (eGFR>60 ml/min per 1.73 m2) and 24 samples with reduced GFR (eGFR<60 ml/min per 1.73 m2).27 This eGFR cutoff was used as a CKD definition in the included GWASs. To match the transcript data with GWAS cases, we included samples from patients with diabetic and hypertensive CKD.
Linear correlation analysis identified the association of 34 CRATs with eGFR (P<0.05) (Table 1). The correlation between the expression of seven CRATs and eGFR remained significant, even after Benjamini–Hochberg-based multiple testing correction. The expression of multiple novel transcripts showed excellent correlation with kidney function. For example, expression levels of Family with sequence similarity 47, member E (FAM47E) (Figure 1A), Plexin domain-containing 1 (PLXDC1) (Figure 1B), Vascular endothelial growth factor A (VEGFA) (Figure 1C), and Membrane-associated guanylate kinase (MAGI2) (Figure 1D) correlated with eGFR. In normal nondisease human kidney tissue, proteins coded by FAM47E (Figure 1E) and VEGFA (Figure 1G) were highly expressed in glomeruli. Immunostaining studies (from the Human Protein Atlas) showed that the protein encoded by PLXDC1 (also known as Tumor endothelial marker 7 [TEM7]) exhibited glomerular endothelial-specific expression in normal human kidney tissue (Figure 1F). MAGI2 seemed to have a podocyte-specific expression pattern (Figure 1H), potentially indicating its role in this cell type. Interestingly, FAM47E, PLXDC1, and MAGI2 have not been identified in GWASs as potential causal genes in the vicinity of CKD risk loci. We also separately examined the expression and correlation of diabetic CKD associated transcripts and their correlation with glomerular gene expression (Supplemental Table 5). In summary, the analysis highlighted that the expression of several CRATs in glomeruli correlates with renal function.
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Table 1: Expression levels of 34 transcripts (CRATs) in glomeruli showed significant correlation with eGFR
Figure 1: Correlation between CRAT expression in glomeruli and renal function. The
y axis shows the relative normalized glomerular expressions of (A)
FAM47E, (B)
PLXDC1, (C)
VEGFA, and (D)
MAGI2. The
x axes show the eGFR for each sample. Each dot represents one individual miscrodissected glomerular sample. The lines represent the fitted linear correlation values. Immunohistochemistry shows the protein expression in human glomeruli ([E]
FAM47E, [F]
PLXDC1, [G]
VEGFA, and [H]
MAGI2). Scale bars, 100
μm. Reprinted from
www.proteinatlas.org.
CRAT Expression in Normal and Diseased Human Kidney Tubule Samples
Next, we analyzed the expression levels of 226 CRATs in 95 microdissected human kidney tubule samples. Samples were obtained from patients with a wide range of kidney function (Supplemental Table 4): 56 samples with normal eGFR (eGFR>60 ml/min per 1.73 m2) and 39 samples with kidney disease (eGFR<60 ml/min per 1.73 m2). We performed a binary analysis by comparing the expression levels of CRATs in control versus CKD samples. Using statistical correction for multiple testing (Benjamini–Hochberg corrected P value<0.05), 73 CRATs (from 226 CRATs) showed differential expression when CKD tubule samples were compared to controls (Supplemental Table 6).
We also looked for linear correlation between CRATs and 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 metabolic enzyme, Acyl-CoA synthetase medium chain family member 5 (ACSM5), also highly correlated with renal function and showed high protein expression in tubule epithelial cells (Figure 2, C and F). For external validation, we used a gene expression dataset containing genome-wide transcription profiling from 41 microdissected tubule samples. The clinical characteristics of these samples are described in Supplemental Table 7. Samples in this dataset were different from the primary dataset, and a slightly different method was used for microarray probe labeling. Although this dataset was much smaller with a narrower GFR range, we confirmed the significant linear correlation of 51 transcripts, highlighting the importance of these CRATs (Table 2). Next, we also specifically examined the correlation of the diabetic CKD-associated polymorphisms (rs12437854, rs7583877, and rs1617640) and transcript changes only in diabetic kidney disease (Supplemental Table 5). The analysis highlighted that Procollagen C-endopeptidase enhancer (PCOLCE) and Thyroid hormone receptor interactor 6 (TRIP6) in the vicinity of diabetic CKD SNPs correlate with kidney function. In summary, the gene expression and kidney function correlation analysis underscored CRATs for future prioritization.
