β-N-Methylamino-L-alanine (BMAA) perturbs alanine, aspartate and glutamate metabolism pathways in human neuroblastoma cells as determined by metabolic profiling

β-Methylamino-L-alanine (BMAA) is a non-proteinogenic amino acid that induces long-term cognitive deficits, as well as an increased neurodegeneration and intracellular fibril formation in the hippocampus of adult rodents following short-time neonatal exposure and in vervet monkey brain following long-term exposure. It has also been proposed to be involved in the etiology of neurodegenerative disease in humans. The aim of this study was to identify metabolic effects not related to excitotoxicity or oxidative stress in human neuroblastoma SH-SY5Y cells. The effects of BMAA (50, 250, 1000 µM) for 24 h on cells differentiated with retinoic acid were studied. Samples were analyzed using LC-MS and NMR spectroscopy to detect altered intracellular polar metabolites. The analysis performed, followed by multivariate pattern recognition techniques, revealed significant perturbations in protein biosynthesis, amino acid metabolism pathways and citrate cycle. Of specific interest were the BMAA-induced alterations in alanine, aspartate and glutamate metabolism and as well as alterations in various neurotransmitters/neuromodulators such as GABA and taurine. The results indicate that BMAA can interfere with metabolic pathways involved in neurotransmission in human neuroblastoma cells

NMR-based metabolic profiling in healthy individuals overfed different types of fat : links to changes in liver fat accumulation and lean tissue mass.

BACKGROUND: Overeating different dietary fatty acids influence the amount of liver fat stored during weight gain, however, the mechanisms responsible are unclear. We aimed to identify non-lipid metabolites that may differentiate between saturated (SFA) and polyunsaturated fatty acid (PUFA) overfeeding using a non-targeted metabolomic approach. We also investigated the possible relationships between plasma metabolites and body fat accumulation. METHODS: In a randomized study (LIPOGAIN study), n=39 healthy individuals were overfed with muffins containing SFA or PUFA. Plasma samples were precipitated with cold acetonitrile and analyzed by nuclear magnetic resonance (NMR) spectroscopy. Pattern recognition techniques were used to overview the data, identify variables contributing to group classification and to correlate metabolites with fat accumulation. RESULTS: We previously reported that SFA causes a greater accumulation of liver fat, visceral fat and total body fat, whereas lean tissue levels increases less compared with PUFA, despite comparable weight gain. In this study, lactate and acetate were identified as important contributors to group classification between SFA and PUFA (P&lt;0.05). Furthermore, the fat depots (total body fat, visceral adipose tissue and liver fat) and lean tissue correlated (P(corr)&gt;0.5) all with two or more metabolites (for example, branched amino acids, alanine, acetate and lactate). The metabolite composition differed in a manner that may indicate higher insulin sensitivity after a diet with PUFA compared with SFA, but this needs to be confirmed in future studies. CONCLUSION: A non-lipid metabolic profiling approach only identified a few metabolites that differentiated between SFA and PUFA overfeeding. Whether these metabolite changes are involved in depot-specific fat storage and increased lean tissue mass during overeating needs further investigation.Rosqvist, Engskog, Haglöf, Riséus and Pettersson contributed equally to this work.</p

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BACKGROUND: Overeating different dietary fatty acids influence the amount of liver fat stored during weight gain, however, the mechanisms responsible are unclear. We aimed to identify non-lipid metabolites that may differentiate between saturated (SFA) and polyunsaturated fatty acid (PUFA) overfeeding using a non-targeted metabolomic approach. We also investigated the possible relationships between plasma metabolites and body fat accumulation. METHODS: In a randomized study (LIPOGAIN study), n=39 healthy individuals were overfed with muffins containing SFA or PUFA. Plasma samples were precipitated with cold acetonitrile and analyzed by nuclear magnetic resonance (NMR) spectroscopy. Pattern recognition techniques were used to overview the data, identify variables contributing to group classification and to correlate metabolites with fat accumulation. RESULTS: We previously reported that SFA causes a greater accumulation of liver fat, visceral fat and total body fat, whereas lean tissue levels increases less compared with PUFA, despite comparable weight gain. In this study, lactate and acetate were identified as important contributors to group classification between SFA and PUFA (P&lt;0.05). Furthermore, the fat depots (total body fat, visceral adipose tissue and liver fat) and lean tissue correlated (P(corr)&gt;0.5) all with two or more metabolites (for example, branched amino acids, alanine, acetate and lactate). The metabolite composition differed in a manner that may indicate higher insulin sensitivity after a diet with PUFA compared with SFA, but this needs to be confirmed in future studies. CONCLUSION: A non-lipid metabolic profiling approach only identified a few metabolites that differentiated between SFA and PUFA overfeeding. Whether these metabolite changes are involved in depot-specific fat storage and increased lean tissue mass during overeating needs further investigation.Rosqvist, Engskog, Haglöf, Riséus and Pettersson contributed equally to this work.</p

