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Biology subjects

Amgalan, B.

Publications and source records attributed to Amgalan, B..

2 recordsLinked to original sources

Mutational Signatures as Sensors of Environmental Exposures: Role of Smoking in COVID-19 Vulnerabilities

BackgroundEnvironmental exposures such as smoking are widely recognized risk factors in the emergence of lung diseases including lung cancer and acute respiratory distress syndrome (ARDS). However, the strength of environmental exposures is difficult to measure, making it challenging to understand their impacts. On the other hand, some COVID-19 patients develop ARDS in an unfavorable disease progression and smoking has been suggested as a potential risk factor among others. Yet initial studies on COVID-19 cases reported contradictory results on the effects of smoking on the disease - some suggest that smoking might have a protective effect against it while other studies report an increased risk. A better understanding of how the exposure to smoking and other environmental factors affect biological processes relevant to SARS-CoV-2 infection and unfavorable disease progression is needed. ApproachIn this study, we utilize mutational signatures associated with environmental factors as sensors of their exposure level. Many environmental factors including smoking are mutagenic and leave characteristic patterns of mutations, called mutational signatures, in affected genomes. We postulated that analyzing mutational signatures, combined with gene expression, can shed light on the impact of the mutagenic environmental factors to the biological processes. In particular, we utilized mutational signatures from lung adenocarcinoma (LUAD) data set collected in TCGA to investigate the role of environmental factors in COVID-19 vulnerabilities. Integrating mutational signatures with gene expression in normal tissues and using a pathway level analysis, we examined how the exposure to smoking and other mutagenic environmental factors affects the infectivity of the virus and disease progression. ResultsBy delineating changes associated with smoking in pathway-level gene expression and cell type proportions, our study demonstrates that mutational signatures can be utilized to study the impact of exogenous mutagenic factors on them. Consistent with previous findings, our analysis showed that smoking mutational signature (SBS4) is associated with activation of cytokine-mediated signaling pathways, leading to inflammatory responses. Smoking related changes in cell composition were also observed, including the correlation of SBS4 with the expansion of goblet cells. On the other hand, increased basal cells and decreased ciliated cells in proportion were associated with the strength of a different mutational signature (SBS5), which is present abundantly but not exclusively in smokers. In addition, we found that smoking increases the expression levels of genes that are up-regulated in severe COVID-19 cases. Jointly, these results suggest an unfavorable impact of smoking on the disease progression and also provide novel findings on how smoking impacts biological processes in lung.

systems biology↗

RepairSig: Deconvolution of DNA damage and repaircontributions to the mutational landscape of cancer

Many mutagenic processes leave characteristic imprints on cancer genomes known as mutational signatures. These signatures have been of recent interest regarding their applicability in studying processes shaping the mutational landscape of cancer. In particular, pinpointing the presence of altered DNA repair pathways can have important therapeutic implications. However, mutational signatures of DNA repair deficiencies are often hard to infer. This challenge emerges as a result of deficient DNA repair processes acting by modifying the outcome of other mutagens. Thus, they exhibit non-additive effects that are not depicted by the current paradigm for modeling mutational processes as independent signatures. To close this gap, we present RO_SCPLOWEPAIRC_SCPLOWSO_SCPLOWIGC_SCPLOW, a method that accounts for interactions between DNA damage and repair and is able to uncover unbiased signatures of deficient DNA repair processes. In particular, RO_SCPLOWEPAIRC_SCPLOWSO_SCPLOWIGC_SCPLOW was able to replace three MMR deficiency signatures previously proposed to be active in breast cancer, with just one signature strikingly similar to the experimentally derived signature. As the first method to model interactions between mutagenic processes, RO_SCPLOWEPAIRC_SCPLOWSO_SCPLOWIGC_SCPLOW is an important step towards biologically more realistic modeling of mutational processes in cancer. The source code for RO_SCPLOWEPAIRC_SCPLOWSO_SCPLOWIGC_SCPLOW is publicly available at https://github.com/ncbi/RepairSig.

bioinformatics↗