Untargeted Metabolomics Strategy Research Articles

Untargeted metabolomics of strawberry (Fragaria x ananassa 'Camarosa') fruit from plants grown under osmotic stress conditions.

Plants activate defense mechanisms to cope with adverse environmental conditions, leading to the accumulation and / or depletion of general and specialized metabolites. In this study, a multiplatform untargeted metabolomics strategy was employed to evaluate metabolic changes in strawberry fruit of cv. Camarosa grown under osmotic stress conditions. Liquid chromatography-mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-MS) data from strawberries grown under two water-deficit conditions, irrigated at 95% crop evapotranspiration (ETc) and 85% ETc, and one excess salt condition with a 80 mmol L-1 NaCl solution, were analyzed to determine treatment effects on fruit metabolism. Multivariate principal component analysis, orthogonal projections to latent structures-discriminant analysis (OPLS-DA), and univariate statistical analyses were applied to the data set. While multivariate analyses showed group separation by treatment, T-tests and fold change revealed 12 metabolites differentially accumulated in strawberries from different treatments-among them phenolic compounds, glycerophospholipids, phytosterols, carbohydrates, and an aromatic amino acid. Untargeted metabolomic analysis allowed for the annotation of compounds differentially accumulated in strawberry fruit from plants grown under osmotic stress and non-stressed plants. The metabolic disturbance in plants under stress involved metabolites associated with the inhibition of reactive oxygen species and cell-wall and membrane lipid biosynthesis, which might serve as osmotic stress biomarkers. © 2019 Society of Chemical Industry.

Insights into the Diversity of Secondary Metabolites of Planktothrix Using a Biphasic Approach Combining Global Genomics and Metabolomics.

Cyanobacteria are an ancient lineage of slow-growing photosynthetic bacteria and a prolific source of natural products with diverse chemical structures and potent biological activities and toxicities. The chemical identification of these compounds remains a major bottleneck. Strategies that can prioritize the most prolific strains and novel compounds are of great interest. Here, we combine chemical analysis and genomics to investigate the chemodiversity of secondary metabolites based on their pattern of distribution within some cyanobacteria. Planktothrix being a cyanobacterial genus known to form blooms worldwide and to produce a broad spectrum of toxins and other bioactive compounds, we applied this combined approach on four closely related strains of Planktothrix. The chemical diversity of the metabolites produced by the four strains was evaluated using an untargeted metabolomics strategy with high-resolution LC–MS. Metabolite profiles were correlated with the potential of metabolite production identified by genomics for the different strains. Although, the Planktothrix strains present a global similarity in terms of a biosynthetic cluster gene for microcystin, aeruginosin, and prenylagaramide for example, we found remarkable strain-specific chemodiversity. Only few of the chemical features were common to the four studied strains. Additionally, the MS/MS data were analyzed using Global Natural Products Social Molecular Networking (GNPS) to identify molecular families of the same biosynthetic origin. In conclusion, we depict an efficient, integrative strategy for elucidating the chemical diversity of a given genus and link the data obtained from analytical chemistry to biosynthetic genes of cyanobacteria.

An untargeted metabolomic strategy based on liquid chromatography-mass spectrometry to study high glucose-induced changes in HK-2 cells

Diabetes mellitus is a major health concern nowadays. It is estimated that 40% of diabetics are affected by diabetic nephropathy, one of the complications derived from high glucose blood levels which can lead to chronic loss of kidney function. It is now clear that the renal proximal tubule plays a critical role in the progression of diabetic nephropathy but research focused on studying the molecular mechanisms involved is still needed. The aim of this work was to develop a liquid chromatography-mass spectrometry platform to carry out, for the first time, the untargeted metabolomic analysis of high glucose-induced changes in cultured human proximal tubular HK-2 cells. In order to find the metabolites which were affected by high glucose and to expand the metabolite coverage, intra- and extracellular fluid from HK-2 cells exposed to high glucose (25 mM), normal glucose (5.5 mM) or osmotic control (5.5 mM glucose +19.5 mM mannitol) were analyzed by two complementary chromatographic modes: hydrophilic interaction and reversed-phase liquid chromatography. Non-supervised principal components analysis showed a good separation among the three groups of samples. Statistically significant variables were chosen for further metabolite identification. Different metabolic pathways were affected mainly those derived from amino acidic, polyol, and nitrogenous bases metabolism.

