Biology subjects

Oza, V. H.

Publications and source records attributed to Oza, V. H..

8 recordsLinked to original sources

bioRxiv2026

Barrier Immune Memory is Promoted by Intestinal Epithelial Cell Presentation of Injected Bacterial Antigens

The contributions of antigen compartmentalization to recognition differences between CD4 and CD8 T cells have long been appreciated, but little is known of how subcellular localization of different antigens expressed by a single pathogen impacts T cell immunity. By tracking a clonal CD4 T cell response to its cognate epitope shuttled between different virulence proteins of the enteropathogenic bacterium, Citrobacter rodentium (Cr), we find a remarkable bias in the magnitude and quality of the response contingent on whether antigen remains bacterially associated or is introduced into intestinal epithelial cells colonized by the bacterium. Only proteins injected into the cytosol of colonocytes via the type 3 secretion system (T3SS) of Cr were found to recruit robust antigen-specific T cell responses to the infected mucosa and give rise to CD4 resident memory T (Trm) cells that populate the mucosal epithelium--and this required direct presentation of these antigens by infected epithelial cells. Single-cell transcriptomic analyses revealed that sustained, bidirectional epithelial-T cell communication was required both to elicit epithelial barrier-protective T cell help and to promote transcriptional networks that program a tissue-residency rather than central memory fate. These results establish a central role for antigen presentation by non-professional APCs in controlling memory fate decisions by CD4 T cells, with important implications for development of successful mucosal vaccines.

immunology↗

bioRxiv2023

Altered Glia-Neuron Communication in Alzheimer's Disease Affects WNT, p53, and NFkB Signaling Determined by snRNA-seq

BackgroundAlzheimers disease is the most common cause of dementia and is characterized by amyloid-{beta} plaques, tau neurofibrillary tangles, and neuronal loss. Although neuronal loss is a primary hallmark of Alzheimers disease, it is known that non-neuronal cell populations are ultimately responsible for maintaining brain homeostasis and neuronal health through neuron-glia and glial cell crosstalk. Many signaling pathways have been proposed to be dysregulated in Alzheimers disease, including WNT, TGF{beta}, p53, mTOR, NFkB, and Pi3k/Akt signaling. Here, we predict altered cell-cell communication between glia and neurons. MethodsUsing public snRNA-sequencing data generated from postmortem human prefrontal cortex, we predicted altered cell-cell communication between glia (astrocytes, microglia, oligodendrocytes, and oligodendrocyte progenitor cells) and neurons (excitatory and inhibitory). We confirmed interactions in a second and third independent orthogonal dataset. We determined cell-type-specificity using Jaccard Similarity Index and investigated the downstream effects of altered interactions in inhibitory neurons through gene expression and transcription factor activity analyses of signaling mediators. Finally, we determined changes in pathway activity in inhibitory neurons. ResultsCell-cell communication between glia and neurons is altered in Alzheimers disease in a cell-type-specific manner. As expected, ligands are more cell-type-specific than receptors and targets. We identified ligand-receptor pairs in three independent datasets and found involvement of the Alzheimers disease risk genes APP and APOE across datasets. Most of the signaling mediators of these interactions were not differentially expressed, however, the mediators that are also transcription factors had differential activity between AD and control. Namely, MYC and TP53, which are associated with WNT and p53 signaling, respectively, had decreased TF activity in Alzheimers disease, along with decreased WNT and p53 pathway activity in inhibitory neurons. Additionally, inhibitory neurons had both increased NFkB signaling pathway activity and increased TF activity of NFIL3, an NFkB signaling-associated transcription factor. ConclusionsCell-cell communication between glia and neurons in Alzheimers disease is altered in a cell-type-specific manner involving Alzheimers disease risk genes. Signaling mediators had altered transcription factor activity suggesting altered glia-neuron interactions may dysregulate signaling pathways including WNT, p53, and NFkB in inhibitory neurons.

genomics↗

bioRxiv2023

Cell-type-specific gene expression and regulation in the cerebral cortex and kidney of atypical Setbp1S858R Schinzel Giedion Syndrome mice

