Jie Ma

We have previously demonstrated the cardioprotective effects of exosomes derived from mesenchymal stem cells (MSCs). It is well known that the activation of Akt is involved in stem cell-induced cardioprotection. In the present study, we investigated whether exosomes released from Akt-overexpressing MSCs showed a beneficial effect on cardioprotection and angiogenesis. MSCs were collected from human umbilical cord (hucMSCs), and Akt was transfected into hucMSCs (Akt-hucMSCs) by using an adenovirus transfection system. Exosomes were isolated from control hucMSCs (Exo) and Akt-hucMSCs (Akt-Exo). An acute myocardial infarction model was created by ligation of the left anterior decedent coronary artery (LAD) in rats. Various source exosomes (400 µg of protein) were infused via the tail vein immediately after LAD ligation. The cardiac function was evaluated by using echocardiography after different treatments for 1 and 5 weeks, respectively. Endothelial cell proliferation, migration, and tube-like structure formation, as well as chick allantoic membrane assay, were used to evaluate the angiogenetic effects of Akt-Exo. The results indicated that cardiac function was significantly improved in the animals treated with Akt-Exo. In addition, Akt-Exo significantly accelerated endothelial cell proliferation and migration, tube-like structure formation in vitro, and blood vessel formation in vivo. The expression of platelet-derived growth factor D (PDGF-D) was significantly upregulated in Akt-Exo. However, the angiogenesis was abrogated in endothelial cells treated with the exosomes obtained from MSCs transfected with PDGF-D-siRNA. Our studies suggest that exosomes obtained from Akt-modified hucMSCs are more effective in myocardial infarction therapy through promoting angiogenesis. PDGF-D plays an important role in Akt-Exo-mediated angiogenesis. Stem Cells Translational Medicine 2017;6:51-59.

Critical-sized bone defect repair in patients with diabetes mellitus remains a challenge in clinical treatment because of dysfunction of macrophage polarization and the inflammatory microenvironment in the bone defect region. Three-dimensional (3D) bioprinted scaffolds loaded with live cells and bioactive factors can improve cell viability and the inflammatory microenvironment and further accelerating bone repair. Here, we used modified bioinks comprising gelatin, gelatin methacryloyl (GelMA), and 4-arm poly (ethylene glycol) acrylate (PEG) to fabricate 3D bioprinted scaffolds containing BMSCs, RAW264.7 macrophages, and BMP-4-loaded mesoporous silica nanoparticles (MSNs). Addition of MSNs effectively improved the mechanical strength of GelMA/gelatin/PEG scaffolds. Moreover, MSNs sustainably released BMP-4 for long-term effectiveness. In 3D bioprinted scaffolds, BMP-4 promoted the polarization of RAW264.7 to M2 macrophages, which secrete anti-inflammatory factors and thereby reduce the levels of pro-inflammatory factors. BMP-4 released from MSNs and BMP-2 secreted from M2 macrophages collectively stimulated the osteogenic differentiation of BMSCs in the 3D bioprinted scaffolds. Furthermore, in calvarial critical-size defect models of diabetic rats, 3D bioprinted scaffolds loaded with MSNs/BMP-4 induced M2 macrophage polarization and improved the inflammatory microenvironment. And 3D bioprinted scaffolds with MSNs/BMP-4, BMSCs, and RAW264.7 cells significantly accelerated bone repair. In conclusion, our results indicated that implanting 3D bioprinted scaffolds containing MSNs/BMP-4, BMSCs, and RAW264.7 cells in bone defects may be an effective method for improving diabetic bone repair, owing to the direct effects of BMP-4 on promoting osteogenesis of BMSCs and regulating M2 type macrophage polarization to improve the inflammatory microenvironment and secrete BMP-2.

In light of the rapid accumulation of large-scale omics datasets, numerous studies have attempted to characterize the molecular and clinical features of cancers from a multi-omics perspective. However, there are great challenges in integrating multi-omics using machine learning methods for cancer subtype classification. In this study, MoGCN, a multi-omics integration model based on graph convolutional network (GCN) was developed for cancer subtype classification and analysis. Genomics, transcriptomics and proteomics datasets for 511 breast invasive carcinoma (BRCA) samples were downloaded from the Cancer Genome Atlas (TCGA). The autoencoder (AE) and the similarity network fusion (SNF) methods were used to reduce dimensionality and construct the patient similarity network (PSN), respectively. Then the vector features and the PSN were input into the GCN for training and testing. Feature extraction and network visualization were used for further biological knowledge discovery and subtype classification. In the analysis of multi-dimensional omics data of the BRCA samples in TCGA, MoGCN achieved the highest accuracy in cancer subtype classification compared with several popular algorithms. Moreover, MoGCN can extract the most significant features of each omics layer and provide candidate functional molecules for further analysis of their biological effects. And network visualization showed that MoGCN could make clinically intuitive diagnosis. The generality of MoGCN was proven on the TCGA pan-kidney cancer datasets. MoGCN and datasets are public available at https://github.com/Lifoof/MoGCN. Our study shows that MoGCN performs well for heterogeneous data integration and the interpretability of classification results, which confers great potential for applications in biomarker identification and clinical diagnosis.

Most human cancers develop from stem and progenitor cell populations through the sequential accumulation of various genetic and epigenetic alterations. Cancer stem cells have been identified from medulloblastoma (MB), but a comprehensive understanding of MB stemness, including the interactions between the tumor immune microenvironment and MB stemness, is lacking. Here, we employed a trained stemness index model based on an existent one-class logistic regression (OCLR) machine-learning method to score MB samples; we then obtained two stemness indices, a gene expression-based stemness index (mRNAsi) and a DNA methylation-based stemness index (mDNAsi), to perform an integrated analysis of MB stemness in a cohort of primary cancer samples (n = 763). We observed an inverse trend between mRNAsi and mDNAsi for MB subgroup and metastatic status. By applying the univariable Cox regression analysis, we found that mRNAsi significantly correlated with overall survival (OS) for all MB patients, whereas mDNAsi had no significant association with OS for all MB patients. In addition, by combining the Lasso-penalized Cox regression machine-learning approach with univariate and multivariate Cox regression analyses, we identified a stemness-related gene expression signature that accurately predicted survival in patients with Sonic hedgehog (SHH) MB. Furthermore, positive correlations between mRNAsi and prognostic copy number aberrations in SHH MB, including MYCN amplifications and GLI2 amplifications, were detected. Analyses of the immune microenvironment revealed unanticipated correlations of MB stemness with infiltrating immune cells. Lastly, using the Connectivity Map, we identified potential drugs targeting the MB stemness signature. Our findings based on stemness indices might advance the development of objective diagnostic tools for quantitating MB stemness and lead to novel biomarkers that predict the survival of patients with MB or the efficacy of strategies targeting MB stem cells.