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

Examination of internal metabolome and VOCs profile of brewery yeast and their mutants producing beer with improved aroma

Volatile organic compounds (VOCs) are metabolites pivotal in determining the aroma of various products. A well-known VOC producer of industrial importance is Saccharomyces cerevisiae, partially responsible for flavor of beers and wines. We identified VOCs in beers produced by yeast strains characterized by improved aroma obtained in UV-induced mutagenesis. We observed significant increase in concentration of compounds in strains: 1214uv16 (2-phenylethyl acetate, 2- phenylethanol), 1214uv31 (2-ethyl henxan-1-ol), 1214uv33 (ethyl decanoate, caryophyllene). We observed decrease in production of 2-phenyethyl acetate in strain 1214uv33. Analysis of intracellular metabolites based on 1H NMR revealed that intracellular phenylalanine concentration was not changed in strains producing more phenylalanine related VOCs (1214uv16 and 1214uv33), so regulation of this pathway seems to be more sophisticated than is currently assumed. Metabolome analysis surprisingly showed the presence of 3-hydroxyisobutyrate, a product of valine degradation, which is considered to be absent in S. cerevisiae. Our results show that our knowledge of yeast metabolism including VOC production has gaps regarding synthesis pathways for individual metabolites and regulation mechanisms. Detailed analysis of 1214uv16 and 1214uv33 may enhance our knowledge of the regulatory mechanisms of VOC synthesis in yeast, and analysis of strain 1214uv31 may reveal the pathway of 2-ethyl henxan-1-ol biosynthesis.

Multivariate curve resolution-based data fusion approaches applied in 1H NMR metabolomic analysis of healthy cohorts

BACKGROUND

Metabolomics plays a critical role in deciphering metabolic alterations within individuals, demanding the use of sophisticated analytical methodologies to navigate its intricate complexity. While many studies focus on single biofluid types, simultaneous analysis of multiple matrices enhances understanding of complex biological mechanisms. Consequently, the development of data fusion methods enabling multiblock analysis becomes essential for comprehensive insights into metabolic dynamics.

RESULTS

This study introduces a novel guideline for jointly analyzing diverse metabolomic datasets (serum, urine, metadata) with a focus on metabolic differences between groups within a healthy cohort. The guideline presents two fusion strategies, 'Low-Level data fusion' (LLDF) and 'Mid-Level data fusion' (MLDF), employing a sequential application of Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS), linking the outcomes of successive analyses. MCR-ALS is a versatile method for analyzing mixed data, adaptable at various stages of data processing-encompassing resonance integration, data compression, and exploratory analysis. The LLDF and MLDF strategies were applied to 1H NMR spectral data extracted from urine and serum samples, coupled with biochemical metadata sourced from 145 healthy volunteers.

SIGNIFICANCE

Both methodologies effectively integrated and analysed multiblock datasets, unveiling the inherent data structure and variables associated with discernible factors among healthy cohorts. While both approaches successfully detected sex-related differences, the MLDF strategy uniquely revealed components linked to age. By applying this analysis, we aim to enhance the interpretation of intricate biological mechanisms and uncover variations that may not be easily discernible through individual data analysis.

Targeted 1-H-NMR wine analyses revealed specific metabolomic signatures of yeast populations belonging to the Saccharomyces genus

This study aimed to explore the non-volatile metabolomic variability of a large panel of strains (44) belonging to the Saccharomyces cerevisiae and Saccharomyces uvarum species in the context of the wine alcoholic fermentation. For the S. cerevisiae strains flor, fruit and wine strains isolated from different anthropic niches were compared. This phenotypic survey was achieved with a special focus on acidity management by using natural grape juices showing opposite level of acidity. A 1H NMR based metabolomics approach was developed for quantifying fifteen wine metabolites that showed important quantitative variability within the strains. Thanks to the robustness of the assay and the low amount of sample required, this tool is relevant for the analysis of the metabolomic profile of numerous wines. The S. cerevisiae and S. uvarum species displayed significant differences for malic, succinic, and pyruvic acids, as well as for glycerol and 2,3-butanediol production. As expected, S. uvarum showed weaker fermentation fitness but interesting acidifying properties. The three groups of S. cerevisiae strains showed different metabolic profiles mostly related to their production and consumption of organic acids. More specifically, flor yeast consumed more malic acid and produced more acetic acid than the other S. cerevisiae strains which was never reported before. These features might be linked to the ability of flor yeasts to shift their metabolism during wine oxidation.

