Back

Multi-Omic, Multi-Tissue Responses to Acute Exercise in Sedentary Adults: Findings from the Molecular Transducers of Physical Activity Consortium

MoTrPAC Study Group, ; Katz, D. H.; Jin, C. A.; Many, G. M.; Smith, G. R.; Keshishian, H.; Clark, N. M.; Iyer, G.; Ahn, C.; Lindholm, M. E.; Sagendorf, T. J.; Amar, D.; Barber, J. L.; Brandt, A. R.; Coen, P. M.; Ge, Y.; Hart, P.; Hsu, F.-C.; Jaeger, B. C.; Jimenez-Morales, D.; Leach, D. T.; Mani, D. R.; Montalvo, S.; Pincas, H.; Rao, P.; Sanford, J. A.; Smith, K. S.; Vetr, N. G.; Adkins, J. N.; Ashley, E. A.; Carr, S. A.; Miller, M. E.; Montgomery, S. B.; Nair, V. D.; Robbins, J. M.; Snyder, M. P.; Sparks, L. M.; Tracy, R.; Walsh, M. J.; Wheeler, M. T.; Xia, A. Y.; Sealfon, S. C.; Gerszten, R

2026-03-03 systems biology
10.64898/2026.02.27.702183 bioRxiv
Show abstract

Regular physical activity represents one of the greatest mechanisms for maintaining human health, yet the underlying molecular transducers of these benefits remain incompletely understood. Multi-omic assays now provide new opportunities to study the coordinated molecular responses of body tissues to different exercise modalities. The Molecular Transducers of Physical Activity Consortium (MoTrPAC) was established to address this need by creating a molecular map of the response to physical activity. Described here is the first human cohort of MoTrPAC: sedentary adults enrolled prior to study suspension during the COVID-19 pandemic (N=175) randomized to either endurance or resistance exercise, or non-exercise control. From these participants, we detail their global acute molecular response in skeletal muscle, adipose tissue, and blood, integrated at multiple levels: tissue, exercise modality, timepoint, and omic category. These analyses characterize key molecular pathways, identify central regulators, and implicate novel candidate exerkines in mediating multi-organ exercise effects.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.