Ketogenic diets slow melanoma growth in vivo regardless of tumor genetics and metabolic plasticity.

BACKGROUND: Growing evidence supports the use of low-carbohydrate/high-fat ketogenic diets as an adjunctive cancer therapy. However, it is unclear which genetic, metabolic, or immunological factors contribute to the beneficial effect of ketogenic diets. Therefore, we investigated the effect of ketogenic diets on the progression and metabolism of genetically and metabolically heterogeneous melanoma xenografts, as well as on the development of melanoma metastases in mice with a functional immune system. METHODS: Mice bearing BRAF mutant, NRAS mutant, and wild-type melanoma xenografts as well as mice bearing highly metastatic melanoma allografts were fed with a control diet or ketogenic diets, differing in their triglyceride composition, to evaluate the effect of ketogenic diets on tumor growth and metastasis. We performed an in-depth targeted metabolomics analysis in plasma and xenografts to elucidate potential antitumor mechanisms in vivo. RESULTS: We show that ketogenic diets effectively reduced tumor growth in immunocompromised mice bearing genetically and metabolically heterogeneous human melanoma xenografts. Furthermore, the ketogenic diets exerted a metastasis-reducing effect in the immunocompetent syngeneic melanoma mouse model. Targeted analysis of plasma and tumor metabolomes revealed that ketogenic diets induced distinct changes in amino acid metabolism. Interestingly, ketogenic diets reduced the levels of alpha-amino adipic acid, a biomarker of cancer, in circulation to levels observed in tumor-free mice. Additionally, alpha-amino adipic acid was reduced in xenografts by ketogenic diets. Moreover, the ketogenic diets increased sphingomyelin levels in plasma and the hydroxylation of sphingomyelins and acylcarnitines in tumors. CONCLUSIONS: Ketogenic diets induced antitumor effects toward melanoma regardless of the tumors´ genetic background, its metabolic signature, and the host immune status. Moreover, ketogenic diets simultaneously affected multiple metabolic pathways to create an unfavorable environment for melanoma cell proliferation, supporting their potential as a complementary nutritional approach to melanoma therapy.

Targeted Metabolomics Identifies Plasma Biomarkers in Mice with Metabolically Heterogeneous Melanoma Xenografts.

Melanomas are genetically and metabolically heterogeneous, which influences therapeutic efficacy and contributes to the development of treatment resistance in patients with metastatic disease. Metabolite phenotyping helps to better understand complex metabolic diseases, such as melanoma, and facilitates the development of novel therapies. Our aim was to characterize the tumor and plasma metabolomes of mice bearing genetically different melanoma xenografts. We engrafted the human melanoma cell lines A375 (BRAF mutant), WM47 (BRAF mutant), WM3000 (NRAS mutant), and WM3311 (BRAF, NRAS, NF1 triple-wildtype) and performed a broad-spectrum targeted metabolomics analysis of tumor and plasma samples obtained from melanoma-bearing mice as well as plasma samples from healthy control mice. Differences in ceramide and phosphatidylcholine species were observed between melanoma subtypes irrespective of the genetic driver mutation. Furthermore, beta-alanine metabolism differed between melanoma subtypes and was significantly enriched in plasma from melanoma-bearing mice compared to healthy mice. Moreover, we identified beta-alanine, p-cresol sulfate, sarcosine, tiglylcarnitine, two dihexosylceramides, and one phosphatidylcholine as potential melanoma biomarkers in plasma. The present data reflect the metabolic heterogeneity of melanomas but also suggest a diagnostic biomarker signature for melanoma screening.

Role of coenzymes in cancer metabolism.

Cancer is a heterogeneous set of diseases characterized by the rewiring of cellular signaling and the reprogramming of metabolic pathways to sustain growth and proliferation. In past decades, studies were focused primarily on the genetic complexity of cancer. Recently, increasing number of studies have discovered several mutations among metabolic enzymes in different tumor cells. Most of the enzymes are regulated by coenzymes, organic cofactors, that function as intermediate carrier of electrons or functional groups that are transferred during the reaction. However, the precise role of cofactors is not well elucidated. In this review, we discuss several metabolic enzymes associated to cancer metabolism rewiring, whose inhibition may represent a therapeutic target. Such enzymes, upon expression or inhibition, may impact also the coenzymes levels, but only in few cases, it was possible to direct correlate coenzymes changes with a specific enzyme. In addition, we also summarize an up-to-date information on biological role of some coenzymes, preclinical and clinical studies, that have been carried out in various cancers and their outputs.

