Construction and Investigation of Competing Endogenous RNA Networks and Candidate Genes Involved in SARS-CoV-2 Infection

Qi, Mingran; Liu, Bin; Li, Shuai; Ni, Zhaohui; Fan, Li

doi:10.2147/ijgm.s335162

Cited by 9 publications

(9 citation statements)

References 54 publications

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“…IFI6 (also known as G1P3, ISG16, or IFI6-16) has been previously described as an IFN-induced gene ( 36 , 37 ), which is induced by infection of many RNA viruses such as influenza ( 38 ), flaviviruses ( 15 , 17 ) and SARS-CoV-2 ( 39 ). To confirm that IFI6 is an ISG in our cell culture systems, epithelial human lung adenocarcinoma-derived A549 cells were treated with poly(I:C), an analog of dsRNA, or with IFNα, for 16 h, and the levels of IFI6 were measured by RT-qPCR, and compared to the levels in mock-treated cells.…”

Section: Resultsmentioning

confidence: 99%

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Interferon alpha inducible protein 6 is a negative regulator of innate immune responses by modulating RIG-I activation

et al. 2023

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Interferons (IFNs), IFN-stimulated genes (ISGs), and inflammatory cytokines mediate innate immune responses, and are essential to establish an antiviral response. Within the innate immune responses, retinoic acid-inducible gene I (RIG-I) is a key sensor of virus infections, mediating the transcriptional induction of IFNs and inflammatory proteins. Nevertheless, since excessive responses could be detrimental to the host, these responses need to be tightly regulated. In this work, we describe, for the first time, how knocking-down or knocking-out the expression of IFN alpha-inducible protein 6 (IFI6) increases IFN, ISG, and pro-inflammatory cytokine expression after the infections with Influenza A Virus (IAV), Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), and Sendai Virus (SeV), or poly(I:C) transfection. We also show how overexpression of IFI6 produces the opposite effect, in vitro and in vivo, indicating that IFI6 negatively modulates the induction of innate immune responses. Knocking-out or knocking-down the expression of IFI6 diminishes the production of infectious IAV and SARS-CoV-2, most likely because of its effect on antiviral responses. Importantly, we report a novel interaction of IFI6 with RIG-I, most likely mediated through binding to RNA, that affects RIG-I activation, providing a molecular mechanism for the effect of IFI6 on negatively regulating innate immunity. Remarkably, these new functions of IFI6 could be targeted to treat diseases associated with an exacerbated induction of innate immune responses and to combat viral infections, such as IAV and SARS-CoV-2.

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Section: Resultsmentioning

confidence: 99%

“…IFI6 is an ISG whose expression is induced by many viruses ( 15 , 17 , 38 , 39 ), such as IAV ( Figure 1C ), and SARS-CoV-2 ( Figure 1D ). Many ISGs display antiviral functions ( 1 ).…”

Section: Discussionmentioning

confidence: 99%

Interferon alpha inducible protein 6 is a negative regulator of innate immune responses by modulating RIG-I activation

et al. 2023

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“…Studies have shown that IFIT2 and IFI44L are strongly induced in bronchoalveolar lavage (BAL) cells and peripheral blood mononuclear cells (PBMCs) of COVID-19 patients, leading to enhance antiviral and immune modulatory functions (Zhu et al 2020 ; Shaath et al 2020 ). Transcriptome analysis of PBMCs also highlighted the potential role of IFI6 in responses to SARS-COV-2 in comparison to other respiratory viruses (Shaath et al 2020 ; Qi et al 2021 ). According to our findings, IL1R2 is a downregulated NP marker for COVID-19 patients.…”

Section: Discussionmentioning

confidence: 99%

Identifying novel host-based diagnostic biomarker panels for COVID-19: a whole-blood/nasopharyngeal transcriptome meta-analysis

