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Biology subjects

Brunelle, B.

Publications and source records attributed to Brunelle, B..

2 recordsLinked to original sources

Target-enrichment sequencing yields valuable genomic data for difficult-to-culture bacteria of public health importance

Genomic data contribute invaluable information to the epidemiological investigation of pathogens of public health importance. However, whole genome sequencing (WGS) of bacteria typically relies on culture, which represents a major hurdle for generating such data for a wide range of species for which culture is challenging. In this study, we assessed the use of culture-free target-enrichment sequencing as a method for generating genomic data for two bacterial species: 1) Bacillus anthracis, which causes anthrax in both people and animals and whose culture requires high level containment facilities; and 2) Mycoplasma amphoriforme, a fastidious emerging human respiratory pathogen. We obtained high quality genomic data for both species directly from clinical samples, with sufficient coverage (>15X) for confident variant calling over at least 80% of the baited genomes for over two thirds of the samples tested. Higher qPCR cycle threshold (Ct) values (indicative of lower pathogen concentrations in the samples), pooling libraries prior to capture, and lower captured library concentration were all statistically associated with lower capture efficiency. The Ct value had the highest predictive value, explaining 52% of the variation in capture efficiency. Samples with Ct values [≤] 30 were over 6 times more likely to achieve the threshold coverage than those with a Ct > 30. We conclude that target-enrichment sequencing provides a valuable alternative to standard WGS following bacterial culture and creates opportunities for an improved understanding of the epidemiology and evolution of many clinically important pathogens for which culture is challenging. Data summaryThe authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. Scripts used in this study can be accessed on GitHub at https://github.com/tristanpwdennis/bactocap. All sequence data generated during this study have been deposited in the European Nucleotide Archive (ENA) Sequence Read Archive (SRA) under project accession numbers PRJEB46822 (B. anthracis) and PRJEB50216 (M. amphoriforme). Accession numbers for individual samples, along with metadata, laboratory parameters and sequence quality metrics, are available at the University of Glasgows data repository, Enlighten, at http://dx.doi.org/10.5525/gla.researchdata.1249.

molecular biology↗

Improved microbial community characterization of 16S rRNA via metagenome hybridization capture enrichment

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. However, shotgun metagenomic sequencing is 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 < 80% sequence similarity 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 in part to the extent and curation of the reference databases considered.

microbiology↗