Prediction of SARS-CoV Interaction with Host Proteins during Lung Aging Reveals a Potential Role for TRIB3 in COVID-19

Moraes, Diogo de; Paiva, Brunno Vivone Buquete; Cury, Sarah Santiloni; Ludwig, Raissa G.; Araújo, João Pessoa; Mori, Marcelo A.; Carvalho, Robson Francisco

doi:10.14336/ad.2020.1112

Cited by 15 publications

(12 citation statements)

References 45 publications

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“…In this module, we also identified several hub-high traffic genes that were crucial in regulating the host immune response during COVID-19. These hub-high traffic genes included CDKN2A (308,309), OASL (282,310), CCL5 (311)(312)(313)(314)(315)(316), KIF2C (4), and CD8A (317). Among them, the CCL5 hub-high traffic gene has the most important role in regulating the inflammatory response and the host immune system; however, its excessive expression can amplify inflammatory responses toward immunopathology (e.g., cytokine storm) (311,314).…”

Section: Midnightblue Modulementioning

confidence: 99%

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

et al. 2021

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BackgroundThe recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.MethodsRNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.ResultsBased on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19’s main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.ConclusionThis study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

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Section: Midnightblue Modulementioning

confidence: 99%

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

et al. 2021

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“…P-HIPSTer is a broad protein catalog of the virus-human interactions upon structural information with an experimental validation rate of approximately 76% [ 12 ]. Although this interactome is a good predictor of different SARS-CoV strains, our research has used P-HIPSTer to identify proteins potentially interacting with SARS-CoV-2 in other functional tissues [ 20 , 21 ]. More recently, a multi-omics study revealed significant cellular interactions related to the perturbations of SARS-CoV-2 and SARS-CoV at different levels [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Aging whole blood transcriptome reveals candidate genes for SARS-CoV-2-related vascular and immune alterations

et al. 2021

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“…In a study with isolated VERO E6 cell lines expressing TMPRSS2, Matsuyama et al [19] have demonstrated its high susceptibility to SARS-CoV-2 infection, suggesting that the infection is enhanced by the expression of this gene. In other way, the lack of gene expression may also influence the susceptibility of SARS-CoV-2 infection, as argued by Moraes et al [20], who suggests that a decreased expression of the gene tribbles homolog 3 (TRIB3) with age in lung cells of male patients is associated with their vulnerability to develop severe COVID-19, since its protein predicted role of interaction with the nucleocapsid protein and the RNA-dependent RNA polymerase of the Human Coronavirus, would prevent the infection.…”

Section: Brazilian Journal Of Health Reviewmentioning

confidence: 96%

Patients with genetic disorders as risk group for COVID-19

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Santos

2020

BJHR

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The coronavirus disease (COVID-19) pandemic has seriously affected the global population and has raised concerns about the effect of this new disease on individuals with genetic disorders, many of which have a systemic effect, including impairment of the respiratory system. A lack of published literature about the consequences of SARS-CoV-2 infection in genetic disorders is observed, in this sense, the present review analyses the current knowledge about COVID-19 and its impacts on patients with genetic disorders, also genetic variants as factors of susceptibility and possible risks and preventive measures to those patients, highlighting the role of the restriction to social contact in prevention.

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Prediction of SARS-CoV Interaction with Host Proteins during Lung Aging Reveals a Potential Role for TRIB3 in COVID-19

Cited by 15 publications

References 45 publications

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Aging whole blood transcriptome reveals candidate genes for SARS-CoV-2-related vascular and immune alterations

Patients with genetic disorders as risk group for COVID-19

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