Epithelial-mesenchymal transition (EMT) is a critical process for tumor invasion and metastasis. Hypoxia may induce EMT, and upregulated β-catenin expression has been found in various tumors. In this study, we investigate the role of β-catenin in hypoxia-induced EMT in hepatocellular carcinoma (HCC). Induction of EMT in HCC cell lines by hypoxia was confirmed by altered morphology, expression change of EMT-associated markers and enhanced invasion capacity. We showed that hypoxia-induced EMT could be enhanced by addition of recombinant Wnt3a while it was repressed by β-catenin small interfering RNA. An interaction between β-catenin and hypoxia-induced factor-1α (hif-1α) was found, and an underlying competition for β-catenin between hif-1α and T-cell factor-4 was implied. Notably, increased hif-1α activity was accompanied with more significant EMT features. We also showed that the pro-EMT effect of β-catenin in hypoxia was deprived in the absence of hif-1α. Moreover, β-catenin was found to be responsible for the maintenance of viability and proliferation for tumor cells undergoing hypoxia. We further showed a correlation between hif-1α and β-catenin expression, and corresponding expression of EMT-associated markers in human HCC tissues. Our results suggest that Wnt/β-catenin signaling enhances hypoxia-induced EMT in HCC by increasing the EMT-associated activity of hif-1α and preventing tumor cell death.

Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

COVID-19 is mainly associated with respiratory distress syndrome, but a subset of patients often present gastrointestinal (GI) symptoms. Imbalances of gut microbiota have been previously linked to respiratory virus infection. Understanding how the gut–lung axis affects the progression of COVID-19 can provide a novel framework for therapies and management. In this study, we examined the gut microbiota of patients with COVID-19 (n = 47) and compared it to healthy controls (n = 19). Using shotgun metagenomic sequencing, we have identified four microorganisms unique in COVID-19 patients, namely Streptococcus thermophilus, Bacteroides oleiciplenus, Fusobacterium ulcerans, and Prevotella bivia. The abundances of Bacteroides stercoris, B. vulgatus, B. massiliensis, Bifidobacterium longum, Streptococcus thermophilus, Lachnospiraceae bacterium 5163FAA, Prevotella bivia, Erysipelotrichaceae bacterium 6145, and Erysipelotrichaceae bacterium 2244A were enriched in COVID-19 patients, whereas the abundances of Clostridium nexile, Streptococcus salivarius, Coprococcus catus, Eubacterium hallii, Enterobacter aerogenes, and Adlercreutzia equolifaciens were decreased (p < 0.05). The relative abundance of butyrate-producing Roseburia inulinivorans is evidently depleted in COVID-19 patients, while the relative abundances of Paraprevotella sp. and the probiotic Streptococcus thermophilus were increased. We further identified 30 KEGG orthology (KO) modules overrepresented, with 7 increasing and 23 decreasing modules. Notably, 15 optimal microbial markers were identified using the random forest model to have strong diagnostic potential in distinguishing COVID-19. Based on Spearman’s correlation, eight species were associated with eight clinical indices. Moreover, the increased abundance of Bacteroidetes and decreased abundance of Firmicutes were also found across clinical types of COVID-19. Our findings suggest that the alterations of gut microbiota in patients with COVID-19 may influence disease severity. Our COVID-19 classifier, which was cross-regionally verified, provides a proof of concept that a set of microbial species markers can distinguish the presence of COVID-19.

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SARS-CoV-2 is the etiological agent responsible for the ongoing pandemic of coronavirus disease 2019 (COVID-19). The main protease of SARS-CoV-2, 3CLpro, is an attractive target for antiviral inhibitors due to its indispensable role in viral replication and gene expression of viral proteins. The search of compounds that can effectively inhibit the crucial activity of 3CLpro, which results to interference of the virus life cycle, is now widely pursued. Here, we report that epigallocatechin-3-gallate (EGCG), an active ingredient of Chinese herbal medicine (CHM), is a potent inhibitor of 3CLpro with half-maximum inhibitory concentration (IC50) of 0.874 ± 0.005 μM. In the study, we retrospectively analyzed the clinical data of 123 cases of COVID-19 patients, and found three effective Traditional Chinese Medicines (TCM) prescriptions. Multiple strategies were performed to screen potent inhibitors of SARS-CoV-2 3CLpro from the active ingredients of TCMs, including network pharmacology, molecular docking, surface plasmon resonance (SPR) binding assay and fluorescence resonance energy transfer (FRET)-based inhibition assay. The SPR assay showed good interaction between EGCG and 3CLpro with KD ~6.17 μM, suggesting a relatively high affinity of EGCG with SARS-CoV-2 3CLpro. Our results provide critical insights into the mechanism of action of EGCG as a potential therapeutic agent against COVID-19.