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Record W3095700049 · doi:10.1101/2020.11.05.369322

All-trans Retinoic Acid Regulates miR-106a-5p Inhibition of Autophagic in Developing Cleft Palates

2020· preprint· en· W3095700049 on OpenAlexaff
Lungang Shi, Yan Liang, Lijing Yang, Binchen Li, Binna Zhang, Congyuan Zhen, Jufeng Fan, Shijie Tang

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCleft Lip and Palate Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPI3K/AKT/mTOR pathwayCell biologyAutophagyBiologyPTENProtein kinase BRetinoic acidEmbryonic stem cellCellular differentiationStem cellHomeobox protein NANOGChemistryMolecular biologySignal transductionCell cultureBiochemistryApoptosisInduced pluripotent stem cell

Abstract

fetched live from OpenAlex

Abstract Background All-trans retinoic acid (atRA) results in cleft palate, but the cellular and molecular mechanisms underlying the teratogenic effects on palatal development have not been fully elucidated. Autophagy interruption has been reported to seriously affect embryonic-cell differentiation and development. This study aimed to verify whether atRA-induced cleft palate occurs because atRA blocks autophagy and stemness of embryonic palatal mesenchyme (MEPM) cells, which are maintained via the phosphatase and tensin homolog (PTEN)/protein kinase B (Akt)/mammalian target of rapamycin (mTOR) autophagic signaling pathway, and inhibits osteogenic-differentiation potential of MEPM cells, which could lead to the development of cleft palate. Methods To assess the stemness and pluripotency of MEPM cells, we analyzed their surfacemarkers using immunofluorescence (IF) and flow cytometry (FCM). Differentiation potentials, such as osteogenic, adipogenic, and chondrogenic differentiation, were induced. We also investigated the role of the PTEN/Akt/mTOR autophagic signaling pathway, which maintains the stemness and pluripotency of MEPM cells. Using transmission electron microscopy (TEM), Western blot analysis, quantitative reverse transcriptase polymerase chain reaction (RT-qPCR), messenger ribonucleic acid (mRNA) microarray, dual-luciferase reporter system, and exosomes, we found that atRA blocks autophagy and osteogenic differentiation of MEPM cells through micro-ribonucleic acid (miR)-106a-5p by targeting the PTEN/Akt/mTOR autophagic pathway. Results In vitro purified MEPM cells expressed cell surface markers similar to those of mouse bone marrow stem cells. Additionally, in vitro MEPM cells were ectomesenchymal and expressed the neural-crest marker human natural killer-1 (HNK-1), the mesodermal marker vimentin, and the ectodermal marker nestin. They were also positive for in vitro MEPM markers, including platelet-derived growth factor alpha (PDGFRα), ephrin B1 (Efnb1), odd-skipped related 2 (Osr2), and Meox2, as well as for stemness markers including POU class 5 homeobox 4 (Oct4), Nanog, and sex-determining region Y-related HMG box 2 (Sox2). MEPM cell pluripotency was retained through activation of the PTEN/Akt/mTOR autophagic signaling pathway. We found that atRA blocked MEPM cell pluripotency to inhibit osteogenic differentiation via miR-106a-5p targeting of PTEN mRNA and subsequent suppression of the PTEN/Akt/mTOR autophagic pathway. Conclusions In vitro cultured MEPM cells are ectomesenchymal stem cells that have strong osteogenic differentiation potential, and MEPM pluripotency is regulated by autophagy via the PTEN/AKT/mTOR signaling pathway. atRA disrupts MEPM cell pluripotency through PTEN/AKT/mTOR signaling inactivation where miR-106a-5p targets PTEN mRNA to reduce osteogenic differentiation of MEPM cells and results in the development of cleft palates. Our findings provide new insight into the mechanism underlying the development of cleft palate, and miR-106a-5p may act as a prenatal screening biomarker for cleft palate as well as a new diagnostic and therapeutic target.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.021
GPT teacher head0.255
Teacher spread0.234 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

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