Schinzel Giedion Syndrome (SGS) is an ultra-rare autosomal dominant Mendelian disease presenting with abnormalities spanning multiple organ systems. The most notable phenotypes involve severe developmental delay, progressive brain atrophy, and drug-resistant seizures. SGS is caused by spontaneous variants in SETBP1, which encodes for the epigenetic hub SETBP1 transcription factor (TF). SETBP1 variants causing classical SGS cluster at the degron, disrupting SETBP1 protein degradation resulting in toxic accumulation, while those located outside cause milder atypical SGS. Due to the multi-system phenotype, we evaluated gene expression and regulatory programs altered in atypical SGS by snRNA-seq of cerebral cortex and kidney of Setbp1S858R heterozygous mice (corresponds to the human likely pathogenic SETBP1S867R variant) compared to matched wild-type mice by constructing cell-type-specific regulatory networks. Setbp1 was differentially expressed in excitatory neurons, but known SETBP1 targets were differentially expressed and regulated in many cell types. Our findings suggest molecular drivers underlying neurodevelopmental phenotypes in classical SGS also drive atypical SGS, persist after birth, and are present in the kidney. Our results indicate SETBP1s role as an epigenetic hub leads to cell-type-specific differences in TF activity, gene targeting, and regulatory rewiring. This research provides a framework for investigating cell-type-specific variant impact on gene expression and regulation.

genomics↗

bioRxiv2023

Inferring chromosomal instability from copy number aberrations as a measure of chromosomal instability across human cancers

BackgroundCancer is a complex disease that is the second leading cause of death in the United States. Despite research efforts, the ability to manage cancer and select optimal therapeutic responses for each patient remains elusive. Chromosomal instability (CIN) is primarily a product of segregation errors wherein one or many chromosomes, in part or whole, vary in number. CIN is an enabling characteristic of cancer, contributes to tumor-cell heterogeneity, and plays a crucial role in the multistep tumorigenesis process, especially in tumor growth and initiation and in response to treatment. AimsMultiple studies have reported different metrics for analyzing copy number aberrations as surrogates of CIN from DNA copy number variation data. However, these metrics differ in how they are calculated with respect to the type of variation, the magnitude of change, and the inclusion of breakpoints. Here we compared metrics capturing CIN as either numerical aberrations, structural aberrations, or a combination of the two across 33 cancer data sets from The Cancer Genome Atlas (TCGA). Methods and resultsUsing CIN inferred by methods in the CINmetrics R package, we evaluated how six copy number CIN surrogates compared across TCGA cohorts by assessing each across tumor types, as well as how they associate with tumor stage, metastasis, and nodal involvement, and with respect to patient sex. ConclusionsWe found that the tumor type impacts how well any two given CIN metrics correlate. While we also identified overlap between metrics regarding their association with clinical characteristics and patient sex, there was not complete agreement between metrics. We identified several cases where only one CIN metric was significantly associated with a clinical characteristic or patient sex for a given tumor type. Therefore, caution should be used when describing CIN based on a given metric or comparing it to other studies.

genomics↗

bioRxiv2023

CoSIA: an R Bioconductor package for CrOss Species Investigation and Analysis

SummaryHigh throughput sequencing technologies have enabled cross-species comparative transcriptomic studies; however, there are numerous challenges for these studies due to biological and technical factors. We developed CoSIA (Cross-Species Investigation and Analysis), an Bioconductor R package and Shiny app that provides an alternative framework for cross-species transcriptomic comparison of non-diseased wild-type RNA sequencing gene expression data from Bgee across tissues and species (human, mouse, rat, zebrafish, fly, and nematode) through visualization of variability, diversity, and specificity metrics. Availability and Implementationhttps://github.com/lasseignelab/CoSIA ContactBrittany Lasseigne (bnp0001@uab.edu) Supplementary informationSee Supplementary Files

bioinformatics↗

bioRxiv2023

Signature reversion of three disease-associated gene signatures prioritizes cancer drug repurposing candidates

