Biology subjects

Asquith, M.

Publications and source records attributed to Asquith, M..

3 recordsLinked to original sources

bioRxiv2021

Axial spondyloarthritis patients have altered mucosal IgA response to oral and fecal microbiota

ObjectiveTo investigate whether axial spondyloarthritis (AxSpA) patients have an altered immunoglobulin A (IgA) response in the gut and oral microbial communities. MethodsWe performed 16S rRNA gene (16S) sequencing on IgA positive (IgA+) and IgA negative (IgA-) fractions (IgA-SEQ) from feces (n=17 AxSpA; n=14 healthy) and saliva (n=17 AxSpA; n=12 healthy), as well as on IgA-unsorted fecal and salivary samples. PICRUSt2 was used to predict microbial metabolic potential in AxSpA patients and healthy controls (HCs). ResultsIgA-SEQ revealed enrichment of several microbes in the fecal (Akkermansia, Ruminococcaceae, Lachnospira) and salivary (Prevotellaceae, Actinobacillus) microbiome in AxSpA patients as compared with HCs. Fecal microbiome from AxSpA patients showed a trend towards increased alpha diversity of the IgA+ fraction and decreased diversity in the IgA- fraction in comparison with HCs, while the salivary microbiome exhibits a significant decrease in alpha diversity in both IgA+ and IgA- fractions. Increased IgA coating of Clostridiales Family XIII correlated with disease severity. Inferred metagenomic analysis suggests perturbation of metabolites and metabolic pathways for inflammation (oxidative phosphorylation, glutathione metabolism) and metabolism (propanoate and butanoate metabolism) in AxSpA patients. ConclusionsAnalyses of fecal and salivary microbes from AxSpA patients reveal distinct populations of immunoreactive microbes using novel IgA-SEQ approach, which were not captured by comparing their relative abundance with HCs. Predictive metagenomic analysis revealed perturbation of metabolites/metabolic pathways in AxSpA patients. Future studies on these immunoreactive microbes may lead to better understanding of the functional role of IgA in maintaining microbial structure and human health.

immunology↗

bioRxiv2019

HLA alleles associated with risk of ankylosing spondylitis and rheumatoid arthritis influence the gut microbiome

ObjectivesHLA alleles affect susceptibility to more than 100 diseases, but the mechanisms to account for these genotype-disease associations are largely unknown. HLA-alleles strongly influence predisposition to ankylosing spondylitis (AS) and rheumatoid arthritis (RA). Both AS and RA patients have discrete intestinal and faecal microbiome signatures. Whether these changes are cause or consequence of the diseases themselves is unclear. To distinguish these possibilities, we examine the effect of HLA-B27 and HLA-DRB1 RA-risk alleles on the composition of the intestinal microbiome in healthy individuals.\n\nMethods568 samples from 6 intestinal sites were collected from 107 otherwise healthy unrelated subjects and stool samples from 696 twin pairs from the TwinsUK cohort. Microbiome profiling was performed using sequencing of the 16S rRNA bacterial marker gene. All patients were genotyped using the Illumina CoreExome SNP microarray, and HLA genotypes were imputed from these data.\n\nResultsAssociation was observed between HLA-B27 genotype, and RA-risk HLA-DRB1 alleles, and overall microbial composition (P=0.0002 and P=0.00001 respectively). These associations were replicated in the TwinsUK cohort stool samples (P=0.023 and P=0.033 respectively).\n\nConclusionsThis study shows that the changes in intestinal microbiome composition seen in AS and RA are at least partially due to effects of HLA-B27 and -DRB1 on the gut microbiome. These findings support the hypothesis that HLA alleles operate to cause or increase the risk of these diseases through interaction with the intestinal microbiome, and suggest that therapies targeting the microbiome may be effective in their prevention and/or treatment.

microbiology↗

bioRxiv2018

Controlling for contaminants in low biomass 16S rRNA gene sequencing experiments

BackgroundMicrobial communities are commonly studied using culture-independent methods such as 16S rRNA gene sequencing. However, one challenge in accurately characterizing microbial communities is exogenous bacterial DNA contamination. This is particularly problematic for sites of low microbial biomass such as the urinary tract, placenta, and lower airway. Computational approaches have been proposed as a post-processing step to identify and remove potential contaminants, but their performance has not been independently evaluated.\n\nTo identify the impact of decreasing microbial biomass on polymicrobial 16S rRNA gene sequencing experiments, we used a serial dilution of a mock microbial community. We evaluated two computational approaches to identify and remove contaminants: 1) identifying sequences that have an inverse correlation with DNA concentration implemented in Decontam and 2) predicting the proportion of experimental sample arising from defined contaminant sources implemented in SourceTracker.\n\nResultsAs expected, the proportion of contaminant bacterial DNA increased with decreasing starting microbial biomass, with 79.12% of the most dilute sample arising from contaminant sequences. Inclusion of contaminant sequences in analyses leads to overinflated diversity estimates (up to 12 times greater than the expected values) and distorts microbiome composition. SourceTracker successfully removed over 98% of contaminants when the experimental environments are well defined. However, SourceTracker performed poorly when the experimental environment is unknown, failing to remove the majority of contaminants. Decontam successfully removed 74-91% of contaminants regardless of prior knowledge of the experimental environment.\n\nConclusionsOur study indicates that computational methods can reduce the amount of contaminants in 16S rRNA gene sequencing experiments. The appropriate computational approach for removing contaminant sequences from an experiment depends on the prior knowledge about the microbial environment under investigation and can be evaluated with a dilution series of a mock microbial community.

bioinformatics↗