Time-resolved decoding of metabolic signatures of in vitro growth of the hemibiotrophic pathogen Colletotrichum sublineolum

Metabolomics has emerged as a powerful approach to comprehensively interrogate cellular biochemistry. As such, we applied an untargeted liquid chromatography-mass spectrometry metabolomic strategy to elucidate metabolome changes in the anthracnose-causing hemibiotrophic sorghum pathogen, Colletotrichum sublineolum. An in vitro batch culture study model with different carbon sources, glucose, arabinose and rhamnose, were used to support fungal growth over a period of twelve days. Metabolites representing the intracellular and extracellular (secreted) metabolomes were extracted with methanol and subjected to LC-MS analyses. Chemometric modelling revealed a metabolic variation trajectory, comprising three distinct stages that metabolically describe the adaptation of the fungus to diminishing nutrients. Selected marker gene expression indicated stage one (0–3 d.p.i) as corresponding to the early logarithmic phase. Stage two can be interpreted as an intermediate transitionary stage with stage three corresponding to the stationary phase (9–12 d.p.i). Stage one was characterised by up-regulation of endo-metabolites such as ferricrocin, fatty acids and flavone-conjugates, while stage three was characterised by the secretion of phytotoxins, including colletotrichin and colletotric acid. Ultimately, results from our in vitro model reveal previously unknown insights into the dynamic aspects of metabolome reprogramming in the growth phases of Colletotrichum spp as determined by nutrients obtainable from plant cell walls.

Untargeted metabolomics by high resolution mass spectrometry coupled to normal and reversed phase liquid chromatography as a tool to study the in vitro biotransformation of new psychoactive substances

In 2016, several synthetic cathinones were seized by the State Bureau of Criminal Investigation Bavaria in Germany. Due to their previous appearances in other countries their metabolism was already investigated in human urine as well as different in vitro models. These investigations were conducted using ordinary metabolism studies for drugs of abuse by using general knowledge about drug metabolism and visual comparison of mass spectra. The present study aimed to use untargeted metabolomics to support and improve those methods that highly depend on the investigators experience. Incubations were conducted using pooled human liver microsomes (pHLM) and the two cathinones 1-phenyl-2-(1-pyrrolidinyl)-1-butanone and 1-phenyl-2-(1-pyrrolidinyl)-1-heptanone. Samples were analyzed by LC-HRMS/MS using a metabolomics workflow consisting of a reversed phase or normal phase separation followed by electrospray ionization and full scan in positive or negative mode. LC-MS data was afterwards statistically evaluated using principal component analysis, t-distributed stochastic neighborhood embedding, and hierarchical clustering. Significant features were then identified using MS/MS. The workflow revealed 24 significant features after 1-phenyl-2-(1-pyrrolidinyl)-1-butanone and 39 after 1-phenyl-2-(1-pyrrolidinyl)-1-heptanone incubation, consisting of adducts, artifacts, isomers, and metabolites. The applied untargeted metabolomics strategy was able to find almost all of the metabolites that were previously described for 1-phenyl-2-(1-pyrrolidinyl)-1-butanone in literature as well as three additional metabolites. Concerning 1-phenyl-2-(1-pyrrolidinyl)-1-heptanone biotransformation in pHLM, merely four metabolites described in primary human hepatocytes and human urine were not found. This study revealed that untargeted metabolomics workflows are well suited to support biotransformation studies at least of the investigated compounds in pHLM.

