Biology subjects

Menon, R.

Publications and source records attributed to Menon, R..

3 recordsLinked to original sources

bioRxiv2018

An eQTL landscape of kidney tissue in human nephrotic syndrome

Expression quantitative trait loci (eQTL) studies illuminate the genetics of gene expression and, in disease research, can be particularly illuminating when using the tissues directly impacted by the condition. In nephrology, there is a paucity of eQTLs studies of human kidney. Here, we used whole genome sequencing (WGS) and microdissected glomerular (GLOM) & tubulointerstitial (TI) transcriptomes from 187 patients with nephrotic syndrome (NS) to describe the eQTL landscape in these functionally distinct kidney structures.\n\nUsing MatrixEQTL, we performed cis-eQTL analysis on GLOM (n=136) and TI (n=166). We used the Bayesian \"Deterministic Approximation of Posteriors\" (DAP) to fine-map these signals, eQtlBma to discover GLOM-or TI-specific eQTLs, and single cell RNA-Seq data of control kidney tissue to identify cell-type specificity of significant eQTLs. We integrated eQTL data with an IgA Nephropathy (IGAN) GWAS to perform a transcriptome-wide association study (TWAS).\n\nWe discovered 894 GLOM eQTLs and 1767 TI eQTLs at FDR <0.05. 14% and 19% of GLOM & TI eQTLs, respectively, had > 1 independent signal associated with its expression. 12% and 26% of eQTLs were GLOM-specific and TI-specific, respectively. GLOM eQTLs were most significantly enriched in podocyte transcripts and TI eQTLs in proximal tubules. The IGAN TWAS identified significant GLOM & TI genes, primarily at the HLA region.\n\nIn this study of NS patients, we discovered GLOM & TI eQTLs, identified those that were tissue-specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles. These data are publicly available for browsing and download at http://nephqtl.org.

genomics↗

bioRxiv2018

Single-cell analysis of progenitor cell dynamics and lineage specification of the human fetal kidney

The mammalian kidney develops through repetitive and reciprocal interactions between the ureteric bud and the metanephric mesenchyme to give rise to the entire collecting system and the nephrons, respectively. Most of our knowledge of the developmental regulators driving this process has been gained from the study of gene expression and functional genetics in mice and other animal models. In order to shed light on human kidney development, we have used singlecell transcriptomics to characterize gene expression in different cell population, and to study individual cell dynamics and lineage trajectories during development. Single cell transcriptome analyses of 3,865 cells identified 17 clusters of specific cell types as defined by their gene expression profile, including markers of ureteric bud tip- and metanephric mesenchyme-specific progenitors, as well as their intermediate and differentiated lineages including the mature collecting ducts, the renal vesicle and comma- and s-shaped bodies, immature and mature podocytes, proximal tubules, loops of Henle and distal tubules. Other lineages identified include mesangium and cortical and medullary interstitium, endothelial and immune cells as well as hematopoietic cells. Novel markers for these cell types were revealed in the analysis as well as components of key signaling pathways driving renal development in animal models. Altogether, we provide a comprehensive and dynamic gene expression array of the human developing kidney at the single-cell level.

developmental biology↗

bioRxiv2017

Interactions between species introduce spurious associations in microbiome studies

Microbiota contribute to many dimensions of host phenotype, including disease. To link specific microbes to specific phenotypes, microbiome-wide association studies compare microbial abundances between two groups of samples. Abundance differences, however, reflect not only direct associations with the phenotype, but also indirect effects due to microbial interactions. We found that microbial interactions could easily generate a large number of spurious associations that provide no mechanistic insight. Using techniques from statistical physics, we developed a method to remove indirect associations and applied it to the largest dataset on pediatric inflammatory bowel disease. Our method corrected the inflation of p-values in standard association tests and showed that only a small subset of associations is directly linked to the disease. Direct associations had a much higher accuracy in separating cases from controls and pointed to immunomodulation, butyrate production, and the brain-gut axis as important factors in the inflammatory bowel disease.

microbiology↗