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Background: Insulin regulates metabolism via the PI3K/Akt pathway. Results: A kinome siRNA screen identified PFKFB3, a glycolysis regulator, as a modulator of insulin action. Manipulation of PFKFB3 activity or glycolysis affected insulin signaling. Conclusion: Intracellular metabolism modulates important signal transduction pathways. Significance: The novel link between glycolysis and growth factor signaling has important implications for the treatment of metabolic diseases.

Cancer is one of the leading causes of mortality in humans, and recent work has focused on the area of immuno-oncology, in which the immune system is used to specifically target cancerous cells. Ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1) is an emerging therapeutic target in human cancers owing to its role in degrading cyclic GMP-AMP (cGAMP), an agonist of the stimulator of interferon genes (STING). The available structures of ENPP1 are of the mouse enzyme, and no structures are available with anything other than native nucleotides. Here, the first X-ray crystal structures of the human ENPP1 enzyme in an apo form, with bound nucleotides and with two known inhibitors are presented. The availability of these structures and a robust crystallization system will allow the development of structure-based drug-design campaigns against this attractive cancer therapeutic target.

Objective:Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavored to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans.Methods:We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single gene, gene set, and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in three independent human data sets (n=115).Results:This GEM of 93 genes substantially improved diagnosis of IR compared with routine clinical measures across multiple independent data sets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between β-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action.Conclusions:This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the β-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation.