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.

/pdf/identification-of-covid-19-specific-immune-markers-using-a-3tf0xkz8.pdf

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

In December 2019, an outbreak of novel coronavirus pneumonia spread over Wuhan, Hubei Province, China, which then developed into a significant global health public event, giving rise to substantial economic losses. We downloaded throat swab expression profiling data of COVID-19 positive and negative patients from the Gene Expression Omnibus (GEO) database to mine novel diagnostic biomarkers. XGBoost was used to construct the model and select feature genes. Subsequently, we constructed COVID-19 classifiers such as MARS, KNN, SVM, MIL, and RF using machine learning methods. We selected the KNN classifier with the optimal MCC value from these classifiers using the IFS method to identify 24 feature genes. Finally, we used principal component analysis to classify the samples and found that the 24 feature genes could effectively be used to classify COVID-19-positive and negative patients. Additionally, we analyzed the possible biological functions and signaling pathways in which the 24 feature genes were involved by GO and KEGG enrichment analyses. The results demonstrated that these feature genes were primarily enriched in biological functions such as viral transcription and viral gene expression and pathways such as Coronavirus disease-COVID-19. In summary, the 24 feature genes we identified were highly effective in classifying COVID-19 positive and negative patients, which could serve as novel markers for COVID-19.

/pdf/xgboost-based-feature-learning-method-for-mining-covid-19-2yj6egn4.pdf

XGBoost-Based Feature Learning Method for Mining COVID-19 Novel Diagnostic Markers

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.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.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.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. 

/pdf/identifying-novel-host-based-diagnostic-biomarker-panels-for-1ndwf50a.pdf

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

Rapid and robust induction of antiviral genes is essential for the host to clear the invaded viruses. In addition to the IRF3/7-IFN-I-STAT1 signaling axis, the XAF1-IRF1 positive feedback loop synergistically or independently drives the transcription of antiviral genes. ABSTRACT XIAP-associated factor 1 (XAF1) is an interferon (IFN)-stimulated gene (ISG) that enhances IFN-induced apoptosis. However, it is unexplored whether XAF1 is essential for the host fighting against invaded viruses. Here, we find that XAF1 is significantly upregulated in the host cells infected with emerging RNA viruses, including influenza, Zika virus (ZIKV), and SARS-CoV-2. IFN regulatory factor 1 (IRF1), a key transcription factor in immune cells, determines the induction of XAF1 during antiviral immunity. Ectopic expression of XAF1 protects host cells against various RNA viruses independent of apoptosis. Knockout of XAF1 attenuates host antiviral innate immunity in vitro and in vivo, which leads to more severe lung injuries and higher mortality in the influenza infection mouse model. XAF1 stabilizes IRF1 protein by antagonizing the CHIP-mediated degradation of IRF1, thus inducing more antiviral IRF1 target genes, including DDX58, DDX60, MX1, and OAS2. Our study has described a protective role of XAF1 in the host antiviral innate immunity against RNA viruses. We have also elucidated the molecular mechanism that IRF1 and XAF1 form a positive feedback loop to induce rapid and robust antiviral immunity. IMPORTANCE Rapid and robust induction of antiviral genes is essential for the host to clear the invaded viruses. In addition to the IRF3/7-IFN-I-STAT1 signaling axis, the XAF1-IRF1 positive feedback loop synergistically or independently drives the transcription of antiviral genes. Moreover, XAF1 is a sensitive and reliable gene that positively correlates with the viral infection, suggesting that XAF1 is a potential diagnostic marker for viral infectious diseases. In addition to the antitumor role, our study has shown that XAF1 is essential for antiviral immunity. XAF1 is not only a proapoptotic ISG, but it also stabilizes the master transcription factor IRF1 to induce antiviral genes. IRF1 directly binds to the IRF-Es of its target gene promoters and drives their transcriptions, which suggests a unique role of the XAF1-IRF1 loop in antiviral innate immunity, particularly in the host defect of IFN-I signaling such as invertebrates.

XAF1 Protects Host against Emerging RNA Viruses by Stabilizing IRF1-Dependent Antiviral Immunity

Abstract COVID-19 is a global pandemic impacting the daily living of millions. As variants of the virus evolve, a complete comprehension of the disease and drug targets becomes a decisive duty. The Omicron variant, for example, has a notably high transmission rate verified in 155 countries. We performed integrative transcriptomic and network analyses to identify drug targets and diagnostic biomarkers and repurpose FDA-approved drugs for SARS-CoV-2. Upon the enrichment of 464 differentially expressed genes, pathways regulating the host cell cycle were significant. Regulatory and interaction networks featured hsa-mir-93-5p and hsa-mir-17-5p as blood biomarkers while hsa-mir-15b-5p as an antiviral agent. MYB, RRM2, ERG, CENPF, CIT, and TOP2A are potential drug targets for treatment. HMOX1 is suggested as a prognostic biomarker. Enhancing HMOX1 expression by neem plant extract might be a therapeutic alternative. We constructed a drug-gene network for FDA-approved drugs to be repurposed against the infection. The key drugs retrieved were members of anthracyclines, mitotic inhibitors, anti-tumor antibiotics, and CDK1 inhibitors. Additionally, hydroxyquinone and digitoxin are potent TOP2A inhibitors. Hydroxyurea, cytarabine, gemcitabine, sotalol, and amiodarone can also be redirected against COVID-19. The analysis enforced the repositioning of fluorouracil and doxorubicin, especially that they have multiple drug targets, hence less probability of resistance. 

/pdf/sars-cov-2-potential-drugs-drug-targets-and-biomarkers-a-1mrk9rsa.pdf

SARS-CoV-2 potential drugs, drug targets, and biomarkers: a viral-host interaction network-based analysis

Coronavirus disease 2019 (COVID-19) is a global epidemic disease caused by a novel virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing serious adverse effects on human health. In this study, we obtained a blood leukocytes sequencing data set of COVID-19 patients from the GEO database and obtained differentially expressed genes (DEGs). We further analyzed these DEGs by protein-protein interaction analysis and Gene Ontology enrichment analysis and identified the DEGs closely related to SARS-CoV-2 infection. Then, we constructed a six-gene model (comprising IFIT3, OASL, USP18, XAF1, IFI27, and EPSTI1) by logistic regression analysis and calculated the area under the ROC curve (AUC) for the diagnosis of COVID-19. The AUC values of the training group, testing group, and entire group were 0.930, 0.914, and 0.921, respectively. The six genes were highly expressed in patients with COVID-19 and positively correlated with the expression of SARS-CoV-2 invasion-related genes (ACE2, TMPRSS2, CTSB, and CTSL). The risk score calculated by this model was also positively correlated with the expression of TMPRSS2, CTSB, and CTSL, indicating that the six genes were closely related to SARS-CoV-2 infection. In conclusion, we comprehensively analyzed the functions of DEGs in the blood leukocytes of patients with COVID-19 and constructed a six-gene model that may contribute to the development of new diagnostic and therapeutic ideas for COVID-19. Moreover, these six genes may be therapeutic targets for COVID-19.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmv.27093

Yuan Liu

Papers

Genome-wide screening of SARS-CoV-2 infection-related genes based on the blood leukocytes sequencing data set of patients with COVID-19.