Development of simultaneous targeted metabolite quantification and untargeted metabolomics strategy using dual-column liquid chromatography coupled with tandem mass spectrometry

Targeted quantification and untargeted global profiling are the two mainstream approaches, a merging of which could provide enhanced analytical potential in metabolomics research. Here, a simultaneous targeted quantification and untargeted metabolomics (STQUM) strategy was developed for more efficient, accurate and comprehensive metabolomics research by using ultra-high-performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-HRMS/MS). First, we selected 110 cancer-related metabolites as targets and established a dual LC sequential separation method for simultaneous analysis of strong and weak polar metabolites. In order to achieve efficient acquisition for synchronous qualitative and quantitative analysis, high-resolution, data-dependent parallel reaction monitoring (PRM) method and data-independent all ion fragmentation (AIF) method were established. Their performance in targeted confirmation and quantification, and untargeted analysis were systematically investigated and assessed. In total, 78 metabolites were confidently confirmed in positive ion mode in both PRM and AIF assays, in which 73 metabolites can be accurately quantified. In addition, simultaneously untargeted profiling of 4651 features of high reliability and validity were achieved. Both AIF and PRM methods revealed high confidence, sensitivity and accuracy. In the STQUM approach, another 15 metabolites could be accurately quantified in negative ion mode. The method offers a new perspective for merging the hypothesis-based targeted quantitative validation and untargeted biomarkers discovery in one run for improved analysis efficiency and integrity.

Strategy for Screening of Antioxidant Compounds from Two Ulmaceae Species Based on Liquid Chromatography-Mass Spectrometry.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics implies that annotated metabolites can serve as potential markers of the associated bioactivities of plant extracts. Firstly, we selected Aphananthe aspera and Zelkova serrata (Family: Ulmaceae) from 16 Korean plant species based on their distinct principal component analysis (PCA) patterns in LC-MS datasets and antioxidant activity assays. Further, we chose 40% solid-phase extraction (SPE) extracts of the two species displaying the highest antioxidant activities coupled with distinct PCA patterns. Examining the metabolite compositions of the 40% SPE extracts, we observed relatively higher abundances of quercetin, kaempferol, and isorhamnetin O-glucosides for A. aspera, whereas quercetin, isorhamnetin O-glucuronides, and procyanidin dimer were relatively higher in Z. serrata. These metabolites were clearly distinguished in pathway map and displayed strong positive correlations with antioxidant activity. Further, we performed preparative high-performance liquid chromatography (prep-HPLC) analysis coupled with the 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) assay to validate their functional correlations. As a result, quercetin O-sophoroside was determined as the main antioxidant in A. aspera, while isorhamnetin O-glucuronide and procyanidin dimer were the primary antioxidants in Z. serrata. The current study suggests that the LC-MS-based untargeted metabolomics strategy can be used to illuminate subtle metabolic disparities as well as compounds associated with bioactivities.

A novel approach based on untargeted lipidomics reveals differences in the lipid pattern among durum and common wheat

In the present work the possibility of using an untargeted metabolomic strategy to discriminate between common and durum wheat lipidome for an authenticity purpose was explored. A first study was conducted by analyzing 52 samples from two durum and common wheat varieties. Afterwards, an extended and independent sample set (173 samples and five varieties) was used as a confirmatory study to verify the stability and consistency of the models obtained. Putatively identified markers were evaluated applying ROC curves resulting in individual marker AUC >90% both in preliminary and confirmatory study. In addition, digalactosyl diglyceride (DGDG) 36:4 was shown to be an effective marker differentiating between authentic durum wheat and its adulterated admixture down to 3% adulteration level, which is the maximum contamination level allowed by Italian legislation. The results demonstrated that untargeted lipidomics, in conjunction with chemometric tools has a significant potential for screening and detection of wheat fraud.

Untargeted metabolomics based on ultra-high-performance liquid chromatography-high-resolution mass spectrometry merged with chemometrics: A new predictable tool for an early detection of mycotoxins.

