Metabolomics for mitochondrial and cancer studies

Metabolomics, a high-throughput global metabolite analysis, is a burgeoning field, and in recent times has shown substantial evidence to support its emerging role in cancer diagnosis, cancer recurrence, and prognosis, as well as its impact in identifying novel cancer biomarkers and developing cancer therapeutics. Newly evolving advances in disease diagnostics and therapy will further facilitate future growth in the field of metabolomics, especially in cancer, where there is a dire need for sensitive and more affordable diagnostic tools and an urgency to develop effective therapies and identify reliable biomarkers to predict accurately the response to a therapy. Here, we review the application of metabolomics in cancer and mitochondrial studies and its role in enabling the understanding of altered metabolism and malignant transformation during cancer growth and metastasis. The recent developments in the area of metabolic flux analysis may help to close the gap between clinical metabolomics research and the development of cancer metabolome. In the era of personalized medicine with more and more patient specific targeted therapies being used, we need reliable, dynamic, faster, and yet sensitive biomarkers both to track the disease and to develop and evolve therapies during the course of treatment. Recent advances in metabolomics along with the novel strategies to analyze, understand, and construct the metabolic pathways opens this window of opportunity in a very cost-effective manner.     

Metabolomics as a diagnostic tool for hepatology: validation in a naturally occurring canine model

Human hepatopathies are a diagnostic challenge, with many distinct diseases having similar clinical signs and laboratory findings. Naturally occurring canine hepatic disease provides an excellent model for human diseases and similar diagnostic dilemmas exist; differentiating canine congenital portosystemic vascular anomalies (PVA) from acquired hepatopathies is difficult and traditionally requires invasive diagnostic procedures. The emerging post-genomic science of metabolomics is concerned with detecting global changes of populations of endogenous low molecular weight metabolites in biological samples and offers the possibility of identifying surrogate profiles of disease. Metabolomics couples sensitive metabolite analysis with sophisticated pattern recognition techniques. In this study, a metabolomic strategy has been employed to assess metabolite changes in the plasma of dogs with congenital PVA and acquired hepatic disease. Plasma samples were collected from 25 dogs, comprising 9 dogs with congenital PVA, 6 with acquired hepatopathy and 10 with non-hepatic disorders. Low molecular weight metabolites were analyzed by liquid chromatography-mass spectrometry (LC-MS). Following identification of metabolites, multivariate data analysis was used to compare profiles amongst groups. The analysis demonstrated significant disturbances in the plasma bile acid and phospholipid profiles of dogs with portovascular anomalies. In contrast to traditional laboratory parameters, the metabolomic strategy was able to produce a clear segregation between all three study groups. In conclusion, this study demonstrates the potential of metabolomics as a diagnostic tool for naturally occurring hepatic disease. With further validation, this approach will improve diagnostic capabilities, provide an insight into pathogenetic mechanisms, and ultimately inform therapeutic decision making in clinical hepatology. This work was supported in part by grants from The Royal Society, Petplan Charitable Trust and The Waltham Foundation.     

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.     

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.     

Pharmaco-metabolomics: an emerging "omics" tool for the personalization of anticancer treatments and identification of new valuable therapeutic targets

In the post-genomics era, metabolomics represents a new "omics" approach that in the last decade has received increased attention in the field of oncology. Metabolomics is based on the holistic study of the metabolic profile that characterizes a specific phenotype in a biological system. The metabolic profile provides a readout of the metabolic state of an individual that cannot be obtained directly from DNA genotyping, gene expression, or proteomic profiling analyses. The translational value of metabonomics in the oncology field has been demonstrated by the identification of diagnostic and prognostic biomarkers. The so-called pharmaco-metabolomic approach that is currently emerging aims to identify the individual metabolomic characteristics able to predict drug effectiveness and/or toxicity. This review presents the potential role of pharmaco-metabolomics in the future of anticancer pharmacology to achieve customized anticancer treatments and new, targeted therapeutic approaches.     

Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis

Over-representation analysis (ORA) is one of the commonest pathway analysis approaches used for the functional interpretation of metabolomics datasets. Despite the widespread use of ORA in metabolomics, the community lacks guidelines detailing its best-practice use. Many factors have a pronounced impact on the results, but to date their effects have received little systematic attention. Using five publicly available datasets, we demonstrated that changes in parameters such as the background set, differential metabolite selection methods, and pathway database used can result in profoundly different ORA results. The use of a non-assay-specific background set, for example, resulted in large numbers of false-positive pathways. Pathway database choice, evaluated using three of the most popular metabolic pathway databases (KEGG, Reactome, and BioCyc), led to vastly different results in both the number and function of significantly enriched pathways. Factors that are specific to metabolomics data, such as the reliability of compound identification and the chemical bias of different analytical platforms also impacted ORA results. Simulated metabolite misidentification rates as low as 4% resulted in both gain of false-positive pathways and loss of truly significant pathways across all datasets. Our results have several practical implications for ORA users, as well as those using alternative pathway analysis methods. We offer a set of recommendations for the use of ORA in metabolomics, alongside a set of minimal reporting guidelines, as a first step towards the standardisation of pathway analysis in metabolomics.     

Challenges and Opportunities in Cancer Metabolomics

Challenges in metabolomics for a given spectrum of disease are more or less comparable, ranging from the accurate measurement of metabolite abundance, compound annotation, identification of unknown constituents, and interpretation of untargeted and analysis of high throughput targeted metabolomics data leading to the identification of biomarkers. However, metabolomics approaches in cancer studies specifically suffer from several additional challenges and require robust ways to sample the cells and tissues in order to tackle the constantly evolving cancer landscape. These constraints include, but are not limited to, discriminating the signals from given cell types and those that are cancer specific, discerning signals that are systemic and confounded, cell culture‐based challenges associated with cell line identities and media standardizations, the need to look beyond Warburg effects, citrate cycle, lactate metabolism, and identifying and developing technologies to precisely and effectively sample and profile the heterogeneous tumor environment. This review article discusses some of the current and pertinent hurdles in cancer metabolomics studies. In addition, it addresses some of the most recent and exciting developments in metabolomics that may address some of these issues. The aim of this article is to update the oncometabolomics research community about the challenges and potential solutions to these issues.     

Urine metabolomics analysis for biomarker discovery and detection of jaundice syndrome in patients with liver disease

Metabolomics is a powerful new technology that allows for the assessment of global metabolic profiles in easily accessible biofluids and biomarker discovery in order to distinguish between diseased and nondiseased status information. Deciphering the molecular networks that distinguish diseases may lead to the identification of critical biomarkers for disease aggressiveness. However, current diagnostic methods cannot predict typical Jaundice syndrome (JS) in patients with liver disease and little is known about the global metabolomic alterations that characterize JS progression. Emerging metabolomics provides a powerful platform for discovering novel biomarkers and biochemical pathways to improve diagnostic, prognostication, and therapy. Therefore, the aim of this study is to find the potential biomarkers from JS disease by using a nontarget metabolomics method, and test their usefulness in human JS diagnosis. Multivariate data analysis methods were utilized to identify the potential biomarkers. Interestingly, 44 marker metabolites contributing to the complete separation of JS from matched healthy controls were identified. Metabolic pathways (Impact-value≥0.10) including alanine, aspartate, and glutamate metabolism and synthesis and degradation of ketone bodies were found to be disturbed in JS patients. This study demonstrates the possibilities of metabolomics as a diagnostic tool in diseases and provides new insight into pathophysiologic mechanisms.     

1H NMR metabolomics identification of markers of hypoxia-induced metabolic shifts in a breast cancer model system

