Exercise hormone irisin is a critical regulator of cognitive function.
Mohammad R IslamSophia ValarisMichael F YoungErin B HaleyRenhao LuoSabrina F BondSofia MazueraRobert R KitchenBarbara J CaldaroneLuis E B BettioBrian R ChristieAngela B SchmiderRoy J SobermanAntoine BesnardMark P JedrychowskiHyeonwoo KimHua TuEunhee KimSe Hoon ChoiRudolph E TanziBruce M SpiegelmanChristiane D WrannPublished in: Nature metabolism (2021)
Identifying secreted mediators that drive the cognitive benefits of exercise holds great promise for the treatment of cognitive decline in ageing or Alzheimer's disease (AD). Here, we show that irisin, the cleaved and circulating form of the exercise-induced membrane protein FNDC5, is sufficient to confer the benefits of exercise on cognitive function. Genetic deletion of Fndc5/irisin (global Fndc5 knock-out (KO) mice; F5KO) impairs cognitive function in exercise, ageing and AD. Diminished pattern separation in F5KO mice can be rescued by delivering irisin directly into the dentate gyrus, suggesting that irisin is the active moiety. In F5KO mice, adult-born neurons in the dentate gyrus are morphologically, transcriptionally and functionally abnormal. Importantly, elevation of circulating irisin levels by peripheral delivery of irisin via adeno-associated viral overexpression in the liver results in enrichment of central irisin and is sufficient to improve both the cognitive deficit and neuropathology in AD mouse models. Irisin is a crucial regulator of the cognitive benefits of exercise and is a potential therapeutic agent for treating cognitive disorders including AD.
Keyphrases
- cognitive decline
- high intensity
- physical activity
- resistance training
- mild cognitive impairment
- transcription factor
- high fat diet induced
- type diabetes
- machine learning
- cell proliferation
- gene expression
- mouse model
- sars cov
- preterm infants
- mass spectrometry
- adipose tissue
- metabolic syndrome
- young adults
- liquid chromatography
- preterm birth
- combination therapy
- gene therapy
- deep learning