An Exome-wide Association Study for Type 2 Diabetes-Attributed End-Stage Kidney Disease in African Americans

Application of multiple machine learning approaches to determine key pyroptosis molecules in type 2 diabetes mellitus

Objective

Pyroptosis, a lytic and inflammatory programmed cell death, has been implicated in type 2 diabetes mellitus (T2DM) and its complications. Nonetheless, it remains elusive exactly which pyroptosis molecule exerts an essential role in T2DM, and this study aims to solve such issue.

Methods

Transcriptional profiling datasets of T2DM, i.e., GSE20966, GSE95849, and GSE26168, were acquired. Four machine learning models, namely, random forest, support vector machine, extreme gradient boosting, and generalized linear modeling, were built based on pyroptosis genes. A nomogram of key pyroptosis genes was also generated, and the clinical value was appraised via calibration curves and decision curve analysis. Immune infiltration was inferred utilizing CIBERSORT. Drug-druggable target relationships were acquired from the Drug Gene Interaction Database. Through WGCNA, key pyroptosis-relevant genes were selected.

Results

Most pyroptosis genes exhibited upregulation in T2DM relative to controls, indicating the activity of pyroptosis in T2DM. The SVM model composed of BAK1, CHMP2B, NLRP6, PLCG1, and TIRAP exhibited the best performance in T2DM diagnosis, with AUC = 1. The nomogram can predict the risk of T2DM for clinical practice. NK cells resting exhibited a lower abundance in T2DM versus normal specimens, with a higher abundance of neutrophils. NLRP6 was positively linked with neutrophils. Drugs (keracyanin, 9,10-phenanthrenequinone, diclofenac, phosphomethylphosphonic acid adenosyl ester, acetaminophen, cefixime, aspirin, ustekinumab) potentially targeted the key pyroptosis genes. Additionally, CHMP2B-relevant genes were determined.

Conclusion

Altogether, this work proposes the key pyroptosis genes in T2DM, which might become possible molecules for the management and treatment of T2DM and its complications.

Whole-exome sequencing study identifies four novel gene loci associated with diabetic kidney disease

Diabetic kidney disease (DKD) is recognized as an important public health challenge. However, its genomic mechanisms are poorly understood. To identify rare variants for DKD, we conducted a whole-exome sequencing (WES) study leveraging large cohorts well-phenotyped for chronic kidney disease and diabetes. Our two-stage WES study included 4372 European and African ancestry participants from the Chronic Renal Insufficiency Cohort and Atherosclerosis Risk in Communities studies (stage 1) and 11 487 multi-ancestry Trans-Omics for Precision Medicine participants (stage 2). Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test implemented within our mixed model framework. We identified four novel exome-wide significant DKD-related loci through initiating diabetes. In single-variant analyses, participants carrying a rare, in-frame insertion in the DIS3L2 gene (rs141560952) exhibited a 193-fold increased odds [95% confidence interval (CI): 33.6, 1105] of DKD compared with noncarriers (P = 3.59 × 10-9). Likewise, each copy of a low-frequency KRT6B splice-site variant (rs425827) conferred a 5.31-fold higher odds (95% CI: 3.06, 9.21) of DKD (P = 2.72 × 10-9). Aggregate gene-based analyses further identified ERAP2 (P = 4.03 × 10-8) and NPEPPS (P = 1.51 × 10-7), which are both expressed in the kidney and implicated in renin-angiotensin-aldosterone system modulated immune response. In the largest WES study of DKD, we identified novel rare variant loci attaining exome-wide significance. These findings provide new insights into the molecular mechanisms underlying DKD.

A Custom Target Next-Generation Sequencing 70-Gene Panel and Replication Study to Identify Genetic Markers of Diabetic Kidney Disease

Diabetic kidney disease (DKD) has been pointed out as a prominent cause of chronic and end-stage renal disease (ESRD). There is a genetic predisposition to DKD, although clinically relevant loci are yet to be identified. We utilized a custom target next-generation sequencing 70-gene panel to screen a discovery cohort of 150 controls, DKD and DKD-ESRD patients. Relevant SNPs for the susceptibility and clinical evolution of DKD were replicated in an independent validation cohort of 824 controls and patients. A network analysis aiming to assess the impact of variability along specific pathways was also conducted. Forty-eight SNPs displayed significantly different frequencies in the study groups. Of these, 28 with p-values lower than 0.01 were selected for replication. MYH9 rs710181 was inversely associated with the risk of DKD (OR = 0.52 (0.28–0.97), p = 0.033), whilst SOWAHB rs13140552 and CNDP1 rs4891564 were not carried by cases or controls, respectively (p = 0.044 and 0.023). In addition, the RGMA rs1969589 CC genotype was significantly correlated with lower albumin-to-creatinine ratios in the DKD patients (711.8 ± 113.0 vs. 1375.9 ± 474.1 mg/g for TC/TT; mean difference = 823.5 (84.46–1563.0); p = 0.030). No biological pathway stood out as more significantly affected by genetic variability. Our findings reveal new variants that could be useful as biomarkers of DKD onset and/or evolution.

Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline

Rapid decline of glomerular filtration rate estimated from creatinine (eGFRcrea) is associated with severe clinical endpoints. In contrast to cross-sectionally assessed eGFRcrea, the genetic basis for rapid eGFRcrea decline is largely unknown. To help define this, we meta-analyzed 42 genome-wide association studies from the Chronic Kidney Diseases Genetics Consortium and United Kingdom Biobank to identify genetic loci for rapid eGFRcrea decline. Two definitions of eGFRcrea decline were used: 3 mL/min/1.73m2/year or more ("Rapid3"; encompassing 34,874 cases, 107,090 controls) and eGFRcrea decline 25% or more and eGFRcrea under 60 mL/min/1.73m2 at follow-up among those with eGFRcrea 60 mL/min/1.73m2 or more at baseline ("CKDi25"; encompassing 19,901 cases, 175,244 controls). Seven independent variants were identified across six loci for Rapid3 and/or CKDi25: consisting of five variants at four loci with genome-wide significance (near UMOD-PDILT (2), PRKAG2, WDR72, OR2S2) and two variants among 265 known eGFRcrea variants (near GATM, LARP4B). All these loci were novel for Rapid3 and/or CKDi25 and our bioinformatic follow-up prioritized variants and genes underneath these loci. The OR2S2 locus is novel for any eGFRcrea trait including interesting candidates. For the five genome-wide significant lead variants, we found supporting effects for annual change in blood urea nitrogen or cystatin-based eGFR, but not for GATM or LARP4B. Individuals at high compared to those at low genetic risk (8-14 vs. 0-5 adverse alleles) had a 1.20-fold increased risk of acute kidney injury (95% confidence interval 1.08-1.33). Thus, our identified loci for rapid kidney function decline may help prioritize therapeutic targets and identify mechanisms and individuals at risk for sustained deterioration of kidney function.

Targeted Next-Generation Sequencing Identifies Pathogenic Variants in Diabetic Kidney Disease

INTRODUCTION

Diabetes is the most common cause of chronic kidney disease (CKD). For patients with diabetes and CKD, the underlying cause of their kidney disease is often assumed to be a consequence of their diabetes. Without histopathological confirmation, however, the underlying cause of their disease is unclear. Recent studies have shown that next-generation sequencing (NGS) provides a promising avenue toward uncovering and establishing precise genetic diagnoses in various forms of kidney disease.

METHODS

Here, we set out to investigate the genetic basis of disease in nondiabetic kidney disease (NDKD) and diabetic kidney disease (DKD) patients by performing targeted NGS using a custom panel comprising 345 kidney disease-related genes.

RESULTS

Our analysis identified rare diagnostic variants based on ACMG-AMP guidelines that were consistent with the clinical diagnosis of 19% of the NDKD patients included in this study. Similarly, 22% of DKD patients were found to carry rare pathogenic/likely pathogenic variants in kidney disease-related genes included on our panel. Genetic variants suggestive of NDKD were detected in 3% of the diabetic patients included in this study.

DISCUSSION/CONCLUSION

Our findings suggest that rare variants in kidney disease-related genes in a diabetic background may play a role in the pathogenesis of DKD and NDKD in patients with diabetes.

