Metabolic and Lipidomic Reprogramming in Renal Cell Carcinoma Subtypes Reflects Regions of Tumor Origin.

BACKGROUND: Renal cell carcinoma (RCC) consists of prognostic distinct subtypes derived from different cells of origin (eg, clear cell RCC [ccRCC], papillary RCC [papRCC], and chromophobe RCC [chRCC]). ccRCC is characterized by lipid accumulation and metabolic alterations, whereas data on metabolic alterations in non-ccRCC are limited. OBJECTIVE: We assessed metabolic alterations and the lipid composition of RCC subtypes and ccRCC-derived metastases. Moreover, we elucidated the potential of metabolites/lipids for subtype classification and identification of therapeutic targets. DESIGN, SETTING, AND PARTICIPANTS: Metabolomic/lipidomic profiles were quantified in ccRCC (n=58), chRCC (n=19), papRCC (n=14), corresponding nontumor tissues, and metastases (n=9) through a targeted metabolomic approach. Transcriptome profiling was performed in corresponding samples and compared with expression data of The Cancer Genome Atlas cohorts (patients with ccRCC, n=452; patients with papRCC, n=260; and patients with chRCC, n=59). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: In addition to cluster analyses, metabolomic/transcriptomic data were analyzed to evaluate metabolic differences of ccRCC and chRCC using Welch's t test or paired t test as appropriate. Where indicated, p values were adjusted for multiple testing using Bonferroni or Benjamini-Hochberg correction. RESULTS AND LIMITATIONS: Based on their metabolic profiles, RCC subtypes clustered into two groups separating ccRCC and papRCC from chRCC, which mainly reflected the different cells of origin. ccRCC-derived metastases clustered with primary ccRCCs. In addition to differences in certain lipids (lysophosphatidylcholines and sphingomyelins), the coregulation network of lipids differed between ccRCC and chRCC. Consideration of metabolic gene expression indicated, for example, alterations of the polyamine pathway at metabolite and transcript levels. In vitro treatment of RCC cells with the ornithine-decarboxylase inhibitor difluoromethylornithine resulted in reduced cell viability and mitochondrial activity. Further evaluation of clinical utility was limited by the retrospective study design and cohort size. CONCLUSIONS: In summary, we provide novel insight into the metabolic profiles of ccRCC and non-ccRCC, thereby confirming the different ontogeny of RCC subtypes. Quantification of differentially regulated metabolites/lipids improves classification of RCC with an impact on the identification of novel therapeutic targets. PATIENT SUMMARY: Several subtypes of renal cell carcinoma (RCC) with different metastatic potentials and prognoses exist. In the present study, we provide novel insight into the metabolism of these different subtypes, which improves classification of subtypes and helps identify novel targets for RCC therapy.

Quantitative Metabolomics in Alzheimer's Disease: Technical Considerations for Improved Reproducibility.

Metabolomics is the comprehensive analysis of small molecules (metabolites) that are intermediates or endpoints of metabolism. Since metabolites change more rapidly to both external and internal stimuli than genes and proteins, metabolomics provides a more sensitive tool to study physiological changes to a wide range of factors such age, medication, or disease status. Therefore, metabolomics is being increasingly used for the study of several pathological states, including complex diseases like Alzheimer's disease (AD).Both untargeted and targeted metabolomics have been applied for AD and both have provided diagnostic algorithms that accurately discriminate healthy patients from patients with AD by combining different metabolites. However, none of these algorithms have been replicated in larger, different cohorts, and a consensus in methodology has been claimed by the scientific community. The AbsoluteIDQ® p180 Kit (Biocrates, Life Science AG, Innsbruck, Austria) is to date the only commercially available, validated, and standardized assay that measures up to 188 metabolites in biological samples. This kit unifies methodology in a common user manual and provides quantitative measurements of metabolites, thus facilitating an easier comparison among studies and reducing the technical variability that might contribute to replication failures. Nevertheless, recent studies showed no replication even when using this kit, suggesting that additional measures should be taken to achieve replication of metabolite-based discriminative algorithms. The aim of this chapter is to provide technical guidance on how to apply quantitative metabolomic data to the definition of discriminative algorithms for the diagnosis of neurodegenerative diseases such as AD. This chapter will provide an overview of technical aspects on the whole process, from blood sampling to raw data handling, and will highlight several technical aspects in the process that could hamper replication attempts even when using validated and standardized assays, such as the AbsoluteIDQ® p180 Kit.

Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss-mediated metabolic improvements.

Health has been defined as the capability of the organism to adapt to challenges. In this study, we tested to what extent comprehensively phenotyped individuals reveal differences in metabolic responses to a standardized mixed meal tolerance test (MMTT) and how these responses change when individuals experience moderate weight loss. Metabolome analysis was used in 70 healthy individuals. with profiling of ∼300 plasma metabolites during an MMTT over 8 h. Multivariate analysis of plasma markers of fatty acid catabolism identified 2 distinct metabotype clusters (A and B). Individuals from metabotype B showed slower glucose clearance, had increased intra-abdominal adipose tissue mass and higher hepatic lipid levels when compared with individuals from metabotype A. An NMR-based urine analysis revealed that these individuals also to have a less healthy dietary pattern. After a weight loss of ∼5.6 kg over 12 wk, only the subjects from metabotype B showed positive changes in the glycemic response during the MMTT and in markers of metabolic diseases. Our study in healthy individuals demonstrates that more comprehensive phenotyping can reveal discrete metabotypes with different outcomes in a dietary intervention and that markers of lipid catabolism in plasma could allow early detection of the metabolic syndrome.-Fiamoncini, J., Rundle, M., Gibbons, H., Thomas, E. L., Geillinger-Kästle, K., Bunzel, D., Trezzi, J.-P., Kiselova-Kaneva, Y., Wopereis, S., Wahrheit, J., Kulling, S. E., Hiller, K., Sonntag, D., Ivanova, D., van Ommen, B., Frost, G., Brennan, L., Bell, J. Daniel, H. Plasma metabolome analysis identifies distinct human metabotypes in the postprandial state with different susceptibility to weight loss-mediated metabolic improvements.

Genome-Wide Association Studies of Metabolites in Patients with CKD Identify Multiple Loci and Illuminate Tubular Transport Mechanisms.

Background The kidneys have a central role in the generation, turnover, transport, and excretion of metabolites, and these functions can be altered in CKD. Genetic studies of metabolite concentrations can identify proteins performing these functions.Methods We conducted genome-wide association studies and aggregate rare variant tests of the concentrations of 139 serum metabolites and 41 urine metabolites, as well as their pairwise ratios and fractional excretions in up to 1168 patients with CKD.Results After correction for multiple testing, genome-wide significant associations were detected for 25 serum metabolites, two urine metabolites, and 259 serum and 14 urinary metabolite ratios. These included associations already known from population-based studies. Additional findings included an association for the uremic toxin putrescine and variants upstream of an enzyme catalyzing the oxidative deamination of polyamines (AOC1, P-min=2.4×10-12), a relatively high carrier frequency (2%) for rare deleterious missense variants in ACADM that are collectively associated with serum ratios of medium-chain acylcarnitines (P-burden=6.6×10-16), and associations of a common variant in SLC7A9 with several ratios of lysine to neutral amino acids in urine, including the lysine/glutamine ratio (P=2.2×10-23). The associations of this SLC7A9 variant with ratios of lysine to specific neutral amino acids were much stronger than the association with lysine concentration alone. This finding is consistent with SLC7A9 functioning as an exchanger of urinary cationic amino acids against specific intracellular neutral amino acids at the apical membrane of proximal tubular cells.Conclusions Metabolomic indices of specific kidney functions in genetic studies may provide insight into human renal physiology.

Comprehensive Metabolomic and Lipidomic Profiling of Human Kidney Tissue: A Platform Comparison.

