Tumor cells display progressive changes in metabolism that correlate with malignancy, including development of a lipogenic phenotype. How stored fats are liberated and remodeled to support cancer pathogenesis, however, remains unknown. Here, we show that the enzyme monoacylglycerol lipase (MAGL) is highly expressed in aggressive human cancer cells and primary tumors, where it regulates a fatty acid network enriched in oncogenic signaling lipids that promotes migration, invasion, survival, and in vivo tumor growth. Overexpression of MAGL in non-aggressive cancer cells recapitulates this fatty acid network and increases their pathogenicity -- phenotypes that are reversed by an MAGL inhibitor. Interestingly, impairments in MAGL-dependent tumor growth are rescued by a high-fat diet, indicating that exogenous sources of fatty acids can contribute to malignancy in cancers lacking MAGL activity. Together, these findings reveal how cancer cells can co-opt a lipolytic enzyme to translate their lipogenic state into an array of pro-tumorigenic signals.

Proteins circulating in the blood are critical for age-related disease processes; however, the serum proteome has remained largely unexplored. To this end, 4137 proteins covering most predicted extracellular proteins were measured in the serum of 5457 Icelanders over 65 years of age. Pairwise correlation between proteins as they varied across individuals revealed 27 different network modules of serum proteins, many of which were associated with cardiovascular and metabolic disease states, as well as overall survival. The protein modules were controlled by cis- and trans-acting genetic variants, which in many cases were also associated with complex disease. This revealed co-regulated groups of circulating proteins that incorporated regulatory control between tissues and demonstrated close relationships to past, current, and future disease states.

Plk4 phosphorylates itself in trans to prevent accumulation and self-limit kinase activity, which may be important for regulating centriole duplication.

Serine hydrolases (SHs) are one of the largest and most diverse enzyme classes in mammals. They play fundamental roles in virtually all physiological processes and are targeted by drugs to treat diseases such as diabetes, obesity, and neurodegenerative disorders. Despite this, we lack biological understanding for most of the 110þ predicted mammalian metabolic SHs, in large part because of a dearth of assays to assess their biochemical activities and a lack of selective inhibitors to probe their function in living systems. We show here that the vast majority (>80%) of mammalian metabolic SHs can be labeled in proteomes by a single, active site-directed fluorophosphonate probe. We exploit this universal activitybased assay in a library-versus-library format to screen 70þ SHs against 140þ structurally diverse carbamates. Lead inhibitors were discovered for ∼40% of the screened enzymes, including many poorly characterized SHs. Global profiles identified carbamate inhibitors that discriminate among highly sequence-related SHs and, conversely, enzymes that share inhibitor sensitivity profiles despite lacking sequence homology. These findings indicate that sequence relatedness is not a strong predictor of shared pharmacology within the SH superfamily. Finally, we show that lead carbamate inhibitors can be optimized into pharmacological probes that inactivate individual SHs with high specificity in vivo.enzymology | mass spectrometry | profiling | proteomics A major challenge facing biological researchers in the 21st century is the functional characterization of the large number of unannotated gene products identified by genome sequencing efforts (1). Many proteins partly or completely uncharacterized with respect to their biochemical activities belong to expansive, sequence-related families (2). Although such membership can inform on the general mechanistic class to which a protein belongs (e.g., enzyme, receptor, or channel), it is insufficient to predict specific biochemical and physiological functions, which require knowledge of substrates, ligands, and interacting biomolecules. On the contrary, membership within a large protein family can even present a barrier to achieving these goals by frustrating the implementation of standard genetic and pharmacological methods to probe protein function. For example, targeted gene disruption of one member of a protein superfamily may result in cellular compensation from other family members.Problems are also encountered when attempting to develop specific inhibitors and/or ligands for uncharacterized members of large protein families, where at least two major experimental issues must be addressed. First, there is an intrinsic difficulty facing ligand discovery for uncharacterized proteins, which often lack the functional information required to develop high-quality assays for compound screening. Creative solutions to this problem have emerged for specific protein classes, such as G-protein coupled receptors (GPCRs) (3) and kinases (4, 5), where generic assays have been develop...