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Record W2990411477 · doi:10.1071/rdv32n2ab182

182 Bisphenol A, but not bisphenol S, affects key microRNAs during bovine oocyte maturation

2019· article· en· W2990411477 on OpenAlexaff
Reem Sabry, Leanne Stalker, Jonathan LaMarre, Laura A. Favetta

Bibliographic record

VenueReproduction Fertility and Development · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsOocytemicroRNABiologyEpigeneticsMessenger RNAComplementary DNACell biologyAndrologyReal-time polymerase chain reactionTranscriptomeGene expressionGeneGeneticsEmbryoMedicine

Abstract

fetched live from OpenAlex

Oocyte maturation involves crucial hormone-dependent events that are uniquely susceptible to toxic insults by endocrine disrupting chemicals (EDCs). Emerging evidence suggests that small RNAs, including microRNAs (miRNAs), may be key participants in the response to EDCs. Bisphenol A (BPA) and bisphenol S (BPS) are chemicals with detrimental health effects, with BPA negatively affecting oocyte quality. The mode of action of bisphenols at the epigenetic level is not clear. Several miRNAs have been identified as crucial regulators of gene expression during development. This study aimed to examine key miRNAs in response to BPA or BPS treatment during oocyte maturation. Primary forms (pri-miRNA) and mature forms of miR-21, miR-155, miR-34c, and miR-146a were quantified by quantitative (q)PCR in IVM bovine cumulus-oocyte complexes (COCs) and in vitro cultured (IVC) cumulus cells treated with BPA and BPS at physiologically significant doses (0.05 mg mL−1). Total RNA, containing both forms of microRNAs, was isolated from pools of 40 COCs on a minimum of three biological replicates. Primary miRNAs and mature miRNAs were reverse transcribed (RT) using qScript cDNA and microRNA/cDNA kits, respectively, and quantified by qPCR, with three technical replicates for each biological one. In addition, mRNA and protein quantification of the downstream target DNMT3A in IVC cumulus cells further enhanced our understanding of EDC interference in epigenetic regulations in female reproduction. Total RNA was isolated from cumulus cells, mRNA (1 μg) and mature miRNAs (0.5 μg) were RT separately and cDNA was quantified by qPCR, as described above. Expression values were normalized against two housekeeping genes selected by GeNorm analysis. Twenty micrograms of proteins extracted by sonication were loaded on a 8% acrylamide gel and analysed by western blotting. Densitometry analysis was performed on 3 separate blots with protein levels normalized to the loading control, β-actin. One-way ANOVA was used to determine statistical differences among treatment groups with P < 0.05 considered statistically significant. Results showed that BPA significantly increased miR-21 in COCs (P = 0.02) and cumulus cells (P = 0.01), increased pri-miR-21 in oocytes (P = 0.03), suppressed miR-34c in cumulus cells (P = 0.02), increased miR-155 in denuded oocytes (P = 0.04), and had no effect on miR-146a. No changes were observed in response to BPS. Experiments in IVC cumulus cells showed similar miRNA profiles: miR-21 and miR-155 were significantly overexpressed in BPA-treated cells (P = 0.04); however, miR-34c and miR-146a were not affected. Messenger RNA levels of DNMT3A increased (P = 0.02) and protein levels of DNMT3A decreased in response to BPA (P = 0.005). Overall, this study presents novel findings of BPA-induced miRNA dysregulation in IVM bovine oocytes and in IVC bovine cumulus cells. We can speculate that miR-21 participates in the BPA-induced dysregulation of DNMT3A, contributing to decreased fertility. This study did not show any effect of BPS on microRNA expression, suggesting an alternative mechanistic pathway for this analogue.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.396
Threshold uncertainty score0.826

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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 teacher head, 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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