Metabolic Phenotyping Study of Mouse Brain Following Microbiome Disruption by C.difficile Colonization.

Clostridioides difficile infection (CDI) is responsible for an increasing number of cases of post-antibiotic diarrhea worldwide, which has high severity and mortality among hospitalized elderly patients. The disruption of gut microbiota due to antibacterial medication facilitates the intestinal colonization of C. difficile. In the present study, a murine model was used to investigate the potential effects of antibiotic administration and subsequent colonization by C. difficile, as well as the effects of three different 10-day treatments (metronidazole, probiotics, and fecal microbiota transplantation), on the brain metabolome for the first time. Four different metabolomic-based methods (targeted HILIC-MS/MS, untargeted RP-LC-HRMS/MS, targeted GC-MS/MS, and untargeted GC-MS) were applied, resulting in the identification of 217 unique metabolites in the brain extracts, mainly glycerophospholipids, glycerolipids, amino acids, carbohydrates, and fatty acids. Univariate and multivariate statistical analysis revealed that CDI, as well as the subsequent treatments, altered significantly several brain metabolites, probably due to gut dysbiosis, and affected the brain through the gut-brain axis. Notably, none of the therapeutic approaches completely restored the brain metabolic profile to the original, healthy, and non-infected phenotype, even after 10 days of treatment.

Metabolic consequences for mice lacking Endosialin: LC-MS/MS-based metabolic phenotyping of serum from C56Bl/6J Control and CD248 knock-out mice.

INTRODUCTION: The Endosialin/CD248/TEM1 protein is expressed in adipose tissue and its expression increases with obesity. Recently, genetic deletion of CD248 has been shown to protect mice against atherosclerosis on a high fat diet. OBJECTIVES: We investigated the effect of high fat diet feeding on visceral fat pads and circulating lipid profiles in CD248 knockout mice compared to controls. METHODS: From 10 weeks old, CD248-/- and +/+ mice were fed either chow (normal) diet or a high fat diet for 13 weeks. After 13 weeks the metabolic profiles and relative quantities of circulating lipid species were assessed using ultra high performance liquid chromatography-quadrupole time-of flight mass spectrometry (UHPLC-MS) with high resolution accurate mass (HRAM) capability. RESULTS: We demonstrate a specific reduction in the size of the perirenal fat pad in CD248-/- mice compared to CD248+/+, despite similar food intake. More strikingly, we identify significant, diet-dependent differences in the serum metabolic phenotypes of CD248 null compared to age and sex-matched wildtype control mice. Generalised protection from HFD-induced lipid accumulation was observed in CD248 null mice compared to wildtype, with particular reduction noted in the lysophosphatidylcholines, phosphatidylcholines, cholesterol and carnitine. CONCLUSIONS: Overall these results show a clear and protective metabolic consequence of CD248 deletion in mice, implicating CD248 in lipid metabolism or trafficking and opening new avenues for further investigation using anti-CD248 targeting agents.

Metabolic Phenotyping Study of Mouse Brains Following Acute or Chronic Exposures to Ethanol.

The chronic and acute effect of ethanol administration on the metabolic phenotype of mouse brain was studied in a C57BL/6 mouse model of ethanol abuse using both untargeted and targeted ultra performance liquid chromatography-tandem mass spectrometry. Two experiments based on either chronic (8 week) exposure to ethanol of both male and female mice or acute exposure of male mice for 11 days, plus 2 oral gavage doses of 25% ethanol, were undertaken. Marked differences were found in amino acids, nucleotides, nucleosides, and related metabolites as well as a number of different lipids. Using untargeted metabolite profiling, acute ethanol exposure found significant decreases in several metabolites including nucleosides, fatty acids, glycerophosphocholine, and a number of phospholipids, while chronic exposure resulted in increases in several amino acids with notable decreases in adenosine, acetylcarnitine, and galactosylceramides. Similarly, targeted metabolite analysis, focusing on the hydrophilic fraction of the brain tissue extract, identified significant decreases in the metabolism of amino acids and derivatives, as well as purine degradation especially after chronic exposure to ethanol.

