Acarose is an anti-diabetic drug and exhibits anti-arthritic effects. We hypothesized that acarbose influences the gut microbiota to affect the course of arthritis and tested this hypothesis in a collagen-induced arthritis (CIA) murine model. Acarbose in drinking water was administered via gastric gavage started prior to or at the time of CIA induction. Gut microbiota were evaluated with 16S rRNA gene sequencing from fecal pellets collected prior to arthritis induction, during onset of arthritis, and after treatment. Immune response was evaluated by measuring changes in T helper-17 (Th17) and T regulatory (Treg) cells in the spleen and intestine, as well as serum cytokine levels. Before induction of CIA, acarbose significantly reduced the incidence of arthritis and attenuated clinical severity of arthritis. The frequency of Th17 cells was significantly decreased in the intestinal lamina propria in acarbose treated mice. Mice that were treated with acarbose showed significantly increased CD4 + CD25 + Foxp3 + Treg cells with elevation of Helios and CCR6. A remarkable alteration in microbial community was observed in acarbose treated mice. Bacterial diversity and richness in mice with arthritis were significantly lower than those in acarbose treated groups. The frequency of Firmicutes was significantly reduced after arthritis onset but was restored after treatment with acarbose. The frequency of Lactobacillus, Anaeroplasma, Adlercreutzia, RF39 and Corynebacterium was significantly higher in control groups than in acarbose treated, while Oscillospira, Desulfovibrio and Ruminococcus enriched in acarbose treated group. Miglitol, another aglucosidase inhibitor showed a similar but less potent anti-arthritic effect to that of acarbose. These data demonstrate that acarbose alleviated CIA through regulation of Th17/Treg cells in the intestinal mucosal immunity, which may have resulted from the impact of acarbose on gut microbial community. Inexpensive antidiabetic drugs with an excellent safety profile are potentially useful for managing rheumatoid arthritis.

When creating figures, it is important to consider that individuals with color vision deficiency (CVD) may not perceive all colors. While there are several CVD-friendly color palettes, they are often insufficient for working with microbiome data. Here, we introduce microshades , an R package for creating CVD-accessible microbiome figures.

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BackgroundLittle is known about the relationship of proximal urogenital microbiomes in the bladder and the vagina and how this contributes to bladder health. In this study, we use a microbial ecology and network framework to understand the dynamics of interactions/co-occurrences of bacteria in the bladder and vagina in women with and without urgency urinary incontinence (UUI).MethodsWe collected vaginal swabs and catheterized urine specimens from 20 women with UUI (cases) and 30 women without UUI (controls). We sequenced the V4 region of the bacterial 16S rRNA gene and evaluated using alpha and beta diversity metrics. We used microbial network analysis to detect interactions in the microbiome and the betweenness centrality measure to identify central bacteria in the microbial network. Bacteria exhibiting maximum betweenness centrality are considered central to the microbe-wide networks and likely maintain the overall microbial network structure.ResultsThere were no significant differences in the vaginal or bladder microbiomes between cases and controls using alpha and beta diversity. Silhouette metric analysis identified two distinct microbiome clusters in both the bladder and vagina. One cluster was dominated by Lactobacillus genus while the other was more diverse. Network-based analyses demonstrated that vaginal and bladder microbial networks were different between cases and controls. In the vagina, there were similar numbers of genera and subgroup clusters in each network for cases and controls. However, cases tend to have more unique bacterial co-occurrences. While Bacteroides and Lactobacillus were the central bacteria with the highest betweenness centrality in controls, Aerococcus had the highest centrality in cases and correlated with bacteria commonly associated with bacterial vaginosis. In the bladder, cases have less than half as many network clusters compared to controls. Lactobacillus was the central bacteria in both groups but associated with several known uropathogens in cases. The number of shared bacterial genera between the bladder and the vagina differed between cases and controls, with cases having larger overlap (43%) compared to controls (29%).ConclusionOur study shows overlaps in microbial communities of bladder and vagina, with higher overlap in cases. We also identified differences in the bacteria that are central to the overall community structure.

In the past few decades, ecologists have developed many diversity indices to describe within and between sample diversity. Consequently, it can be difficult to determine which index to choose and how the distribution of microbial communities affect these indices. We've developed an interactive application, , that dynamically visualizes different alpha or beta diversity ShinyDiversity indices. In enabling users to select and simultaneously visualize different indices, our application aims to facilitate understanding of how the microbial data affects selected indices.