UV-Vis absorption spectrophotometry and LC-DAD-MS-ESI(+)-ESI(-) coupled to chemometrics analysis of the monitoring of sulfamethoxazole degradation by chlorination, photodegradation, and chlorination/photodegradation

Sulfamethoxazole (SMX) is one of the most widely used antibiotics worldwide and has been detected at high concentrations in wastewater treatment plant effluents and river waters. In this study, the SMX degradation process combining the simultaneous chlorine oxidation and UV photodegradation is assessed and compared with both photodegradation and chlorine oxidation processes individually. Photodegradation and Chlorine/UV tests were performed using Suntest CPS equipment. Different experimental techniques, including UV-Visible spectrophotometry and liquid chromatography coupled to a diode array detector and positive and negative ionization mass spectrometry (LC-DAD-MS-ESI(+)-ESI(-)), were used to evaluate the degradation reaction of SMX. All the analytical data generated have been processed with the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) method to monitor, resolve, and identify the several transformation products generated during the studied degradation processes. A new data fusion analysis strategy is proposed to examine the three processes simultaneously (with only photodegradation, only chlorination, and simultaneous chlorination+photodegradation). Combined with the analysis of different analytical techniques individually (spectrophotometry, LC-DAD, and LC-MS), the fusion of all generated data improved the description of the degradation processes. Detection using DAD allowed a better correspondence among the species monitored spectrophotometrically (UV-Vis) with those analyzed chromatographically. On the other side, detection using MS in both positive and negative acquisition modes allowed resolving a larger number of chemical compounds (specially SMX degradation subproducts) that could not be detected by UV-Vis spectrometry. The results obtained permitted the comparison of the effects produced by the three different degradation processes.

Linking MS1 and MS2 signals in positive and negative modes of LC-HRMS in untargeted metabolomics using the ROIMCR approach

Data-independent acquisition (DIA) mode in liquid chromatography (LC) high-resolution mass spectrometry (HRMS) has emerged as a powerful strategy in untargeted metabolomics for detecting a broad range of metabolites. However, the use of this approach also represents a challenge in the analysis of the large datasets generated. The regions of interest (ROI) multivariate curve resolution (MCR) approach can help in the identification and characterization of unknown metabolites in their mixtures by linking their MS1 and MS2 DIA spectral signals. In this study, it is proposed for the first time the analysis of MS1 and MS2 DIA signals in positive and negative electrospray ionization modes simultaneously to increase the coverage of possible metabolites present in biological systems. In this work, this approach has been tested for the detection and identification of the amino acids present in a standard mixture solution and in fish embryo samples. The ROIMCR analysis allowed for the identification of all amino acids present in the analyzed mixtures in both positive and negative modes. The methodology allowed for the direct linking and correspondence between the MS signals in their different acquisition modes. Overall, this approach confirmed the advantages and possibilities of performing the proposed ROIMCR simultaneous analysis of mass spectrometry signals in their differing acquisition modes in untargeted metabolomics studies.

Non-target ROIMCR LC-MS analysis of the disruptive effects of TBT over time on the lipidomics of Daphnia magna

INTRODUCTION

This study has investigated the temporal disruptive effects of tributyltin (TBT) on lipid homeostasis in Daphnia magna. To achieve this, the study used Liquid Chromatography-Mass Spectrometry (LC-MS) analysis to analyze biological samples of Daphnia magna treated with TBT over time. The resulting data sets were multivariate and three-way, and were modeled using bilinear and trilinear non-negative factor decomposition chemometric methods. These methods allowed for the identification of specific patterns in the data and provided insight into the effects of TBT on lipid homeostasis in Daphnia magna.

OBJECTIVES

Investigation of how are the changes in the lipid concentrations of Daphnia magna pools when they were exposed with TBT and over time using non-targeted LC-MS and advanced chemometric analysis.

METHODS

The simultaneous analysis of LC-MS data sets of Daphnia magna samples under different experimental conditions (TBT dose and time) were analyzed using the ROIMCR method, which allows the resolution of the elution and mass spectra profiles of a large number of endogenous lipids. Changes obtained in the peak areas of the elution profiles of these lipids caused by the dose of TBT treatment and the time after its exposure are analyzed by principal component analysis, multivariate curve resolution-alternative least square, two-way ANOVA and ANOVA-simultaneous component analysis.

RESULTS

87 lipids were identified. Some of these lipids are proposed as Daphnia magna lipidomic biomarkers of the effects produced by the two considered factors (time and dose) and by their interaction. A reproducible multiplicative effect between these two factors is confirmed and the optimal approach to model this dataset resulted to be the application of the trilinear factor decomposition model.

CONCLUSION

The proposed non-targeted LC-MS lipidomics approach resulted to be a powerful tool to investigate the effects of the two factors on the Daphnia magna lipidome using chemometric methods based on bilinear and trilinear factor decomposition models, according to the type of interaction between the design factors.

