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

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Caveolae are vesicular invaginations of the plasma membrane. The chief structural proteins of caveolae are the caveolins. Caveolins form a scaffold onto which many classes of signaling molecules can assemble to generate preassembled signaling complexes. In addition to concentrating these signal transducers within a distinct region of the plasma membrane, caveolin binding may functionally regulate the activation state of caveolae-associated signaling molecules. Because the responsibilities assigned to caveolae continue to increase, this review will focus on: (i) caveolin structure/function and (ii) caveolae-associated signal transduction. Studies that link caveolae to human diseases will also be considered.The Caveolin Gene Family: Caveolin-1, -2, and -3 Molecular cloning has identified three distinct caveolin genes (1-6), caveolin-1, caveolin-2, and caveolin-3. Two isoforms of caveolin-1 (Cav-1␣ and Cav-1␤) are derived from alternate initiation during translation. Caveolin-1 and -2 are most abundantly expressed in adipocytes, endothelial cells, and fibroblastic cell types, whereas the expression of caveolin-3 is muscle-specific.Caveolin proteins interact with themselves to form homo-and hetero-oligomers (7-9), which directly bind cholesterol (10) and require cholesterol for insertion into model lipid membranes (10,11). Caveolin oligomers may also interact with glycosphingolipids (12). These protein-protein and protein-lipid interactions are thought to be the driving force for caveolae formation (7). In addition, the caveolin gene family is structurally and functionally conserved from worms (Caenorhabditis elegans) to man (13), supporting the idea that caveolins play an essential role.Caveolin-1 assumes an unusual topology. A central hydrophobic domain (residues 102-134) is thought to form a hairpin-like structure within the membrane. As a consequence, both the N-terminal domain (residues 1-101) and the C-terminal domain (residues 135-178) face the cytoplasm. A 41-amino acid region of the N-terminal domain (residues 61-101) directs the formation of caveolin homooligomers (7), whereas the 44-amino acid C-terminal domain acts as a bridge to allow these homo-oligomers to interact with each other, thereby forming a caveolin-rich scaffold (14).Recent co-immunoprecipitation and dual labeling experiments directly show that caveolin-1 and -2 form a stable hetero-oligomeric complex and are strictly co-localized (9). Caveolin-2 localization corresponds to caveolae membranes as visualized by immunoelectron microscopy (9). Thus, caveolin-2 may function as an "accessory protein" in conjunction with caveolin-1. Caveolin-interacting ProteinsA number of studies support the hypothesis that caveolin proteins provide a direct means for resident caveolae proteins to be sequestered within caveolae microdomains. These caveolin-interacting proteins include G-protein ␣ subunits, Ha-Ras, Src family tyrosine kinases, endothelial NOS, 1 EGF-R and related receptor tyrosine kinases, and protein kinase C isoforms (11, 15-18, 20 -32).Heterotri...

Here, we propose a new model for understanding the Warburg effect in tumor metabolism. Our hypothesis is that epithelial cancer cells induce the Warburg effect (aerobic glycolysis) in neighboring stromal fibroblasts. These cancer-associated fibroblasts, then undergo myo-fibroblastic differentiation, and secrete lactate and pyruvate (energy metabolites resulting from aerobic glycolysis). Epithelial cancer cells could then take up these energy-rich metabolites and use them in the mitochondrial TCA cycle, thereby promoting efficient energy production (ATP generation via oxidative phosphorylation), resulting in a higher proliferative capacity. In this alternative model of tumorigenesis, the epithelial cancer cells instruct the normal stroma to transform into a wound-healing stroma, providing the necessary energy-rich micro-environment for facilitating tumor growth and angiogenesis. In essence, the fibroblastic tumor stroma would directly feed the epithelial cancer cells, in a type of host-parasite relationship. We have termed this new idea the "Reverse Warburg Effect." In this scenario, the epithelial tumor cells "corrupt" the normal stroma, turning it into a factory for the production of energy-rich metabolites. This alternative model is still consistent with Warburg's original observation that tumors show a metabolic shift towards aerobic glycolysis. In support of this idea, unbiased proteomic analysis and transcriptional profiling of a new model of cancer-associated fibroblasts (caveolin-1 (Cav-1) deficient stromal cells), shows the upregulation of both (1) myo-fibroblast markers and (2) glycolytic enzymes, under normoxic conditions. We validated the expression of these proteins in the fibroblastic stroma of human breast cancer tissues that lack stromal Cav-1. Importantly, a loss of stromal Cav-1 in human breast cancers is associated with tumor recurrence, metastasis, and poor clinical outcome. Thus, an absence of stromal Cav-1 may be a biomarker for the "Reverse Warburg Effect," explaining its powerful predictive value.

Awareness that the metabolic phenotype of cells within tumours is heterogeneous - and distinct from that of their normal counterparts - is growing. In general, tumour cells metabolize glucose, lactate, pyruvate, hydroxybutyrate, acetate, glutamine, and fatty acids at much higher rates than their nontumour equivalents; however, the metabolic ecology of tumours is complex because they contain multiple metabolic compartments, which are linked by the transfer of these catabolites. This metabolic variability and flexibility enables tumour cells to generate ATP as an energy source, while maintaining the reduction-oxidation (redox) balance and committing resources to biosynthesis - processes that are essential for cell survival, growth, and proliferation. Importantly, experimental evidence indicates that metabolic coupling between cell populations with different, complementary metabolic profiles can induce cancer progression. Thus, targeting the metabolic differences between tumour and normal cells holds promise as a novel anticancer strategy. In this Review, we discuss how cancer cells reprogramme their metabolism and that of other cells within the tumour microenvironment in order to survive and propagate, thus driving disease progression; in particular, we highlight potential metabolic vulnerabilities that might be targeted therapeutically.