Natural variation in tolerance to sub-zero temperatures among populations of Arabidopsis lyrata ssp. petraea.

BACKGROUND: Temperature is one of the most important abiotic factors limiting plant growth and productivity. Many plants exhibit cold acclimation to prepare for the likelihood of freezing as temperatures decrease towards 0 °C. The physiological mechanisms associated with enabling increased tolerance to sub-zero temperatures vary between species and genotypes. Geographically and climatically diverse populations of Arabidopsis lyrata ssp. petraea were examined for their ability to survive, maintain functional photosynthetic parameters and cellular electrolyte leakage integrity after being exposed to sub-zero temperatures. The duration of cold acclimation prior to sub-zero temperatures was also manipulated (2 and 14 days). RESULTS: We found that there was significant natural variation in tolerances to sub-zero temperatures among populations of A. petraea. The origin of the population affected the acclimation response and survival after exposure to sub-zero temperatures. Cold acclimation of plants prior to sub-zero temperatures affected the maximum quantum efficiency of photosystem II (PSII) (Fv/Fm) in that plants that were cold acclimated for longer periods had higher values of Fv/Fm as a result of sub-zero temperatures. The inner immature leaves were better able to recover Fv/Fm from sub-zero temperatures than mature outer leaves. The Irish population (Leitrim) acclimated faster, in terms of survival and electrolyte leakage than the Norwegian population (Helin). CONCLUSION: The ability to survive, recover photosynthetic processes and cellular electrolyte leakage after exposure to sub-zero temperatures is highly dependent on the duration of cold acclimation.

Metabolic Clustering Analysis as a Strategy for Compound Selection in the Drug Discovery Pipeline for Leishmaniasis.

A lack of viable hits, increasing resistance, and limited knowledge on mode of action is hindering drug discovery for many diseases. To optimize prioritization and accelerate the discovery process, a strategy to cluster compounds based on more than chemical structure is required. We show the power of metabolomics in comparing effects on metabolism of 28 different candidate treatments for Leishmaniasis (25 from the GSK Leishmania box, two analogues of Leishmania box series, and amphotericin B as a gold standard treatment), tested in the axenic amastigote form of Leishmania donovani. Capillary electrophoresis-mass spectrometry was applied to identify the metabolic profile of Leishmania donovani, and principal components analysis was used to cluster compounds on potential mode of action, offering a medium throughput screening approach in drug selection/prioritization. The comprehensive and sensitive nature of the data has also made detailed effects of each compound obtainable, providing a resource to assist in further mechanistic studies and prioritization of these compounds for the development of new antileishmanial drugs.

Complex Interplay between Sphingolipid and Sterol Metabolism Revealed by Perturbations to the Leishmania Metabolome Caused by Miltefosine.

With the World Health Organization reporting over 30,000 deaths and 200,000 to 400,000 new cases annually, visceral leishmaniasis is a serious disease affecting some of the world's poorest people. As drug resistance continues to rise, there is a huge unmet need to improve treatment. Miltefosine remains one of the main treatments for leishmaniasis, yet its mode of action (MoA) is still unknown. Understanding the MoA of this drug and parasite response to treatment could help pave the way for new and more successful treatments for leishmaniasis. A novel method has been devised to study the metabolome and lipidome of Leishmania donovani axenic amastigotes treated with miltefosine. Miltefosine caused a dramatic decrease in many membrane phospholipids (PLs), in addition to amino acid pools, while sphingolipids (SLs) and sterols increased. Leishmania major promastigotes devoid of SL biosynthesis through loss of the serine palmitoyl transferase gene (ΔLCB2) were 3-fold less sensitive to miltefosine than wild-type (WT) parasites. Changes in the metabolome and lipidome of miltefosine-treated L. major mirrored those of L. donovani A lack of SLs in the ΔLCB2 mutant was matched by substantial alterations in sterol content. Together, these data indicate that SLs and ergosterol are important for miltefosine sensitivity and, perhaps, MoA.

