Co-expression network analysis identified key genes in association with mesenchymal stem cell osteogenic differentiation

Genome-wide association study of growth curve parameters reveals novel genomic regions and candidate genes associated with metatarsal bone traits in chickens

The growth and development of chicken bones have an enormous impact on the health and production performance of chickens. However, the development pattern and genetic regulation of the chicken skeleton are poorly understood. This study aimed to evaluate metatarsal bone growth and development patterns in chickens via non-linear models, and to identify the genetic determinants of metatarsal bone traits using a genome-wide association study (GWAS) based on growth curve parameters. Data on metatarsal length (MeL) and metatarsal circumference (MeC) were obtained from 471 F2 chickens (generated by crossing broiler sires, derived from a line selected for high abdominal fat, with Baier layer dams) at 4, 6, 8, 10, and 12 weeks of age. Four non-linear models (Gompertz, Logistic, von Bertalanffy, and Brody) were used to fit the MeL and MeC growth curves. Subsequently, the estimated growth curve parameters of the mature MeL or MeC (A), time-scale parameter (b), and maturity rate (K) from the non-linear models were utilized as substitutes for the original bone data in GWAS. The Logistic and Brody models displayed the best goodness-of-fit for MeL and MeC, respectively. Single-trait and multi-trait GWASs based on the growth curve parameters of the Logistic and Brody models revealed 4 618 significant single nucleotide polymorphisms (SNPs), annotated to 332 genes, associated with metatarsal bone traits. The majority of these significant SNPs were located on Gallus gallus chromosome (GGA) 1 (167.433-176.318 Mb), GGA2 (96.791-103.543 Mb), GGA4 (65.003-83.104 Mb) and GGA6 (64.685-95.285 Mb). Notably, we identified 12 novel GWAS loci associated with chicken metatarsal bone traits, encompassing 35 candidate genes. In summary, the combination of single-trait and multi-trait GWASs based on growth curve parameters uncovered numerous genomic regions and candidate genes associated with chicken bone traits. The findings benefit an in-depth understanding of the genetic architecture underlying metatarsal growth and development in chickens.

CCT2 prevented β-catenin proteasomal degradation to sustain cancer stem cell traits and promote tumor progression in epithelial ovarian cancer

BACKGROUND

Epithelial ovarian cancer (EOC) is featured by rapid progression and dismal outcomes clinically. Chaperonin Containing TCP1 Subunit 2 (CCT2) was identified as a crucial regulator for tumor progression, however, its exact role in EOC remained largely unknown.

METHODS

CCT2 expression and prognostic value in EOC samples were assessed according to TCGA dataset. Proliferation and mobility potentials were assessed by CCK8, colony-formation, wound healing, and Transwell assays. Cancer stem cell (CSC) traits were evaluated by RT-PCR, WB assays, sphere-forming assay and chemoresistance analysis. Bioinformatic analysis, co-IP assays and ubiquitin assays were performed to explore the mechanisms of CCT2 on EOC cells.

RESULTS

CCT2 highly expressed in EOC tissues and predicted poor prognosis of EOC patients by TCGA analysis. Silencing CCT2 significantly restrained cell proliferation, migration, and invasion. Moreover, CCT2 could effectively trigger epithelial-mesenchymal transition to confer extensive invasion potentials to EOC cells, Importantly, CCT2 positively correlated with CSC markers in EOC, and CCT2 knockdown impaired CSC traits and sensitize EOC cells to conventional chemotherapy regimens. Contrarily, overexpressing CCT2 achieved opposite results. Mechanistically, CCT2 exerted its pro-oncogene function by triggering Wnt/β-catenin signaling. Specifically, CCT2 could recruit HSP105-PP2A complex, a well-established dephosphorylation complex, to β-catenin via direct physical interaction to prevent phosphorylation-induced proteasomal degradation of β-catenin, resulting in intracellular accumulation of active β-catenin and increased signaling activity.

CONCLUSIONS

CCT2 was a novel promotor for EOC progression and a crucial sustainer for CSC traits mainly by preventing β-catenin degradation. Targeting CCT2 may represent a promising therapeutic strategy for EOC.

