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1.
Comput Math Methods Med ; 2022: 8268067, 2022.
Article En | MEDLINE | ID: mdl-35126637

BACKGROUND: Tendon-to-bone healing is a difficult process in treatment of rotator cuff tear (RCT). In addition, diabetes is an important risk factor for poor tendon-to-bone healing. Therefore, we investigated the specific mechanisms through which diabetes affects tendon-to-bone healing by regulating the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR). METHODS: Tendon-derived stem cells (TDSCs) were extracted from rats after which their proliferative capacities were evaluated by the MTT assay. The expression levels of CFTR and tendon-related markers were determined by qRT-PCR. Then, bioinformatics analyses and dual luciferase reporter gene assays were used to identify miRNAs with the ability to bind CFTR mRNA. Finally, CFTR was overexpressed in TDSCs to validate the specific mechanisms through which the high glucose microenvironment inhibits tendon-to-bone healing. RESULTS: The high glucose microenvironment downregulated mRNA expression levels of tendon-related markers and CFTR in TDSCs cultured with different glucose concentrations. Additionally, bioinformatics analyses revealed that let-7b-5p may be regulated by the high glucose microenvironment and can regulate CFTR levels. Moreover, a dual luciferase reporter gene assay was used to confirm that let-7b-5p targets and binds CFTR mRNA. Additional experiments also confirmed that overexpressed CFTR effectively reversed the negative effects of the hyperglycaemic microenvironment and upregulation of let-7b-5p on TDSC proliferation and differentiation. These findings imply that the hyperglycemic microenvironment inhibits CFTR transcription and, consequently, proliferation and differentiation of TDSCs in vitro by upregulating let-7b-5p. CONCLUSIONS: A hyperglycemic microenvironment inhibits TDSC proliferation in vitro via the let-7b-5p/CFTR pathway, and this is a potential mechanism in diabetes-induced poor tendon-to-bone healing.


Bone and Bones/physiopathology , Cystic Fibrosis Transmembrane Conductance Regulator/metabolism , Hyperglycemia/physiopathology , MicroRNAs/metabolism , Tendons/physiopathology , Wound Healing/physiology , Animals , Cell Proliferation , Cells, Cultured , Computational Biology , Cystic Fibrosis Transmembrane Conductance Regulator/genetics , Disease Models, Animal , Down-Regulation , Humans , Hyperglycemia/genetics , Hyperglycemia/pathology , MicroRNAs/genetics , Rats , Rats, Sprague-Dawley , Rotator Cuff Injuries/genetics , Rotator Cuff Injuries/pathology , Rotator Cuff Injuries/physiopathology , Signal Transduction , Stem Cell Niche/genetics , Stem Cell Niche/physiology , Tendons/pathology , Wound Healing/genetics
2.
J Int Med Res ; 48(10): 300060520954722, 2020 Oct.
Article En | MEDLINE | ID: mdl-33021861

OBJECTIVE: Osteoporosis and osteoarthritis are metabolic skeletal disorders. This study aimed to identify specific networks of competitive endogenous RNA (ceRNA) in osteoporosis that differ from those in osteoarthritis. METHODS: The dataset GSE74209 was downloaded from the Gene Expression Omnibus, and differentially expressed microRNAs (DEmiRNAs) in osteoporotic samples and osteoarthritic samples were identified. After predicting target genes and linked long noncoding (lnc)RNAs, ceRNA networks of DEmiRNAs were constructed. The nodes that overlapped between ceRNA networks and the Comparative Toxicogenomics Database were selected as key candidates. RESULTS: Fifteen DEmiRNAs (including 2 downregulated and 13 upregulated miRNAs) were identified in osteoporotic samples versus osteoarthritic samples; these targeted 161 genes and linked to 60 lncRNAs. The ceRNA network consisted of 6 DEmiRNAs, 63 target genes, and 53 lncRNAs. After searching the Comparative Toxicogenomics Database and mining the literature, 2 lncRNAs (MALAT1 and NEAT1), 2 DEmiRNAs (hsa-miR-32-3p, downregulated; and hsa-miR-22-3p, upregulated) and 6 genes (SP1, PTEN, ESR1, ERBB3, CSF1R, and CDK6) that relate to cell death, growth, and differentiation were identified as key candidates separating osteoporosis from osteoarthritis. CONCLUSIONS: Two miRNA-ceRNA networks (including NEAT1/MALAT1-hsa-miR-32-3p-SP1/FZD6 and NEAT1/MALAT1-hsa-miR-22-3p-PTEN/ESR1/ERBB3/CSF1R/CDK6) might have crucial and specific roles in osteoporosis.


MicroRNAs , Osteoporosis , RNA, Long Noncoding , Gene Regulatory Networks , Humans , MicroRNAs/genetics , Osteoporosis/genetics , RNA, Long Noncoding/genetics , RNA, Messenger
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