Characterizing phenotype with tracer based metabolomics

In-vivo tracking of deuterium metabolism in mouse organs using LC-MS/MS

Mass spectrometry-based metabolomics with stable isotope labeling (SIL) is an established tool for sensitive and precise analyses of tissue metabolism, its flux, and pathway activities in diverse models of physiology and disease. Despite the simplicity and broad applicability of deuterium (2H)-labeled precursors for tracing metabolic pathways with minimal biological perturbations, they are rarely employed in LC-MS/MS-guided metabolomics. In this study, we have developed a LC-MS/MS-guided workflow to trace deuterium metabolism in mouse organs following 2H7 -glucose infusion. The workflow includes isotopically labeled glucose infusion, mouse organ isolation and metabolite extraction, zwitterion-based hydrophilic interaction liquid chromatography (HILIC) coupled to high-resolution tandem mass spectrometry, targeted data acquisition for sensitive detection of deuterated metabolites, a spectral library of over 400 metabolite standards, and multivariate data analysis with pathway mapping. The optimized method was validated for matrix effects, normalization, and quantification to provide both tissue metabolomics and tracking the in-vivo metabolic fate of deuterated glucose through key metabolic pathways. We quantified more than 100 metabolites in five major mouse organ tissues (liver, kidney, brain, brown adipose tissue, and heart). Furthermore, we mapped isotopologues of deuterated metabolites from glycolysis, tricarboxylic acid (TCA) cycle, and amino acid pathways, which are significant for studying both health and various diseases. This study will open new avenues in LC-MS based analysis of 2H-labeled tissue metabolism research in animal models and clinical settings.

Untargeted metabolomics to evaluate antifungal mechanism: a study of Cophinforma mamane and Candida albicans interaction

Microbial interactions between filamentous fungi and yeast are still not fully understood. To evaluate a potential antifungal activity of a filamentous fungus while highlighting metabolomic changes, co-cultures between an endophytic strain of Cophinforma mamane (CM) and Candida albicans (CA) were performed. The liquid cultures were incubated under static conditions and metabolite alterations during the course were investigated by ultra-performance liquid chromatography-tandem mass spectrophotometry (UPLC-MS/MS). Results were analyzed using MS-DIAL, MS-FINDER, METLIN, Xcalibur, SciFinder, and MetaboAnalyst metabolomics platforms. The metabolites associated with catabolic processes, including the metabolism of branched-chain amino acids, carnitine, and phospholipids were upregulated both in the mono and co-cultures, indicating fungal adaptability to environmental stress. Several metabolites, including C20 sphinganine 1-phosphate, myo-inositol, farnesol, gamma-undecalactone, folinic acid, palmitoleic acid, and MG (12:/0:0/0:0) were not produced by CA during co-culture with CM, demonstrating the antifungal mechanism of CM. Our results highlight the crucial roles of metabolomics studies to provide essential information regarding the antifungal mechanism of C. mamane against C. albicans, especially when the lost/undetected metabolites are involved in fungal survival and pathogenicity.

A metabolic data-driven systems pharmacology strategy for decoding and validating the mechanism of Compound Kushen Injection against HCC

ETHNOPHARMACOLOGICAL RELEVANCE

Compound Kushen Injection (CKI) is a widely used TCM formula for treatment of carcinomatous pain and tumors of digestive system including hepatocellular carcinoma (HCC). However, the potential mechanisms of CKI for treatment of HCC have not been systematically and deeply studied.

AIM OF STUDY

A metabolic data-driven systems pharmacology approach was utilized to investigate the potential mechanisms of CKI for treatment of HCC.

MATERIALS AND METHODS

Based on phenotypic data generated by metabolomics and genotypic data of drug targets, a propagation model based on Dijkstra program was proposed to decode the effective network of key genotype-phenotype of CKI in treating HCC. The pivotal pathway was predicted by target propagation mode of our proposed model, and was validated in SMMC-7721 cells and diethylnitrosamine-induced rats.

RESULTS

Metabolomics results indicated that 12 differential metabolites, and 5 metabolic pathways might be involved in the anti-HCC effect of CKI. A total of 86 metabolic related genes that affected by CKI were obtained. The results calculated by propagation model showed that 6475 shortest distance chains might be involved in the anti-HCC effect of CKI. According to the results of propagation mode, EGFR was identified as the core target of CKI for the anti-HCC effect. Finally, EGFR and its related pathway EGFR-STAT3 signaling pathway were validated in vivo and in vitro.

CONCLUSION

The proposed method provides a methodological reference for explaining the underlying mechanism of TCM in treating HCC.

From correlation to causation: analysis of metabolomics data using systems biology approaches

INTRODUCTION

Metabolomics is a well-established tool in systems biology, especially in the top-down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.

OBJECTIVES

This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.

METHODS

We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.

RESULTS

We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.

CONCLUSIONS

Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies.