Differentiating signals to make biological sense - A guide through databases for MS-based non-targeted metabolomics.

Metabolite identification is one of the most challenging steps in metabolomics studies and reflects one of the greatest bottlenecks in the entire workflow. The success of this step determines the success of the entire research, therefore the quality at which annotations are given requires special attention. A variety of tools and resources are available to aid metabolite identification or annotation, offering different and often complementary functionalities. In preparation for this article, almost 50 databases were reviewed, from which 17 were selected for discussion, chosen for their online ESI-MS functionality. The general characteristics and functions of each database is discussed in turn, considering the advantages and limitations of each along with recommendations for optimal use of each tool, as derived from experiences encountered at the Centre for Metabolomics and Bioanalysis (CEMBIO) in Madrid. These databases were evaluated considering their utility in non-targeted metabolomics, including aspects such as identifier assignment, structural assignment and interpretation of results.

Applications of Metabolomics in Cancer Studies.

Since the start of metabolomics as a field of research, the number of studies related to cancer has grown to such an extent that cancer metabolomics now represents its own discipline. In this chapter, the applications of metabolomics in cancer studies are explored. Different approaches and analytical platforms can be employed for the analysis of samples depending on the goal of the study and the aspects of the cancer metabolome being investigated. Analyses have concerned a range of cancers including lung, colorectal, bladder, breast, gastric, oesophageal and thyroid, amongst others. Developments in these strategies and methodologies that have been applied are discussed, in addition to exemplifying the use of cancer metabolomics in the discovery of biomarkers and in the assessment of therapy (both pharmaceutical and nutraceutical). Finally, the application of cancer metabolomics in personalised medicine is presented.

Monitoring cancer prognosis, diagnosis and treatment efficacy using metabolomics and lipidomics.

INTRODUCTION: Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics. OBJECTIVES: Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case-control, prognostic and longitudinal study designs. METHODS: A literature search of the current relevant primary research was performed. RESULTS: Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case-control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance. CONCLUSION: Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.

A Single In-Vial Dual Extraction Strategy for the Simultaneous Lipidomics and Proteomics Analysis of HDL and LDL Fractions.

A single in-vial dual extraction (IVDE) procedure for the subsequent analysis of lipids and proteins in the high-density lipoprotein (HDL) and low-density lipoprotein (LDL) fractions derived from the same biological sample is presented. On the basis of methyl-tert-butyl ether (MTBE) extraction, IVDE leads to the formation of three phases: a protein pellet at the bottom, an aqueous phase with polar compounds, and an ether phase with lipophilic compounds. After sample extraction, performed within a high-performance liquid chromatography vial insert, the ether phase was directly injected for lipid fingerprinting, while the protein pellet, after evaporation of the remaining sample, was used for proteomics analysis. Human HDL and LDL isolates were used to test the suitability of the IVDE methodology for lipid and protein analysis from a single sample in terms of data quality and matching composition to that of HDL and LDL. Subsequently, HDL and LDL fractions isolated from ApoE-KO and wild-type mice were used to validate the capacity of IVDE for revealing changes in lipid and protein abundance. Results indicate that IVDE can be successfully used for the subsequent analysis of lipids and proteins with the advantages of time saving, simplicity, and reduced sample amount.

Looking into aqueous humor through metabolomics spectacles - exploring its metabolic characteristics in relation to myopia.