Mechanistic exploration of the activities of poly(lactic-co-glycolic acid)-loaded nanoparticles of betulinic acid against hepatocellular carcinoma at cellular and molecular levels

The effectiveness of betulinic acid (B) and PLGA loaded nanoparticles of B (Bnp) against hepatocellular carcinoma (HCC) was established and reported earlier. In continuation of our previous report, the present study described the molecular mechanisms of their antineoplastic responses. In this context, the antineoplastic properties of both B and Bnp were evaluated on DEN-induced HCC rat model. The quantitative real-time polymerase chain reaction and western blot analyses revealed that HCC was developed through lower expressions of e-NOS, BAX, BAD, Cyt C and higher expressions of i-NOS, Bcl-xl, Bcl-2. B and Bnp normalised the expressions of these apoptogenic markers. Particularly, both activated i-NOS and e-NOS mediated Bcl-2 family proteins→CytC→Caspase 3 and 9 signalling cascades. The ¹H-NMR-based metabolomics study also demonstrated that the perturbed metabolites in DEN-induced rat serum restored to the normal level following both treatments. Moreover, the antineoplastic potential of Bnp was found to be comparable with the marketed product, 5-flurouracil.

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

Preclinical Evaluation of Dimethyl Itaconate Against Hepatocellular Carcinoma via Activation of the e/iNOS-Mediated NF-κB-Dependent Apoptotic Pathway

Hepatocellular carcinoma (HCC) is one of the most common tumors affecting a large population worldwide, with the fifth and seventh greatest mortality rates among men and women, respectively, and the third prime cause of mortality among cancer victims. Dimethyl itaconate (DI) has been reported to be efficacious in colorectal cancer by decreasing IL-1β release from intestinal epithelial cells. In this study, diethylnitrosamine (DEN)-induced HCC in male albino Wistar rats was treated with DI as an anticancer drug. The function and molecular mechanism of DI against HCC in vivo were assessed using histopathology, enzyme-linked immunosorbent assay (ELISA), and Western blot studies. Metabolomics using ¹H-NMR was used to investigate metabolic profiles. As per molecular insights, DI has the ability to trigger mitochondrial apoptosis through iNOS- and eNOS-induced activation of the NF-κB/Bcl-2 family of proteins, CytC, caspase-3, and caspase-9 signaling cascade. Serum metabolomics investigations using ¹H-NMR revealed that aberrant metabolites in DEN-induced HCC rats were restored to normal following DI therapy. Furthermore, our data revealed that the DI worked as an anti-HCC agent. The anticancer activity of DI was shown to be equivalent to that of the commercial chemotherapeutic drug 5-fluorouracil.

Purine Auxotrophic Starvation Evokes Phenotype Similar to Stationary Phase Cells in Budding Yeast

Purine auxotrophy is an abundant trait among eukaryotic parasites and a typical marker for many budding yeast strains. Supplementation with an additional purine source (such as adenine) is necessary to cultivate these strains. If not supplied in adequate amounts, purine starvation sets in. We explored purine starvation effects in a model organism, a budding yeast Saccharomyces cerevisiae ade8 knockout, at the level of cellular morphology, central carbon metabolism, and global transcriptome. We observed that purine-starved cells stopped their cycle in G1/G0 state and accumulated trehalose, and the intracellular concentration of AXP decreased, but adenylate charge remained stable. Cells became tolerant to severe environmental stresses. Intracellular RNA concentration decreased, and massive downregulation of ribosomal biosynthesis genes occurred. We proved that the expression of new proteins during purine starvation is critical for cells to attain stress tolerance phenotype Msn2/4p targets are upregulated in purine-starved cells when compared to cells cultivated in purine-rich media. The overall transcriptomic response to purine starvation resembles that of stationary phase cells. Our results demonstrate that the induction of a strong stress resistance phenotype in budding yeast can be caused not only by natural starvation, but also starvation for metabolic intermediates, such as purines.

