Combination of CE-MS and advancedchemometricmethods for high-throughput metabolic profiling

Non-targeted LC-MS and CE-MS for biomarker discovery in bioreactors: Influence of separation, mass spectrometry and data processing tools

Liquid separation coupled to mass spectrometry is often used for non-targeted analyses in various fields, such as metabolomics. However, the combination of non-standardized methods, various mass spectrometers (MS) and processing tools for data evaluation affect biomarker discovery potentially. Here, we present a comprehensive study of these factors based on non-targeted liquid chromatography coupled to time-of-flight (TOF) and Orbitrap MS and capillary zone electrophoresis to Orbitrap analyses of the same bioreactor samples, describing the correlation of its gas yield with changing feature signal intensity. The three datasets were processed with MZmine 2 and XCMS online and subsequential Partial Least Square Regression (PLSR) with Variable Importance in Projection (VIP) ranking for feature prioritization. The six feature tables were compared to evaluate their overlap of shared features and the influence of the processing software and MS instrument on the VIP values and fold changes. The overlaps, defined as a fraction of one feature table found in the comparative table, were from 27% to 57% for the comparison of MZmine and XCMS and from 15% to 50% between Orbitrap and TOF data sets, respectively. Considering the most relevant features only (VIP >1.5), the overlaps were increased significantly in all cases from 26% to 95%. For the same data set, both VIP values and fold changes were well correlated, however, varied significantly between TOF and Orbitrap. CE-MS showed higher total feature numbers compared to LC-MS, most likely due to its more appropriate selectivity, different sample preparation, and/or the sensitive nano-ESI interface. Since only less than 10% of MS/MS data overlapped, CE-MS provided complementary information to LC-MS. Overall, our systematic study proves the benefits of using different separation techniques and processing tools but also indicates a significant influence of mass spectrometry on comprehensive biomarker discovery.

Enhancing confidence of metabolite annotation in Capillary Electrophoresis-Mass Spectrometry untargeted metabolomics with relative migration time and in-source fragmentation

Capillary electrophoresis coupled to mass spectrometry is a power tool in untargeted metabolomics studies to analyze charged and polar compounds. However, identification is a challenge due to the variability of migration times and the lack of MS/MS spectra in CE-TOF-MS, the type of instruments most frequently employed. We present here a CE-MS search platform incorporated in CEU Mass Mediator to annotate metabolites with a confidence level L2. For its the development we analyzed 226 compounds using two fragmentor voltages: 100 and 200 V. The information obtained, such as relative migration times (RMT) and in-source fragments, were incorporated into the platform. In addition, we validated the CE-MS search functionality using different types of biological samples such as plasma samples (human, rat, and rabbit), mouse macrophages, and human urine. The RMT tolerance percentage for the search of metabolites has been determined, establishing 5% for all compounds, except for the compounds migrating in the electro-osmotic flow, for which the tolerance should be of 10%. It has also been demonstrated the robustness of the in-source fragmentation, which makes possible the annotation of compounds by means of their fragmentation pattern. As an example, 3-methylhistidine and 1-methilhistidine, whose RMT are very close, have been annotated. Studies of the fragmentation mechanisms of acyl-L-carnitines have shown that in-source fragmentation follows the general fragmentation rules and is a suitable alternative to MS/MS.

Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage

Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.

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.

Deciphering the Underlying Metabolomic and Lipidomic Patterns Linked to Thermal Acclimation in Saccharomyces cerevisiae

Temperature is one of the most critical parameters for yeast growth, and it has deep consequences in many industrial processes where yeast is involved. Nevertheless, the metabolic changes required to accommodate yeast cells at high or low temperatures are still poorly understood. In this work, the ultimate responses of these induced transcriptomic effects have been examined using metabolomics-derived strategies. The yeast metabolome and lipidome have been characterized by 1D proton nuclear magnetic resonance spectroscopy and ultra-high-performance liquid chromatography-mass spectrometry at four temperatures, corresponding to low, optimal, high, and extreme thermal conditions. The underlying pathways that drive the acclimation response of yeast to these nonoptimal temperatures were evaluated using multivariate curve resolution-alternating least-squares. The analysis revealed three different thermal profiles (cold, optimal, and high temperature), which include changes in the lipid composition, secondary metabolic pathways, and energy metabolism, and we propose that they reflect the acclimation strategy of yeast cells to low and high temperatures. The data suggest that yeast adjusts membrane fluidity by changing the relative proportions of the different lipid families (acylglycerides, phospholipids, and ceramides, among others) rather than modifying the average length and unsaturation levels of the corresponding fatty acids.

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.

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.

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.

(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.

Classification of congenital disorders of glycosylation based on analysis of transferrin glycopeptides by capillary liquid chromatography-mass spectrometry

In this work, we describe a multivariate data analysis approach for data exploration and classification of the complex and large data sets generated to study the alteration of human transferrin (Tf) N-glycopeptides in patients with congenital disorders of glycosylation (CDG). Tf from healthy individuals and two types of CDG patients (CDG-I and CDG-II) is purified by immunoextraction from serum samples before trypsin digestion and separation by capillary liquid chromatography mass spectrometry (CapLC-MS). Following a targeted data analysis approach, partial least squares discriminant analysis (PLS-DA) is applied to the relative abundance of Tf glycopeptide glycoforms obtained after integration of the extracted ion chromatograms of the different samples. The performance of PLS-DA for classification of the different samples and for providing a novel insight into Tf glycopeptide glycoforms alteration in CDGs is demonstrated. Only six out of fourteen of the detected glycoforms are enough for an accurate classification. This small glycoform set may be considered a sensitive and specific novel biomarker panel for CDGs.

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.