Genetic-Variation-Driven Gene-Expression Changes Highlight Genes with Important Functions for Kidney Disease

Genetic Studies Highlight the Role of TET2 and INO80 in DNA Damage Response and Kidney Disease Pathogenesis

Genome-wide association studies (GWAS) have identified over 800 loci associated with kidney function, yet the specific genes, variants, and pathways involved remain elusive. By integrating kidney function GWAS, human kidney expression and methylation quantitative trait analyses, we identified Ten-Eleven Translocation (TET) DNA demethylase 2: TET2 as a novel kidney disease risk gene. Utilizing single-cell chromatin accessibility and CRISPR-based genome editing, we highlight GWAS variants that influence TET2 expression in kidney proximal tubule cells. Experiments using kidney-tubule-specific Tet2 knockout mice indicated its protective role in cisplatin-induced acute kidney injury, as well as chronic kidney disease and fibrosis, induced by unilateral ureteral obstruction or adenine diet. Single-cell gene profiling of kidneys from Tet2 knockout mice and TET2- knock-down tubule cells revealed the altered expression of DNA damage repair and chromosome segregation genes, notably including INO80 , another kidney function GWAS target gene itself. Remarkably both TET2- null and INO80- null cells exhibited an increased accumulation of micronuclei after injury, leading to the activation of cytosolic nucleotide sensor cGAS-STING. Genetic deletion of cGAS or STING in kidney tubules or pharmacological inhibition of STING protected TET2 null mice from disease development. In conclusion, our findings highlight TET2 and INO80 as key genes in the pathogenesis of kidney diseases, indicating the importance of DNA damage repair mechanisms.

ACSS2 gene variants determine kidney disease risk by controlling de novo lipogenesis in kidney tubules

Worldwide, over 800 million people are affected by kidney disease, yet its pathogenesis remains elusive, hindering the development of novel therapeutics. In this study, we used kidney-specific expression of quantitative traits and single-nucleus open chromatin analysis to show that genetic variants linked to kidney dysfunction on chromosome 20 target the acyl-CoA synthetase short-chain family 2 (ACSS2). By generating ACSS2-KO mice, we demonstrated their protection from kidney fibrosis in multiple disease models. Our analysis of primary tubular cells revealed that ACSS2 regulated de novo lipogenesis (DNL), causing NADPH depletion and increasing ROS levels, ultimately leading to NLRP3-dependent pyroptosis. Additionally, we discovered that pharmacological inhibition or genetic ablation of fatty acid synthase safeguarded kidney cells against profibrotic gene expression and prevented kidney disease in mice. Lipid accumulation and the expression of genes related to DNL were elevated in the kidneys of patients with fibrosis. Our findings pinpoint ACSS2 as a critical kidney disease gene and reveal the role of DNL in kidney disease.

A fast and globally optimal solution for RNA-seq quantification

Alignment-based RNA-seq quantification methods typically involve a time-consuming alignment process prior to estimating transcript abundances. In contrast, alignment-free RNA-seq quantification methods bypass this step, resulting in significant speed improvements. Existing alignment-free methods rely on the Expectation-Maximization (EM) algorithm for estimating transcript abundances. However, EM algorithms only guarantee locally optimal solutions, leaving room for further accuracy improvement by finding a globally optimal solution. In this study, we present TQSLE, the first alignment-free RNA-seq quantification method that provides a globally optimal solution for transcript abundances estimation. TQSLE adopts a two-step approach: first, it constructs a k-mer frequency matrix A for the reference transcriptome and a k-mer frequency vector b for the RNA-seq reads; then, it directly estimates transcript abundances by solving the linear equation ATAx = ATb. We evaluated the performance of TQSLE using simulated and real RNA-seq data sets and observed that, despite comparable speed to other alignment-free methods, TQSLE outperforms them in terms of accuracy. TQSLE is freely available at https://github.com/yhg926/TQSLE.

Elevated serum albumin-to-creatinine ratio as a protective factor on outcomes after heart transplantation

Background

The purpose of this study was to investigate the prognostic significance of serum albumin to creatinine ratio (ACR) in patients receiving heart transplantation of end-stage heart failure.

Methods

From January 2015 to December 2020, a total of 460 patients who underwent heart transplantation were included in this retrospective analysis. According to the maximum Youden index, the optimal cut-off value was identified. Kaplan-Meier methods were used to describe survival rates, and multivariable analyses were conducted with Cox proportional hazard models. Meanwhile, logistic regression analysis was applied to evaluate predictors for postoperative complications. The accuracy of risk prediction was evaluated by using the concordance index (C-index) and calibration plots.

Results

The optimal cut-off value was 37.54 for ACR. Univariable analysis indicated that recipient age, IABP, RAAS, BB, Hb, urea nitrogen, D-dimer, troponin, TG, and ACR were significant prognostic factors of overall survival (OS). Multivariate analysis showed that preoperative ACR (HR: 0.504, 95% = 0.352-0.722, P < 0.001) was still an independent prognostic factor of OS. The nomogram for predicting 1-year and 5-year OS in patients who underwent heart transplantation without ACR (C-index = 0.631) and with ACR (C-index = 0.671). Besides, preoperative ACR level was a significant independent predictor of postoperative respiratory complications, renal complications, liver injury, infection and in-hospital death. Moreover, the calibration plot showed good consistency between the predictions by the nomogram for OS and the actual outcomes.

Conclusion

Our research showed that ACR is a favorable prognostic indicator in patients of heart transplantation.

A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records

BACKGROUND

The identification of new uses for existing drug therapies has the potential to identify treatments for comorbid conditions that have the added benefit of glycemic control while also providing a rapid, low-cost approach to drug (re)discovery.

METHODS

We developed and tested a genetically-informed drug-repurposing pipeline for diabetes management. This approach mapped genetically-predicted gene expression signals from the largest genome-wide association study for type 2 diabetes mellitus to drug targets using publicly available databases to identify drug-gene pairs. These drug-gene pairs were then validated using a two-step approach: 1) a self-controlled case-series (SCCS) using electronic health records from a discovery and replication population, and 2) Mendelian randomization (MR).

FINDINGS

After filtering on sample size, 20 candidate drug-gene pairs were validated and various medications demonstrated evidence of glycemic regulation including two anti-hypertensive classes: angiotensin-converting enzyme inhibitors as well as calcium channel blockers (CCBs). The CCBs demonstrated the strongest evidence of glycemic reduction in both validation approaches (SCCS HbA1c and glucose reduction: -0.11%, p = 0.01 and -0.85 mg/dL, p = 0.02, respectively; MR: OR = 0.84, 95% CI = 0.81, 0.87, p = 5.0 x 10-25).

INTERPRETATION

Our results support CCBs as a strong candidate medication for blood glucose reduction in addition to cardiovascular disease reduction. Further, these results support the adaptation of this approach for use in future drug-repurposing efforts for other conditions.

FUNDING

National Institutes of Health, Medical Research Council Integrative Epidemiology Unit at the University of Bristol, UK Medical Research Council, American Heart Association, and Department of Veterans Affairs (VA) Informatics and Computing Infrastructure and VA Cooperative Studies Program.

