Biology subjects

Pouyabahar, D.

Publications and source records attributed to Pouyabahar, D..

3 recordsLinked to original sources

bioRxiv2023

Revealing the Grammar of Small RNA Secretion Using Interpretable Machine Learning

Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes 1,2. However, the mechanisms that govern their sorting and secretion are still not well understood. In this study, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequence. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis, and confirmed that primary RNA sequence is a major determinant in small RNA secretion. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins, e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. We used exoCLIP to reveal the RNA interactome of HNRNPA2B1 and RBM24 in extracellular vesicles. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into complex processes such as small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.

systems biology↗

bioRxiv2022

Single cell profiling reveals strain-specific differences in myeloid inflammatory potential in the rat liver

Liver transplantation is currently the only treatment for end-stage liver disease and acute liver failure. Liver transplant rejection is among the most lethal complications of transplantation, and therapeutic development is limited by our lack of a comprehensive understanding of the cellular landscape of the liver. The laboratory rat (Rattus norvegicus), ideal in size as a model for surgical procedures, is a strong platform to study liver biology in the context of liver transplantation. Liver allograft rejection is known to be strain-specific in the rat model, although the transplantation is accepted without rejection in some strains, it leads to acute rejection in others. To shed light on the cellular landscape of the rat liver and build a foundation for strain comparison, we present a comprehensive single-cell transcriptomics map of the healthy rat liver of Lewis and Dark Agouti strains. Using a novel computational pipeline we developed to guide the detailed annotation of our rat liver atlas, we discovered that hepatic myeloid cells have strong Lewis and Dark Agouti strain-specific differences focused on inflammatory signaling pathways. We experimentally validated these strain-specific differences in myeloid inflammatory potential in vitro using intracellular cytokine staining. Our work provides the first examination of the multi-strain healthy rat liver by single cell transcriptomics and uncovers key insights into strain-specific differences in this valuable model animal. SummaryThe laboratory rat (Rattus norvegicus) is a standard model animal for orthotopic liver transplantation. Transplanting a liver from a Dark agouti (DA) to a Lewis (LEW) strain rat leads to transplant rejection and the reverse procedure leads to tolerance. Understanding this strain difference may help explain the cellular drivers of liver allograft rejection post-transplant. This study uses single-cell transcriptomics to better understand the complex cellular composition of the rat liver and unravels cellular and molecular sources of inter-strain hepatic variation. We generated single-cell transcriptomic maps of the livers of healthy DA and LEW rat strains and developed a novel, factor analysis-based bioinformatics pipeline to study data covariates, such as strain and batch. Using this approach, we discovered variations within hepatocyte and myeloid populations that explain how the states of these cells differ between strains in the healthy rat, which may explain why these strains respond differently to liver transplants.

genomics↗

bioRxiv2021

Single-cell profiling of healthy human kidney reveals features of sex-based transcriptional programs and tissue-specific immunity

Maintaining organ homeostasis requires complex functional synergy between distinct cell types, a snapshot of which is glimpsed through the simultaneously broad and granular analysis provided by single-cell atlases. Knowledge of the transcriptional programs underpinning the complex and specialized functions of human kidney cell populations at homeostasis is limited by difficulty accessing healthy, fresh tissue. Here, we present a single-cell perspective of healthy human kidney from 19 living donors, with equal contribution from males and females, profiling the transcriptome of 27677 high-quality cells to map healthy kidney at high resolution. Our sex-balanced dataset revealed sex-based differences in gene expression within proximal tubular cells, specifically, increased anti-oxidant metallothionein genes in females and the predominance of aerobic metabolism-related genes in males. Functional differences in metabolism were confirmed between male and female proximal tubular cells, with male cells exhibiting higher oxidative phosphorylation and higher levels of energy precursor metabolites. Within the immune niche, we identified kidney-specific lymphocyte populations with unique transcriptional profiles indicative of kidney-adapted functions and validated findings by flow cytometry. We observed significant heterogeneity in resident myeloid populations and identified an MRC1+ LYVE1+ FOLR2+ C1QC+ population as the predominant myeloid population in healthy kidney. This study provides a detailed cellular map of healthy human kidney, revealing novel insights into the complexity of renal parenchymal cells and kidney-resident immune populations.

immunology↗