Genetic and transcriptional analysis of human host response to healthy gut microbiome
Bibliographic record
Abstract
Abstract Many studies have demonstrated the importance of the gut microbiome in healthy and disease states. However, establishing the causality of host-microbiome interactions in humans is still challenging. Here, we describe a novel experimental system to define the transcriptional response induced by the microbiome in human cells and to shed light on the molecular mechanisms underlying host-gut microbiome interactions. In primary human colonic epithelial cells, we identified over 6,000 genes that change expression at various time points following co-culturing with the gut microbiome of a healthy individual. The differentially expressed genes are enriched for genes associated with several microbiome-related diseases, such as obesity and colorectal cancer. In addition, our experimental system allowed us to identify 87 host SNPs that show allele-specific expression in 69 genes. Furthermore, for 12 SNPs in 12 different genes, allele-specific expression is conditional on the exposure to the microbiome. Of these 12 genes, eight have been associated with diseases linked to the gut microbiome, specifically colorectal cancer, obesity and type 2 diabetes. Our study demonstrates a scalable approach to study host-gut microbiome interactions and can be used to identify putative mechanisms for the interplay between host genetics and microbiome in health and disease.
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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 | 0.000 | 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) | 0.001 | 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 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".