Discovering common pathogenetic processes between COVID-19 and diabetes mellitus by differential gene expression pattern analysis

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Abstract

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the newly discovered coronavirus, SARS-CoV-2. Increased severity of COVID-19 has been observed in patients with diabetes mellitus (DM). This study aimed to identify common transcriptional signatures, regulators and pathways between COVID-19 and DM. We have integrated human whole-genome transcriptomic datasets from COVID-19 and DM, followed by functional assessment with gene ontology (GO) and pathway analyses. In peripheral blood mononuclear cells (PBMCs), among the upregulated differentially expressed genes (DEGs), 32 were found to be commonly modulated in COVID-19 and type 2 diabetes (T2D), while 10 DEGs were commonly downregulated. As regards type 1 diabetes (T1D), 21 DEGs were commonly upregulated, and 29 DEGs were commonly downregulated in COVID-19 and T1D. Moreover, 35 DEGs were commonly upregulated in SARS-CoV-2 infected pancreas organoids and T2D islets, while 14 were commonly downregulated. Several GO terms were found in common between COVID-19 and DM. Prediction of the putative transcription factors involved in the upregulation of genes in COVID-19 and DM identified RELA to be implicated in both PBMCs and pancreas. Here, for the first time, we have characterized the biological processes and pathways commonly dysregulated in COVID-19 and DM, which could be in the next future used for the design of personalized treatment of COVID-19 patients suffering from DM as comorbidity.

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limma powers differential expression analyses for RNA-sequencing and microarray studies

Matthew E. Ritchie, Belinda Phipson, Di Wu … (2015)

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

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Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study

Xiaobo Yang, Yuan Yu, Jiqian Xu … (2020)

Summary Background An ongoing outbreak of pneumonia associated with the severe acute respiratory coronavirus 2 (SARS-CoV-2) started in December, 2019, in Wuhan, China. Information about critically ill patients with SARS-CoV-2 infection is scarce. We aimed to describe the clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia. Methods In this single-centered, retrospective, observational study, we enrolled 52 critically ill adult patients with SARS-CoV-2 pneumonia who were admitted to the intensive care unit (ICU) of Wuhan Jin Yin-tan hospital (Wuhan, China) between late December, 2019, and Jan 26, 2020. Demographic data, symptoms, laboratory values, comorbidities, treatments, and clinical outcomes were all collected. Data were compared between survivors and non-survivors. The primary outcome was 28-day mortality, as of Feb 9, 2020. Secondary outcomes included incidence of SARS-CoV-2-related acute respiratory distress syndrome (ARDS) and the proportion of patients requiring mechanical ventilation. Findings Of 710 patients with SARS-CoV-2 pneumonia, 52 critically ill adult patients were included. The mean age of the 52 patients was 59·7 (SD 13·3) years, 35 (67%) were men, 21 (40%) had chronic illness, 51 (98%) had fever. 32 (61·5%) patients had died at 28 days, and the median duration from admission to the intensive care unit (ICU) to death was 7 (IQR 3–11) days for non-survivors. Compared with survivors, non-survivors were older (64·6 years [11·2] vs 51·9 years [12·9]), more likely to develop ARDS (26 [81%] patients vs 9 [45%] patients), and more likely to receive mechanical ventilation (30 [94%] patients vs 7 [35%] patients), either invasively or non-invasively. Most patients had organ function damage, including 35 (67%) with ARDS, 15 (29%) with acute kidney injury, 12 (23%) with cardiac injury, 15 (29%) with liver dysfunction, and one (2%) with pneumothorax. 37 (71%) patients required mechanical ventilation. Hospital-acquired infection occurred in seven (13·5%) patients. Interpretation The mortality of critically ill patients with SARS-CoV-2 pneumonia is considerable. The survival time of the non-survivors is likely to be within 1–2 weeks after ICU admission. Older patients (>65 years) with comorbidities and ARDS are at increased risk of death. The severity of SARS-CoV-2 pneumonia poses great strain on critical care resources in hospitals, especially if they are not adequately staffed or resourced. Funding None.

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Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

Yingyao Zhou, Bin Zhou, Lars Pache … (2019)

A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.

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Author and article information

Contributors

Md Rezanur Rahman:

ORCID: https://orcid.org/0000-0002-8739-8714

Tania Islam

Md Shahjaman

Md Rafiqul Islam

Salvo Danilo Lombardo

Placido Bramanti

Rosella Ciurleo

Alessia Bramanti

Andrey Tchorbanov

Francesco Fisicaro

Paolo Fagone:

ORCID: https://orcid.org/0000-0002-6694-1992

Ferdinando Nicoletti

Manuela Pennisi

Journal

Journal ID (nlm-ta): Brief Bioinform

Journal ID (iso-abbrev): Brief Bioinform

Journal ID (publisher-id): bib

Title: Briefings in Bioinformatics

Publisher: Oxford University Press

ISSN (Print): 1467-5463

ISSN (Electronic): 1477-4054

Publication date (Electronic): 17 July 2021

Publication date PMC-release: 17 July 2021

Electronic Location Identifier: bbab262

Affiliations

Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University , Kushtia, Bangladesh

Department of Biochemistry and Biotechnology, Khwaja Yunus Ali University , Enayetpur, Sirajganj, Bangladesh

Department of Biotechnology and Genetic Engineering, Faculty of Biological Sciences, Islamic University , Kushtia, Bangladesh

Department of Statistics, Begum Rokeya University , Rangpur, Bangladesh

Faculty of Health, Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT) , Brisbane, Australia

Department of Pharmacy, Faculty of Biological Science and Technology, Jashore University of Science and Technology , Jashore, Bangladesh

CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences , Lazarettgasse 14, AKH BT 25.3, A-1090 Vienna, Austria

IRCCS Centro Neurolesi “Bonino-Pulejo”, Via Provinciale Palermo , Contrada Casazza, 98124 Messina, Italy

Laboratory of Experimental Immunology, Institute of Microbiology , Bulgarian Academy of Sciences , Sofia, Bulgaria

National Institute of Immunology , Sofia, Bulgaria

Department of Biomedical and Biotechnological Sciences, University of Catania , 95124 Catania CT, Italy

Author notes

Corresponding author. Paolo Fagone, Department of Biomedical and Biotechnological Sciences, University of Catania, 95124 Catania, Italy. Tel.: +39 095 4781274; E-mail: paolofagone@ 123456yahoo.it

Author information

Md Rezanur Rahman https://orcid.org/0000-0002-8739-8714

Paolo Fagone https://orcid.org/0000-0002-6694-1992

Article

Publisher ID: bbab262

DOI: 10.1093/bib/bbab262

PMC ID: 8344483

PubMed ID: 34260684

SO-VID: 28d2a7e4-c908-4d5d-be56-c24e3b6f6bc6

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date received : 4 March 2021

Date revision received : 28 May 2021

Date accepted : 21 June 2021

Page count

Pages: 12

Funding

Funded by: Centro Neurolesi Bonino-Pulejo;

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ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: covid-19,diabetes mellitus,blood gene expression,transcriptional signatures,molecular pathways

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ScienceOpen disciplines: Bioinformatics & Computational biology

Keywords: covid-19, diabetes mellitus, blood gene expression, transcriptional signatures, molecular pathways

Discovering common pathogenetic processes between COVID-19 and diabetes mellitus by differential gene expression pattern analysis

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Novel Coronavirus Disease COVID-19

Most cited references 63

limma powers differential expression analyses for RNA-sequencing and microarray studies

Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study

Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

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