Machine Learning and Chemical Imaging to Elucidate Enzyme Immobilization for Biocatalysis

Biocatalysis has rapidly become an essential tool in the scientific and industrial communities for the development of efficient, safe, and sustainable chemical syntheses. Immobilization of the biocatalyst, typically an engineered enzyme, offers significant advantages, including increased enzyme stability and control, resistance to environmental change, and enhanced reusability. Determination and optimization of the spatial and chemical distribution of immobilized enzymes are critical for proper functionality; however, analytical methods currently employed for doing so are frequently inadequate. Machine learning, in the form of multivariate curve resolution, with Raman hyperspectral imaging is presented herein as a potential method for investigating the spatial and chemical distribution of evolved pantothenate kinase immobilized onto two diverse, microporous resins. An exhaustive analysis indicates that this method can successfully resolve, both spatially and spectrally, all chemical species involved in enzyme immobilization, including the enzyme, both resins, and other key components. Quantitation of the spatial coverage of immobilized enzymes, a key parameter used for process development, was accomplished. Optimal analytical parameters were determined by the evaluation of different excitation wavelengths. Exploratory chemometric approaches, including principal component analysis, were utilized to investigate the chemical species embedded within the data sets and their relationships. The totality of this information can be utilized for an enhanced understanding of enzyme immobilization processes and can allow for the further implementation of biocatalysis within the scientific and pharmaceutical communities.

Assessment of substrate biodegradability improvement in anaerobic Co-digestion using a chemometrics-based metabolomic approach

Anaerobic co-digestion (AcoD) can increase methane production of anaerobic digesters in plants treating wastewater sludge by improving the nutrient balance needed for the microorganisms to grow in the digesters, resulting in a faster process stabilization. Substrate mixture proportions are usually optimized in terms of biogas production, while the metabolic biodegradability of the whole mixture is neglected in this optimisation. In this aim, we developed a strategy to assess AcoD using metabolomics data. This strategy was explored in two different systems. Specifically, we investigated the co-digestion of wastewater sludge with different proportions of either grass or fish waste using untargeted High Performance Liquid Chromatography coupled to Mass Spectrometry (HPLC-MS) metabolomics and chemometrics methods. The analysis of these data revealed that adding grass waste did not improve the metabolic biodegradability of wastewater sludge. Conversely, a synergistic effect in the metabolic biodegradability was observed when fish waste was used, this effect being the highest for 25% of fish waste. In conclusion, metabolomics can be regarded as a promising tool both for characterizing the biochemical processes occurring during anaerobic digestion, and for providing a better understanding of the anaerobic digestion processes.

Targeting redox metabolism: the perfect storm induced by acrylamide poisoning in the brain

Exposure to acrylamide may lead to different neurotoxic effects in humans and in experimental animals. To gain insights into this poorly understood type of neurotoxicological damage, we used a multi-omic approach to characterize the molecular changes occurring in the zebrafish brain exposed to acrylamide at metabolite, transcript and protein levels. We detected the formation of acrylamide adducts with thiol groups from both metabolites and protein residues, leading to a quasi-complete depletion of glutathione and to the inactivation of different components of the thioredoxin system. We propose that the combined loss-of-function of both redox metabolism-related systems configure a perfect storm that explains many acrylamide neurotoxic effects, like the dysregulation of genes related to microtubules, presynaptic vesicle alteration, and behavioral alterations. We consider that our mechanistical approach may help developing new treatments against the neurotoxic effects of acrylamide and of other neurotoxicants that may share its toxic mode of action.

Bio-Inspired Silver Nanoparticles Impose Metabolic and Epigenetic Toxicity to Saccharomyces cerevisiae

Silver nanoparticles (AgNPs) have many applications in various fields, including biomedical applications. Due to the broad range of applications, they are considered as the leading fraction of manufactured nanoparticles. AgNPs are synthesized by different types of chemical and biological (green) methods. Previously, biologically synthesized AgNPs were considered safe for the environment and humans. However, new toxicity evidence have initiated a more careful assessment to delineate the toxicity mechanisms associated with these nanoparticles. This study demonstrates the use of aqueous gooseberry extract for AgNP preparation in a time- and cost-effective way. Ultraviolet-visible spectroscopy, X-ray diffraction, transmission electron microscopy, and dynamic light scattering confirm the formation of AgNPs, with an average size between 50 and 100 nm. Untargeted ¹H-nuclear magnetic resonance-based metabolomics revealed manyfold up- and down-regulation in the concentration of 55 different classes of annotated metabolites in AgNP-exposed yeast Saccharomyces cerevisiae cells. Based on their chemical nature and cellular functions, these metabolites are classified into amino acids, glycolysis and the tricarboxylic acid (TCA) cycle, organic acids, nucleotide metabolism, urea cycle, and lipid metabolism. Transcriptome analysis revealed that the genes involved in oxidative stress mitigation maintain their expression levels, whereas the genes of the TCA cycle and lipid metabolism show drastic down-regulation upon AgNP exposure. Moreover, they can induce alteration in histone epigenetic marks by altering the methylation and acetylation of selected histone H3 and H4 proteins. Altogether, we conclude that the selected dose of biologically synthesized AgNPs impose toxicity by modulating the transcriptome, epigenome, and metabolome of eukaryotic cells, which eventually cause disequilibrium in cellular metabolism leading to toxicity.

