Maladaptive immunity to the microbiota promotes neuronal hyperinnervation and itch via IL-17A
Bibliographic record
Abstract
The interaction between the immune system and the somatosensory system plays a fundamental role in the regulation of diverse biological processes. Chronic itch is a common yet hard-to-treat symptom of many inflammatory skin conditions. One hallmark of chronic itch is the hyperinnervation of the skin by sensory fibers, yet what drives this aberrant nerve growth or how it contributes to disease progression remains unclear. Here, we identify IL-17A and immunity to skin microbiota as key triggers of sensory neuron plasticity and pruritus. In a murine model of psoriatic itch, we show that exposure to Staphylococcus aureus prior to experimental psoriasis results in heightened skin inflammation, increased itch, and marked hyperinnervation of CGRPα + sensory neurons. Accordingly, single-nuclei RNA sequencing of dorsal root ganglia reveals that microbiota-driven inflammation induces a regenerative transcriptional program in sensory neurons, including upregulation of axonal growth, injury response, and IL-17RA signaling pathways. Mechanistically, we show that IL-17A/IL-17RA signaling within TRPV1 + sensory neurons drives hyperinnervation and pruritus, establishing a causal link between IL-17A and microbiota-driven immune responses in sensory circuit remodeling. Further, we identify sensory hyperinnervation as a key driver of chronic itch and inflammation. Collectively, we reveal that aberrant IL-17A signaling in sensory neurons, triggered by dysregulated microbiota immunity, promotes neuronal remodeling that amplifies itch and inflammation. These findings provide a framework for targeting microbiota–neuroimmune interactions as a therapeutic strategy for pruritus.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".