Table 2: In tubules, expression levels of 92 transcripts (CRATs) showed significant correlation with eGFR
Figure 2: Correlation between CRAT expression in tubules and renal function. Expressions of (A)
SLC34A1, (B)
SLC7A9, and (C)
ACSM5 correlate with eGFR in tubule samples. The
x axes represent eGFR (ml/min per 1.73 m
2), 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 values. Immunohistochemistry shows tubular-specific expression of (D)
SLC34A1, (E)
SLC7A9, and (F)
ACSM5. Scale bars, 100
μm. Reprinted from
www.proteinatlas.org.
Transcript Levels around the UMOD Locus
We specifically further investigated expression changes of the UMOD transcript, because it is a potential causal gene underlying the polymorphism of some of the best characterized CKD-associated SNPs on chromosome 16 (rs12917707, rs4293393, and rs11864909). This gene encodes one of the most abundant proteins in human urine; Uromodulin or Tamm–Horsfall protein. Furthermore, functional studies seem to link UMOD expression both as a biomarker and a causal gene for CKD development.28 We found that UMOD transcript levels showed a highly significant linear correlation with renal function (P=1.9×10−7) in tubule samples (Figure 3A). Figure 3, B–E, shows results of immunohistochemistry staining from samples used for the transcriptomic analysis, indicating the excellent correlation between uromodulin protein expression and its transcript levels.
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 μm.
Although UMOD has emerged as an important causal gene for CKD, unexpectedly, we found that three other nearby genes were also highly expressed in renal tubules, and their expression strongly correlated with eGFR. To illustrate this observation, Figure 4, A–C shows the chromosome 16 locus, including three leading SNPs (rs12917707, rs4293393, and rs11864909) with polymorphisms that best correlate with CKD. Closest genes to these polymorphisms are UMOD and PDILT (Protein disulfide isomerase-like, testis expressed). The expression level of one of the flanking genes PDILT was nearly undetectable, but our RNA sequencing analysis confirmed high UMOD transcript levels in human kidney samples (Figure 4B). Unfortunately, PDILT probes were absent from the human U133 chips, and therefore, the correlation between PDILT and renal function could not be analyzed. However, we observed that ACSM5 (Figure 2, C and F) and ACSM2A/B were highly expressed in human kidney tubule samples (Figure 4, A and B). Furthermore, we also validated the transcript expression of UMOD, Glycoprotein 2 (GP2), ACSM5, ACSM2A/B, ACSM1, and PDILT by quantitative real-time RT-PCR (QRT-PCR) (Supplemental Table 8) to confirm the microarray results (Figure 4C). ACSM5 and ACSM2A/2B genes (ACSM family members) encode three genes in the α-fatty acid oxidation pathway. Interestingly, these transcripts not only showed high expression in the kidney, but also, their 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 5 region (Figure 4, 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.
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 m
2). (A and D) Color coding represents the baseline expression of the 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, and
ACSM2B and (F) FYN binding protein (
FYB) and
DAB2. 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).
Next, we examined whether in the proximity 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 eGFR (P=8×10−8). We found that, around the rs267734 polymorphism on chromosome 1, both Ceramide synthase 2 (CERS2; P=1.01×10−5) and Annexin A9 (ANXA9) (P=2.2×10−6) transcripts correlated with eGFR in tubule samples. In addition, transcript level of Cathepsin S (CTSS) correlated with renal function both in tubules and glomeruli (P=1.77×10−6 and P=1.8×10−4, respectively). On chromosome 5 at the rs11959928 polymorphism, both Disabled homolog 2 (DAB2, a putative mitogen-responsive phosphoprotein) and FYN binding protein (FYB) showed strong correlation with renal function (P=3.68×10−5 and P=3×10−8, respectively) (Figure 4, D–F).
On the basis of renal expression and renal function association, we could prioritize potential target and/or causal genes for CKD development for 39 of 44 examined loci. As mentioned earlier, there was no gene around rs12437854, and the only nearby gene (WDR72) around rs491567 had no probe on the human U133 chips, albeit WDR72 is highly expressed in human kidney (Supplemental Table 2) by RNA sequencing analysis. No nearby transcript showed association with renal function for three SNPs (rs1394125, rs7805747, and rs4744712) (Supplemental Figure 3). The correlation between these loci and kidney function would need to be re-evaluated.