Regions of Interest Multivariate Curve Resolution Liquid Chromatography with Data-Independent Acquisition Tandem Mass Spectrometry

New data-independent acquisition (DIA) modes coupled to chromatographic separations are opening new perspectives in the processing of massive mass spectrometric (MS) data using chemometric methods. In this work, the application of the regions of interest multivariate curve resolution (ROIMCR) method is shown for the simultaneous analysis of MS1 and MS2 DIA raw data obtained by liquid chromatography coupled to quadrupole-time-of-flight MS analysis. The ROIMCR method proposed in this work relies on the intrinsic bilinear structure of the MS1 and MS2 experimental data which allows us for the fast direct resolution of the elution and spectral profiles of all sample constituents giving measurable MS signals, without needing any further data pretreatment such as peak matching, alignment, or modeling. Compound annotation and identification can be achieved directly by the comparison of the ROIMCR-resolved MS1 and MS2 spectra with those from standards or from mass spectral libraries. ROIMCR elution profiles of the resolved components can be used to build calibration curves for the prediction of their concentrations in complex unknown samples. The application of the proposed procedure is shown for the analysis of mixtures of per- and polyfluoroalkyl substances in standard mixtures, spiked hen eggs, and gull egg samples, where these compounds tend to accumulate.

A Multi-Level Systems Biology Analysis of Aldrin's Metabolic Effects on Prostate Cancer Cells

Although numerous studies support a dose-effect relationship between Endocrine disruptors (EDs) and the progression and malignancy of tumors, the impact of a chronic exposure to non-lethal concentrations of EDs in cancer remains unknown. More specifically, a number of studies have reported the impact of Aldrin on a variety of cancer types, including prostate cancer. In previous studies, we demonstrated the induction of the malignant phenotype in DU145 prostate cancer (PCa) cells after a chronic exposure to Aldrin (an ED). Proteins are pivotal in the regulation and control of a variety of cellular processes. However, the mechanisms responsible for the impact of ED on PCa and the role of proteins in this process are not yet well understood. Here, two complementary computational approaches have been employed to investigate the molecular processes underlying the acquisition of malignancy in prostate cancer. First, the metabolic reprogramming associated with the chronic exposure to Aldrin in DU145 cells was studied by integrating transcriptomics and metabolomics via constraint-based metabolic modeling. Second, gene set enrichment analysis was applied to determine (i) altered regulatory pathways and (ii) the correlation between changes in the transcriptomic profile of Aldrin-exposed cells and tumor progression in various types of cancer. Experimental validation confirmed predictions revealing a disruption in metabolic and regulatory pathways. This alteration results in the modification of protein levels crucial in regulating triacylglyceride/cholesterol, linked to the malignant phenotype observed in Aldrin-exposed cells.

A biochemical and lipidomic approach to perceive Halimione portulacoides (L.) response to mercury: An environmental perspective

The impact of hazardous materials, such as Hg, on life is far from being understood and due to the high number of polluted sites it has generated great concern. A biochemical and lipidomic approach was used to assess the effects of Hg on the saltmarsh halophyte Halimione portulacoides. Plants were collected at two sites of a Hg contaminated saltmarsh. Hg accumulation and distribution in the plant, biochemical parameters (antioxidant and metabolic) and lipid profiles were determined and compared between plant organs and sites (s1 and s2). Hg did not induce antioxidant enzyme activity. Lipid profiles changed under Hg exposure, especially in leaves, decreasing the unsaturation level, the membrane fluidity and stability, and evidencing that membrane lipid remodeling influences plant tolerance to Hg. This knowledge can help select the most appropriate methodologies for the restoration of Hg polluted hotspots, curtailing a serious environmental problem threatening saltmarshes.

Phenotypic and Metabolomic Characterization of 3D Lung Cell Cultures Exposed to Airborne Particulate Matter from Three Air Quality Network Stations in Catalonia

Air pollution constitutes an environmental problem that it is known to cause many serious adverse effects on the cardiovascular and respiratory systems. The chemical characterization of particulate matter (PM) is key for a better understanding of the associations between chemistry and toxicological effects. In this work, the chemical composition and biological effects of fifteen PM₁₀ air filter samples from three air quality stations in Catalonia with contrasting air quality backgrounds were investigated. Three-dimensional (3D) lung cancer cell cultures were exposed to these sample extracts, and cytotoxicity, reactive oxygen species (ROS) induction, metabolomics, and lipidomics were explored. The factor analysis method Multivariate Curve Resolution-Alternating Least-Squares (MCR-ALS) was employed for an integrated interpretation of the associations between chemical composition and biological effects, which could be related to urban traffic emission, biomass burning smoke, and secondary aerosols. In this pilot study, a novel strategy combining new approach methodologies and chemometrics provided new insights into the biomolecular changes in lung cells associated with different sources of air pollution. This approach can be applied in further research on air pollution toxicity to improve our understanding of the causality between chemistry and its effects.

Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches

In this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensional gas or liquid chromatography, the main novelty of this work is the demonstration of their applicability to LC×LC-MS datasets. Besides, steps such as peak alignment and modelling, commonly applied in this type of data analysis, are not required with the approaches proposed here. In a first step, regions of interest (ROI) strategy is used for the spectral compression of the LC×LC-MS datasets. Then the first strategy consists of building a calibration curve from the areas obtained in this ROI compression step. Alternatively, the ROI intensity matrices can be used as input for a second analysis step employing the multivariate curve resolution alternating least squares (MCR-ALS) method. The main benefit of MCR-ALS is the resolution of elution and spectral profiles for each of the analytes in the mixture, even in the case of strong coelutions and high signal overlapping. Classical MCR-ALS based calibration curve from the peak areas resolved only applying non-negativity constraints (second strategy) is compared to the results obtained when an area correlation constraint is imposed during the ALS optimization (third strategy). All in all, similar quantification results were achieved by the three approaches but, especially in prediction studies, the more accurate quantification is obtained when the calibration curve is built from the peak areas obtained with MCR-ALS when the area correlation constraint is imposed.

Temporal air quality (NO2, O3, and PM10) changes in urban and rural stations in Catalonia during COVID-19 lockdown: an association with human mobility and satellite data

In this study, changes in air quality by NO₂, O₃, and PM₁₀ in Barcelona metropolitan area and other parts of Catalonia during the COVID-19 lockdown with respect to pre-lockdown and to previous years (2018 and 2019) were evaluated. Selected air monitoring stations included 3 urban (Gràcia, Vall d'Hebron, and Granollers), 1 control site (Fabra Observatory), 1 semi-urban (Manlleu), and 3 rural (Begur, Bellver de Cerdanya, and Juneda). NO₂ lockdown levels showed a diminution, which in relative terms was maximum in two rural stations (Bellver de Cerdanya, - 63% and Begur, - 61%), presumably due to lower emissions from the ceasing hotel and ski resort activities during eastern holidays. In absolute terms and from an epidemiologic perspective, decrease in NO₂, also reinforced by the high amount of rainfall registered in April 2020, was more relevant in the urban stations around Barcelona. O₃ levels increased in the transited urban stations (Gràcia, + 42%, and Granollers, + 64%) due to the lower titration effect by NO_x. PM₁₀ lockdown levels decreased, mostly in Gràcia, Vall d'Hebron, and Granollers (- 35, - 39%, and - 39%, respectively) due to traffic depletion (- 90% in Barcelona's transport). Correlation among mobility index in Barcelona (- 100% in retail and recreation) and contamination was positive for NO₂ and PM₁₀ and negative for O₃ (P < 0.001). Satellite images evidenced two hotspots of NO₂ in Spain (Madrid and Barcelona) in April 2018 and 2019 that disappeared in 2020. Overall, the benefits of lockdown on air quality in Catalonia were evidenced with NO₂, O₃ and PM₁₀ levels below WHOAQG values in most of stations opposed to the excess registered in previous years.

Understanding temporal and spatial changes of O3 or NO2 concentrations combining multivariate data analysis methods and air quality transport models

The application of the multivariate curve resolution method to the analysis of temporal and spatial data variability of hourly measured O₃ and NO₂ concentrations at nineteen air quality monitoring stations across Catalonia, Spain, during 2015 is shown. Data analyzed included ground-based experimental measurements and predicted concentrations by the CALIOPE air quality modelling system at three horizontal resolutions (Europe at 12 × 12 km², Iberian Peninsula at 4 × 4 km² and Catalonia at 1 × 1 km²). Results obtained in the analysis of these different data sets allowed a better understanding of O₃ and NO₂ concentration changes as a sum of a small number of different contributions related to daily sunlight radiation, seasonal dynamics, traffic emission patterns, and local station environments (urban, suburban and rural). The evaluation of O₃ and NO₂ concentrations predicted by the CALIOPE system revealed some differences among data sets at different spatial resolutions. NO₂ predictions, showed in general a better performance than O₃ predictions for the three model resolutions, specially at urban stations. Our results confirmed that the application of the trilinearity constraint during the multivariate curve resolution factor analysis decomposition of the analyzed data sets is a useful tool to facilitate the understanding of the resolved variability sources.

Coupling of spectrometric, chromatographic, and chemometric analysis in the investigation of the photodegradation of sulfamethoxazole

A workflow is proposed for the study of the photodegradation process of the sulfamethoxazole (SMX) based on the combination of different experimental techniques, including liquid chromatography, mass spectrometry, UV-Visible spectrophotometry, and the treatment of all the analytical data with advanced chemometric methods. SMX, which is one of the most widely used antibiotics worldwide and has been found at remarkable concentrations in various rivers and effluents over all Europe, was degraded in the laboratory under a controlled source of UV radiation, which simulates the environmental solar radiation (Suntest). Kinetic monitoring of the photodegradation process was performed using UV-Visible spectrophotometric measurements and by further Liquid Chromatography with Diode Array Detector and Mass Spectrometry analysis (LC-DAD-MS). Additionally, the acid-base properties were also investigated to see how the pH can affect the speciation of this substance during the photodegradation process. Based on the Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) application, the proposed chemometric method coped with the large amounts of data generated by the different analytical techniques used to monitor the evolution of the photodegradation process. Their simultaneous analysis involved applying a data fusion strategy and an advanced MCR-ALS constrained analysis, which allowed and improved the description of the complete degradation process, detecting the different species of the reaction, and identifying the possible transformation products formed. A total number of six species were resolved in the degradation process of SMX. In addition to the initial SMX, a second species corresponded to a conformational isomer, and the other four species represented different photoproducts, which have also been identified. Furthermore, three different acid-base species of SMX were obtained, and their pK_a values were estimated.