Different functions of AKT1 and AKT2 in molecular pathways, cell migration and metabolism in colon cancer cells

AKT is a central protein in many cellular pathways such as cell survival, proliferation, glucose uptake, metabolism, angiogenesis, as well as radiation and drug response. The three isoforms of AKT (AKT1, AKT2 and AKT3) are proposed to have different physiological functions, properties and expression patterns in a cell type-dependent manner. As of yet, not much is known about the influence of the different AKT isoforms in the genome and their effects in the metabolism of colorectal cancer cells. In the present study, DLD-1 isogenic AKT1, AKT2 and AKT'/2 knockout colon cancer cell lines were used as a model system in conjunction with the parental cell line in order to further elucidate the differences between the AKT isoforms and how they are involved in various cellular pathways. This was done using genome wide expression analyses, metabolic profiling and cell migration assays. In conclusion, downregulation of genes in the cell adhesion, extracellular matrix and Notch-pathways and upregulation of apoptosis and metastasis inhibitory genes in the p53-pathway, confirm that the knockout of both AKT1 and AKT2 will attenuate metastasis and tumor cell growth. This was verified with a reduction in migration rate in the AKT1 KO and AKT2 KO and most explicitly in the AKT1/2 KO. Furthermore, the knockout of AKT1, AKT2 or both, resulted in a reduction in lactate and alanine, suggesting that the metabolism of carbohydrates and glutathione was impaired. This was further verified in gene expression analyses, showing downregulation of genes involved in glucose metabolism. Additionally, both AKT1 KO and AKT2 KO demonstrated an impaired fatty acid metabolism. However, genes were upregulated in the Wnt and cell proliferation pathways, which could oppose this effect. AKT inhibition should therefore be combined with other effectors to attain the best effect

NMR Spectroscopy-Based Metabolic Profiling of Drug-Induced Changes In Vitro Can Discriminate between Pharmacological Classes

Drug-induced changes in mammalian cell line models have already been extensively profiled at the systemic mRNA level and subsequently used to suggest mechanisms of action for new substances, as well as to support drug repurposing, i.e., identifying new potential indications for drugs already licensed for other pharmacotherapy settings. The seminal work in this field, which includes a large database and computational algorithms for pattern matching, is known as the “Connectivity Map” (CMap). However, the potential of similar exercises at the metabolite level is still largely unexplored. Only recently, the first high-throughput metabolomic assay pilot study was published, which involved screening the metabolic response to a set of 56 kinase inhibitors in a 96-well format. Here, we report results from a separately developed metabolic profiling assay, which leverages ¹H NMR spectroscopy to the quantification of metabolic changes in the HCT116 colorectal cancer cell line, in response to each of 26 compounds. These agents are distributed across 12 different pharmacological classes covering a broad spectrum of bioactivity. Differential metabolic profiles, inferred from multivariate spectral analysis of 18 spectral bins, allowed clustering of the most-tested drugs, according to their respective pharmacological class. A more-advanced supervised analysis, involving one multivariate scattering matrix per pharmacological class and using only 3 spectral bins (3 metabolites), showed even more distinct pharmacology-related cluster formations. In conclusion, this type of relatively fast and inexpensive profiling seems to provide a promising alternative to that afforded by mRNA expression analysis, which is relatively slow and costly. As also indicated by the present pilot study, the resulting metabolic profiles do not seem to provide as information-rich signatures as those obtained using systemic mRNA profiling, but the methodology holds strong promise for significant refinement