Ldlr-/- and ApoE-/- mice better mimic the human metabolite signature of increased carotid intima media thickness compared to other animal models of cardiovascular disease.

BACKGROUND AND AIMS: Preclinical experiments on animal models are essential to understand the mechanisms of cardiovascular disease (CVD). Metabolomics allows access to the metabolic perturbations associated with CVD in heart and vessels. Here we assessed which potential animal CVD model most closely mimics the serum metabolite signature of increased carotid intima-media thickness (cIMT) in humans, a clinical parameter widely accepted as a surrogate of CVD. METHODS: A targeted mass spectrometry assay was used to quantify and compare a series of blood metabolites between 1362 individuals (KORA F4 cohort) and 5 animal CVD models: ApoE-/-, Ldlr-/-, and klotho-hypomorphic mice (kl/kl) and SHRSP rats with or without salt feeding. The metabolite signatures were obtained using linear regressions adjusted for various co-variates. RESULTS: In human, increased cIMT [quartile Q4 vs. Q1] was associated with 26 metabolites (9 acylcarnitines, 2 lysophosphatidylcholines, 9 phosphatidylcholines and 6 sphingomyelins). Acylcarnitines correlated preferentially with serum glucose and creatinine. Phospholipids correlated preferentially with cholesterol (total and LDL). The human signature correlated positively and significantly with Ldlr-/- and ApoE-/- mice, while correlation with kl/kl mice and SHRP rats was either negative and non-significant. Human and Ldlr-/- mice shared 11 significant metabolites displaying the same direction of regulation: 5 phosphatidylcholines, 1 lysophosphatidylcholines, 5 sphingomyelins; ApoE-/- mice shared 10. CONCLUSIONS: The human cIMT signature was partially mimicked by Ldlr-/- and ApoE-/- mice. These animal models might help better understand the biochemical and molecular mechanisms involved in the vessel metabolic perturbations associated with, and contributing to metabolic disorders in CVD.

Model-driven discovery of long-chain fatty acid metabolic reprogramming in heterogeneous prostate cancer cells.

Epithelial-mesenchymal-transition promotes intra-tumoral heterogeneity, by enhancing tumor cell invasiveness and promoting drug resistance. We integrated transcriptomic data for two clonal subpopulations from a prostate cancer cell line (PC-3) into a genome-scale metabolic network model to explore their metabolic differences and potential vulnerabilities. In this dual cell model, PC-3/S cells express Epithelial-mesenchymal-transition markers and display high invasiveness and low metastatic potential, while PC-3/M cells present the opposite phenotype and higher proliferative rate. Model-driven analysis and experimental validations unveiled a marked metabolic reprogramming in long-chain fatty acids metabolism. While PC-3/M cells showed an enhanced entry of long-chain fatty acids into the mitochondria, PC-3/S cells used long-chain fatty acids as precursors of eicosanoid metabolism. We suggest that this metabolic reprogramming endows PC-3/M cells with augmented energy metabolism for fast proliferation and PC-3/S cells with increased eicosanoid production impacting angiogenesis, cell adhesion and invasion. PC-3/S metabolism also promotes the accumulation of docosahexaenoic acid, a long-chain fatty acid with antiproliferative effects. The potential therapeutic significance of our model was supported by a differential sensitivity of PC-3/M cells to etomoxir, an inhibitor of long-chain fatty acid transport to the mitochondria.

Persistently Altered Metabolic Phenotype following Perinatal Excitotoxic Brain Injury.

Excitotoxicity plays a key role during insults to the developing brain such as neonatal encephalopathy, stroke, and encephalopathy of prematurity. Such insults affect many thousands of infants each year. Excitotoxicity causes frank lesions due to cell death and gliosis and disturbs normal developmental process, leading to deficits in learning, memory, and social integration that persist into adulthood. Understanding the underlying processes of the acute effects of excitotoxicity and its persistence during brain maturation provides an opportunity to identify mechanistic or diagnostic biomarkers, thus enabling and designing possible therapies. We applied mass spectrometry to provide metabolic profiles of brain tissue and plasma over time following an excitotoxic lesion (intracerebral ibotenate) to the neonatal (postnatal day 5) mouse brain. We found no differences between the plasma from the control (PBS-injected) and excitotoxic (ibotenate-injected) groups over time (on postnatal days 8, 9, 10, and 30). In the brain, we found that variations in amino acids (arginine, glutamine, phenylananine, and proline) and glycerophospholipids were sustaining acute and delayed (tertiary) responses to injury. In particular, the effect of the excitotoxic lesion on the normal profile of development was linked to alterations in a fingerprint of glycerophospolipids and amino acids. Specifically, we identified increases in the amino acids glutamine, proline, serine, threonine, tryptophan, valine, and the sphingolipid SM C26:1, and decreases in the glycerophospholipids, i.e., the arachidonic acid-containing phosphatidylcholine (PC aa) C30:2 and the PC aa C32:3. This study demonstrates that metabolic profiling is a useful approach to identify acute and tertiary effects in an excitotoxic lesion model, and generating a short list of targets with future potential in the hunt for identification, stratification, and possibly therapy.

