Genes and Diseases: Insights from Transcriptomics Studies

Kolobkov, Dmitry; Свиридова, Д. А.; Абилев, С. К.; Kuzovlev, Аrtem; Salnikova, Lyubov E.

doi:10.3390/genes13071168

Cited by 6 publications

(6 citation statements)

References 37 publications

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“…2020 ). Essential-for-life genes, which also showed an increased excess of rare HI variants in severe COVID-19, are enriched in genes intolerant to HI variants and disease genes ( Dickinson et al, 2016 ), which are often overexpressed in disease-related tissues and exhibit high levels of network connectivity ( Kolobkov et al, 2022 ). We can hypothesize that essential genes can be mutually regulated with more genes with fewer steps required to influence the core genes.…”

Section: Discussionmentioning

confidence: 99%

“…Our next approach was to analyze the lists of genes associated with human diseases affecting certain organs and systems. We used our previously constructed gene list of disease genes described in detail elsewhere ( Kolobkov et al, 2022 ). Shortly, we generated a set of 9,972 genes from five gene-phenotype databases (OMIM, ORPHANET, DDG2P, DisGeNet and MalaCards) and a report of the IUIS.…”

Section: Methodsmentioning

confidence: 99%

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COVID-19 severity: does the genetic landscape of rare variants matter?

et al. 2023

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Rare variants affecting host defense against pathogens may be involved in COVID-19 severity, but most rare variants are not expected to have a major impact on the course of COVID-19. We hypothesized that the accumulation of weak effects of many rare functional variants throughout the exome may contribute to the overall risk in patients with severe disease. This assumption is consistent with the omnigenic model of the relationship between genetic and phenotypic variation in complex traits, according to which association signals tend to spread across most of the genome through gene regulatory networks from genes outside the major pathways to disease-related genes. We performed whole-exome sequencing and compared the burden of rare variants in 57 patients with severe and 29 patients with mild/moderate COVID-19. At the whole-exome level, we observed an excess of rare, predominantly high-impact (HI) variants in the group with severe COVID-19. Restriction to genes intolerant to HI or damaging missense variants increased enrichment for these classes of variants. Among various sets of genes, an increased signal of rare HI variants was demonstrated predominantly for primary immunodeficiency genes and the entire set of genes associated with immune diseases, as well as for genes associated with respiratory diseases. We advocate taking the ideas of the omnigenic model into account in COVID-19 studies.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

COVID-19 severity: does the genetic landscape of rare variants matter?

et al. 2023

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“…One of the main differences between core and peripheral genes in our study was the significantly lower number of tissue-specific genes among peripheral genes. It has been shown that disease-causing genes are often tissue-specific and, in a healthy state, are expressed at a higher level in those tissues that are affected in pathology [ 33 , 53 , 54 ]. In this context, one of the possible explanations for why biologically important peripheral genes from the near-core FPs remain peripheral is their low tissue specificity.…”

Section: Discussionmentioning

confidence: 99%

“…We next compared these three sets of genes for the number of genes that may be biologically important in relation to the development and course of acute infection. As biologically important, we considered the following gene groups: haploinsufficient [31], essential for life [32], intolerant to loss-offunction variants and intolerant to missense variants (https://storage.googleapis.com/ gnomadpublic/release/2.1.1/constraint/gnomad.v2.1.1.lof_metrics.by_gene.txt.bgz (accessed on 21 June 2022)), linked to SARS-CoV-2 infection and/or COVID-19 disease from the GENCODE project (https://www.gencodegenes.org/human/covid19_genes.html# (accessed on 12 May 2023)), and immune tissue-specific [33]. Other tissue-specific or nonspecific genes were included in the analysis for the comparison with immune tissuespecific genes.…”

Section: Theoretical Phase: Gene List Constructionmentioning

confidence: 99%

Rare Variants in Primary Immunodeficiency Genes and Their Functional Partners in Severe COVID-19

Khadzhieva,

Kolobkov,

Kashatnikova

et al. 2023

Biomolecules

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The development of severe COVID-19, which is a complex multisystem disease, is thought to be associated with many genes whose action is modulated by numerous environmental and genetic factors. In this study, we focused on the ideas of the omnigenic model of heritability of complex traits, which assumes that a small number of core genes and a large pool of peripheral genes expressed in disease-relevant tissues contribute to the genetics of complex traits through interconnected networks. We hypothesized that primary immunodeficiency disease (PID) genes may be considered as core genes in severe COVID-19, and their functional partners (FPs) from protein–protein interaction networks may be considered as peripheral near-core genes. We used whole-exome sequencing data from patients aged ≤ 45 years with severe (n = 9) and non-severe COVID-19 (n = 11), and assessed the cumulative contribution of rare high-impact variants to disease severity. In patients with severe COVID-19, an excess of rare high-impact variants was observed at the whole-exome level, but maximal association signals were detected for PID + FP gene subsets among the genes intolerant to LoF variants, haploinsufficient and essential. Our exploratory study may serve as a model for new directions in the research of host genetics in severe COVID-19.

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“…High-throughput technologies continue to produce vast quantities of molecular data, be it genomic, transcriptomic, proteomic, or otherwise. These different omics data help to reveal a complex, interconnected cellular landscape, and the analysis of omics data can highlight specific molecular features that may be implicated in a disease or biological condition [ 1 – 3 ]. In parallel with the proliferation of omics data, there exist large and expanding databases containing protein–protein interactions (PPI) [ 4 – 6 ].…”

Section: Introductionmentioning

confidence: 99%

AMEND: active module identification using experimental data and network diffusion

2023

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Background Molecular interaction networks have become an important tool in providing context to the results of various omics experiments. For example, by integrating transcriptomic data and protein–protein interaction (PPI) networks, one can better understand how the altered expression of several genes are related with one another. The challenge then becomes how to determine, in the context of the interaction network, the subset(s) of genes that best captures the main mechanisms underlying the experimental conditions. Different algorithms have been developed to address this challenge, each with specific biological questions in mind. One emerging area of interest is to determine which genes are equivalently or inversely changed between different experiments. The equivalent change index (ECI) is a recently proposed metric that measures the extent to which a gene is equivalently or inversely regulated between two experiments. The goal of this work is to develop an algorithm that makes use of the ECI and powerful network analysis techniques to identify a connected subset of genes that are highly relevant to the experimental conditions. Results To address the above goal, we developed a method called Active Module identification using Experimental data and Network Diffusion (AMEND). The AMEND algorithm is designed to find a subset of connected genes in a PPI network that have large experimental values. It makes use of random walk with restart to create gene weights, and a heuristic solution to the Maximum-weight Connected Subgraph problem using these weights. This is performed iteratively until an optimal subnetwork (i.e., active module) is found. AMEND was compared to two current methods, NetCore and DOMINO, using two gene expression datasets. Conclusion The AMEND algorithm is an effective, fast, and easy-to-use method for identifying network-based active modules. It returned connected subnetworks with the largest median ECI by magnitude, capturing distinct but related functional groups of genes. Code is freely available at https://github.com/samboyd0/AMEND.

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Genes and Diseases: Insights from Transcriptomics Studies

Cited by 6 publications

References 37 publications

COVID-19 severity: does the genetic landscape of rare variants matter?

COVID-19 severity: does the genetic landscape of rare variants matter?

Rare Variants in Primary Immunodeficiency Genes and Their Functional Partners in Severe COVID-19

AMEND: active module identification using experimental data and network diffusion

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