Differential gene expression profiling reveals potential biomarkers and pharmacological compounds against SARS-CoV-2: Insights from machine learning and bioinformatics approaches

Abstract

The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has created an urgent global situation. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability and disease comorbidities. The pandemic continues to spread worldwide, despite intense efforts to develop multiple vaccines and therapeutic options against COVID-19. However, the precise role of SARS-CoV-2 in the pathophysiology of the nasopharyngeal tract (NT) is still unfathomable. This study utilized machine learning approaches to analyze 22 RNA-seq data from COVID-19 patients (n = 8), recovered individuals (n = 7), and healthy individuals (n = 7) to find disease-related differentially expressed genes (DEGs). We compared dysregulated DEGs to detect critical pathways and gene ontology (GO) connected to COVID-19 comorbidities. We found 1960 and 153 DEG signatures in COVID-19 patients and recovered individuals compared to healthy controls. In COVID-19 patients, the DEG–miRNA, and DEG–transcription factors (TFs) interactions network analysis revealed that E2F1, MAX, EGR1, YY1, and SRF were the highly expressed TFs, whereas hsa-miR-19b, hsa-miR-495, hsa-miR-340, hsa-miR-101, and hsa-miR-19a were the overexpressed miRNAs. Three chemical agents (Valproic Acid, Alfatoxin B1, and Cyclosporine) were abundant in COVID-19 patients and recovered individuals. Mental retardation, mental deficit, intellectual disability, muscle hypotonia, micrognathism, and cleft palate were the significant diseases associated with COVID-19 by sharing DEGs. Finally, the detected DEGs mediated by TFs and miRNA expression indicated that SARS-CoV-2 infection might contribute to various comorbidities. Our results provide the common DEGs between COVID-19 patients and recovered humans, which suggests some crucial insights into the complex interplay between COVID-19 progression and the recovery stage, and offer some suggestions on therapeutic target identification in COVID-19 caused by the SARS-CoV-2.

SARS‐CoV‐2 infection alters the gut microbiome in diabetes patients: A cross‐sectional study from Bangladesh

“…It can be rationally inferred that dysbiosis of the microbiome induced by COVID-19 infection along with T2DM can substantially decrease vital metabolic pathways in the gut, resulting in an increased number of opportunistic microbial communities. 15,48 The present study, although presented a number of remarkable outcomes, was not devoid of limitations. First, the number of patients included in the study is low compared to other studies on gut microbiome.…”

“…It was also revealed that genes coding for synthesis and degradation of ketone bodies, CHO metabolism, amino sugar and nucleotide sugar metabolism, glycolysis and gluconeogenesis, ascorbate and aldarate metabolism, and C5‐branched dibasic acid metabolism were overexpressed in the gut microbiomes of diabetes (CD and D) patients compared to non‐diabetes (CDW) subjects. It can be rationally inferred that dysbiosis of the microbiome induced by COVID‐19 infection along with T2DM can substantially decrease vital metabolic pathways in the gut, resulting in an increased number of opportunistic microbial communities 15,48 …”

Uncovering strain- and age-dependent differences in innate immune response to SARS-CoV-2 infection in nasal epithelia using 10X single-cell sequencing

“…Furthermore, RPL4 was also commonly DTU in infected vs control datasets, which was also found to be DTU in bulk RNA-seq of Caco-2 cells infected with WT SARS-CoV-2 (Chang et al, 2022). RPL4 has been found to be differentially expressed in nasopharyngeal samples of COVID-19 patients vs controls with bulk RNA-seq (Hoque et al, 2022). The protein of this gene has also been shown to be a primary interactor of SARS-CoV-2 proteins (Biji et al, 2021).…”

Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells

“…Several studies have demonstrated the effect of COVID-19 on MT-CO1 expression. In 2022, researchers found low mRNA levels of MT-CO1 in patients with COVID-19 [70], and another article noted that MT-CO1 was downregulated in patients with COVID-19 and recovered individuals [71]. The differential expression of MT-CO1 in vascular endothelial cells due to COVID-19 may contribute to a range of cardiovascular diseases.…”