Stephen Kanyerezi, Patricia Nabisubi, Grace Kebirungi, Ivan Sserwadda, Benson R. Kidenya, Daudi Jjingo, Gerald Mboowa
{"title":"Metagenomics insights into the microbial resistome and virulome composition of Kampala’s wastewater","authors":"Stephen Kanyerezi, Patricia Nabisubi, Grace Kebirungi, Ivan Sserwadda, Benson R. Kidenya, Daudi Jjingo, Gerald Mboowa","doi":"10.12688/openresafrica.15040.1","DOIUrl":null,"url":null,"abstract":"Background Antimicrobial-resistant (AMR) infections represent a major global health threat, causing approximately 700,000 deaths each year directly due to AMR-related issues worldwide. In Africa, 42.6% of countries lack sufficient data on AMR, highlighting a crucial gap in our reports. Consequently, there's a pressing need for thorough AMR surveillance data. Urban sewage, harboring a diverse array of microbes from sizable and mostly healthy populations, offers an excellent sampling opportunity. This study set out to identify and assess the microbes present in urban sewage in Kampala, while also analyzing the microbial resistome and virulome associated with urban sewage. Methods Samples were gathered from two wastewater treatment facilities, capturing data from both wet and dry seasons to reflect population behavior across seasons. DNA was extracted from these samples and underwent shotgun metagenomics sequencing. The resulting FastQ files were analyzed using a tailored metagenomics approach to identify microbial profiles, antibiotic-resistant genes, and virulence factors. Results In the pathobiome examined, Pseudomonas psychrophila, a fish pathogen, was the most prevalent, while Klebsiella pneumoniae was the least prevalent. Analysis identified 23 resistant genes, primarily conferring resistance to tetracyclines. Additionally, 29 virulence factors were identified, with a predominant association with bacterial motility. Notably, all of these virulence factors were found within Pseudomonas aeruginosa strain PAO1. Conclusion The utilization of shotgun metagenomics in sewage analysis is crucial for ongoing monitoring of microbial diversity and antimicrobial resistance. This approach uncovers intricate details that would be challenging or costly to obtain through conventional methods like PCR and culture-based techniques.","PeriodicalId":74358,"journal":{"name":"Open research Africa","volume":" 8","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Open research Africa","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12688/openresafrica.15040.1","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background Antimicrobial-resistant (AMR) infections represent a major global health threat, causing approximately 700,000 deaths each year directly due to AMR-related issues worldwide. In Africa, 42.6% of countries lack sufficient data on AMR, highlighting a crucial gap in our reports. Consequently, there's a pressing need for thorough AMR surveillance data. Urban sewage, harboring a diverse array of microbes from sizable and mostly healthy populations, offers an excellent sampling opportunity. This study set out to identify and assess the microbes present in urban sewage in Kampala, while also analyzing the microbial resistome and virulome associated with urban sewage. Methods Samples were gathered from two wastewater treatment facilities, capturing data from both wet and dry seasons to reflect population behavior across seasons. DNA was extracted from these samples and underwent shotgun metagenomics sequencing. The resulting FastQ files were analyzed using a tailored metagenomics approach to identify microbial profiles, antibiotic-resistant genes, and virulence factors. Results In the pathobiome examined, Pseudomonas psychrophila, a fish pathogen, was the most prevalent, while Klebsiella pneumoniae was the least prevalent. Analysis identified 23 resistant genes, primarily conferring resistance to tetracyclines. Additionally, 29 virulence factors were identified, with a predominant association with bacterial motility. Notably, all of these virulence factors were found within Pseudomonas aeruginosa strain PAO1. Conclusion The utilization of shotgun metagenomics in sewage analysis is crucial for ongoing monitoring of microbial diversity and antimicrobial resistance. This approach uncovers intricate details that would be challenging or costly to obtain through conventional methods like PCR and culture-based techniques.