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Excitation of Vagal Afferent Neurons by Fecal Supernatant from Inflammatory Bowel Disease Patients

2022· article· en· W4225403983 on OpenAlexafffund
Ayssar A. Tashtush, David E. Reed, Alan Lomax

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsAfferentInflammatory bowel diseaseMedicineFecesGastroenterologyDiseaseInternal medicineInflammationPathologyChemistryBiologyMicrobiology

Abstract

fetched live from OpenAlex

Background The gut‐ microbiota‐brain axis has received increasing attention recently due to evidence that colonic microbes can affect brain function and behavior. Recent studies have demonstrated that vagal afferent neurons may be an important conduit between the gut microbiota and the brain, as it is capable of detecting mediators released from the gut microbiota. However, it is unknown whether i) the gut microbiota from healthy human stool donors affects nodose ganglion (NG) neurophysiology or ii) gut microbial dysbiosis during inflammatory bowel disease (IBD) impacts the function of vagal afferent neurons. Hypothesis Remodeling of gut microbiota during IBD increases secretion of mediators that change the excitability of vagal afferent neurons. Methods To examine the effect of IBD patients’ fecal supernatant (FS; 1:20 dilution) on the excitability of mouse vagal afferent neurons, NG neurons from C57/Bl6 mice were collected, dissociated, and incubated overnight with FS from 5 active Crohn’s disease (CD) patients, 7 active ulcerative colitis (UC) patients, and 5 healthy volunteers (HV). Current and voltage‐clamp recordings were used to assess changes in neuronal excitability and ion channel function. Results CD and UC FS significantly increased the excitability of NG neurons through a reduction in the rheobase of 40% (CD:60 cells vs . control: 54 cells), P <0.0001, Mann‐Whitney test) and 23% (n= 50 cells vs . control: 46 cells, P = 0.0038, Mann‐Whitney test) compared to their individual vehicle control neurons, respectively. This decrease in rheobase was accompanied by a two‐fold increase in the number of action potentials elicited at twice rheobase ( P <0.01, Mann‐Whitney test). However, neither resting membrane potential, nor input resistance was altered in NG neurons treated with IBD FS compared with vehicle control neurons. HV FS had no effect on NG excitability. CD and UC FS significantly reduced voltage‐gated K + currents ( P= 0.0075 and P <0.0001, two‐way ANOVA followed by Sidak's multiple comparison test, respectively), but had no effect on voltage‐gated Na + currents. The excitatory effect of CD and UC FS of NG neurons was blocked by the cysteine protease inhibitor (E64) (30 nM), but not the serine protease inhibitor (FUT175) (10 μM). In all CD patients, pre‐incubation of E64 blocked the increase in excitability by CD patient FS (CD rheobase:40.6 ± 3.6 pA vs .CD+ E64 rheobase: 73.9 ± 4.5 pA) ( P<0.0001 , one‐way ANOVA followed by Tukey's multiple comparison). However, E64 was only able to block the excitatory effect of FS from 5 out of 7 UC patients FS (UC rheobase:43.8 ± 4.7 pA vs . UC+ E64 rheobase: 83.5 ± 4.3 pA) ( P =0.0092, one‐way ANOVA followed by Tukey's multiple comparison). The protease‐activated receptor 2 (PAR2) antagonist GB83 (10 μM) also blocked the effect of the CD and UC patient supernatant on NG neurons ( P=0.0081 and P =0.0116, Kruskal‐Wallis test, respectively). Conclusion FS from active IBD patients contain mediators that can excite NG neurons. Cysteine proteases directly mediates the effect of IBD FS on NG neurons by activation of PAR‐2. Signaling pathways downstream of PAR‐2 activation lead to inhibition of voltage‐gated K + currents.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.011
GPT teacher head0.220
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2022
Admission routes2
Has abstractyes

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