In order to explore the early detection of mycotoxins in wheat three standardized approaches (Fusarium disease severity, PCR assays for Fusarium spp. identification and mycotoxin quantification) and a novel untargeted metabolomics strategy were jointly assessed. In the first phase of this research, standardized approaches were able to quantify mycotoxins and identify Fusarium spp. Then, an UHPLC-QTOF metabolic fingerprinting method was developed to investigate plant-pathogen cross-talk. At the same time, chemometrics analysis demonstrated to be a powerful tool in order to distinguish low and strong infection levels. Combining these results, the cross-talk plant pathogen related to the early detection of mycotoxins was discovered. As a rapid response to fungal infection an overexpression of phosphatidic acids was discovered. By contrast, when the infection became stronger an increase of oxylipins and diacylglycerols was revealed.

Integrating targeted glycomics and untargeted metabolomics to investigate the processing chemistry of herbal medicines, a case study on Rehmanniae Radix

It is well-recognized that multiple components, the majority of which are secondary metabolites and carbohydrates, collectively contribute to the therapeutic effects of herbal medicines. The chemical characterization of herbal medicines has focused extensively on secondary metabolites but has largely overlooked carbohydrates. Here, we proposed an integrated chromatographic technique based targeted glycomics and untargeted metabolomics strategy simultaneously determining carbohydrates and secondary metabolites for the overall chemical profiling of herbal medicines; this strategy was successfully exemplified in an investigation of processing chemistry of Rehmanniae Radix (RR), a Chinese medicinal herb. It was demonstrated that the integrated strategy holistically illuminated the variations in the glycome and metabolome of RR samples processed by the traditionally-adopted nine cycles of steaming and drying, and further elucidated the processing-induced chemical transformation mechanisms of carbohydrates and secondary metabolites, and thereby revealed the inherent chemical connections between carbohydrates and secondary metabolites. The result suggested that the proposed strategy meets the technical demands for the overall chemical characterization of herbal medicines, and therefore could serve as a powerful tool for deciphering the scientific basis of herbal medicines.

An Integrated Metabolomic and Genomic Mining Workflow To Uncover the Biosynthetic Potential of Bacteria.

Microorganisms are a rich source of bioactives; however, chemical identification is a major bottleneck. Strategies that can prioritize the most prolific microbial strains and novel compounds are of great interest. Here, we present an integrated approach to evaluate the biosynthetic richness in bacteria and mine the associated chemical diversity. Thirteen strains closely related to Pseudoalteromonas luteoviolacea isolated from all over the Earth were analyzed using an untargeted metabolomics strategy, and metabolomic profiles were correlated with whole-genome sequences of the strains. We found considerable diversity: only 2% of the chemical features and 7% of the biosynthetic genes were common to all strains, while 30% of all features and 24% of the genes were unique to single strains. The list of chemical features was reduced to 50 discriminating features using a genetic algorithm and support vector machines. Features were dereplicated by tandem mass spectrometry (MS/MS) networking to identify molecular families of the same biosynthetic origin, and the associated pathways were probed using comparative genomics. Most of the discriminating features were related to antibacterial compounds, including the thiomarinols that were reported from P.luteoviolacea here for the first time. By comparative genomics, we identified the biosynthetic cluster responsible for the production of the antibiotic indolmycin, which could not be predicted with standard methods. In conclusion, we present an efficient, integrative strategy for elucidating the chemical richness of a given set of bacteria and link the chemistry to biosynthetic genes. IMPORTANCE We here combine chemical analysis and genomics to probe for new bioactive secondary metabolites based on their pattern of distribution within bacterial species. We demonstrate the usefulness of this combined approach in a group of marine Gram-negative bacteria closely related to Pseudoalteromonas luteoviolacea, which is a species known to produce a broad spectrum of chemicals. The approach allowed us to identify new antibiotics and their associated biosynthetic pathways. Combining chemical analysis and genetics is an efficient "mining" workflow for identifying diverse pharmaceutical candidates in a broad range of microorganisms and therefore of great use in bioprospecting.

