Diindoles produced from commensal microbiota metabolites function as endogenous CAR/Nr1i3 ligands
Bibliographic record
Abstract
Numerous studies have demonstrated the correlation between human gut bacteria and host physiology, mediated primarily via nuclear receptors (NRs). Despite this body of work, the systematic identification and characterization of microbe-derived ligands that regulate NRs remain a considerable challenge. In this study, we discover a series of diindole molecules produced from commensal bacteria metabolites that act as specific agonists for the orphan constitutive androstane receptor (CAR). Using various biophysical analyses we show that their nanomolar affinities are comparable to those of synthetic CAR agonists, and that they can activate both rodent and human CAR orthologues, which established synthetic agonists cannot. We also find that the diindoles, diindolylmethane (DIM) and diindolylethane (DIE) selectively up-regulate bona fide CAR target genes in primary human hepatocytes and mouse liver without causing significant side effects. These findings provide new insights into the complex interplay between the gut microbiome and host physiology, as well as new tools for disease treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".