Maleknia

Tavassolifar

Mottaghitalab

et al. 2022

Mol Med

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Background Regardless of improvements in controlling the COVID-19 pandemic, the lack of comprehensive insight into SARS-COV-2 pathogenesis is still a sophisticated challenge. In order to deal with this challenge, we utilized advanced bioinformatics and machine learning algorithms to reveal more characteristics of SARS-COV-2 pathogenesis and introduce novel host response-based diagnostic biomarker panels. Methods In the present study, eight published RNA-Seq datasets related to whole-blood (WB) and nasopharyngeal (NP) swab samples of patients with COVID-19, other viral and non-viral acute respiratory illnesses (ARIs), and healthy controls (HCs) were integrated. To define COVID-19 meta-signatures, Gene Ontology and pathway enrichment analyses were applied to compare COVID-19 with other similar diseases. Additionally, CIBERSORTx was executed in WB samples to detect the immune cell landscape. Furthermore, the optimum WB- and NP-based diagnostic biomarkers were identified via all the combinations of 3 to 9 selected features and the 2-phases machine learning (ML) method which implemented k-fold cross validation and independent test set validation. Results The host gene meta-signatures obtained for SARS-COV-2 infection were different in the WB and NP samples. The gene ontology and enrichment results of the WB dataset represented the enhancement in inflammatory host response, cell cycle, and interferon signature in COVID-19 patients. Furthermore, NP samples of COVID-19 in comparison with HC and non-viral ARIs showed the significant upregulation of genes associated with cytokine production and defense response to the virus. In contrast, these pathways in COVID-19 compared to other viral ARIs were strikingly attenuated. Notably, immune cell proportions of WB samples altered in COVID-19 versus HC. Moreover, the optimum WB- and NP-based diagnostic panels after two phases of ML-based validation included 6 and 8 markers with an accuracy of 97% and 88%, respectively. Conclusions Based on the distinct gene expression profiles of WB and NP, our results indicated that SARS-COV-2 function is body-site-specific, although according to the common signature in WB and NP COVID-19 samples versus controls, this virus also induces a global and systematic host response to some extent. We also introduced and validated WB- and NP-based diagnostic biomarkers using ML methods which can be applied as a complementary tool to diagnose the COVID-19 infection from non-COVID cases.

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“…IFI6 is induced by interferon, and the IFI6 protein may be involved in the regulation of cell apoptosis ( Jia et al, 2020 ). Researchers compared and analyzed the transcriptional data of cells with SARS-CoV-2 and other viral infections and found that IFI6 may be a potential target for intervention in COVID-19 ( Qi et al, 2021 ). IFI6 can protect uninfected cells by preventing virus-induced endoplasmic reticulum invagination ( Richardson et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

Huang

Liao

et al. 2022

Front. Mol. Biosci.

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Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4+ T cells, CD8+ T cells, monocytes, dendritic cells, B cells, and natural killer cells. Various immune cells are involved and make up the complex and unique immune system response to COVID-19, providing characteristics that set it apart from other respiratory infectious diseases. In the present study, we identified cell markers for differentiating COVID-19 from common inflammatory responses, non-COVID-19 severe respiratory diseases, and healthy populations based on single-cell profiling of the gene expression of six immune cell types by using Boruta and mRMR feature selection methods. Some features such as IFI44L in B cells, S100A8 in monocytes, and NCR2 in natural killer cells are involved in the innate immune response of COVID-19. Other features such as ZFP36L2 in CD4+ T cells can regulate the inflammatory process of COVID-19. Subsequently, the IFS method was used to determine the best feature subsets and classifiers in the six immune cell types for two classification algorithms. Furthermore, we established the quantitative rules used to distinguish the disease status. The results of this study can provide theoretical support for a more in-depth investigation of COVID-19 pathogenesis and intervention strategies.

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Construction and Investigation of Competing Endogenous RNA Networks and Candidate Genes Involved in SARS-CoV-2 Infection

Cited by 9 publications

References 54 publications

Interferon alpha inducible protein 6 is a negative regulator of innate immune responses by modulating RIG-I activation

Interferon alpha inducible protein 6 is a negative regulator of innate immune responses by modulating RIG-I activation

Identifying novel host-based diagnostic biomarker panels for COVID-19: a whole-blood/nasopharyngeal transcriptome meta-analysis

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

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