Biology subjects

SAHA, S.

Publications and source records attributed to SAHA, S..

3 recordsLinked to original sources

bioRxiv2022

DHFR metabolic activity controls neurogenic transitions in the developing Human and mouse neocortex

One-carbon/folate (1C) metabolism supplies methyl groups required for DNA and histone methylation, and is involved in the maintenance of self-renewal in stem cells. Dihydrofolate reductase (DHFR), a key enzyme in 1C metabolism, is highly expressed in Human and mouse neural progenitors at the early stages of neocortical development. Here, we investigated the role of DHFR in the developing neocortex and report that reducing its activity in Human cerebral organoids and mouse embryonic neocortex accelerates indirect neurogenesis, a hallmark of mammalian brain evolution, thereby affecting neuronal composition of the neocortex. Further, we show that decreasing DHFR activity in neural progenitors leads to a reduction in One-carbon/folate metabolites and correlates with modifications of H3K4me3 methylation. Our findings reveal an unanticipated role for DHFR in controlling specific steps of neocortex development and indicate that variations in 1C metabolic cues impact cell fate transitions.

developmental biology↗

bioRxiv2022

Dose-dependent disruption of hepatic zonation by 2,3,7,8-tetrachlorodibenzo-p-dioxin in mice: integration of single-nuclei RNA sequencing and spatial transcriptomics.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) dose-dependently induces the development of hepatic fat accumulation and inflammation with fibrosis in mice initially in the portal region. Conversely, differential gene and protein expression is first detected in the central region. To further investigate cell-specific and spatially resolved dose-dependent changes in gene expression elicited by TCDD, single-nuclei RNA sequencing and spatial transcriptomics were used for livers of male mice gavaged with TCDD every 4 days for 28 days. The proportion of 11 cell (sub)types across 131,613 nuclei dose-dependently changed with 68% of all portal and central hepatocyte nuclei in control mice being overtaken by macrophages following TCDD treatment. We identified 368 (portal fibroblasts) to 1,339 (macrophages) differentially expressed genes. Spatial analyses revealed initial loss of portal identity that eventually spanned the entire liver lobule with increasing dose. Induction of R-spondin 3 (Rspo3) and pericentral Apc, suggested dysregulation of the Wnt/{beta}-catenin signaling cascade in zonally resolved steatosis. Collectively, the integrated results suggest disruption of zonation contributes to the pattern of TCDD-elicited NAFLD pathologies. SYNOPSIS O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY Single-nuclei RNA sequencing (snRNAseq) and spatial transcriptomics were integrated to investigate cell-specific and spatially resolved dose-dependent changes elicited by TCDD. We show that TCDD causes a loss of zonal characteristics that disrupts spatially defined metabolic functions. - Dose-dependent analyses show higher responsiveness of central hepatocytes despite hepatotoxicity occurring initially in the portal region. - Integration of snRNAseq and spatial transcriptomics demonstrates a loss of hepatocytes with portal characteristics. - TCDD disrupted spatially resolved expression of {beta}-catenin signaling members that are critical in maintaining liver zonation. - Spatial transcriptomics and snRNAseq shows induction of R-spondin3 from nonparenchymal cells which serve as cue for the {beta}-catenin pathway.

pharmacology and toxicology↗

bioRxiv2021

Multiview Graph Learning for single-cell RNA sequencing data

Characterizing the underlying topology of gene regulatory networks is one of the fundamental problems of systems biology. Ongoing developments in high throughput sequencing technologies has made it possible to capture the expression of thousands of genes at the single cell resolution. However, inherent cellular heterogeneity and high sparsity of the single cell datasets render void the application of regular Gaussian assumptions for constructing gene regulatory networks. Additionally, most algorithms aimed at single cell gene regulatory network reconstruction, estimate a single network ignoring group-level (cell-type) information present within the datasets. To better characterize single cell gene regulatory networks under different but related conditions we propose the joint estimation of multiple networks using multiview graph learning (mvGL). The proposed method is developed based on recent works in graph signal processing (GSP) for graph learning, where graph signals are assumed to be smooth over the unknown graph structure. Graphs corresponding to the different datasets are regularized to be similar to each other through a learned consensus graph. We further kernelize mvGL with the kernel selected to suit the structure of single cell data. An efficient algorithm based on prox-linear block coordinate descent is used to optimize mvGL. We study the performance of mvGL using synthetic data generated with a diverse set of parameters. We further show that mvGL successfully identifies well-established regulators in a mouse embryonic stem cell differentiation study and a cancer clinical study of medulloblastoma.

genomics↗