The mechanistic target of rapamycin complex 1 (mTORC1) is a master driver of growth that influences development, reproduction, and lifespan by responding to a diverse set of environmental cues, including amino acids. Known mTORC1 direct phosphorylation targets regulate translation, autophagy, and metabolism, but it is likely that unknown targets are involved in the many complex aspects of mTORC1 action. We have identified potential novel mTORC1 target genes and pathways by following a systems biology approach. We conducted bioinformatic analysis of known mTOR phosphoproteome/interactome proteins by pathway enrichment analysis and identified predicted TOR binding sites, then performed high-throughput in vivoexperiments in C. elegansto identify genetic interactors of mTORC1. For the genetic screens we used a loss of function mutation in
raga-1(the amino-acid signal transducer RagA), which provides a partial loss of mTORC1 function, and searched for clones that enhance defects of RagA mutants in growth and development. This strategy identified new predicted mTORC1 targets, and new processes predicted to be regulated by mTORC1. We next used a machine-learning system to predict a new set of candidate mTORC1 targets. We applied a random-forest algorithm to these screening results, looking for genes/proteins with similar characteristics to our interactors. We found that this new group of candidates exhibited a higher than expected frequency of genetic interaction with
raga-1(mTORC1), validating our strategy. We will discuss how these predicted downstream direct and indirect targets of mTORC1 have identified unexpected mechanisms that appear to be regulated by mTORC1 as it promotes growth.