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Record W3006913961 · doi:10.1093/jcag/gwz047.208

A209 ROLE OF SEROTONIN-AUTOPHAGY AXIS IN REGULATION OF EPITHELIAL CELL FUNCTION AND MICROBIOTA COMPOSITION IN GUT

2020· article· en· W3006913961 on OpenAlexaff
Sabah Haq, H Wang, Jeansok J. Kim, E Y Kwon, Suhrid Banskota, Jensine A. Grondin, Nida Hassan, John H. Brumell, Dana J. Philpott, Waliul I. Khan

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsAutophagyGut floraProinflammatory cytokineColitisDysbiosisBarrier functionInflammationInflammatory bowel diseaseIntestinal epitheliumIntestinal mucosaBiologyCell biologyImmunologyChemistryEpitheliumInternal medicineBiochemistryMedicineApoptosisDisease

Abstract

fetched live from OpenAlex

Abstract Background Serotonin (5-hydroxytryptamine; 5-HT), an enteric signalling molecule mainly produced by the enterochromaffin (EC) cells of the intestinal epithelium regulates various processes of the gut. Tryptophan hydroxylase 1 (Tph1) is the rate-limiting enzyme of 5-HT biosynthesis in EC cells. In inflammatory bowel disease (IBD) and experimental colitis, there are alterations in 5-HT content and microbiota composition in the gut. Previously we reported, Tph1-deficient (Tph1-/-) mice with reduced 5-HT in the gut exhibit reduced susceptibility to colitis. The mechanism by which 5-HT regulates colitis is unknown. Autophagy, a catabolic process regulates the function of intestinal epithelial cells (IECs), gut microbiota, and protects against intestinal inflammation. Both aberrant 5-HT signalling and autophagy is implicated in colitis. It is unclear whether they interact in regulation of production of pro-inflammatory cytokines from IECs and gut microbiota composition in relation to colitis. Our hypothesis is, an increase in 5-HT signalling inhibits autophagy in the IECs, which results in up-regulation of colitis by increasing the production of pro-inflammatory cytokines, and by selection for a more colitogenic microbiota. Aims To define the role of 5-HT-autophagy axis in the production of pro-inflammatory cytokines from IECs and gut microbiota composition in intestinal inflammation. Methods We investigated level of autophagy with or without 5% dextran sodium sulphate (DSS) in colons, mucosal scraping and IECs of Tph1-/- and their wild-type (WT) littermates. In addition, autophagy and proinflammatory cytokine production were investigated in human colonic epithelial cells (HT-29) following stimulation by 5-HT. We evaluated colitis and gut microbiota composition in WT, Tph1-/-, epithelial-specific autophagy gene Atg7 deficient (Atg7ΔIEC), and Atg7ΔIECTph1-/- (double knock out; DKO) mice. Results Tph1 -/- mice, with less 5-HT in the gut than WT mice following DSS administration exhibited an up-regulation of autophagy markers in the colon, mucosal scraping and IECs along with reduction of colitis severity. 5-HT treatment of HT-29 cells resulted in down-regulation of autophagy and upregulation of pro-inflammatory cytokine, IL-8. DKO mice exhibited increased severity of DSS-colitis, and altered microbiota composition compared to Tph1-/- mice. Conclusions These findings suggest, an increase in 5-HT in colitis inhibits autophagy in the IECs that contribute to alteration of the gut microbiota and disease severity. Blocking 5-HT signalling may promote autophagy in the IECs and alleviate the severity of colitis. Understanding the contribution of 5-HT in autophagy may identify new therapeutic target in IBD and other intestinal inflammatory conditions that exhibit dysregulated autophagy. Funding Agencies CAG, CIHR

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.004
GPT teacher head0.188
Teacher spread0.185 · 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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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