Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.     

Mammalian alpha beta hydrolase domain (ABHD) proteins: Lipid metabolizing enzymes at the interface of cell signaling and energy metabolism

Dysregulation of lipid metabolism underlies many chronic diseases such as obesity, diabetes, cardiovascular disease, and cancer. Therefore, understanding enzymatic mechanisms controlling lipid synthesis and degradation is imperative for successful drug discovery for these human diseases. Genes encoding α/β hydrolase fold domain (ABHD) proteins are present in virtually all reported genomes, and conserved structural motifs shared by these proteins predict common roles in lipid synthesis and degradation. However, the physiological substrates and products for these lipid metabolizing enzymes and their broader role in metabolic pathways remain largely uncharacterized. Recently, mutations in several members of the ABHD protein family have been implicated in inherited inborn errors of lipid metabolism. Furthermore, studies in cell and animal models have revealed important roles for ABHD proteins in lipid metabolism, lipid signal transduction, and metabolic disease. The purpose of this review is to provide a comprehensive summary surrounding the current state of knowledge regarding mammalian ABHD protein family members. In particular, we will discuss how ABHD proteins are ideally suited to act at the interface of lipid metabolism and signal transduction. Although, the current state of knowledge regarding mammalian ABHD proteins is still in its infancy, this review highlights the potential for the ABHD enzymes as being attractive targets for novel therapies targeting metabolic disease.     

Model of Tryptophan Metabolism,Readily Scalable Using Tissue-specific Gene Expression Data

Tryptophan is utilized in various metabolic routes including protein synthesis, serotonin, and melatonin synthesis and the kynurenine pathway. Perturbations in these pathways have been associated with neurodegenerative diseases and cancer. Here we present a comprehensive kinetic model of the complex network of human tryptophan metabolism based upon existing kinetic data for all enzymatic conversions and transporters. By integrating tissue-specific expression data, modeling tryptophan metabolism in liver and brain returned intermediate metabolite concentrations in the physiological range. Sensitivity and metabolic control analyses identified expected key enzymes to govern fluxes in the branches of the network. Combining tissue-specific models revealed a considerable impact of the kynurenine pathway in liver on the concentrations of neuroactive derivatives in the brain. Moreover, using expression data from a cancer study predicted metabolite changes that resembled the experimental observations. We conclude that the combination of the kinetic model with expression data represents a powerful diagnostic tool to predict alterations in tryptophan metabolism. The model is readily scalable to include more tissues, thereby enabling assessment of organismal tryptophan metabolism in health and disease.     

Stochasticity in Protein Levels Drives Colinearity of Gene Order in Metabolic Operons of Escherichia coli

In bacterial genomes, gene order is not random. This is most evident when looking at operons, these often encoding enzymes involved in the same metabolic pathway or proteins from the same complex. Is gene order within operons nonrandom, however, and if so why? We examine this issue using metabolic operons as a case study. Using the metabolic network of Escherichia coli, we define the temporal order of reactions. We find a pronounced trend for genes to appear in operons in the same order as they are needed in metabolism (colinearity). This is paradoxical as, at steady state, enzymes abundance should be independent of order within the operon. We consider three extensions of the steady-state model that could potentially account for colinearity: (1) increased productivity associated with higher expression levels of the most 5′ genes, (2) a faster metabolic processing immediately after up-regulation, and (3) metabolic stalling owing to stochastic protein loss. We establish the validity of these hypotheses by employing deterministic and stochastic models of enzyme kinetics. The stochastic stalling hypothesis correctly and uniquely predicts that colinearity is more pronounced both for lowly expressed operons and for genes that are not physically adjacent. The alternative models fail to find any support. These results support the view that stochasticity is a pervasive problem to a cell and that gene order evolution can be driven by the selective consequences of fluctuations in protein levels.     

