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Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes.
Islam, M Babul; Chowdhury, Utpala Nanda; Nain, Zulkar; Uddin, Shahadat; Ahmed, Mohammad Boshir; Moni, Mohammad Ali.
  • Islam MB; Department of Electrical and Electronic Engineering, University of Rajshahi, Rajshahi, Bangladesh.
  • Chowdhury UN; Department of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh.
  • Nain Z; Department of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh.
  • Uddin S; Complex Systems Research Group & Project Management Program, Faculty of Engineering, The University of Sydney, NSW, 2006, Australia.
  • Ahmed MB; School of Material Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea.
  • Moni MA; Healthy Ageing Theme, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia; WHO Collaborating Centre on eHealth, UNSW Digital Health, School of Public Health and Community Medicine, Faculty of Medicine, UNSW Sydney, NSW, 2052, Australia. Electronic address: m.moni@unsw.edu.au.
Comput Biol Med ; 136: 104668, 2021 09.
Article in English | MEDLINE | ID: covidwho-1322052
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: MicroRNAs / Diabetes Mellitus, Type 2 / COVID-19 Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Comput Biol Med Year: 2021 Document Type: Article Affiliation country: J.compbiomed.2021.104668

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Full text: Available Collection: International databases Database: MEDLINE Main subject: MicroRNAs / Diabetes Mellitus, Type 2 / COVID-19 Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Comput Biol Med Year: 2021 Document Type: Article Affiliation country: J.compbiomed.2021.104668