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Record W3040648832 · doi:10.3389/fmicb.2020.01461

Selenomethionine Suppressed TLR4/NF-κB Pathway by Activating Selenoprotein S to Alleviate ESBL Escherichia coli-Induced Inflammation in Bovine Mammary Epithelial Cells and Macrophages

2020· article· en· W3040648832 on OpenAlex
Cuicui Zhuang, Gang Liu, Herman W. Barkema, Man Zhou, Siyu Xu, Sadeeq ur Rahman, Yongxia Liu, John P. Kastelic, Jian Gao, Bo Han

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Microbiology · 2020
Typearticle
Languageen
FieldNursing
TopicSelenium in Biological Systems
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsInflammationEscherichia coliSelenoproteinTLR4MicrobiologyNF-κBChemistryBiologyGeneImmunologyBiochemistryOxidative stressSuperoxide dismutase

Abstract

fetched live from OpenAlex

Inflammation is the hallmark of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli-induced bovine mastitis. Organic selenium can activate pivotal proteins in immune responses and regulate the immune system. The present study aimed to investigate whether selenomethionine (SeMet) attenuates ESBL E. coli-induced inflammation in bovine mammary epithelial cells (bMECs) and macrophages. Cells were treated with 0, 5/10, 10/20, 20/40 or 40/60 μM SeMet for 12 h and/or inoculated with ESBL-E. coli (MOI=5) for 4/6 h, respectively. We assessed inflammatory responses, including selenoprotein S (SeS), Toll-like receptor 4 (TLR4), Ikappa-B (IκB), phospho-NF-κB p65 (Ser536), IL-1β, tumor necrosis factor-α (TNF-α) and lactate dehydrogenase (LDH) activities. Treatment with 40/60 μM SeMet promoted cell viability and inhibited LDH activities in both bMECs and macrophages. Inoculation with ESBL-E. coli reduced cell viability, which was attenuated by SeMet treatment in bMECs and macrophages. SeMet increased ESBL E. coli-induced downregulation of SeS and decreased LDH activities, TLR4, IκB, phospho-NF-κB p65 (Ser536), IL-1β and TNF-α protein expressions in bMECs and macrophages. In addition, knockdown of SeS promoted protein expression of TLR4-mediated NF-κB pathway and BAY 11-708 inhibited TNF-α and IL-1β protein levels in bMECs and macrophages after ESBL-E. coli treatment. Moreover, ESBL-E. coli inoculation increased monocyte chemoattractant protein 1 (MCP-1), C–C motif ligand 3 (CCL-3) and CCL-5 mRNA expressions in bMECs. In conclusion, ESBL-E. coli induced expression of MCP-1, CCL-3 and CCL-5 in bMECs and then recruited and activated macrophages, whereas SeMet attenuated ESBL E. coli-induced inflammation through activated SeS-mediated TLR4/NF-κB signaling pathway in bMECs and macrophages.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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