Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment

Abstract

Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in genomic scope. In contrast, shotgun metagenomic methods sample more genomic regions with fewer sequence acquisition biases, but are much more expensive (even with moderate sequencing depth) and computationally challenging. Here, we develop a set of 16S rRNA sequence capture baits that offer a potential middle ground with the advantages from both approaches for investigating microbial communities. These baits cover the diversity of all 16S rRNA sequences available in the Greengenes (v. 13.5) database, with no sequence having <78% sequence identity to at least one bait for all segments of 16S. The use of our baits provide comparable results to 16S amplicon libraries and shotgun metagenomic libraries when assigning taxonomic units from 16S sequences within the metagenomic reads. We demonstrate that 16S rRNA capture baits can be used on a range of microbial samples (i.e., mock communities and rodent fecal samples) to increase the proportion of 16S rRNA sequences (average > 400-fold) and decrease analysis time to obtain consistent community assessments. Furthermore, our study reveals that bioinformatic methods used to analyze sequencing data may have a greater influence on estimates of community composition than library preparation method used, likely due in part to the extent and curation of the reference databases considered. Thus, enriching existing aliquots of shotgun metagenomic libraries and obtaining modest numbers of reads from them offers an efficient orthogonal method for assessment of bacterial community composition.

Escaping the fate of Sisyphus: assessing resistome hybridization baits for antimicrobial resistance gene capture

“…In this series of analyses, it is possible to use all available sequencing reads (see Data Processing and Analysis) (Beaudry et al ., 2021). An reads per kilobase per million mapped reads (RPKM) analysis of our resistance mock community revealed that resistance genes in enriched libraries are sequenced at a much higher sequencing depth than those in unenriched libraries, in all but one resistance class.…”

“…water, clinical built environments, etc.) (Beaudry et al ., 2021). Our ready‐to‐use publicly available bait set is a cost‐effective way to monitor AMR [i.e.…”

“…to detect new variants and overcome some level of genetic bias) while being highly sensitive. Hybridization sequence capture bait sets have been designed and used in microbial diversity studies by designing baits to the 16S rRNA (Gasc and Peyret, 2018; Beaudry et al ., 2021), to study a defined set of pathogens or genes [e.g. virulence genes in Vibrio spp., human infecting viruses (Lasa et al ., 2019; Metsky et al ., 2019)] and to study antibiotic resistance (Noyes et al ., 2017; Lanza et al ., 2018; Guitor et al ., 2019).…”

A breath of fresh air in microbiome science: shallow shotgun metagenomics for a reliable disentangling of microbial ecosystems

“…However, under specific circumstances, even shotgun metagenomics may not detect certain microorganisms from challenging samples, such as sub-dominant microorganisms or within samples dominated by a large amount of host DNA in host-related environments. In these circumstances, a DNA filtering step or a targeted DNA approach is mandatory [10] ; otherwise, an even deeper shotgun sequencing is necessary, increasing the costs of these analyses. In this context, the hybridization capture targeting of the 16S rRNA gene, or other molecular markers, could be a complementary strategy to explore the microbial community at the species level [11] .…”

“…In this perspective, we would like to encourage the scientific community to investigate poorly characterized microbiomes through culturomics experiments to gain access to the genome sequence of novel microbial species not already discovered. In this context, it has been shown that unknown microorganisms, also referred to as microbial dark matter, can be easily found in unexplored environments, such as rural human populations, exotic animals, soils, and waters [10,16] . A fundamental step to uncover the complexity of microbiomes is to retrieve genomic sequences not already classified, for example, through the DNA sequencing of putative novel species identified using peptide mass fingerprints by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) [17] .…”