Investigation of the effect of genotype and agronomic conditions on metabolomic profiles of selected strawberry cultivars with different sensitivity to environmental stress

Strawberry is one of the most economically important and widely cultivated fruit crops across the world, so that there is a growing need to develop new analytical methodologies for the authentication of variety and origin, as well as the assessment of agricultural and processing practices. In this work, an untargeted metabolomic strategy based on gas chromatography mass spectrometry (GC–MS) combined with multivariate statistical techniques was used for the first time to characterize the primary metabolome of different strawberry cultivars and to study metabolite alterations in response to multiple agronomic conditions. For this purpose, we investigated three varieties of strawberries with different sensitivity to environmental stress (Camarosa, Festival and Palomar), cultivated in soilless systems using various electrical conductivities, types of coverage and substrates. Metabolomic analysis revealed significant alterations in primary metabolites between the three strawberry cultivars grown under different crop conditions, including sugars (fructose, glucose), organic acids (malic acid, citric acid) and amino acids (alanine, threonine, aspartic acid), among others. Therefore, it could be concluded that GC–MS based metabolomics is a suitable tool to differentiate strawberry cultivars and characterize metabolomic changes associated with environmental and agronomic conditions.

Metabolomic fingerprint of severe obesity is dynamically affected by bariatric surgery in a procedure-dependent manner

Obesity is associated with multiple diseases. Bariatric surgery is the most effective therapy for severe obesity that can reduce body weight and obesity-associated morbidity. The metabolic alterations associated with obesity and respective changes after bariatric surgery are incompletely understood. We comprehensively assessed metabolic alterations associated with severe obesity and distinct bariatric procedures. In our longitudinal observational study, we applied a (1)H-nuclear magnetic resonance-based global, untargeted metabolomics strategy on human serum samples that were collected before and repeatedly ≤1 y after distinct bariatric procedures [i.e., a sleeve gastrectomy, proximal Roux-en Y gastric bypass (RYGB), and distal RYGB]. For comparison, we also analyzed serum samples from normal-weight and less-obese subjects who were matched for 1-y postoperative body mass index (BMI) values of the surgical groups. We identified a metabolomic fingerprint in obese subjects that was clearly discriminated from that of normal-weight subjects. Furthermore, we showed that bariatric surgery (sleeve gastrectomy and proximal and distal RYGB) dynamically affected this fingerprint in a procedure-dependent manner, thereby establishing new fingerprints that could be discriminated from those of BMI-matched and normal-weight control subjects. Metabolites that largely contributed to the metabolomic fingerprints of severe obesity were aromatic and branched-chain amino acids (elevated), metabolites related to energy metabolism (pyruvate and citrate; elevated), and metabolites suggested to be derived from gut microbiota (formate, methanol, and isopropanol; all elevated). Our data indicate that bariatric surgery, irrespective of the specific kind of procedure used, reverses most of the metabolic alterations associated with obesity and suggest profound changes in gut microbiome-host interactions after the surgery. This trial was registered at clinicaltrials.gov as NCT02480322.

Metabolic fingerprinting reveals a novel candidate biomarker for prednisolone treatment in cattle

The use of glucocorticoids as growth promoters for meat-producing animals is strictly regulated within the European Union. However, in the past few years, a higher frequency of non-compliant bovine urine samples for prednisolone has been noticed, which could not be directly related to fraudulent use of prednisolone. As such, questions have risen about the origin of this compound. Unfortunately, at present, no decisive strategy has been established to discriminate between endogenous and exogenous prednisolone. In this study, an untargeted metabolomics strategy, based on Orbitrap and QqTOF mass spectrometry, was deployed to reveal urinary biomarkers, which are indicative for the exogenous administration of the synthetic glucocorticoid prednisolone. For this purpose, prednisolone was administered intramuscularly and per os to 12 bovines and a total of 2700 urine samples were collected before, during and after treatment. Multivariate statistical data analysis (i.e. OPLS-DA) revealed four differentiating metabolites that allowed discrimination between urine samples collected before and during prednisolone administration. None of these compounds were present in urine containing endogenous prednisolone, of which the formation was induced by the administration of a synthetic analogue of adrenocorticotropic hormone. Only one metabolite was retained as a highly suitable biomarker during growth-promoting and therapeutic prednisolone treatment, with 93.4 % sensitivity and 96.3 % specificity. Besides, this compound could be detected up to 4 days after a single therapeutic per os prednisolone administration. Based on accurate mass, isotope pattern, and MS/MS spectra, this compound was putatively annotated and is suggested as an actionable biomarker for exogenous prednisolone administration.