The Warburg effect: a balance of flux analysis

Cancer metabolism is characterized by increased macromolecular syntheses through coordinated increases in energy and substrate metabolism. The observation that cancer cells produce lactate in an environment of oxygen sufficiency (aerobic glycolysis) is a central theme of cancer metabolism known as the Warburg effect. Aerobic glycolysis in cancer metabolism is accompanied by increased pentose cycle and anaplerotic activities producing energy and substrates for macromolecular synthesis. How these processes are coordinated is poorly understood. Recent advances have focused on molecular regulation of cancer metabolism by oncogenes and tumor suppressor genes which regulate numerous enzymatic steps of central glucose metabolism. In the past decade, new insights in cancer metabolism have emerged through the application of stable isotopes particularly from 13C carbon tracing. Such studies have provided new evidence for system-wide changes in cancer metabolism in response to chemotherapy. Interestingly, experiments using metabolic inhibitors on individual biochemical pathways all demonstrate similar system-wide effects on cancer metabolism as in targeted therapies. Since biochemical reactions in the Warburg effect place competing demands on available precursors, high energy phosphates and reducing equivalents, the cancer metabolic system must fulfill the condition of balance of flux (homeostasis). In this review, the functions of the pentose cycle and of the tricarboxylic acid (TCA) cycle in cancer metabolism are analyzed from the balance of flux point of view. Anticancer treatments that target molecular signaling pathways or inhibit metabolism alter the invasive or proliferative behavior of the cancer cells by their effects on the balance of flux (homeostasis) of the cancer metabolic phenotype.

Metabolomics of adherent mammalian cells by capillary electrophoresis-mass spectrometry: HT-29 cells as case study

In this work, the optimization of an effective protocol for cell metabolomics is described with special emphasis in the sample preparation and subsequent analysis of intracellular metabolites from adherent mammalian cells by capillary electrophoresis-mass spectrometry. As case study, colon cancer HT-29 cells, a human cell model to investigate colon cancer, are employed. The feasibility of the whole method for cell metabolomics is demonstrated via a fast and sensitive profiling of the intracellular metabolites HT-29 cells by capillary electrophoresis-time-of-flight mass spectrometry (CE-TOF MS). The suitability of this methodology is further corroborated through the examination of the metabolic changes in the polyamines pathway produced in colon cancer HT-29 cells by difluoromethylornithine (DFMO), a known potent ornithine decarboxylase inhibitor. The selection of the optimum extraction conditions allowed a higher sample volume injection that led to an increase in CE-TOF MS sensitivity. Following a non-targeted metabolomics approach, 10 metabolites (namely, putrescine, ornithine, gamma-aminobutyric acid (GABA), oxidized and reduced glutathione, 5'-deoxy-5'-(methylthio)adenosine, N-acetylputrescine, cysteinyl-glycine, spermidine and an unknown compound) were found to be significantly altered by DFMO (p<0.05) in HT-29 cells. In addition to the effect of DFMO on polyamine metabolism, minor modifications of other metabolic pathways (e.g., related to intracellular thiol redox state) were observed.

Cyclin-dependent kinases 4 and 6 control tumor progression and direct glucose oxidation in the pentose cycle

Cyclin-dependent kinases CDK4 and CDK6 are essential for the control of the cell cycle through the G(1) phase. Aberrant expression of CDK4 and CDK6 is a hallmark of cancer, which would suggest that CDK4 and CDK6 are attractive targets for cancer therapy. Herein, we report that calcein AM (the calcein acetoxymethyl-ester) is a potent specific inhibitor of CDK4 and CDK6 in HCT116 human colon adenocarcinoma cells, inhibiting retinoblastoma protein (pRb) phosphorylation and inducing cell cycle arrest in the G(1) phase. The metabolic effects of calcein AM on HCT116 cells were also evaluated and the flux between the oxidative and non-oxidative branches of the pentose phosphate pathway was significantly altered. To elucidate whether these metabolic changes were due to the inhibition of CDK4 and CDK6, we also characterized the metabolic profile of a CDK4, CDK6 and CDK2 triple knockout of mouse embryonic fibroblasts. The results show that the metabolic profile associated with the depletion of CDK4, CDK6 and CDK2 coincides with the metabolic changes induced by calcein AM on HCT116 cells, thus confirming that the inhibition of CDK4 and CDK6 disrupts the balance between the oxidative and non-oxidative branches of the pentose phosphate pathway. Taken together, these results indicate that low doses of calcein can halt cell division and kill tumor cells. Thus, selective inhibition of CDK4 and CDK6 may be of greater pharmacological interest, since inhibitors of these kinases affect both cell cycle progression and the robust metabolic profile of tumors.

