Chemometric evaluation of Saccharomyces cerevisiae metabolic profiles using LC-MS

Effect of Cold Shock Pretreatment Combined with Perforation-Mediated Passive Modified Atmosphere Packaging on Storage Quality of Cucumbers

This study evaluated the application of cold shock combined with perforation-mediated passive modified atmosphere packaging technology (CS-PMAP) for cucumber preservation through physicochemical, sensory, and nutritional qualities. The effectiveness of CS-PMAP in maintaining the quality of fresh cucumbers was studied; cucumbers were pretreated with cold shock and then packed into perforated polyethylene bags (bag size of 20 × 30 cm; film thickness of 0.07 mm; and two holes in each bag with a diameter of 6 mm), while the cucumbers without cold shock were considered as the control. Storage of the samples was performed at (13 ± 2) °C for 20 days to determine the quality changes in terms of gas composition, weight loss, skin color, texture, total soluble solids (TSS), ascorbic acid, malondialdehyde (MDA), and volatile organic compounds (VOCs). The CS-PMAP showed a significant improvement in maintaining firmness, TSS, ascorbic acid, and flavor profile of cucumbers; the control samples without cold shock showed higher weight loss and MDA levels. Results of this study confirmed that CS-PMAP has potential use in the storage of cucumbers.

Alternating in-source fragmentation with single-stage high-resolution mass spectrometry with high annotation confidence in non-targeted metabolomics

Non-targeted metabolomics is increasingly applied in various applications for understanding biological processes and finding novel biomarkers in living organisms. However, high-confidence identity confirmation of metabolites in complex biological samples is still a significant bottleneck, especially when using single-stage mass analysers. In the current study, a complete workflow for alternating in-source fragmentation on a time-of-flight mass spectrometry (TOFMS) instrument for non-targeted metabolomics is presented. Hydrophilic interaction liquid chromatography (HILIC) was employed to assess polar metabolites in yeast following ESI parameter optimization using experimental design principles, which revealed the key influence of fragmentor voltage for this application. Datasets from alternating in-source fragmentation high resolution mass spectrometry (HRMS) were evaluated using open-source data processing tools combined with public reference mass spectral databases. The significant influence of the selected fragmentor voltages on the abundance of the primary analyte ion of interest and the extent of in-source fragmentation allowed an optimum selection of qualifier fragments for the different metabolites. The new acquisition and evaluation workflow was implemented for the non-targeted analysis of yeast extract samples whereby more than 130 metabolites were putatively annotated with more than 40% considered to be of high confidence. The presented workflow contains a fully elaborated acquisition and evaluation methodology using alternating in-source fragmentor voltages suitable for peak annotation and metabolite identity confirmation for non-targeted metabolomics applications performed on a single-stage HRMS platform.

Protein-biomolecule interactions play a major role in shaping corona proteome: studies on milk interacted dietary particles

Despite the significance of surface absorbed proteins in determining the biological identity of nanoparticles (NPs) entering the human body, little is known about the surface corona and factors that shape their formation on dietary particles used as food additives. In this study, food grade NPs of silica and titania and their food additive counterparts (E551 and E171) were interacted with milk proteins or with skimmed milk and the levels of protein adsorption were quantified. Characteristics of proteins correlating with their level of adsorption to NPs were determined using partial least squares regression analysis. Results from individual protein-particle interactions revealed the significance of factors such as zeta potential, hydrophobicity and hydrodynamic size of particles, and protein characteristics such as the number of beta strands, isoelectric points, the number of amino acid units (Ile, Tyr, Ala, Gly, Pro, Asp, and Arg), and phosphorylation sites on their adsorption to particles. Similar regression analysis was performed to identify the characteristics of twenty abundant and enriched proteins (identified using LC-MS/MS analysis) for their association with the surface corona of milk-interacted particles. Contrary to individual protein-particle interactions, protein characteristics such as helices, turns, protein structures, disulfide bonds, the number of amino acid units (Cys, Met, Leu, and Trp), and Fe binding sites were significant for their association with the surface corona of milk interacted particles. This difference in factors identified from individual proteins and milk interacted particles suggested possible interactions of proteins with surface adsorbed biomolecules as revealed by scanning transmission X-ray microscopy and other biochemical assays.