Computational Assessment of the Expression-modulating Potential for Non-coding Variants

Large-scale genome-wide association studies (GWAS) and expression quantitative trait locus (eQTL) studies have identified multiple non-coding variants associated with genetic diseases by affecting gene expression. However, pinpointing causal variants effectively and efficiently remains a serious challenge. Here, we developed CARMEN, a novel algorithm to identify functional non-coding expression-modulating variants. Multiple evaluations demonstrated CARMEN's superior performance over state-of-the-art tools. Applying CARMEN to GWAS and eQTL datasets further pinpointed several causal variants other than the reported lead single-nucleotide polymorphisms (SNPs). CARMEN scales well with the massive datasets, and is available online as a web server at http://carmen.gao-lab.org.

Autophagy and kidney aging

Autophagy is a highly conserved intracellular degradation system in eukaryotes that maintains cellular and tissue homeostasis. Upon autophagy induction, cytoplasmic components are engulfed by a double-membrane organelle called the autophagosome that fuses with a lysosome to degrade its contents. In recent years, it has become clear that autophagy becomes dysregulated with aging, which leads to age-related diseases. Kidney function is particularly prone to age-related decline, and aging is the most significant risk factor for chronic kidney disease. This review first discuss the relationship between autophagy and kidney aging. Second, we describe how age-related dysregulation of autophagy occurs. Finally, we discuss the potential of autophagy-targeting drugs to ameliorate human kidney aging and the approaches necessary to discover such agents.

Molecular pathways that drive diabetic kidney disease

Kidney disease is a major driver of mortality among patients with diabetes and diabetic kidney disease (DKD) is responsible for close to half of all chronic kidney disease cases. DKD usually develops in a genetically susceptible individual as a result of poor metabolic (glycemic) control. Molecular and genetic studies indicate the key role of podocytes and endothelial cells in driving albuminuria and early kidney disease in diabetes. Proximal tubule changes show a strong association with the glomerular filtration rate. Hyperglycemia represents a key cellular stress in the kidney by altering cellular metabolism in endothelial cells and podocytes and by imposing an excess workload requiring energy and oxygen for proximal tubule cells. Changes in metabolism induce early adaptive cellular hypertrophy and reorganization of the actin cytoskeleton. Later, mitochondrial defects contribute to increased oxidative stress and activation of inflammatory pathways, causing progressive kidney function decline and fibrosis. Blockade of the renin-angiotensin system or the sodium-glucose cotransporter is associated with cellular protection and slowing kidney function decline. Newly identified molecular pathways could provide the basis for the development of much-needed novel therapeutics.

Determining the Relationship Between Blood Pressure, Kidney Function, and Chronic Kidney Disease: Insights From Genetic Epidemiology

BACKGROUND

It is well established that decreased kidney function can increase blood pressure (BP), but it is unproven whether moderately elevated BP causes chronic kidney disease (CKD) or glomerular hyperfiltration.

METHODS

311 119 White British UK Biobank participants were included in logistic regression analyses to estimate the odds of CKD (defined as long-term kidney replacement therapy, estimated glomerular filtration rate [eGFR]< 60mL/min/1.73m², or urinary albumin:creatinine ratio ≥3 mg/mmol) associated with higher genetically predicted BP using genetic risk scores comprising 219 systolic and 223 diastolic BP loci. Analyses estimating associations with clinical categories of eGFR and urinary albumin:creatinine ratio were also conducted, with an eGFR ≥120 mL (min·1.73m²) considered evidence of glomerular hyperfiltration.

RESULTS

21 623 participants had CKD: 7781 with reduced eGFR and 15 500 with albuminuria. 1828 participants had an eGFR ≥120 mL/min/1.73m². Each genetically predicted 10 mmHg higher systolic BP and 5 mmHg higher diastolic BP were associated with a 37% (95% CI, 1.29-1.45) and 19% (1.14-1.25) higher odds of CKD, respectively. Associations were evident for both the reduced eGFR and albuminuria components of the CKD outcome. The odds of hyperfiltration (versus an eGFR ≥60 and <90 mL/min/1.73m² were 49% higher (95% CI, 1.21-1.84) for each genetically predicted 10 mmHg higher systolic BP. Associations with CKD and hyperfiltration were similar irrespective of preexisting diabetes, vascular disease, or different levels of adiposity.

CONCLUSIONS

In this general population, genetic epidemiological evidence supports a causal role of life-long differences in BP for decreased kidney function, glomerular hyperfiltration, and albuminuria. Physiological autoregulation may not afford complete renal protection against the moderate BP elevations.

Youth versus adult-onset type 2 diabetic kidney disease: Insights into currently known structural differences and the potential underlying mechanisms

Type 2 diabetes (T2D) is a global health pandemic with significant humanitarian, economic, and societal implications, particularly for youth and young adults who are experiencing an exponential rise in incident disease. Youth-onset T2D has a more aggressive phenotype than adult-onset T2D, and this translates to important differences in rates of progression of diabetic kidney disease (DKD). We hypothesize that youth-onset DKD due to T2D may exhibit morphometric, metabolic, and molecular characteristics that are distinct from adult-onset T2D and develop secondary to inherent differences in renal energy expenditure and substrate metabolism, resulting in a central metabolic imbalance. Kidney structural changes that are evident at the onset of puberty also serve to exacerbate the organ’s baseline high rates of energy expenditure. Additionally, the physiologic state of insulin resistance seen during puberty increases the risk for kidney disease and is exacerbated by both concurrent diabetes and obesity. A metabolic mismatch in renal energetics may represent a novel target for pharmacologic intervention, both for prevention and treatment of DKD. Further investigation into the underlying molecular mechanisms resulting in DKD in youth-onset T2D using metabolomics and RNA sequencing of kidney tissue obtained at biopsy is necessary to expand our understanding of early DKD and potential targets for therapeutic intervention. Furthermore, large-scale clinical trials evaluating the duration of kidney protective effects of pharmacologic interventions that target a metabolic mismatch in kidney energy expenditure are needed to help mitigate the risk of DKD in youth-onset T2D.

Genome-Wide Association Study for eGFR in a Taiwanese Population

BACKGROUND AND OBJECTIVES

Chronic kidney disease (CKD) is a global public health issue associated with large economic burdens. CKD contributes to higher risks of cardiovascular complications, kidney failure, and mortality. The incidence and prevalence rates of kidney failure in Taiwan have remained the highest in the world.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Assessing genetic factors that influence kidney function in specific populations has substantial clinical relevance. We investigated associations of genetic variants with eGFR. The quality control filtering and genotype imputation resulted in 10,008 Taiwan Biobank participants and 6,553,511 variants for final analyses. We examined these loci with in silico replication in individuals of European and African ancestry.

RESULTS

Our results revealed one significant locus (4q21.1) and three suggestive significant loci (17q23.2, 22q13.2, and 3q29) for eGFR in the Taiwanese population. In total, four conditional-independent single nucleotide polymorphisms were identified as the most important variants within these regions, including rs55948430 (Coiled-Coil Domain Containing 158), rs1010269 (BCAS3), rs56108505 (MKL1), and rs34796810 (upstream of DLG1). By performing a meta-analysis, we found that the 4q21.1 and 17q23.2 loci were successfully replicated in the European population, whereas only the 17q23.2 locus was replicated in African ancestry. Therefore, these two loci are suggested to be transethnic loci, and the other two eGFR-associated loci (22q13.2 and 3q29) are likely population specific.