Transcriptomic effects of tributyltin (TBT) in zebrafish eleutheroembryos. A functional benchmark dose analysis

Exposure to the antifouling tributyltin (TBT) has been related to imposex in mollusks and to obesogenicity, adipogenesis and masculinization in fish. To understand the underlying molecular mechanisms, we evaluated dose-response effects of TBT (1.7-56 nM) in zebrafish eleutheroembryos transcriptome exposed from 2 to 5 days post-fertilization. RNA-sequencing analysis identified 3238 differentially expressed transcripts in eleutheroembryos exposed to TBT. Benchmark dose analyses (BMD) showed that the point of departure (PoD) for transcriptomic effects (9.28 nM) was similar to the metabolomic PoD (11.5 nM) and about one order of magnitude lower than the morphometric PoD (67.9 nM) or the median lethal concentration (LC₅₀: 93.6 nM). Functional analysis of BMD transcriptomic data identified steroid metabolism and cholesterol and vitamin D₃ biosynthesis as the most sensitive pathways to TBT (<50% PoD). Conversely, transcripts related to general stress and DNA damage became affected only at doses above the PoD. Therefore, our results indicate that transcriptomes can act as early molecular indicators of pollutant exposure, and illustrates their usefulness for the mechanistic identification of the initial toxic events. As the estimated molecular PoDs are close to environmental levels, we concluded that TBT may represent a substantial risk in some natural environments.

Photostability study of multicomponent drug formulations via MCR-ALS: The case of the hydrochlorothiazide-amiloride mixture

The kinetics and photodegradation mechanism of the pharmaceutical mixture of hydrochlorothiazide (HCT) and amiloride (AML) has been studied in depth using a chemometric approach. Water solutions of HCT and AML, separately or in binary mixtures, were irradiated with forced light at different pH values (3, 7, 9 and 12). Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) modelling has been applied to the experimental data recorded by UV spectrophotometry and HPLC-UV/MS. 78 data sets were collected and their chemometric processing has allowed the simultaneous determination of the behaviour of the two drugs in the mixture when exposed to light and the dependence of their photodegradation kinetics on pH. MCR-ALS has been applied using three different implementations. Soft-MCR-ALS and hybrid Hard/Soft-MCR-ALS have been used to resolve the experimental data and to get the equilibrium and kinetic parameters of the investigated chemical processes. A third implementation of the MCR-ALS method has been used in the analysis of the incomplete data sets obtained when UV spectrophotometric and HPLC-UV/MS data were simultaneously analysed, using a row- and column-wise incomplete augmented data matrix arrangement. In these matrices, information from HPLC-UV detector was used as a bridge between the data recorded by UV spectrophotometry (acid-base and kinetic reactions monitoring) and the data obtained by HPLC-MS.

Patterns of cyanobacterial abundance in a major drinking water reservoir: what 3 years of comprehensive monitoring data reveals?

The present study provides a detailed analysis of the factors influencing variation in cyanobacterial communities of a large shallow off-river drinking water reservoir on the east coast of Australia. Receiving multiple inflows from two unprotected mixed land-use catchments, the Grahamstown Reservoir is a model example of a reservoir which is highly vulnerable to adverse water quality issues, including phytoplankton blooms and the resulting filtration, toxin and taste and odour problems produced. The spatial and temporal distributions of cyanobacteria were assessed for a period of 3 years (January 2012-December 2014) based on samples collected from three monitoring stations within the reservoir. Relationships between cyanobacterial abundance and a range of environmental factors were evaluated by application of multivariate curve resolution-alternating least squares (MCR-ALS) analysis.Results of the analysis indicated that among the 22 physico-chemical variables and 14 cyanobacterial taxa measured, the vertical temperature gradient within the water column and nutrient availability were the most powerful explanatory factors for the observed temporal and spatial distribution patterns in the densities of cyanobacterial taxa. The abundance patterns of the dominant cyanobacterial taxa-Aphanocapsa, Aphanothece, Microcystis and Pseudanabaena-were strongly linked with rainfall and run-off patterns into the reservoir, while Coelosphaerium and Microcystis were the taxa most influenced by the apparent occurrence of thermal stratification. The findings demonstrate the capacity of rigorous multivariate data analysis to identify more subtle relationships between water quality variables, catchment factors and cyanobacterial growth in drinking water reservoirs.

