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Autophagy receptor OPTN (optineurin) regulates mesenchymal stem cell fate and bone-fat balance during aging by clearing FABP3

2021· dataset· en· W4394532851 on OpenAlexaff
Zhengzhao Liu, Chun‐Gu Hong, Wenbao Hu, Menglu Chen, Ran Duan, Hongming Li, Tao Yue, Jia Cao, Zhenxing Wang, Chun‐Yuan Chen, Xiong‐Ke Hu, Ben Wu, Haoming Liu, Yi‐Juan Tan, Jianghua Liu, Zhong‐Wei Luo, Yan Zhang, Shan‐Shan Rao, Mingjie Luo, Hao Yin, Yiyi Wang, Kun Xia, Lang Xu, Siyuan Tang, Ronggui Hu, Hui Xie

Bibliographic record

VenueFigshare · 2021
Typedataset
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsOptineurinAutophagyMesenchymal stem cellCell biologyBalance (ability)BiologyNeuroscienceGenetics

Abstract

fetched live from OpenAlex

Senile osteoporosis (OP) is often concomitant with decreased autophagic activity. OPTN (optineurin), a macroautophagy/autophagy (hereinafter referred to as autophagy) receptor, is found to play a pivotal role in selective autophagy, coupling autophagy with bone metabolism. However, its role in osteogenesis is still mysterious. Herein, we identified Optn as a critical molecule of cell fate decision for bone marrow mesenchymal stem cells (MSCs), whose expression decreased in aged mice. Aged mice revealed osteoporotic bone loss, elevated senescence of MSCs, decreased osteogenesis, and enhanced adipogenesis, as well as optn–/ – mice. Importantly, restoring Optn by transplanting wild-type MSCs to optn–/ – mice or infecting optn–/ – mice with Optn-containing lentivirus rescued bone loss. The introduction of a loss-of-function mutant of OptnK193R failed to reestablish a bone-fat balance. We further identified FABP3 (fatty acid binding protein 3, muscle and heart) as a novel selective autophagy substrate of OPTN. FABP3 promoted adipogenesis and inhibited osteogenesis of MSCs. Knockdown of FABP3 alleviated bone loss in optn–/ – mice and aged mice. Our study revealed that reduced OPTN expression during aging might lead to OP due to a lack of FABP3 degradation via selective autophagy. FABP3 accumulation impaired osteogenesis of MSCs, leading to the occurrence of OP. Thus, reactivating OPTN or inhibiting FABP3 would open a new avenue to treat senile OP. Abbreviations: ADIPOQ: adiponectin, C1Q and collagen domain containing; ALPL: alkaline phosphatase, liver/bone/kidney; BGLAP/OC/osteocalcin: bone gamma carboxyglutamate protein; BFR/BS: bone formation rate/bone surface; CALCOCO2/NDP52: calcium binding and coiled-coil domain 2; CDKN1A/p21: cyclin-dependent kinase inhibitor 1A; CDKN2A/p16: cyclin dependent kinase inhibitor 2A; CDKN2B/p15: cyclin dependent kinase inhibitor 2B; CEBPA: CCAAT/enhancer binding protein (C/EBP), alpha; COL1A1: collagen, type I, alpha 1; Ct. BV/TV: cortical bone volume fraction; Ct. Th: cortical thickness; Es. Pm: endocortical perimeter; FABP4/Ap2: fatty acid binding protein 4, adipocyte; H2AX: H2A.X variant histone; HE: hematoxylin and eosin; MAP1LC3B: microtubule-associated protein 1 light chain 3 beta; MAR: mineral apposition rate; MSCs: bone marrow mesenchymal stem cells; NBR1: NBR1, autophagy cargo receptor; OP: osteoporosis; OPTN: optineurin; PDB: Paget disease of bone; PPARG: peroxisome proliferator activated receptor gamma; Ps. Pm: periosteal perimeter; qRT-PCR: quantitative real-time PCR; γH2AX: Phosphorylation of the Serine residue of H2AX; ROS: reactive oxygen species; RUNX2: runt related transcription factor 2; SA-GLB1: senescence-associated (SA)-GLB1 (galactosidase, beta 1); SP7/Osx/Osterix: Sp7 transcription factor 7; SQSTM1/p62: sequestosome 1; TAX1BP1: Tax1 (human T cell leukemia virus type I) binding protein 1; Tb. BV/TV: trabecular bone volume fraction; Tb. N: trabecular number; Tb. Sp: trabecular separation; Tb. Th: trabecular thickness; μCT: micro computed tomography.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.014
GPT teacher head0.248
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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