CONCLUSIONS

We identified four susceptibility loci on 4q21.1, 17q23.2, 22q13.2, and 3q29 that associated with kidney-related traits in a Taiwanese population. The 22q13.2 (MKL1) and 3q29 (DLG1) were prioritized as critical candidates. Functional analyses delineated novel pathways related to kidney physiology in Taiwanese and East Asian ancestries.

Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease

AIMS/HYPOTHESIS

Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets.

METHODS

We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets.

RESULTS

The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR<60 ml/min per 1.73 m2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10-9; although not withstanding correction for multiple testing, p>9.3×10-9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN-RESP18, GPR158, INIP-SNX30, LSM14A and MFF; p<2.7×10-6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10-6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p<1.5×10-11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10-8] and negatively with tubulointerstitial fibrosis [p=2.0×10-9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10-16], and SNX30 expression correlated positively with eGFR [p=5.8×10-14] and negatively with fibrosis [p<2.0×10-16]).

CONCLUSIONS/INTERPRETATION

Altogether, the results point to novel genes contributing to the pathogenesis of DKD.

DATA AVAILABILITY

The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages ( https://t1d.hugeamp.org/downloads.html ; https://t2d.hugeamp.org/downloads.html ; https://hugeamp.org/downloads.html ).

Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease

More than 800 million people suffer from kidney disease, yet the mechanism of kidney dysfunction is poorly understood. In the present study, we define the genetic association with kidney function in 1.5 million individuals and identify 878 (126 new) loci. We map the genotype effect on the methylome in 443 kidneys, transcriptome in 686 samples and single-cell open chromatin in 57,229 kidney cells. Heritability analysis reveals that methylation variation explains a larger fraction of heritability than gene expression. We present a multi-stage prioritization strategy and prioritize target genes for 87% of kidney function loci. We highlight key roles of proximal tubules and metabolism in kidney function regulation. Furthermore, the causal role of SLC47A1 in kidney disease is defined in mice with genetic loss of Slc47a1 and in human individuals carrying loss-of-function variants. Our findings emphasize the key role of bulk and single-cell epigenomic information in translating genome-wide association studies into identifying causal genes, cellular origins and mechanisms of complex traits.

Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals

Reduced glomerular filtration rate (GFR) can progress to kidney failure. Risk factors include genetics and diabetes mellitus (DM), but little is known about their interaction. We conducted genome-wide association meta-analyses for estimated GFR based on serum creatinine (eGFR), separately for individuals with or without DM (nDM = 178,691, nnoDM = 1,296,113). Our genome-wide searches identified (i) seven eGFR loci with significant DM/noDM-difference, (ii) four additional novel loci with suggestive difference and (iii) 28 further novel loci (including CUBN) by allowing for potential difference. GWAS on eGFR among DM individuals identified 2 known and 27 potentially responsible loci for diabetic kidney disease. Gene prioritization highlighted 18 genes that may inform reno-protective drug development. We highlight the existence of DM-only and noDM-only effects, which can inform about the target group, if respective genes are advanced as drug targets. Largely shared effects suggest that most drug interventions to alter eGFR should be effective in DM and noDM.

Synthesis of broad-specificity activity-based probes for exo-β-mannosidases

Exo-β-mannosidases are a broad class of stereochemically retaining hydrolases that are essential for the breakdown of complex carbohydrate substrates found in all kingdoms of life. Yet the detection of exo-β-mannosidases in complex biological samples remains challenging, necessitating the development of new methodologies. Cyclophellitol and its analogues selectively label the catalytic nucleophiles of retaining glycoside hydrolases, making them valuable tool compounds. Furthermore, cyclophellitol can be readily redesigned to enable the incorporation of a detection tag, generating activity-based probes (ABPs) that can be used to detect and identify specific glycosidases in complex biological samples. Towards the development of ABPs for exo-β-mannosidases, we present a concise synthesis of β-manno-configured cyclophellitol, cyclophellitol aziridine, and N-alkyl cyclophellitol aziridines. We show that these probes covalently label exo-β-mannosidases from GH families 2, 5, and 164. Structural studies of the resulting complexes support a canonical mechanism-based mode of action in which the active site nucleophile attacks the pseudoanomeric centre to form a stable ester linkage, mimicking the glycosyl enzyme intermediate. Furthermore, we demonstrate activity-based protein profiling using an N-alkyl aziridine derivative by specifically labelling MANBA in mouse kidney tissue. Together, these results show that synthetic manno-configured cyclophellitol analogues hold promise for detecting exo-β-mannosidases in biological and biomedical research.

Discovery of novel eGFR-associated multiple independent signals using a quasi-adaptive method

A decreased estimated glomerular filtration rate (eGFR) leading to chronic kidney disease is a significant public health problem. Kidney function is a heritable trait, and recent application of genome-wide association studies (GWAS) successfully identified multiple eGFR-associated genetic loci. To increase statistical power for detecting independent associations in GWAS loci, we improved our recently developed quasi-adaptive method estimating SNP-specific alpha levels for the conditional analysis, and applied it to the GWAS meta-analysis results of eGFR among 783,978 European-ancestry individuals. Among known eGFR loci, we revealed 19 new independent association signals that were subsequently replicated in the United Kingdom Biobank (n = 408,608). These associations have remained undetected by conditional analysis using the established conservative genome-wide significance level of 5 × 10^-8. Functional characterization of known index SNPs and novel independent signals using colocalization of conditional eGFR association results and gene expression in cis across 51 human tissues identified two potentially causal genes across kidney tissues: TSPAN33 and TFDP2, and three candidate genes across other tissues: SLC22A2, LRP2, and CDKN1C. These colocalizations were not identified in the original GWAS. By applying our improved quasi-adaptive method, we successfully identified additional genetic variants associated with eGFR. Considering these signals in colocalization analyses can increase the precision of revealing potentially functional genes of GWAS loci.

Emerging Insights Into Chronic Renal Disease Pathogenesis in Hypertension From Human and Animal Genomic Studies

The pathogenic links between elevated blood pressure and chronic kidney disease remain obscure. This article examines progress in population genetics and in animal models of hypertension and chronic kidney disease. It also provides a critique of the application of genome-wide association studies to understanding the heritability of renal function. Emerging themes identified indicate that heritable risk of chronic kidney disease in hypertension can arise from genetic variation in (1) glomerular and tubular protein handling mechanisms; (2) autoregulatory capacity of the renal vasculature; and (3) innate and adaptive immune mechanisms. Increased prevalence of hypertension-associated chronic kidney disease that occurs with aging may reflect amplification of heritable risks by normal aging processes affecting immunity and autoregulation.

Genome-wide association studies identify the role of caspase-9 in kidney disease

Genome-wide association studies (GWAS) have identified hundreds of genetic risk regions for kidney dysfunction [estimated glomerular filtration rate (eGFR)]; however, the causal genes, cell types, and pathways are poorly understood. Integration of GWAS and human kidney expression of quantitative trait analysis using Bayesian colocations, transcriptome-wide association studies, and summary-based Mendelian randomization studies prioritized caspase-9 (CASP9) as a kidney disease risk gene. Human kidney single-cell epigenetic and immunostaining studies indicated kidney tubule cells as a disease-causing cell type. Mice with genetic deletion or pharmacological inhibition of CASP9 showed lower apoptosis while having improved mitophagy, resulting in dampened activation of cytosolic nucleotide sensing pathways (cGAS-STING), reduction of inflammation, and protection from acute kidney disease or renal fibrosis. In summary, here, we prioritized CASP9 as an eGFR GWAS target gene and demonstrated the causal role of CASP9 in kidney disease development via improving mitophagy and lowering inflammation and apoptosis.

