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Enteric Microbiota Contribute to Behavioral Alterations Observed in Mice with Colitis

2020· article· en· W3016363231 on OpenAlexaffabout
Fernando Vicentini, Lateece Griffin, Catherine M. Keenan, Jean‐Baptiste Cavin, Kristoff Nieves, Simon A. Hirota, Keith A. Sharkey

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDysbiosisGut floraColitisGut–brain axisContext (archaeology)ImmunologyGastrointestinal tractInflammatory bowel diseaseMedicineInflammationMicrobiomeIrritable bowel syndromeDiseaseInternal medicineBiologyBioinformatics

Abstract

fetched live from OpenAlex

The enteric microbiota has been recognized as an essential regulator of both gut and brain physiology, a complex interaction generally termed the microbiota‐gut‐brain axis. Disturbances to gastrointestinal physiology lead to alterations in the composition of the enteric microbiota, whereas dysbiosis can also contribute to pathophysiology. Inflammatory bowel diseases (IBD) are chronic, relapsing and remitting inflammatory conditions of the gastrointestinal tract, associated with microbial dysbiosis. Interestingly, IBD patients exhibit an increased incidence of mental illness (i.e. anxiety and depression), often termed “sickness behavior”, even during the remitting phase of their disease. It is unclear if alterations in the enteric microbiota associated with IBD are responsible for the observed modification in brain function and behavior. Here, we hypothesized that sickness behavior is driven by alterations in microbial composition, which occur in the context of intestinal inflammation. We tested whether transfer of the microbiota from colitic mice, exhibiting sickness behaviour, into healthy counterparts would induce behavioral changes. Male mice (C57Bl/6J; 8 weeks old) were used in all experiments. Colitis was induced by administration of 2.5% dextran sodium sulfate (DSS) in the drinking water for 5 days. Colonic inflammation was assessed by measuring fecal lipocalin‐2 and the expression of pro‐inflammatory mediators via qPCR. Cecal matter from donor mice (control or DSS treated) were collected for fecal microbiota transplant (FMT). FMT was performed via oral gavage in antibiotic‐treated recipient mice. Gut bacteria were evaluated by 16S rRNA sequencing in cecal samples. Anxiety‐ and depression‐like behavior were assessed by elevated plus maze and tail suspension test, respectively. Brain samples were processed for qPCR analysis. DSS‐treated mice exhibited clinical disease, reflected by body weight loss, increased fecal lipocalin‐2 and elevated colonic pro‐inflammatory cytokine transcripts. An increase in anxiety‐like behavior was observed in mice with colitis, although no alterations in depression‐like behavior were detected. Colitic mice exhibited a unique microbial community. Transferring cecal material from colitic mice into recipient, antibiotic‐treated mice, recapitulated alterations in behavior seen in colitic donors, as shown by increased anxiety‐like behavior and unexpectedly, increased depression‐like behavior. These behavioral changes occurred in the absence of colonic or brain inflammation in the recipient mice, but were associated with changes in stress‐related gene expression (i.e. Crh ). Colitis‐associated sickness behavior can be transmitted to antibiotic‐treated recipients via FMT, which occurs in the absence of overt intestinal or neuroinflammation. Support or Funding Information This work was supported by Canadian Institutes of Health Research. F.A.V. is funded by the National Counsel of Technological and Scientific Development (CNPq, Brazil).

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.028
GPT teacher head0.270
Teacher spread0.242 · 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 designObservational
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 routes2
Has abstractyes

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