Tracer-based metabolomics: concepts and practices

Tracer-based metabolomics is a systems biology tool that combines advances in tracer methodology for physiological studies, high throughput "-omics" technologies and constraint based modeling of metabolic networks. It is different from the commonly known metabolomics or metabonomics in that it is a targeted approach based on a metabolic network model in cells. Because of its complexity, it is the least understood among the various "-omics." In this review, the development of concepts and practices of tracer-based metabolomics is traced from the early application of radioactive isotopes in metabolic studies to the recent application of stable isotopes and isotopomer analysis using mass spectrometry; and from the modeling of biochemical reactions using flux analysis to the recent theoretical formulation of the constraint based modeling. How these newer experimental methods and concepts of constraint-based modeling approaches can be applied to metabolic studies is illustrated by examples of studies in determining metabolic responses of cells to pharmacological agents and nutrient environment changes.

Inhibition of protein phosphorylation in MIA pancreatic cancer cells: confluence of metabolic and signaling pathways

Oxythiamine (OT), a transketolase inhibitor, is known to inhibit pancreatic cancer cell proliferation. In this study, we investigated the effect of inhibition of the transketolase pathway on signaling pathways in MIA PaCa cancer cells using in-house proteomic techniques. We hypothesized that OT alter protein phosphorylation thus affecting cell cycle arrest and cell proliferation. MIA PaCa-2 cells were cultured in media containing an algal (15)N amino acid mixture at 50% enrichment, with and without OT, to determine protein expression and synthesis. Analysis of cell lysates using two-dimensional gel electrophoresis matrix assisted laser desorption and ionization time-of-flight and time-of-flight mass spectrometry (2-DE-MALDI-TOF/TOF MS) identified 12 phosphor proteins that were significantly suppressed by OT treatment. Many of these proteins are involved in regulation of cycle activities and apoptosis. Among the proteins identified, expression of the phosphor heat shock protein 27 (Hsp27) was dramatically inhibited by OT treatment while the level of its total protein remained unchanged. Hsp27 expression and phosphorylation is known to be associated with drug resistance and cancer cell survival. The changes in phosphorylation of key proteins of cancer proliferation and survival suggest that protein phosphorylation is the confluence of the effects of OT on metabolic and signaling pathways.

Systems analysis of energy metabolism elucidates the affected respiratory chain complex in Leigh's syndrome

Leigh's syndrome is a complex neurological disease with little known correlation between causes and symptoms. Mutations in pyruvate dehydrogenase and electron transport chain complexes have been associated with this syndrome, although the identification of affected enzymes is difficult, if not impossible, with non-invasive clinical tests. In this study, isotopomer analysis is used to characterize the metabolic phenotype of normal and Leigh's syndrome fibroblasts (GM01503), thereby identifying affected enzymes in the diseased cells. Fibroblasts are grown with DMEM media enriched with (13)C labeled glucose. Amino acids from media and proteins as well as lactate are analyzed with GC-MS to identify their label distributions. A computational model accounting for all major pathways in fibroblast metabolism (including 430 metabolites and 508 reactions) is built to determine the metabolic steady states of the normal and Leigh's cell lines based on measured substrate uptake and secretion rates and isotopomer data. Results show that (i) Leigh's syndrome affected cells have slower metabolism than control fibroblasts as evidenced by their overall slower substrate utilization and lower secretion of end products; (ii) intracellular fluxes predicted by the models, some of which are validated by biochemical studies published in the literature, show that the respiratory chain in Leigh's affected cells can produce ATP at a similar rate as the controls, but with a more restricted flux range; and (iii) mutations causing the defects observed in the Leigh's cells are likely to be in succinate cytochrome c reductase.

Pentose phosphate cycle oxidative and nonoxidative balance: A new vulnerable target for overcoming drug resistance in cancer

The metabolic network of cancer cells confers adaptive mechanisms against many chemotherapeutic agents, but also presents critical constraints that make the cells vulnerable to perturbation of the network due to drug therapy. To identify these fragilities, combination therapies based on targeting the nucleic acid synthesis metabolic network at multiple points were tested. Results showed that cancer cells overcome single hit strategies through different metabolic network adaptations, demonstrating the robustness of cancer cell metabolism. Analysis of these adaptations also identified the maintenance of pentose phosphate cycle oxidative and nonoxidative balance to be critical for cancer cell survival and vulnerable to chemotherapeutic intervention. The vulnerability of cancer cells to the imbalance on pentose phosphate cycle was demonstrated by phenotypic phase plane analysis.

Precision mapping of the metabolome

The global study of the structure and dynamics of metabolic networks has been hindered by a lack of techniques that identify metabolites and their biochemical relationship in complex mixtures. The recent application of Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) to metabolomic analysis suggests a way to tackle the problem. A lower-cost alternative to high-field FTICR-MS, the Orbitrap mass analyzer, promises accelerated activity in this area. Here, we show how the ultra-high mass accuracy and resolution provided by this new generation of mass spectrometers can help to identify metabolites and connect them into metabolic networks. Data from perturbation studies and isotope-tracking experiments can complement this information to create metabolic maps de novo and chart unexplored areas of metabolism.