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Record W4401900864 · doi:10.1101/2024.08.26.609823

Maternal progesterone and adipose mPRε in pregnancy regulate the embryonic nutritional state

2024· preprint· en· W4401900864 on OpenAlexaff
Keita Watanabe, Mayu Yamano, Junki Miyamoto, Ryuji Ohue‐Kitano, Yuki Masujima, Daiki Sasahara, Yuki Mouri, Nozomu Kono, Shunsuke Inuki, Fumitaka Osakada, Kentaro Nagaoka, Junken Aoki, Yuki Sugiura, Hiroaki Ohno, Eiji Kondoh, Ikuo Kimura

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldMedicine
TopicBirth, Development, and Health
Canadian institutionsASTER
Fundersnot available
KeywordsAdipose tissuePregnancyEmbryonic stem cellState (computer science)EndocrinologyBiologyComputer scienceGeneticsGene

Abstract

fetched live from OpenAlex

Abstract Sex steroid hormones such as progesterone play a pivotal role in reproductive functions and maintaining pregnancy; however, the impact of progesterone on the interaction between mother and embryo is unclear. Here, we demonstrate that the relationship between maternal progesterone and membrane progesterone receptor epsilon (mPRε) in adipose tissue regulates embryonic nutritional environment and growth after birth in mice. The activation of adipose mPRε by increased progesterone during pregnancy enhanced maternal insulin resistance through the production of prostaglandins, thereby efficiently providing glucose to embryos. The offspring of mPRε-deficient mothers exhibited metabolic dysfunction, whereas mPRε-deficient mothers with high-fat-diet-induced obesity exhibited improved insulin sensitivity. These findings establish the importance of progesterone as a nutritional regulator between mother and embryo, and suggest that mPRε modulators could be developed to treat pregnant glycemic control disorders such as gestational diabetes mellitus, as well as metabolic syndrome in offspring. Graphical Abstract

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 score0.045
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0450.005

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 head0.017
GPT teacher head0.247
Teacher spread0.231 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicBirth, Development, and Health→French-language works237,207→