Transgenic fluorescent zebrafish lines that have revolutionized biomedical research

Since its debut in the biomedical research fields in 1981, zebrafish have been used as a vertebrate model organism in more than 40,000 biomedical research studies. Especially useful are zebrafish lines expressing fluorescent proteins in a molecule, intracellular organelle, cell or tissue specific manner because they allow the visualization and tracking of molecules, intracellular organelles, cells or tissues of interest in real time and in vivo. In this review, we summarize representative transgenic fluorescent zebrafish lines that have revolutionized biomedical research on signal transduction, the craniofacial skeletal system, the hematopoietic system, the nervous system, the urogenital system, the digestive system and intracellular organelles.

The Aggressive Diabetic Kidney Disease in Youth-Onset Type 2 Diabetes: Pathogenetic Mechanisms and Potential Therapies

Youth-onset Type 2 Diabetes Mellitus (T2DM) represents a major burden worldwide. In the last decades, the prevalence of T2DM became higher than that of Type 1 Diabetes Mellitus (T1DM), helped by the increasing rate of childhood obesity. The highest prevalence rates of youth-onset T2DM are recorded in China (520 cases/100,000) and in the United States (212 cases/100,000), and the numbers are still increasing. T2DM young people present a strong hereditary component, often unmasked by social and environmental risk factors. These patients are affected by multiple coexisting risk factors, including obesity, hyperglycemia, dyslipidemia, insulin resistance, hypertension, and inflammation. Juvenile T2DM nephropathy occurs earlier in life compared to T1DM-related nephropathy in children or T2DM-related nephropathy in adult. Diabetic kidney disease (DKD) is T2DM major long term microvascular complication. This review summarizes the main mechanisms involved in the pathogenesis of the DKD in young population and the recent evolution of treatment, in order to reduce the risk of DKD progression.

A single genetic locus controls both expression of DPEP1/CHMP1A and kidney disease development via ferroptosis

Genome-wide association studies (GWAS) have identified loci for kidney disease, but the causal variants, genes, and pathways remain unknown. Here we identify two kidney disease genes Dipeptidase 1 (DPEP1) and Charged Multivesicular Body Protein 1 A (CHMP1A) via the triangulation of kidney function GWAS, human kidney expression, and methylation quantitative trait loci. Using single-cell chromatin accessibility and genome editing, we fine map the region that controls the expression of both genes. Mouse genetic models demonstrate the causal roles of both genes in kidney disease. Cellular studies indicate that both Dpep1 and Chmp1a are important regulators of a single pathway, ferroptosis and lead to kidney disease development via altering cellular iron trafficking.

Association of IL1R1 Coding Variant With Plasma-Level Soluble ST2 and Risk of Aortic Dissection

Objective: Aortic dissection (AD) is characterized by an acute onset, rapid progress, and high mortality. Levels of soluble ST2 (sST2) on presentation are elevated in patients with acute AD, which can be used to discriminate AD patients from patients with chest pain. sST2 concentrations were found to be highly heritable in the general population. The aim of this study was to investigate the associations of variations in ST2-related gene expression with sST2 concentrations and AD risk. Methods: This case-control study involving a total of 2,277 participants were conducted, including 435 AD patients and age- and sex-matched 435 controls in the discovery stage, and 464 patients and 943 controls in the validation stage. Eight ST2-related genes were selected by systematic review. Tag single-nucleotide polymorphisms (SNPs) were screened out from the Chinese population of the 1,000 Genomes Database. Twenty-one ST2-related SNPs were genotyped, and plasma sST2 concentrations were measured. Results: In the discovery stage, rs13019803 located in IL1R1 was significantly associated with AD after Bonferroni correction (p = 0.0009) and was correlated with circulating sST2 levels in patients with type A AD(AAD) [log-sST2 per C allele increased by 0.180 (95%) CI: 0.002 - 0.357] but not in type B. Combining the two stages together, rs13019803C was associated with plasma sST2 level in AAD patients [log-sST2 increased by 0.141 (95% CI: 0.055-0.227) for per C allele]. Odds ratio of rs13019803 on the risk of AAD is 1.67 (95% CI: 1.33-2.09). Conclusions: The IL1R1 SNP rs13019803C is associated with higher sST2 levels and increased risk of AAD.

Kidney disease genetic risk variants alter lysosomal beta-mannosidase (MANBA) expression and disease severity

More than 800 million people in the world suffer from chronic kidney disease (CKD). Genome-wide association studies (GWAS) have identified hundreds of loci where genetic variants are associated with kidney function; however, causal genes and pathways for CKD remain unknown. Here, we performed integration of kidney function GWAS and human kidney-specific expression quantitative trait analysis and identified that the expression of beta-mannosidase (MANBA) was lower in kidneys of subjects with CKD risk genotype. We also show an increased incidence of renal failure in subjects with rare heterozygous loss-of-function coding variants in MANBA using phenome-wide association analysis of 40,963 subjects with exome sequencing data. MANBA is a lysosomal gene highly expressed in kidney tubule cells. Deep phenotyping revealed structural and functional lysosomal alterations in human kidneys from subjects with CKD risk alleles and mice with genetic deletion of Manba Manba heterozygous and knockout mice developed more severe kidney fibrosis when subjected to toxic injury induced by cisplatin or folic acid. Manba loss altered multiple pathways, including endocytosis and autophagy. In the absence of Manba, toxic acute tubule injury induced inflammasome activation and fibrosis. Together, these results illustrate the convergence of common noncoding and rare coding variants in MANBA in kidney disease development and demonstrate the role of the endolysosomal system in kidney disease development.

Association between MANBA Gene Variants and Chronic Kidney Disease in a Korean Population

Chronic kidney disease (CKD), a damaged condition of the kidneys, is a global public health problem that can be caused by diabetes, hypertension, and other disorders. Recently, the MANBA gene was identified in CKD by integrating CKD-related variants and kidney expression quantitative trait loci (eQTL) data. This study evaluated the effects of MANBA gene variants on CKD and kidney function-related traits using a Korean cohort. We also analyzed the association of MANBA gene variants with kidney-related traits such as the estimated glomerular filtration rate (eGFR), and blood urea nitrogen (BUN), creatinine, and uric acid levels using linear regression analysis. As a result, 14 single nucleotide polymorphisms (SNPs) were replicated in CKD (p < 0.05), consistent with previous studies. Among them, rs4496586, which was the most significant for CKD and kidney function-related traits, was associated with a decreased CKD risk in participants with the homozygous minor allele (CC), increased eGFR, and decreased creatinine and uric acid concentrations. Furthermore, the association analysis between the rs4496586 genotype and MANBA gene expression in human tubules and glomeruli showed high MANBA gene expression in the minor allele carriers. In conclusion, this study demonstrated that MANBA gene variants were associated with CKD and kidney function-related traits in a Korean cohort.

