Analysis of receptors responsible for the dysfunction of the human immune system by different viral infections
This article has been Reviewed by the following groups
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
- Evaluated articles (ScreenIT)
Abstract
There are difficulties in creating direct anti-viral drugs for all viruses, including new, suddenly arising infections, such as COVID-19. Therefore, pathogenetic therapy is often used to treat severe viral infections. Despite significant distinctions in the etiopathogenesis of viral diseases, they are often associated with the substantial dysfunction of the immune system. To identify shared mechanisms of immune dysfunction during infection by nine different viruses (cytomegalovirus, Ebstein-Barr virus, human T-cell leukemia virus type 1, Hepatitis B and C viruses, human immunodeficiency virus, Dengue virus, SARS-CoV, and SARS-CoV-2), we applied analysis of corresponding transcription profiles from peripheral blood mononuclear cells (PBMC). As a result, we revealed common pathways, cellular processes, and master regulators for studied viral infections. We found that all nine viral infections cause immune activation, exhaustion, cell proliferation disruption, and increased susceptibility to apoptosis. An application of network analysis allowed us to identify receptors of PBMC that are the proteins at the top of signaling pathways, which may be responsible for the observed transcription changes. The identified relationships between some of them and virus-induced immune disfunction are new, with little or no information in the literature, e.g., receptors for autocrine motility factor, insulin, prolactin, angiotensin II, and immunoglobulin epsilon.
Article activity feed
-
SciScore for 10.1101/2022.01.26.477819: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Collection of transcription datasets: We performed a comprehensive search across two transcriptomics databases: Gene Expression Omnibus (GEO) [30], and ArrayExpress [31] using the following query: “(virus OR viral) AND (lymphocytes OR “B cells” OR “T cells” OR monocytes OR “NK cells” OR PBMC OR “peripheral blood mononuclear” OR neutrophils OR “whole blood”).” We selected datasets containing data on gene transcription in blood cells measured by microarrays or RNA sequencing and obtained from both viral-infected and uninfected people. Gene Expression Omnibussuggested: (Gene Expression …SciScore for 10.1101/2022.01.26.477819: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Collection of transcription datasets: We performed a comprehensive search across two transcriptomics databases: Gene Expression Omnibus (GEO) [30], and ArrayExpress [31] using the following query: “(virus OR viral) AND (lymphocytes OR “B cells” OR “T cells” OR monocytes OR “NK cells” OR PBMC OR “peripheral blood mononuclear” OR neutrophils OR “whole blood”).” We selected datasets containing data on gene transcription in blood cells measured by microarrays or RNA sequencing and obtained from both viral-infected and uninfected people. Gene Expression Omnibussuggested: (Gene Expression Omnibus (GEO, RRID:SCR_005012)ArrayExpresssuggested: (ArrayExpress, RRID:SCR_002964)Genome Enhancer pipeline: To identify differentially regulated genes and master regulators (MRs), we used the Genome Enhancer tool [32] developed by geneXplain GmbH [33]. Genome Enhancer toolsuggested: NonePathway enrichment analysis: To identify KEGG pathways [35] that were differentially regulated in PBMC from a particular viral infection-related group of patients compared to healthy control, we peformed pathway enrichment analysis [36] using the “enrichr” function from the “enrichR” R package [37]. KEGGsuggested: (KEGG, RRID:SCR_012773)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-
