Biology subjects

Bousounis, P.

Publications and source records attributed to Bousounis, P..

7 recordsLinked to original sources

bioRxiv2024

cGAS-STING is responsible for aging of telomerase deficient zebrafish.

Telomere shortening occurs in multiple tissues throughout aging. When telomeres become critically short, they trigger DNA damage responses and p53 stabilization, leading to apoptosis or replicative senescence. In vitro, cells with short telomeres activate the cGAS-STING innate immune pathway resulting in type I interferon inflammation and senescence. However, the consequences of these events to the organism are not yet understood. Here, we show that sting is responsible for premature aging of telomerase-deficient zebrafish. We generated sting-/- tert-/- double mutants and observed a thorough rescue of tert-/- phenotypes. At the cellular level, lack of cGAS-STING in ter t mutants resulted in reduced senescence, increased cell proliferation, and low inflammation despite similar short telomeres. Critically, absence of sting function resulted in dampening of the DNA damage response and low p53 levels. At the organism level, sting-/- tert-/- zebrafish regained fertility, delayed cachexia, and cancer incidence, resulting in increased healthspan and lifespan of telomerase mutants.

cell biology↗

bioRxiv2023

PD-L1 blockade immunotherapy rewires cancer emergency myelopoiesis

Immune checkpoint blockade (ICB) immunotherapy has revolutionized cancer treatment, demonstrating exceptional clinical responses in a wide range of cancers. Despite the success, a significant proportion of patients still fail to respond, highlighting the existence of unappreciated mechanisms of immunotherapy resistance. Delineating such mechanisms is paramount to minimize immunotherapy failures and optimize the clinical benefit. Herein, we reveal that immunotherapy with PD-L1 blockage antibody (PDL1) in tumour-bearing mice targets the hematopoietic stem and progenitor cells (HSPCs) in the bone marrow (BM), mediating their exit from quiescence and promoting their proliferation. Notably, disruption of the PDL1/PD1 axis induces transcriptomic reprogramming in HSPCs, from both individuals with Hodgkin lymphoma (HL) and tumour-bearing mice shifting towards an inflammatory state. Functionally, transplantation of HSPCs isolated from PDL1-treated tumor-bearing mice exhibited resistance to cancer-associated myelopoiesis as evident by the generation of reduced frequencies of myeloid-derived suppressor cells (MDSCs) compared to cells from control-treated mice. Our findings shed light on unrecognized mechanisms of action of ICB immunotherapy in cancer, which involves targeting of BM-driven HSPCs and reprogramming of emergency myelopoiesis.

immunology↗

bioRxiv2020

SCReadCounts: Estimation of cell-level SNVs from scRNA-seq data

Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAFRNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of celllevel allele expression of germline heterozygous SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel recurrent somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available (https://github.com/HorvathLab/NGS) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source.

bioinformatics↗

bioRxiv2020

scReQTL: an approach to correlate SNVs to gene expression from individual scRNA-seq datasets

Recently, pioneering eQTLs studies on single cell RNA-seq (scRNA-seq) data have revealed new and cell-specific regulatory SNVs. Because eQTLs correlate genotypes and gene expression across multiple individuals, they are confined to SNVs with sufficient population frequency. Here, we present an alternative sc-eQTL approach - scReQTL - wherein we substitute the genotypes with expressed Variant Allele Fraction (VAFRNA) at heterozygous SNV sites. Our approach employs the advantage that, when estimated from multiple cells, VAFRNA can be used to assess effects of rare SNVs in a single individual. ScReQTLs are enriched in known genetic interactions, therefore can be used to identify novel regulatory SNVs.

genomics↗

bioRxiv2019

Estimating allele-specific expression of SNVs from 10x Genomics Single-Cell RNA-Sequencing Data

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10x Genomics platform. We include in the analysis 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), with an average sequencing reads over 120K/cell (more than 4 billion scRNA-seq reads total). High quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimate the expressed Variant Allele Fraction (VAFRNA) from SNV-aware alignments and analyze its variance and distribution (mono- and bi-allelic) at different cutoffs for required minimal number of sequencing reads. Our analysis shows that when assessing SNV loci covered by a minimum of 3 unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at minimum of 10 reads, nearly 90% of the SNVs are bi-allelic. Consistent with single cell studies on RNA velocity and models of transcriptional burst kinetics, we observe a substantially higher rate of monoallelic expression among intronic SNVs, signifying the usefulness of scVAFRNA to assess dynamic cellular processes. Our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3-based library generation protocol of 10x Genomics scRNA-seq data can be highly informative in SNV-based analyses.

genomics↗

bioRxiv2019

RsQTL: correlation of expressed SNVs with splicing using RNA-sequencing data

RsQTL is a tool for identification of splicing quantitative trait loci (sQTLs) from RNA-sequencing (RNA-seq) data by correlating the variant allele fraction at expressed SNV loci in the transcriptome (VAFRNA) with the proportion of molecules spanning local exon-exon junctions at loci with differential intron excision (percent spliced in, PSI). We exemplify the method on sets of RNA-seq data from human tissues obtained though the Genotype-Tissue Expression Project (GTEx). RsQTL does not require matched DNA and can identify a subset of expressed sQTL loci. Due to the dynamic nature of VAFRNA, RsQTL is applicable for the assessment of conditional and dynamic variation-splicing relationships. Availability and implementationhttps://github.com/HorvathLab/RsQTL. Contacthorvatha@gwu.edu or jsein@gwmail.gwu.edu Supplementary InformationRsQTL_Supplementary_Data.zip

bioinformatics↗

bioRxiv2018

ReQTL - an allele-level measure of variation-expression genomic relationships

MotivationBy testing for association of DNA genotypes with gene expression levels, expression quantitative trait locus (eQTL) analyses have been instrumental in understanding how thousands of single nucleotide variants (SNVs) may affect gene expression. As compared to DNA genotypes, RNA genetic variation represents a phenotypic trait that reflects the actual allele content of the studied system. RNA genetic variation can be measured at expressed genome regions, and differs from the DNA genotype in sites subjected to regulatory forces. Therefore, assessment of correlation between RNA genetic variation and gene expression can reveal regulatory genomic relationships in addition to eQTLs.\n\nResultsWe introduce ReQTL, an eQTL modification which substitutes the DNA allele count for the variant allele frequency (VAF) at expressed SNV loci in the transcriptome. We exemplify the method on sets of RNA-sequencing data from human tissues obtained though the Genotype-Tissue Expression Project (GTEx) and demonstrate that ReQTL analyses show consistently high performance and sufficient power to identify both previously known and novel molecular associations. The majority of the SNVs implicated in significant cis-ReQTLs identified by our analysis were previously reported as significant cis-eQTL loci. Notably, trans ReQTL loci in our data were substantially enriched in RNA-editing sites. In summary, ReQTL analyses are computationally feasible and do not require matched DNA data, hence they have a high potential to facilitate the discovery of novel molecular interactions through exploration of the increasingly accessible RNA-sequencing datasets.\n\nAvailability and implementationSample scripts used in our ReQTL analyses are available with the Supplementary Material (ReQTL_sample_code).\n\nContacthorvatha@gwu.edu or lfspurr@gwmail.gwu.edu\n\nSupplementary InformationRe_QTL_Supplementary_Data.zip

genomics↗