Biology subjects

Shooshtari, P.

Publications and source records attributed to Shooshtari, P..

8 recordsLinked to original sources

bioRxiv2026

Maternal BMI and Placental Transcriptomic Changes: A Meta-Analysis of Gene Expression at the Maternal-Fetal Interface

ObjectiveMaternal body mass index (BMI) is often used as a measure of metabolic status and increased or decreased maternal BMI is associated with a heightened risk of cardiometabolic diseases across generations. The placenta mediates these maternal metabolic cues; however, its genome-wide transcriptional adaptations in response to maternal BMI remain incompletely defined. MethodsTo delineate placental genes, pathways, and interaction clusters whose transcript abundance varies with maternal pre-pregnancy BMI through a genome-wide meta-analysis of human placental RNA-sequencing datasets. Placental RNA-seq reads from four publicly available cohorts (n = 146) were mapped to the GRCh38 reference genome and differentially expressed genes were identified. An independent microarray cohort (n = 19) was re-analysed separately to facilitate cross-platform comparison. Functional enrichment employed GO, KEGG, and STRING protein-interaction resources. ResultsMeta-analysis of 146 RNA-seq samples identified eight genes with genome-wide significance in placentae from underweight pregnancies including inflammatory signaling gene MAP4K1 and metabolic enzyme PSPH, while overweight and obese categories revealed nominally significant differential expression. KEGG analysis demonstrated significant downregulation of oxidative phosphorylation with increasing maternal BMI, and protein-protein interaction networks revealed inflammatory mediators as central nodes in overweight and obese groups. Independent microarray validation corroborated key findings, including consistent downregulation of oxidative phosphorylation in obesity. ConclusionMaternal BMI is associated with placental transcriptomic signatures involving inflammatory, metabolic, and hormonal pathways, with consistent downregulation of oxidative phosphorylation across platforms. This genome-wide meta-analysis provides a reproducible catalogue of BMI-responsive placental transcripts that may contribute to developmental programming of offspring health. Article highlightsO_LIMeta-analysis of 146 RNA-seq samples across four cohorts spanning the BMI spectrum C_LIO_LIEight genes reached genome-wide significance in underweight placentae C_LIO_LIOxidative phosphorylation downregulated with increasing maternal BMI across platforms C_LIO_LIInflammatory mediators emerged as central nodes in obesity-associated PPI networks C_LIO_LIIndependent microarray validation corroborated RNA-seq pathway findings C_LI

bioinformatics↗

bioRxiv2025

B cell subsets have different capacities for phagocytosis and subsequent presentation of antigen to cognate T cells

B cells have been shown to be phagocytic under some circumstances. However, the phagocytic capacity of different B cell subsets and how this is linked to Antigen (Ag) presentation or other functions has not been characterized. To address this, we developed 2 {micro}m phagocytic Ag conjugated bead targets that target phagocytic pathways including the BCR, scavenger, Fc, and complement receptors to study potential pathways by which B cells phagocytose both cognate and non-cognate Ags. We found that while follicular B2 (Fo B), marginal zone, and B1 B cells are highly phagocytic of BCR-engaging targets through their BCR, only peritoneal cavity B1 cells could uptake non-cognate Ag-coated beads or bacteria. Despite this, B1 cells were not effective at presenting Ag to activate cognate T cells or at killing phagocytosed bacteria. Finally, analysis of scRNA-seq data revealed that these differences in phagocytic capacity could not be explained by differential expression of relevant phagocytic receptors, implying that there is likely some form of regulation in place preventing non-cognate Ag uptake by Fo B cells. Our work will help contribute to a better understanding of non-classical Ag uptake mechanisms employed by B cells and their relevance to inflammation.

immunology↗

bioRxiv2024

Integrative Data Analysis to Uncover Transcription Factors Involved in Gene Dysregulation of Nine Autoimmune and Inflammatory Diseases

