Microbe and host interaction in gastrointestinal homeostasis
Bibliographic record
Abstract
RATIONALE: Researchers in psychiatry and neuroscience are increasingly recognizing the importance of gut-brain communication in mental health. Both genetics and environmental factors influence gut microbiota composition and function. This study examines host-microbe signaling at the gastrointestinal barrier to identify bottom-up mechanisms of microbiota-brain communication. OBJECTIVES: We examined differences in gut microbiota composition and fecal miRNA profiles in BALB/c and C57BL/6 mice, in relation to gastrointestinal homeostasis and evaluated the response to perturbation of the gut microbiota by broad-spectrum antibiotic treatment. METHODS AND RESULTS: Differences in the gut microbiota composition between BALB/c and C57BL/6 mice, evaluated by fecal 16S rRNA gene sequencing, included significant differences in genera Prevotella, Alistipes, Akkermansia, and Ruminococcus. Significant differences in fecal miRNA profiles were determined using the nCounter NanoString platform. A BLASTn analysis identified conserved fecal miRNA target regions in bacterial metagenomes with 14 significant correlations found between fecal miRNA and predicted taxa relative abundance in our dataset. Treatment with broad-spectrum antibiotics for 2 weeks resulted in a host-specific physiological response at the gastrointestinal barrier including a decrease in barrier permeability in BALB/c mice and alterations in the expression of barrier regulating genes in both strains. Genera Parabacteroides and Bacteroides were associated with changes in barrier function. CONCLUSIONS: The results of this study provide insight into how specific taxa influence gut barrier integrity and function. More generally, these data in the context of recent published studies makes a significant contribution to our understanding of host-microbe interactions providing new knowledge that can be harnessed by us and others in future mechanistic studies.
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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 | 0.000 | 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".