Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes. (September 2021)

• Record Type: Journal Article
• Authors: Islam, M. Babul; Chowdhury, Utpala Nanda; Nain, Zulkar; Uddin, Shahadat; Ahmed, Mohammad Boshir; Moni, Mohammad Ali
• Is Part Of: Computers in biology and medicine. Volume 136(2021)
• Publication Date: 2021-09
• Subjects: COVID-19 -- SARS-CoV-2 -- Type 2 diabetes -- Differentially expressed genes -- Protein–protein interactions -- Drug molecules; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104668
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This research aims to determine the genetic factors and processes contributing to the synergistic severity of SARS-CoV-2 infection among T2D patients through bioinformatics approaches. We analyzed two sets of transcriptomic data of SARS-CoV-2 infection obtained from lung epithelium cells and PBMCs, and two sets of T2D data from pancreatic islet cells and PBMCs to identify the associated differentially expressed genes (DEGs) followed by their functional enrichment analyses in terms of protein-protein interaction (PPI) to detect hub-proteins and associated comorbidities, transcription factors (TFs), microRNAs (miRNAs) as well as the potential drug candidates. In PPI analysis, four potential hub-proteins (i.e., BIRC3, C3, MME, and IL1B) were identified among 25 DEGs shared between the disease pair. Enrichment analyses using the mutually overlapped DEGs revealed the most prevalent GO and cell signalling pathways, including TNF signalling, cytokine-cytokine receptor interaction, and IL-17 signalling, which are related to cytokine activities. Furthermore, as significant TFs, we identified IRF1, KLF11, FOSL1, and CREB3L1 while miRNAs including miR-1-3p, 34a-5p, 16–5p, 155–5p, 20a-5p, and let-7b-5p were found to be noteworthy. The findings illustrated the significant association between COVID-19 and T2D at the molecular level. These genetic determinants can further be explored for their specific roles in disease progression and therapeutic intervention, while significant pathways can also be studied as molecular checkpoints. Finally, the identified drug candidates may be evaluated for their potency to minimize the severity of COVID-19 patients with pre-existing T2D. Highlights: This study aims to explore the significant genetic contribution of SARS-CoV-2 infections to the mortality and severity of T2D patients. Transcriptomic analysis leveraging microarray and RNA-Seq data stipulated the contributory concordant genes in COVID-19 and T2D. Gene ontology and signalling pathways shared between SARS-CoV-2 and T2D assist to have better insight about the coexistence of the disease pair. The hub genes identified in protein-protein interaction analysis could accelerate the therapeutic development to fight against COVID-19. Gained TFs and miRNAs could be source of molecular check-point while protein-drug interaction offered putative drug candidates for further study.