Hypoxia can promote invasive behavior in cancer cells and alters the response to therapeutic intervention as a result of changes in the expression many genes, including genes involved in intermediary metabolism. Although metabolomics technologies are capable of simultaneously measuring a wide range of metabolites in an untargeted manner, these methods have been relatively under utilized in the study of cancer cell responses to hypoxia. Thus, (1)H NMR metabolomics was used to examine the effects of hypoxia in the MDA-MB-231 human breast cancer cell line, both in vitro and in vivo. Cell cultures were compared with respect to their metabolic responses during growth under either hypoxic (1% O(2)) or normoxic conditions. Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify a set of metabolites that were responsive to hypoxia. Via intracardiac administration, MDA-MB-231 cells were also used to generate widespread metastatic disease in immuno-compromised mice. Serum metabolite analysis was conducted to compare animals with and without a large tumor burden. Intriguingly, using a cross-plot of the OPLS loadings, both the in vitro and in vivo samples yielded a subset of metabolites that were significantly altered by hypoxia. These included primarily energy metabolites and amino acids, indicative of known alterations in energy metabolism, and possibly protein synthesis or catabolism. The results suggest that the metabolite pattern identified might prove useful as a marker for intra-tumoral hypoxia.     

Can metabolomics improve tuberculosis diagnostics?

With one third of the global human population suffering from tuberculosis (TB) in 2001, the disease was officially declared a worldwide emergency by the World Health Organization. Since then this disease has grown to epidemic proportions and the recent emergence of drug resistance, the rising incidence of HIV/TB co-infection, and the inability of the currently used vaccination, diagnostic and treatment protocols to control this pandemic, has made TB a research topic inviting urgent attention. The implementation of metabolomics, a relatively new research approach, which is defined as the study of all the small molecular weight compounds or metabolites in a system or sample, using highly sensitive and specific analytical techniques, shows promise in the quest to eradicate this disease. In this context, we describe here the advantages and limitations of the currently available TB diagnostic techniques, and the role that metabolomics has played in the identification of new biomarkers, not only leading to innovative approaches for TB diagnostics, but also to a better understanding of the intra-host changes induced by Mycobacterium tuberculosis infection and active disease.     

Apoptosis in prostate carcinogenesis

Development of effective therapeutic modalities for the treatment of human cancer relies heavily upon understanding the molecular alterations that result in initiation and progression of the tumorigenic process. Many of the molecular changes identified in human prostate tumorigenesis so far play key roles in apoptosis regulation. Apoptosis represents a universal and exquisitely efficient cellular suicide pathway. Since the therapeutic goal is to trigger tumor-selective apoptotic cell death (without clinically significant effects on the host), elucidation of the mechanisms underlying apoptosis deregulation will lead to the identification of specific cellular components for targeting therapeutic interventions. As our understanding of its vital role in the development and growth of the prostate gland has expanded, numerous genes that encode apoptotic regulators have been identified that are severely impaired in prostate cancer cells. In addition, the expression of apoptotic modulators within prostatic tumors appears to correlate with tumor sensitivity to traditional therapies such as hormonal ablation and radiotherapy. No strict correlation between apoptosis induction and a patient's long-term prognosis has emerged, perhaps due to the fact that the ability to achieve initial remission alone does not adequately predict long-term outcome. This review will encompass the known molecular changes intimately involved in the apoptotic pathway which have potential prognostic value in disease progression, as well as therapeutic significance in the enhancement of the apoptotic response to novel and established treatment strategies for the treatment of androgen-dependent and androgen-independent prostatic tumors. The main focus will be on the role of the transforming growth factor-beta (TGF-beta) signaling pathway, bcl-2 and the bcl-2 family members, the caspase cascade (apoptosis executioners), and the Fas pathway in induction and regulation of apoptosis following therapeutic stimuli for the management of advanced prostate cancer.     

Towards the Disease Biomarker in an Individual Patient Using Statistical Health Monitoring

In metabolomics, identification of complex diseases is often based on application of (multivariate) statistical techniques to the data. Commonly, each disease requires its own specific diagnostic model, separating healthy and diseased individuals, which is not very practical in a diagnostic setting. Additionally, for orphan diseases such models cannot be constructed due to a lack of available data. An alternative approach adapted from industrial process control is proposed in this study: statistical health monitoring (SHM). In SHM the metabolic profile of an individual is compared to that of healthy people in a multivariate manner. Abnormal metabolite concentrations, or abnormal patterns of concentrations, are indicated by the method. Subsequently, this biomarker can be used for diagnosis. A tremendous advantage here is that only data of healthy people is required to construct the model. The method is applicable in current–population based –clinical practice as well as in personalized health applications. In this study, SHM was successfully applied for diagnosis of several orphan diseases as well as detection of metabotypic abnormalities related to diet and drug intake.     

