Background
Bone marrow-derived stem cells (BMSCs) and chondrocytes have been reported to present “dedifferentiation” and “phenotypic loss” during the chondrogenic differentiation process in cartilage tissue engineering, and cartilage progenitor cells (CPCs) are novel seeding cells for cartilage tissue engineering. In our previous study, cartilage progenitor cells from different subtypes of cartilage tissue were isolated and identified in vitro, but the study on in vivo chondrogenic characteristics of cartilage progenitor cells remained rarely. In the current study, we explored the feasibility of combining cartilage progenitor cells with poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) to produce tissue-engineered cartilage and compared the proliferation ability and chondrogenic characteristics of cartilage progenitor cells with those of bone marrow-derived stem cells and chondrocytes.
Methods
These three cells combined with PHBV were cultured in vitro for 1 week without chondrogenic induction and then transplanted subcutaneously into nude mice for 6 weeks. The cell-PHBV constructs were evaluated by gross observation, histological staining, glycosaminoglycan content measurement, biomechanical analysis and RT-PCR.
Results
The chondrocyte-PHBV constructs and CPC-PHBV constructs became an ivory-whitish cartilage-like tissue, while the BMSC-PHBV constructs became vascularized 6 weeks after the subcutaneous implantation. Histological examination showed that many typical cartilage structures were present in the chondrocyte group, some typical cartilage structures were observed in the CPC group, while no typical cartilage structures were observed in the BMSC group.
Conclusions
Cartilage progenitor cells may undergo chondrogenesis without chondrogenic induction and are better at chondrogenesis than BMSCs but worse than chondrocytes in the application of cartilage tissue engineering.
Numerous examples of microbial phase-separated biomolecular condensates have now been identified following advances in fluorescence imaging and single molecule microscopy technologies. The structure, function, and potential applications of these microbial condensates are currently receiving a great deal of attention. By neatly compartmentalizing proteins and their interactors in membrane-less organizations while maintaining free communication between these macromolecules and the external environment, microbial cells are able to achieve enhanced metabolic efficiency. Typically, these condensates also possess the ability to rapidly adapt to internal and external changes. The biological functions of several phase-separated condensates in small bacterial cells show evolutionary convergence with the biological functions of their eukaryotic paralogs. Artificial microbial membrane-less organelles are being constructed with application prospects in biocatalysis, biosynthesis, and biomedicine. In this review, we provide an overview of currently known biomolecular condensates driven by liquid-liquid phase separation (LLPS) in microbial cells, and we elaborate on their biogenesis mechanisms and biological functions. Additionally, we highlight the major challenges and future research prospects in studying microbial LLPS.
A Monte Carlo algorithm is developed to simulate the radical polymerization and copolymerization in microflow reactor. The algorithm couples the stochastic reaction process, based on Gillespie's theory, with material diffusion and flow. The simulation results agree well with the experimental kinetic data reported in the literature. The method can be used to predict the polymerization kinetics, molecular weight and molecular weight distribution, and composition and sequence length distribution (both instantaneous and cumulative) of the polymer products obtained from microflow reactors. The effects of various reaction parameters, such as flow rate, initial concentration, and temperature, on the polymerization kinetics are also discussed.
BackgroundAs a novel immune checkpoint, CD73 has been reported to play prominent roles in several malignancies. However, the significance of CD73 in melanoma remains ambiguous. This study sought to reveal the impact of CD73 on the tumor microenvironment (TME) and patients’ prognosis, and to investigate whether CD73 could be a therapeutic target in Chinese melanomas, which were dominated by acral and mucosal subtypes.MethodsTwo independent Chinese cohorts of 194 patients with melanoma were enrolled. CD73 and PD-L1 expression as well as CD8+ and CD56+ cell infiltrations were evaluated by immunohistochemistry in 194 resected melanoma samples. Clinical outcomes of patients were assessed utilizing the Kaplan-Meier plotter and Cox proportional hazard analysis. RNA-seq data was obtained from TCGA database. Gene set functional annotations were performed based on GO, KEGG and GSEA analysis. CIBERSORT, ssGSEA and TIMER were used to explore the association between CD73 and immune infiltration. These findings were validated by establishing tumor xenograft model, and functions of tumor-infiltrating immune cells were examined by flow cytometry and immunofluorescence.ResultsHigh CD73 expression showed poorer clinical outcomes and was identified as an independent prognostic indicator for survival in two cohorts. Expression of CD73 was more prevalent than PD-L1 in Chinese melanoma cohorts (54.6% vs 23.2%). Co-expression of both immune checkpoints was infrequent (12.9%) in melanoma, and 54.4% of PD-L1 negative cases showed elevated expression of CD73. CD73high tumors showed a microenvironment with fewer CD8+ T cells and CD56+ NK cells infiltration, which displayed a dysfunctional phenotype. With the treatment of CD73 inhibitor APCP, the amount of CD8+ T cells and CD56+ NK cells infiltrated in tumors was elevated and the immunosuppressive effect of CD73 was eliminated.ConclusionsHigh CD73 expression was associated with an inhibitory TME and adverse clinical outcomes of melanoma. In comparison to PD-L1, CD73 was more prevalent and possessed more definite prognostic significance. Therefore, it may serve as a prognostic indicator and immunotherapeutic target next to PD-L1 in melanoma for Chinese population.
Background: Melanoma is the most dangerous form of skin cancer because of its high metastatic potential.Potential-N6-methyladenosine (m6A)-related long noncoding RNAs (pMRlncRNAs) play a vital role in malignancy. The identification of prognostic-related pMRlncRNAs and development of risk signatures could improve the prognosis and promote the precise treatment of melanoma.Methods: Gene expression and relevant clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic-related pMRlncRNAs were selected using univariate Cox regression analysis. Patients with melanoma were classified into different subtypes using the "ConsensusClusterPlus" package, and the ESTIMATE algorithm was applied to depict their immune landscape. A pMRlncRNA risk signature was developed using least absolute shrinkage and selection operator regression analysis and verified using survival analysis and receiver operating characteristic curves. Gene set enrichment analysis (GSEA) was used to investigate the underlying biological pathways. The relationships between risk score and clinicopathological characteristics, as well as programmed cell death-ligand 1 (PD-L1) expression level, were investigated. A nomogram with calibration curves was established to comprehensively predict the outcome of melanoma.Results: Fifteen pMRlncRNAs were significantly associated with overall survival (OS). Two cluster subtypes were identified by consensus clustering. Patients in cluster 2 were associated with better OS, higher PD-L1 expression level, lower T stage, and higher ESTIMATEScore, ImmuneScore, and StromalScore than those in cluster 1. There were differences in immune cell infiltration between the 2 clusters. Ten pMRlncRNAs with prognostic value were selected to develop a risk signature, that functioned as an independent prognostic factor for melanoma. Patients with low-risk scores had a better prognosis in general. The area under the curve (AUC) value (0.720), as well as 1-, 3-, and 5-year calibration curves, revealed that the risk signature has suitable predictive power for prognosis. GSEA revealed 10 pathways that might play important roles in melanoma. Moreover, patients with high-risk scores were associated with advanced T stage, cluster 1, lower ImmuneScore, and higher PD-L1 expression level.
Conclusions:We developed a novel 10-pMRlncRNA risk signature that could elucidate the crucial role of pMRlncRNAs in the immune landscape of melanoma and predict prognosis.
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