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Record W4385077305 · doi:10.3168/jdsc.2022-0333

Effect of pH and lipopolysaccharide on tight junction regulators and inflammatory markers in intestinal cells as an experimental model of weaning transition in dairy calves

2023· article· en· W4385077305 on OpenAlexafffund
B.C. Agustinho, Adam Mark, A.H. Laarman, Denise Konetchy, Pedram Rezamand

Bibliographic record

VenueJDS Communications · 2023
Typearticle
Languageen
FieldNeuroscience
TopicBarrier Structure and Function Studies
Canadian institutionsUniversity of Alberta
FundersNational Institute of Food and AgricultureUniversity of Alberta
KeywordsLipopolysaccharideWeaningTransition (genetics)Inflammatory responseImmunologyBiologyChemistryCell biologyAnimal scienceInflammationGeneticsGene

Abstract

fetched live from OpenAlex

Acidic conditions combined with the presence of lipopolysaccharide (LPS) may increase the permeability of gastrointestinal epithelium. Feeding starch-rich starter to dairy calves is associated with ruminal acidosis and decreases the pH of other segments of the gastrointestinal tract, and that affects the tight junction regulator. The objective was to evaluate the effect of the combination of different pH (7.4 vs. 6.0) and LPS concentrations (0, 0.5, 10 ng/mL) in intestinal cells on tight junction regulators, inflammatory markers, and permeability. The human colon carcinoma Caco-2 cell line was used with the main treatment of pH and LPS in a 2 × 3 factorial arrangement. The pH was acidic (pH 6.0) or physiologic (pH 7.4), whereas LPS was 0, 0.5, or 10 ng/mL. After cells reached 70%–80% of confluence, the media were replaced with each respective treatment medium. Cells were treated for 3 h for mRNA abundance analysis, 3 and 6 h for protein abundance determination, and 3, 6, 12, and 24 h for permeability determination. Protein abundance of the myosin light-chain kinase (MYLK) and toll-like receptor 4 (TLR4) were measured by western blot. The mRNA abundance of IL-8, MYLK, peroxisome proliferator activated receptor gamma, and nuclear factor kappa B (Nfkb1) was determined by real-time, quantitative PCR. Paracellular permeability was determined with Lucifer yellow after 21 d of incubation. Cell culture was performed in biological triplicate; each biological replicate for real-time, quantitative PCR had 2 technical replicates, and for protein abundance and permeability assay had one technical replicate. The MIXED procedure of SAS (SAS Institute Inc.) was used with LPS, pH, and pH × LPS as fixed effects. Significance was declared at P ≤ 0.05 and tendencies when 0.05 < P ≤ 0.10. Increasing LPS doses did not affect the protein abundance of MYLK and TLR4, nor mRNA abundance of MYLK and PPRG. The LPS tended to increase mRNA abundance of IL-8 while pH × LPS interactively increased mRNA abundance of Nfkb1, where mRNA abundance of Nfkb1 was lower in cells exposed to pH 6.0 when combined with 0 and 10 ng/mL of LPS. Contrary to expectations, LPS did not affect the permeability of Caco-2 cells. The mRNA abundance of MYLK was greater at pH 6.0 versus pH 7.4. Also, protein abundance of TLR4 was lower at pH 6.0 than pH 7.4, and it decreased when exposure increased to 6 h. In addition, mRNA abundance of IL-8 was lower at pH 6.0 versus pH 7.4. Permeability was greater at pH 6.0 versus 7.4 after 6, 12, and 24 h of treatment. In summary, the effect of LPS and its interaction with pH showed less impact than expected on dependent variables measured, which might be attributed to the adopted clinically achievable LPS doses likely not being high enough to draw a strong response as observed in the literature. On the other hand, pH was far more relevant, modulating mRNA abundance of inflammatory markers, tight junction regulators, and permeability in in vitro colon cell models.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
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 head0.027
GPT teacher head0.292
Teacher spread0.265 · 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

Citations7
Published2023
Admission routes2
Has abstractyes

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