Metabolic network failures in Alzheimer's disease: A biochemical road map.

INTRODUCTION: The Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance. METHODS: Fasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted. RESULTS: Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aß1-42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease. DISCUSSION: Metabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery.

Serum metabolites predict response to angiotensin II receptor blockers in patients with diabetes mellitus.

BACKGROUND: Individual patients show a large variability in albuminuria response to angiotensin receptor blockers (ARB). Identifying novel biomarkers that predict ARB response may help tailor therapy. We aimed to discover and validate a serum metabolite classifier that predicts albuminuria response to ARBs in patients with diabetes mellitus and micro- or macroalbuminuria. METHODS: Liquid chromatography-tandem mass spectrometry metabolomics was performed on serum samples. Data from patients with type 2 diabetes and microalbuminuria (n = 49) treated with irbesartan 300 mg/day were used for discovery. LASSO and ridge regression were performed to develop the classifier. Improvement in albuminuria response prediction was assessed by calculating differences in R(2) between a reference model of clinical parameters and a model with clinical parameters and the classifier. The classifier was externally validated in patients with type 1 diabetes and macroalbuminuria (n = 50) treated with losartan 100 mg/day. Molecular process analysis was performed to link metabolites to molecular mechanisms contributing to ARB response. RESULTS: In discovery, median change in urinary albumin excretion (UAE) was -42 % [Q1-Q3: -69 to -8]. The classifier, consisting of 21 metabolites, was significantly associated with UAE response to irbesartan (p < 0.001) and improved prediction of UAE response on top of the clinical reference model (R(2) increase from 0.10 to 0.70; p < 0.001). In external validation, median change in UAE was -43 % [Q1-Q35: -63 to -23]. The classifier improved prediction of UAE response to losartan (R(2) increase from 0.20 to 0.59; p < 0.001). Specifically ADMA impacting eNOS activity appears to be a relevant factor in ARB response. CONCLUSIONS: A serum metabolite classifier was discovered and externally validated to significantly improve prediction of albuminuria response to ARBs in diabetes mellitus.

Prognostic clinical and molecular biomarkers of renal disease in type 2 diabetes.

Diabetic kidney disease occurs in ∼ 25-40% of patients with type 2 diabetes. Given the high risk of progressive renal function loss and end-stage renal disease, early identification of patients with a renal risk is important. Novel biomarkers may aid in improving renal risk stratification. In this review, we first focus on the classical panel of albuminuria and estimated glomerular filtration rate as the primary clinical predictors of renal disease and then move our attention to novel biomarkers, primarily concentrating on assay-based multiple/panel biomarkers, proteomics biomarkers and metabolomics biomarkers. We focus on multiple biomarker panels since the molecular processes of renal disease progression in type 2 diabetes are heterogeneous, rendering it unlikely that a single biomarker significantly adds to clinical risk prediction. A limited number of prospective studies of multiple biomarkers address the predictive performance of novel biomarker panels in addition to the classical panel in type 2 diabetes. However, the prospective studies conducted so far have small sample sizes, are insufficiently powered and lack external validation. Adequately sized validation studies of multiple biomarker panels are thus required. There is also a paucity of studies that assess the effect of treatments on novel biomarker panels and determine whether initial treatment-induced changes in novel biomarkers predict changes in long-term renal outcomes. Such studies can not only improve our healthcare but also our understanding of the mechanisms of actions of existing and novel drugs and may yield biomarkers that can be used to monitor drug response. We conclude that this will be an area to focus research on in the future.

Alterations in Human Liver Metabolome during Prolonged Cryostorage.

