Gene Expression Meta-Analysis Reveals Interferon-Induced Genes Associated With SARS Infection in Lungs

CsMYC2 is involved in the regulation of phenylpropanoid biosynthesis induced by trypsin in cucumber (Cucumis sativus) during storage

Trypsin has a new activity of scavenging superoxide anion and generating hydrogen peroxide. Trypsin can significantly improve the storage quality of C. sativus. To illustrate the mechanism of trypsin-induced resistance in fruits and vegetables, an integrated analysis of widely targeted metabolomics and transcriptomics was carried out. Transcriptomic results showed that 1068 genes highly related to phenylpropanoid biosynthesis gathered in the brown module were obtained by WGCNA. In KEGG analysis, differentially expressed genes (DEGs) were also highly enriched in EIP (Environmental Information Processing) pathways "Plant hormone signal transduction (map04075)" and "MAPK signaling pathway-plant (map04016)". Next, 87 genes were identified as the leading edge by GSEA analysis. So far, CsMYC2 was highlighted as a key transcription factor that regulates phenylpropanoid biosynthesis identified by GSEA and WGCNA. Furthermore, the major route of biosynthesis of phenylpropanoid compounds including coumarins, lignins, chlorogenic acid, flavonoids, and derivatives regulated by trypsin was also illustrated by both transcriptomic and metabolomic data. Results of O2PLS showed that CsMYC2 was positively correlated with Rosmarinic acid-3-O-glucoside, Epigallocatechin, Quercetin-3-O-sophoroside (Baimaside), and so on. Correlation between CsMYC2, phenylpropanoid related genes, and metabolites in C. sativus was illustrated by co-expression networks. Roles of CsMYC2 were further checked in C. sativus by VIGS. The results of this study might give new insight into the exploration of the postharvest resistance mechanism of C. sativus induced by trypsin and provide useful information for the subsequent mining of resistance genes in C. sativus.

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

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.

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

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.

Immune-Related Protein Interaction Network in Severe COVID-19 Patients toward the Identification of Key Proteins and Drug Repurposing

Coronavirus disease 2019 (COVID-19) is still an active global public health issue. Although vaccines and therapeutic options are available, some patients experience severe conditions and need critical care support. Hence, identifying key genes or proteins involved in immune-related severe COVID-19 is necessary to find or develop the targeted therapies. This study proposed a novel construction of an immune-related protein interaction network (IPIN) in severe cases with the use of a network diffusion technique on a human interactome network and transcriptomic data. Enrichment analysis revealed that the IPIN was mainly associated with antiviral, innate immune, apoptosis, cell division, and cell cycle regulation signaling pathways. Twenty-three proteins were identified as key proteins to find associated drugs. Finally, poly (I:C), mitomycin C, decitabine, gemcitabine, hydroxyurea, tamoxifen, and curcumin were the potential drugs interacting with the key proteins to heal severe COVID-19. In conclusion, IPIN can be a good representative network for the immune system that integrates the protein interaction network and transcriptomic data. Thus, the key proteins and target drugs in IPIN help to find a new treatment with the use of existing drugs to treat the disease apart from vaccination and conventional antiviral therapy.