Zinc oxide nanoparticles impose metabolic toxicity by de-regulating proteome and metabolome in Saccharomyces cerevisiae

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Untargeted proteomic and metabolic approaches provide complete toxicity assessment.

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ZnO-NPs de-regulate the proteome and metabolome of S. cerevisiae.

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ZnO-NPs affect the key metabolites of central metabolic pathways.

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Protein and/or metabolite can be used as biomarker specific to the ZnO-NPs induced toxicity.

As zinc oxide nanoparticles are being increasingly used in various applications, it is important to assess their potential toxic implications. Stress responses and adaptations are primarily controlled by modulation in cellular proteins (enzyme) and concentration of metabolites. To date proteomics or metabolomics applications in nanotoxicity assessment have been applied to a restricted extent. Here we utilized 2DE and ¹H NMR based proteomics and metabolomics respectively to delineate the toxicity mechanism of zinc oxide nanoparticles (ZnO-NPs) in budding yeast S. cerevisiae. We found that the physiological and metabolic processes were altered in the S. cerevisiae upon ZnO-NPs exposure. Almost 40% proteins were down-regulated in ZnO-NPs (10 mg L⁻¹) exposed cell as compared to control. Metabolomics and system biology based pathway analysis, revealed that ZnO-NPs repressed a wide range of key metabolites involved in central carbon metabolism, cofactors synthesis, amino acid and fatty acid biosynthesis, purines and pyrimidines, nucleoside and nucleotide biosynthetic pathways. These metabolic changes may be associated with the energy metabolism, antioxidation, DNA and protein damage and membrane stability. We concluded that untargeted proteomic and metabolic approaches provide more complete measurements and suggest probable molecular mechanisms of nanomaterials toxicity.

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.

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.

Novel 1,3,4-thiadiazoles inhibit colorectal cancer via blockade of IL-6/COX-2 mediated JAK2/STAT3 signals as evidenced through data-based mathematical modeling

We attempted a preclinical study using DMH-induced CRC rat model to evaluate the antitumor potential of our recently synthesized 1,3,4-thiadiazoles. The molecular insights were confirmed through ELISA, qRT-PCR and western blot analyses. The CRC condition was produced in response to COX-2 and IL-6 induced activation of JAK2/STAT3 which, in turn, was due to the enhanced phosphorylation of JAK2 and STAT3. The treatment with 1,3,4-thiadiazole derivatives (VR24 and VR27) caused the significant blockade of this signaling pathway. The behavior of STAT3 populations in response to IL-6 and COX-2 stimulations was further confirmed through data-based mathematical modeling using the quantitative western blot data. Finally, VR24 and VR27 restored the perturbed metabolites associated to DMH-induced CRC as evidenced through ¹H NMR based serum metabolomics. The tumor protecting ability of VR24 and VR27 was found comparable or to some degree better than the marketed chemotherapeutics, 5-flurouracil.

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.

Omics in Zebrafish Teratogenesis

The genome revolution represents a complete change on our view of biological systems. The quantitative determination of changes in all major molecular components of the living cells, the "omics" approach, opened whole new fields for all health sciences. Genomics, transcriptomics, proteomics, metabolomics, and others, together with appropriate prediction and modeling tools, will mark the future of developmental toxicity assessment both for wildlife and humans. This is especially true for disciplines, like teratology, which rely on studies in model organisms, as studies at lower levels of organization are difficult to implement. Rodents and frogs have been the favorite models for studying human reproductive and developmental disorders for decades. Recently, the study of the development of zebrafish embryos (ZE) is becoming a major alternative tool to adult animal testing. ZE intrinsic characteristics makes this model a unique system to analyze in vivo developmental alterations that only can be studied applying in toto approaches. Moreover, under actual legislations, ZE is considered as a replacement model (and therefore, excluded from animal welfare regulations) during the first 5 days after fertilization. Here we review the most important components of the zebrafish toolbox available for analyzing early stages of embryotoxic events that could eventually lead to teratogenesis.

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.