Metabolite profiling of tissue samples is a promising approach for the characterization of cancer pathways and tumor classification based on metabolic features. Here, we present an analytical method for nontargeted metabolomics of kidney tissue. Capitalizing on different chemical properties of metabolites allowed us to extract a broad range of molecules covering small polar molecules and less polar lipid classes that were analyzed by LC-QTOF-MS after HILIC and RP chromatographic separation, respectively. More than 1000 features could be reproducibly extracted and analyzed (CV < 30%) in porcine and human kidney tissue, which were used as surrogate matrices for method development. To further assess assay performance, cross-validation of the nontargeted metabolomics platform to a targeted metabolomics approach was carried out. Strikingly, from 102 metabolites that could be detected on both platforms, the majority (>90%) revealed Spearman's correlation coefficients ≥0.3, indicating that quantitative results from the nontargeted assay are largely comparable to data derived from classical targeted assays. Finally, as proof of concept, the method was applied to human kidney tissue where a clear differentiation between kidney cancer and nontumorous material could be demonstrated on the basis of unsupervised statistical analysis.

Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals.

INTRODUCTION: Recently, quantitative metabolomics identified a panel of 10 plasma lipids that were highly predictive of conversion to Alzheimer's disease (AD) in cognitively normal older individuals (n = 28, area under the curve [AUC] = 0.92, sensitivity/specificity of 90%/90%). METHODS: Quantitative targeted metabolomics in serum using an identical method as in the index study. RESULTS: We failed to replicate these findings in a substantially larger study from two independent cohorts-the Baltimore Longitudinal Study of Aging ([BLSA], n = 93, AUC = 0.642, sensitivity/specificity of 51.6%/65.7%) and the Age, Gene/Environment Susceptibility-Reykjavik Study ([AGES-RS], n = 100, AUC = 0.395, sensitivity/specificity of 47.0%/36.0%). In analyses applying machine learning methods to all 187 metabolite concentrations assayed, we find a modest signal in the BLSA with distinct metabolites associated with the preclinical and symptomatic stages of AD, whereas the same methods gave poor classification accuracies in the AGES-RS samples. DISCUSSION: We believe that ours is the largest blood biomarker study of preclinical AD to date. These findings underscore the importance of large-scale independent validation of index findings from biomarker studies with relatively small sample sizes.

Identification of active elementary flux modes in mitochondria using selectively permeabilized CHO cells.

Metabolic compartmentation is a key feature of mammalian cells. Mitochondria are the powerhouse of eukaryotic cells, responsible for respiration and the TCA cycle. We accessed the mitochondrial metabolism of the economically important Chinese hamster ovary (CHO) cells using selective permeabilization. We tested key substrates without and with addition of ADP. Based on quantified uptake and production rates, we could determine the contribution of different elementary flux modes to the metabolism of a substrate or substrate combination. ADP stimulated the uptake of most metabolites, directly by serving as substrate for the respiratory chain, thus removing the inhibitory effect of NADH, or as allosteric effector. Addition of ADP favored substrate metabolization to CO2 and did not enhance the production of other metabolites. The controlling effect of ADP was more pronounced when we supplied metabolites to the first part of the TCA cycle: pyruvate, citrate, α-ketoglutarate and glutamine. In the second part of the TCA cycle, the rates were primarily controlled by the concentrations of C4-dicarboxylates. Without ADP addition, the activity of the pyruvate carboxylase-malate dehydrogenase-malic enzyme cycle consumed the ATP produced by oxidative phosphorylation, preventing its accumulation and maintaining metabolic steady state conditions. Aspartate was taken up only in combination with pyruvate, whose uptake also increased, a fact explained by complex regulatory effects. Isocitrate dehydrogenase and α-ketoglutarate dehydrogenase were identified as the key regulators of the TCA cycle, confirming existent knowledge from other cells. We have shown that selectively permeabilized cells combined with elementary mode analysis allow in-depth studying of the mitochondrial metabolism and regulation.

Non-stationary 13C metabolic flux analysis of Chinese hamster ovary cells in batch culture using extracellular labeling highlights metabolic reversibility and compartmentation.