Untargeted metabolomics of prostate cancer zwitterionic and positively charged compounds in urine

Prostate cancer, a leading cause of cancer-related deaths worldwide, principally occurs in over 50-year-old men. Nowadays there is urgency to discover biomarkers alternative to prostate-specific antigen, as it cannot discriminate patients with benign prostatic hyperplasia from clinically significant forms of prostatic cancer. In the present paper, 32 benign prostatic hyperplasia and 41 prostatic cancer urine samples were collected and analyzed. Polar and positively charged metabolites were therein investigated using an analytical platform comprising an up to 40-fold analyte enrichment step by graphitized carbon black solid-phase extraction, HILIC separation, and untargeted high-resolution mass spectrometry analysis. These classes of compounds are often neglected in common metabolomics experiments even though previous studies reported their significance in cancer biomarker discovery. The complex metabolomics big datasets, generated by the UHPLC-HRMS, were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest data and their analysis by the Multivariate Curve-Resolution Alternating Least Squares chemometrics method. This approach allowed the resolution and tentative identification of the metabolites differentially expressed by the two data sets. Among these, amino acids and carnitine derivatives were tentatively identified highlighting the importance of the proposed methodology for cancer biomarker research.

Combination of UHPLC-MS/MS-molecular networking approach and FTICR-MS for the metabolic profiling of Saccharomyces cerevisiae

Natural products are a reliable source of bioactive molecules and represent an industrial and pharmaceutical stake. Indeed, the model yeast species Saccharomyces cerevisiae is a well-known eukaryotic organism largely used as a biotechnological tool, but still a topical subject of study. In this work, the exploration of Saccharomyces cerevisiae is taken further through an untargeted metabolomics workflow. The aim is to enrich databases and bring new information about the standard S. cerevisiae strain in a given medium. Analytical methods and bioinformatics tools were combined in a high-throughput methodology useable to dereplicate many types of biological extracts and cartography secondary metabolites. Ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) analyses were carried out and spectral data were pre-processed to build molecular networks. Annotations were attributed to compounds through comparison with databases and manual investigation of networks. Ultra-high-resolution Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) brought additional information thanks to a higher dynamic range and enhanced UHPLC-MS/MS results by unveiling ambiguities and bringing accurate molecular formulae. Therefore, accurate and reliable annotated features resulted from the UHPLC-MS/MS data while FTICR-MS provided an overall cartography of metabolites thanks to van Krevelen diagrams. Various small molecules such as amino acids derivatives and indole alkaloids have been determined for the first time in this yeast. The complementarity of FTICR-MS and UHPLC-MS/MS for secondary metabolite annotation brought this new mapping of S. cerevisiae.

Untargeted metabolomics changes on Gammarus pulex induced by propranolol, triclosan, and nimesulide pharmaceutical drugs

The presence of pharmaceuticals and personal care products (PPCPs) in natural water resources due to incomplete removal in Wastewater Treatment Plants (WWTPs) is a serious environmental concern at present. In this work, the effects of three pharmaceuticals (propranolol, triclosan, and nimesulide) on Gammarus pulex metabolic profiles at different doses and times of exposure have been investigated by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). The complex data sets generated in the different exposure experiments were analyzed with the ROIMCR procedure, based on the selection of the MS regions of interest (ROI) data and on their analysis by the Multivariate Curve-Resolution Alternating Least Squares (MCR-ALS) chemometrics method. This approach, allowed the resolution and identification of the metabolites present in the analyzed samples, as well as the estimation of their concentration changes due to the exposure experiments. ANOVA Simultaneous Component Analysis (ASCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were then conducted to assess the changes in the concentration of the metabolites for the three pharmaceuticals at the different conditions of exposure. The three tested pharmaceuticals changed the concentrations of metabolites, which were related to different KEGG functional classes. These changes summarize the biochemical response of Gammarus pulex to the exposure by the three investigated pharmaceuticals. Possible pathway alterations related to protein synthesis and oxidative stress were observed in the concentration of identified metabolites.

