Biology subjects

Khomtchouk, B. B.

Publications and source records attributed to Khomtchouk, B. B..

8 recordsLinked to original sources

bioRxiv2022

Targeting long non-coding RNA NUDT6 enhances smooth muscle cell survival and limits vascular disease progression

Long non-coding RNAs (lncRNAs) orchestrate various biological processes and regulate the development of cardiovascular diseases. Their potential therapeutic benefit to tackle disease progression has recently been extensively explored. Our study investigates the role of lncRNA Nudix Hydrolase 6 (NUDT6) and its antisense target Fibroblast Growth Factor 2 (FGF2) in two vascular pathologies: abdominal aortic aneurysms (AAA) and carotid artery disease. Using tissue samples from both diseases, we detected a substantial increase of NUDT6, whereas FGF2 was downregulated. Targeting Nudt6 in vivo with antisense oligonucleotides in three murine and one porcine animal models of carotid artery disease and AAA limited disease progression. Restoration of FGF2 upon Nudt6 knockdown improved vessel wall morphology and fibrous cap stability. Overexpression of NUDT6 in vitro impaired smooth muscle cell (SMC) migration, while limiting their proliferation and augmenting apoptosis. By employing RNA pulldown followed by mass spectrometry as well as RNA immunoprecipitation, we identified Cysteine and Glycine Rich Protein 1 (CSRP1) as another direct NUDT6 interaction partner, regulating cell motility and SMC differentiation. Overall, the present study identifies NUDT6 as a well-conserved antisense transcript of FGF2. NUDT6 silencing triggers SMC survival and migration and could serve as a novel RNA-based therapeutic strategy in vascular diseases.

molecular biology↗

bioRxiv2021

Epidermal Growth Factor Receptor Inhibition Prevents Caveolin-1-dependent Calcifying Extracellular Vesicle Biogenesis

Chronic kidney disease (CKD) increases the risk of cardiovascular disease, including vascular calcification, leading to higher mortality. Release of calcifying extracellular vesicles (EVs) by vascular smooth muscle cells (VSMCs) promotes the ectopic mineralization of vessel walls. Caveolin-1 (CAV1) protein plays a key role in genesis of calcifying EVs in VSMCs. Epidermal growth factor receptor (EGFR) co-localizes with and influences the intracellular trafficking of CAV1. Using a diet-induced mouse model of CKD, we measured serum EGFR and assessed the potential of EGFR inhibition to prevent vascular calcification. Mice with CKD developed widespread vascular calcification, which associated with increased serum levels of EGFR. We computationally analyzed 7651 individuals in the Multi-Ethnic Study of Atherosclerosis (MESA) and Framingham cohorts to assess potential correlations between coronary artery calcium and single nucleotide polymorphisms (SNPs) associated with elevated serum levels of EGFR. Individuals in the MESA and Framingham cohorts with SNPs associated with increased serum EGFR exhibit elevated coronary artery calcium. In both the CKD mice and human VSMC culture, EGFR inhibition significantly reduced vascular calcification by mitigating the release of CAV1-positive calcifying EVs. EGFR inhibition also increased bone mineral density in CKD mice. Given that EGFR inhibitors exhibit clinical safety and efficacy in other pathologies, the current data suggest that EGFR may be an ideal target to prevent pathological vascular calcification.

pathology↗

bioRxiv2020

NERO: A Biomedical Named-entity (Recognition) Ontology with a Large, Annotated Corpus Reveals Meaningful Associations Through Text Embedding

Machine reading is essential for unlocking valuable knowledge contained in the millions of existing biomedical documents. Over the last two decades 1,2, the most dramatic advances in machine-reading have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in machine reading methodology and automated knowledge extraction systems in the same way that ImageNet 4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named-entity analysis tool for biomedicine: (a) a new, Named-Entity Recognition Ontology (NERO) developed specifically for describing entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named-entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named-entity recognition automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.

systems biology↗

bioRxiv2020

Codon usage bias levels predict taxonomic identity and genetic composition

In this study, we investigate how an organisms codon usage bias levels can serve as a predictor and classifier of various genomic and evolutionary features across the three kingdoms of life (archaea, bacteria, eukarya). We perform secondary analysis of existing genetic datasets to build several artificial intelligence (AI) and machine learning models trained on over 13,000 organisms that show it is possible to accurately predict an organisms DNA type (nuclear, mitochondrial, chloroplast) and taxonomic identity simply using its genetic code (64 codon usage frequencies). By leveraging advanced AI and machine learning methods to accurately identify evolutionary origins and genetic composition from codon usage patterns, our study suggests that the genetic code can be utilized to train accurate machine learning classifiers of taxonomic and phylogenetic features. Our dataset and analyses are made publicly available on Github and the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Codon+usage) to facilitate open-source reproducibility and community engagement.

