Autophagy is a cellular catabolic process that relies on the cooperation of autophagosomes and lysosomes. During starvation, the cell expands both compartments to enhance degradation processes. We found that starvation activates a transcriptional program that controls major steps of the autophagic pathway, including autophagosome formation, autophagosome-lysosome fusion and substrate degradation. The transcription factor EB (TFEB), a master gene for lysosomal biogenesis, coordinated this program by driving expression of autophagy and lysosomal genes. Nuclear localization and activity of TFEB were regulated by serine phosphorylation mediated by the extracellular signal-regulated kinase 2, whose activity was tuned by the levels of extracellular nutrients. Thus, a mitogen-activated protein kinase-dependent mechanism regulates autophagy by controlling the biogenesis and partnership of two distinct cellular organelles.

A lysosome-to-nucleus signalling mechanism senses and regulates the lysosome via mTOR and TFEBUnder basal conditions TFEB, a master regulator of lysosomal biogenesis, is sequestered in the cytosol due to mTORC1-dependent phosphorylation at the lysosomal membrane. Nutrient starvation or lysosomal dysfunction inhibit mTORC1 activity and induce nuclear translocation of TFEB inducing target gene expression.

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based Critical Assessment of protein Function Annotation (CAFA) experiment. Fifty-four methods representing the state-of-the-art for protein function prediction were evaluated on a target set of 866 proteins from eleven organisms. Two findings stand out: (i) today’s best protein function prediction algorithms significantly outperformed widely-used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is significant need for improvement of currently available tools.