GM2-GM3 gangliosides ratio is dependent on GRP94 through down-regulation of GM2-AP cofactor in brain metastasis cells

GRP94 is an ATP-dependent chaperone able to regulate pro-oncogenic signaling pathways. Previous studies have shown a critical role of GRP94 in brain metastasis (BrM) pathogenesis and progression. In this work, an untargeted lipidomic analysis revealed that some lipid species were altered in GRP94-deficient cells, specially GM2 and GM3 gangliosides. The catalytic pathway of GM2 is affected by the low enzymatic activity of β-Hexosaminidase (HexA), responsible for the hydrolysis of GM2 to GM3. Moreover, a deficiency of the GM2-activator protein (GM2-AP), the cofactor of HexA, is observed without alteration of gene expression, indicating a post-transcriptional alteration of GM2-AP in the GRP94-ablated cells. One plausible explanation of these observations is that GM2-AP is a client of GRP94, resulting in defective GM2 catabolic processing and lysosomal accumulation of GM2 in GRP94-ablated cells. Overall, given the role of gangliosides in cell surface dynamics and signaling, their imbalance might be linked to modifications of cell behaviour acquired in BrM progression. This work indicates that GM2-AP could be an important factor in ganglioside balance maintenance. These findings highlight the relevance of GM3 and GM2 gangliosides in BrM and reveal GM2-AP as a promising diagnosis and therapeutic target in BrM research.

Stoichiometric gene-to-reaction associations enhance model-driven analysis performance: Metabolic response to chronic exposure to Aldrin in prostate cancer

BACKGROUND

Genome-scale metabolic models (GSMM) integrating transcriptomics have been widely used to study cancer metabolism. This integration is achieved through logical rules that describe the association between genes, proteins, and reactions (GPRs). However, current gene-to-reaction formulation lacks the stoichiometry describing the transcript copies necessary to generate an active catalytic unit, which limits our understanding of how genes modulate metabolism. The present work introduces a new state-of-the-art GPR formulation that considers the stoichiometry of the transcripts (S-GPR). As case of concept, this novel gene-to-reaction formulation was applied to investigate the metabolic effects of the chronic exposure to Aldrin, an endocrine disruptor, on DU145 prostate cancer cells. To this aim we integrated the transcriptomic data from Aldrin-exposed and non-exposed DU145 cells through S-GPR or GPR into a human GSMM by applying different constraint-based-methods.

RESULTS

Our study revealed a significant improvement of metabolite consumption/production predictions when S-GPRs are implemented. Furthermore, our computational analysis unveiled important alterations in carnitine shuttle and prostaglandine biosynthesis in Aldrin-exposed DU145 cells that is supported by bibliographic evidences of enhanced malignant phenotype.

CONCLUSIONS

The method developed in this work enables a more accurate integration of gene expression data into model-driven methods. Thus, the presented approach is conceptually new and paves the way for more in-depth studies of aberrant cancer metabolism and other diseases with strong metabolic component with important environmental and clinical implications.

Unravelling the mechanisms of PFOS toxicity by combining morphological and transcriptomic analyses in zebrafish embryos

Exposure to PFOS (perfluorooctanesulfonate) has been related to toxic effects on lipid metabolism, immunological response, and different endocrine systems. We present here a transcriptomic analysis of zebrafish embryos exposed to different concentrations of PFOS (0.03-1.0 mg/L) from 48 to 120 hpf. No major survival or morphological alterations (swimming bladder inflation, kyphosis, eye separation and size…) were observed below the 1.0 mg/L mark. Conversely, we observed significant increase in transcripts related to lipid transport and metabolism even at the lowest used concentration. In addition, we observed a general decrease on transcripts related to natural immunity and defense again infections, which adds to the recent concerns about PFOS as immunotoxicant, particularly in humans. Derived PoD (Point of Departure) values for transcriptional changes (0.011 mg/L) were about 200-fold lower than the corresponding PoD values for morphometric effects (2.53 mg/L), and close to levels observed in human blood serum or bird eggs. Our data suggest that currently applicable tolerable levels of PFOS in commercial goods should be re-evaluated, taking into account its potential effects on lipid metabolism and the immune system.

ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets

BACKGROUND

The analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each "feature" (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field.

RESULTS

Here, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com ) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications.

CONCLUSIONS

The methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data.