Delta-Integration of Single Gene Shapes the Whole Metabolomic Short-Term Response to Ethanol of Recombinant Saccharomyces cerevisiae Strains

In yeast engineering, metabolic burden is often linked to the reprogramming of resources from regular cellular activities to guarantee recombinant protein(s) production. Therefore, growth parameters can be significantly influenced. Two recombinant strains, previously developed by the multiple δ-integration of a glucoamylase in the industrial Saccharomyces cerevisiae 27P, did not display any detectable metabolic burden. In this study, a Fourier Transform InfraRed Spectroscopy (FTIR)-based assay was employed to investigate the effect of δ-integration on yeast strains’ tolerance to the increasing ethanol levels typical of the starch-to-ethanol industry. FTIR fingerprint, indeed, offers a holistic view of the metabolome and is a well-established method to assess the stress response of microorganisms. Cell viability and metabolomic fingerprints have been considered as parameters to detecting any physiological and/or metabolomic perturbations. Quite surprisingly, the three strains did not show any difference in cell viability but metabolomic profiles were significantly altered and different when the strains were incubated both with and without ethanol. A LC/MS untargeted workflow was applied to assess the metabolites and pathways mostly involved in these strain-specific ethanol responses, further confirming the FTIR fingerprinting of the parental and recombinant strains. These results indicated that the multiple δ-integration prompted huge metabolomic changes in response to short-term ethanol exposure, calling for deeper metabolomic and genomic insights to understand how and, to what extent, genetic engineering could affect the yeast metabolome.

Metabolomic analysis of the effects of cadmium and copper treatment in Oryza sativa L. using untargeted liquid chromatography coupled to high resolution mass spectrometry and all-ion fragmentation

While the knowledge of plant metabolomes has increased in the last few years, their response to the presence of toxicants is still poorly understood. Here, we analyse the metabolomic changes in Japanese rice (Oryza sativa var. Japonica) upon exposure to heavy metals (Cd(ii) and Cu(ii)) in concentrations from 10 to 1000 μM. After harvesting, rice metabolites were extracted from aerial parts of the plants and analysed by HPLC (HILIC TSK gel amide-80 column) coupled to a mass spectrometer quadrupole-Orbitrap (Q-Exactive). Full scan and all ion fragmentation (AIF) mass spectrometry modes were used during the analysis. The proposed untargeted metabolomics data analysis strategy is based on the application of the multivariate curve resolution alternating least squares (MCR-ALS) method for feature detection, allowing the simultaneous resolution of pure chromatographic profiles and mass spectra of all metabolites present in the analysed rice extracts. All-ion fragmentation data were used to confirm the identification of MCR-ALS resolved metabolites. A total of 112 metabolites were detected, and 97 of them were subsequently identified and confirmed. Pathway analysis of the observed metabolic changes suggested an underlying similarity of the responses of the plant to Cd(ii) and Cu(ii), although the former treatment appeared to be the more severe of the two. In both cases, secondary metabolism and amino acid-, purine-, carbon- and glycerolipid-metabolism pathways were affected, in a pattern consistent with reduction in plant growth and/or photosynthetic capacity and with induction of defence mechanisms to reduce cell damage.

Phenotypic and lipidomic characterization of primary human epidermal keratinocytes exposed to simulated solar UV radiation

BACKGROUND

Ultraviolet (UV) radiation is known to be one of the most important environmental hazards acting on the skin. The most part of UV radiation is absorbed in the epidermis, where keratinocytes are the most abundant and exposed cell type. Lipids have an important role in skin biology, not only for their important contribution to the maintenance of the permeability barrier but also for the production and storage of energy, membrane organization and cell signalling functions. However, the effects on the lipid composition of keratinocytes under UV radiation are little explored.

OBJECTIVE

The present work aims to explore the effects on the phenotype and lipid content of primary human keratinocytes exposed to simulated solar UV radiation.

METHODS

Keratinocytes were exposed to a single (acute exposure) and repeated simulated solar UV irradiations for 4 weeks (chronic exposure). Cell viability and morphology were explored, as well as the production of reactive oxygen species. Then, lipid extracts were analysed through liquid chromatography coupled to mass spectrometry (LC-MS) and the data generated was processed using the ROIMCR chemometric methodology together with partial least squares discriminant analysis (PLS-DA), to finally reveal the most relevant lipid changes that occurred in keratinocytes upon UV irradiation. Also, the potential induction of keratinocyte differentiation was explored by measuring the increase of involucrin.

