MétaCan
Menu
Back to cohort
Record W2956085547 · doi:10.7554/elife.45282

Fetal and trophoblast PI3K p110α have distinct roles in regulating resource supply to the growing fetus in mice

2019· article· en· W2956085547 on OpenAlexaff
Jorge López‐Tello, Vicente Pérez-García, Jaspreet Khaira, Laura C. Kusinski, Wendy N. Cooper, Adam Andreani, Imogen Grant, Edurne Fernández de Liger, Brian Lam, Myriam Hemberger, Ionel Sandovici, Miguel Constância, Amanda N. Sferruzzi‐Perri

Bibliographic record

VenueeLife · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Syndromes and Imprinting
Canadian institutionsUniversity of Calgary
FundersErasmus+Biotechnology and Biological Sciences Research CouncilRoyal SocietyMedical Research CouncilEuropean Cooperation in Science and TechnologyWellcome Trust
KeywordsTrophoblastConceptusBiologyPlacentaFetusCell biologyPhenotypeEmbryonic stem cellGeneticsGenePregnancy

Abstract

fetched live from OpenAlex

Studies suggest that placental nutrient supply adapts according to fetal demands. However, signaling events underlying placental adaptations remain unknown. Here we demonstrate that phosphoinositide 3-kinase p110α in the fetus and the trophoblast interplay to regulate placental nutrient supply and fetal growth. Complete loss of fetal p110α caused embryonic death, whilst heterozygous loss resulted in fetal growth restriction and impaired placental formation and nutrient transport. Loss of trophoblast p110α resulted in viable fetuses, abnormal placental development and a failure of the placenta to transport sufficient nutrients to match fetal demands for growth. Using RNA-seq we identified genes downstream of p110α in the trophoblast that are important in adapting placental phenotype. Using CRISPR/Cas9 we showed loss of p110α differentially affects gene expression in trophoblast and embryonic stem cells. Our findings reveal important, but distinct roles for p110α in the different compartments of the conceptus, which control fetal resource acquisition and growth.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.407

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 head0.005
GPT teacher head0.210
Teacher spread0.206 · 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 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".

Quick stats

Citations45
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueeLifeSame topicGenetic Syndromes and ImprintingFrench-language works237,207