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.     

Protein arrays as tools for serum autoantibody marker discovery in cancer

Protein array technology has begun to play a significant role in the study of protein–protein interactions and in the identification of antigenic targets of serum autoantibodies in a variety of autoimmune disorders. More recently, this technology has been applied to the identification of autoantibody signatures in cancer.The identification of tumour-associated antigens (TAAs) recognised by the patient's immune response represents an exciting approach to identify novel diagnostic cancer biomarkers and may contribute towards a better understanding of the molecular mechanisms involved. Circulating autoantibodies have not only been used to identify TAAs as diagnostic/prognostic markers and potential therapeutic targets, they also represent excellent biomarkers for the early detection of tumours and potential markers for monitoring the efficacy of treatment. Protein array technology offers the ability to screen the humoral immune response in cancer against thousands of proteins in a high throughput technique, thus readily identifying new panels of TAAs. Such an approach should not only aid in improved diagnostics, but has already contributed to the identification of complex autoantibody signatures that may represent disease subgroups, early diagnostics and facilitated the analysis of vaccine trials.     

Inflammatory-induced hibernation in the fetus: priming of fetal sheep metabolism correlates with developmental brain injury

Prenatal inflammation is considered an important factor contributing to preterm birth and neonatal mortality and morbidity. The impact of prenatal inflammation on fetal bioenergetic status and the correlation of specific metabolites to inflammatory-induced developmental brain injury are unknown. We used a global metabolomics approach to examine plasma metabolites differentially regulated by intrauterine inflammation. Preterm-equivalent sheep fetuses were randomized to i.v. bolus infusion of either saline-vehicle or LPS. Blood samples were collected at baseline 2 h, 6 h and daily up to 10 days for metabolite quantification. Animals were killed at 10 days after LPS injection, and brain injury was assessed by histopathology. We detected both acute and delayed effects of LPS on fetal metabolism, with a long-term down-regulation of fetal energy metabolism. Within the first 3 days after LPS, 121 metabolites were up-regulated or down-regulated. A transient phase (4-6 days), in which metabolite levels recovered to baseline, was followed by a second phase marked by an opposing down-regulation of energy metabolites, increased pO(2) and increased markers of inflammation and ADMA. The characteristics of the metabolite response to LPS in these two phases, defined as 2 h to 2 days and at 6-9 days, respectively, were strongly correlated with white and grey matter volumes at 10 days recovery. Based on these results we propose a novel concept of inflammatory-induced hibernation of the fetus. Inflammatory priming of fetal metabolism correlated with measures of brain injury, suggesting potential for future biomarker research and the identification of therapeutic targets.     

Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study

ABSTRACT: BACKGROUND: The use of biological molecular network information for diagnostic and prognostic purposes and elucidation of molecular disease mechanism is a key objective in systems biomedicine. The network of regulatory miRNA-target and functional protein interactions is a rich source of information to elucidate the function and the prognostic value of miRNAs in cancer. The objective of this study is to identify miRNAs that have high influence on target protein complexes in prostate cancer as a case study. This could provide biomarkers or therapeutic targets relevant for prostate cancer treatment. RESULTS: Our findings demonstrate that a miRNA's functional role can be explained by its target protein connectivity within a physical and functional interaction network. To detect miRNAs with high influence on target protein modules, we integrated miRNA and mRNA expression profiles with a sequence based miRNA-target network and human functional and physical protein interactions (FPI). miRNAs with high influence on target protein complexes play a role in prostate cancer progression and are promising diagnostic or prognostic biomarkers. We uncovered several miRNA-regulated protein modules which were enriched in focal adhesion and prostate cancer genes. Several miRNAs such as miR-96, miR-182, and miR-143 demonstrated high influence on their target protein complexes and could explain most of the gene expression changes in our analyzed prostate cancer data set. CONCLUSIONS: We describe a novel method to identify active miRNA-target modules relevant to prostate cancer progression and outcome. miRNAs with high influence on protein networks are valuable biomarkers that can be used in clinical investigations for prostate cancer treatment.     

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.