Drug repurposing is promising because approving a drug for a new indication requires fewer resources than approving a new drug. Signature reversion detects drug perturbations most inversely related to the disease-associated gene signature to identify drugs that may reverse that signature. We assessed the performance and biological relevance of three approaches for constructing disease-associated gene signatures (i.e, limma, DESeq2, and MultiPLIER) and prioritized the resulting drug repurposing candidates for four low-survival human cancers. Our results were enriched for candidates that had been used in clinical trials or performed well in the PRISM drug screen. Additionally, we found that pamidronate and nimodipine, drugs predicted to be efficacious against the brain tumor glioblastoma (GBM), inhibited the growth of a GBM cell line and cells isolated from a patient derived xenograft (PDX). Our results demonstrate that by applying multiple disease-associated gene signature methods, we prioritized several drug repurposing candidates for low-survival cancers. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=155 HEIGHT=200 SRC="FIGDIR/small/532074v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1c5d402org.highwire.dtl.DTLVardef@e1dc86org.highwire.dtl.DTLVardef@54e6feorg.highwire.dtl.DTLVardef@96856_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2022

Prioritized polycystic kidney disease drug targets and repurposing candidates from pre-cystic and cystic mouse model gene expression reversion

Autosomal dominant polycystic kidney disease (ADPKD) is one of the most prevalent monogenic human diseases. It is mostly caused by pathogenic variants in PKD1 or PKD2 genes that encode interacting transmembrane proteins polycystin-1 (PC1) and polycystin-2 (PC2). Among many pathogenic processes described in ADPKD, those associated with cAMP signaling, inflammation, and metabolic reprogramming appear to regulate the disease manifestations. Tolvaptan, a vasopressin receptor-2 antagonist that regulates cAMP pathway, is the only FDA-approved ADPKD therapeutic. Tolvaptan reduces renal cyst growth and kidney function loss, but it is not tolerated by many patients and is associated with idiosyncratic liver toxicity. Therefore, additional therapeutic options for ADPKD treatment are needed. As drug repurposing of FDA-approved drug candidates can significantly decrease the time and cost associated with traditional drug discovery, we used the computational approach signature reversion to detect inversely related drug response gene expression signatures from the Library of Integrated Network-Based Cellular Signatures (LINCS) database and identified compounds predicted to reverse disease-associated transcriptomic signatures in three publicly available Pkd2 kidney transcriptomic data sets of mouse ADPKD models. We focused on a pre-cystic model for signature reversion, as it was less impacted by confounding secondary disease mechanisms in ADPKD, and then compared the resulting candidates target differential expression in the two cystic mouse models. We further prioritized these drug candidates based on their known mechanism of action, FDA status, targets, and by functional enrichment analysis. With this in-silico approach, we prioritized 29 unique drug targets differentially expressed in Pkd2 ADPKD cystic models and 16 prioritized drug repurposing candidates that target them, including bromocriptine and mirtazapine, which can be further tested in-vitro and in-vivo. Collectively, these indicate drug targets and repurposing candidates that may effectively treat pre-cystic as well as cystic ADPKD. O_FIG O_LINKSMALLFIG WIDTH=110 HEIGHT=200 SRC="FIGDIR/small/518863v2_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@18af02org.highwire.dtl.DTLVardef@89d45corg.highwire.dtl.DTLVardef@d4fffdorg.highwire.dtl.DTLVardef@1f244b3_HPS_FORMAT_FIGEXP M_FIG Graphical abstract of the study created with Biorender.com. C_FIG

genomics↗

bioRxiv2021

CINmetrics: An R package for chromosomal instability analysis

Genomic instability is an important hallmark of cancer and more recently has been identified in others like neurodegenrative diseases. Chromosomal instability, as a measure of genomic instability, has been used to characterize clinical and biological phenotypes associated with these diseases by measuring structural and numerical chromosomal alterations. There have been multiple chromosomal instability scores developed across many studies in the literature; however, these scores have not been compared because of the lack of a single tool available to calculate and facilitate these various metrics. Here, we provide an R package CINmetrics, that calculates six different chromosomal instability scores and allows direct comparison between them. We also demonstrate how these scores differ by applying CINmetrics to breast cancer data from The Cancer Genome Atlas (TCGA). The package is available on CRAN at https://cran.r-project.org/package=CINmetrics and on github at https://github.com/lasseignelab/CINmetrics.

cancer biology↗