BACKGROUND: Mapping the intracellular fluxes for established mammalian cell lines becomes increasingly important for scientific and economic reasons. However, this is being hampered by the high complexity of metabolic networks, particularly concerning compartmentation. RESULTS: Intracellular fluxes of the CHO-K1 cell line central carbon metabolism were successfully determined for a complex network using non-stationary 13C metabolic flux analysis. Mass isotopomers of extracellular metabolites were determined using [U-13C6] glucose as labeled substrate. Metabolic compartmentation and extracellular transport reversibility proved essential to successfully reproduce the dynamics of the labeling patterns. Alanine and pyruvate reversibility changed dynamically even if their net production fluxes remained constant. Cataplerotic fluxes of cytosolic phosphoenolpyruvate carboxykinase and mitochondrial malic enzyme and pyruvate carboxylase were successfully determined. Glycolytic pyruvate channeling to lactate was modeled by including a separate pyruvate pool. In the exponential growth phase, alanine, glycine and glutamate were excreted, and glutamine, aspartate, asparagine and serine were taken up; however, all these amino acids except asparagine were exchanged reversibly with the media. High fluxes were determined in the pentose phosphate pathway and the TCA cycle. The latter was fueled mainly by glucose but also by amino acid catabolism. CONCLUSIONS: The CHO-K1 central metabolism in controlled batch culture proves to be robust. It has the main purpose to ensure fast growth on a mixture of substrates and also to mitigate oxidative stress. It achieves this by using compartmentation to control NADPH and NADH availability and by simultaneous synthesis and catabolism of amino acids.

Metabolic control at the cytosol-mitochondria interface in different growth phases of CHO cells.

Metabolism at the cytosol-mitochondria interface and its regulation is of major importance particularly for efficient production of biopharmaceuticals in Chinese hamster ovary (CHO) cells but also in many diseases. We used a novel systems-oriented approach combining dynamic metabolic flux analysis and determination of compartmental enzyme activities to obtain systems level information with functional, spatial and temporal resolution. Integrating these multiple levels of information, we were able to investigate the interaction of glycolysis and TCA cycle and its metabolic control. We characterized metabolic phases in CHO batch cultivation and assessed metabolic efficiency extending the concept of metabolic ratios. Comparing in situ enzyme activities including their compartmental localization with in vivo metabolic fluxes, we were able to identify limiting steps in glycolysis and TCA cycle. Our data point to a significant contribution of substrate channeling to glycolytic regulation. We show how glycolytic channeling heavily affects the availability of pyruvate for the mitochondria. Finally, we show that the activities of transaminases and anaplerotic enzymes are tailored to permit a balanced supply of pyruvate and oxaloacetate to the TCA cycle in the respective metabolic states. We demonstrate that knowledge about metabolic control can be gained by correlating in vivo metabolic flux dynamics with time and space resolved in situ enzyme activities.

Dynamics of growth and metabolism controlled by glutamine availability in Chinese hamster ovary cells.

The physiology of animal cells is characterized by constantly changing environmental conditions and adapting cellular responses. Applied dynamic metabolic flux analysis captures metabolic dynamics and can be applied to industrially relevant cultivation conditions. We investigated the impact of glutamine availability or limitation on the physiology of CHO K1 cells in eight different batch and fed-batch cultivations. Varying glutamine availability resulted in global metabolic changes. We observed dose-dependent effects of glutamine in batch cultivation. Identifying metabolic links from the glutamine metabolism to specific metabolic pathways, we show that glutamine feeding results in its coupling to tricarboxylic acid cycle fluxes and in its decoupling from metabolic waste production. We provide a mechanistic explanation of the cellular responses upon mild or severe glutamine limitation and ammonia stress. The growth rate of CHO K1 decreased with increasing ammonia levels in the supernatant. On the other hand, growth, especially culture longevity, was stimulated at mild glutamine-limiting conditions. Flux rearrangements in the pyruvate and amino acid metabolism compensate glutamine limitation by consumption of alternative carbon sources and facilitating glutamine synthesis and mitigate ammonia stress as result of glutamine abundance.

Quenching methods for the analysis of intracellular metabolites.

Sampling and quenching methods for intracellular metabolite analysis in mammalian cells in adherent and suspension culture are described. Quenching of adherent cells is achieved by application of hot air after removal of the supernatant by suction. For suspension cultures, the addition of excess ice-cold saline results in a rapid inactivation of metabolism and significant dilution of extracellular metabolites. Medium carryover is prevented by rinsing the cells with washing solution. Separation of supernatant from suspension cells via centrifugation is incomplete due to required short centrifugation time. Thus, it is necessary to determine the reproducible cell recovery after quenching.