Transcriptome-wide association analysis identifies DACH1 as a kidney disease risk gene that contributes to fibrosis

Genome-wide association studies (GWAS) for kidney function identified hundreds of risk regions; however, the causal variants, target genes, cell types, and disease mechanisms remain poorly understood. Here, we performed transcriptome-wide association studies (TWAS), summary Mendelian randomization, and MetaXcan to identify genes whose expression mediates the genotype effect on the phenotype. Our analyses identified Dachshund homolog 1 (DACH1), a cell-fate determination factor. GWAS risk variant was associated with lower DACH1 expression in human kidney tubules. Human and mouse kidney single-cell open chromatin data (snATAC-Seq) prioritized estimated glomerular filtration rate (eGFR) GWAS variants located on an intronic regulatory region in distal convoluted tubule cells. CRISPR-Cas9-mediated gene editing confirmed the role of risk variants in regulating DACH1 expression. Mice with tubule-specific Dach1 deletion developed more severe renal fibrosis both in folic acid and diabetic kidney injury models. Mice with tubule-specific Dach1 overexpression were protected from folic acid nephropathy. Single-cell RNA sequencing, chromatin immunoprecipitation, and functional analysis indicated that DACH1 controls the expression of cell cycle and myeloid chemotactic factors, contributing to macrophage infiltration and fibrosis development. In summary, integration of GWAS, TWAS, single-cell epigenome, expression analyses, gene editing, and functional validation in different mouse kidney disease models identified DACH1 as a kidney disease risk gene.

Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney

The kidney is an organ of key relevance to blood pressure (BP) regulation, hypertension and antihypertensive treatment. However, genetically mediated renal mechanisms underlying susceptibility to hypertension remain poorly understood. We integrated genotype, gene expression, alternative splicing and DNA methylation profiles of up to 430 human kidneys to characterize the effects of BP index variants from genome-wide association studies (GWASs) on renal transcriptome and epigenome. We uncovered kidney targets for 479 (58.3%) BP-GWAS variants and paired 49 BP-GWAS kidney genes with 210 licensed drugs. Our colocalization and Mendelian randomization analyses identified 179 unique kidney genes with evidence of putatively causal effects on BP. Through Mendelian randomization, we also uncovered effects of BP on renal outcomes commonly affecting patients with hypertension. Collectively, our studies identified genetic variants, kidney genes, molecular mechanisms and biological pathways of key relevance to the genetic regulation of BP and inherited susceptibility to hypertension.

Conventional and Genetic Evidence on the Association between Adiposity and CKD

BACKGROUND

The size of any causal contribution of central and general adiposity to CKD risk and the underlying mechanism of mediation are unknown.

METHODS

Data from 281,228 UK Biobank participants were used to estimate the relevance of waist-to-hip ratio and body mass index (BMI) to CKD prevalence. Conventional approaches used logistic regression. Genetic analyses used Mendelian randomization (MR) and data from 394 waist-to-hip ratio and 773 BMI-associated loci. Models assessed the role of known mediators (diabetes mellitus and BP) by adjusting for measured values (conventional analyses) or genetic associations of the selected loci (multivariable MR).

RESULTS

Evidence of CKD was found in 18,034 (6.4%) participants. Each 0.06 higher measured waist-to-hip ratio and each 5-kg/m² increase in BMI were associated with 69% (odds ratio, 1.69; 95% CI, 1.64 to 1.74) and 58% (1.58; 1.55 to 1.62) higher odds of CKD, respectively. In analogous MR analyses, each 0.06-genetically-predicted higher waist-to-hip ratio was associated with a 29% (1.29; 1.20 to 1.38) increased odds of CKD, and each 5-kg/m² genetically-predicted higher BMI was associated with a 49% (1.49; 1.39 to 1.59) increased odds. After adjusting for diabetes and measured BP, chi-squared values for associations for waist-to-hip ratio and BMI fell by 56%. In contrast, mediator adjustment using multivariable MR found 83% and 69% reductions in chi-squared values for genetically-predicted waist-to-hip ratio and BMI models, respectively.

CONCLUSIONS

Genetic analyses suggest that conventional associations between central and general adiposity with CKD are largely causal. However, conventional approaches underestimate mediating roles of diabetes, BP, and their correlates. Genetic approaches suggest these mediators explain most of adiposity-CKD-associated risk.

Integrated multi-omics approaches to improve classification of chronic kidney disease

Chronic kidney diseases (CKDs) are currently classified according to their clinical features, associated comorbidities and pattern of injury on biopsy. Even within a given classification, considerable variation exists in disease presentation, progression and response to therapy, highlighting heterogeneity in the underlying biological mechanisms. As a result, patients and clinicians experience uncertainty when considering optimal treatment approaches and risk projection. Technological advances now enable large-scale datasets, including DNA and RNA sequence data, proteomics and metabolomics data, to be captured from individuals and groups of patients along the genotype-phenotype continuum of CKD. The ability to combine these high-dimensional datasets, in which the number of variables exceeds the number of clinical outcome observations, using computational approaches such as machine learning, provides an opportunity to re-classify patients into molecularly defined subgroups that better reflect underlying disease mechanisms. Patients with CKD are uniquely poised to benefit from these integrative, multi-omics approaches since the kidney biopsy, blood and urine samples used to generate these different types of molecular data are frequently obtained during routine clinical care. The ultimate goal of developing an integrated molecular classification is to improve diagnostic classification, risk stratification and assignment of molecular, disease-specific therapies to improve the care of patients with CKD.

ReQTL: identifying correlations between expressed SNVs and gene expression using RNA-sequencing data

MOTIVATION

By testing for associations between DNA genotypes and gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation at expressed SNV loci can be estimated using the proportion of alleles bearing the variant nucleotide (variant allele fraction, VAFRNA). VAFRNA is a continuous measure which allows for precise allele quantitation in loci where the RNA alleles do not scale with the genotype count. We describe a method to correlate VAFRNA with gene expression and assess its ability to identify genetically regulated expression solely from RNA-sequencing (RNA-seq) datasets.

RESULTS

We introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression (GTEx) project and demonstrate that ReQTL analyses are computationally feasible and can identify a subset of expressed eQTL loci.

AVAILABILITY AND IMPLEMENTATION

A toolkit to perform ReQTL analyses is available at https://github.com/HorvathLab/ReQTL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Uromodulin (Tamm-Horsfall protein): guardian of urinary and systemic homeostasis

Biology has taught us that a protein as abundantly made and conserved among species as Tamm-Horsfall protein (THP or uromodulin) cannot just be a waste product serving no particular purpose. However, for many researchers, THP is merely a nuisance during urine proteome profiling or exosome purification and for clinicians an enigmatic entity without clear disease implications. Thanks to recent human genetic and correlative studies and animal modeling, we now have a renewed appreciation of this highly prevalent protein in not only guarding urinary homeostasis, but also serving as a critical mediator in systemic inter-organ signaling. Beyond a mere barrier that lines the tubules, or a surrogate for nephron mass, mounting evidence suggests that THP is a multifunctional protein critical for modulating renal ion channel activity, salt/water balance, renal and systemic inflammatory response, intertubular communication, mineral crystallization and bacterial adhesion. Indeed, mutations in THP cause a group of inherited kidney diseases, and altered THP expression is associated with increased risks of urinary tract infection, kidney stone, hypertension, hyperuricemia and acute and chronic kidney diseases. Despite the recent surge of information surrounding THP's physiological functions and disease involvement, our knowledge remains incomplete regarding how THP is normally regulated by external and intrinsic factors, how precisely THP deficiency leads to urinary and systemic pathophysiology and in what clinical settings THP can be used as a theranostic biomarker and a target for modulation to improve patient outcomes.

Complexities of Understanding Function from CKD-Associated DNA Variants

Genome-wide association studies (GWASs) have facilitated the unbiased discovery of hundreds of genomic loci associated with CKD and kidney function. The vast majority of disease-associated DNA variants are noncoding. Those that are causal in CKD pathogenesis likely modulate transcription of target genes in a cell type-specific manner. To gain novel biological insights into mechanisms driving the development of CKD, the causal variants (which are usually not the most significant variant reported in a GWAS), their target genes, and causal cell types need to be identified. This functional validation requires a large number of new data sets, complex bioinformatics analyses, and experimental cellular and in vivo studies. Here, we review the basic principles and some of the current approaches being leveraged to assign functional significance to a genotype-phenotype association.

Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis

We investigated type 2 diabetes (T2D) genetic susceptibility via multi-ethnic meta-analysis of 228,499 cases and 1,178,783 controls in the Million Veteran Program, DIAMANTE, Biobank Japan, and other studies. We report 568 associations, including 286 autosomal, 7 X chromosomal, and 25 identified in ancestry-specific analyses that were previously unreported. Transcriptome-wide association analysis detected 3,568 T2D-associations with genetically predicted gene expression in 687 novel genes; of these, 54 are known to interact with FDA-approved drugs. A polygenic risk score was strongly associated with increased risk of T2D-related retinopathy and modestly associated with chronic kidney disease (CKD), peripheral artery disease (PAD), and neuropathy. We investigated the genetic etiology of T2D-related vascular outcomes in MVP and observed statistical SNP-T2D interactions at 13 variants, including coronary heart disease, CKD, PAD, and neuropathy. These findings may help to identify potential therapeutic targets for T2D and genomic pathways that link T2D to vascular outcomes.

Unravelling the complex genetics of common kidney diseases: from variants to mechanisms

Genome-wide association studies (GWAS) have identified hundreds of loci associated with kidney-related traits such as glomerular filtration rate, albuminuria, hypertension, electrolyte and metabolite levels. However, these impressive, large-scale mapping approaches have not always translated into an improved understanding of disease or development of novel therapeutics. GWAS have several important limitations. Nearly all disease-associated risk loci are located in the non-coding region of the genome and therefore, their target genes, affected cell types and regulatory mechanisms remain unknown. Genome-scale approaches can be used to identify associations between DNA sequence variants and changes in gene expression (quantified through bulk and single-cell methods), gene regulation and other molecular quantitative trait studies, such as chromatin accessibility, DNA methylation, protein expression and metabolite levels. Data obtained through these approaches, used in combination with robust computational methods, can deliver robust mechanistic inferences for translational exploitation. Understanding the genetic basis of common kidney diseases means having a comprehensive picture of the genes that have a causal role in disease development and progression, of the cells, tissues and organs in which these genes act to affect the disease, of the cellular pathways and mechanisms that drive disease, and of potential targets for disease prevention, detection and therapy.

MEDEA: analysis of transcription factor binding motifs in accessible chromatin

Deciphering the interplay between chromatin accessibility and transcription factor (TF) binding is fundamental to understanding transcriptional regulation, control of cellular states, and the establishment of new phenotypes. Recent genome-wide chromatin accessibility profiling studies have provided catalogs of putative open regions, where TFs can recognize their motifs and regulate gene expression programs. Here, we present motif enrichment in differential elements of accessibility (MEDEA), a computational tool that analyzes high-throughput chromatin accessibility genomic data to identify cell-type-specific accessible regions and lineage-specific motifs associated with TF binding therein. To benchmark MEDEA, we used a panel of reference cell lines profiled by ENCODE and curated by the ENCODE Project Consortium for the ENCODE-DREAM Challenge. By comparing results with RNA-seq data, ChIP-seq peaks, and DNase-seq footprints, we show that MEDEA improves the detection of motifs associated with known lineage specifiers. We then applied MEDEA to 610 ENCODE DNase-seq data sets, where it revealed significant motifs even when absolute enrichment was low and where it identified novel regulators, such as NRF1 in kidney development. Finally, we show that MEDEA performs well on both bulk and single-cell ATAC-seq data. MEDEA is publicly available as part of our Glossary-GENRE suite for motif enrichment analysis.

Modeling Lysosomal Storage Diseases in the Zebrafish

Lysosomal storage diseases (LSDs) are a family of 70 metabolic disorders characterized by mutations in lysosomal proteins that lead to storage material accumulation, multiple-organ pathologies that often involve neurodegeneration, and early mortality in a significant number of patients. Along with the necessity for more effective therapies, there exists an unmet need for further understanding of disease etiology, which could uncover novel pathways and drug targets. Over the past few decades, the growth in knowledge of disease-associated pathways has been facilitated by studies in model organisms, as advancements in mutagenesis techniques markedly improved the efficiency of model generation in mammalian and non-mammalian systems. In this review we highlight non-mammalian models of LSDs, focusing specifically on the zebrafish, a vertebrate model organism that shares remarkable genetic and metabolic similarities with mammals while also conferring unique advantages such as optical transparency and amenability toward high-throughput applications. We examine published zebrafish LSD models and their reported phenotypes, address organism-specific advantages and limitations, and discuss recent technological innovations that could provide potential solutions.

ASEP: Gene-based detection of allele-specific expression across individuals in a population by RNA sequencing

Allele-specific expression (ASE) analysis, which quantifies the relative expression of two alleles in a diploid individual, is a powerful tool for identifying cis-regulated gene expression variations that underlie phenotypic differences among individuals. Existing methods for gene-level ASE detection analyze one individual at a time, therefore failing to account for shared information across individuals. Failure to accommodate such shared information not only reduces power, but also makes it difficult to interpret results across individuals. However, when only RNA sequencing (RNA-seq) data are available, ASE detection across individuals is challenging because the data often include individuals that are either heterozygous or homozygous for the unobserved cis-regulatory SNP, leading to sample heterogeneity as only those heterozygous individuals are informative for ASE, whereas those homozygous individuals have balanced expression. To simultaneously model multi-individual information and account for such heterogeneity, we developed ASEP, a mixture model with subject-specific random effect to account for multi-SNP correlations within the same gene. ASEP only requires RNA-seq data, and is able to detect gene-level ASE under one condition and differential ASE between two conditions (e.g., pre- versus post-treatment). Extensive simulations demonstrated the convincing performance of ASEP under a wide range of scenarios. We applied ASEP to a human kidney RNA-seq dataset, identified ASE genes and validated our results with two published eQTL studies. We further applied ASEP to a human macrophage RNA-seq dataset, identified genes showing evidence of differential ASE between M0 and M1 macrophages, and confirmed our findings by results from cardiometabolic trait-relevant genome-wide association studies. To the best of our knowledge, ASEP is the first method for gene-level ASE detection at the population level that only requires the use of RNA-seq data. With the growing adoption of RNA-seq, we believe ASEP will be well-suited for various ASE studies for human diseases.

