Back

IFN-driven lipid synthesis shutdown in CD4⁺ T cells during acute SIV infection and persistent OXPHOS with ART initiation

Kim, J.; Ambikan, A.; Harrison-Gleason, J. P.; Yerlioglu, K. L.; Filipovic, I.; Abdelraouf, R. A.; Ananias-Saez, C.; Taylor, E. B.; Arainga, M.; Bose, D.; Villinger, F. J.; Neogi, U.; Martinelli, E.

2026-02-13 immunology
10.64898/2026.02.12.705596 bioRxiv
Show abstract

Cellular metabolism regulates HIV/SIV replication and reservoir establishment, yet how infection and antiretroviral therapy initiation (ARTi) shape the metabolism of CD4 Tcells--main HIV target--in vivo remains poorly defined. Using the SIVmac239 macaque model, we integrated single-cell metabolic profiling (MIST), transcriptomics, lipidomics, genome-scale metabolic modeling, and functional assays to characterize their metabolic remodeling. At peak viremia, CD4 T cells exhibited a marked shutdown of de novo fatty-acid (FA) synthesis, reflected by acetyl-CoA carboxylase-1 (ACC1) downregulation, inhibition of lipid-anabolic reactions, and depletion of membrane phospholipids. This metabolic state was driven by strong type I interferon (IFN-I) responses, and IFN-I exposure was sufficient to suppress ACC1 in vitro. Pharmacologic inhibition of FA synthesis independently enhanced Tcell activation and reduced HIV replication, indicating direct antiviral and immunomodulatory effects. Following ARTi, most metabolic pathways were broadly suppressed, whereas mitochondrial oxidative phosphorylation (OXPHOS) remained elevated. Together, these findings identify IFN-driven FA synthesis shutdown and persistent OXPHOS as defining metabolic features of early HIV/SIV infection and treatment initiation, highlighting these pathways as potential targets to limit viral replication and reservoir formation.

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.