Evidence map›Paper›PMID 34340124›Full record

ArticleComputers in biology and medicine2021

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

M Babul Islam, Utpala Nanda Chowdhury, Zulkar Nain, Shahadat Uddin, Mohammad Boshir Ahmed, Mohammad Ali Moni

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PubMed Full text at PMC Publisher
In one paragraph

Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.3field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 16 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 5 institutions in 3 countries.

M Babul IslamDepartment of Electrical and Electronic Engineering, University of Rajshahi, Rajshahi, Bangladesh.
Utpala Nanda ChowdhuryDepartment of Computer Science and Engineering, University of Rajshahi, Rajshahi, Bangladesh.
Zulkar NainDepartment of Biotechnology and Genetic Engineering, Islamic University, Kushtia, Bangladesh.
Shahadat UddinComplex Systems Research Group & Project Management Program, Faculty of Engineering, The University of Sydney, NSW, 2006, Australia.
Mohammad Boshir AhmedSchool of Material Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea.
Mohammad Ali MoniHealthy 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.
University of Rajshahi · BDGarvan Institute of Medical Research · AUGwangju Institute of Science and Technology · KRIslamic University · BDThe University of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19Diabetes Mellitus, Type 2MicroRNAsComputational BiologyHumansSARS-CoV-2MicroRNAsCOVID-19Differentially expressed genesDrug moleculesProtein–protein interactionsSARS-CoV-2Type 2 diabetes

Identifiers

PMID34340124
PMCPMC8299293
OpenAlexW3186150863

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.