Effect of psychiatric drugs on Daphnia magna oxylipin profiles

Neuro-active pharmaceuticals have been reported to act as endocrine disruptors enhancing reproduction in the model crustacean Daphnia manga at environmental concentrations of ng/L. Oxylipins and more specifically eicosanoids, which are lipid mediators formed from polyunsaturated fatty acids (PUFAs), are known to regulate reproduction together with other physiological processes in insects. In D. magna, the biosynthesis of eicosanoids and their putative role in the regulation of reproduction has been studied using transcriptomics, genomics and exposures to cyclooxygenase inhibitors. Quantification of eicosanoids and oxylipins derived from PUFAs upon exposure to pharmaceuticals is therefore crucial for a better understanding of the mode of action of neuro-active pharmaceuticals on aquatic invertebrates. The aim of this study was to investigate shifts in the oxylipin profile in D. magna adults upon exposure to environmental concentrations of the three psychiatric drugs, fluoxetine, diazepam and carbamazepine, with known effects of enhancing offspring production. Oxylipin profiles were determined in whole organism tissues using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Up to 28 different oxylipins belonging to arachidonic (AA), linoleic acid (LA), α-linoleic acid (α-LA) and eicosapentaenoic acid (EPA) pathways were detected and quantified in D. magna adults. Exposure to the selected psychiatric drugs showed that fluoxetine enhanced the accumulation of the cyclooxygenase (COX) product 12-hydroxyheptadecatrienoic acid (12-HHTrE), whereas diazepam increased the concentration of eicosanoids belonging to the lipoxygenase (LOX) and cytochrome P450 (CYP) pathways (HETEs, EpOMEs, HODEs, HOTrEs and HEPEs) from the AA, LA, αLA and EPA pathways. Carbamazepine had little effect and only one LA-derived compound from the LOX pathway (13-HODE) increased significantly. This means that despite having different modes of action in humans, fluoxetine and diazepam up-regulated eicosanoid pathways in D. magna, closely related to known biologically active products that regulate reproduction in insects.

Dose-dependent transcriptomic responses of zebrafish eleutheroembryos to Bisphenol A

Despite the abundant literature on the adverse effects of Bisphenol A (BPA) as endocrine disruptor, its toxicity mechanisms are still poorly understood. We present here a study of its effects on the zebrafish eleutheroembryo transcriptome at concentrations ranging from 0.1 to 4 mg L^-1, this latter representing the lowest observed effect concentration (LOEC) found in our study at three different macroscopical endpoints (survival, hatching and swim bladder inflation). Multivariate data analysis methods identified both monotonic and bi-phasic patterns of dose-dependent responses. Functional analyses of genes affected by BPA exposure suggest an interaction of BPA with different signaling pathways, being the estrogenic and retinoid receptors two likely targets. In addition, we identified an apparently unrelated inhibitory effect on, among others, visual function genes. We interpret our data as the result of a sum of underlying, independent molecular mechanisms occurring simultaneously at the exposed animals, well below the macroscopic LOEC, but related to at least some of the observed morphological alterations, particularly in eye size and yolk sac resorption. Our data supports the idea that the physiological effects of BPA cannot be only explained by its rather weak interaction with the estrogen receptor, and that multivariate analyses are required to analyze the effects of toxicants like BPA, which interact with different cellular targets producing complex phenotypes.

Metabolomic changes induced by nicotine in adult zebrafish skeletal muscle

Acute exposure to nicotinic agonists induces myotoxicity in zebrafish embryos. The main goal of this work was to evaluate the potential myotoxicity of nicotine acetylcholine receptor agonists on adult zebrafish muscle tissue by using nicotine as a model compound. Liquid chromatography coupled to high resolution mass spectrometry (LC-HRMS) datasets were processed with different chemometric tools based on the selection of Regions of Interest (ROI) and Multivariate Curve-Resolution (ROI-MCR procedure) Alternating Least Squares (ALS) for the analysis of different exposure experiments. Analysis of Variance Simultaneous Component Analysis (ASCA) of changes on metabolite peak profile areas showed significant nicotine 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). Most of the changes observed in the concentrations of different metabolites are probably secondary to the observed hyperlocomotion, as they have been also observed in humans after strenuous muscular exercise. The absence of myotoxicity might be related with the reduced calcium permeability of adult muscle-type nicotinic acetylcholine receptors (nAChRs).

Untargeted lipidomic evaluation of hydric and heat stresses on rice growth

Environmental stresses are the major factors that limit the geographical distribution of plants. As a consequence, plants have developed different strategies to adapt to these environmental changes among which can be outlined the maintenance of membranes' integrity and fluidity. Lipids are key molecules for this environmental adaptation and a comprehensive understand of the molecular mechanisms underlying is still required. Here, lipidome changes in Japanese rice (Oryza sativa var. Japonica) upon heat and hydric stresses are assessed using an untargeted approach based on liquid chromatography coupled with mass spectrometry (LC-MS). The obtained data were analyzed using different multivariate data analysis tools. A total number of 298 lipids responded to these abiotic stresses, and 128 of them were tentatively identified. Diacylglycerols (DG), triacylglycerols (TG), phosphatidylcholines (PC) and phosphatidylethanolamines (PE) were the most altered lipid families heat and hydric stress. Interpretation of the obtained results showed relevant changes related to the unsaturation degree in the identified lipids. In the case of heat stress, a decrease in the unsaturation degree of lipids can be linked to an increase in the cell membranes' rigidity. In contrast, the hydric stress produced an increase in the lipids unsaturation degree causing an increase in the cell membranes' fluidity, in an attempt to adapt to these non-optimal conditions.

