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Intraluminal Nutrients Modulate Intracellular Calcium Activity in the Enteric Nervous System of Adult Mice

2019· article· en· W3175522398 on OpenAlexaffabout
Jean‐Baptiste Cavin, Joel C. Glover, Wallace K. MacNaughton, Keith A. Sharkey

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsCalgary Laboratory ServicesHotchkiss Brain InstituteUniversity of Calgary
Fundersnot available
KeywordsCalciumCalcium in biologyCalcium imagingEnteric nervous systemPerfusionBiologyConfocalIntracellularJejunumPremovement neuronal activityCalcium metabolismElectrophysiologySmall intestineCell biologyBiochemistryInternal medicineEndocrinologyNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Background It has long been hypothesized that enteric neurons are able to detect nutrients present in the intestinal lumen, but direct evidence in intact preparations of the intestine is lacking. Understanding how enteric neurons react to changes in dietary constituents will open new avenues for discoveries on the relation between nutrition, modulation of gut function, and health. Aim To measure neuronal activity in the enteric nervous system in response to luminal nutrients. Methods We have designed chambers allowing us to visualize, record and quantify intracellular calcium activity in 3D live‐cell confocal recordings of neurons from intact, whole thickness segments of mouse intestine. Twenty mice expressing the genetically encoded calcium reporter Gcamp6 under the control of the pan‐neuronal Wnt1 promoter were used to record intracellular calcium activity in neurons from the jejunum and colon, while perfusing different nutrients through the lumen. Krebs solution was perfused as control and Ensure®, 10% Intralipid® or 20mM Glucose were used as stimuli. 3D videos of calcium fluorescence in the neuronal network were acquired with a Nikon A1R confocal microscope and calcium dynamics were analyzed using the Imaris software (Bitplane). Results are expressed as relative changes in fluorescence intensity (ΔF/F) ± SEM among the neuronal populations studied. Results Perfusion of the intestine with Ensure® (liquid meal replacement) increased the fluorescence intensity in reactive neurons by 670 ± 270% in the jejunum and 403 ± 62% in the colon. Peak amplitude was measured on average 5–6 minutes after the initiation of the nutrient perfusion. Perfusion with 10% Intralipid® (lipid emulsion) increased the fluorescence intensity in reactive neurons by 300 ± 180% in the jejunum and 150 ± 15% in the colon. On the other hand, perfusion of the intestine with Glucose (20mM in Krebs solution) lowered baseline fluorescence intensity in a population of neurons by −91 ± 3% in the jejunum and −80 ± 3% in the colon. The percentage of neurons reacting to intraluminal stimuli was dependent on the stimulus and was different between the jejunum and the colon, indicating that subpopulations of neurons might differentially react to different nutrient stimuli. Conclusions We report the existence of slow intracellular calcium changes in the neurons in the intestine that vary depending on the nutrient content of the intestinal lumen. The origin and functional consequences of these changes in intracellular calcium activity as well as the identity of responding neurons are currently under investigation. Support or Funding Information Funding sources: Canadian institutes of Health Research, Alberta Innovates, Human Frontier Science Program. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.249
Teacher spread0.234 · 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
Published2019
Admission routes2
Has abstractyes

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