Bioinformatics Analysis Identified miR-584-5p and Key miRNA-mRNA Networks Involved in the Osteogenic Differentiation of Human Periodontal Ligament Stem Cells

Human periodontal ligament cells (PDLCs) play an important role in periodontal tissue stabilization and function. In the process of osteogenic differentiation of PDLSCs, the regulation of molecular signal pathways are complicated. In this study, the sequencing results of three datasets on GEO were used to comprehensively analyze the miRNA-mRNA network during the osteogenic differentiation of PDLSCs. Using the GSE99958 and GSE159507, a total of 114 common differentially expressed genes (DEGs) were identified, including 62 up-regulated genes and 52 down-regulated genes. GO enrichment analysis was performed. The up-regulated 10 hub genes and down-regulated 10 hub genes were screened out by protein-protein interaction network (PPI) analysis and STRING in Cytoscape. Similarly, differentially expressed miRNAs (DEMs) were selected by limma package from GSE159508. Then, using the miRwalk website, we further selected 11 miRNAs from 16 DEMs that may have a negative regulatory relationship with hub genes. In vitro RT-PCR verification revealed that nine DEMs and 18 hub genes showed the same trend as the RNA-seq results during the osteogenic differentiation of PDLSCs. Finally, using miR-584-5p inhibitor and mimics, it was found that miR-584-5p negatively regulates the osteogenic differentiation of PDLSCs in vitro. In summary, the present results found several potential osteogenic-related genes and identified candidate miRNA-mRNA networks for the further study of osteogenic differentiation of PDLSCs.

Identification of a Twelve-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival for Medulloblastoma

BACKGROUND

Medulloblastoma is the common pediatric malignant tumor with poor prognosis in cerebellum. However, MB is always with clinical heterogeneity. To provide patients with more clinically beneficial treatment strategies, there is a pressing need to develop a new prognostic prediction model as a supplement to the prediction outcomes of clinical judgment.

MATERIALS AND METHODS

Four datasets of mRNA expression and clinical data were downloaded from gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and functionally enriched among GSE50161, GSE74195, GSE86574. Then we used STRING and Cytoscape to constructed and analyze protein-protein interaction network (PPI) and hub genes. Univariate cox regression analysis was performed to identify overall survival-related hub genes in an unique dataset from GSE85217 as train cohort. Lasso Cox regression model was used to construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, univariate and multivariate Cox regression analysis were used to assess the prognostic capacity of the twelve-gene signature. A unique dataset from GSE85217 was downloaded to further validate the results. Finally, we established the nomogram by using the gene signature and validated it with ROC curve. Gene set enrichment analysis (GSEA) was carried out to further investigate its potential molecular mechanism. Besides, the twelve genes expression at the mRNA and protein levels was validated using external database such as Oncomine, cBioportal and HPA, respectively.

RESULTS

A twelve-gene signature comprising FOXM1, NEK2, CCT2, ACTL6A, EIF4A3, CCND2, ABL1, SYNCRIP, ITGB1, NRXN2, ENAH, and UMPS was established to predict overall survival of medulloblastoma. The ROC curve showed good performance in survival prediction in both the train cohort and the validation cohort. The twelve-gene signature could stratify patients into a high risk and low risk group which had significantly different survival. Univariate and multivariate Cox regression revealed that the twelve-gene signature was an independent prognostic factor in medulloblastoma. Nomogram, which included twelve-gene signatures, was established and showed some clinical benefit.

CONCLUSION

Our study identified a twelve-gene signature and established a prognostic nomogram that reliably predicts overall survival in medulloblastoma. The above results will help us to better analyze the pathogenesis and treatment of medulloblastoma in the future.

