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Metabolic pathways that contribute to adiposity and ageing are activated by the mammalian target of rapamycin complex 1 (mTORC1) and p70 ribosomal protein S6 kinase 1 (S6K1) axis. However, known mTORC1-S6K1 targets do not account for observed loss-of-function phenotypes, suggesting that there are additional downstream effectors of this pathway. Here we identify glutamyl-prolyl-tRNA synthetase (EPRS) as an mTORC1-S6K1 target that contributes to adiposity and ageing. Phosphorylation of EPRS at Ser999 by mTORC1-S6K1 induces its release from the aminoacyl tRNA multisynthetase complex, which is required for execution of noncanonical functions of EPRS beyond protein synthesis. To investigate the physiological function of EPRS phosphorylation, we generated Eprs knock-in mice bearing phospho-deficient Ser999-to-Ala (S999A) and phospho-mimetic (S999D) mutations. Homozygous S999A mice exhibited low body weight, reduced adipose tissue mass, and increased lifespan, similar to S6K1-deficient mice and mice with adipocyte-specific deficiency of raptor, an mTORC1 constituent. Substitution of the EprsS999D allele in S6K1-deficient mice normalized body mass and adiposity, indicating that EPRS phosphorylation mediates S6K1-dependent metabolic responses. In adipocytes, insulin stimulated S6K1-dependent EPRS phosphorylation and release from the multisynthetase complex. Interaction screening revealed that phospho-EPRS binds SLC27A1 (that is, fatty acid transport protein 1, FATP1), inducing its translocation to the plasma membrane and long-chain fatty acid uptake. Thus, EPRS and FATP1 are terminal mTORC1-S6K1 axis effectors that are critical for metabolic phenotypes.
Pubmed ID: 28178239 RIS Download
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View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 2nd, 2023. Sequence composition based classifier for metagenomic sequences. It works by capturing signatures of each sequence based on the sequence composition. Each sequence is modeled as a walk in a de Bruijn graph with underlying Markov chain properties. ClaMS captures stationary parameters of the underlying Markov chain as well as structural parameters of the underlying de Bruijn graph to form this signature. In practice, for each sequence to binned, such a signature is computed and matched to similar signatures computed for the training sets. The best match that also qualifies the normalized distance cut-off wins. In the case that the best match does not qualify this cut-off, the sequence remains un-binned.
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