Biology subjects

Alomran, N.

Publications and source records attributed to Alomran, N..

9 recordsLinked to original sources

bioRxiv2024

TAD hierarchy restricts poised LTR activation and loss of TAD hierarchy promotes LTR co-option in cancer

Transposable elements (TEs) are abundant in the human genome, and they provide the sources for genetic and functional diversity. The regulation of TEs expression and their functional consequences in physiological conditions and cancer development remain to be fully elucidated. Previous studies suggested TEs are repressed by DNA methylation and chromatin modifications. The effect of 3D chromatin topology on TE regulation remains elusive. Here, by integrating transcriptome and 3D genome architecture studies, we showed that haploinsufficient loss of NIPBL selectively activates alternative promoters at the long terminal repeats (LTRs) of the TE subclasses. This activation occurs through the reorganization of topologically associating domain (TAD) hierarchical structures and recruitment of proximal enhancers. These observations indicate that TAD hierarchy restricts transcriptional activation of LTRs that already possess open chromatin features. In cancer, perturbation of the hierarchical chromatin topology can lead to co-option of LTRs as functional alternative promoters in a context-dependent manner and drive aberrant transcriptional activation of novel oncogenes and other divergent transcripts. These data uncovered a new layer of regulatory mechanism of TE expression beyond DNA and chromatin modification in human genome. They also posit the TAD hierarchy dysregulation as a novel mechanism for alternative promoter-mediated oncogene activation and transcriptional diversity in cancer, which may be exploited therapeutically.

cancer biology↗

bioRxiv2022

Exploring the utility of ssDNA aptamers directed against snake venom toxins as new therapeutics for tropical snakebite envenoming

Snakebite is a neglected tropical disease that causes considerable death and disability in the tropical world. Although snakebite can cause a variety of pathologies in victims, haemotoxic effects are particularly common and are typically characterised by haemorrhage and/or venom-induced consumption coagulopathy. Antivenoms are the mainstay therapy for treating the toxic effects of snakebite, but despite saving thousands of lives annually, these therapies are associated with limited cross-snake species efficacy due to venom variation, which ultimately restricts their therapeutic utility to particular geographical regions. In this study, we sought to explore the potential of ssDNA aptamers as toxin-specific inhibitory alternatives to antibodies. As a proof of principle model, we selected snake venom serine protease toxins, which are responsible for contributing to venom-induced coagulopathy following snakebite envenoming, as our target. Using SELEX technology, we selected ssDNA aptamers against recombinantly expressed versions of the fibrinogenolytic SVSPs Ancrod from the venom of Calloselasma rhodostoma and Batroxobin from Bothrops atrox. From the resulting pool of specific ssDNA aptamers directed against each target, we identified candidates that exhibited low nanomolar binding affinities to their targets. Downstream ALISA, fibrinogenolysis, and coagulation profiling experiments demonstrated that the candidate aptamers were able to recognise native and recombinant SVSP toxins and inhibit toxin- and venom-induced prolongation of plasma clotting times and consumption of fibrinogen, with inhibitory potencies highly comparable to commercial polyvalent antivenoms. Our findings demonstrate that rationally selected toxin-specific aptamers can exhibit broad in vitro cross-reactivity against toxins found in different snake venoms and are capable of inhibiting toxins in pathologically relevant in vitro and ex vivo models of venom activity. These data highlight the potential utility of ssDNA aptamers as novel toxin-inhibiting therapeutics of value for tackling snakebite envenoming.

pharmacology and toxicology↗

bioRxiv2022

Exploring the utility of recombinantly expressed snake venom serine protease toxins as immunogens for generating experimental snakebite antivenoms

Snakebite is a neglected tropical disease that causes high rates of global mortality and morbidity. Although snakebite can cause a variety of pathologies in victims, haemotoxic effects are particularly common and are typically characterised by haemorrhage and/or venom-induced consumption coagulopathy. Despite polyclonal antibody-based antivenoms being the mainstay life-saving therapy for snakebite, they are associated with limited cross-snake species efficacy, as there is often extensive toxin variation between snake venoms, including those used as immunogens for antivenom production. This restricts the therapeutic utility of any antivenom to certain geographical regions. In this study, we explored the feasibility of using recombinantly expressed toxins as immunogens to stimulate focused, pathology-specific, antibodies to broadly counteract specific toxins associated with snakebite envenoming. Three snake venom serine proteases (SVSP) toxins, sourced from geographically diverse and medically important viper snake venoms were successfully expressed in HEK293F mammalian cells and used for murine immunisation. Analyses of the resulting antibody responses revealed that ancrod and RVV-V stimulated the strongest immune responses, and that experimental antivenoms directed against these recombinant SVSP toxins, and a mixture of the three different immunogens, extensively recognised and exhibited immunological binding towards a variety of native snake venoms. While the experimental antivenoms showed some reduction in abnormal clotting parameters stimulated by the toxin immunogens and crude venom, specifically reducing the depletion of fibrinogen levels and prolongation of prothrombin times, fibrinogen degradation experiments revealed they broadly protected against venom- and toxin-induced fibrinogenolytic functional activities. Overall, our findings further strengthen the case for the use of recombinant venom toxins as supplemental immunogens to stimulate focused and desirable antibody responses capable of neutralising venom-induced pathological effects, and therefore potentially circumventing some of the limitations associated with current snakebite therapies.

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

GeTallele: a mathematical model and a toolbox for integrative analysis and visualization of DNA and RNA allele frequencies

BackgroundAsymmetric allele expression typically indicates functional and/or structural features associated with the underlying genetic variants. When integrated, RNA and DNA allele frequencies can reveal patterns characteristic of a wide-range of biological traits, including ploidy changes, genome admixture, allele-specific expression and gene-dosage transcriptional response. ResultsTo assess RNA and DNA allele frequencies from matched sequencing datasets, we introduce a method for generating model distributions of variant allele frequencies (VAF) with a given variant read probability. In contrast to other methods, based on whole sequences or single SNV, proposed methodology uses continuous multi-SNV genomic regions. The methodology is implemented in a GeTallele toolbox that provides a suite of functions for integrative analysis, statistical assessment and visualization of Genome and Transcriptome allele frequencies. Using model VAF probabilities, GeTallele allows estimation and comparison of variant read probabilities (VAF distributions) in a sequencing dataset. We demonstrate this functionality across cancer DNA and RNA sequencing datasets. ConclusionBased on our evaluation, variant read probabilities can serve as a dependable indicator to assess gene and chromosomal allele asymmetries and to aid calls of genomic events in matched sequencing RNA and DNA datasets. ContactP.M.Slowinski@exeter.ac.uk

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↗