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

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 lacks 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 (Y seq ) 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 (r 2 = 0.98) between spiked and detected concentrations of internal standards. High-throughput capability of the qmNGS approach was demonstrated with artificial E. 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 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 requires high-throughput gene quantification. The quantitative mNGS (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.

A review of the emergence of antibiotic resistance in bioaerosols and its monitoring methods

“…The emergence and spread of bioaerosols in indoor and outdoor environments have become worldwide public health issues that cause adverse events such as severe acute respiratory syndrome (SARS-CoV-2), the H1N1 influenza pandemic, and allergic reactions (Haig et al 2016 ; Chen et al 2019 ; Li et al 2021a ). According to previous studies (Heald and Spracklen 2009 ; Woo et al 2018 ), bacterial and fungal species are predominant in bioaerosols, and estimated yearly amounts of 1.4–4.6 and 28–50 Tg are emitted into the Earth’s atmosphere, respectively.…”

“…Extensive studies have attempted to identify ARGs and ARBs in wastewater, soil, sediment, compost, and manure environments with the aim of obtaining a better understanding of the antibiotic contamination of the environment (Xu et al 2016 ; Chen et al 2019 ; Yoo et al 2020 ; Li et al 2021a ). Although air pollutants have attracted interest due to their direct effects on public health, the studies on the health-related effects of air pollution conducted to date have relied heavily on the PM mass concentrations without considering biological parameters such as ARGs or ARBs (Li et al 2018 ).…”

A quantitative metagenomic approach to determine population concentrations with examination of quantitative limitations

“…Both of these approaches depend on the accuracy of time-and resource-consuming ancillary measurements that can be error prone and the latter is not suitable for quantitative viral metagenomes (viromes), as viruses lack conserved genes. Alternatively, DNA standards have been added to samples at known concentrations to facilitate absolute abundance measurements in metagenomes (6)(7)(8)(9)(10)(11)(12)(13)(14)(15). With this approach, target gene or genome counts in the metagenome are quantified using the number of reads that map to the standards and the known standard concentrations (6,8,(11)(12)(13)15).…”

“…Alternatively, DNA standards have been added to samples at known concentrations to facilitate absolute abundance measurements in metagenomes (6)(7)(8)(9)(10)(11)(12)(13)(14)(15). With this approach, target gene or genome counts in the metagenome are quantified using the number of reads that map to the standards and the known standard concentrations (6,8,(11)(12)(13)15).…”

“…Beyond detection, accurate target quantification relies on accurate read mapping to sequences of interest. Previous studies implementing quantitative metagenomics determined target concentrations by mapping reads onto targets from databases (i.e., reference-based quantification) (7,(9)(10)(11). Targets can also be quantified in metagenomes by mapping reads onto contigs assembled from the same sample (i.e., contig-based quantification).…”