PTBGRP: predicting phage-bacteria interactions with graph representation learning on microbial heterogeneous information network.

Identifying the potential bacteriophages (phage) candidate to treat bacterial infections plays an essential role in the research of human pathogens. Computational approaches are recognized as a valid way to predict bacteria and target phages. However, most of the current methods only utilize lower-order biological information without considering the higher-order connectivity patterns, which helps to improve the predictive accuracy. Therefore, we developed a novel microbial heterogeneous interaction network (MHIN)-based model called PTBGRP to predict new phages for bacterial hosts. Specifically, PTBGRP first constructs an MHIN by integrating phage-bacteria interaction (PBI) and six bacteria-bacteria interaction networks with their biological attributes. Then, different representation learning methods are deployed to extract higher-level biological features and lower-level topological features from MHIN. Finally, PTBGRP employs a deep neural network as the classifier to predict unknown PBI pairs based on the fused biological information. Experiment results demonstrated that PTBGRP achieves the best performance on the corresponding ESKAPE pathogens and PBI dataset when compared with state-of-art methods. In addition, case studies of Klebsiella pneumoniae and Staphylococcus aureus further indicate that the consideration of rich heterogeneous information enables PTBGRP to accurately predict PBI from a more comprehensive perspective. The webserver of the PTBGRP predictor is freely available at http://120.77.11.78/PTBGRP/.

GSPHI: A novel deep learning model for predicting phage-host interactions via multiple biological information.

Emerging evidence suggests that due to the misuse of antibiotics, bacteriophage (phage) therapy has been recognized as one of the most promising strategies for treating human diseases infected by antibiotic-resistant bacteria. Identification of phage-host interactions (PHIs) can help to explore the mechanisms of bacterial response to phages and provide new insights into effective therapeutic approaches. Compared to conventional wet-lab experiments, computational models for predicting PHIs can not only save time and cost, but also be more efficient and economical. In this study, we developed a deep learning predictive framework called GSPHI to identify potential phage and target bacterium pairs through DNA and protein sequence information. More specifically, GSPHI first initialized the node representations of phages and target bacterial hosts via a natural language processing algorithm. Then a graph embedding algorithm structural deep network embedding (SDNE) was utilized to extract local and global information from the interaction network, and finally, a deep neural network (DNN) was applied to accurately detect the interactions between phages and their bacterial hosts. In the drug-resistant bacteria dataset ESKAPE, GSPHI achieved a prediction accuracy of 86.65 % and AUC of 0.9208 under the 5-fold cross-validation technique, significantly better than other methods. In addition, case studies in Gram-positive and negative bacterial species demonstrated that GSPHI is competent in detecting potential Phage-host interactions. Taken together, these results indicate that GSPHI can provide reasonable candidate sensitive bacteria to phages for biological experiments. The webserver of the GSPHI predictor is freely available at http://120.77.11.78/GSPHI/.

Microbial functional communities and the antibiotic resistome profile in a high-selenium ecosystem.

Enshi City, in the Hubei Province of China, is known as the world capital of selenium with the most abundant selenium resource. An important selenium hyperaccumulator plant, Cardamine violifolia, was found to naturally grow in this high-selenium ecosystem. However, relatively little is known about the impact of the selenium levels on microbial community and functional shifts in C. violifolia rhizosphere. Here, we tested the hypothesis that underground microbial diversity and function vary along a selenium gradient, including antibiotic resistance genes (ARGs). Comprehensive metagenomic analyses, such as taxonomic investigation, functional detection, and ARG annotation, showed that selenium, mercury, cadmium, lead, arsenic, and available phosphorus and potassium were correlated with microbial diversity and function. Thaumarchaeota was exclusively dominant in the highest selenium concentration of mine outcrop, and Rhodanobacter and Nitrospira were predominant in the high-selenium ecosystem. The plant C. violifolia enriched a high concentration of selenium in the rhizosphere compared to those in the bulk soil, and it recruited Variovorax and Polaromonas in its rhizosphere. Microbial abundance showed a trend of increasing first and then decreasing from low to high selenium concentrations. Annotation of ARGs showed that the multidrug resistance genes adeF, mtrA, and poxtA, the aminoglycoside resistance gene rpsL, and the sulfonamide resistant gene sul2 were enriched in the high-selenium system. It was discovered that putative antibiotic resistant bacteria displayed obvious differences in the farmland and the soils with various selenium concentrations, indicating that a high-selenium ecosystem harbors the specific microbes with a higher capacity to enrich or resist selenium, toxic metals, or antibiotics. Taken together, these results reveal the effects of selenium concentration and the selenium hyperaccumulator plant C. violifolia on shaping the microbial functional community and ARGs. Metalloid selenium-inducible antibiotic resistance is worth paying attention to in future.