First Insights into microbial changes within an Inflammatory Bowel Disease Family Cohort study
Rausch, P.; Ratjen, I.; Tittmann, L.; Enderle, J.; Wacker, E. M.; Jaeger, K.; Ruehlemann, M. C.; Ellul, P.; Kruse, R.; Halfvarsson, J.; Roggenbuck, D.; Ellinghaus, D.; Jacobs, G.; Krawczak, M.; Schreiber, S.; Bang, C.; Lieb, W.; Franke, A.
Show abstract
BackgroundThe prospective Kiel Inflammatory Bowel Disease (IBD) Family Cohort Study (KINDRED cohort) was initiated in 2013 to systematically and extensively collect data and biosamples from index IBD patients and their relatives (e.g., blood, stool), a population at high risk for IBD development. Regular follow-ups were conducted to collect updated health and lifestyle information, to obtain new biosamples, and to capture the incidence of IBD during development. By combining taxonomic and imputed functional microbial data collected at successive time points with extensive anthropometric, medical, nutritional, and social information, this study aimed to characterize the factors influencing the microbiota in health and disease via detailed ecological analyses. ResultsUsing two dysbiosis metrics (MD-index, GMHI) trained on the German KINDRED cohort, we identified strong and generalizable gradients within and across different IBD cohorts, which correspond strongly with IBD pathologies, physiological manifestations of inflammation (e.g., Bristol stool score, ASCA IgA/IgG), genetic risk for IBD, and general risk of disease onset. Anthropometric and medical factors influencing transit time strongly modify bacterial communities. Various Enterobacteriaceae (e.g., Klebsiella sp.) and opportunistic Clostridia pathogens (e.g., C. XIVa clostridioforme), characterize in combination with ectopic oral taxa (e.g. Veillonella sp., Cand. Saccharibacteria sp., Fusobacterium nucleatum) the distinct and chaotic IBD-specific communities. Functionally, amino acid metabolism and flagellar assembly are beneficial, while mucolytic functions are associated with IBD. ConclusionsOur findings demonstrate broad-scale ecological patterns which indicate drastic state transitions of communities into characteristically chaotic communities in IBD patients. These patterns appear to be universal across cohorts and influence physiological signs of inflammation, display high resilience, but show only little heritability/intrafamily transmission.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The host genotype actively shapes its microbiome across generations 95%
- Transplantation of bacteriophages from ulcerative colitis patients shifts the gut bacteriome and exacerbates severity of DSS-colitis 94%
- Colonization during a key developmental window reveals microbiota-dependent shifts in growth and immunity during undernutrition 94%
Similar papers in this journal
- Escherichia coli strains from patients with inflammatory bowel diseases have disease-specific genomic adaptations 96%
- Intestinal receptor of SARS-CoV-2 in inflamed IBD tissue is downregulated by HNF4A in ileum and upregulated by interferon regulating factors in colon 95%
- Susceptibility to inflammatory bowel diseases promotes invasive carcinomas in a murine model of ATF6-driven colon cancer 94%
Similar papers in this journal
- Blood-borne immune cells carry low biomass DNA remnants of microbes in patients with colorectal cancer or inflammatory bowel disease 96%
- Enhancing Recovery from Gut Microbiome Dysbiosis and Alleviating DSS-Induced Colitis in Mice with a Consortium of Rare Short-Chain Fatty Acid-Producing Bacteria 96%
Similar papers in this journal
Similar papers in this journal
- Two microbiota subtypes identified in Irritable Bowel Syndrome with distinct responses to the low FODMAP diet 96%
- Specific gut pathobionts escape antibody coating and are enriched during flares in patients with severe Crohn's disease 95%
- CARD9 in Neutrophils Protects from Colitis and Controls Mitochondrial Metabolism and Cell Survival 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.