RESULTS

Under acute irradiation, cell viability and morphology were not altered. However, a general increase of phosphatidylcholines (PC) phosphatidylethanolamines (PE) and phosphatidylglycerol (PG) together with a slight sphingomyelin (SM) decrease were found in UV irradiated cells, among other changes. In addition, keratinocyte cultures did not present any differentiation hallmark. Contrary to acute-irradiated cells, in chronic exposures, cell viability was reduced and keratinocytes presented an altered morphology. Also, hallmarks of differentiation, such as the increase of involucrin protein and the autophagy induction were detected. Among the main lipid changes that accompanied this phenotype, the increase of long-chain ceramides, lysoPC and glycerolipid species were found.

CONCLUSION

Important lipid changes were detected under acute and chronic UV irradiation. The lipid profile under chronic exposure may represent a lipid fingerprint of the keratinocyte differentiation phenotype.

Untargeted metabolomic on urine samples after α-lipoic acid and/or eicosapentaenoic acid supplementation in healthy overweight/obese women

Background

Eicosapentaenoic acid (EPA) and α-lipoic acid (α-LA) have been investigated for their beneficial effects on obesity and cardiovascular risk factors. In the current research, the goal was to evaluate metabolomic changes following the dietary supplementation of these two lipids, alone or combined in healthy overweight/obese sedentary women following an energy-restricted diet. For this purpose, an untargeted metabolomics approach was conducted on urine samples using liquid chromatography coupled with time of flight mass spectrometry (HPLC-TOF-MS).

Methods

This is a short-term double blind placebo-controlled study with a parallel nutritional design that lasted 10 weeks. Participants were assigned to one of the 4 experimental groups [Control, EPA (1.3 g/d), α-LA (0.3 g/d) and EPA+α-LA (1.3 g/d + 0.3 g/d)]. All intervention groups followed an energy-restricted diet of 30% less than total energy expenditure. Clinically relevant biochemical measurements were analyzed. Urine samples (24 h) were collected at baseline and after 10 weeks. Untargeted metabolomic analysis on urine samples was carried out, and principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were performed for the pattern recognition and characteristic metabolites identification.

Results

Urine samples were scattered in the PCA scores plots in response to the supplementation with α-LA. Totally, 28 putative discriminant metabolites in positive ionization, and 6 in negative ionization were identified among groups clearly differentiated according to the α-LA administration. Remarkably is the presence of an ascorbate intermediate metabolite (one of the isomers of trihydroxy-dioxohexanoate, or dihydroxy–oxohexanedionate) in the groups supplemented with α-LA. This fact might be associated with antioxidant properties of both α-LA and ascorbic acid. Correlations between phenotypical parameters and putative metabolites of provided additional information on whether there is a direct or inverse relationship between them. Especially interesting are the negative correlation between ascorbate intermediate metabolite and asymmetric dimethylarginine (ADMA) and the positive one between superoxide dismutase (SOD) and α-LA supplementation.

Conclusions

This metabolomic approach supports that the beneficial effects of α-LA administration on body weight reduction may be partly explained by the antioxidant properties of this organosulfur carboxylic acid mediated by isomers of trihydroxy-dioxohexanoate, or dihydroxy–oxohexanedionate.

Trial registration

Clinicaltrials.gov NCT01138774.

Electronic supplementary material

The online version of this article (10.1186/s12944-018-0750-4) contains supplementary material, which is available to authorized users.

Untargeted lipidomic analysis of primary human epidermal melanocytes acutely and chronically exposed to UV radiation

Ultraviolet (UV) radiation present in sunlight has been related to harmful effects on skin such as premature aging and skin cancer.

Metabolic disruption of zebrafish (Danio rerio) embryos by bisphenol A. An integrated metabolomic and transcriptomic approach

Although bisphenol A (BPA) is commonly recognized as an endocrine disruptor, the metabolic consequences of its exposure are still poorly understood. In this study, we present a non-targeted LC-MS based metabolomic analysis in combination with a full-genome, high-throughput RNA sequencing (RNA-Seq) to reveal the metabolic effects and the subjacent regulatory pathways of exposing zebrafish embryos to BPA during the first 120 hours post-fertilization. We applied multivariate data analysis methods to extract biochemical information from the LC-MS and RNA-Seq complex datasets and to perform testable predictions of the phenotypic adverse effects. Metabolomic and transcriptomic data revealed a similar subset of altered pathways, despite the large difference in the number of identified biomarkers (around 50 metabolites and more than 1000 genes). These results suggest that even a moderate coverage of zebrafish metabolome may be representative of the global metabolic changes. These multi-omic responses indicate a specific metabolic disruption by BPA affecting different signaling pathways, such as retinoid and prostaglandin metabolism. The combination of transcriptomic and metabolomic data allowed a dynamic interpretation of the results that could not be drawn from either single dataset. These results illustrate the utility of -omic integrative analyses for characterizing the physiological effects of toxicants beyond the mere indication of the affected pathways.

