Protein Expression of AEBP1, MCM4, and FABP4 Differentiate Osteogenic, Adipogenic, and Mesenchymal Stromal Stem Cells

AEBP1 restores osteoblastic differentiation under dexamethasone treatment by activating PI3K/AKT signalling

Adipocyte enhancer-binding protein 1 (AEBP1) is closely implicated in osteoblastic differentiation and bone fracture; this research aimed to investigate the effect of AEBP1 on restoring osteoblastic differentiation under dexamethasone (Dex) treatment, and its interaction with the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) pathway. Pre-osteoblastic MC3T3-E1 cells were cultured in osteogenic medium and treated by Dex to mimic steroid-induced osteonecrosis cellular model. They were then further transfected with control or AEBP1-overexpressed lentiviral vectors. Finally, cells were treated with the PI3K inhibitor LY294002, with or without AEBP1-overexpressed lentiviral vectors. AEBP1 expression showed a downward trend in MC3T3-E1 cells under Dex treatment in a dose-dependent manner. AEBP1-overexpressed lentiviral vectors increased relative cell viability, alkaline phosphatase (ALP) staining, Alizarin red staining and osteoblastic differentiation markers including osteocalcin (OCN), osteopontin (OPN), collagen type I alpha 1 (COL1A1), runt-related transcription factor 2 (RUNX2) and bone morphogenetic protein 2 (BMP2), but decreased cell apoptosis rate in MC3T3-E1 cells under Dex treatment; besides, AEBP1-overexpressed lentiviral vectors positively regulated p-PI3K and p-AKT expressions. Furthermore, LY294002 treatment decreased relative cell viability, Alizarin red staining, osteoblastic differentiation markers including OCN, OPN, RUNX2 and BMP, increased cell apoptosis rate and did not affect ALP staining in MC3T3-E1 cells under Dex treatment; meanwhile, LY294002 treatment weakened the effect of AEBP1 overexpression vectors on the above cell functions. AEBP1 restores osteoblastic differentiation under Dex treatment by activating the PI3K/AKT pathway.

Artificial Intelligence (AI): A Potential Game Changer in Regenerative Orthopedics-A Scoping Review

Background and Aims

Regenerative orthopedics involves approaches like stem cell therapy, platelet-rich plasma (PRP) therapy, the use of biological scaffold implants, tissue engineering, etc. We aim to present a scoping review of the role of artificial intelligence (AI) in different treatment approaches of regenerative orthopedics.

Methods

Using the PRISMA guidelines, a search for articles for the last ten years (2013-2024) on PubMed was done, using several keywords. We have discussed the state-of-the-art, strengths/benefits, and limitations of the published research, and provide a useful resource for the way ahead in future for researchers working in this area.

Results

Using the eligibility criteria out of 82 initially screened publications, we included 18 studies for this review. We noticed that the treatment responses to regenerative treatments depend on several factors; hence, to facilitate better comprehensive and patient-specific treatments, AI technology is very useful. Machine learning (ML) and deep learning (DL) are a few of the most frequently used AI techniques. They use a data-driven approach for training models to make human-like decisions. Data are fed to the ML/DL algorithm and the trained model makes classifications or predictions based on its learning.

Conclusion

The area of regenerative orthopedics is highly sophisticated and significantly aids in providing cost-effective and non-invasive treatments to patients suffering from orthopedic ailments and injuries. Due to its promising future, the use of AI in regenerative orthopedics is an emerging and promising research field; however, its universal clinical applications are associated with some ethical considerations, which need addressing.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s43465-024-01189-1.

Identification and Validation of Hub Genes and Construction of miRNA-Gene and Transcription Factor-Gene Networks in Adipogenesis of Mesenchymal Stem Cells

Background

Adipogenic differentiation stands as a crucial pathway in the range of differentiation options for mesenchymal stem cells (MSCs), carrying significant importance in the fields of regenerative medicine and the treatment of conditions such as obesity and osteoporosis. However, the exact mechanisms that control the adipogenic differentiation of MSCs are not yet fully understood.