Aqueous humor is the transparent fluid found in the anterior chamber of the eye that provides the metabolic requirements to the avascular tissues surrounding it. Despite the fact that metabolomics could be a powerful tool in the characterization of this biofluid and in revealing metabolic signatures of common ocular diseases such as myopia, it has never to our knowledge previously been applied in humans. In this research a novel method for the analysis of aqueous humor is presented to show its application in the characterization of this biofluid using CE-MS. The method was extended to a dual platform method (CE-MS and LC-MS) in order to compare samples from patients with different severities of myopia in order to explore the disease from the metabolic phenotype point of view. With this method, a profound knowledge of the metabolites present in human aqueous humor has been obtained: over 40 metabolites were reproducibly and simultaneously identified from a low volume of sample by CE-MS, including among others, a vast number of amino acids and derivatives. When this method was extended to study groups of patients with high or low myopia in both CE-MS and LC-MS, it has been possible to identify over 20 significantly different metabolite and lipid signatures that distinguish patients based on the severity of myopia. Among these, the most notable higher abundant metabolites in high myopia were aminooctanoic acid, arginine, citrulline and sphinganine while features of low myopia were aminoundecanoic acid, dihydro-retinoic acid and cysteinylglycine disulfide. This dual platform approach offered complementarity such that different metabolites were detected in each technique. Together the experiments presented provide a whelm of valuable information about human aqueous humor and myopia, proving the utility of non-targeted metabolomics for the first time in analyzing this type of sample and the metabolic phenotype of this disease.

To treat or not to treat: metabolomics reveals biomarkers for treatment indication in chronic lymphocytic leukaemia patients.

In chronic lymphocytic leukaemia (CLL), the clinical course of patients is heterogeneous. Some present an aggressive disease onset and require immediate therapy, while others remain without treatment for years. Current disease staging systems developed by Rai and Binet may be useful in forecasting patient survival time, but do not discriminate between stable and progressive forms of the disease in the early stages. Recently ample attention has been directed towards identifying new disease prognostic markers capable of predicting clinical aggressiveness at diagnosis. In the present study serum samples from stable (n = 51) and progressive (n = 42) CLL patients and controls (n = 45) were used with aim to discover metabolic indicators of disease status. First an LC-MS based metabolic fingerprinting method was used to analyse selected samples in order to find a potential markers discriminating aggressive from indolent patients. Ten of these discovered markers were validated on the whole set of samples with an independent analytical technique. Linoleamide (p = 0.002) in addition to various acylcarnitines (p = 0.001-0.000001) showed to be significant markers of CLL in its aggressive form. Acetylcarnitine (p = 0.05) and hexannoylcarnitine (p = 0.005) were also distinguishable markers of indolent subjects. Forming a panel of selected acylcarnitines and fatty acid amides, it was possible to reach a potentially highly specific and sensitive diagnostic approach (AUC = 0.766).

Metabolic profiling reveals potential metabolic markers associated with Hypoxia Inducible Factor-mediated signalling in hypoxic cancer cells.

Hypoxia inducible factors (HIFs) plays an important role in oxygen compromised environments and therefore in tumour survival. In this research, metabolomics has been applied to study HIFs metabolic function in two cell models: mouse hepatocellular carcinoma and human colon carcinoma, whereby the metabolism has been profiled for a range of oxygen potentials. Wild type cells have been compared to cells deficient in HIF signalling to reveal its effect on cellular metabolism under normal oxygen conditions as well as low oxygen, hypoxic and anoxic environments. Characteristic responses to hypoxia that were conserved across both cell models involved the anti-correlation between 2-hydroxyglutarate, 2-oxoglutarate, fructose, hexadecanoic acid, hypotaurine, pyruvate and octadecenoic acid with 4-hydroxyproline, aspartate, cysteine, glutamine, lysine, malate and pyroglutamate. Further to this, network-based correlation analysis revealed HIF specific pathway responses to each oxygen condition that were also conserved between cell models. From this, 4-hydroxyproline was revealed as a regulating hub in low oxygen survival of WT cells while fructose appeared to be in HIF deficient cells. Pathways surrounding these hubs were built from the direct connections of correlated metabolites that look beyond traditional pathways in order to understand the mechanism of HIF response to low oxygen environments.

Missing value imputation strategies for metabolomics data.

The origin of missing values can be caused by different reasons and depending on these origins missing values should be considered differently and dealt with in different ways. In this research, four methods of imputation have been compared with respect to revealing their effects on the normality and variance of data, on statistical significance and on the approximation of a suitable threshold to accept missing data as truly missing. Additionally, the effects of different strategies for controlling familywise error rate or false discovery and how they work with the different strategies for missing value imputation have been evaluated. Missing values were found to affect normality and variance of data and k-means nearest neighbour imputation was the best method tested for restoring this. Bonferroni correction was the best method for maximizing true positives and minimizing false positives and it was observed that as low as 40% missing data could be truly missing. The range between 40 and 70% missing values was defined as a "gray area" and therefore a strategy has been proposed that provides a balance between the optimal imputation strategy that was k-means nearest neighbor and the best approximation of positioning real zeros.