Analysis of neurobehavioural data by chemometric methods in ecotoxicological studies

Incorporation of chemometric tools in behavioural data management workflows allows for the early identification of most relevant endpoints complementarily to statistical confirmatory approaches. In this work, the effects of two model neurotoxicants, chlorpyrifos (CPF) and nicotine, exposures on behavioural profiles of adult zebrafish at three different times (2, 6 and 24h) were evaluated using open field test (OFT) paradigm experiments. Two chemometric methods like Principal Component Analysis (PCA) and Analysis of Variance-Simultaneous Component Analysis (ASCA) have been used to interpret the changes observed in the obtained behavioural data. A decreased of the locomotor activity, an anxiolytic effect and an altered exploratory behaviour were the most affected behavioural endpoints in the CPF exposures. However, an increase of the locomotor activity and an anxiogenic effect were observed in the nicotine exposures. Finally, an excellent correlation between the ASCA results and the results obtained using traditional statistical procedures for both compounds were encountered.

Differential Off-line LC-NMR (DOLC-NMR) Metabolomics To Monitor Tyrosine-Induced Metabolome Alterations in Saccharomyces cerevisiae

A novel differential off-line LC-NMR approach (DOLC-NMR) was developed to capture and quantify nutrient-induced metabolome alterations in Saccharomyces cerevisiae. Off-line coupling of HPLC separation and ¹H NMR spectroscopy supported by automated comparative bucket analyses, followed by quantitative ¹H NMR using ERETIC 2 (electronic reference to access in vivo concentrations), has been successfully used to quantitatively record changes in the metabolome of S. cerevisiae upon intervention with the aromatic amino acid l-tyrosine. Among the 33 metabolites identified, glyceryl succinate, tyrosol acetate, tyrosol lactate, tyrosol succinate, and N-acyl-tyrosine derivatives such as N-(1-oxooctyl)-tyrosine are reported for the first time as yeast metabolites. Depending on the chain length, N-(1-oxooctyl)-, N-(1-oxodecanyl)-, N-(1-oxododecanyl)-, N-(1-oxomyristinyl)-, N-(1-oxopalmityl)-, and N-(1-oxooleoyl)-l-tyrosine imparted a kokumi taste enhancement above their recognition thresholds ranging between 145 and 1432 μmol/L (model broth). Finally, carbon module labeling (CAMOLA) and carbon bond labeling (CABOLA) experiments with ¹³C₆-glucose as the carbon source confirmed the biosynthetic pathway leading to the key metabolites; for example, the aliphatic side chain of N-(1-oxooctyl)-tyrosine could be shown to be generated via de novo fatty acid biosynthesis from four C₂-carbon modules (acetyl-CoA) originating from glucose.

Untargeted assignment and automatic integration of 1H NMR metabolomic datasets using a multivariate curve resolution approach

In this article, we propose the use of the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometrics method to resolve the ¹H NMR spectra and concentration of the individual metabolites in their mixtures in untargeted metabolomics studies. A decision tree-based strategy is presented to optimally select and implement spectra estimates and equality constraints during MCR-ALS optimization. The proposed method has been satisfactorily evaluated using different ¹H NMR metabolomics datasets. In a first study, ¹H NMR spectra of the metabolites in a simulated mixture were successfully recovered and assigned. In a second study, more than 30 metabolites were characterized and quantified from an experimental unknown mixture analyzed by ¹H NMR. In this work, MCR-ALS is shown to be a convenient tool for metabolite investigation and sample screening using ¹H NMR, and it opens a new path for performing metabolomics studies with this chemometric technique.

