Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Rowland J

PROVIDER: S-EPMC6390171 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Rowland Joshua J Akbarov Artur A Eales James J Xu Xiaoguang X Dormer John P JP Guo Hui H Denniff Matthew M Jiang Xiao X Ranjzad Parisa P Nazgiewicz Alicja A Prestes Priscilla Ribeiro PR Antczak Andrzej A Szulinska Monika M Wise Ingrid A IA Zukowska-Szczechowska Ewa E Bogdanski Pawel P Woolf Adrian S AS Samani Nilesh J NJ Charchar Fadi J FJ Tomaszewski Maciej M

Kidney international 20190301 3

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 ...[more]

PMID: 30784661

Similar Datasets

Project description:An unrealized potential to understand the genetic basis of aging in humans, is to consider the immense survival advantage of the rare individuals who live 100 years or more. The Longevity Gene Study was initiated in 1998 at the Albert Einstein College of Medicine to investigate longevity genes in a selected population: the "oldest old" Ashkenazi Jews, 95 years of age and older, and their children. The study proved the principle that some of these subjects are endowed with longevity-promoting genotypes. Here we reason that some of the favorable genotypes act as mechanisms that buffer the deleterious effect of age-related disease genes. As a result, the frequency of deleterious genotypes may increase among individuals with extreme lifespan because their protective genotype allows disease-related genes to accumulate. Thus, studies of genotypic frequencies among different age groups can elucidate the genetic determinants and pathways responsible for longevity. Borrowing from evolutionary theory, we present arguments regarding the differential survival via buffering mechanisms and their target age-related disease genes in searching for aging and longevity genes. Using more than 1,200 subjects between the sixth and eleventh decades of life (at least 140 subjects in each group), we corroborate our hypotheses experimentally. We study 66 common allelic site polymorphism in 36 candidate genes on the basis of their phenotype. Among them we have identified a candidate-buffering mechanism and its candidate age-related disease gene target. Previously, the beneficial effect of an advantageous cholesteryl ester transfer protein (CETP-VV) genotype on lipoprotein particle size in association with decreased metabolic and cardiovascular diseases, as well as with better cognitive function, have been demonstrated. We report an additional advantageous effect of the CETP-VV (favorable) genotype in neutralizing the deleterious effects of the lipoprotein(a) (LPA) gene. Finally, using literature-based interaction discovery methods, we use the set of longevity genes, buffering genes, and their age-related target disease genes to construct the underlying subnetwork of interacting genes that is expected to be responsible for longevity. Genome wide, high-throughput hypothesis-free analyses are currently being utilized to elucidate unknown genetic pathways in many model organisms, linking observed phenotypes to their underlying genetic mechanisms. The longevity phenotype and its genetic mechanisms, such as our buffering hypothesis, are similar; thus, the experimental corroboration of our hypothesis provides a proof of concept for the utility of high-throughput methods for elucidating such mechanisms. It also provides a framework for developing strategies to prevent some age-related diseases by intervention at the appropriate level.

Project description:With unknown etiology, scleroderma (SSc) is a multifaceted disease characterized by immune activation, vascular complications, and excessive fibrosis in internal organs. Genetic studies, including candidate gene association studies, genome-wide association studies, and whole-exome sequencing have supported the notion that while genetic susceptibility to SSc appears to be modest, SSc patients are genetically predisposed to this disease. The strongest genetic association for SSc lies within the MHC region, with loci in HLA-DRB1, HLA-DQB1, HLA-DPB1, and HLA-DOA1 being the most replicated. The non-HLA genes associated with SSc are involved in various functions, with the most robust associations including genes for B and T cell activation and innate immunity. Other pathways include genes involved in extracellular matrix deposition, cytokines, and autophagy. Among these genes, IRF5, STAT4, and CD247 were replicated most frequently while SNPs rs35677470 in DNASE1L3, rs5029939 in TNFAIP3, and rs7574685 in STAT4 have the strongest associations with SSc. In addition to genetic predisposition, it became clear that environmental factors and epigenetic influences also contribute to the development of SSc. Epigenetics, which refers to studies that focus on heritable phenotypes resulting from changes in chromatin structure without affecting the DNA sequence, is one of the most rapidly expanding fields in biomedical research. Indeed extensive epigenetic changes have been described in SSc. Alteration in enzymes and mediators involved in DNA methylation and histone modification, as well as dysregulated non-coding RNA levels all contribute to fibrosis, immune dysregulation, and impaired angiogenesis in this disease. Genes that are affected by epigenetic dysregulation include ones involved in autoimmunity, T cell function and regulation, TGFβ pathway, Wnt pathway, extracellular matrix, and transcription factors governing fibrosis and angiogenesis. In this review, we provide a comprehensive overview of the current findings of SSc genetic susceptibility, followed by an extensive description and a systematic review of epigenetic research that has been carried out to date in SSc. We also summarize the therapeutic potential of drugs that affect epigenetic mechanisms, and outline the future prospective of genomics and epigenomics research in SSc.

Project description:BackgroundMultiomics data analysis based on high-throughput sequencing technology has become a hotspot in tumor investigation. The present study aimed to explore prognostic biomarkers via investigating DNA copy number variation (CNV) and methylation variation (MET) data in prostate cancer.MethodsWe obtained the messenger RNA (mRNA) expression, CNV, and methylated data of prostate adenocarcinoma (PRAD) samples via The Cancer Genome Atlas (TCGA)-PRAD cohort. We calculated and assessed the associations between CNV and RNA sequencing (RNA-seq), and between MET and RNA-seq via Pearson correlation coefficients. We then used the "iCluster" package to perform multigroup cluster analysis with CNVcor gene CNV data, METcor gene methylation data, and CNVcor and METcor gene mRNA data. The univariate Cox analysis was used to screen significant hub genes, and multivariate Cox analysis was used to construct risk a model. The nomogram was constructed based on "rms" package, and the immune infiltrating patterns were compared between high- and low-risk groups.ResultsA total of 477 PRAD samples with complete CNV, methylation, mRNA, and matched clinical information were included in our study. A list of 10,073 CNVcor genes and 9841 METcor genes were confirmed with a significance level of P<0.01. We found that CNVcor is more likely to appear on chromosome (chr)8, chr17, and chr10, while METcor is more likely to appear on chr1, chr19, and chr17. Based on the core genes, we finally classified the samples into 4 subtypes, incorporating iC1 (iCluster) (92 samples), iC2 (79 samples), iC3 (165 samples), and iC4 (141 samples). Furthermore, we constructed the prognostic model for PRAD based on the 5 genes (IER3, AOX1, PRKCDBP, UBD, and FBLN5). Nomograms incorporating risk score and other clinical variables were further constructed, and these nomograms exhibited superior predictive ability. We further compared the differential immune infiltrating patterns in 2 risk groups and found significantly low levels of infiltrating cluster of differentiation (CD)8+ T cells in high-risk samples.ConclusionsOur study integrated the multi-omics data to elucidate the molecular features of PRAD and pivotal genes for predicting prognosis.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets