Interferon regulatory factor 7 impairs cellular metabolism in aging adipose-derived stromal cells

ALDH2 regulates mesenchymal stem cell senescence via modulation of mitochondrial homeostasis

Although mitochondrial aldehyde dehydrogenase 2 (ALDH2) is involved in aging and aging-related diseases, its role in the regulation of human mesenchymal stem cell (MSC) senescence has not been investigated. This study aimed to determine the role of ALDH2 in regulating MSC senescence and illustrate the potential mechanisms. MSCs were isolated from young (YMSCs) and aged donors (AMSCs). Senescence-associated β-galactosidase (SA-β-gal) staining and Western blotting were used to assess MSC senescence. Reactive oxygen species (ROS) generation and mitochondrial membrane potential were determined to evaluate mitochondrial function. We showed that the expression of ALDH2 increased alongside cellular senescence of MSCs. Overexpression of ALDH2 accelerated YMSC senescence whereas down-regulation alleviated premature senescent phenotypes of AMSCs. Transcriptome and biochemical analyses revealed that an elevated ROS level and mitochondrial dysfunction contributed to ALDH2 function in MSC senescence. Using molecular docking, we identified interferon regulatory factor 7 (IRF7) as the potential target of ALDH2. Mechanistically, ectopic expression of ALDH2 led to mitochondrial dysfunction and accelerated senescence of MSCs by increasing the stability of IRF7 through a direct physical interaction. These effects were partially reversed by knockdown of IRF7. These findings highlight a crucial role of ALDH2 in driving MSC senescence by regulating mitochondrial homeostasis, providing a novel potential strategy against human aging-related diseases.

Transcriptional control of metabolism by interferon regulatory factors

Interferon regulatory factors (IRFs) comprise a family of nine transcription factors in mammals. IRFs exert broad effects on almost all aspects of immunity but are best known for their role in the antiviral response. Over the past two decades, IRFs have been implicated in metabolic physiology and pathophysiology, partly as a result of their known functions in immune cells, but also because of direct actions in adipocytes, hepatocytes, myocytes and neurons. This Review focuses predominantly on IRF3 and IRF4, which have been the subject of the most intense investigation in this area. IRF3 is located in the cytosol and undergoes activation and nuclear translocation in response to various signals, including stimulation of Toll-like receptors, RIG-I-like receptors and the cGAS-STING pathways. IRF3 promotes weight gain, primarily by inhibiting adipose thermogenesis, and also induces inflammation and insulin resistance using both weight-dependent and weight-independent mechanisms. IRF4, meanwhile, is generally pro-thermogenic and anti-inflammatory and has profound effects on lipogenesis and lipolysis. Finally, new data are emerging on the role of other IRF family members in metabolic homeostasis. Taken together, data indicate that IRFs serve as critical yet underappreciated integrators of metabolic and inflammatory stress.

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.

A passage-dependent network for estimating the in vitro senescence of mesenchymal stromal/stem cells using microarray, bulk and single cell RNA sequencing

Long-term in vitro culture of human mesenchymal stem cells (MSCs) leads to cell lifespan shortening and growth stagnation due to cell senescence. Here, using sequencing data generated in the public domain, we have established a specific regulatory network of "transcription factor (TF)-microRNA (miRNA)-Target" to provide key molecules for evaluating the passage-dependent replicative senescence of mesenchymal stem cells for the quality control and status evaluation of mesenchymal stem cells prepared by different procedures. Short time-series expression miner (STEM) analysis was performed on the RNA-seq and miRNA-seq databases of mesenchymal stem cells from various passages to reveal the dynamic passage-related changes of miRNAs and mRNAs. Potential miRNA targets were predicted using seven miRNA target prediction databases, including TargetScan, miRTarBase, miRDB, miRWalk, RNA22, RNAinter, and TargetMiner. Then use the TransmiR v2.0 database to obtain experimental-supported transcription factor for regulating the selected miRNA. More than ten sequencing data related to mesenchymal stem cells or mesenchymal stem cells reprogramming were used to validate key miRNAs and mRNAs. And gene set variation analysis (GSVA) was performed to calculate the passage-dependent signature. The results showed that during the passage of mesenchymal stem cells, a total of 29 miRNAs were gradually downregulated and 210 mRNA were gradually upregulated. Enrichment analysis showed that the 29 miRNAs acted as multipotent regulatory factors of stem cells and participated in a variety of signaling pathways, including TGF-beta, HIPPO and oxygen related pathways. 210 mRNAs were involved in cell senescence. According to the target prediction results, the targets of these key miRNAs and mRNAs intersect to form a regulatory network of "TF-miRNA-Target" related to replicative senescence of cultured mesenchymal stem cells, across 35 transcription factor, 7 miRNAs (has-mir-454-3p, has-mir-196b-5p, has-mir-130b-5p, has-mir-1271-5p, has-let-7i-5p, has-let-7a-5p, and has-let-7b-5p) and 7 predicted targets (PRUNE2, DIO2, CPA4, PRKAA2, DMD, DDAH1, and GATA6). This network was further validated by analyzing datasets from a variety of mesenchymal stem cells subculture and lineage reprogramming studies, as well as qPCR analysis of early passages mesenchymal stem cells versus mesenchymal stem cells with senescence morphologies (SA-β-Gal⁺). The "TF-miRNA-Target" regulatory network constructed in this study reveals the functional mechanism of miRNAs in promoting the senescence of MSCs during in vitro expansion and provides indicators for monitoring the quality of functional mesenchymal stem cells during the preparation and clinical application.

Comparative transcriptomic analysis of rabbit interscapular brown adipose tissue whitening under physiological conditions

Interscapular brown adipose tissue (iBAT) of both rabbits and humans exhibits a similar whitening phenomenon under physiological conditions. However, a detailed characterization of iBAT whitening in them is still lacking. Here, we chose rabbits as a model to gain a better understanding of the molecular signature changes during the whitening process of iBAT by transcriptomic analysis of rabbit iBAT at day 1, day 14, 1 month and 4 months after birth. We applied non-invasive MRI imaging to monitor the whitening process and correlated these changes with analysis of morphological, histological and molecular features. Principal component analysis (PCA) of differentially expressed genes delineated three major phases for the whitening process as Brown, Transition and Whitened BAT phases. RNA-sequencing data revealed that whitening of iBAT was an orchestrated process where multiple types of cells and tissues participated in a variety of physiological processes including neovascularization, formation of new nervous networks and immune regulation. Several key metabolic and signalling pathways contributed to whitening of iBAT, and immune cells and immune regulation appeared to play an overarching role.