Comparative analysis of 1H NMR and 1H-13C HSQC NMR metabolomics to understand the effects of medium composition in yeast growth

In nuclear magnetic resonance (NMR) metabolomics, most of the studies have been focused on the analysis of one-dimensional proton (1D ¹H) NMR, whereas the analysis of other nuclei, such as ¹³C, or other NMR experiments are still underrepresented. The preference of 1D ¹H NMR metabolomics lies on the fact that it has good sensitivity and a short acquisition time, but it lacks spectral resolution because it presents a high degree of overlap. In this study, the growth metabolism of yeast ( Saccharomyces cerevisiae) was analyzed by 1D ¹H NMR and by two-dimensional (2D) ¹H-¹³C heteronuclear single quantum coherence (HSQC) NMR spectroscopy, leading to the detection of more than 50 metabolites with both analytical approaches. These two analyses allow for a better understanding of the strengths and intrinsic limitations of the two types of NMR approaches. The two data sets (1D and 2D NMR) were investigated with PCA, ASCA, and PLS DA chemometric methods, and similar results were obtained regardless of the data type used. However, data-analysis time for the 2D NMR data set was substantially reduced when compared with the data analysis of the corresponding ¹H NMR data set because, for the 2D NMR data, signal overlap was not a major problem and deconvolution was not required. The comparative study described in this work can be useful for the future design of metabolomics workflows, to assist in the selection of the most convenient NMR platform and to guide the posterior data analysis of biomarker selection.

Assessment of tissue-specific multifactor effects in environmental -omics studies of heterogeneous biological samples: Combining hyperspectral image information and chemometrics

The use of hyperspectral imaging techniques in biological studies has increased in the recent years. Hyperspectral images (HSI) provide chemical information and preserve the morphology and original structure of heterogeneous biological samples, which can be potentially useful in environmental -omics studies when effects due to several factors, e.g., contaminant exposure, phenotype,…, at a specific tissue level need to be investigated. Yet, no available strategies exist to exploit adequately this kind of information. This work offers a novel chemometric strategy to pass from the raw image information to useful knowledge in terms of statistical assessment of the multifactor effects of interest in -omic studies. To do so, unmixing of the hyperspectral image measurement is carried out to provide tissue-specific information. Afterwards, several specific ANOVA-Simultaneous Component Analysis (ASCA) models are generated to properly assess and interpret the diverse effect of the factors of interest on the spectral fingerprints of the different tissues characterized. The unmixing step is performed by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) on multisets of biological images related to each studied condition and provides reliable HSI spectral signatures and related image maps for each specific tissue in the regions imaged. The variability associated with these signatures within a population is obtained through an MCR-based resampling step on representative pixel subsets of the images analyzed. All spectral fingerprints obtained for a particular tissue in the different conditions studied are used to obtain the related ASCA model that will help to assess the significance of the factors studied on the tissue and, if relevant, to describe the associated fingerprint modifications. The potential of the approach is assessed in a real case of study linked to the investigation of the effect of exposure time to chlorpyrifos-oxon (CPO) on ocular tissues of different phenotypes of zebrafish larvae from Raman HSI of eye cryosections. The study allowed the characterization of melanin, crystalline and internal eye tissue and the phenotype, exposure time and the interaction of the two factors were found to be significant in the changes found in all kind of tissues. Factor-related changes in the spectral fingerprint were described and interpreted per each kind of tissue characterized.

Assessment of endocrine disruptors effects on zebrafish (Danio rerio) embryos by untargeted LC-HRMS metabolomic analysis

Bisphenol A (BPA), perfluorooctane sulfonate (PFOS), and tributyltin (TBT) are emerging endocrine disruptors (EDCs) with still poorly defined mechanisms of toxicity and metabolic effects in aquatic organisms. We used an untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomic approach to study the effects of sub-lethal doses of these three EDCs on the metabolic profiles of zebrafish embryos exposed from 48 to 120hpf (hours post fertilization). Advanced chemometric data analysis methods were used to reveal effects on the subjacent regulatory pathways. EDC treatments induced changes in concentrations of about 50 metabolites for TBT and BPA, and of 25 metabolites for PFOS. The analysis of the corresponding metabolic changes suggested the presence of similar underlying zebrafish responses to BPA, TBT and PFOS affecting the metabolism of glycerophospholipids, amino acids, purines and 2-oxocarboxylic acids. We related the changes in glycerophospholipid metabolism to alterations in absorption of the yolk sack, the main source of nutrients (including lipids) for the developing embryo, linking the molecular markers with adverse phenotypic effects. We propose a general mode of action for all three chemical compounds, probably related to their already described interaction with the PPAR/RXR complex, combined with specific effects on different signaling pathways resulting in particular alterations in the zebrafish embryos metabolism.

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.

Differential gene transcription across the life cycle in Daphnia magna using a new all genome custom-made microarray

Background

Unravelling the link between genes and environment across the life cycle is a challenging goal that requires model organisms with well-characterized life-cycles, ecological interactions in nature, tractability in the laboratory, and available genomic tools. Very few well-studied invertebrate model species meet these requirements, being the waterflea Daphnia magna one of them. Here we report a full genome transcription profiling of D. magna during its life-cycle. The study was performed using a new microarray platform designed from the complete set of gene models representing the whole transcribed genome of D. magna.

