Biology subjects

Davin, S.

Publications and source records attributed to Davin, S..

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↗

bioRxiv2021

The impact of genetic background and sex on the phenotype of IL-23 induced murine arthritis

BackgroundOverexpression of IL-23 in adult mice by means of hydrodynamic tail vein injection of IL-23 minicircles has been reported to result in spondyloarthritis-like disease. The impact of genetic background and sex on the disease phenotype in this model has not been investigated. MethodsWe compared male B10.RIII mice with male C57BL/6 mice, and male with female B10.RIII mice after hydrodynamic injection of IL-23 enhanced episomal vector (EEV) at 8-12 weeks of age. We monitored clinical arthritis scores, paw swelling, and body weight. Animals were euthanized after two weeks and tissues were harvested for histology, flow cytometry and gene expression analysis. Serum cytokine levels were determined by ELISA. FindingsMale B10.RIII mice developed arthritis in the forepaws and feet within 6 days after IL-23 EEV injection; they also exhibited psoriasis-like skin disease, colitis, weight loss, and osteopenia. In contrast to previous reports, we did not observe spondylitis or uveitis. Male C57BL/6 mice injected with IL-23 EEV had serum IL-23 levels comparable with B10.RIII mice and developed skin inflammation, colitis, weight loss, and osteopenia but failed to develop arthritis. Female B10.RIII mice had more severe arthritis than male B10.RIII mice but did not lose weight. ConclusionsSystemic IL-23 overexpression results in spondyloarthritis-like disease in B10.RIII mice. The development of extra-articular manifestations but absence of arthritis in C57BL/6 mice suggests organ-specific genetic control mechanisms of IL-23 driven inflammation. Discrepancies regarding the phenotype of IL-23 induced disease in different labs and the sexual dimorphism observed in this study warrant further exploration.

immunology↗

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↗