Exercise and obesity have opposite impact on muscle, fat tissues, researchers demonstrate

Exercise training is a well-known means of maintaining and restoring good health; however, the molecular mechanisms underlying the benefits of exercise are not yet completely understood. A new paper by researchers at Joslin Diabetes Center in Cell Metabolism sheds light on the complex physiological response to exercise.

Taking advantage of recent single-cell technologies and advancements in computational biology, a team led by Laurie J. Goodyear, PhD, senior investigator of Integrative Physiology and Metabolism at Joslin Diabetes Center, launched a collaboration with a computational biology and artificial intelligence lab at Massachusetts Institute of Technology led by Manolis Kellis, PhD, to investigate how three metabolic tissues respond to exercise and to high-fat diet-induced obesity at single-cell resolution. These first-of-their-kind results provide a reference atlas of the single-cell changes induced by the exercise and obesity in two different types of fat and muscle. The investigators determined that there are opposite responses to exercise and obesity across all three tissues and highlight prominent molecular pathways modulated by exercise and obesity.

“Regular physical exercise is a well-established intervention for prevention and treatment of obesity and diabetes, and our goal is to set the foundation for understanding the molecular changes and cell types mediating the systemic effects of exercise and obesity in different tissues throughout the body,” said Goodyear, also a professor of medicine at Harvard Medical School. “The results of this study are going to serve as a tremendous resource that can lead to so much other work – not just from our laboratory but from other labs, too – that could eventually lead to the discovery of novel therapeutic options for obesity and other chronic metabolic diseases.”

Goodyear and colleagues focused the current investigation on two kinds of white adipose tissue – or fat – and skeletal muscle taken from mice which were either trained or sedentary, and fed either a healthful chow diet or fed a high-fat diet (HFD) intended to mimic the typical Western diet. This effectively provided four groups of mice; chow-fed/sedentary, chow-fed/active, HFD/sedentary and HFD/active. Diet treatments were for six weeks, and exercise training was done by housing mice with free access to a running wheel for three weeks.

After three weeks of the exercise intervention, the animals’ tissues were analyzed with single-cell RNA sequencing, providing the researchers with a plethora of new data. Among the most striking findings, the scientists observed that genes governing extracellular modelling (ECM) and circadian rhythm were regulated by both exercise and obesity across all three tissue types. Obesity up-regulated ECM-related pathways, while exercise down-regulated them. Conversely, exercise up-regulated circadian-related pathways, and obesity down-regulated them.

“With respect to the circadian rhythm, we saw very quiet cells that weren’t metabolically active with the high-fat diet group,” said co-first author Pasquale Nigro, PhD, a senior member of the Goodyear lab at Joslin and an instructor in medicine at Harvard Medical School. “We discovered that exercise reversed this. It seemed that, when the circadian system is upregulated, cells become re-activated.”

“As one of the most effective strategies to maintain a healthy body and mind, exercise is increasingly understood to induce tissue-specific and shared adaptations in the context of many other diseases beyond obesity,” said co-first author Maria Vamvini, MD, staff physician at Joslin and instructor in medicine at Harvard Medical School. “By combining our knowledge as physiologists with the computational biology skills of the Kellis lab at MIT, we’ve been able to develop a single-cell atlas with more than 200,000 cells and 53 annotated cell types. This resource has the potential to help our research team as well as others reveal fundamental exercise-induced changes in a diverse set of diseases and physiological contexts such as cancer and aging.  This teamwork stands out as a model for what we can accomplish through collaboration.”

Additional authors included co-first author Jiekun Yang, PhD, from the Kellis lab, Computer Science and Artificial Intelligence Laboratory at Massachusetts Institute of Technology who performed the computational analysis for this project with co-corresponding author Manolis Kellis, PhD, who is.  Other co-authors from the Kellis lab included: Li-Lun Ho, Kiki Galani, Yosuke Tanigawa, Ashley Renfro and Leandro Agudelo.  Additional co-authors from the Section on Integrative Physiology and Metabolism, Joslin Diabetes Center included Nicholas Carbone, Michael F. Hirshman and Roeland J. W. Middelbeek. Other co-authors: Marcus Alvarez, Päivi Pajukanta of David Geffen School of Medicine at UCLA; Markku Laasko of University of Eastern Finland; and Kevin Grove of Novo Nordisk Research Center.

This work was supported by Novo Nordisk Research Center, Seattle, Washington; the National Institutes of Health (R01D K 099511, 5P30-DK dash 36836, U24H G 009446, U G3 NS 115064, T32-D K 110919, T32-D K 007260, F32-D K 126432, K23-D K 114550, R018 G 008155, R01A G 067151, R01 HG 010505, R01D K 132775) and Joslin Diabetes Center P&F.

Grove is an employee of Novo Nordisk. All other authors report no conflicts of interest.


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