Early life microbiota colonization programs nociceptor sensitivity by regulating NGF production in mast cells
Bibliographic record
Abstract
Recent evidence suggests that the gut microbiota can influence pain sensitivity, highlighting the potential for microbiota-targeted pain interventions. During early life, both the microbiota and nociceptors are fine-tuned and respond to environmental factors, however, little is known about how they interact with each other. Using germ-free and gnotobiotic models, we demonstrate that microbiota colonization controls nociceptor sensitivity, partly by modulating mast cell production of nerve growth factor (NGF). We report that germ-free mice respond less to thermal and capsaicin-induced stimulation, which correlates with reduced trafficking of TRPV1 to the cell membrane of nociceptors. In germ-free mice, mast cells express lower levels of NGF. Hyposensitivity to thermal and capsaicin-induced stimulation, reduced TRPV1 trafficking, and decreased NGF expression are reversed when mice are colonized at birth, but not when colonization occurs after weaning. Inhibition of mast cell degranulation and NGF signaling during the first weeks of life in colonized mice leads to a hyposensitive phenotype in adulthood, demonstrating a role for mast cells and NGF signaling in linking early life colonization with nociceptor sensitivity. These findings implicate the early life microbiota in shaping mast cell NGF production and nociceptor sensitivity later in life. SIGNIFICANCE STATEMENT: Nociceptors are specialized sensory neurons that detect and transduce painful stimuli. During the early postnatal period, nociceptors are influenced by sensory experiences and the environment. Our findings demonstrate that gut microbiota colonization is essential in setting the threshold of nociceptor responses to painful stimuli. We show that early-life bacterial colonization controls the production of nerve growth factor by mast cells, affecting our sensitivity to pain later in life. Our study highlights the potential for developing new pain treatments that target the gut microbiome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".