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.
Phages are prevalent in diverse environments and play major ecological roles attributed to their tremendous diversity and abundance. Among these viruses, transposable phages (TBPs) are exceptional in terms of their unique lifestyle, especially their replicative transposition. Although several TBPs have been isolated and the life cycle of the representative phage Mu has been extensively studied, the diversity distribution and ecological functions of TBPs on the global scale remain unknown. Here, by mining TBPs from enormous microbial genomes and viromes, we established a TBP genome dataset (TBPGD), that expands the number of accessible TBP genomes 384-fold. TBPs are prevalent in diverse biomes and show great genetic diversity. Based on taxonomic evaluations, we propose the categorization of TBPs into four viral groups, including 11 candidate subfamilies. TBPs infect multiple bacterial phyla, and seem to infect a wider range of hosts than non-TBPs. Diverse auxiliary metabolic genes (AMGs) are identified in the TBP genomes, and genes related to glycoside hydrolases and pyrimidine deoxyribonucleotide biosynthesis are highly enriched. Finally, the influences of TBPs on their hosts are experimentally examined by using the marine bacterium Shewanella psychrophila WP2 and its infecting transposable phage SP2. Collectively, our findings greatly expand the genetic diversity of TBPs, and comprehensively reveal their potential influences in various ecosystems.
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