Materials and Methods

We procured datasets, namely GSE36923, GSE80614, GSE107789, and GSE113253, from the Gene Expression Omnibus database. These datasets enabled us to perform a systematic analysis, including the identification of differentially expressed genes (DEGs) pre- and postadipogenic differentiation in MSCs. Subsequently, we conducted an exhaustive analysis of DEGs common to all four datasets. To gain further insights, we subjected these overlapped DEGs to comprehensive gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Following the construction of protein-protein interaction (PPI) networks, we meticulously identified a cohort of hub genes pivotal to the adipogenic differentiation process and validated them using real-time quantitative polymerase chain reaction. Subsequently, we ventured into the construction of miRNA-gene and TF-gene interaction networks.

Results

Our rigorous analysis revealed a total of 18 upregulated DEGs and 12 downregulated DEGs that consistently appeared across all four datasets. Notably, the peroxisome proliferator-activated receptor signaling pathway, regulation of lipolysis in adipocytes, and the adipocytokine signaling pathway emerged as the top-ranking pathways significantly implicated in the regulation of these DEGs. Subsequent to the construction of the PPI network, we identified and validated 10 key node genes, namely IL6, FABP4, ADIPOQ, LPL, PLIN1, RBP4, ACACB, NT5E, KRT19, and G0S2. Our endeavor to construct miRNA-gene interaction networks led to the discovery of the top 10 pivotal miRNAs, including hsa-mir-27a-3p, hsa-let-7b-5p, hsa-mir-1-3p, hsa-mir-124-3p, hsa-mir-155-5p, hsa-mir-16-5p, hsa-mir-101-3p, hsa-mir-21-3p, hsa-mir-146a-5p, and hsa-mir-148b-3p. Furthermore, the construction of TF-gene interaction networks revealed the top 10 critical TFs: ZNF501, ZNF512, YY1, EZH2, ZFP37, ZNF2, SOX13, MXD3, ELF3, and TFDP1.

Conclusions

In summary, our comprehensive study has successfully unraveled the pivotal hub genes that govern the adipogenesis of MSCs. Moreover, the meticulously constructed miRNA-gene and TF-gene interaction networks are poised to significantly augment our comprehension of the intricacies underlying MSC adipogenic differentiation, thus providing a robust foundation for future advances in regenerative biology.

Proteomic analyses identify HK1 and ATP5A to be overexpressed in distant metastases of lung adenocarcinomas compared to matched primary tumors

Lung cancer is the leading cause of cancer-related deaths worldwide with lung adenocarcinoma (LUAD) being the most common type. Genomic studies of LUAD have advanced our understanding of its tumor biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD are still insufficiently explored. The prognosis for lung cancer patients is still mostly determined by the stage of disease at the time of diagnosis. Focusing on late-stage metastatic LUAD with poor prognosis, we compared the proteomic profiles of primary tumors and matched distant metastases to identify relevant and potentially druggable differences. We performed high-performance liquid chromatography (HPLC) and electrospray ionization tandem mass spectrometry (ESI-MS/MS) on a total of 38 FFPE (formalin-fixed and paraffin-embedded) samples. Using differential expression analysis and unsupervised clustering we identified several proteins that were differentially regulated in metastases compared to matched primary tumors. Selected proteins (HK1, ATP5A, SRI and ARHGDIB) were subjected to validation by immunoblotting. Thereby, significant differential expression could be confirmed for HK1 and ATP5A, both upregulated in metastases compared to matched primary tumors. Our findings give a better understanding of tumor progression and metastatic spreads in LUAD but also demonstrate considerable inter-individual heterogeneity on the proteomic level.

