In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. A key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field.

Mutations that cause defects in levels of the signaling lipid phosphatidylinositol 3,5-bisphosphate [PI(3,5)P ₂ ] lead to profound neurodegeneration in mice. Moreover, mutations in human FIG4 predicted to lower PI(3,5)P ₂ levels underlie Charcot–Marie–Tooth type 4J neuropathy and are present in selected cases of amyotrophic lateral sclerosis. In yeast and mammals, PI(3,5)P ₂ is generated by a protein complex that includes the lipid kinase Fab1/Pikfyve, the scaffolding protein Vac14, and the lipid phosphatase Fig4. Fibroblasts cultured from Vac14 ^−/− and Fig4 ^−/− mouse mutants have a 50% reduction in the levels of PI(3,5)P ₂ , suggesting that there may be PIKfyve-independent pathways that generate this lipid. Here, we characterize a Pikfyve gene-trap mouse ( Pikfyve ^{β -geo/ β -geo} ), a hypomorph with ∼10% of the normal level of Pikfyve protein. shRNA silencing of the residual Pikfyve transcript in fibroblasts demonstrated that Pikfyve is required to generate all of the PI(3,5)P ₂ pool. Surprisingly, Pikfyve also is responsible for nearly all of the phosphatidylinositol-5-phosphate (PI5P) pool. We show that PI5P is generated directly from PI(3,5)P ₂ , likely via 3′-phosphatase activity. Analysis of tissues from the Pikfyve ^{β -geo/ β -geo} mouse mutants reveals that Pikfyve is critical in neural tissues, heart, lung, kidney, thymus, and spleen. Thus, PI(3,5)P ₂ and PI5P have major roles in multiple organs. Understanding the regulation of these lipids may provide insights into therapies for multiple diseases.

AMPK ensures cell survival during energetic stress by stimulating the kinase Akt through mTORC2.

The kinase complex mechanistic target of rapamycin 1 (mTORC1) plays an important role in controlling growth and metabolism. We report here that the stepwise formation of phosphatidylinositol 3-phosphate (PI(3)P) and phosphatidylinositol 3,5-bisphosphate (PI(3,5)P₂) regulates the cell type–specific activation and localization of mTORC1. PI(3)P formation depends on the class II phosphatidylinositol 3-kinase (PI3K) PI3K-C2α, as well as the class III PI3K Vps34, while PI(3,5)P₂ requires the phosphatidylinositol-3-phosphate-5-kinase PIKFYVE. In this paper, we show that PIKFYVE and PI3K-C2α are necessary for activation of mTORC1 and its translocation to the plasma membrane in 3T3-L1 adipocytes. Furthermore, the mTORC1 component Raptor directly interacts with PI(3,5)P₂. Together these results suggest that PI(3,5)P₂ is an essential mTORC1 regulator that defines the localization of the complex.

Mining of integrated public transcriptomic and ChIP-Seq (cistromic) datasets can illuminate functions of mammalian cellular signaling pathways not yet explored in the research literature. Here, we designed a web knowledgebase, the Signaling Pathways Project (SPP), which incorporates community classifications of signaling pathway nodes (receptors, enzymes, transcription factors and co-nodes) and their cognate bioactive small molecules. We then mapped over 10,000 public transcriptomic or cistromic experiments to their pathway node or biosample of study. To enable prediction of pathway node-gene target transcriptional regulatory relationships through SPP, we generated consensus ‘omics signatures, or consensomes, which ranked genes based on measures of their significant differential expression or promoter occupancy across transcriptomic or cistromic experiments mapped to a specific node family. Consensomes were validated using alignment with canonical literature knowledge, gene target-level integration of transcriptomic and cistromic data points, and in bench experiments confirming previously uncharacterized node-gene target regulatory relationships. To expose the SPP knowledgebase to researchers, a web browser interface was designed that accommodates numerous routine data mining strategies. SPP is freely accessible athttps://www.signalingpathways.org.