Extensive amino acid polymorphism at the pgm locus is consistent with adaptive protein evolution in Drosophila melanogaster

PGM plays a central role in the glycolytic pathway at the branch point leading to glycogen metabolism and is highly polymorphic in allozyme studies of many species. We have characterized the nucleotide diversity across the Pgm gene in Drosophila melanogaster and D. simulans to investigate the role that protein polymorphism plays at this crucial metabolic branch point shared with several other enzymes. Although D. melanogaster and D. simulans share common allozyme mobility alleles, we find these allozymes are the result of many different amino acid changes at the nucleotide level. In addition, specific allozyme classes within species contain several amino acid changes, which may explain the absence of latitudinal clines for PGM allozyme alleles, the lack of association of PGM allozymes with the cosmopolitan In(3L)P inversion, and the failure to detect differences between PGM allozymes in functional studies. We find a significant excess of amino acid polymorphisms within D. melanogaster when compared to the complete absence of fixed replacements with D. simulans. There is also strong linkage disequilibrium across the 2354 bp of the Pgm locus, which may be explained by a specific amino acid haplotype that is high in frequency yet contains an excess of singleton polymorphisms. Like G6pd, Pgm shows strong evidence for a branch point enzyme that exhibits adaptive protein evolution.     

Compartmentation in plant metabolism

Cell fractionation and immunohistochemical studies in the last 40 years have revealed the extensive compartmentation of plant metabolism. In recent years, new protein mass spectrometry and fluorescent-protein tagging technologies have accelerated the flow of information, especially for Arabidopsis thaliana, but the intracellular locations of the majority of proteins in the plant proteome are still not known. Prediction programs that search for targeting information within protein sequences can be applied to whole proteomes, but predictions from different programs often do not agree with each other or, indeed, with experimentally determined results. The compartmentation of most pathways of primary metabolism is generally covered in plant physiology textbooks, so the focus here is mainly on newly discovered metabolic pathways in plants or pathways that have recently been revised. Ultimately, all of the pathways of plant metabolism are interconnected, and a major challenge facing plant biochemists is to understand the regulation and control of metabolic networks. One of the best-characterized networks links sucrose synthesis in the cytosol with photosynthetic CO(2) fixation and starch synthesis in the chloroplasts. One of the key features of this network is how the transport of pathway intermediates and signal metabolites across the chloroplast envelope conveys information between the two compartments, influencing the regulation of several enzymes to co-ordinate fluxes through the different pathways. It is widely accepted that chloroplasts and mitochondria originated from prokaryotic endosymbionts, and that new transporters and regulatory networks evolved to integrate metabolism in these organelles with the rest of the cell. Curiously, the present-day locations of many metabolic pathways within the cell often do not reflect their evolutionary origin, and there is evidence of extensive shuffling of enzymes and whole pathways between compartments during the evolution of plants.     

Metabolomics Analysis of Metabolic Effects of Nicotinamide Phosphoribosyltransferase (NAMPT) Inhibition on Human Cancer Cells

Nicotinamide phosphoribosyltransferase (NAMPT) plays an important role in cellular bioenergetics. It is responsible for converting nicotinamide to nicotinamide adenine dinucleotide, an essential molecule in cellular metabolism. NAMPT has been extensively studied over the past decade due to its role as a key regulator of nicotinamide adenine dinucleotide–consuming enzymes. NAMPT is also known as a potential target for therapeutic intervention due to its involvement in disease. In the current study, we used a global mass spectrometry–based metabolomic approach to investigate the effects of FK866, a small molecule inhibitor of NAMPT currently in clinical trials, on metabolic perturbations in human cancer cells. We treated A2780 (ovarian cancer) and HCT-116 (colorectal cancer) cell lines with FK866 in the presence and absence of nicotinic acid. Significant changes were observed in the amino acids metabolism and the purine and pyrimidine metabolism. We also observed metabolic alterations in glycolysis, the citric acid cycle (TCA), and the pentose phosphate pathway. To expand the range of the detected polar metabolites and improve data confidence, we applied a global metabolomics profiling platform by using both non-targeted and targeted hydrophilic (HILIC)-LC-MS and GC-MS analysis. We used Ingenuity Knowledge Base to facilitate the projection of metabolomics data onto metabolic pathways. Several metabolic pathways showed differential responses to FK866 based on several matches to the list of annotated metabolites. This study suggests that global metabolomics can be a useful tool in pharmacological studies of the mechanism of action of drugs at a cellular level.     