Food Metabolome in Clinical Nutrition Research: from Dietary Patterns to Discovering Disease Risk Biomarkers. Evidence from PREDIMED Study

Diet plays an important role in the prevention and development of chronic diseases. Interventional and observational studies have provided evidence of the health benefits of dietary bioactive compounds. The assessment of dietary intake has been estimated using self‐reported questionnaires, which provide less reliable data than nutritional biomarkers. Nutrimetabolomics has been proposed as a tool for discovering new biomarkers of exposure.Different clinical studies provides complementary information about dietary exposure, and thus in the discovery of new biomarkers. In the current study we applied an HPLC‐Q‐ToF‐MS untargeted metabolomics strategy followed by multivariate analysis (OSC‐PLS‐DA) to characterize the urinary metabolome of subjects who followed an acute or chronic intervention of the food item or dietary pattern, as well as in a free‐living population from the PREDIMED cohort (www.predimed.org). Discriminating metabolites were associated with complex metabolic pattern linked to fatty acids, microbial metabolites, compounds from tryptophan & serotonin pathway, phytochemicals, processing‐derived compounds and endogenous metabolites. Diet‐related differences in urinary metabolome were associated with food digestion, microbiota metabolism and endogenous metabolism.Nutrimetabolomics has the potential to unveil the metabolic signature that differentiates healthy from metabolically perturbed subjects, access unexplored metabolic pathways impacted by diet, and elucidate the inter‐individual differences in the effects of dietary interventions.

A metabolomics‐driven approach to predict cocoa product consumption by designing a multimetabolite biomarker model in free‐living subjects from the PREDIMED study

The aim of the current study was to apply an untargeted metabolomics strategy to characterize a model of cocoa intake biomarkers in a free-living population. An untargeted HPLC-q-ToF-MS based metabolomics approach was applied to human urine from 32 consumers of cocoa or derived products (CC) and 32 matched control subjects with no consumption of cocoa products (NC). The multivariate statistical analysis (OSC-PLS-DA) showed clear differences between CC and NC groups. The discriminant biomarkers identified were mainly related to the metabolic pathways of theobromine and polyphenols, as well as to cocoa processing. Consumption of cocoa products was also associated with reduced urinary excretions of methylglutarylcarnitine, which could be related to effects of cocoa exposure on insulin resistance. To improve the prediction of cocoa consumption, a combined urinary metabolite model was constructed. ROC curves were performed to evaluate the model and individual metabolites. The AUC values (95% CI) for the model were 95.7% (89.8-100%) and 92.6% (81.9-100%) in training and validation sets, respectively, whereas the AUCs for individual metabolites were <90%. The metabolic signature of cocoa consumption in free-living subjects reveals that combining different metabolites as biomarker models improves prediction of dietary exposure to cocoa.

Abstract 4406: Role of KRAS in modulating the metabolomic profile and the response of NSCLC cells to PI3K/mTOR and AMPK interfering agents

