Back

Immunoregulatory effect of metformin in monocytes exposed to SARS-CoV-2 spike protein subunit 1

Maurmann, R. M.; Davis, K.; Mosalmanzadeh, N.; Schmitt, B. L.; Pence, B. D.

2025-09-12 immunology
10.1101/2025.09.12.675877 bioRxiv
Show abstract

BackgroundSevere COVID-19 is characterized by a hyperinflammatory state associated with an exacerbated inflammatory activation of monocytes and macrophages in the respiratory tract. Metformin has been identified as a potent monocyte inflammatory suppressor, and it has been demonstrated to attenuate inflammation in COVID-19. The mechanisms underlying metformin anti-inflammatory effects are, however, unclear. We thus sought to investigate metformins main interactions and their respective isolated effects in modulating monocyte inflammatory response to SARS-CoV-2 stimulation. MethodsClassical human monocytes were isolated from healthy 18-40-year-old individuals and stimulated in vitro with recombinant spike protein subunit 1 (rS1) to assess glycolytic and oxidative metabolic responses by Seahorse extracellular flux analysis, and inflammatory gene expression by qPCR. Stimulated monocytes were either pre-treated with metformin, rotenone, S1QEL, or A769662. ResultsMonocytes stimulated in vitro with rS1 showed an increased glycolytic response associated with production of pro-inflammatory cytokines. Metformin pre-treatment reduced glycolytic activation while partially suppressing inflammation. Rotenone-dependent mitochondrial complex I inhibition was not able to replicate the same effect, and neither complex I specific ROS scavenging. Conversely, A769662 induced AMPK activation led to suppressed glycolytic inflammatory response and cytokine expression pattern similar to metformin, thus suggesting AMPK modulation as a possible central component for metformins mode of action upon S1 stimulation. ConclusionsIn summary, further investigation into the interactions underlying AMPK activity on monocytes in the context of SARS-CoV-2 may provide a better elucidation of metformins anti-inflammatory effect.

Published in Immunometabolism · not in our set (fewer than 10 published preprints to learn from) · training set

Matching journals

The top 10 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.