Equivolumetric Protocol Generates Library Sizes Proportional to Total Microbial Load in 16S Amplicon Sequencing

Abstract

High-throughput sequencing of 16S rRNA amplicon has been extensively employed to perform microbiome characterization worldwide. As a culture-independent methodology, it has allowed high-level profiling of sample bacterial composition directly from samples. However, most studies are limited to information regarding relative bacterial abundances (sample proportions), ignoring scenarios in which sample microbe biomass can vary widely. Here, we use an equivolumetric protocol for 16S rRNA amplicon library preparation capable of generating Illumina sequencing data responsive to input DNA, recovering proportionality between observed read counts and absolute bacterial abundances within each sample. Under specified conditions, we show that the estimation of colony-forming units (CFU), the most common unit of bacterial abundance in classical microbiology, is challenged mostly by resolution and taxon-to-taxon variation. We propose Bayesian cumulative probability models to address such issues. Our results indicate that predictive errors vary consistently below one order of magnitude for total microbial load and abundance of observed bacteria. We also demonstrate our approach has the potential to generalize to previously unseen bacteria, but predictive performance is hampered by specific taxa of uncommon profile. Finally, it remains clear that high-throughput sequencing data are not inherently restricted to sample proportions only, and such technologies bear the potential to meet the working scales of traditional microbiology.

Healthcare-Associated Infections-Related Bacteriome and Antimicrobial Resistance Profiling: Assessing Contamination Hotspots in a Developing Country Public Hospital

“…Previous studies have also shown agreement between CD and CI methods, however, they emphasize that HTS-based CI methods are able to capture greater bacterial diversity, not restricted to specific groups, and that, even with their limitations, must be considered as important tools for investigation of bacterial communities in hospital environments and to HAI control (Oberauner et al, 2013;Comar et al, 2019). The CI approach also has the increment of providing a big picture, both by identifying the bacterial diversity closest to reality, as well as by screening AMR profiles, using leaner laboratory tests allied to robust bioinformatics pipelines (Boers et al, 2019;Chiu and Miller, 2019;Cruz et al, 2021).…”

“…Bacterial identification of CI and CD analyzes was performed by high-throughput sequencing of V3/V4 16S rRNA using primers 341F and 806R (Wang and Qian, 2009;Caporaso et al, 2012). Amplicon sequencing library preparation and sequencing was carried out as previously described (Christoff et al, 2020;Cruz et al, 2021), in a two-step equivolumetric PCR protocol: first PCR uses V3/V4 universal primers containing a partial Illumina adaptor, based on TruSeq structure adapter (Illumina Inc., CA, United States) that allows a second PCR with the indexing sequences. All PCR reactions were performed in triplicates and with a negative reaction control.…”

“…The sequenced reads obtained were subjected to demultiplexing processes along with primers and adapters trimming. Application of quality filters was performed using a bioinformatics pipeline previously described (Cruz et al, 2021). Reads were analyzed with the Deblur package v.1.1.0 (Amir et al, 2017) to remove possible erroneous reads and identical sequences were grouped into oligotypes (clusters with 100% identity).…”

“…The resulting oligotype tables were processed and normalized as previously described (Cruz et al, 2021). Oligotype sequences served as input for FastTree 2.1 software (Price et al, 2010) to construct phylogenetic trees and allow beta-diversity analysis with UniFrac distances (Lozupone and Knight, 2005).…”

A primer and discussion on DNA-based microbiome data and related bioinformatics analyses

“…This alternative data could be quantitative PCR data, flow cytometry data (Vandeputte et al 2017) or spiked-in sequences of known abundances (Zemb et al 2020). Different sample preparation protocols prior to DNA sequencing can also help retain information about differential absolute amounts of DNA across samples as well (Cruz et al 2021). These are exciting approaches, but they have not been validated across many datasets and at the moment there is no consensus regarding which methods perform best.…”