Kezia Drane , Roger Huerlimann , Rhondda Jones , Anna Whelan , Madoc Sheehan , Ellen Ariel , Robert Kinobe
{"title":"抗菌药耐药性分子检测方法的一致性:对污水厂进水的横断面研究。","authors":"Kezia Drane , Roger Huerlimann , Rhondda Jones , Anna Whelan , Madoc Sheehan , Ellen Ariel , Robert Kinobe","doi":"10.1016/j.mimet.2024.107069","DOIUrl":null,"url":null,"abstract":"<div><div>Methods that are used to characterise microbiomes and antimicrobial resistance genes (ARGs) in wastewater are not standardised. We used shotgun metagenomic sequencing (SM-Seq), RNA sequencing (RNA-seq) and targeted qPCR to compare microbial and ARG diversity in the influent to a municipal wastewater treatment plant in Australia. ARGs were annotated with CARD-RGI and MEGARes databases, and bacterial diversity was characterised by 16S rRNA gene sequencing and SM-Seq, with species annotation in SILVA/GreenGenes databases or Kraken2 and the NCBI nucleotide database respectively. CARD and MEGARes identified evenly distributed ARG profiles but MEGARes detected a richer array of ARGs (richness = 475 vs 320). Qualitatively, ARGs encoding for aminoglycoside, macrolide-lincosamide-streptogramin and multidrug resistance were the most abundant in all examined databases. RNA-seq detected only 32 % of ARGs identified by SM-Seq, but there was concordance in the qualitative identification of aminoglycoside, macrolide-lincosamide, phenicol, sulfonamide and multidrug resistance by SM-Seq and RNA-seq. qPCR confirmed the detection of some ARGs, including <em>OXA</em>, <em>VEB</em> and <em>EREB</em>, that were identified by SM-Seq and RNA-seq in the influent. For bacteria, SM-Seq or 16S rRNA gene sequencing were equally effective in population profiling at phyla or class level. However, SM-Seq identified a significantly higher species richness (richness = 15,000 vs 3750). These results demonstrate that SM-Seq with gene annotation in CARD and MEGARes are equally sufficient for surveillance of antimicrobial resistance in wastewater. For more precise ARG identification and quantification however, MEGARes presented a better resolution. The functionality of detected ARGs was not confirmed, but general agreement on the putative phenotypic resistance profile by antimicrobial class was observed between RNA-Seq and SM-Seq.</div></div>","PeriodicalId":16409,"journal":{"name":"Journal of microbiological methods","volume":"228 ","pages":"Article 107069"},"PeriodicalIF":1.9000,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Concordance in molecular methods for detection of antimicrobial resistance: A cross sectional study of the influent to a wastewater plant\",\"authors\":\"Kezia Drane , Roger Huerlimann , Rhondda Jones , Anna Whelan , Madoc Sheehan , Ellen Ariel , Robert Kinobe\",\"doi\":\"10.1016/j.mimet.2024.107069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Methods that are used to characterise microbiomes and antimicrobial resistance genes (ARGs) in wastewater are not standardised. We used shotgun metagenomic sequencing (SM-Seq), RNA sequencing (RNA-seq) and targeted qPCR to compare microbial and ARG diversity in the influent to a municipal wastewater treatment plant in Australia. ARGs were annotated with CARD-RGI and MEGARes databases, and bacterial diversity was characterised by 16S rRNA gene sequencing and SM-Seq, with species annotation in SILVA/GreenGenes databases or Kraken2 and the NCBI nucleotide database respectively. CARD and MEGARes identified evenly distributed ARG profiles but MEGARes detected a richer array of ARGs (richness = 475 vs 320). Qualitatively, ARGs encoding for aminoglycoside, macrolide-lincosamide-streptogramin and multidrug resistance were the most abundant in all examined databases. RNA-seq detected only 32 % of ARGs identified by SM-Seq, but there was concordance in the qualitative identification of aminoglycoside, macrolide-lincosamide, phenicol, sulfonamide and multidrug resistance by SM-Seq and RNA-seq. qPCR confirmed the detection of some ARGs, including <em>OXA</em>, <em>VEB</em> and <em>EREB</em>, that were identified by SM-Seq and RNA-seq in the influent. For bacteria, SM-Seq or 16S rRNA gene sequencing were equally effective in population profiling at phyla or class level. However, SM-Seq identified a significantly higher species richness (richness = 15,000 vs 3750). These results demonstrate that SM-Seq with gene annotation in CARD and MEGARes are equally sufficient for surveillance of antimicrobial resistance in wastewater. For more precise ARG identification and quantification however, MEGARes presented a better resolution. The functionality of detected ARGs was not confirmed, but general agreement on the putative phenotypic resistance profile by antimicrobial class was observed between RNA-Seq and SM-Seq.