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Identification of Differentially Expressed Genes and Protein-Protein Interaction in Patients With COVID-19 and
Fahim Alam Nobel1, Mohammad Kamruzzaman1, Mohammad Asaduzzaman2
1Biochemistry and Molecular Biology, Mawlana Bhashani Science and Technology University, Tangail, BGD.
Insights
This study reveals key gene expression differences in patients with both COVID-19 and diabetes, identifying potential therapeutic targets. Understanding these molecular links can improve outcomes for vulnerable diabetic populations.
Area of Science:
- Molecular Biology
- Genomics
- Bioinformatics
Background:
- The COVID-19 pandemic has disproportionately affected individuals with pre-existing conditions, notably diabetes.
- A strong correlation exists between severe COVID-19 outcomes and diabetes, necessitating deeper molecular investigation.
Purpose of the Study:
- To analyze transcriptome data from COVID-19 and diabetic peripheral neuropathy patients.
- To identify common differentially expressed genes (DEGs) and understand their molecular interactions and regulatory networks.
- To propose potential therapeutic drug molecules for identified mutual DEGs.
Main Methods:
- Differential gene expression analysis using R programming language.
- Functional annotation using Gene Ontology (GO), KEGG, Bio-Planet, Reactome, and Wiki pathways.
- Protein-protein interaction (PPI) network construction and topological analysis.
- Gene regulatory network (GRN) exploration.
Main Results:
- Identification of overlapping DEGs between COVID-19 and diabetic peripheral neuropathy cohorts.
- Construction of a PPI network revealing key gene modules and hub genes.
- Functional enrichment analysis highlighting pathways involved in the molecular interplay.
- Suggestion of potential drug candidates targeting identified mutual DEGs.
Conclusions:
- The study elucidates molecular mechanisms underlying COVID-19 in diabetic patients.
- Identified hub genes and pathways offer insights into disease pathogenesis.
- Potential therapeutic targets and drug molecules were proposed for improved patient outcomes.
Abstract:
The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact globally, resulting in a higher death toll and persistent health issues for survivors, particularly those with pre-existing medical conditions. Numerous studies have demonstrated a strong correlation between catastrophic COVID-19 results and diabetes. To gain deeper insights, we analysed the transcriptome dataset from COVID-19 and diabetic peripheral neuropathic patients. Using the R programming language, differentially expressed genes (DEGs) were identified and classified based on up and down regulations. The overlaps of DEGs were then explored between these groups. Functional annotation of those common DEGs was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Bio-Planet, Reactome, and Wiki pathways. A protein-protein interaction (PPI) network was created with bioinformatics tools to understand molecular interactions. Through topological analysis of the PPI network, we determined hub gene modules and explored gene regulatory networks (GRN). Furthermore, the study extended to suggesting potential drug molecules for the identified mutual DEG based on the comprehensive analysis. These approaches may contribute to understanding the molecular intricacies of COVID-19 in diabetic peripheral neuropathy patients through insights into potential therapeutic interventions.