Integration of single-cell datasets reveals novel transcriptomic signatures of β-cells in human type 2 diabetes

Pancreatic islet β-cell failure is key to the onset and progression of type 2 diabetes (T2D). The advent of single-cell RNA sequencing (scRNA-seq) has opened the possibility to determine transcriptional signatures specifically relevant for T2D at the β-cell level. Yet, applications of this technique have been underwhelming, as three independent studies failed to show shared differentially expressed genes in T2D β-cells. We performed an integrative analysis of the available datasets from these studies to overcome confounding sources of variability and better highlight common T2D β-cell transcriptomic signatures. After removing low-quality transcriptomes, we retained 3046 single cells expressing 27 931 genes. Cells were integrated to attenuate dataset-specific biases, and clustered into cell type groups. In T2D β-cells (n = 801), we found 210 upregulated and 16 downregulated genes, identifying key pathways for T2D pathogenesis, including defective insulin secretion, SREBP signaling and oxidative stress. We also compared these results with previous data of human T2D β-cells from laser capture microdissection and diabetic rat islets, revealing shared β-cell genes. Overall, the present study encourages the pursuit of single β-cell RNA-seq analysis, preventing presently identified sources of variability, to identify transcriptomic changes associated with human T2D and underscores specific traits of dysfunctional β-cells across different models and techniques.

Gene loci associated with insulin secretion in islets from non-diabetic mice

Genetic susceptibility to type 2 diabetes is primarily due to β-cell dysfunction. However, a genetic study to directly interrogate β-cell function ex vivo has never been previously performed. We isolated 233,447 islets from 483 Diversity Outbred (DO) mice maintained on a Western-style diet, and measured insulin secretion in response to a variety of secretagogues. Insulin secretion from DO islets ranged >1,000-fold even though none of the mice were diabetic. The insulin secretory response to each secretagogue had a unique genetic architecture; some of the loci were specific for one condition, whereas others overlapped. Human loci that are syntenic to many of the insulin secretion QTL from mouse are associated with diabetes-related SNPs in human genome-wide association studies. We report on three genes, Ptpn18, Hunk and Zfp148, where the phenotype predictions from the genetic screen were fulfilled in our studies of transgenic mouse models. These three genes encode a non-receptor type protein tyrosine phosphatase, a serine/threonine protein kinase, and a Krϋppel-type zinc-finger transcription factor, respectively. Our results demonstrate that genetic variation in insulin secretion that can lead to type 2 diabetes is discoverable in non-diabetic individuals.

Single-cell transcriptomics unveils gene regulatory network plasticity

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies.

RESULTS

We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer's disease. Using tools from graph theory, we compute an unbiased quantification of a gene's biological relevance and accurately pinpoint key players in organ function and drivers of diseases.

CONCLUSIONS

Our approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis, significantly broadening the biological insights that can be obtained with this leading technology.

Genetic Susceptibility to Chronic Kidney Disease - Some More Pieces for the Heritability Puzzle

Chronic kidney disease (CKD) is a major global health problem with an increasing prevalence partly driven by aging population structure. Both genomic and environmental factors contribute to this complex heterogeneous disease. CKD heritability is estimated to be high (30-75%). Genome-wide association studies (GWAS) and GWAS meta-analyses have identified several genetic loci associated with CKD, including variants in UMOD, SHROOM3, solute carriers, and E3 ubiquitin ligases. However, these genetic markers do not account for all the susceptibility to CKD, and the causal pathways remain incompletely understood; other factors must be contributing to the missing heritability. Less investigated biological factors such as telomere length; mitochondrial proteins, encoded by nuclear genes or specific mitochondrial DNA (mtDNA) encoded genes; structural variants, such as copy number variants (CNVs), insertions, deletions, inversions and translocations are poorly covered and may explain some of the missing heritability. The sex chromosomes, often excluded from GWAS studies, may also help explain gender imbalances in CKD. In this review, we outline recent findings on molecular biomarkers for CKD (telomeres, CNVs, mtDNA variants, sex chromosomes) that typically have received less attention than gene polymorphisms. Shorter telomere length has been associated with renal dysfunction and CKD progression, however, most publications report small numbers of subjects with conflicting findings. CNVs have been linked to congenital anomalies of the kidney and urinary tract, posterior urethral valves, nephronophthisis and immunoglobulin A nephropathy. Information on mtDNA biomarkers for CKD comes primarily from case reports, therefore the data are scarce and diverse. The most consistent finding is the A3243G mutation in the MT-TL1 gene, mainly associated with focal segmental glomerulosclerosis. Only one GWAS has found associations between X-chromosome and renal function (rs12845465 and rs5987107). No loci in the Y-chromosome have reached genome-wide significance. In conclusion, despite the efforts to find the genetic basis of CKD, it remains challenging to explain all of the heritability with currently available methods and datasets. Although additional biomarkers have been investigated in less common suspects such as telomeres, CNVs, mtDNA and sex chromosomes, hidden heritability in CKD remains elusive, and more comprehensive approaches, particularly through the integration of multiple -"omics" data, are needed.