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Record W3180950005 · doi:10.1093/hmg/ddab182

Aberrant lung lipids cause respiratory impairment in a <i>Mecp2</i>-deficient mouse model of Rett syndrome

2021· article· en· W3180950005 on OpenAlexafffund
Neeti Vashi, Cameron Ackerley, Martin Post, Monica J. Justice

Bibliographic record

VenueHuman Molecular Genetics · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics and Neurodevelopmental Disorders
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsMECP2BiologyRett syndromeRespiratory systemCell biologyEndocrinologyGeneticsGeneAnatomyPhenotype

Abstract

fetched live from OpenAlex

Severe respiratory impairment is a prominent feature of Rett syndrome, an X-linked disorder caused by mutations in methyl CpG-binding protein 2 (MECP2). Despite MECP2's ubiquitous expression, respiratory anomalies are attributed to neuronal dysfunction. Here, we show that neutral lipids accumulate in mouse Mecp2-mutant lungs, whereas surfactant phospholipids decrease. Conditional deletion of Mecp2 from lipid-producing alveolar epithelial 2 (AE2) cells causes aberrant lung lipids and respiratory symptoms, whereas deletion of Mecp2 from hindbrain neurons results in distinct respiratory abnormalities. Single-cell RNA sequencing of AE2 cells suggests lipid production and storage increase at the expense of phospholipid synthesis. Lipid production enzymes are confirmed as direct targets of MECP2-directed nuclear receptor co-repressor 1/2 transcriptional repression. Remarkably, lipid-lowering fluvastatin improves respiratory anomalies in Mecp2-mutant mice. These data implicate autonomous pulmonary loss of MECP2 in respiratory symptoms for the first time and have immediate impacts on patient care.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.253
Teacher spread0.235 · 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

Citations6
Published2021
Admission routes2
Has abstractyes

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