Recent surges in mass spectrometry-based proteomics studies demand a concurrent rise in speedy and optimized data processing tools and pipelines. Although several stand-alone bioinformatics tools exist that provide protein-protein interaction (PPI) data, we developed Protein Interaction Network Extractor (PINE) as a fully automated, user-friendly, graphical user interface application for visualization and exploration of global proteome and post-translational modification (PTM) based networks. PINE also supports overlaying differential expression, statistical significance thresholds, and PTM sites on functionally enriched visualization networks to gain insights into proteome-wide regulatory mechanisms and PTM-mediated networks. To illustrate the relevance of the tool, we explore the total proteome and its PTM-associated relationships in two different nonalcoholic steatohepatitis (NASH) mouse models to demonstrate different context-specific case studies. The strength of this tool relies in its ability to (1) perform accurate protein identifier mapping to resolve ambiguity, (2) retrieve interaction data from multiple publicly available PPI databases, and (3) assimilate these complex networks into functionally enriched pathways, ontology categories, and terms. Ultimately, PINE can be used as an extremely powerful tool for novel hypothesis generation to understand underlying disease mechanisms.

Heritable epigenetic factors can contribute to complex disease etiology. Here we examine the contribution of DNA methylation to complex traits that are precursors to heart disease, diabetes, and osteoporosis. We profiled DNA methylation in the liver using bisulfite sequencing in 90 mouse inbred strains, genome-wide expression levels, proteomics, metabolomics, and 68 clinical traits and performed epigenome-wide association studies (EWAS). We found associations with numerous clinical traits including bone density, insulin resistance, expression, and protein and metabolite levels. A large proportion of associations were unique to EWAS and were not identified using GWAS. Methylation levels were regulated by genetics largely in cis, but we also found evidence of trans regulation, and we demonstrate that genetic variation in the methionine synthase reductase gene Mtrr affects methylation of hundreds of CpGs throughout the genome. Our results indicate that natural variation in methylation levels contributes to the etiology of complex clinical traits.

Altered metabolism is a hallmark of cancer growth, forming the conceptual basis for development of metabolic therapies as cancer treatments. We performed in vivo metabolic profiling and molecular analysis of lung squamous cell carcinoma (SCC) to identify metabolic nodes for therapeutic targeting. Lung SCCs adapt to chronic mTOR inhibition and suppression of glycolysis through the GSK3α/β signaling pathway, which upregulates glutaminolysis. Phospho-GSK3α/β protein levels are predictive of response to single-therapy mTOR inhibition while combinatorial treatment with the glutaminase inhibitor CB-839 effectively overcomes therapy resistance. In addition, we identified a conserved metabolic signature in a broad spectrum of hypermetabolic human tumors that may be predictive of patient outcome and response to combined metabolic therapies targeting mTOR and glutaminase.

Altered metabolism is a hallmark of cancer growth, forming the conceptual basis for development of metabolic therapies as cancer treatments. We performed in vivo metabolic profiling and molecular analysis of lung squamous cell carcinoma (SCC) to identify metabolic nodes for therapeutic targeting. Lung SCCs adapt to chronic mTOR inhibition and suppression of glycolysis through the GSK3α/β signaling pathway, which upregulates glutaminolysis. Phospho-GSK3α/β protein levels are predictive of response to single-therapy mTOR inhibition while combinatorial treatment with the glutaminase inhibitor CB-839 effectively overcomes therapy resistance. In addition, we identified a conserved metabolic signature in a broad spectrum of hypermetabolic human tumors that may be predictive of patient outcome and response to combined metabolic therapies targeting mTOR and glutaminase.

Copy number alteration (CNA) profiling of human tumors has revealed recurrent patterns of DNA amplifications and deletions across diverse cancer types. These patterns are suggestive of conserved selection pressures during tumor evolution but cannot be fully explained by known oncogenes and tumor suppressor genes. Using a pan-cancer analysis of CNA data from patient tumors and experimental systems, here we show that principal component analysis-defined CNA signatures are predictive of glycolytic phenotypes, including ¹⁸F-fluorodeoxy-glucose (FDG) avidity of patient tumors, and increased proliferation. The primary CNA signature is enriched for p53 mutations and is associated with glycolysis through coordinate amplification of glycolytic genes and other cancer-linked metabolic enzymes. A pan-cancer and cross-species comparison of CNAs highlighted 26 consistently altered DNA regions, containing 11 enzymes in the glycolysis pathway in addition to known cancer-driving genes. Furthermore, exogenous expression of hexokinase and enolase enzymes in an experimental immortalization system altered the subsequent copy number status of the corresponding endogenous loci, supporting the hypothesis that these metabolic genes act as drivers within the conserved CNA amplification regions. Taken together, these results demonstrate that metabolic stress acts as a selective pressure underlying the recurrent CNAs observed in human tumors, and further cast genomic instability as an enabling event in tumorigenesis and metabolic evolution.