Autoimmune and inflammatory diseases are a group of > 80 complex diseases caused by loss of immune tolerance for self-antigens. The biological mechanisms of autoimmune diseases are largely unknown, preventing the development of effective treatment options. Integrative analysis of genome-wide association studies and chromatin accessibility data has shown that the risk variants of autoimmune diseases are enriched in open chromatin regions of immune cells, supporting their role in gene regulation. However, we still lack a systematic and unbiased identification of transcription factors involved in disease gene regulation. We hypothesized that for some of the disease-relevant transcription factors, their binding to DNA is affected at multiple genomic sites rather than a single site, and these effects are cell-type specific. We developed a statistical approach to assess enrichment of transcription factors in being affected by disease risk variants at multiple genomic sites. We used genetic association data of nine autoimmune diseases and identified 99% credible set SNPs for each trait. We then integrated the credible SNPs and chromatin accessibility data of 376 samples comprising 35 unique cell types, and employed a probabilistic model to identify the SNPs that are likely to change binding probability of certain transcription factors at specific cell types. Finally, for each transcription factor, we used a statistical test to assess whether the credible SNPs show enrichments in terms of changing the binding probability of that transcription factor at multiple sites. Our analysis resulted in identification of significantly enriched transcription factors and their relevant cell types for each trait. The prioritized immune cell types varied across the diseases. Our analysis proved that our predicted regulatory sites are active enhancers or promoters in the relevant cell types. Additionally, our pathway analysis showed that the majority of the significant biological pathways are immune-related. In summary, our study has identified disease-relevant transcription factors and their relevant cell types, and will facilitate discovering specific gene regulatory mechanisms of autoimmune diseases.

genomics↗

bioRxiv2023

EZH2 deletion does not impact acinar cell regeneration but restricts progression to pancreatic cancer in mice

Enhancer of Zeste Homologue 2 (EZH2) is part of the Polycomb Repressor Complex 2, which induces trimethylation of lysine 27 on histone 3 (H3K27me3) and promotes genes repression. EZH2 is overexpressed in many cancers including pancreatic ductal adenocarcinoma (PDAC). Previous studies in mice attributed both pro-oncogenic and tumor suppressive functions to EZH2. Deletion of the EZH2 enhances de novo KRAS-driven neoplasia following pancreatic injury by preventing acinar cell regeneration, while increased EZH2 expression in PDAC is correlated to poor prognosis, suggesting a context-dependant effect for EZH2 in PDAC progression. In this study, we examined EZH2 function in pre-and early neoplastic stages of PDAC. Using an inducible model to generate deletion of EZH2 only in adult acinar cells (EZH2{Delta}SET), we showed loss of EZH2 activity did not prevent acinar cell regeneration in the absence of oncogenic KRAS (KRASG12D), nor lead to increased PanIN formation in the presence of KRASG12D in adult mice. However, loss of EZH2 did reduce recruitment of inflammatory cells and, when combined with a PDAC model, promoted widespread PDAC progression. Loss of EZH2 function also correlated to remodeling of the tumor microenvironment, which favors cancer cell progression. This study suggests expression of EZH2 in adult acinar cells restricts PDAC initiation and progression by affecting both the tumour microenvironment and acinar cell differentiation.

cancer biology↗

bioRxiv2023

KRASG12D-mediated PanIN progression in mice is affected by gene driving cre recombinase expression

The fundamental biology of pancreatic ductal adenocarcinoma has been greatly impacted by the characterization of genetically modified mouse models that allow temporal and spatial activation of oncogenic KRAS (KRASG12D). The most commonly used model involves targeted insertion of a cre recombinase into the Ptf1a gene. However, this approach disrupts the Ptf1a gene, resulting in haploinsufficiency that likely affects sensitivity to oncogenic KRAS (KRASG12D). The goal of this study was to determine if Ptf1a haploinsufficiency affected the acinar cell response to KRASG12D before and after induction of pancreatic injury. We performed morphological and molecular analysis of three mouse lines that express a tamoxifen-inducible cre recombinase to activate KRASG12D in acinar cells of the pancreas. The cre-recombinase was targeted to the acinar-specific transcription factor genes, Ptf1a and Mist1/Bhlha15, or expressed within a BAC-derived Elastase transgene. Up to two months after tamoxifen induction of KRASG12D, morphological changes were negligible. However, induction of pancreatic injury by cerulein resulted in stark differences in tissue morphology between lines within seven days, which were maintained for at least five weeks after injury. Ptf1acreERT pancreata showed widespread PanIN lesions and fibrosis, while the Mist1creERT and Ela-creERT models showed reduced amounts of pre-neoplastic lesions. RNA-seq analysis prior to inducing injury suggested Ptf1acreERT and Mist1creERT lines have unique profiles of gene expression that predict a differential response to injury. Multiplex analysis of pancreatic tissue confirmed different inflammatory responses between the lines. These findings suggest understanding the mechanisms underlying the differential response to KRASG12D will help in further defining the intrinsic KRAS-driven mechanisms of neoplasia initiation.

