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Record W2596316031 · doi:10.1093/biolreprod/87.s1.125

Nitric Oxide Synthase Activity Is Critical for the LH-Induced Ovulatory Cascade in Bovine Granulosa Cells.

2012· article· en· W2596316031 on OpenAlexaffabout
Gustavo Zamberlam, Alysson Macedo, Gustavo Zemke, Fatiha Sahmi, Christopher A. Price

Bibliographic record

VenueBiology of Reproduction · 2012
Typearticle
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsAmphiregulinInternal medicineEndocrinologyBiologyOvulationEpiregulinNitric oxide synthaseOvarian follicleGranulosa cellNitric oxideReceptorOvaryEpidermal growth factorHormoneMedicine

Abstract

fetched live from OpenAlex

Fertility in dairy and beef cows is negatively affected by stressors such as lamness and high milk yield, and one site of action of these stressors is the process of ovulation. Rupture of the follicle is initiated by the preovulatory surge of LH, which stimulates granulosa cell release of the EGF-like ligands epiregulin (EREG) and amphiregulin (AREG), which activate EGF receptors in granulosa and cumulus cells to stimulate prostaglandin synthase 2 (PTGS2, also known as COX2) expression, prostaglandin secretion and downstream expression of cumulus expansion genes. Various factors have been implicated in the control of this process, including endogenous nitric oxide (NO). In rodents, NO has been associated either positively or negatively with ovulation. The objectives of the present study were to determine (1) if endogenous NO is critical for expression of genes critical for the ovulatory cascade in bovine granulosa cells, (2) and to determine if nitric oxide synthase (NOS) enzymes are regulated by LH. We employed a short-term bovine granulosa cell (GC) culture system in which AREG, EREG and PTGS2 mRNA levels are acutely upregulated by LH. The first series of experiments was performed to determine the importance of NO in the preovulatory cascade. Pretreatment of cells with a NOS inhibitor, L-NAME, effectively blocked the effect of LH on EREG, AREG and PTGS2 mRNA abundance at 6 hours post-LH, and on PTGS2 protein levels at 12 hours post LH challenge (P<0.05). To determine if NO is necessary for downstream, EGFR-dependent signaling, cells were pretreated with L-NAME and challenged with EGF; EGF alone increased mRNA encoding EREG within 1 h, and this was inhibited by pretreatment with L-NAME. EGF alone increased abundance of mRNA encoding AREG and the nuclear orphan receptors NR5A1 and NR5A2 after 8 h, and L-NAME inhibited the effect of EGF. A second series of experiments was performed to determine the regulation of NOS mRNA abundance in bovine GC. Time- and dose-response experiments demonstrated that LH had a significant stimulatory effect on NOS3 (eNOS) mRNA abundance (P<0.05), although transcriptional regulation of NOS3 was not observed until 12 h of LH treatment, much later than that of EREG mRNA (1-3 h). Messenger RNA encoding NOS2 (iNOS) was detected at very low levels and was not investigated further. Further experiments demonstrated that EGF stimulated NOS3 mRNA levels in a dose-dependent manner (P<0.05). As some studies report a positive feedback loop between prostaglandin E2 (PGE2) and EGF-like factors in cumulus cells, we tested whether PGE2 stimulates NOS3 mRNA levels in mural granulosa cells. The results confirmed that PGE2 stimulates EREG and also NOS3 mRNA levels (P<0.05). Moreover, when cells were pretreated with indomethacin, a PTGS2 inhibitor, EGF was not capable of inducing NOS3 and EREG mRNA levels (P<0.05), suggesting that the transcriptional regulation of NOS3 is prostaglandin dependent. To our knowledge, this is the first study indicating an interaction between nitric oxide and EGF-like factors during the preovulatory period. Taken together, these results suggest that endogenous NOS activity is critical for LH-induced ovulatory cascade in granulosa cells and NO may be essential for ovulation in cattle. Supported by FRQNT and NSERC Canada.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.329

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.057
GPT teacher head0.350
Teacher spread0.293 · 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".

Quick stats

Citations2
Published2012
Admission routes2
Has abstractyes

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