Gene expression profiles in mesenchymal stromal cells from bone marrow, adipose tissue and lung tissue of COPD patients and controls

Optimizing Mesenchymal Stem Cells for Regenerative Medicine: Influence of Diabetes, Obesity, Autoimmune, and Inflammatory Conditions on Therapeutic Efficacy: A Review

Mesenchymal stem cells (MSCs) are a promising tool that may be used in regenerative medicine. Thanks to their ability to differentiate and paracrine signaling, they can be used in the treatment of many diseases. Undifferentiated MSCs can support the regeneration of surrounding tissues through secreted substances and exosomes. This is possible thanks to the production of growth factors. These factors stimulate the growth of neighboring cells, have an anti-apoptotic effect, and support angiogenesis, and MSCs also have an immunomodulatory effect. The level of secreted factors may vary depending on many factors. Apart from the donor's health condition, it is also influenced by the source of MSCs, methods of harvesting, and even the banking of cells. This work is a review of research on how the patient's health condition affects the properties of obtained MSCs. The review discusses the impact of the patient's diabetes, obesity, autoimmune diseases, and inflammation, as well as the impact of the source of MSCs and methods of harvesting and banking cells on the phenotype, differentiation capacity, anti-inflammatory, angiogenic effects, and proliferation potential of MSCs. Knowledge about specific clinical factors allows for better use of the potential of stem cells and more appropriate targeting of procedures for collecting, multiplying, and banking these cells, as well as for their subsequent use. This article aims to review the characteristics, harvesting, banking, and paracrine signaling of MSCs and their role in diabetes, obesity, autoimmune and inflammatory diseases, and potential role in regenerative medicine.

Analysis and Validation of Critical Signatures and Immune Cell Infiltration Characteristics in Doxorubicin-Induced Cardiotoxicity by Integrating Bioinformatics and Machine Learning

Purpose

Doxorubicin-induced cardiotoxicity (DIC) is a severe side reaction in cancer chemotherapy that greatly impacts the well-being of cancer patients. Currently, there is still an insufficiency of effective and reliable biomarkers in the field of clinical practice for the early detection of DIC. This study aimed to determine and validate the potential diagnostic and predictive values of critical signatures in DIC.

Methods

We obtained high-throughput sequencing data from the GEO database and performed data analysis and visualization using R software, GO, KEGG and Cytoscape. Machine learning methods and weighted gene coexpression network (WGCNA) were used to identify key genes for diagnostic model construction. Receiver operating characteristic (ROC) analysis and a nomogram were used to assess their diagnostic values. A multiregulatory network was built to reveal the possible regulatory relationships of critical signatures. Cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) analysis was used to investigate differential immune cell infiltration. Additionally, a cell and animal model were constructed to investigate the relationship between the identified genes and DIC.

Results

Among the 3713 differentially expressed genes, three key genes (CSGALNACT1, ZNF296 and FANCB) were identified. A nomogram and ROC curves based on three key genes showed excellent diagnostic predictive performance. The regulatory network analysis showed that the TFs CREB1, EP300, FLI1, FOXA1, MAX, and MAZ modulated three key genes. An analysis of immune cell infiltration indicated that many immune cells (activated NK cells, M0 macrophages, activated dendritic cells and neutrophils) might be related to the progression of DIC. Furthermore, there may be various degrees of correlation between the three critical signatures and immune cells. RT‒qPCR demonstrated that the mRNA expression of CSGALNACT1 and ZNF296 was significantly upregulated, while FANCB was significantly downregulated in DOX-treated cardiomyocytes in vitro and in vivo.

Conclusion

Our study suggested that the differential expression of CSGALNACT1, ZNF296 and FANCB is associated with cardiotoxicity and is also involved in immune cell infiltration in DIC. They might be potential biomarkers for the early occurrence of DIC.

When GETomics meets aging and exercise in COPD

The term GETomics has been recently proposed to illustrate that human health and disease are actually the final outcome of many dynamic, interacting and cumulative gene (G) - environment (E) interactions that occur through the lifetime (T) of the individual. According to this new paradigm, the final outcome of any GxE interactions depends on both the age of the individual at which such GxE interaction occurs as well as on the previous, cumulative history of previous GxE interactions through the induction of epigenetic changes and immune memory (both lasting overtime). Following this conceptual approach, our understanding of the pathogenesis of chronic obstructive pulmonary disease (COPD) has changed dramatically. Traditionally believed to be a self-inflicted disease induced by tobacco smoking occurring in older men and characterized by an accelerated decline of lung function with age, now we understand that there are many other risk factors associated with COPD, that it occurs also in females and young individuals, that there are different lung function trajectories through life, and that COPD is not always characterized by accelerated lung function decline. In this paper we discuss how a GETomics approach to COPD may open new perspectives to better understand its relationship with exercise limitation and the ageing process.