Antimicrobial resistance (AMR) is a global crisis for human public health which threatens the effective prevention and control of ever-increasing infectious diseases. The advent of pandrug-resistant bacteria makes most, if not all, available antibiotics invalid. Meanwhile, the pipeline of novel antibiotics development stagnates, which prompts scientists and pharmacists to develop unconventional antimicrobials. Bacteriophage-derived endolysins are cell wall hydrolases which could hydrolyze the peptidoglycan layer from within and outside of bacterial pathogens. With high specificity, rapid action, high efficiency, and low risk of resistance development, endolysins are believed to be among the best alternative therapeutic agents to treat multidrug resistant (MDR) bacteria. As of now, endolysins have been applied to diverse aspects. In this review, we comprehensively introduce the structures and activities of endolysins and summarize the latest application progress of recombinant endolysins in the fields of medical treatment, pathogen diagnosis, food safety, and agriculture.

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/.

Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.

The probiotic potential and mechanisms of Lactobacillus helveticus against Escherichia coli (ETEC) induced diarrhea remain poorly understood, despite its robust physiological traits and long history of safe use. Here, we compared the anti-diarrheal efficacy of four L. helveticus strains (H1-H4) isolated from different geographical origins to unravel the mechanisms of action in ETEC-infected mice. Surprisingly, feeding with L. helveticus strains significantly reduced pro-inflammatory cytokines tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ) and interleukin-6 (IL-6), alleviated jejunal damage, and restored the colon length. Among them, L. helveticus H3 exhibited the best protective effects through upregulation of aquaporin 3 (AQP3) and interleukin-10 (IL-10) and downregulation of heat-stable enterotoxin (ST) and toll-like receptor 4 (TLR4). Notably, ETEC infection disrupted intestinal homeostasis of microbiota by enriching Akkermansia and Escherichia-Shigella and suppressing Lactobacillus, Odoribacter, Alistipes, Ligilactobacillus, and Bifidobacterium; whereas the supplementation with L. helveticus could effectively restore the balance of gut microbiota. Moreover, the strain H3 significantly enhanced the production of several short-chain fatty acids (SCFAs), including butyric, propionic, isobutyric, valeric, isovaleric, and caproic acids, compared to the others. Comparative genome analysis revealed that the strain H3 had more genes that likely contributed to SCFA production, toxin detoxification, and colonization capacity, which support its better efficacy in alleviating ETEC-induced diarrhea. Collectively, our study suggests that L. helveticus alleviates ETEC-induced diarrhea by rebalancing gut microbiota and is a promising probiotic for treating infectious diarrhea.