Assessment of chlorpyrifos toxic effects in zebrafish (Danio rerio) metabolism

In this work the effect of chlorpyrifos exposure on metabolic profiles of zebrafish muscle was evaluated by liquid chromatography coupled to high resolution mass spectrometry. Different chemometric tools based on the selection of Regions of Interest and on Multivariate Curve-Resolution-Alternating Least Squares are proposed for the analysis of the complex data sets generated in the different exposure experiments. Analysis of Variance Simultaneous Component Analysis of changes on metabolite peak profile areas showed significant chlorpyrifos concentration and exposure time-dependent changes, clearly differentiating between exposed and non-exposed samples and between short (2 h) and long exposure times (6 h or 24 h). The changes observed in the concentrations of 50 muscle metabolites are indicative of induction of oxidative stress, of a general disruption of neurotransmitter metabolism, and of muscle exhaustion. These three effects are intimately related to the toxicity of chlorpyrifos.

Diet-sourced carbon-based nanoparticles induce lipid alterations in tissues of zebrafish (Danio rerio) with genomic hypermethylation changes in brain

With rising environmental levels of carbon-based nanoparticles (CBNs), there is an urgent need to develop an understanding of their biological effects in order to generate appropriate risk assessment strategies. Herein, we exposed zebrafish via their diet to one of four different CBNs: C₆₀ fullerene (C₆₀), single-walled carbon nanotubes (SWCNT), short multi-walled carbon nanotubes (MWCNTs) or long MWCNTs. Lipid alterations in male and female zebrafish were explored post-exposure in three target tissues (brain, gonads and gastrointestinal tract) using ‘omic’ procedures based in liquid chromatography coupled with mass spectrometry (LC-MS) data files. These tissues were chosen as they are often target tissues following environmental exposure. Marked alterations in lipid species are noted in all three tissues. To further explore CBN-induced brain alterations, Raman microspectroscopy analysis of lipid extracts was conducted. Marked lipid alterations are observed with males responding differently to females; in addition, there also appears to be consistent elevations in global genomic methylation. This latter observation is most profound in female zebrafish brain tissues post-exposure to short MWCNTs or SWCNTs (P < 0.05). This study demonstrates that even at low levels, CBNs are capable of inducing significant cellular and genomic modifications in a range of tissues. Such alterations could result in modified susceptibility to other influences such as environmental exposures, pathology and, in the case of brain, developmental alterations.

Targeted metabolomics of Gammarus pulex following controlled exposures to selected pharmaceuticals in water

The effects of pharmaceuticals and personal care products (PPCPs) on aquatic organisms represent a significant current concern. Herein, a targeted metabolomics approach using liquid chromatography-high resolution mass spectrometry (LC-HRMS) is presented to characterise concentration changes in 29 selected metabolites following exposures of aquatic invertebrates, Gammarus pulex, to pharmaceuticals. Method performance revealed excellent linearity (R(2)>0.99), precision (0.1-19%) and lower instrumental limits of detection (0.002-0.20ng) for all metabolites studied. Three pharmaceuticals were selected representing the low, middle and high range of measured acute measured toxicities (of a total of 26 compounds). Gammarids were exposed to both the no-observed-adverse-effect-level (NOAEL) and the lowest-observed-adverse-effect-level (LOAEL) of triclosan (0.1 and 0.3mgL(-1)), nimesulide (0.5 and 1.4mgL(-1)) and propranolol (100 and 153mgL(-1)) over 24h. Quantitative metabolite profiling was then performed. Significant changes in metabolite concentrations relative to controls are presented and display distinct clustered trends for each pharmaceutical. Approximately 37% (triclosan), 33% (nimesulide) and 46% (propranolol) of metabolites showed statistically significant time-related effects. Observed changes are also discussed with respect to internal concentrations of the three pharmaceuticals measured using a method based on pulverised liquid extraction, solid phase extraction and LC-MS/MS. Potential metabolic pathways that may be affected by such exposures are also discussed. This represents the first study focussing on quantitative, targeted metabolomics of this lower trophic level benthic invertebrate that may elucidate biomarkers for future risk assessment.

