bioRxiv Science⌕ Search

bioRxiv · 10.1101/2023.10.01.560393

Disrupted Ecology and H. parainfluenzae Distinguish the Gut Microbiota of an Ethnic Minority Predisposed to Type 2 Diabetes Mellitus

Abstract

PurposeDecreased gut microbiota production of short-chain fatty acids (SCFAs) has been implicated in type 2 diabetes mellitus (T2DM) disease progression. Most microbiome studies focus on ethnic majorities. This study aims to understand microbiome differences between an ethnic majority (the Dutch) and minority (the South-Asian Surinamese (SAS)) group with a lower and higher prevalence of T2DM, respectively. MethodsMicrobiome data from the Healthy Life in an Urban Setting (HELIUS) cohort were used. The 16S rRNA V4 region was sequenced. Two age- and gender-matched groups were compared: the Dutch (n = 41) and SAS (n = 43). Microbial compositions were generated via DADA2. Alpha and beta-diversity and Principal Coordinate Analysis (PCoA) were computed. DESeq2 differential bacterial abundance and LEfSe biomarker analyses were performed to determine discriminating features. Co-occurrence networks were constructed to examine gut ecology. ResultsA tight cluster of bacterial abundances was observed in the Dutch women, which overlapped with some of the SAS microbiomes. The Dutch gut contained a more interconnected microbial ecology, whereas the SAS network was dispersed. Bacteroides caccae, Butyricicoccus, Alistipes putredinis, Coprococcus comes, Odoribacter splanchnicus, and Lachnospira characterized the Dutch gut. Haemophilus, Bifidobacterium, and Anaerostipes hadrus characterized the SAS gut. All but Lachnospira and certain strains of Haemophilus are known SCFA producers. ConclusionThe Dutch gut microbiome was distinguished from the SAS by diverse, differentially abundant SCFA-producing taxa with significant cooperation. The dynamic ecology observed in the Dutch was lost in the SAS. The higher prevalence of T2DM in the SAS may be associated with the dysbiosis observed.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nayman, E. I., Schwartz, B. A., Polmann, M., Gumabong, A. C., Nieuwdorp, M., Cickovski, T., Mathee, K.. 2023-10-03. Disrupted Ecology and H. parainfluenzae Distinguish the Gut Microbiota of an Ethnic Minority Predisposed to Type 2 Diabetes Mellitus. https://doi.org/10.1101/2023.10.01.560393

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

A population-scale landscape of the subgingival microbiome reveals divergent routes to periodontal dysbiosis

Periodontitis is an archetypical mucosal inflammatory disease in which microbiome dysbiosis at the tooth-epithelial interface interacts with host genetic and behavioral risk factors to drive immune-mediated tissue destruction. Although subgingival microbiome compositional shifts are thought to parallel disease severity, microbiome variation at the population-level and its relationship to periodontal clinical phenotypes and disease-modifying factors remain poorly defined. Here, we use unsupervised manifold learning to map the compositional landscape of the subgingival microbiome in 1,355 adults spanning periodontal health to severe periodontitis. We identified eight latent microbiome states organized along a branching continuum from eubiosis to dysbiosis. An intermediate microbial configuration marked ecological destabilization and bifurcation into two distinct periodontitis-associated dysbiotic trajectories, distinguished by links to gingival inflammation and smoking. Although the microbiome trajectories broadly tracked periodontal destruction, a minority of individuals showed discordant microbiome-clinical phenotypes, with some individuals with periodontitis retaining otherwise eubiotic microbiomes enriched for low-abundance pathobionts, while some cases of health or mild disease had highly dysbiotic communities, suggesting distinct host susceptibility. Together, these findings define a population-scale ecological landscape of the subgingival microbiome, reveal divergent trajectories to periodontal dysbiosis, and highlight heterogeneity in the relationship between microbial community structure and clinical disease expression.

microbiology↗

Beta-lactam enhancement against methicillin-resistant Staphylococcus aureus by cell wall blockade is autolysis-dependent: a butyrolactone derivative as case in point

Methicillin-resistant Staphylococcus aureus (MRSA) is non-susceptible to beta-lactams. Blockade of cell wall biosynthesis is a potential target for beta-lactam enhancement but requires further investigation. A butyrolactone derivative enhanced beta-lactams against MRSA strains by reducing the availability of D-Ala-D-Ala. Unlike D-cycloserine, it did not inhibit D-Ala-D-Ala ligase (Ddl). Nor did it show an additive or synergistic effect when combined with cycloserine, indicating a unique mechanism for blocking cell wall precursor production that does not involve the traditional Lipid II pathway. Notably, beta-lactam potentiation by our chemical or D-cycloserine was highly dependent on the intrinsic autolytic ability of the tested MRSA strains. Strains that resisted lysis upon Triton X-100 exposure showed a minimal increase in beta-lactam susceptibility, whereas highly autolytic strains showed significant changes in their beta-lactam MICs. We have thus identified autolytic ability as the Achilles Heel in the strategy of targeting cell wall biosynthesis for beta-lactam potentiation.

microbiology↗

Rapid and largely reversible shifts in the canine fecal metabolome during dietary change

Diet can rapidly change the fecal metabolome, but less is known about recovery after the original diet is restored. We used untargeted UPLC-MS metabolomics to analyze 72 fecal samples from nine Pumi dogs during an owner-managed switch from dry food to raw food and back to dry food. Diet phase accounted for a large proportion of variation in both ionization modes. More than 13,000 LC-MS features changed at the first sampling point after the switch to raw food, with a similarly large response after return to dry food. Among features significant in both comparisons, more than 99% changed in opposite directions. At the final sampling point, no positive-mode (ESI+) features and only 13 negative-mode (ESI-) features differed from the second dry-food baseline under the same threshold. BARF-associated patterns persisted in analyses excluding individual dogs and in pedigree-adjusted candidate models, although individual feature effects depended on normalization. Putative metabolites from several biochemical classes differed in their response and recovery. The fecal metabolome therefore changed rapidly and returned largely toward baseline, with differences among dogs.

microbiology↗