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Record W2595364407 · doi:10.1093/biolreprod/87.s1.293

Microarray Analysis of Bovine Oocyte from Distinct Follicle Sizes Reveals Gradual Transcripts Accumulation.

2012· article· en· W2595364407 on OpenAlexaff
Rémi Labrecque, Marc‐André Sirard

Bibliographic record

VenueBiology of Reproduction · 2012
Typearticle
Languageen
FieldMedicine
TopicReproductive Biology and Fertility
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsOocyteBlastocystBiologyFolliculogenesisAndrologyTranscriptomeFollicular phaseFollicleEmbryoIn vitro maturationOvarian follicleHuman fertilizationReproductive technologyEmbryogenesisCell biologyGeneGeneticsGene expressionEndocrinology

Abstract

fetched live from OpenAlex

In vitro maturation and in vitro fertilization of bovine oocyte obtained from slaughterhouse-derived ovaries is a routine procedure in many labs to study various questions related to reproductive biology. However, even after more than 25 years of improvements of this system, from optimization of culture media to morphological selection of the best cumulus-oocyte complex, success rate remains quite low with an average blastocyst rate around 30%. One model of oocyte competence implies the follicular size where the oocyte originates. Effectively, higher blastocyst rate are obtained with oocyte from larger follicles compare to the smaller one. Furthermore, it is well known that during folliculogenesis, the oocyte needs to accumulate all the transcripts needed to ensure the development until the maternal to embryonic transition. Therefore, the aim of this project is to compare the transcriptome of oocyte originating from follicle of specific sizes in a sequential way. Bovine slaughterhouse-derived ovaries were used and oocytes were collected and divided in four groups: oocytes from follicles < 3 mm of diameter; 3 to 5 mm; 5 to 8 mm and > 8 mm. Three pools of ten oocytes per group were used to perform a transcriptomic analysis with a bovine embryo-specific 44K Agilent slide (EmbryoGene). We used a reference design (<3 mm group as a reference) to compare with the three other groups. Concerning the differentially expressed genes, we found 12, 710 and 240 probes up or down-regulated (fold-change >2; p < 0.05) in the contrast <3mm vs 3-5mm; <3mm vs 5-8mm and <3mm vs >8mm respectively. When we looked at the number of probes that are common between these three contrasts, we found 1667 probes in common (fold change ≠1, p<0.05). Interestingly, the majority of these probes appears to change either in one direction or in other across the follicular stages studied (812 probes with a positive fold change for three contrasts and 722 probes with a negative fold change in three contrasts). For example, transcriptional adaptor 1 (TADA1), which is part of a chromatin-modifying complex, showed constant increase at the transcript level with fold change >1.5 in each contrast. While within bgcn homolog (WIBG), a key regulator of the exon jonction complex known to enhance translation of spliced mRNAs, showed constant decrease of its mRNA level along the follicular growth. These results suggest the relative similarity between the first two groups (<3mm and 3-5mm) while the other two groups reveals increasing differences compared to the reference. The majority of changes in term of differentially expressed genes in the oocyte during those stages of follicular growth seem to follow a general trend (global increase or decrease), which reinforces the idea that the competence acquisition is a gradual process. So, a comprehensive transcriptomic analysis of the oocyte from different follicle size in a sequential scheme will help us to better understand the competence acquisition during the follicular growth in an in vitro system.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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

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 head0.064
GPT teacher head0.336
Teacher spread0.272 · 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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