Integrated Analyses of Mouse Stem Cell Transcriptomes Provide Clues for Stem Cell Maintenance and Transdifferentiation

In vivo cell fate reprogramming has emerged as a new method for understanding cell plasticity and as potential treatment for tissue regeneration. Highly efficient and precise reprogramming requires fully understanding of the transcriptomes which function within different cell types. Here, we adopt weighted gene co-expression network analysis (WGCNA) to explore the molecular mechanisms of self-renewal in several well-known stem cell types, including embryonic stem cells (ESC), primordial germ cells (PGC), spermatogonia stem cells (SSC), neural stem cells (NSC), mesenchymal stem cells (MSC), and hematopoietic stem cells (HSC). We identified 37 core genes that were up-regulated in all of the stem cell types examined, as well as stem cell correlated gene co-expression networks. The validation of the co-expression genes revealed a continued protein-protein interaction network that included 823 nodes and 3113 edges. Based on the topology, we identified six densely connected regions within the continued protein-protein interaction network. The SSC specific genes Itgam, Cxcr6, and Agtr2 bridged four densely connected regions that consisted primarily of HSC-, NSC-, and MSC-correlated genes. The expression levels of identified stem cell related transcription factors were confirmed consistent with bioinformatics prediction in ESCs and NSCs by qPCR. Exploring the mechanisms underlying adult stem cell self-renewal will aid in the understanding of stem cell pool maintenance and will promote more accurate and efficient strategies for tissue regeneration and repair.

Novel gene signatures for prognosis prediction in ovarian cancer

Ovarian cancer (OV) is one of the leading causes of cancer deaths in women worldwide. Late diagnosis and heterogeneous treatment result to poor survival outcomes for patients with OV. Therefore, we aimed to develop novel biomarkers for prognosis prediction from the potential molecular mechanism of tumorigenesis. Eight eligible data sets related to OV in GEO database were integrated to identify differential expression genes (DEGs) between tumour tissues and normal. Enrichment analyses discovered DEGs were most significantly enriched in G2/M checkpoint signalling pathway. Subsequently, we constructed a multi‐gene signature based on the LASSO Cox regression model in the TCGA database and time‐dependent ROC curves showed good predictive accuracy for 1‐, 3‐ and 5‐year overall survival. Utility in various types of OV was validated through subgroup survival analysis. Risk scores formulated by the multi‐gene signature stratified patients into high‐risk and low‐risk, and the former inclined worse overall survival than the latter. By incorporating this signature with age and pathological tumour stage, a visual predictive nomogram was established, which was useful for clinicians to predict survival outcome of patients. Furthermore, SNRPD1 and EFNA5 were selected from the multi‐gene signature as simplified prognostic indicators. Higher EFNA5 expression or lower SNRPD1 indicated poorer outcome. The correlation between signature gene expression and clinical characteristics was observed through WGCNA. Drug‐gene interaction was used to identify 16 potentially targeted drugs for OV treatment. In conclusion, we established novel gene signatures as independent prognostic factors to stratify the risk of OV patients and facilitate the implementation of personalized therapies.

In Vitro Mechanical and Biological Properties of 3D Printed Polymer Composite and β-Tricalcium Phosphate Scaffold on Human Dental Pulp Stem Cells

3D printed biomaterials have been extensively investigated and developed in the field of bone regeneration related to clinical issues. However, specific applications of 3D printed biomaterials in different dental areas have seldom been reported. In this study, we aimed to and successfully fabricated 3D poly (lactic-co-glycolic acid)/β-tricalcium phosphate (3D-PLGA/TCP) and 3D β-tricalcium phosphate (3D-TCP) scaffolds using two relatively distinct 3D printing (3DP) technologies. Conjunctively, we compared and investigated mechanical and biological responses on human dental pulp stem cells (hDPSCs). Physicochemical properties of the scaffolds, including pore structure, chemical elements, and compression modulus, were characterized. hDPSCs were cultured on scaffolds for subsequent investigations of biocompatibility and osteoconductivity. Our findings indicate that 3D printed PLGA/TCP and β-tricalcium phosphate (β-TCP) scaffolds possessed a highly interconnected and porous structure. 3D-TCP scaffolds exhibited better compressive strength than 3D-PLGA/TCP scaffolds, while the 3D-PLGA/TCP scaffolds revealed a flexible mechanical performance. The introduction of 3D structure and β-TCP components increased the adhesion and proliferation of hDPSCs and promoted osteogenic differentiation. In conclusion, 3D-PLGA/TCP and 3D-TCP scaffolds, with the incorporation of hDPSCs as a personalized restoration approach, has a prospective potential to repair minor and critical bone defects in oral and maxillofacial surgery, respectively.