Glycolysis downregulation is a hallmark of HIV-1 latency and sensitizes infected cells to oxidative stress.
Iart Luca ShytajFrancesco Andrea ProcopioMohammad TarekIrene Carlon-AndresHsin-Yao TangAaron R GoldmanMohamedHusen MunshiVirender Kumar PalMattia ForcatoSheetal SreeramKonstantin LeskovFengchun YeBojana LucicNicolly CruzLishomwa C NdhlovuSilvio BicciatoSergi Padilla-ParraRicardo Sobhie DiazAmit SinghMarina LusicJonathan KarnDavid Alvarez-CarbonellAndrea SavarinoPublished in: EMBO molecular medicine (2021)
HIV-1 infects lymphoid and myeloid cells, which can harbor a latent proviral reservoir responsible for maintaining lifelong infection. Glycolytic metabolism has been identified as a determinant of susceptibility to HIV-1 infection, but its role in the development and maintenance of HIV-1 latency has not been elucidated. By combining transcriptomic, proteomic, and metabolomic analyses, we here show that transition to latent HIV-1 infection downregulates glycolysis, while viral reactivation by conventional stimuli reverts this effect. Decreased glycolytic output in latently infected cells is associated with downregulation of NAD+ /NADH. Consequently, infected cells rely on the parallel pentose phosphate pathway and its main product, NADPH, fueling antioxidant pathways maintaining HIV-1 latency. Of note, blocking NADPH downstream effectors, thioredoxin and glutathione, favors HIV-1 reactivation from latency in lymphoid and myeloid cellular models. This provides a "shock and kill effect" decreasing proviral DNA in cells from people living with HIV/AIDS. Overall, our data show that downmodulation of glycolysis is a metabolic signature of HIV-1 latency that can be exploited to target latently infected cells with eradication strategies.
Keyphrases
- antiretroviral therapy
- hiv aids
- induced apoptosis
- hiv infected
- hiv positive
- human immunodeficiency virus
- oxidative stress
- hiv testing
- hepatitis c virus
- cell cycle arrest
- signaling pathway
- men who have sex with men
- cell proliferation
- acute myeloid leukemia
- cell death
- immune response
- machine learning
- bone marrow
- reactive oxygen species