Quantitative Proteomic Analysis of Human Lung Tumor Xenografts Treated with the Ectopic ATP Synthase Inhibitor Citreoviridin

ATP synthase is present on the plasma membrane of several types of cancer cells. Citreoviridin, an ATP synthase inhibitor, selectively suppresses the proliferation and growth of lung cancer without affecting normal cells. However, the global effects of targeting ectopic ATP synthase in vivo have not been well defined. In this study, we performed quantitative proteomic analysis using isobaric tags for relative and absolute quantitation (iTRAQ) and provided a comprehensive insight into the complicated regulation by citreoviridin in a lung cancer xenograft model. With high reproducibility of the quantitation, we obtained quantitative proteomic profiling with 2,659 proteins identified. Bioinformatics analysis of the 141 differentially expressed proteins selected by their relative abundance revealed that citreoviridin induces alterations in the expression of glucose metabolism-related enzymes in lung cancer. The up-regulation of enzymes involved in gluconeogenesis and storage of glucose indicated that citreoviridin may reduce the glycolytic intermediates for macromolecule synthesis and inhibit cell proliferation. Using comprehensive proteomics, the results identify metabolic aspects that help explain the antitumorigenic effect of citreoviridin in lung cancer, which may lead to a better understanding of the links between metabolism and tumorigenesis in cancer therapy.     

Sequence divergence and diversity suggests ongoing functional diversification of vertebrate NAD metabolism

NAD is not only an important cofactor in redox reactions but has also received attention in recent years because of its physiological importance in metabolic regulation, DNA repair and signaling. In contrast to the redox reactions, these regulatory processes involve degradation of NAD and therefore necessitate a constant replenishment of its cellular pool. NAD biosynthetic enzymes are common to almost all species in all clades, but the number of NAD degrading enzymes varies substantially across taxa. In particular, vertebrates, including humans, have a manifold of NAD degrading enzymes which require a high turnover of NAD. As there is currently a lack of a systematic study of how natural selection has shaped enzymes involved in NAD metabolism we conducted a comprehensive evolutionary analysis based on intraspecific variation and interspecific divergence. We compare NAD biosynthetic and degrading enzymes in four eukaryotic model species and subsequently focus on human NAD metabolic enzymes and their orthologs in other vertebrates. We find that the majority of enzymes involved in NAD metabolism are subject to varying levels of purifying selection. While NAD biosynthetic enzymes appear to experience a rather high level of evolutionary constraint, there is evidence for positive selection among enzymes mediating NAD-dependent signaling. This is particularly evident for members of the PARP family, a diverse protein family involved in DNA damage repair and programmed cell death. Based on haplotype information and substitution rate analysis we pinpoint sites that are potential targets of positive selection. We also link our findings to a three dimensional structure, which suggests that positive selection occurs in domains responsible for DNA binding and polymerization rather than the NAD catalytic domain. Taken together, our results indicate that vertebrate NAD metabolism is still undergoing functional diversification.     

Up-regulation of vitamin B1 homeostasis genes in breast cancer

An increased carbon flux and exploitation of metabolic pathways for the rapid generation of biosynthetic precursors is a common phenotype observed in breast cancer. To support this metabolic phenotype, cancer cells adaptively regulate the expression of glycolytic enzymes and nutrient transporters. However, activity of several enzymes involved in glucose metabolism requires an adequate supply of cofactors. In particular, vitamin B1 (thiamine) is utilized as an essential cofactor for metabolic enzymes that intersect at critical junctions within the glycolytic network. Intracellular availability of thiamine is facilitated by the activity of thiamine transporters and thiamine pyrophosphokinase-1 (TPK-1). Therefore, the objective of this study was to establish if the cellular determinants regulating thiamine homeostasis differ between breast cancer and normal breast epithelia. Employing cDNA arrays of breast cancer and normal breast epithelial tissues, SLC19A2, SLC25A19 and TPK-1 were found to be significantly up-regulated. Similarly, up-regulation was also observed in breast cancer cell lines compared to human mammary epithelial cells. Thiamine transport assays and quantitation of intracellular thiamine and thiamine pyrophosphate established a significantly greater extent of thiamine transport and free thiamine levels in breast cancer cell lines compared to human mammary epithelial cells. Overall, these findings demonstrate an adaptive response by breast cancer cells to increase cellular availability of thiamine.     

