At a Supra-Physiological Concentration, Human Sexual Hormones Act as Quorum-Sensing Inhibitors
Bibliographic record
Abstract
N-acylhomoserine lactone (AHL)-mediated quorum-sensing (QS) regulates virulence functions in plant and animal pathogens such as Agrobacterium tumefaciens and Pseudomonas aeruginosa. A chemolibrary of more than 3500 compounds was screened using two bacterial AHL-biosensors to identify QS-inhibitors (QSIs). The purity and structure of 15 QSIs selected through this screening were verified using HPLC MS/MS tools and their activity tested on the A. tumefaciens and P. aeruginosa bacterial models. The IC50 value of the identified QSIs ranged from 2.5 to 90 µg/ml, values that are in the same range as those reported for the previously identified QSI 4-nitropyridine-N-oxide (IC50 24 µg/ml). Under the tested culture conditions, most of the identified QSIs did not exhibit bacteriostatic or bactericidal activities. One third of the tested QSIs, including the plant compound hordenine and the human sexual hormone estrone, decreased the frequency of the QS-regulated horizontal transfer of the tumor-inducing (Ti) plasmid in A. tumefaciens. Hordenine, estrone as well as its structural relatives estriol and estradiol, also decreased AHL accumulation and the expression of six QS-regulated genes (lasI, lasR, lasB, rhlI, rhlR, and rhlA) in cultures of the opportunist pathogen P. aeruginosa. Moreover, the ectopic expression of the AHL-receptors RhlR and LasR of P. aeruginosa in E. coli showed that their gene-regulatory activity was affected by the QSIs. Finally, modeling of the structural interactions between the human hormones and AHL-receptors LasR of P. aeruginosa and TraR of A. tumefaciens confirmed the competitive binding capability of the human sexual hormones. This work indicates potential interferences between bacterial and eukaryotic hormonal communications.
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) | 0.002 | 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".