Biology subjects

Holmes, S. P.

Publications and source records attributed to Holmes, S. P..

4 recordsLinked to original sources

bioRxiv2018

Metagenomic analysis with strain-level resolution reveals fine-scale variation in the human pregnancy microbiome

Recent studies suggest that the microbiome has an impact on gestational health and outcome. However, characterization of the pregnancy-associated microbiome has largely relied on 16S rRNA gene amplicon-based surveys. Here, we describe an assembly-driven, metagenomics-based, longitudinal study of the vaginal, gut, and oral microbiomes in 292 samples from ten subjects sampled every three weeks throughout pregnancy. 1.53 Gb of non-human sequence was assembled into scaffolds, and functional genes were predicted for gene-and pathway-based analyses. Vaginal assemblies were binned into 97 draft quality genomes. Redundancy analysis (RDA) of microbial community composition at all three body sites revealed gestational age to be a significant source of variation in patterns of gene abundance. In addition, health complications were associated with variation in community functional gene composition in the mouth and gut. The diversity of Lactobacillus iners-dominated communities in the vagina, unlike most other vaginal community types, significantly increased with gestational age. The genomes of co-occurring Gardnerella vaginalis strains with predicted distinct functions were recovered in samples from two subjects. In seven subjects, gut samples contained strains of the same Lactobacillus species that dominated the vaginal community of that same subject, and not other Lactobacillus species; however, these within-host strains were divergent. CRISPR spacer analysis suggested shared phage and plasmid populations across body sites and individuals. This work underscores the dynamic behavior of the microbiome during pregnancy and suggests the potential importance of understanding the sources of this behavior for fetal development and gestational outcome.

microbiology↗

bioRxiv2017

Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data

BackgroundThe accuracy of microbial community surveys based on marker-gene and metagenomic sequencing (MGS) suffers from the presence of contaminants -- DNA sequences not truly present in the sample. Contaminants come from various sources, including reagents. Appropriate laboratory practices can reduce contamination, but do not eliminate it. Here we introduce decontam (https://github.com/benjjneb/decontam), an open-source R package that implements a statistical classification procedure that identifies contaminants in MGS data based on two widely reproduced patterns: contaminants appear at higher frequencies in low-concentration samples, and are often found in negative controls.\n\nResultsdecontam classified amplicon sequence variants (ASVs) in a human oral dataset consistently with prior microscopic observations of the microbial taxa inhabiting that environment and previous reports of contaminant taxa. In metagenomics and marker-gene measurements of a dilution series, decontam substantially reduced technical variation arising from different sequencing protocols. The application of decontam to two recently published datasets corroborated and extended their conclusions that little evidence existed for an indigenous placenta microbiome, and that some low-frequency taxa seemingly associated with preterm birth were contaminants.\n\nConclusionsdecontam improves the quality of metagenomic and marker-gene sequencing by identifying and removing contaminant DNA sequences. decontam integrates easily with existing MGS workflows, and allows researchers to generate more accurate profiles of microbial communities at little to no additional cost.

bioinformatics↗

bioRxiv2017

Strain-Specific Human Natural Killer Cell Recognition of Influenza A Virus

AbstractInnate Natural killer (NK) cells employ an array of surface receptors to detect altered self induced by infection or malignancy. Despite their decisive role in early antiviral immunity, the cellular mechanisms governing if or how they discriminate between viral infections remain unresolved. Here, we demonstrate that while human NK cells are capable of reducing infection levels of distinct influenza A strains, the A/California/07/2009 (pH1N1) strain induces a significantly more robust IFN-{gamma} response than A/Victoria/361/2011 (H3N2) and all other strains tested. This surprising degree of strain specificity results in part from the inability of the pH1N1 strain to downregulate the activating ligands CD112 (Nectin-2) and CD54 (ICAM-1) as efficiently as the H3N2 strain, leading to enhanced NK cell detection and IFN-{gamma} secretion. A network analysis of differentially expressed transcripts identifies the interferon /{beta} receptor (IFNAR) pathway as an additional, critical determinant of this strain-specific response. Strain-specific downregulation of NK cell activating ligands and modulation of type I IFN production represents a previously unrecognized influenza immunoevasion tactic and could present new opportunities to modulate the quality and quantity of the innate antiviral response for therapeutic benefit.\n\nOne Sentence SummaryHuman natural killer cells distinguish between Influenza A strains using a combinatorial cytokine priming and receptor-ligand signaling mechanism.

immunology↗

bioRxiv2017

Exact sequence variants should replace operational taxonomic units in marker gene data analysis

Recent advances have made it possible to analyze high-throughput marker-gene sequencing data without resorting to the customary construction of molecular operational taxonomic units (OTUs): clusters of sequencing reads that differ by less than a fixed dissimilarity threshold. New methods control errors sufficiently that sequence variants (SVs) can be resolved exactly, down to the level of single-nucleotide differences over the sequenced gene region. The benefits of finer taxonomic resolution are immediately apparent, and arguments for SV methods have focused on their improved resolution. Less obvious, but we believe more important, are the broad benefits deriving from the status of SVs as consistent labels with intrinsic biological meaning identified independently from a reference database. Here we discuss how those features grant SVs the combined advantages of closed-reference OTUs -- including computational costs that scale linearly with study size, simple merging between independently processed datasets, and forward prediction -- and of de novo OTUs -- including accurate diversity measurement and applicability to communities lacking deep coverage in reference databases. We argue that the improvements in reusability, reproducibility and comprehensiveness are sufficiently great that SVs should replace OTUs as the standard unit of marker gene analysis and reporting.

microbiology↗