The gut microbiome restores intrinsic and extrinsic nerve function in germ‐free mice accompanied by changes in calbindin
Bibliographic record
Abstract
BACKGROUND: The microbiome is essential for normal myenteric intrinsic primary afferent neuron (IPAN) excitability. These neurons control gut motility and modulate gut-brain signaling by exciting extrinsic afferent fibers innervating the enteric nervous system via an IPAN to extrinsic fiber sensory synapse. We investigated effects of germ-free (GF) status and conventionalization on extrinsic sensory fiber discharge in the mesenteric nerve bundle and IPAN electrophysiology, and compared these findings with those from specific pathogen-free (SPF) mice. As we have previously shown that the IPAN calcium-dependent slow afterhyperpolarization (sAHP) is enhanced in GF mice, we also examined the expression of the calcium-binding protein calbindin in these neurons in these different animal groups. METHODS: IPAN sAHP and mesenteric nerve multiunit discharge were recorded using ex vivo jejunal gut segments from SPF, GF, or conventionalized (CONV) mice. IPANs were excited by adding 5 μM TRAM-34 to the serosal superfusate. We probed for calbindin expression using immunohistochemical techniques. KEY RESULTS: SPF mice had a 21% increase in mesenteric nerve multiunit firing rate and CONV mice a 41% increase when IPANs were excited by TRAM-34. For GF mice, this increase was barely detectable (2%). TRAM-34 changed sAHP area under the curve by -77 for SPF, +3 for GF, or -54% for CONV animals. Calbindin-immunopositive neurons per myenteric ganglion were 36% in SPF, 24% in GF, and 52% in CONV animals. CONCLUSIONS & INFERENCES: The intact microbiome is essential for normal intrinsic and extrinsic nerve function and gut-brain signaling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".