Biology subjects

Effernberger, M.

Publications and source records attributed to Effernberger, M..

2 recordsLinked to original sources

bioRxiv2025

Escherichia-Shigella expansion and metabolite dysregulation in type 3c diabetes

Emerging evidence supports a bidirectional gut-pancreas axis in which microbial dysbiosis, barrier dysfunction, and altered metabolite fluxes contribute to pancreatogenic diabetes (T3cDM). Whether gut microbial changes reflect systemic metabolic disturbances or primarily arise from exocrine pancreatic insufficiency (EPI) remains unclear. We profiled the gut microbiome of 48 outpatients with T3cDM, type 1 diabetes (T1DM), and healthy controls. Genus-level 16S rRNA data were analysed using cross-validated LASSO logistic regression and patient-specific community metabolic models. T3cDM showed reduced -diversity and distinct {beta}-diversity compared with T1DM and controls. Key compositional shifts included enrichment of Enterobacteriaceae (notably Escherichia-Shigella) and Streptococcaceae in T3cDM. LASSO models discriminated T3cDM from T1DM (AUC 0.867; accuracy 0.818), highlighting Blautia, Escherichia-Shigella, Streptococcus, Clostridium, and Faecalibacterium as predictors. Metabolic modelling indicated elevated Escherichia-Shigella growth in T3cDM and disease-specific metabolite fluxes. Gut microbial shifts in T3cDM predominantly reflect EPI rather than systemic metabolic disturbances characteristic of T1DM, underscoring the central role of exocrine pancreatic dysfunction in shaping the gut microbiome and its metabolic activity.

microbiology↗

bioRxiv2020

Human liver microbiota modeling strategy at the early onset of fibrosis

To understand the pathophysiological impact of liver microbiota on the early stages of fibrosis we identified the corresponding microbiota sequences and overcome the impact of different group size and patient origins with adapted statistical approaches. Liver samples with low liver fibrosis scores (F0, F1, F2) were collected from Romania(n=36), Austria(n=10), Italy(n=19), and Spain(n=17). The 16SrDNA gene was sequenced. We considered the frequency, sparsity, unbalanced sample size between cohorts to identify taxonomic profiles and statistical differences. Multivariate analyses, including adapted spectral clustering with L1-penalty fair-discriminant strategies, and predicted metagenomics were used to identify that 50 % of liver taxa were Enterobacteriaceae and Pseudomonadaceae. The Caulobacteraceae, Flavobacteriaceae and Propionibacteriaceae discriminated between F0 and F1. The preQ0 biosynthesis and pathways involving glucoryranose and glycogen degradation were negatively associated with liver fibrosis F1-F2 vs F0. Altogether, our results suggest a role of bacterial translocation to the liver in the progression of fibrosis. This statistical approach can identify microbial signatures and overcome issues regarding sample size differences, the impact of environment, and sets of analyses.

pathology↗