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Record W3008129165 · doi:10.1093/jcag/gwz047.259

A260 THE LACK OF CHROMOGRANIN-A MODIFIES THE GUT MICROBIOTA COMPOSITION AND REGULATES EXPERIMENTAL COLONIC INFLAMMATION

2020· article· en· W3008129165 on OpenAlexaff
Nour Eissa, A. Diarra, Hayam Hussein, Çharles N. Bernstein, Jean‐Eric Ghia

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMicrobiomeChromogranin ABiologyGut floraDysbiosisFecesMicrobiologyImmunologyBioinformaticsImmunohistochemistry

Abstract

fetched live from OpenAlex

Abstract Background Ulcerative colitis (UC)is characterized by distinct changes in the gut microbiome and elevated chromogranin-A (CHGA) level, which seem to be a relevant pathogenetic mechanism.CHGA, a prohormone produced by enterochromaffin (EC) cells and cleaved into several bioactive peptides, regulates experimental colonic inflammation. In the rodent, intra-rectal infusion of catestatin, a Chga-derived peptide, alters the distal colonic microbial composition. However, the interplay between CHGA, as a pro-hormone, and the gut microbiome remains elusive. Aims in homoeostatic and pathophysiologic conditions, we investigated the functional consequences of the lack of Chgaon the distal colonic microbiota. Methods Acute colitis (5 % dextran sulfate sodium [DSS], 5 days) was induced in Chga-C57BL/6-deficient (Chga-/-) and wild-type (Chga+/+)mice. Feces and mucosa-associated microbiota (MAM) samples were collected and the V4 region of 16s rRNA was subjected to Miseq Illumina sequencing. Alpha diversity was calculated using Shannon’s diversity index. OTU abundances were summarized using the Bray-Curtis index and non-metric multidimensional scaling (NMDS) analysis to visualize microbiome similarities and a permutational analysis of variance (PERMANOVA) to test the significance of groups were performed respectively. Results In non-colitic homoeostatic condition, the absence of Chga (Chga-/) significantly increased the bacterial richness and modified the bacterial community composition at the genera level between the groups, represented by increased abundance of Lactobacillus species and reduced abundance of Helicobacter& Oscillospira species compared to Chga+/+mice in fecal and colonic MAM. Moreover, the absence of Chga (Chga-/-) resulted in a significant change in the alpha-diversity of fecal and colonic MAM compared to Chga+/+mice. DSS induced-colitis resulted in a significant microbial dysbiosis in Chga+/+mice, however, deletion of Chgaprotected against DSS-induced colitis and reduced the microbial dysbiosis, reduced the family of Rikenellaceaeand maintained the abundance of Bacteroides species, compared to wild-type (Chga+/+). Conclusions The lack of CHGA regulates the biodiversity and the composition of the colonic gut microbiota suggesting a cross-talk between the EC cell and the microbiome. Therefore, targeting CHGA could provide a novel therapeutic strategy by regulating the gut microbiome in physiological and pathophysiological conditions. Funding Agencies 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.016
GPT teacher head0.208
Teacher spread0.192 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

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