Prolonged antibiotic treatment induces a diabetogenic intestinal microbiome that accelerates diabetes in NOD mice
Bibliographic record
Abstract
Accumulating evidence supports that the intestinal microbiome is involved in Type 1 diabetes (T1D) pathogenesis through the gut-pancreas nexus. Our aim was to determine whether the intestinal microbiota in the non-obese diabetic (NOD) mouse model played a role in T1D through the gut. To examine the effect of the intestinal microbiota on T1D onset, we manipulated gut microbes by: (1) the fecal transplantation between non-obese diabetic (NOD) and resistant (NOR) mice and (2) the oral antibiotic and probiotic treatment of NOD mice. We monitored diabetes onset, quantified CD4+T cells in the Peyer's patches, profiled the microbiome and measured fecal short-chain fatty acids (SCFA). The gut microbiota from NOD mice harbored more pathobionts and fewer beneficial microbes in comparison with NOR mice. Fecal transplantation of NOD microbes induced insulitis in NOR hosts suggesting that the NOD microbiome is diabetogenic. Moreover, antibiotic exposure accelerated diabetes onset in NOD mice accompanied by increased T-helper type 1 (Th1) and reduced Th17 cells in the intestinal lymphoid tissues. The diabetogenic microbiome was characterized by a metagenome altered in several metabolic gene clusters. Furthermore, diabetes susceptibility correlated with reduced fecal SCFAs. In an attempt to correct the diabetogenic microbiome, we administered VLS#3 probiotics to NOD mice but found that VSL#3 colonized the intestine poorly and did not delay diabetes. We conclude that NOD mice harbor gut microbes that induce diabetes and that their diabetogenic microbiome can be amplified early in life through antibiotic exposure. Protective microbes like VSL#3 are insufficient to overcome the effects of a diabetogenic microbiome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".