{"title":"仅利用基因组信息预测整个埃希氏菌属的菌株级噬菌体-宿主相互作用","authors":"Baptiste Gaborieau, Hugo Vaysset, Florian Tesson, Inès Charachon, Nicolas Dib, Juliette Bernier, Tanguy Dequidt, Héloïse Georjon, Olivier Clermont, Pascal Hersen, Laurent Debarbieux, Jean-Damien Ricard, Erick Denamur, Aude Bernheim","doi":"10.1038/s41564-024-01832-5","DOIUrl":null,"url":null,"abstract":"Predicting bacteriophage infection of specific bacterial strains promises advancements in phage therapy and microbial ecology. Whether the dynamics of well-established phage–host model systems generalize to the wide diversity of microbes is currently unknown. Here we show that we could accurately predict the outcomes of phage–bacteria interactions at the strain level in natural isolates from the genus Escherichia using only genomic data (area under the receiver operating characteristic curve (AUROC) of 86%). We experimentally established a dataset of interactions between 403 diverse Escherichia strains and 96 phages. Most interactions are explained by adsorption factors as opposed to antiphage systems which play a marginal role. We trained predictive algorithms and pinpoint poorly predicted interactions to direct future research efforts. Finally, we established a pipeline to recommend tailored phage cocktails, demonstrating efficiency on 100 pathogenic E. coli isolates. This work provides quantitative insights into phage–host specificity and supports the use of predictive algorithms in phage therapy. Phage–host interactions are computationally predicted using only genomic information, highlighting future research directions and enabling generation of custom phage cocktails.","PeriodicalId":18992,"journal":{"name":"Nature Microbiology","volume":"9 11","pages":"2847-2861"},"PeriodicalIF":20.5000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of strain level phage–host interactions across the Escherichia genus using only genomic information\",\"authors\":\"Baptiste Gaborieau, Hugo Vaysset, Florian Tesson, Inès Charachon, Nicolas Dib, Juliette Bernier, Tanguy Dequidt, Héloïse Georjon, Olivier Clermont, Pascal Hersen, Laurent Debarbieux, Jean-Damien Ricard, Erick Denamur, Aude Bernheim\",\"doi\":\"10.1038/s41564-024-01832-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Predicting bacteriophage infection of specific bacterial strains promises advancements in phage therapy and microbial ecology. Whether the dynamics of well-established phage–host model systems generalize to the wide diversity of microbes is currently unknown. Here we show that we could accurately predict the outcomes of phage–bacteria interactions at the strain level in natural isolates from the genus Escherichia using only genomic data (area under the receiver operating characteristic curve (AUROC) of 86%). We experimentally established a dataset of interactions between 403 diverse Escherichia strains and 96 phages. Most interactions are explained by adsorption factors as opposed to antiphage systems which play a marginal role. We trained predictive algorithms and pinpoint poorly predicted interactions to direct future research efforts. Finally, we established a pipeline to recommend tailored phage cocktails, demonstrating efficiency on 100 pathogenic E. coli isolates. This work provides quantitative insights into phage–host specificity and supports the use of predictive algorithms in phage therapy. Phage–host interactions are computationally predicted using only genomic information, highlighting future research directions and enabling generation of custom phage cocktails.\",\"PeriodicalId\":18992,\"journal\":{\"name\":\"Nature Microbiology\",\"volume\":\"9 11\",\"pages\":\"2847-2861\"},\"PeriodicalIF\":20.5000,\"publicationDate\":\"2024-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Microbiology\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.nature.com/articles/s41564-024-01832-5\",\"RegionNum\":1,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Microbiology","FirstCategoryId":"99","ListUrlMain":"https://www.nature.com/articles/s41564-024-01832-5","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MICROBIOLOGY","Score":null,"Total":0}
Prediction of strain level phage–host interactions across the Escherichia genus using only genomic information
Predicting bacteriophage infection of specific bacterial strains promises advancements in phage therapy and microbial ecology. Whether the dynamics of well-established phage–host model systems generalize to the wide diversity of microbes is currently unknown. Here we show that we could accurately predict the outcomes of phage–bacteria interactions at the strain level in natural isolates from the genus Escherichia using only genomic data (area under the receiver operating characteristic curve (AUROC) of 86%). We experimentally established a dataset of interactions between 403 diverse Escherichia strains and 96 phages. Most interactions are explained by adsorption factors as opposed to antiphage systems which play a marginal role. We trained predictive algorithms and pinpoint poorly predicted interactions to direct future research efforts. Finally, we established a pipeline to recommend tailored phage cocktails, demonstrating efficiency on 100 pathogenic E. coli isolates. This work provides quantitative insights into phage–host specificity and supports the use of predictive algorithms in phage therapy. Phage–host interactions are computationally predicted using only genomic information, highlighting future research directions and enabling generation of custom phage cocktails.
期刊介绍:
Nature Microbiology aims to cover a comprehensive range of topics related to microorganisms. This includes:
Evolution: The journal is interested in exploring the evolutionary aspects of microorganisms. This may include research on their genetic diversity, adaptation, and speciation over time.
Physiology and cell biology: Nature Microbiology seeks to understand the functions and characteristics of microorganisms at the cellular and physiological levels. This may involve studying their metabolism, growth patterns, and cellular processes.
Interactions: The journal focuses on the interactions microorganisms have with each other, as well as their interactions with hosts or the environment. This encompasses investigations into microbial communities, symbiotic relationships, and microbial responses to different environments.
Societal significance: Nature Microbiology recognizes the societal impact of microorganisms and welcomes studies that explore their practical applications. This may include research on microbial diseases, biotechnology, or environmental remediation.
In summary, Nature Microbiology is interested in research related to the evolution, physiology and cell biology of microorganisms, their interactions, and their societal relevance.