65 Exploring molecular mechanisms behind Lactobacillus protection offered to Caenorhabditis elegans: the role of neurotransmitters
Bibliographic record
Abstract
Abstract Lactobacillus are commonly used as probiotics in livestock production to improve animal gut health and performance. We previously reported the selection of Lactobacillus zeae LB1, which was able to reduce Salmonella infection in chickens and pigs, through the life-span assay of Caenorhabditis elegans infected with Salmonella Typhimurium DT104. To understand the molecular mechanisms behind the probiotic effect, the present study used C. elegans as a model to investigate the influence of LB1 on the behavior plasticity and roles of both serotonin and dopamine in C. elegans responding to Salmonella infection and LB1 protection. Pre-exposure to LB1 did not elicit aversive olfactory behavior of both the wild-type nematode (N2) and mutants defective in serotonin (tph-1) or dopamine (cat-2) production towards DT104, although LB1 was much more attractive than DT104 to C. elegans. Life-span studies showed that both the mutants succumbed faster than N2 to DT104 infection. Pre-exposure to LB1 significantly increased the survival level of both N2 and mutant tph-1. However, LB1 provided no protection to mutant cat-2. Supplementation of dopamine restored both the resistance of mutant cat-2 to Salmonella infection and the protection from LB1 to mutant cat-2. These results suggest that both serotonin and dopamine have a role in the host defense of C. elegans to Salmonella infection, and that the LB1 protection effect was not dependent on modifying olfactory preference of the nematode, but mediated by dopamine. Additionally, the p38-mitogen activated protein kinase signaling and IGFR-1/DAF-16 signaling pathways may have also participated in the protection under dopamine regulation.
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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".