Stimulatory and Toxic Effects of Neurotransmitters on the lux Operon-Dependent Bioluminescence of Escherichia coli K12 TGI
Bibliographic record
Abstract
Background: The normal functioning of the brain requires neuromediators, i.e., substances that transmit messages between nervous cells. Neurochemicals also function as signals that are involved in communication among the microorganisms that inhabit the human organism. While the impact of “classical” neurotransmitters including catecholamines, serotonin, and histamine on microorganisms has been investigated in a number of recent publications, this work provides evidence for the stimulatory and inhibitory (toxic) effects of some other important neurochemicals that have not received sufficient attention in the literature.Methods: The biosensor was based on a GM Escherichia coli K12 strain (TGI) that contained the lux operon of the luminescent soil bacterium Photorhabdus luminescencens ZMI. The biosensor was exposed to the action of the tested neurotransmitters for 15 mins to 144 hrs. The intensity of bacterial luminescence (counts / second) was monitored in the control and the experimental samples with an 1251 BioOrbit luminometer (Finland).Results: Neurochemicals such as putrescine, acetylcholine, taurin, and indole were found to stimulate, at low concentrations (0.1-10 µM), the luminescence of the strain E. coli K12 TGI containing the lux operon from Photorhabdus luminescencens ZMI. At higher concentrations, putrescine, taurin, and indole exerted a weak toxic influence, i.e. they marginally attenuated the luminescence of E. coli K12 TGI.Conclusions: Based on the data obtained, a regulatory, presumably receptor-dependent, effect is exerted by the tested neurochemicals on the bacterium E. coli K12 TGI, in an analogy to their impact on nervous, immune, and other specialized types of eukaryotic cells. However, high neurochemical concentrations are likely to produce nonspecific effects on the bacterial luciferase system and/or on membrane phosphorylation.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".