Taken together, we identified 104 transcripts of 226 CRATs showing significant correlation with eGFR. We could highlight genes for further prioritization for 39 of 44 loci (89%). Using UMOD, ACSM2A, and VEGFA genes as examples, we showed that these expression changes likely correlate with protein levels. Our results also suggest that not only the closest gene but also, several genes in the close vicinity correlate highly with renal function, indicating their potential importance and their potential coregulation.
Expressions Quantitative Trait Loci Highlight CKD Candidate Genes
Polymorphisms associated with kidney function can also directly control baseline transcript levels in disease-relevant types. To examine whether CKD risk SNPs influence local transcript levels (in cis; i.e., within 1-Mb distance), we examined multiple different datasets where genotype and gene expression correlation data were available. These datasets included the MuTHER (Multiple Tissue Human Expression Resource) and other studies,29–33 where transcript levels were available from liver, adipose, and lymphoblastoid samples. Cis-expressions quantitative trait loci (cis-eQTLs) often can be detected in multiple tissues. We found that 4 SNPs from the previously identified 44 leading SNPs and 16 SNPs in their linkage disequilibrium (r2≥0.8) acted as cis-eQTLs for 11 different transcripts (P<0.05) (Supplemental Table 9). Four of these transcripts (33%) were outside of the 500-kb window that we used to identify CRATs. All 11 transcripts were at least moderately expressed in human kidney tissue. Only one transcript CLTB (Clathrin, light chain B) showed significant linear correlation with eGFR in glomerulus samples (P=0.016). Another transcript, CERS2 (also known as LASS2), showed variation in gene expression in lymphoblastiod tissue on the basis of the rs267734 and rs267738 genotypes. Furthermore, CERS2 was differentially expressed in CKD and highly correlated with eGFR (Table 2 and Supplemental Table 6), making it a potential candidate gene for CKD development.
Unfortunately, we did not have genomic DNA from all analyzed kidney samples to examine genotype and gene expression correlations, but we genotyped 21 control (eGFR>85 ml/min per 1.73 m2) samples for the rs881858 polymorphism. In the same samples, tubule-specific VEGFA transcript levels were determined by QRT-PCR. Tubule-specific VEGFA transcript levels were lower in patients who were homozygous for the major allele on the rs881858 locus compared to heterozygous or minor allele homozygous samples (Figure 5A). Glomerular or tubule-specific VEGFA transcript and protein expression level correlated with GFR (Figures 1C and 5, B–D). These results indicate that the rs881858 polymorphism likely influences VEGF transcript levels and that VEGFA could be an important CKD candidate gene. Additionally, we examined whether genetic polymorphism (rs6420094) on chromosome 5 around SLC34A1 will influence transcript expression. We found that tubule-specific SLC34A1 expression was significantly higher in patients who were homozygous for the major allele on the rs6420094 locus compared with heterozygous or minor allele homozygous samples (Supplemental Figure 4A).
Figure 5: The expression of VEGFA correlates with renal function. The expression of VEGFA is significantly lower (*P=0.025) in samples homozygous for A alleles (A/A; n=7) at the rs881585 locus compared with samples with minor alleles (A/G; n=7 or G/G; n=7) at this locus. (A) Only control samples (eGFR>85 ml/min per 1.73 m2) were used for the analysis. (B) Microarray-based transcript levels of VEGFA correlate with renal function in tubule samples (R 2=0.219, P=1.7×10−6). (C) QRT-PCR–based VEGFA transcript levels (R 2=0.228, P=7.8×10−4) confirm its correlation with kidney function. (D) VEGFA protein expression (by immunohistochemistry) correlates with transcript levels. Counterstained with hematoxylin. Scale bars, 50 μm.
Transcription factor binding analysis around 44 CKD risk-associated polymorphisms indicated that, at 42 loci, multiple binding motifs are altered by the genetic variants (Supplemental Table 10). This altered transcription factor binding can potentially highlight the mechanism of CKD risk variant-associated disease development.
CRATs Form Networks Highlighting the Role of Inflammation and Epithelial Biology
Table 3 summarizes our results and provides comprehensive evaluation of the loci and transcripts studied here. Finally, we examined whether the 104 renal function-correlating CRATs (either in tubule or glomerular samples) in the neighborhood of 39 CKD risk loci show relatedness and can form a network. The network analysis was performed separately on genes that showed positive or negative correlation with kidney function. Genes showing negative correlation with kidney function (higher expression in CKD) clustered at the TNF-α, TGF-β1, and NF-κB/RelA regulatory nodes (Figure 6A). Most members of this cluster are known to play a role in immune function and regulation of inflammation. The second cluster (transcripts with expression that inversely correlated with kidney function) centered at VEGFA and EGF receptor 2 molecules (Figure 6B). As indicated by their name, these molecules play important roles in maintaining epithelial and endothelial functions. In summary, network analysis highlighted the relatedness of the regulated genes and the potential role of epithelial cell biology and inflammation in CKD.