</div></div>\",\"PeriodicalId\":16409,\"journal\":{\"name\":\"Journal of microbiological methods\",\"volume\":\"228 \",\"pages\":\"Article 107069\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-11-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of microbiological methods\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0167701224001817\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of microbiological methods","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167701224001817","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Concordance in molecular methods for detection of antimicrobial resistance: A cross sectional study of the influent to a wastewater plant
Methods that are used to characterise microbiomes and antimicrobial resistance genes (ARGs) in wastewater are not standardised. We used shotgun metagenomic sequencing (SM-Seq), RNA sequencing (RNA-seq) and targeted qPCR to compare microbial and ARG diversity in the influent to a municipal wastewater treatment plant in Australia. ARGs were annotated with CARD-RGI and MEGARes databases, and bacterial diversity was characterised by 16S rRNA gene sequencing and SM-Seq, with species annotation in SILVA/GreenGenes databases or Kraken2 and the NCBI nucleotide database respectively. CARD and MEGARes identified evenly distributed ARG profiles but MEGARes detected a richer array of ARGs (richness = 475 vs 320). Qualitatively, ARGs encoding for aminoglycoside, macrolide-lincosamide-streptogramin and multidrug resistance were the most abundant in all examined databases. RNA-seq detected only 32 % of ARGs identified by SM-Seq, but there was concordance in the qualitative identification of aminoglycoside, macrolide-lincosamide, phenicol, sulfonamide and multidrug resistance by SM-Seq and RNA-seq. qPCR confirmed the detection of some ARGs, including OXA, VEB and EREB, that were identified by SM-Seq and RNA-seq in the influent. For bacteria, SM-Seq or 16S rRNA gene sequencing were equally effective in population profiling at phyla or class level. However, SM-Seq identified a significantly higher species richness (richness = 15,000 vs 3750). These results demonstrate that SM-Seq with gene annotation in CARD and MEGARes are equally sufficient for surveillance of antimicrobial resistance in wastewater. For more precise ARG identification and quantification however, MEGARes presented a better resolution. The functionality of detected ARGs was not confirmed, but general agreement on the putative phenotypic resistance profile by antimicrobial class was observed between RNA-Seq and SM-Seq.
期刊介绍:
The Journal of Microbiological Methods publishes scholarly and original articles, notes and review articles. These articles must include novel and/or state-of-the-art methods, or significant improvements to existing methods. Novel and innovative applications of current methods that are validated and useful will also be published. JMM strives for scholarship, innovation and excellence. This demands scientific rigour, the best available methods and technologies, correctly replicated experiments/tests, the inclusion of proper controls, calibrations, and the correct statistical analysis. The presentation of the data must support the interpretation of the method/approach.
All aspects of microbiology are covered, except virology. These include agricultural microbiology, applied and environmental microbiology, bioassays, bioinformatics, biotechnology, biochemical microbiology, clinical microbiology, diagnostics, food monitoring and quality control microbiology, microbial genetics and genomics, geomicrobiology, microbiome methods regardless of habitat, high through-put sequencing methods and analysis, microbial pathogenesis and host responses, metabolomics, metagenomics, metaproteomics, microbial ecology and diversity, microbial physiology, microbial ultra-structure, microscopic and imaging methods, molecular microbiology, mycology, novel mathematical microbiology and modelling, parasitology, plant-microbe interactions, protein markers/profiles, proteomics, pyrosequencing, public health microbiology, radioisotopes applied to microbiology, robotics applied to microbiological methods,rumen microbiology, microbiological methods for space missions and extreme environments, sampling methods and samplers, soil and sediment microbiology, transcriptomics, veterinary microbiology, sero-diagnostics and typing/identification.