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Transcriptome and Machine Learning Analysis of the Impact of COVID-19 on Mitochondria and Multiorgan Damage

Yu-Yu Chang, An-Chi Wei.

Abstract

The effects of coronavirus disease 2019 (COVID-19) primarily concern the respiratory tract and lungs; however, studies have shown that all organs are susceptible to infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 may involve multiorgan damage from direct viral invasion through angiotensin-converting enzyme 2 (ACE2), through inflammatory cytokine storms, or through other secondary pathways.

Introduction

Patients with coronavirus disease 2019 (COVID-19) experience various respiratory issues. Acute respiratory distress syndrome (ARDS) due to COVID-19 pneumonia is the primary cause of mortality and long-term lung damage. Although the respiratory system is most commonly affected in people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus can impact any organ in the body.

Materials and Methods:

Bioinformatics and machine learning tools were used to analyze multiple publicly available RNA-Seq sample sets from clinical samples. NetworkAnalyst is a comprehensive gene expression profiling and web visualization analysis Ingenuity Pathway Analysis (IPA) provides analysis and development tools for genomics, proteomics, drug toxicology, and metabolic and regulatory pathway studies DAVID (Database for Annotation, Visualization and Integrated Discovery) is a web-based tool for functional evaluation of the gene expression data ClueGO is a Cytoscape plug-in for deciphering functionally grouped gene ontology and pathway annotation networks GSEA (Gene Set Enrichment Analysis) is applied to assess the distribution trend of genes in a specific gene set arranged in a gene table based on their correlation to the phenotype to determine its contribution to the phenotype.

Discussion

Machine learning is often applied for prediction or classification. Here, we employ machine learning in a reverse sense: we first formulated a hypothesis and then used the data that met the hypothesis to perform machine learning. If the accuracy of the prediction was high, the hypothesis had a high probability of being correct in terms of logical inference, which would provide an interpretation for the results obtained through machine learning.

Citation: Chang Y-Y, Wei A-C (2024) Transcriptome and machine learning analysis of the impact of COVID-19 on mitochondria and multiorgan damage. PLoS ONE 19(1): e0297664. https://doi.org/10.1371/journal.pone.0297664

Editor: Bhanwar Lal Puniya, University of Nebraska-Lincoln, UNITED STATES

Received: August 3, 2023; Accepted: January 9, 2024; Published: January 31, 2024.

Copyright: © 2024 Chang, Wei. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All RNA-seq files are downloaded from the NCBI Gene Expression Omnibus (GEO) database (accession numbers GSE152075, and GSE163151, GSE157103, GSE169241, GSE152641).

Funding: This work was supported by the Center for Advanced Computing and Imaging in Biomedicine (NTU-112L900701) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

Competing interests: The authors have declared that no competing interests exist.

 

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