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 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 (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 (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.

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

FIG 1

Correlation between detected sequence base ratio (n_ISF-i/n_TOT, base pairs/base pairs) and spiked DNA concentration ratio (C_ISF-i/C_TOT, nanograms/nanograms) of the ISFs in the three replicate mixed E. coli DNA samples (E1 to E3) and manure samples (M1 to M3).

FIG 2

Average sequencing yield values of the 79 DNA internal standard fragments (ISFs) located on the three blocks (IS-1, -2, and -3) that were spiked at different concentrations (A) and their coefficient of variation (CV) with respect to their spiked concentration ratio (B). Error bars in panel A were standard deviations from six samples.

FIG 3

The impact of different sequencing depths (through resampling) on the distribution of sequencing yield values of the ISFs. Only ISFs spiked at a concentration ratio above the limit of quantification (LOQ) are included in the box plots.

FIG 4

Heat map of the absolute concentrations of ARGs (log₁₀ gene copies/μl) in the DNA samples from the E. coli isolate mixtures (E1 to E3) and cattle manure samples (M1 to M3). Only ARGs detected with relative abundances of >0.5% are presented here.

FIG 5

Comparison of qmNGS and qPCR in quantifying the 16S rRNA gene and five ARGs in the cattle manure samples (M1 to M3). qnrS was not detected by either qmNGS or qPCR.