Results

Up to 93% of the existing 41,317 D. magna gene models showed differential transcription patterns across the developmental stages of D. magna, 59% of which were functionally annotated. Embryos showed the highest number of unique transcribed genes, mainly related to DNA, RNA, and ribosome biogenesis, likely related to cellular proliferation and morphogenesis of the several body organs. Adult females showed an enrichment of transcripts for genes involved in reproductive processes. These female-specific transcripts were essentially absent in males, whose transcriptome was enriched in specific genes of male sexual differentiation genes, like doublesex.

Conclusion

Our results define major characteristics of transcriptional programs involved in the life-cycle, differentiate males and females, and show that large scale gene-transcription data collected in whole animals can be used to identify genes involved in specific biological and biochemical processes.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4725-7) contains supplementary material, which is available to authorized users.

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.

Combining hyperspectral imaging and chemometrics to assess and interpret the effects of environmental stressors on zebrafish eye images at tissue level

Changes on an organism by the exposure to environmental stressors may be characterized by hyperspectral images (HSI), which preserve the morphology of biological samples, and suitable chemometric tools. The approach proposed allows assessing and interpreting the effect of contaminant exposure on heterogeneous biological samples monitored by HSI at specific tissue levels. In this work, the model example used consists of the study of the effect of the exposure of chlorpyrifos-oxon on zebrafish tissues. To assess this effect, unmixing of the biological sample images followed by tissue-specific classification models based on the unmixed spectral signatures is proposed. Unmixing and classification are performed by multivariate curve resolution-alternating least squares (MCR-ALS) and partial least squares-discriminant analysis (PLS-DA), respectively. Crucial aspects of the approach are: (1) the simultaneous MCR-ALS analysis of all images from 1 population to take into account biological variability and provide reliable tissue spectral signatures, and (2) the use of resolved spectral signatures from control and exposed populations obtained from resampling of pixel subsets analyzed by MCR-ALS multiset analysis as information for the tissue-specific PLS-DA classification models. Classification results diagnose the presence of a significant effect and identify the spectral regions at a tissue level responsible for the biological change.

Preprocessing Tools Applied to Improve the Assessment of Aldrin Effects on Prostate Cancer Cells Using Raman Spectroscopy

The study of pollutant effects on living organisms provides information about the possible biological and environmental response to a contaminant. Progression of prostate cancer may be related to exposure to pesticides or other chemical substances. In this work, the effect of the pesticide aldrin on human prostate cancer cells (DU145) is studied using Raman spectroscopy and chemometric techniques. Prostate cancer cell line DU145 has been exposed acutely the pesticide aldrin. Individual Raman spectra coming from control and treated cell populations have been acquired. Partial least squares discriminant analysis (PLSDA) has been used to assess differences among treated and control samples and to identify spectral biomarkers associated with pollutant stress. Some preprocessing methodologies have been tested in order to improve the capability of discrimination between fingerprints. Partial least squares discriminant analysis results suggest that the best normalization-scaling preprocessing combination is provided by Euclidean normalization (EN)-SIMPLISMA-based scaling (SBS). SIMPLISMA-based scaling has been proposed as a scaling method focused on the classification objective, which enhances variables with high relative variation among samples. The most relevant spectral variables related to aldrin effect on DU145 seem to be mainly related to lipids, proteins, and variations in nucleic acids.

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.

Combined effects of salinity, temperature and hypoxia on Daphnia magna metabolism

Metabolic changes of Daphnia magna pools due different abiotic factors linked to global climate change (salinity, temperature and hypoxia) were investigated using untargeted GC-MS and advanced chemometric strategies using a three factors two-level full factorial experimental design (DoE). Effects of these three factors and identity of the metabolites whose concentrations changed because of them were investigated. The simultaneous analysis of GC-MS data sets using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) allowed the resolution of the elution and mass spectra profiles of a large number of D. magna metabolites. Changes in peak areas of these metabolites were then analyzed by Principal Component Analysis (PCA), by ANOVA-Simultaneous Component Analysis (ASCA) and by Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), and the combined effects of the three investigated stressors were assessed. Results confirmed the strong influence of increasing environmental salinity levels on the D. magna metabolome. This impact was specially highlighted by changes on the cellular content of carbohydrates, fatty acids, organic acids and amino acid molecules. In contrast, these effects were less significant for the other two factors (temperature and hypoxia) at the moderate stressing experimental conditions investigated in this work when they were not combined with salinity.

Compression of multidimensional NMR spectra allows a faster and more accurate analysis of complex samples

A variable-filtering approach of multidimensional NMR spectral data is proposed. Filtered data keeps resonance information and original spectral resolution. The denoised data can be easily peak-picked and integrated, allowing a fast and accurate NMR analysis.

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.