Phage–microbe interactions are appealing systems to study coevolution, and have also been increasingly emphasized due to their roles in human health, disease, and the development of novel therapeutics. Phage–microbe interactions leave diverse signals in bacterial and phage genomic sequences, defined as phage–host interaction signals (PHISs), which include clustered regularly interspaced short palindromic repeats (CRISPR) targeting, prophage, and protein–protein interaction signals. In the present study, we developed a novel tool phage–host interaction signal detector (PHISDetector) to predict phage–host interactions by detecting and integrating diverse in silico PHISs, and scoring the probability of phage–host interactions using machine learning models based on PHIS features. We evaluated the performance of PHISDetector on multiple benchmark datasets and application cases. When tested on a dataset of 758 annotated phage–host pairs, PHISDetector yields the prediction accuracies of 0.51 and 0.73 at the species and genus levels, respectively, outperforming other phage–host prediction tools. When applied to on 125,842 metagenomic viral contigs (mVCs) derived from 3042 geographically diverse samples, a detection rate of 54.54% could be achieved. Furthermore, PHISDetector could predict infecting phages for 85.6% of 368 multidrug-resistant (MDR) bacteria and 30% of 454 human gut bacteria obtained from the National Institutes of Health (NIH) Human Microbiome Project (HMP). The PHISDetector can be run either as a web server (http://www.microbiome-bigdata.com/PHISDetector/) for general users to study individual inputs or as a stand-alone version (https://github.com/HIT-ImmunologyLab/PHISDetector) to process massive phage contigs from virome studies.
Nineteen families of phages infecting bacteria or archaea are currently recognized by the International Committee on Taxonomy of Viruses (ICTV). Of these, only two have single-stranded DNA genomes, namely Inoviridae and Microvirida e. The distribution, genetic characteristics, and ecological roles of Microviridae remain largely under explored. Here, using viral metagenomics, we investigate the intestinal virome from human and twenty-four species of animals, as well as freshwater samples, containing abundant sequence reads showing similarity to the Microviridae . Eight hundred and sixty complete or near complete Microviridae -related genomes were generated, showing high levels of co-infections and sequence divergence. Sequence comparison and phylogenetic analysis showed that the Microviridae subfamily Gokushovirinae was highly prevalent and that some strains may qualify as new subfamilies. This study significantly augments our knowledge of the genetic diversity, genome evolution, and distribution in animal species of members of the family Microviridae.
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