Microbes as part of ancestral neuronal circuits: Bacterial produced signals affect neurons controlling eating behavior in <i>Hydra</i>
Bibliographic record
Abstract
Summary Although recent studies indicate the impact of microbes on the central nervous systems and behavior, it remains unclear how the relationship between the functionality of the nervous system, behavior and the microbiota arise. We studied the eating behavior of Hydra, a host that has a simple nervous system and a low-complexity microbiota. To identify the neuronal subpopulations involved, we used a subpopulation specific cell ablation system and calcium imaging. The role of the microbiota was uncovered by reducing the diversity of the natural microbiota. Here, we demonstrate that different neuronal subpopulations are functioning together to control the eating behavior. The microbiota participates in control of the eating behavior since germ-free or mono-colonized animals have drastic difficulties in mouth opening. This was restored by adding a full complement of the microbiota. In summary, we provide a mechanistic explanation of how the eating behavior is controlled in Hydra and how microbes can affect the neuronal circuit. Highlights - Multiple neuronal modules and their networks control complex behavior in an animal lacking a central nervous system. - Its associated microbes participate in these neuronal circuits and influence the eating behavior. - Disorganization of the microbiota negatively impacts this eating behavior. - Glutamate participates in an evolutionary ancient interkingdom language.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".