RNA biology
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Stem cells are a class of undifferentiated cells with great self-renewal and differentiation capabilities that can differentiate into mature cells in specific tissue types. Stem cell differentiation plays critical roles in body homoeostasis, injury repair and tissue generation. The important functions of stem cell differentiation have resulted in numerous studies focusing on the complex molecular mechanisms and various signalling pathways controlling stem cell differentiation. ⋯ Importantly, multiple lines of evidence have shown the abnormal expression of numerous circRNAs during stem cell differentiation, and some play a role in regulating stem cell differentiation, highlighting the role of circRNAs as novel biomarkers of stem cell differentiation and novel targets for stem cell-based therapy. In this review, we systematically summarize and discuss recent advances in our understanding of the roles and underlying mechanisms of circRNAs in modulating stem cell differentiation, thus providing guidance for future studies to investigate stem cell differentiation and stem cell-based therapy. Abbreviations: CircRNAs: circular RNAs; ESCs: embryonic stem cells; ADSCs: adipose-derived mesenchymal stem cells; ecircRNAs: exonic circRNAs; EIciRNAs: exon-intron circRNAs; eiRNAs: circular intronic RNAs; tricRNAs: tRNA intronic circRNAs; pol II: polymerase II; snRNP: small nuclear ribonucleoprotein; m6A: N6-methyladenosine; AGO2: Argonaute 2; RBPs: RNA-binding proteins; MBNL: muscleblind-like protein 1; MSCs: mesenchymal stem cells; hiPSCs: human induced pluripotent stem cells; hiPSC-CMs: hiPSC-derived cardiomyocytes; hBMSCs: human bone marrow mesenchymal stem cells; hADSCs: human adipose-derived mesenchymal stem cells; hDPSCs: human dental pulp stem cells; RNA-seq: high-throughput RNA sequencing; HSCs: haematopoietic stem cells; NSCs: neural stem cells; EpSCs: epidermal stem cells; hESCs: human embryonic stem cells; mESCs: murine embryonic stem cells; MNs: motor neurons; SSUP: small subunit processome; BMSCs: bone marrow-derived mesenchymal stem cells; OGN: osteoglycin; GIOP: glucocorticoid‑induced osteoporosis; CDR1as: cerebellar degeneration-related protein 1 transcript; SONFH: steroid-induced osteogenesis of the femoral head; rBMSCs: rat bone marrow-derived mesenchymal stem cells; QUE: quercetin; AcvR1b: activin A receptor type 1B; BSP: bone sialoprotein; mADSCs: mouse ADSCs; PTBP1: polypyrimidine tract-binding protein; ER: endoplasmic reticulum; hUCMSCs: MSCs derived from human umbilical cord; MSMSCs: maxillary sinus membrane stem cells; SCAPs: stem cells from the apical papilla; MyoD: myogenic differentiation protein 1; MSTN: myostatin; MEF2C: myocyte enhancer factor 2C; BCLAF1: BCL2-associated transcription factor 1; EpSCs: epidermal stem cells; ISCs: intestinal stem cells; NSCs: neural stem cells; Lgr5+ ISCs: crypt base columnar cells; ILCs: innate lymphoid cells.
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Single-cell RNA sequencing (scRNA-seq) technologies allow numerous opportunities for revealing novel and potentially unexpected biological discoveries. scRNA-seq clustering helps elucidate cell-to-cell heterogeneity and uncover cell subgroups and cell dynamics at the group level. Two important aspects of scRNA-seq data analysis were introduced and discussed in the present review: relevant datasets and analytical tools. In particular, we reviewed popular scRNA-seq datasets and discussed scRNA-seq clustering models including K-means clustering, hierarchical clustering, consensus clustering, and so on. ⋯ Two primary evaluation metrics, the Adjusted Rand Index (ARI) and the Normalized Mutual Information (NMI), were used to evaluate these methods. Although unsupervised models can effectively cluster scRNA-seq data, these methods also have challenges. Some suggestions were provided for future research directions.
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The recently discovered clustered regularly interspaced short palindromic repeats (CRISPR)-Cpf1 system expands the genome editing toolbox. This system exhibits several distinct features compared to the widely used CRISPR-Cas9 system, but has reduced gene editing efficiency. ⋯ We also demonstrate that this design can enhance CRISPR-based gene activation. We thus generated an improved CRISPR-Cpf1 system for more efficient gene editing and gene regulation.
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The pathogenesis of neuropathic pain (NP) is characterized by an increased responsiveness of nociceptive neurons in the nervous system. However, the molecular mechanisms underpinning the NP still remain elusive. Recent data suggest that long non-coding RNAs (lncRNAs) regulate expression of NP-associated genes. ⋯ Protein interaction networks were constructed based on the correlation analyses of DE lncRNA target proteins at 7 and 14 days after SNI, respectively. Taken together, our results revealed the profiles of lncRNAs and mRNAs in the rat spinal cord under an NP condition. These lncRNAs and mRNAs may represent new therapeutic targets for the treatment of NP.
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The fragile X syndrome (FXS), the most common form of inherited intellectual disability, is due to the absence of FMRP, a protein regulating RNA metabolism. Recently, an unexpected function of FMRP in modulating the activity of Adenosine Deaminase Acting on RNA (ADAR) enzymes has been reported both in Drosophila and Zebrafish. ADARs are RNA-binding proteins that increase transcriptional complexity through a post-transcriptional mechanism called RNA editing. ⋯ The ADAR2-FMRP co-localization was further observed by double-immunogold Electron Microscopy (EM) in the hippocampus. Moreover, ADAR2-FMRP interaction appeared to be RNA independent. Because changes in the editing pattern are associated with neuropsychiatric and neurodevelopmental disorders, we propose that the increased editing observed in the fmr1-KO mice might contribute to the FXS molecular phenotypes.