Repression of Salmonella Host Cell Invasion by Aromatic Small Molecules from the Human Fecal Metabolome
Bibliographic record
Abstract
ABSTRACT The human microbiome is a collection of microorganisms that inhabit every surface of the body that is exposed to the environment, generally coexisting peacefully with their host. These microbes have important functions, such as producing vitamins, aiding in maturation of the immune system, and protecting against pathogens. We have previously shown that a small-molecule extract from the human fecal microbiome has a strong repressive effect on Salmonella enterica serovar Typhimurium host cell invasion by modulating the expression of genes involved in this process. Here, we describe the characterization of this biological activity. Using a series of purification methods, we obtained fractions with biological activity and characterized them by mass spectrometry. These experiments revealed an abundance of aromatic compounds in the bioactive fraction. Selected compounds were obtained from commercial sources and tested with respect to their ability to repress the expression of hilA , the gene encoding the master regulator of invasion genes in Salmonella . We found that the aromatic compound 3,4-dimethylbenzoic acid acts as a strong inhibitor of hilA expression and of invasion of cultured host cells by Salmonella . Future studies should reveal the molecular details of this phenomenon, such as the signaling cascades involved in sensing this bioactive molecule. IMPORTANCE Microbes constantly sense and adapt to their environment. Often, this is achieved through the production and sensing of small extracellular molecules. The human body is colonized by complex communities of microbes, and, given their biological and chemical diversity, these ecosystems represent a platform where the production and sensing of molecules occur. In previous work, we showed that small molecules produced by microbes from the human gut can significantly impair the virulence of the enteric pathogen Salmonella enterica . Here, we describe a specific compound from the human gut that produces this same effect. The results from this work not only shed light on an important biological phenomenon occurring in our bodies but also may represent an opportunity to develop drugs that can target these small-molecule interactions to protect us from enteric infections and other diseases.
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".