In-source fragmentation and correlation analysis as tools for metabolite identification exemplified with CE-TOF untargeted metabolomics.

The role of non-targeted metabolomics with its discovery power is constantly growing in many different fields of science. However, its biggest advantage of uncovering the unexpected is turning into one of its biggest bottlenecks, particularly in metabolite identification. Among different methods for metabolite identification or ID confirmation, tandem MS analysis plays a very important role. However, this method is limited to only certain types of MS analysers, making for example TOF-MS inaccessible for this type of metabolite identification. To overcome this, in-source fragmentation has been used to fragment molecules and obtain product ions. Since the molecule of interest is not isolated prior to its fragmentation, the acquired spectrum contains many different signals arising from the fragmentation of all compounds present in the sample. Therefore, to assign product ions to their precursors, a novel use of correlation analysis was tested with r ≥0.9 as an assignation of a product ion belonging to the precursor. This method and chosen cut-off was tested on three different sample complexity levels: conducting the analysis on a single standard, mix of co-eluting standards and on a plasma sample. Obtained results clearly proved the effectiveness of the proposed methodology for metabolite ID confirmation. Moreover, the proposed strategy can be successfully applied for semi-quantification of co-eluting molecules with the same monoisotopic mass but that differ in fragmentation pattern. The proposed methodology can greatly improve the robustness and throughput of identification in metabolomics studies by use of TOF-MS, which is crucial to obtain meaningful and trustful results.

Metabolomic evaluation of Mitomycin C and rapamycin in a personalized treatment of pancreatic cancer.

In a personalized treatment designed for a patient with pancreatic cancer resistant to other treatments, the success of Mitomycin C (MMC) has been highlighted. This was revealed in a murine xenograft tumor model encompassing pancreatic adenocarcinoma cells extracted from the patient. The patient was found to exhibit a biallelic inactivation of the PALB2 gene, involved in DNA repair in addition to another mutation in the TSC2 gene that induces susceptibility of the tumor to therapeutic targets of the PI3K-mTOR pathway. The aim of the study was to apply metabolomics to elucidate the modes of action of each therapy, suggesting why MMC was so successful in this patient and why it could be a more popular choice in future pancreatic cancer treatment. The effectiveness of MMC compared to rapamycin (RM), another relevant therapeutic agent has been evaluated through liquid- and gas-chromatography mass spectrometry-based metabolomic analyses of the xenograft tumors. The relative concentrations of many metabolites in the xenograft tumors were found to be increased by MMC relative to other treatments (RM and a combination of both), including a number that are involved in central carbon metabolism (CCM). Metabolic fingerprinting revealed statistically significantly altered pathways including, but not restricted to, the pentose phosphate pathway, glycolysis, TCA cycle, purine metabolism, fatty acid biosynthesis, in addition to many significant lipid and amino acid alterations. Given the genetic background of the patient, it was expected that the combined therapy would be most effective; however, the most effective was MMC alone. It is proposed that the effectiveness of MMC is owed to its direct effect on CCM, a vital region of tumor metabolism.

Metabolomics as a tool for drug discovery and personalised medicine. A review.

Studying the effects of drugs on the metabolome constitutes a huge part of the metabolomics discipline. Whether the approach is associated with drug discovery (altered pathways due to the disease that provide future targets and information into the mechanism of action or resistance, etc.) or pharmacometabolomics (studying the outcome of treatment), there have been many aspiring published articles in this area. With specific experimental design, including fingerprinting analysis with different analytical platforms in a non-targeted way, the approach is advancing towards the discovery of markers for the implication of personalised medicine, while also providing information that could help to improve the efficacy and reduce the side effects associated with a treatment. In this review, the evolution of pharmacometabolomics from other areas of drug efficacy metabolomics studies is explored.

