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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.

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.

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 (CV30%) 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.

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

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-Kastle, 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.

Respiration analysis using isolated mitochondria and electrochemical oxygen sensing has contributed significantly to the knowledge about mitochondrial metabolism, which is involved in energy generation but also in ageing and numerous diseases. Here, we present a high-throughput respiration screening for functional in situ mitochondrial studies in permeabilized Chinese hamster ovary cells. The determination of oxygen uptake rates allowed a quantitative comparison between different conditions and a distinction of substrates into three groups providing an insight into tricarboxylic acid (TCA) cycle regulation. The mitochondrial metabolization of citrate, isocitrate, glutamine, and glutamate was highly stimulated by ADP supply. In contrast, the metabolization of α-ketoglutarate, succinate, fumarate, and malate was little controlled by the energy and redox state. Metabolization of pyruvate was very strictly regulated by several independent mechanisms: phosphorylation, feedback inhibition, but also by the availability of CoA. A moderate stimulation of pyruvate metabolization was accomplished by feeding both pyruvate and aspartate simultaneously. The presented high-throughput respiration screening provides comprehensive information about the effect of single or mixed substrates on mitochondrial metabolic activities, including transport and TCA cycle regulation, and metabolic bottlenecks. This supports the design of efficient mammalian producer strains or feeding strategies, but also the investigation of pathological and toxicological effects related to mitochondrial metabolism.

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.

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.

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.

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.

Introduction The renal cell carcinoma (RCC) subtypes clear cell RCC (ccRCC), papillary RCC (papRCC) and chromophobe RCC (chRCC) are proposed to arise from distinct cell types of the nephron. The subtypes differ not only in their incidence, but also in terms of metastasis risk and prognosis. The most common form ccRCC, accounting for ~75% of the cases, is characterized by reprogramming of tumor metabolism and is therefore assumed to be a metabolic disease. In contrast, little is known about the metabolic alterations in non-ccRCC subtypes. In this study, we applied transcriptomics and metabolomics profiling to identify key pathways altered in specific RCC subtypes and in ccRCC-derived metastases. In addition, the potential of metabolites to improve subtype classification and to identify promising drug targets for new therapeutic interventions was elucidated. Experimental Procedures Genome-wide expression analyses were performed in ccRCC (n=58), chRCC (n=19), papRCC (n=14), matching non-tumor tissues, and ccRCC-derived metastases (n=9) using HTA 2.0 gene expression arrays. The transcriptomic profiles were compared to expression data of RCC cohorts of TCGA (KIRC, n=452; KIRP, n=260; KICH, n=59). In addition, a targeted metabolomics approach using LC/MS technology was applied in these tissues. Results Pathway enrichment analyses based on metabolic gene expression signatures revealed metabolic alterations in proximal vs. distal cells of the nephron. Accordingly, the respective RCC subtypes arising from proximal (ccRCC, papRCC) or distal (chRCC) tubular cells showed major differences in transcriptional activity of metabolic pathways, reflecting their cell type of origin. Based on metabolite levels, hierarchical cluster analyses separated the samples into three groups, differentiating non-tumor tissues, chRCC, as well as ccRCC, ccRCC-derived metastases and papRCC, indicating differences depending on RCC ontogeny also for metabolic profiles. ccRCC-derived metastases clustered with primary ccRCC tumors, irrespective of affected organs. When comparing ccRCC and chRCC subtypes, major differences were found, e.g. for certain biogenic amines of the polyamine pathway. Differential regulation of this pathway was also reflected on transcriptome level. In vitro experiments revealed that interfering with the polyamine pathway by inhibiting the ornithine-decarboxylase with difluoromethylornithine, reduced cell viability and mitochondrial activity in RCC cells. Conclusion In this study, we elucidated the transcriptomic and metabolomic profiles of RCC tumors, which confirm the distinct ontogeny of RCC subtypes from different parts of the nephron. Furthermore, we found that identification and quantification of differentially regulated metabolites can improve RCC classification and represent an attractive opportunity to reveal subtype-specific therapeutic targets. Citation Format: Pascale Fisel, Florian Büttner, Anna Reustle, Verena Klumpp, Stefan Winter, Steffen Rausch, Jörg Hennenlotter, Stephan Kruck, Arnulf Stenzl, Judith Wahrheit, Denise Sonntag, Marcus Scharpf, Falko Fend, Abbas Agaimy, Arndt Hartmann, Jens Bedke, Matthias Schwab, Elke Schaeffeler. Transcriptomic and metabolomic profiles in renal cell carcinoma (RCC) tumors reflect ontogeny of RCC subtypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3485.

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.

An efficient isolation of mitochondria from cells under physiological conditions is crucial for many studies in life sciences but still challenging in many cases such as in metabolic characterization of mitochondria. In this work, four methods for the disruption of Chinese hamster ovary cells were evaluated regarding their influence on mitochondrial integrity and yield. After cell disruption, mitochondria released from cells were separated from the remaining cell homogenate by differential centrifugation. Sonication was shown to be a rapid and sensitive isolation method. Yields of 14.0 ± 0.3 mg raw mitochondrial protein per 108 cells were obtained. The mitochondria were morphologically intact, with membrane integrities of 67% (outer membrane) to 94% (inner membrane). Compared with the methods using Dounce homogenization, digitonin permeabilization, or electroporation for cell disruption the ultrasound method provided the highest yield of isolated mitochondria. Furthermore, this method is rapid (≈ 45 s for disruption), more robust than Dounce homogenization regarding their influence on mitochondrial integrity and especially suitable for preparing a relatively large amount of mitochondria. The results of this work can be helpful for quantitative and dynamic studies of molecular processes related to mitochondria under physiological conditions for many questions in both biomedicine and biotechnology.

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.

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.

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.

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.

Background Glutamine metabolism represents one of the major targets in metabolic engineering and process optimization due to its importance as cellular energy, carbon and nitrogen source. Metabolic flux analysis represents a powerful method to investigate the physiology and metabolism of cells [1]. Classical metabolic flux analysis methods require steady state conditions. However, industrially relevant cultivation conditions, i.e. batch and fed-batch cultivations, are characterized by changing environmental conditions and metabolic shifts [2]. We used dynamic metabolic flux analysis to study the impact of glutamine availability or limitation on the physiology of CHO K1 cells capturing metabolic dynamics during batchand fed-batch cultivations.

Background Metabolomics, aiming at the quantification of all extracellular and intracellular metabolites, is a valuable tool for characterizing, understanding and manipulating the physiology of mammalian cells. While extracellular metabolite analysis is well established, required quenching and extraction procedures for intracellular metabolite analysis in mammalian suspension cells are not yet routinely available. In this study a simple sampling and quenching protocol using ice-cold 0.9% saline as quenching solution [1] was tested on CHO cells. Quenching efficiency, preservation of cell integrity as well as cell separation and the necessity of washing steps were evaluated and possible sources of error are discussed.