Applying single-cell technologies to clinical pathology: progress in nephropathology

Cells represent the basic building blocks of living organisms. Accurate characterisation of cellular phenotype, intercellular signalling networks, and the spatial organisation of cells within organs is crucial to deliver a better understanding of the processes underpinning physiology, and the perturbations that lead to disease. Single-cell methodologies have increased rapidly in scale and scope in recent years and are set to generate important insights into human disease. Here, we review current practices in nephropathology, which are dominated by relatively simple morphological descriptions of tissue biopsies based on their appearance using light microscopy. Bulk transcriptomics have more recently been used to explore glomerular and tubulointerstitial kidney disease, renal cancer, and the responses to injury and alloimmunity in kidney transplantation, generating novel disease insights and prognostic biomarkers. These studies set the stage for single-cell transcriptomic approaches that reveal cell-type-specific gene expression patterns in health and disease. These technologies allow genome-wide disease susceptibility genes to be interpreted with the knowledge of the specific cell populations within organs that express them, identifying candidate cell types for further study. Single-cell technologies are also moving beyond assaying individual cellular transcriptomes, to measuring the epigenetic landscape of single cells. Single-cell antigen-receptor gene sequencing also enables specific T- and B-cell clones to be tracked in different tissues and disease states. In the coming years these rich 'multi-omic' descriptions of kidney disease will enable histopathological descriptions to be comprehensively integrated with molecular phenotypes, enabling better disease classification and prognostication and the application of personalised treatment strategies. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Dnmt3a and Dnmt3b-Decommissioned Fetal Enhancers are Linked to Kidney Disease

BACKGROUND

Cytosine methylation is an epigenetic mark that dictates cell fate and response to stimuli. The timing and establishment of methylation logic during kidney development remains unknown. DNA methyltransferase 3a and 3b are the enzymes capable of establishing de novo methylation.

METHODS

We generated mice with genetic deletion of Dnmt3a and Dnmt3b in nephron progenitor cells (Six2CreDnmt3a/3b) and kidney tubule cells (KspCreDnmt3a/3b). We characterized KspCreDnmt3a/3b mice at baseline and after injury. Unbiased omics profiling, such as whole genome bisulfite sequencing, reduced representation bisulfite sequencing and RNA sequencing were performed on whole-kidney samples and isolated renal tubule cells.

RESULTS

KspCreDnmt3a/3b mice showed no obvious morphologic and functional alterations at baseline. Knockout animals exhibited increased resistance to cisplatin-induced kidney injury, but not to folic acid-induced fibrosis. Whole-genome bisulfite sequencing indicated that Dnmt3a and Dnmt3b play an important role in methylation of gene regulatory regions that act as fetal-specific enhancers in the developing kidney but are decommissioned in the mature kidney. Loss of Dnmt3a and Dnmt3b resulted in failure to silence developmental genes. We also found that fetal-enhancer regions methylated by Dnmt3a and Dnmt3b were enriched for kidney disease genetic risk loci. Methylation patterns of kidneys from patients with CKD showed defects similar to those in mice with Dnmt3a and Dnmt3b deletion.

CONCLUSIONS

Our results indicate a potential locus-specific convergence of genetic, epigenetic, and developmental elements in kidney disease development.

Uncovering genetic mechanisms of kidney aging through transcriptomics, genomics, and epigenomics

Nephrons scar and involute during aging, increasing the risk of chronic kidney disease. Little is known, however, about genetic mechanisms of kidney aging. We sought to define the signatures of age on the renal transcriptome using 563 human kidneys. The initial discovery analysis of 260 kidney transcriptomes from the TRANScriptome of renaL humAn TissuE Study (TRANSLATE) and the Cancer Genome Atlas identified 37 age-associated genes. For 19 of those genes, the association with age was replicated in 303 kidney transcriptomes from the Nephroseq resource. Surveying 42 nonrenal tissues from the Genotype–Tissue Expression project revealed that, for approximately a fifth of the replicated genes, the association with age was kidney-specific. Seventy-three percent of the replicated genes were associated with functional or histological parameters of age-related decline in kidney health, including glomerular filtration rate, glomerulosclerosis, interstitial fibrosis, tubular atrophy, and arterial narrowing. Common genetic variants in four of the age-related genes, namely LYG1, PPP1R3C, LTF and TSPYL5, correlated with the trajectory of age-related changes in their renal expression. Integrative analysis of genomic, epigenomic, and transcriptomic information revealed that the observed age-related decline in renal TSPYL5 expression was determined both genetically and epigenetically. Thus, this study revealed robust molecular signatures of the aging kidney and new regulatory mechanisms of age-related change in the kidney transcriptome.

Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.

Genome-Wide Association Study of Diabetic Kidney Disease Highlights Biology Involved in Glomerular Basement Membrane Collagen

BACKGROUND

Although diabetic kidney disease demonstrates both familial clustering and single nucleotide polymorphism heritability, the specific genetic factors influencing risk remain largely unknown.

METHODS

To identify genetic variants predisposing to diabetic kidney disease, we performed genome-wide association study (GWAS) analyses. Through collaboration with the Diabetes Nephropathy Collaborative Research Initiative, we assembled a large collection of type 1 diabetes cohorts with harmonized diabetic kidney disease phenotypes. We used a spectrum of ten diabetic kidney disease definitions based on albuminuria and renal function.

RESULTS

Our GWAS meta-analysis included association results for up to 19,406 individuals of European descent with type 1 diabetes. We identified 16 genome-wide significant risk loci. The variant with the strongest association (rs55703767) is a common missense mutation in the collagen type IV alpha 3 chain (COL4A3) gene, which encodes a major structural component of the glomerular basement membrane (GBM). Mutations in COL4A3 are implicated in heritable nephropathies, including the progressive inherited nephropathy Alport syndrome. The rs55703767 minor allele (Asp326Tyr) is protective against several definitions of diabetic kidney disease, including albuminuria and ESKD, and demonstrated a significant association with GBM width; protective allele carriers had thinner GBM before any signs of kidney disease, and its effect was dependent on glycemia. Three other loci are in or near genes with known or suggestive involvement in this condition (BMP7) or renal biology (COLEC11 and DDR1).

CONCLUSIONS

The 16 diabetic kidney disease-associated loci may provide novel insights into the pathogenesis of this condition and help identify potential biologic targets for prevention and treatment.

Genome-wide association meta-analyses and fine-mapping elucidate pathways influencing albuminuria

Increased levels of the urinary albumin-to-creatinine ratio (UACR) are associated with higher risk of kidney disease progression and cardiovascular events, but underlying mechanisms are incompletely understood. Here, we conduct trans-ethnic (n = 564,257) and European-ancestry specific meta-analyses of genome-wide association studies of UACR, including ancestry- and diabetes-specific analyses, and identify 68 UACR-associated loci. Genetic correlation analyses and risk score associations in an independent electronic medical records database (n = 192,868) reveal connections with proteinuria, hyperlipidemia, gout, and hypertension. Fine-mapping and trans-Omics analyses with gene expression in 47 tissues and plasma protein levels implicate genes potentially operating through differential expression in kidney (including TGFB1, MUC1, PRKCI, and OAF), and allow coupling of UACR associations to altered plasma OAF concentrations. Knockdown of OAF and PRKCI orthologs in Drosophila nephrocytes reduces albumin endocytosis. Silencing fly PRKCI further impairs slit diaphragm formation. These results generate a priority list of genes and pathways for translational research to reduce albuminuria.

