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Record W2808822996 · doi:10.1038/s41467-018-04913-2

Pulmonary pericytes regulate lung morphogenesis

2018· article· en· W2808822996 on OpenAlexaff
Katsuhiro Kato, Rodrigo Diéguez‐Hurtado, Do Young Park, Seon Pyo Hong, Sakiko Kato-Azuma, Susanne Adams, Martin Stehling, Britta Trappmann, Jeffrey L. Wrana, Gou Young Koh, Ralf H. Adams

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHippo pathway signaling and YAP/TAZ
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsParacrine signallingAutocrine signallingCell biologyMorphogenesisBiologyHepatocyte growth factorLungReceptorMedicineInternal medicineGenetics

Abstract

fetched live from OpenAlex

Blood vessels are essential for blood circulation but also control organ growth, homeostasis, and regeneration, which has been attributed to the release of paracrine signals by endothelial cells. Endothelial tubules are associated with specialised mesenchymal cells, termed pericytes, which help to maintain vessel wall integrity. Here we identify pericytes as regulators of epithelial and endothelial morphogenesis in postnatal lung. Mice lacking expression of the Hippo pathway components YAP and TAZ in pericytes show defective alveologenesis. Mutant pericytes are present in normal numbers but display strongly reduced expression of hepatocyte growth factor leading to impaired activation of the c-Met receptor, which is expressed by alveolar epithelial cells. YAP and TAZ are also required for expression of angiopoietin-1 by pulmonary pericytes, which also controls hepatocyte growth factor expression and thereby alveologenesis in an autocrine fashion. These findings establish that pericytes have important, organ-specific signalling properties and coordinate the behavior of epithelial and vascular cells during lung morphogenesis.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.459

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 science0.0010.000
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 head0.013
GPT teacher head0.287
Teacher spread0.275 · 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

Citations115
Published2018
Admission routes1
Has abstractyes

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