Bibliographic record
Abstract
The studies described in this thesis investigated how changes in the gut microbiota relate to alterations in the immune system and the development of beta-cell dysfunction. Moreover, we addressed the interrelation between intestinal immunoglobulins and gut microbiota in individuals with metabolic diseases. Chapter 1 provides a general introduction to the gut microbiota, the pathogenesis of T2D, and the function of IgA. Chapter 2 summarizes the current literature on gut microbiota, obesity, and T2D. A western diet leads to changes in gut microbial composition and intestinal physiology. Altered intestinal microbial composition promotes obesity by increasing energy harvest and gut permeability that may facilitate the translocation of bacteria or bacterial components into the circulation. Thereby, the gut microbiota can impact the function of metabolic organs. Interventions aiming to restore gut microbial homeostasis, such as ingestion of fibers or therapeutic microbes, are novel strategies to reduce metabolic abnormalities. Inflammation in the pancreas can lead to beta-cell dysfunction with the driving inflammatory triggers yet to be identified. The gut microbiota provides a plausible source of pro-inflammatory molecules. Chapter 3 shows that a high-fat diet significantly altered the gut and pancreatic bacterial signature in mice. Importantly, our findings could not be traced back to contamination, a well-known factor disturbing microbial signal identification in low-abundance samples. Our data point out that the pancreas comprises bacterial DNA. This underscores similar speculations and observations in the field implying that gut microbial components translocate and act on distal organs and tissue. Chapter 4 summarizes the current literature on gut microbiota and metabolic inflammation. T2D is characterized by a chronic state of low-grade inflammation, which has been linked to the development of insulin resistance. Intestinal microbiota drive the development of insulin resistance in obesity and T2D, possibly by initiating an inflammatory response. Overall, there is strong evidence for the tripartite interaction between the gut microbiota and the host’s immune system as well as metabolism. Chapter 5 explores how a (dysbiotic) gut microbiota can influence beta-cell dysfunction. Enterobacteriaceae, which harbors several pro-inflammatory bacteria such as Enterobacter cloacae, was more abundant in the gut of individuals with T2D. These bacteria can express flagellin, an integral part of the locomotive function of bacteria. Flagellin induced a pro-inflammatory response in pancreatic islets, which was at least partly mediated by toll-like receptor (TLR)-5 on resident islet macrophages. This inflammatory response was associated with beta-cell dysfunction, characterized by reduced insulin gene expression, impaired proinsulin processing, and stress-induced insulin hypersecretion. We postulate that increased systemically disseminated flagellin is a contributing factor to beta-cell failure and hence to T2D development in time. Chapter 6 addresses the effect of antibiotics-induced alterations in the gut microbiota on the intestinal antibody response. Individuals with or without metabolic syndrome received oral vancomycin for seven days. Coinciding with a surge in Gram-negative bacteria, fecal levels of the immunogenic bacterial components LPS and flagellin in- creased. Intestinal antibodies significantly increased, whereas peripheral antibodies were mostly unaffected by vancomycin treatment. Typical pro-inflammatory bacteria were mostly coated with IgA after antibiotic treatment. We suggest that the intestinal antibody response after antibiotic treatment prevents systemic inflammation. Chapter 7 addresses the intestinal antibody response after bariatric surgery, an 8-weight-loss surgery with significant consequences for gut microbiota composition. Body weight, fasting glucose, and inflammatory parameters decreased after bariatric surgery, whereas pro-inflammatory bacterial species, LPS, and flagellin increased. Simultaneously, concentrations of LPS- and flagellin-specific intestinal IgA levels increased. Serum antibodies decreased in both groups, along with a lower inflammatory tone. We concluded that intestinal rearrangement by bariatric surgery expands typical pro-inflammatory bacteria, which might be compensated by an improved antibody response. Chapter 8 summarizes the findings and gives future perspectives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".