The importance of post-translational modifications in regulating Saccharomyces cerevisiae metabolism

Regulation of the flow of mass and energy through cellular metabolic networks is fundamental to the operation of all living organisms. Such metabolic fluxes are determined by the concentration of limiting substrates and by the amount and kinetic properties of the enzymes. Regulation of the amount of enzyme can be exerted, on a long-term scale, at the level of gene and protein expression. Enzyme regulation by post-translational modifications (PTMs) and noncovalent binding of allosteric effectors are shorter-term mechanisms that modulate enzyme activity. PTMs, in particular protein phosphorylation, are increasingly being recognized as key regulators in many cellular processes, including metabolism. For example, about half of the enzymes in the Saccharomyces cerevisiae metabolic network have been detected as phosphoproteins, although functional relevance has been demonstrated only in a few cases. Direct regulation of enzymes by PTMs provides one of the fastest ways for cells to adjust to environmental cues and internal stimulus. This review charts the so far identified metabolic enzymes undergoing reversible PTMs in the model eukaryote S. cerevisiae and reviews their underlying mechanistic principles - both at the individual enzyme level and in the context of the entire metabolic network operation.     

Alterations of metabolic genes and metabolites in cancer

Altered metabolic regulation has long been observed in human cancer and broadly used in the clinic for tumor detection. Two recent findings--the direct regulation of metabolic enzymes by frequently mutated cancer genes and frequent mutations of several metabolic enzymes themselves in cancer--have renewed interest in cancer metabolism. Supporting a causative role of altered metabolic enzymes in tumorigenesis, abnormal levels of several metabolites have been found to play a direct role in cancer development. The alteration of metabolic genes and metabolites offer not only new biomarkers for diagnosis and prognosis, but also potential new targets for cancer therapy.     

Dynamic simulation of the human red blood cell metabolic network

We have developed a Mathematica application package to perform dynamic simulations of the red blood cell (RBC) metabolic network. The package relies on, and integrates, many years of mathematical modeling and biochemical work on red blood cell metabolism. The extensive data regarding the red blood cell metabolic network and the previous kinetic analysis of all the individual components makes the human RBC an ideal 'model' system for mathematical metabolic models. The Mathematica package can be used to understand the dynamics and regulatory characteristics of the red blood cell.     

Evolutionary constraint and adaptation in the metabolic network of Drosophila

Organisms must carefully control their metabolism in order to survive. On the other hand, enzymes must adapt in response to evolutionary pressures on the pathways in which they are imbedded. Taking advantage of the newly available whole-genome sequences of 12 Drosophila species, we examined how protein function and metabolic network architecture influence rates of enzyme evolution. We found that despite high overall constraint, there were significant differences in rates of amino acid substitution among functional classes of enzymes. This heterogeneity arises because proteins involved in the metabolism of foreign compounds evolve relatively rapidly, whereas enzymes that act in "core" metabolism exhibit much slower rates of amino acid replacement, suggesting strong selective constraint. Network architecture also influences enzymes' rates of amino acid replacement. In particular, enzymes that share metabolites with many other enzymes are relatively constrained, although apparently not because they are more likely to be essential. Our analyses suggest that this pattern is driven by strong constraint of enzymes acting at branch points in metabolic pathways. We conclude that metabolic network architecture and enzyme function separately affect enzyme evolution rates.     

Comparative gene expression profiling analysis of urothelial carcinoma of the renal pelvis and bladder

Background

Tumor therapy mainly attacks the metabolism to interfere the tumor's anabolism and signaling of proliferative second messengers. However, the metabolic demands of different cancers are very heterogeneous and depend on their origin of tissue, age, gender and other clinical parameters. We investigated tumor specific regulation in the metabolism of breast cancer.

Methods

For this, we mapped gene expression data from microarrays onto the corresponding enzymes and their metabolic reaction network. We used Haar Wavelet transforms on optimally arranged grid representations of metabolic pathways as a pattern recognition method to detect orchestrated regulation of neighboring enzymes in the network. Significant combined expression patterns were used to select metabolic pathways showing shifted regulation of the aggressive tumors.

Results

Besides up-regulation for energy production and nucleotide anabolism, we found an interesting cellular switch in the interplay of biosynthesis of steroids and bile acids. The biosynthesis of steroids was up-regulated for estrogen synthesis which is needed for proliferative signaling in breast cancer. In turn, the decomposition of steroid precursors was blocked by down-regulation of the bile acid pathway.

Conclusion

We applied an intelligent pattern recognition method for analyzing the regulation of metabolism and elucidated substantial regulation of human breast cancer at the interplay of cholesterol biosynthesis and bile acid metabolism pointing to specific breast cancer treatment.