Literature DB >> 34085862

A Quantitative Metagenomic Sequencing Approach for High-Throughput Gene Quantification and Demonstration with Antibiotic Resistance Genes.

Bo Li1, Xu Li2, Tao Yan1.   

Abstract

Comprehensive microbial risk assessment requires high-throughput quantification of diverse microbial risks in the environment. Current metagenomic next-generation sequencing approaches can achieve high-throughput detection of genes indicative of microbial risks but lack quantitative capabilities. This study developed and tested a quantitative metagenomic next-generation sequencing (qmNGS) approach. Numerous xenobiotic synthetic internal DNA standards were used to determine the sequencing yield (Yseq) of the qmNGS approach, which can then be used to calculate absolute concentration of target genes in environmental samples based on metagenomic sequencing results. The qmNGS approach exhibited excellent linearity as indicated by a strong linear correlation (r2 = 0.98) between spiked and detected concentrations of internal standards. High-throughput capability of the qmNGS approach was demonstrated with artificial Escherichia coli mixtures and cattle manure samples, for which 95 ± 3 and 208 ± 4 types of antibiotic resistance genes (ARGs) were detected and quantified simultaneously. The qmNGS approach was further compared with quantitative real-time PCR (qPCR) and demonstrated comparable levels of accuracy and less variation for the quantification of six target genes (16S, tetO, sulI, tetM, ermB, and qnrS). IMPORTANCE Monitoring and comprehensive assessment of microbial risks in the environment require high-throughput gene quantification. The quantitative metagenomic NGS (qmNGS) approach developed in this study incorporated numerous xenobiotic and synthetic DNA internal standard fragments into metagenomic NGS workflow, which are used to determine a new parameter called sequencing yield that relates sequence base reads to absolute concentration of target genes in the environmental samples. The qmNGS approach demonstrated excellent method linearity and comparable performance as the qPCR approach with high-throughput capability. This new qmNGS approach can achieve high-throughput and accurate gene quantification in environmental samples and has the potential to become a useful tool in monitoring and comprehensively assessing microbial risks in the environment.

Entities:  

Keywords:  antibiotic resistance genes; gene quantification; internal DNA standards; qmNGS

Mesh:

Substances:

Year:  2021        PMID: 34085862      PMCID: PMC8373253          DOI: 10.1128/AEM.00871-21

Source DB:  PubMed          Journal:  Appl Environ Microbiol        ISSN: 0099-2240            Impact factor:   4.792


  40 in total

Review 1.  Next-generation sequencing technologies for environmental DNA research.

Authors:  Shadi Shokralla; Jennifer L Spall; Joel F Gibson; Mehrdad Hajibabaei
Journal:  Mol Ecol       Date:  2012-04       Impact factor: 6.185

2.  Quantitative analysis of a deeply sequenced marine microbial metatranscriptome.

Authors:  Scott M Gifford; Shalabh Sharma; Johanna M Rinta-Kanto; Mary Ann Moran
Journal:  ISME J       Date:  2010-09-16       Impact factor: 10.302

3.  Use of internal standards for quantitative metatranscriptome and metagenome analysis.

Authors:  Brandon M Satinsky; Scott M Gifford; Byron C Crump; Mary Ann Moran
Journal:  Methods Enzymol       Date:  2013       Impact factor: 1.600

4.  Real-time PCR for the detection of Escherichia coli O157:H7 in dairy and cattle wastewater.

Authors:  G Spano; L Beneduce; V Terzi; A M Stanca; S Massa
Journal:  Lett Appl Microbiol       Date:  2005       Impact factor: 2.858

5.  Use of amplicon sequencing to improve sensitivity in PCR-based detection of microbial pathogen in environmental samples.

Authors:  Prakit Saingam; Bo Li; Tao Yan
Journal:  J Microbiol Methods       Date:  2018-05-07       Impact factor: 2.363

6.  High-throughput profiling of antibiotic resistance genes in drinking water treatment plants and distribution systems.

Authors:  Like Xu; Weiying Ouyang; Yanyun Qian; Chao Su; Jianqiang Su; Hong Chen
Journal:  Environ Pollut       Date:  2016-02-15       Impact factor: 8.071

7.  The impacts of different high-throughput profiling approaches on the understanding of bacterial antibiotic resistance genes in a freshwater reservoir.

Authors:  Xuan Liu; Peng Xiao; Yunyan Guo; Lemian Liu; Jun Yang
Journal:  Sci Total Environ       Date:  2019-07-24       Impact factor: 7.963

8.  Abundance, diversity and mobility potential of antibiotic resistance genes in pristine Tibetan Plateau soil as revealed by soil metagenomics.

Authors:  Bo Li; Zeng Chen; Fan Zhang; Yongqin Liu; Tao Yan
Journal:  FEMS Microbiol Ecol       Date:  2020-10-01       Impact factor: 4.194

9.  Metagenomic Characterization of Antibiotic Resistance Genes in Full-Scale Reclaimed Water Distribution Systems and Corresponding Potable Systems.

Authors:  Emily Garner; Chaoqi Chen; Kang Xia; Jolene Bowers; David M Engelthaler; Jean McLain; Marc A Edwards; Amy Pruden
Journal:  Environ Sci Technol       Date:  2018-05-17       Impact factor: 9.028

10.  A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome.

Authors:  Imane Allali; Jason W Arnold; Jeffrey Roach; Maria Belen Cadenas; Natasha Butz; Hosni M Hassan; Matthew Koci; Anne Ballou; Mary Mendoza; Rizwana Ali; M Andrea Azcarate-Peril
Journal:  BMC Microbiol       Date:  2017-09-13       Impact factor: 3.605

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  1 in total

1.  A Metagenomic Approach for Characterizing Antibiotic Resistance Genes in Specific Bacterial Populations: Demonstration with Escherichia coli in Cattle Manure.

Authors:  Bo Li; Xu Li; Bing Wang; Tao Yan
Journal:  Appl Environ Microbiol       Date:  2022-03-14       Impact factor: 5.005

  1 in total

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