Detection of bacteria through taste receptors primes the cellular immune response
Bibliographic record
Abstract
Abstract Animals use their sensory system to detect cues in their external environment, then communicate, process, and integrate these cues through the nervous system in order to elicit a specific response. Taste is an important cue used by animals to explore their external environment and can modulate various aspects of animal behavior and physiology. A major ongoing challenge for animals is to detect and respond to the presence of a variety of microbes in their environment. However, to date, the links between the sensory system and the response to pathogenic threats remain poorly understood. Here we show that Drosophila melanogaster larvae use their taste system to detect bacterial peptidoglycans in their environment and respond by modulating the activity of their cellular immune system. We show that specific PeptidoGlycan Receptor Proteins (PGRPs) act in aversive taste neurons, via a non-canonical Immune Deficiency (Imd) pathway. These PGRPs mediate signaling in taste neurons and control immune cells production in the larval hematopoietic organ, the lymph gland. Taste-mediated sensing of bacteria in larvae primes the immune system, and improves survival after infection in adult flies. These results demonstrate that sensory inputs such as taste play an important role in protecting animals from bacterial infection by providing a powerful adaptive response to potential pathogens. Overall, our findings add to the growing list of examples of crosstalk between the nervous and immune systems and provide novel and important mechanisms for linking them. One Sentence Summary Najera Mazariegos et al. demonstrate that organisms can use taste to monitor their environment for potential immune challenges and activate their immune system if they detect bacteria.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".