Influence of missing values substitutes on multivariate analysis of metabolomics data.

Missing values are known to be problematic for the analysis of gas chromatography-mass spectrometry (GC-MS) metabolomics data. Typically these values cover about 10%-20% of all data and can originate from various backgrounds, including analytical, computational, as well as biological. Currently, the most well known substitute for missing values is a mean imputation. In fact, some researchers consider this aspect of data analysis in their metabolomics pipeline as so routine that they do not even mention using this replacement approach. However, this may have a significant influence on the data analysis output(s) and might be highly sensitive to the distribution of samples between different classes. Therefore, in this study we have analysed different substitutes of missing values namely: zero, mean, median, k-nearest neighbours (kNN) and random forest (RF) imputation, in terms of their influence on unsupervised and supervised learning and, thus, their impact on the final output(s) in terms of biological interpretation. These comparisons have been demonstrated both visually and computationally (classification rate) to support our findings. The results show that the selection of the replacement methods to impute missing values may have a considerable effect on the classification accuracy, if performed incorrectly this may negatively influence the biomarkers selected for an early disease diagnosis or identification of cancer related metabolites. In the case of GC-MS metabolomics data studied here our findings recommend that RF should be favored as an imputation of missing value over the other tested methods. This approach displayed excellent results in terms of classification rate for both supervised methods namely: principal components-linear discriminant analysis (PC-LDA) (98.02%) and partial least squares-discriminant analysis (PLS-DA) (97.96%) outperforming other imputation methods.

Metabolomics in cancer biomarker discovery: current trends and future perspectives.

Cancer is one of the most devastating human diseases that causes a vast number of mortalities worldwide each year. Cancer research is one of the largest fields in the life sciences and despite many astounding breakthroughs and contributions over the past few decades, there is still a considerable amount to unveil on the function of cancer. It is well known that cancer metabolism differs from that of normal tissue and an important hypothesis published in the 1950s by Otto Warburg proposed that cancer cells rely on anaerobic metabolism as the source for energy, even under physiological oxygen levels. Following this, cancer central carbon metabolism has been researched extensively and beyond respiration, cancer has been found to involve a wide range of metabolic processes, and many more are still to be unveiled. Studying cancer through metabolomics could reveal new biomarkers for cancer that could be useful for its future prognosis, diagnosis and therapy. Metabolomics is becoming an increasingly popular tool in the life sciences since it is a relatively fast and accurate technique that can be applied with either a particular focus or in a global manner to reveal new knowledge about biological systems. There have been many examples of its application to reveal potential biomarkers in different cancers that have employed a range of different analytical platforms. In this review, approaches in metabolomics that have been employed in cancer biomarker discovery are discussed and some of the most noteworthy research in the field is highlighted.

A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions.

BACKGROUND: Metabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments. RESULTS: Correlation analysis has revealed statistically significant connections between metabolites, where differences in correlations between cells exposed to different oxygen levels have been highlighted as markers of hypoxic metabolism in cancer. Network mapping onto reconstructed human metabolic models is a novel addition to correlation analysis. Correlated metabolites have been mapped onto the Edinburgh human metabolic network (EHMN) with the aim of interlinking metabolites found to be regulated in a similar fashion in response to oxygen. This revealed novel pathways within the metabolic network that may be key to tumour cell survival at low oxygen. Results show that the metabolic responses to lowering oxygen availability can be conserved or specific to a particular cell line. Network-based correlation analysis identified conserved metabolites including malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate. In this way, this method has revealed metabolites not previously linked, or less well recognised, with respect to hypoxia before. Lactate fermentation is one of the key themes discussed in the field of hypoxia; however, malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate, which are connected by a single pathway, may provide a more significant marker of hypoxia in cancer. CONCLUSIONS: Metabolic networks generated for each cell line were compared to identify conserved metabolite pathway responses to low oxygen environments. Furthermore, we believe this methodology will have general application within metabolomics.