Table 3: Summary of the CRATs correlating with eGFR
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-β. (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).
Discussion
Understanding complex trait development is a formidable challenge. The first step is to understand the genetic architecture of the disease. Initial GWASs have provided the first glimpse of critical regions in the genome with variations that are associated with kidney function. The second step is to identify transcripts that are regulated by SNPs. The working hypothesis in the field is that causal polymorphisms alter transcription factor binding, causing changes in transcript levels in target cell types and inducing disease in specific organs. Because there are hundreds of genetic variants associated with disease development, analyzing variants individually is a daunting task. Recently, two complementary methods have been developed and successfully applied to identify genes that are targets of the polymorphisms. The first method uses the transcript levels as quantitative traits to identify polymorphisms that influence their levels (eQTL).34 To perform such analysis, a large human tissue bank from target cell types is necessary where both genetic polymorphisms and transcript levels are analyzed. The second (newer) method uses the cell type-specific cellular epigenome for regulatory element annotation and identifies target transcripts that are associated with genetic variants.35 A critical limitation of these methods is that they only identify transcripts that are influenced by a basal transcription factor, because these datasets are generated from control healthy samples. However, it is possible that polymorphisms control transcription factor binding sites for signal-dependent transcription factors. This would mean that the expression of a CKD causing gene is not altered at baseline but shows differences under stress conditions. Figure 7 summarizes the concept underlying this work. Here, we performed the initial level of such analysis by examining the correlation between transcripts in the vicinity of CKD SNPs and GFR.
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 investigated the relationship between transcript levels around CKD risk variants and kidney function by examining the contribution of genetic and environmental factors.
On the basis of recent observations that close to 90% of target transcripts are within 250 kb of the polymorphism, we defined 306 CRATs. Most prior studies focused on the two flanking genes, ignoring transcripts that are farther away.8,9 These 306 CRATs could be important for future studies as potential candidates for CKD development. We determined their baseline expression patterns using highly accurate RNA sequencing methods. Their strong enrichment in the kidney supports their functional role. 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 and tissue-specific gene expression analysis. Mechanistic studies shall determine the role of these cells in CKD development. Diabetic and hypertensive renal disease are considered nonimmune-mediated renal diseases; however, this dogma might need to be revisited.
The highlight of our work is the identification of novel genes in the vicinity of CKD-associated SNPs that show strong correlation with kidney function; thereby, they are potential candidates for CKD development (for example, FAM47E, PLXDC1, ACSM2A/B, ACSM5, and MAGI2). PLXDC1 (previously known as tumor endothelial marker 7) is primarily associated with angiogenesis in the cancer field, including kidney cancers.36 Recently, its increased expression in diabetic retinopathy has been reported.37 We found that the MAGI2 expression correlates with renal function in glomeruli. Although MAGI2 is expressed in the brain, MAGI2 expression is enriched in podocytes.38 Given the critical role of podocytes in kidney disease development, this gene could be an important candidate. The expression of CERS2 not 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 located 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 α-fatty oxidation pathway, the examination of this pathway warrants additional scrutiny. This interesting coregulatory 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 small expression differences in 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 epithelial and VEGF signaling. These genes show a linear correlation with kidney function, likely indicating the relationship between epithelial and vascular integrity in progressive nephropathy. The second cluster highlighted TNF and TGF-β1; these genes are known to play important roles in inflammation and fibrosis. Expressions of these transcripts showed an inverse correlation with renal function, indicating an increased expression of these genes in CKD. Functional experiments support our findings. Increased inflammation and destruction of functioning epithelial cells are cornerstones of fibrosis development.39,40
A limitation of the study is that it is from a single center, and we did not have genetic and genomic information from the same kidney samples to directly correlate genetic variation and gene expression. Furthermore, as with every human study, the work mostly highlights an association and cannot fully establish causality. Changes of transcript levels do not fully indicate that they are functionally relevant. However, even if some of the identified genes are not causally linked to CKD development, the expression of these transcripts correlates with kidney function in a large collection of human kidney samples. Therefore, these genes could be important potential candidate biomarkers for renal function decline.