genetics↗

bioRxiv2020

HeartBioPortal2.0: new developments and updates for genetic ancestry and cardiometabolic quantitative traits in diverse human populations

Cardiovascular disease (CVD) is the leading cause of death worldwide for both genders and across most racial and ethnic groups. However, different races and ethnicities exhibit different rates of cardiovascular disease and its related cardiorenal and metabolic co-morbidities, suggesting differences in genetic predisposition and risk of onset, as well as socioeconomic and lifestyle factors (diet, exercise, etc.) that act upon an individuals unique underlying genetic background. Here we present HeartBioPortal2.0, a major update to HeartBioPortal, the worlds largest CVD genetics data precision medicine platform for harmonized CVD-relevant genetic variants, which now enables search and analysis of human genetic information related to heart disease across ethnically diverse populations and cardiovascular/renal/metabolic quantitative traits pertinent to CVD pathophysiology. HeartBioPortal2.0 is structured as a cloud-based computing platform and knowledge portal that consolidates a multitude of CVD-relevant next-generation sequencing data modalities into a single powerful query and browsing interface between data and user via a user-friendly web application publicly available to the scientific research community. Since its initial release, HeartBioPortal2.0 has added new cardiovascular/renal/metabolic disease relevant gene expression data as well as genetic association data from numerous large-scale genome-wide association study (GWAS) consortiums such as CARDIoGRAMplusC4D, TOPMed, FinnGen, AFGen, MESA, MEGASTROKE, UK Biobank, CHARGE, Biobank Japan, MyCode, among other studies. In addition, HeartBioPortal2.0 now includes support for quantitative traits and ethnically diverse populations, allowing users to investigate the shared genetic architecture of any gene or its variants across the continuous cardiometabolic spectrum from health (e.g., blood pressure traits) to disease (hypertension), facilitating the understanding of CVD trait genetics that inform health-to-disease transitions and endophenotypes. Custom visualizations in the new and improved user interface (UI), including performance enhancements and new security features such as user authentication collectively re-imagine HeartBioPortals user experience and provide a data commons that co-locates data, storage and computing infrastructure in the context of studying the genetic basis behind the leading cause of global mortality. Database URLhttps://www.heartbioportal.com/

bioinformatics↗

bioRxiv2018

Gaussian-distributed codon frequencies of genomes

DNA encodes protein primary structure using 64 different codons to specify 20 different amino acids and a stop signal. Frequencies of codon occurrence when ordered in descending sequence provide a global characterization of a genomes preference (bias) for using the different codons of the redundant genetic code. Whereas frequency/rank relations have been described by empirical relations, here we propose a statistical model in which two different forms of codon usage co-exist in a genome. We investigate whether such a model can account for the range of codon usages observed in a large set of genomes from different taxa. The differences in frequency/rank relations across these genomes can be expressed in a single parameter, the proportion of the two codon compartments. One compartment uses different codons with weak bias according to a Gaussian distribution of frequency, the other uses different codons with strong bias. In prokaryotic genomes both compartments appear to be present in a wide range of proportions, whereas in eukaryotic genomes the compartment with Gaussian distribution tends to dominate. Codon frequencies that are Gaussian-distributed suggest that many evolutionary conditions are involved in shaping weakly-biased codon usage, whereas strong bias in codon usage suggests dominance of few evolutionary conditions.

evolutionary biology↗

bioRxiv2018

Biochat: a database for natural language processing of Gene Expression Omnibus data

A biological datasets metadata profile (e.g., study description, organism name, sequencing type, etc.) typically contains terse but descriptive textual information that can be used to link it with other similar biological datasets for the purpose of integrating omics data of different types to inform hypotheses and biological questions. Here we present Biochat, a database containing a multi-omics data integration support system to aid in cross-linking Gene Expression Omnibus (GEO) records to each other by metadata similarity through a user-friendly web application. Biochat is publicly available at: http://www.biochat.ai. Biochat source code is hosted at: https://github.com/Bohdan-Khomtchouk/Bio-chat.\n\nDatabase URLhttps://github.com/Bohdan-Khomtchouk/Bio-chat

bioinformatics↗

bioRxiv2017

SUPERmerge: ChIP-Seq Coverage Island Analysis Algorithm For Broad Histone Marks

SUPERmerge is a ChIP-seq read pileup analysis and annotation algorithm for investigating alignment (BAM) files of diffuse histone modification ChIP-seq datasets with broad chromatin domains at a single base pair resolution level. SUPERmerge allows flexible regulation of a variety of read pileup parameters, thereby revealing how read islands aggregate into areas of coverage across the genome and what annotation features they map to within individual biological replicates. SUPERmerge is especially useful for investigating low sample size ChIP-seq experiments in which epigenetic histone modifications (e.g., H3K9me1, H3K27me3) result in inherently broad peaks with a diffuse range of signal enrichment spanning multiple consecutive genomic loci and annotated features.

bioinformatics↗