Identifying Biomarkers for Osteogenic Potency Assay Development

There has been extensive exploration of how cells may serve as advanced therapy medicinal products to treat skeletal pathologies. Osteoblast progenitors responsible for production of extracellular matrix that is subsequently mineralized during bone formation have been characterised as a rare bone marrow subpopulation of cell culture plastic adherent cells. Conveniently, they proliferate to form single-cell derived colonies of fibroblastoid cells, termed colony forming unit fibroblasts that can subsequently differentiate to aggregates resembling small areas of cartilage or bone. However, donor heterogeneity and loss of osteogenic differentiation capacity during extended cell culture have made the discovery of reliable potency assay biomarkers difficult. Nonetheless, functional osteoblast models derived from telomerised human bone marrow stromal cells have allowed extensive comparative analysis of gene expression, microRNA, morphological phenotypes and secreted proteins. This chapter highlights numerous insights into the molecular mechanisms underpinning osteogenic differentiation of multipotent stromal cells and bone formation, discussing aspects involved in the choice of useful biomarkers for functional attributes that can be quantitively measured in osteogenic potency assays.

Spotlight on therapeutic efficiency of mesenchymal stem cells in viral infections with a focus on COVID-19

The SARS-COV-2 virus has infected the world at a very high rate by causing COVID-19 disease. Nearly 507 million individuals have been infected with this virus, with approximately 1.2% of these patients being dead, indicating that this virus has been out of control in many countries. While researchers are investigating how to develop efficient drugs and vaccines versus the COVID-19 pandemic, new superseded treatments have the potential to reduce mortality. The recent application of mesenchymal stem cells (MSCs) in a subgroup of COVID-19 patients with acute respiratory distress has created potential benefits as supportive therapy for this viral contagion in patients with acute conditions and aged patients with severe pneumonia. Consequently, within this overview, we discuss the role and therapeutic potential of MSCs and the challenges ahead in using them to treat viral infections, with highlighting on COVID-19 infection.

Protein Profiling of WERI-RB1 and Etoposide-Resistant WERI-ETOR Reveals New Insights into Topoisomerase Inhibitor Resistance in Retinoblastoma

Chemotherapy resistance is one of the reasons for eye loss in patients with retinoblastoma (RB). RB chemotherapy resistance has been studied in different cell culture models, such as WERI-RB1. In addition, chemotherapy-resistant RB subclones, such as the etoposide-resistant WERI-ETOR cell line have been established to improve the understanding of chemotherapy resistance in RB. The objective of this study was to characterize cell line models of an etoposide-sensitive WERI-RB1 and its etoposide-resistant subclone, WERI-ETOR, by proteomic analysis. Subsequently, quantitative proteomics data served for correlation analysis with known drug perturbation profiles. Methodically, WERI-RB1 and WERI-ETOR were cultured, and prepared for quantitative mass spectrometry (MS). This was carried out in a data-independent acquisition (DIA) mode. The raw SWATH (sequential window acquisition of all theoretical mass spectra) files were processed using neural networks in a library-free mode along with machine-learning algorithms. Pathway-enrichment analysis was performed using the REACTOME-pathway resource, and correlated to the molecular signature database (MSigDB) hallmark gene set collections for functional annotation. Furthermore, a drug-connectivity analysis using the L1000 database was carried out to associate the mechanism of action (MOA) for different anticancer reagents to WERI-RB1/WERI-ETOR signatures. A total of 4756 proteins were identified across all samples, showing a distinct clustering between the groups. Of these proteins, 64 were significantly altered (q < 0.05 & log2FC |>2|, 22 higher in WERI-ETOR). Pathway analysis revealed the “retinoid metabolism and transport” pathway as an enriched metabolic pathway in WERI-ETOR cells, while the “sphingolipid de novo biosynthesis” pathway was identified in the WERI-RB1 cell line. In addition, this study revealed similar protein signatures of topoisomerase inhibitors in WERI-ETOR cells as well as ATPase inhibitors, acetylcholine receptor antagonists, and vascular endothelial growth factor receptor (VEGFR) inhibitors in the WERI-RB1 cell line. In this study, WERI-RB1 and WERI-ETOR were analyzed as a cell line model for chemotherapy resistance in RB using data-independent MS. Analysis of the global proteome identified activation of “sphingolipid de novo biosynthesis” in WERI-RB1, and revealed future potential treatment options for etoposide resistance in RB.