Back

Blood Biochemical Responses to Acute Exercise: Findings from the Molecular Transducers of Physical Activity Consortium (MoTrPAC)

Robbins, J. M.; Katz, D. H.; Many, G.; Rao, P.; Smith, G. R.; Tiwari, G.; Spielmann, G.; Montalvo, S.; Iyer, G.; Amar, D.; Leach, D. T.; Coyne, B. J.; Lindholm, M. E.; Goodpaster, B. H.; Walsh, M. J.; Clish, C. B.; Burant, C. F.; Gerszten, R. E.; MoTrPAC Study Group,

2026-03-04 systems biology
10.64898/2026.03.02.704798 bioRxiv
Show abstract

Exercise benefits numerous organ systems and tissues, however limited knowledge exists about its underlying molecular pathways. Identifying the exercise-induced biochemical changes that occur in the circulation may provide further insights into how exercise confers systemic health changes. Here, we perform large-scale plasma proteomic, metabolomic, and whole blood transcriptional profiling in sedentary human participants undergoing acute endurance exercise (EE), resistance exercise (RE), or a non-exercise control (CON) in up to 7 timepoints over a 24 hour period. We observe 7066 transcript, 189 protein, and 448 metabolite changes in response to EE or RE compared to CON. Our analyses reveal numerous shared biochemical responses between EE and RE modes, but also differences in immune cell responses, lipid metabolism, and pathways reflective of tissue repair and angiogenesis. Taken together, our findings highlight novel temporal and exercise mode-specific blood-based molecular responses to acute exercise, and provide a new resource for the scientific community. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/704798v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1d301cborg.highwire.dtl.DTLVardef@1ad101org.highwire.dtl.DTLVardef@8fcaf8org.highwire.dtl.DTLVardef@568813_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

Matching journals

The top 6 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.