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Common Bean and Chickpea Supplemented Diets Beneficially Enhance the Colonic Microenvironment and Reduce Colitis‐Associated Inflammation

2016· article· en· W2953450397 on OpenAlexaffabout
Krista A. Power, Jennifer M. Monk, Dion Lepp, Lindsay E. Robinson, Wenqing Wu, Christine M. Carey, Daniela Gräf, Adeel Hussain, L. McGillis

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of GuelphAgriculture and Agri-Food Canada
Fundersnot available
KeywordsColitisInflammatory bowel diseaseGoblet cellOccludinGut floraMucusMucin 2MucinCryptInflammationBarrier functionFood scienceBiologyImmunologyMedicineInternal medicineDiseaseTight junctionBiochemistryEpitheliumGene expressionPathology

Abstract

fetched live from OpenAlex

Dietary impacts on the colonic microenvironment (microbiota and mucosal barrier) can play an important role in modulating gut‐associated inflammatory diseases, such as inflammatory bowel disease (IBD).Pulses, such as common beans and chickpeas, are rich sources of gut health promoting bioactives, including non‐digestible fermentable carbohydrates and phenolics, however, research demonstrating whole pulse effects on the colonic microenvironment and subsequent inflammatory pathologies, are lacking. Our objectives were to determine the effects of cooked bean and chickpea diets on critical aspects of colon health (microbiota activity and community structure, and colonic epithelial barrier integrity and function) and subsequent effects on disease severity following colitis‐associated mucosal injury. In study 1, male C57Bl/6 mice were fed a 20% cooked black bean flour supplemented diet (BB) or an isocaloric control diet (CON) for 3 weeks, followed by exposure to dextran sodium sulphate (DSS, 2% w/v, 5d) to test the ability of BB to prime the colon and attenuate the severity of colitis‐induced damage mimicking IBD. In study 2, a similar study design was used except mice were fed CON or diet supplemented with 20% cooked chickpeas. Prior to DSS‐exposure (healthy mice; n=10/diet), BB diet increased i) colon crypt height, goblet cell number, and mucus production, ii) mRNA expression of MUC1‐3, RELMβ, REG3γ, IgA, occludin and JAM‐A, and iii) reduced serum LPS levels compared to CON, indicative of enhanced antimicrobial defense and gut barrier integrity. Furthermore, compared to CON, BB consumption i) increased microbial‐derived cecal short chain fatty acids (SCFAs) acetate, butyrate, and propionate, and ii) altered the colonic microbial community structure (increased Prevotellaceae, Porphyromonadaceae, and S24‐7 and reduced Rikenellaceae, Lachnospiraceae, Streptococcaceae, Erysipelotrichaceae, Peptococcaceae, and Peptostreptococcaceae), as measured by fecal 16S rRNA sequencing. During colitis (n=10/diet), BB‐fed mice had increased colonic mRNA expression of microbial‐responsive and epithelial barrier integrity promoting genes (MUC1‐3, RELMβ, and TFF3) and reduced colonic pro‐inflammatory mediator production (IL‐1β, IL‐6, IFN‐γ, and TNF‐α). Similar effects of chickpea supplementation were observed in healthy (enhanced mucosal barrier and microbial activity and altered microbial community structure) and colitic mice (reduced disease severity). In conclusion, pulse supplementation resulted in an improved colonic microenvironment as demonstrated by beneficial changes to the microbial community structure and activity, and increased colonic mucosal barrier integrity. This led to an attenuation of colonic damage following DSS‐induced colitis. Therefore, regular consumption of pulse foods may beneficially prime the colonic microenvironment to better respond to inflammatory insults which can mitigate development and severity of inflammation‐associated colonic diseases. Support or Funding Information ORF; AAFC‐Pulse Canada

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)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.243
Teacher spread0.236 · 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

Citations2
Published2016
Admission routes2
Has abstractyes

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