Tissue metabolomics requires high sample quality that crucially depends on the biobanking storage protocol. Hence, we systematically analyzed the influence of realistic storage scenarios on the liver metabolome with different storage temperatures and repeated transfer of samples between storage and retrieval environments, simulating the repeated temperature changes affecting unrelated samples stored in the same container as the sample that is to be retrieved. By cycling between storage (-80 °C freezer, liquid nitrogen, cold nitrogen gas) and retrieval (room temperature, -80 °C), assuming three cycles per day and sample, we simulated biobank storage between 3 months and 10 years. Liver tissue metabolome was analyzed by liquid chromatography/mass spectrometry. Most metabolite concentrations changed <5% for the first "year" of time-compressed biobanking simulation, predominantly due to hydrolysis of peptides and lipids. Interestingly, storage temperature affected metabolite concentrations only little, while there was a linear dependence on the number of temperature change cycles. Elevated sample temperature during (prolonged) retrieval time led to a distinctly different signature of metabolite changes that were induced by cycling. Our findings allow giving recommendations for optimized storage protocols and provide signatures that allow detection of deviations from protocol.

The ratio of phosphatidylcholines to lysophosphatidylcholines in plasma differentiates healthy controls from patients with Alzheimer's disease and mild cognitive impairment.

BACKGROUND: Metabolomic processes have been identified as being strongly linked to the development of Alzheimer's disease (AD). Thus, lipid metabolites appear to be highly useful as diagnostic substrates for the diagnosis of AD and mild cognitive impairment (MCI) in plasma. METHODS: We analyzed plasma samples from controls (n = 35), MCI (n = 33), and AD patients (n = 43) using the AbsoluteIDQ p180 Kit (Biocrates Life Sciences), which included quantitative analysis of 40 acylcarnitines, 21 amino acids, 19 biogenic amines, 15 sphingolipids, 90 glycerophospholipids, and sum of hexoses. RESULTS: We found that individual lipid metabolites can differentiate controls from MCI and AD with relevant significance. However, the ratio between PC aa C34:4 and lysoPC a C18:2 differentiates controls from MCI (P = .0000007) and from AD (P = .0000009) with greater significance. CONCLUSIONS: The results provide evidence that the ratio of these two lipid metabolites is useful for diagnosing MCI and AD with an accuracy of 82%-85%.

Complexity and pitfalls of mass spectrometry-based targeted metabolomics in brain research.

Current quantitative metabolomic research in brain tissue is challenged by several analytical issues. To compare data of metabolite pattern, ratios of individual metabolite concentrations and composed classifiers characterizing a distinct state, standardized workup conditions, and extraction medium are crucial. Differences in physicochemical properties of individual compounds and compound classes such as polarity determine extraction yields and, thus, ratios of compounds with varying properties. Also, variations in suppressive effects related to coextracted matrix components affect standards or references and their concentration-dependent responses.The selection of a common tissue extraction protocol is an ill-posed problem because it can be regarded as a multiple objective decision depending on factors such as sample handling practicability, measurement precision, control of matrix effects, and relevance of the chemical assay. This study systematically evaluates the impact of extraction solvents and the impact of the complex brain tissue on measured metabolite levels, taking into account ionization efficiency as well as challenges encountered in the trace-level quantification of the analytes in brain matrices. In comparison with previous studies that relied on nontargeted platforms, consequently emphasizing the global behavior of the metabolomic fingerprint, here we focus on several series of metabolites spanning over extensive polarity, concentration, and molecular mass ranges.

Stereo- and regiospecificity of yeast phytases-chemical synthesis and enzymatic conversion of the substrate analogues neo- and L-chiro-inositol hexakisphosphate.

Phytases are enzymes that catalyze the hydrolysis of phosphate esters in myo-inositol hexakisphosphate (phytic acid). The precise routes of enzymatic dephosphorylation by phytases of the yeast strains Saccharomyces cerevisiae and Pichia rhodanensis have been investigated up to the myo-inositol trisphosphate level, including the absolute configuration of the intermediates. Stereoselective assignment of the myo-inositol pentakisphosphates (D-myo-inositol 1,2,4,5,6-pentakisphosphate and D-myo-inositol 1,2,3,4,5-pentakisphosphate) generated was accomplished by a new method based on enantiospecific enzymatic conversion and HPLC analysis. Via conduritol B or E derivatives the total syntheses of two epimers of myo-inositol hexakisphosphate, neo-inositol hexakisphosphate and L-chiro-inositol hexakisphosphate were performed to examine the specificity of the yeast phytases with these substrate analogues. A comparison of kinetic data and the degradation pathways determined gave the first hints about the molecular recognition of inositol hexakisphosphates by the enzymes. Exploitation of the high stereo- and regiospecificity observed in the dephosphorylation of neo- and L-chiro-inositol hexakisphosphate made it possible to establish enzyme-assisted steps for the synthesis of D-neo-inositol 1,2,5,6-tetrakisphosphate, L-chiro-inositol 1,2,3,5,6-pentakisphosphate and L-chiro-inositol 1,2,3,6-tetrakisphosphate.