Doxorubicin increases oxidative metabolism in HL-1 cardiomyocytes as shown by 13C metabolic flux analysis.

Doxorubicin (DXR), an anticancer drug, is limited in its use due to severe cardiotoxic effects. These effects are partly caused by disturbed myocardial energy metabolism. We analyzed the effects of therapeutically relevant but nontoxic DXR concentrations for their effects on metabolic fluxes, cell respiration, and intracellular ATP. (13)C isotope labeling studies using [U-(13)C(6)]glucose, [1,2-(13)C(2)]glucose, and [U-(13)C(5)]glutamine were carried out on HL-1 cardiomyocytes exposed to 0.01 and 0.02 µM DXR and compared with the untreated control. Metabolic fluxes were calculated by integrating production and uptake rates of extracellular metabolites (glucose, lactate, pyruvate, and amino acids) as well as (13)C-labeling in secreted lactate derived from the respective (13)C-labeled substrates into a metabolic network model. The investigated DXR concentrations (0.01 and 0.02 µM) had no effect on cell viability and beating of the HL-1 cardiomyocytes. Glycolytic fluxes were significantly reduced in treated cells at tested DXR concentrations. Oxidative metabolism was significantly increased (higher glucose oxidation, oxidative decarboxylation, TCA cycle rates, and respiration) suggesting a more efficient use of glucose carbon. These changes were accompanied by decrease of intracellular ATP. We conclude that DXR in nanomolar range significantly changes central carbon metabolism in HL-1 cardiomyocytes, which results in a higher coupling of glycolysis and TCA cycle. The myocytes probably try to compensate for decreased intracellular ATP, which in turn may be the result of a loss of NADH electrons via either formation of reactive oxygen species or electron shunting.

Eukaryotic metabolism: measuring compartment fluxes.

Metabolic compartmentation represents a major characteristic of eukaryotic cells. The analysis of compartmented metabolic networks is complicated by separation and parallelization of pathways, intracellular transport, and the need for regulatory systems to mediate communication between interdependent compartments. Metabolic flux analysis (MFA) has the potential to reveal compartmented metabolic events, although it is a challenging task requiring demanding experimental techniques and sophisticated modeling. At present no ready-made solution can be provided to cope with the complexity of compartmented metabolic networks, but new powerful tools are emerging. This review gives an overview of different strategies to approach this issue, focusing on different MFA methods and highlighting the additional information that should be included to improve the outcome of an experiment and associate estimation procedures.

Metabolic profiling using HPLC allows classification of drugs according to their mechanisms of action in HL-1 cardiomyocytes.

Along with hepatotoxicity, cardiotoxic side effects remain one of the major reasons for drug withdrawals and boxed warnings. Prediction methods for cardiotoxicity are insufficient. High content screening comprising of not only electrophysiological characterization but also cellular molecular alterations are expected to improve the cardiotoxicity prediction potential. Metabolomic approaches recently have become an important focus of research in pharmacological testing and prediction. In this study, the culture medium supernatants from HL-1 cardiomyocytes after exposure to drugs from different classes (analgesics, antimetabolites, anthracyclines, antihistamines, channel blockers) were analyzed to determine specific metabolic footprints in response to the tested drugs. Since most drugs influence energy metabolism in cardiac cells, the metabolite "sub-profile" consisting of glucose, lactate, pyruvate and amino acids was considered. These metabolites were quantified using HPLC in samples after exposure of cells to test compounds of the respective drug groups. The studied drug concentrations were selected from concentration response curves for each drug. The metabolite profiles were randomly split into training/validation and test set; and then analysed using multivariate statistics (principal component analysis and discriminant analysis). Discriminant analysis resulted in clustering of drugs according to their modes of action. After cross validation and cross model validation, the underlying training data were able to predict 50%-80% of conditions to the correct classification group. We show that HPLC based characterisation of known cell culture medium components is sufficient to predict a drug's potential classification according to its mode of action.