Unraveling the functional role of the orphan solute carrier, SLC22A24 in the transport of steroid conjugates through metabolomic and genome-wide association studies

Variation in steroid hormone levels has wide implications for health and disease. The genes encoding the proteins involved in steroid disposition represent key determinants of interindividual variation in steroid levels and ultimately, their effects. Beginning with metabolomic data from genome-wide association studies (GWAS), we observed that genetic variants in the orphan transporter, SLC22A24 were significantly associated with levels of androsterone glucuronide and etiocholanolone glucuronide (sentinel SNPs p-value <1x10-30). In cells over-expressing human or various mammalian orthologs of SLC22A24, we showed that steroid conjugates and bile acids were substrates of the transporter. Phylogenetic, genomic, and transcriptomic analyses suggested that SLC22A24 has a specialized role in the kidney and appears to function in the reabsorption of organic anions, and in particular, anionic steroids. Phenome-wide analysis showed that functional variants of SLC22A24 are associated with human disease such as cardiovascular diseases and acne, which have been linked to dysregulated steroid metabolism. Collectively, these functional genomic studies reveal a previously uncharacterized protein involved in steroid homeostasis, opening up new possibilities for SLC22A24 as a pharmacological target for regulating steroid levels.

Mapping eGFR loci to the renal transcriptome and phenome in the VA Million Veteran Program

Chronic kidney disease (CKD), defined by low estimated glomerular filtration rate (eGFR), contributes to global morbidity and mortality. Here we conduct a transethnic Genome-Wide Association Study of eGFR in 280,722 participants of the Million Veteran Program (MVP), with replication in 765,289 participants from the Chronic Kidney Disease Genetics (CKDGen) Consortium. We identify 82 previously unreported variants, confirm 54 loci, and report interesting findings including association of the sickle cell allele of betaglobin among non-Hispanic blacks. Our transcriptome-wide association study of kidney function in healthy kidney tissue identifies 36 previously unreported and nine known genes, and maps gene expression to renal cell types. In a Phenome-Wide Association Study in 192,868 MVP participants using a weighted genetic score we detect associations with CKD stages and complications and kidney stones. This investigation reinterprets the genetic architecture of kidney function to identify the gene, tissue, and anatomical context of renal homeostasis and the clinical consequences of dysregulation.

The kidney transcriptome, from single cells to whole organs and back

PURPOSE OF REVIEW

Transcriptome analysis of human kidney samples provides an integrated output of genetic, physiological, or environmental inputs. This review summarizes recent findings including gene expression and genetic variation integration, bulk and single cell gene expression analysis, and describes how such studies have improved our understanding of kidney disease development.

RECENT FINDINGS

Bulk or whole tissue analysis of patient kidney samples identified a large number of genes, whose levels correlate with kidney function and/or structural damage. These genes were enriched for metabolic and immune functions. Using expression quantitative trait analysis, genetic variations-driven gene expression can be identified. Recent developments in single cell sequencing defined cell-type-specific gene expression changes and highlighted specific cell types for disease development.

SUMMARY

Recent advancement in whole tissue transcriptomics, specifically incorporating genotype information and single cell data have been powerful to identify kidney disease-associated genes, pathways, and cell types.

Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis

Chronic kidney disease (CKD) is a growing health burden currently affecting 10–15% of adults worldwide. Estimated glomerular filtration rate (eGFR) as a marker of kidney function is commonly used to diagnose CKD. We analyze eGFR data from the Nord-Trøndelag Health Study and Michigan Genomics Initiative and perform a GWAS meta-analysis with public summary statistics, more than doubling the sample size of previous meta-analyses. We identify 147 loci (53 novel) associated with eGFR, including genes involved in transcriptional regulation, kidney development, cellular signaling, metabolism, and solute transport. Additionally, sex-stratified analysis identifies one locus with more significant effects in women than men. Using genetic risk scores constructed from these eGFR meta-analysis results, we show that associated variants are generally predictive of CKD with only modest improvements in detection compared with other known clinical risk factors. Collectively, these results yield additional insight into the genetic factors underlying kidney function and progression to CKD.

Estimated glomerular filtration rate (eGFR) is a measure of kidney function and used to characterize chronic kidney disease. Here, Graham et al. identify 53 novel loci for eGFR in a GWAS meta-analysis, a subset of which are associated with other common diseases, such as diabetes and hypertension, based on PheWAS.

Big science and big data in nephrology

There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of 'big data' holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called 'big science,' with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology.

The Impact of APOL1 on Chronic Kidney Disease and Hypertension

Essential hypertension is a clinical diagnosis based on the presence of an elevated systemic blood pressure on physical examination without a clear inciting cause. It has multiple etiologies and is not a homogeneous disorder. Hypertension contributes to the development and progression of atherosclerotic cardiovascular diseases, and antihypertensive treatment reduces the risk of fatal and nonfatal myocardial infarction, stroke, and congestive heart failure. Although hypertension is frequently present in nondiabetic individuals with low levels of proteinuria and chronic kidney disease, reducing blood pressures in this population does not reliably slow nephropathy progression. Many of these patients with recent African ancestry have the primary kidney disease "solidified glomerulosclerosis" that is strongly associated with renal-risk variants in the apolipoprotein L1 gene (APOL1). This kidney disease contributes to secondarily elevated blood pressures. The APOL1-associated spectrum of nondiabetic nephropathy also includes proteinuric kidney diseases, idiopathic focal segmental glomerulosclerosis, collapsing glomerulopathy, severe lupus nephritis, and sickle cell nephropathy. This article reviews relationships between mild to moderate essential hypertension and chronic kidney disease with a focus on the role of APOL1 in development of hypertension. Available evidence strongly supports that APOL1 renal-risk variants associate with glomerulosclerosis in African Americans, which then causes secondary hypertension, not with essential hypertension per se.

Integrated Functional Genomic Analysis Enables Annotation of Kidney Genome-Wide Association Study Loci

BACKGROUND

Linking genetic risk loci identified by genome-wide association studies (GWAS) to their causal genes remains a major challenge. Disease-associated genetic variants are concentrated in regions containing regulatory DNA elements, such as promoters and enhancers. Although researchers have previously published DNA maps of these regulatory regions for kidney tubule cells and glomerular endothelial cells, maps for podocytes and mesangial cells have not been available.

METHODS

We generated regulatory DNA maps (DNase-seq) and paired gene expression profiles (RNA-seq) from primary outgrowth cultures of human glomeruli that were composed mainly of podocytes and mesangial cells. We generated similar datasets from renal cortex cultures, to compare with those of the glomerular cultures. Because regulatory DNA elements can act on target genes across large genomic distances, we also generated a chromatin conformation map from freshly isolated human glomeruli.

RESULTS

We identified thousands of unique regulatory DNA elements, many located close to transcription factor genes, which the glomerular and cortex samples expressed at different levels. We found that genetic variants associated with kidney diseases (GWAS) and kidney expression quantitative trait loci were enriched in regulatory DNA regions. By combining GWAS, epigenomic, and chromatin conformation data, we functionally annotated 46 kidney disease genes.

CONCLUSIONS

We demonstrate a powerful approach to functionally connect kidney disease-/trait-associated loci to their target genes by leveraging unique regulatory DNA maps and integrated epigenomic and genetic analysis. This process can be applied to other kidney cell types and will enhance our understanding of genome regulation and its effects on gene expression in kidney disease.