cancer biology↗

bioRxiv2020

TMExplorer: A Tumour Microenvironment Single-cell RNAseq Database and Search Tool

Tumour microenvironments (TME) contain a variety of cells including but not limited to stromal fibroblasts, endothelial cells, immune cells, malignant cells, and cells of the tissues of origin, whose interactions likely influence tumour behaviour and response to cancer treatment. The specific composition of the TME can be elucidated using single-cell RNA sequencing (scRNA-seq) by measuring expression profiles of individual cells. Several scRNA-seq datasets from multiple cancer types have been published in recent years, yet we still lack a comprehensive database for the collection and presentation of TME data from these studies in an easily accessible format. We have thus built a database of TME scRNA-seq data, containing 21 TME scRNA-seq datasets from 12 different cancer types. We have also created an R package called TMExplorer, which provides an interface to easily search and access all available datasets and their metadata. Data and metadata are kept in a consistent format across all datasets, with multiple expression formats available depending on the use case. Users can view a table of metadata and select individual datasets or filter them by specific characteristics. Users may also select a specific type of cancer and view all published scRNA-seq data for that cancer type available in our database. Users are provided with an option to save the data in multiple formats in order to view or process it outside of R. Thus, the TMExplorer database and search tool allows for thorough examination of the TME using scRNA-seq in a way that is streamlined and allows for easy integration into already existing scRNA-seq analysis pipelines.

bioinformatics↗

bioRxiv2020

CReSCENT: CanceR Single Cell ExpressioN Toolkit

CReSCENTCanceR Single Cell ExpressioN Toolkit (https://crescent.cloud), is an intuitive and scalable web portal incorporating a containerized pipeline execution engine for standardized analysis of single-cell RNA sequencing (scRNA-seq) data. While scRNA-seq data for tumour specimens are readily generated, subsequent analysis requires high-performance computing infrastructure and user expertise to build analysis pipelines and tailor interpretation for cancer biology. CReSCENT uses public data sets and preconfigured pipelines that are accessible to computational biology non-experts and are user-editable to allow optimization, comparison, and reanalysis for specific experiments. Users can also upload their own scRNA-seq data for analysis and results can be kept private or shared with other users.

bioinformatics↗

bioRxiv2018

OCHROdb: a comprehensive, quality checked database of open chromatin regions from sequencing data

International consortia, including ENCODE, Roadmap Epigenomics, Genomics of Gene Regulation and Blueprint Epigenome have made large-scale datasets of open chromatin regions publicly available. While these datasets are extremely useful for studying mechanisms of gene regulation in disease and cell development, they only identify open chromatin regions in individual samples. A uniform comparison of accessibility of the same regulatory sites across multiple samples is necessary to correlate open chromatin accessibility and expression of target genes across matched cell types. Additionally, although replicate samples are available for majority of cell types, a comprehensive replication-based quality checking of individual regulatory sites is still lacking. We have integrated 828 DNase-I hypersensitive sequencing samples, which we have uniformly processed and then clustered their regulatory regions across all samples. We checked the quality of open-chromatin regions using our replication test. This has resulted in a comprehensive, quality-checked database of Open CHROmatin (OCHROdb) regions for 194 unique human cell types and cell lines which can serve as a reference for gene regulatory studies involving open chromatin. We have made this resource publicly available: users can download the whole database, or query it for their genomic regions of interest and visualize the results in an interactive genome browser.

genomics↗