Abstract Background: Non-small-cell lung cancers (NSCLC) constitute the majority of lung cancer cases, whose risk is associated with environmental factors, such as air pollution, and smoking habits. KRAS mutations are present in about 20% of NSCLC patients, mostly at codon 12 and have been associated to tumor progression and resistance to chemotherapy in many types of cancer. Increasingly evidences seem to highlight that the definition of KRAS status is not sufficient to determine its predictive and/or prognostic role in NSCLC, and indicate that different KRAS mutations could differently impact on crucial cellular processes. An issue still to be addressed is whether different KRAS mutations may modulate cellular metabolism with possible implications on different response to anticancer treatments which specifically inhibit metabolism modulating pathways, such as the PI3K/AKT/mTOR axis. Methods: We generated from the human NSCLC cell line NCI-H1299 clones overexpressing WT KRAS, and the three most common NSCLC KRAS mutations (G12C, G12V and G12D). The clones were used to determine in vitro response to PI3K, mTOR and AMPK interfering agents. We applied an explorative mass spectrometry-based untargeted metabolomics strategy to characterize the largest possible number of metabolites that might distinguish the different KRAS mutated clones. Results: KRAS overexpressing clones resulted sensitive to the PI3K inhibitors BKM-120 and PIK-75 and to the PI3K/mTOR dual inhibitor BEZ-235. Metformin (acting on AMPK) did not display a cytotoxic activity on the clones at concentrations below or equal to 1mM. Metabolomics analyses revealed that clones harboring different KRAS mutations at codon 12 had different metabolic remodellings such as different redox buffering system and different glutamine-dependency. Conclusion: The preliminary findings reported herein indicate that specific KRAS mutations at codon 12 are associated to different metabolomics profiles that might have an impact to the response to anticancer treatments. Combination studies including metformin and PI3K/mTOR interfering agents and more detailed metabolomics analyses are ongoing in order to clarify whether metabolic adaptations dependent on KRAS mutants might account for the behaviour of KRAS overexpressing clones treated with different drugs. Citation Format: Elisa Caiola, Laura Brunelli, Mirko Marabese, Marina Chiara Garassino, Gabriella Farina, Massimo Broggini, Roberta Pastorelli. Role of KRAS in modulating the metabolomic profile and the response of NSCLC cells to PI3K/mTOR and AMPK interfering agents. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4406. doi:10.1158/1538-7445.AM2014-4406

Capturing the metabolomic diversity of KRAS mutants in non-small-cell lung cancer cells

In non-small-cell lung cancer (NSCLC), one-fifth of patients have KRAS mutations, which are considered a negative predictive factor to first-line therapy. Evidence is emerging that not all KRAS mutations have the same biological activities and possible remodeling of cell metabolism by KRAS activation might complicate the scenario. An open question is whether different KRAS mutations at codon-12 affect cellular metabolism differently with possible implications for different responses to cancer treatments. We applied an explorative mass spectrometry-based untargeted metabolomics strategy to characterize the largest possible number of metabolites that might distinguish isogenic NSCLC cells overexpressing mutated forms of KRAS at codon-12 (G12C, G12D, G12V) and the wild-type. The glutamine deprivation assay and real-time PCR were used to confirm the involvement of some of the metabolic pathways highlighted. Cell clones indicated distinct metabolomic profiles in KRAS wild-type and mutants. Clones harboring different KRAS mutations at codon-12 also had different metabolic remodeling, such as a different redox buffering system and different glutamine-dependency not driven by the transcriptional state of enzymes involved in glutaminolysis. These findings indicate that KRAS mutations at codon-12 are associated with different metabolomic profiles that might affect the responses to cancer treatments.

Principal component analysis of HPLC–MS/MS patterns of wheat (Triticum aestivum) varieties

Untargeted metabolomic strategy was chosen to investigate as many small metabolites as possible in a collection of 13 varieties of conventionally grown spring and winter wheat and organic wheat (Triticum aestivum). Metabolites were separated by high-performance liquid chromatography on a reversed-phase column (RP-HPLC) coupled with electrospray ionization tandem mass spectrometry (ESI-MS/MS). The procedure includes extraction of metabolites followed by chromatographic separation using the linear gradient of aqueous formic acid and acetonitrile with subsequent identification of compounds by MS/MS. Discrimination of the metabolomic patterns of different wheat varieties was achieved by principal component analysis (PCA). Results of PCA indicated clear differences in the patterns of wheat varieties. The winter wheat grown in conventional conditions and the spring wheat grown in organic conditions differed from the spring wheat grown in conventional conditions by the higher content of carbohydrates. It could be explained by osmotic stress resistance. Varieties grown under organic conditions could be well distinguished from others by the results of PCA, which points to the existence of an impact of different farming systems.