In summary, this study is one of the first studies to perform a comprehensive functional genomic analysis of CKD-associated GWAS hits. These results highlight multiple new CKD risk-associated candidate genes, that were not originally considered by GWAS experiments. Future candidate molecular and cell biology experiments will be needed to understand the functional role of these CRATs.
Concise Methods
Human Kidney Samples
Kidney samples were obtained from routine surgical nephrectomies and leftover portions of diagnostic kidney biopsies. Only the normal, non-neoplasmatic part of the tissue was used for further investigation. Samples were deidentified, and corresponding clinical information was collected by an individual who was not involved in the research protocol.41,42 The study was approved by the institutional review boards (IRBs) of the Albert Einstein College of Medicine and Montefiore Medical Center (IRB 2002–202) and the University of Pennsylvania (IRB 815796). Glomerular sclerosis and interstitial fibrosis were evaluated using periodic acid–Schiff-stained kidney sections by two independent nephropathologists.
Tissue Handling and Microdissection
The kidney tissue was immediately placed and stored in RNAlater (Ambion) according to the manufacturer’s instruction. The tissue was manually microdissected under a microscope in RNAlater for glomerular and tubular compartment. Dissected tissue was homogenized, and RNA was prepared using RNAeasy mini columns (Qiagen, Valencia, CA) according to the manufacturer’s instructions. RNA quality and quantity were determined using the Laboratory-on-Chip Total RNA PicoKit Agilent BioAnalyzer. Only samples without evidence of degradation were further used (RNA integrity number>6).
Microarray Procedure
Purified total RNAs from 95 tubule samples were amplified using the Ovation Pico WTA System V2 (NuGEN) and labeled with the Encore Biotin Module (NuGEN) according to the manufacturer’s protocol. The purified total RNAs from 51 glomerular samples and 41 tubule 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.
Microarray Data Processing
After hybridization and scanning, raw data files were imported into GeneSpring GX software, version 12.6 (Agilent Technologies). Raw expression levels were summarized using the RMA16 algorithm. Normalized values were generated after log transformation and baseline transformation. GeneSpring GX software then was used for statistical analysis. We used Benjamini–Hochberg multiple testing correction with a P value<0.05. In the case of genes with more probe set identifications, the results with the lowest P values are represented. Statistical analyses for the patient demographics and the linear correlation tests between the gene expression arrays and eGFR were performed using Prism 6 software (GraphPad).
Network Analyses
Transcripts with expression levels showing significant linear correlation with eGFR were exported to Ingenuity Network Analysis software (Ingenuity Systems). This software determines the top canonical pathways by using a ratio (calculated by dividing the number of genes in a given pathway that meet cutoff criteria by the total number of genes that constitute that pathway) and then scoring the pathways using a Fischer exact test (P value<0.05).
RNA Sequencing Analyses
RNA sequencing was carried out on microdissected kidney tubules. Total RNA was 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 with genomic DNA. RNA purity was assessed using the Agilent 2100 Bioanalyzer. Each RNA sample had an A260:A280 ratio>1.8, an RNA integrity number>9, and an A260:A230 ratio>2.2. Single-end 100-bp RNA sequencing was carried out an Illumina HiSeq2000 machine.
RNA sequencing reads were aligned to the human genome (GRCh37/hg19) and transcriptome (hg19 RefSeq from Illumina iGenomes) using the softwares TopHat (version 2.0.9) and Cufflinks (version 2.1.1.Linux_x86_64), respectively.43,44 We counted the number of fragments mapped to each gene annotated in the UCSC hg19. Transcript abundances were measured in fragments per kilobase of exon per million fragments mapped (FPKM). Sequence data can be accessed at the National Center for Biotechnology Information’s Gene Expression Omnibus (Accession number: GSE60119).
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 describe the baseline expression of the CRATs in the kidney.
QRT-PCR
Two-hundred fifty ng RNA was reverse transcribed 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-specific primers. The data were normalized and analyzed using the [INCREMENT][INCREMENT]CT 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 and 3,3′diaminobenzidine for visualizations. Antibody specificity was evaluated separately; secondary antibodies alone showed no positive staining.
Disclosures
The laboratory of K.S. received research support from Boehringer Ingelheim.
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 Meeting of the American Society of Nephrology (November 5–10, 2013, Atlanta, GA).
Published online ahead of print. Publication date available at www.jasn.org.
This article contains supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2014010028/-/DCSupplemental.
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