(1)H NMR metabolomic study of auxotrophic starvation in yeast using Multivariate Curve Resolution-Alternating Least Squares for Pathway Analysis

Disruption of specific metabolic pathways constitutes the mode of action of many known toxicants and it is responsible for the adverse phenotypes associated to human genetic defects. Conversely, many industrial applications rely on metabolic alterations of diverse microorganisms, whereas many therapeutic drugs aim to selectively disrupt pathogens’ metabolism. In this work we analyzed metabolic changes induced by auxotrophic starvation conditions in yeast in a non-targeted approach, using one-dimensional proton Nuclear Magnetic Resonance spectroscopy (¹H NMR) and chemometric analyses. Analysis of the raw spectral datasets showed specific changes linked to the different stages during unrestricted yeast growth, as well as specific changes linked to each of the four tested starvation conditions (L-methionine, L-histidine, L-leucine and uracil). Analysis of changes in concentrations of more than 40 metabolites by Multivariate Curve Resolution – Alternating Least Squares (MCR-ALS) showed the normal progression of key metabolites during lag, exponential and stationary unrestricted growth phases, while reflecting the metabolic blockage induced by the starvation conditions. In this case, different metabolic intermediates accumulated over time, allowing identification of the different metabolic pathways specifically affected by each gene disruption. This synergy between NMR metabolomics and molecular biology may have clear implications for both genetic diagnostics and drug development.

High-Performance Chemical Isotope Labeling Liquid Chromatography-Mass Spectrometry for Profiling the Metabolomic Reprogramming Elicited by Ammonium Limitation in Yeast

Information about how yeast metabolism is rewired in response to internal and external cues can inform the development of metabolic engineering strategies for food, fuel, and chemical production in this organism. We report a new metabolomics workflow for the characterization of such metabolic rewiring. The workflow combines efficient cell lysis without using chemicals that may interfere with downstream sample analysis and differential chemical isotope labeling liquid chromatography mass spectrometry (CIL LC-MS) for in-depth yeast metabolome profiling. Using (12)C- and (13)C-dansylation (Dns) labeling to analyze the amine/phenol submetabolome, we detected and quantified a total of 5719 peak pairs or metabolites. Among them, 120 metabolites were positively identified using a library of 275 Dns-metabolite standards, and 2980 metabolites were putatively identified based on accurate mass matches to metabolome databases. We also applied (12)C- and (13)C-dimethylaminophenacyl (DmPA) labeling to profile the carboxylic acid submetabolome and detected over 2286 peak pairs, from which 33 metabolites were positively identified using a library of 188 DmPA-metabolite standards, and 1595 metabolites were putatively identified. Using this workflow for metabolomic profiling of cells challenged by ammonium limitation revealed unexpected links between ammonium assimilation and pantothenate accumulation that might be amenable to engineering for better acetyl-CoA production in yeast. We anticipate that efforts to improve other schemes of metabolic engineering will benefit from application of this workflow to multiple cell types.

LC-MS based metabolomics and chemometrics study of the toxic effects of copper on Saccharomyces cerevisiae

Copper containing fungicides are used to protect vineyards from fungal infections.

Evaluation of changes induced in rice metabolome by Cd and Cu exposure using LC-MS with XCMS and MCR-ALS data analysis strategies

The comprehensive analysis of untargeted metabolomics data acquired using LC-MS is still a major challenge. Different data analysis tools have been developed in recent years such as XCMS (various forms (X) of chromatography mass spectrometry) and multivariate curve resolution alternating least squares (MCR-ALS)-based strategies. In this work, metabolites extracted from rice tissues cultivated in an environmental test chamber were subjected to untargeted full-scan LC-MS analysis, and the obtained data sets were analyzed using XCMS and MCR-ALS. These approaches were compared in the investigation of the effects of copper and cadmium exposure on rice tissue (roots and aerial parts) samples. Both methods give, as a result of their application, the whole set of resolved elution and spectra profiles of the extracted metabolites in control and metal-treated samples, as well as the values of their corresponding chromatographic peak areas. The effects caused by the two considered metals on rice samples were assessed by further chemometric analysis and statistical evaluation of these peak area values. Results showed that there was a statistically significant interaction between the considered factors (type of metal of treatment and tissue). Also, the discrimination of the samples according to both factors was possible. A tentative identification of the most discriminant metabolites (biomarkers) was assessed. It is finally concluded that both XCMS- and MCR-ALS-based strategies provided similar results in all the considered cases despite the completely different approaches used by these two methods in the chromatographic peak resolution and detection strategies. Finally, advantages and disadvantages of using these two methods are discussed. Graphical Abstract Summary of the workflow for untargeted metabolomics using the compared approaches.

Epithelial-to-mesenchymal transition involves triacylglycerol accumulation in DU145 prostate cancer cells

Epithelial to mesenchymal transition (EMT) is a biological process that plays a crucial role in cancer metastasis.