Aging and obesity prime the methylome and transcriptome of adipose stem cells for disease and dysfunction

Dynamics of DNA methylation during osteogenic differentiation of porcine synovial membrane mesenchymal stem cells from two metabolically distinct breeds

Mesenchymal stem cells (MSCs), with the ability to differentiate into osteoblasts, adipocytes, or chondrocytes, show evidence that the donor cell's metabolic type influences the osteogenic process. Limited knowledge exists on DNA methylation changes during osteogenic differentiation and the impact of diverse donor genetic backgrounds on MSC differentiation. In this study, synovial membrane mesenchymal stem cells (SMSCs) from two pig breeds (Angeln Saddleback, AS; German Landrace, DL) with distinct metabolic phenotypes were isolated, and the methylation pattern of SMSCs during osteogenic induction was investigated. Results showed that most differentially methylated regions (DMRs) were hypomethylated in osteogenic-induced SMSC group. These DMRs were enriched with genes of different osteogenic signalling pathways at different time points including Wnt, ECM, TGFB and BMP signalling pathways. AS pigs consistently exhibited a higher number of hypermethylated DMRs than DL pigs, particularly during the peak of osteogenesis (day 21). Predicting transcription factor motifs in regions of DMRs linked to osteogenic processes and donor breeds revealed influential motifs, including KLF1, NFATC3, ZNF148, ASCL1, FOXI1, and KLF5. These findings contribute to understanding the pattern of methylation changes promoting osteogenic differentiation, emphasizing the substantial role of donor the metabolic type and epigenetic memory of different donors on SMSC differentiation.

Adipose tissue-derived stem cells, in vivo and in vitro models for metabolic diseases

The primary role of adipose tissue stem cells (ADSCs) is to support the function and homeostasis of adipose tissue in physiological and pathophysiological conditions. However, when ADSCs become dysfunctional in diseases such as obesity and cancer, they become impaired, undergo signalling changes, and their epigenome is altered, which can have a dramatic effect on human health. In more recent years, the therapeutic potential of ADSCs in regenerative medicine, wound healing, and for treating conditions such as cancer and metabolic diseases has been extensively investigated with very promising results. ADSCs have also been used to generate two-dimensional (2D) and three-dimensional (3D) cellular and in vivo models to study adipose tissue biology and function as well as intracellular communication. Characterising the biology and function of ADSCs, how it is altered in health and disease, and its therapeutic potential and uses in cellular models is key for designing intervention strategies for complex metabolic diseases and cancer.

Latent transforming growth factor beta binding protein 4: A regulator of mitochondrial function in acute kidney injury

Recently, latent transforming growth factor beta binding protein 4 (LTBP4) was implicated in the pathogenesis of renal damage through its modulation of mitochondrial dynamics. The seminal article written by Su et al. entitled "LTBP4 (Latent Transforming Growth Factor Beta Binding Protein 4) Protects Against Renal Fibrosis via Mitochondrial and Vascular Impacts" uncovers LTBP4's renoprotective role against acute kidney injury via modulating mitochondrial dynamics. Recently, LTBP4 has emerged as a driver in the mitochondrial-dependent modulation of age-related organ pathologies. This article aims to expand our understanding of LTBP4's diverse roles in these diseases in the context of these recent findings.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Emerging technologies in adipose tissue research

Technologies are transforming the understanding of adipose tissue as a complex and dynamic tissue that plays a critical role in energy homoeostasis and metabolic health. This mini-review provides a brief overview of the potential impact of novel technologies in biomedical research and aims to identify areas where these technologies can make the most significant contribution to adipose tissue research. It discusses the impact of cutting-edge technologies such as single-cell sequencing, multi-omics analyses, spatial transcriptomics, live imaging, 3D tissue engineering, microbiome analysis, in vivo imaging, and artificial intelligence/machine learning. As these technologies continue to evolve, we can expect them to play an increasingly important role in advancing our understanding of adipose tissue and improving the treatment of related diseases.