Methods for untargeted analysis of cows’ milk – factors impacting metabolite identification

An integrated strategy to improve data acquisition and metabolite identification by time-staggered ion lists in UHPLC/Q-TOF MS-based metabolomics

Untargeted LC-MS based metabolomics strategies are being increasingly applied in metabolite screening for a wide variety of medical conditions. The long-standing “grand challenge” in the utilization of this approach is metabolite identification – confidently determining the chemical structures of m/z-detected unknowns. Here, we use a novel workflow based on the detection of molecular features of interest by high-throughput untargeted LC-MS analysis of patient body fluids combined with targeted molecular identification of those features using infrared ion spectroscopy (IRIS), effectively providing diagnostic IR fingerprints for mass-isolated targets. A significant advantage of this approach is that in silico predicted IR spectra of candidate chemical structures can be used to suggest the molecular structure of unknown features, thus mitigating the need for the synthesis of a broad range of physical reference standards. Pyridoxine dependent epilepsy (PDE-ALDH7A1) is an inborn error of lysine metabolism, resulting from a mutation in the ALDH7A1 gene that leads to an accumulation of toxic levels of α-aminoadipic semialdehyde (α-AASA), piperideine-6-carboxylate (P6C), and pipecolic acid in body fluids. While α-AASA and P6C are known biomarkers for PDE in urine, their instability makes them poor candidates for diagnostic analysis from blood, which would be required for application in newborn screening protocols. Here, we use combined untargeted metabolomics-IRIS to identify several new biomarkers for PDE-ALDH7A1 that can be used for diagnostic analysis in urine, plasma, and cerebrospinal fluids, and are compatible with analysis in dried blood spots for newborn screening. The identification of these novel metabolites has directly rendered novel insights in the pathophysiology of PDE-ALDH7A1.

Untargeted mass spectrometry-based monitoring of samples is a rapidly growing field. It enables comprehensive analysis of chemical compounds present in a sample by measurement of the accurate mass of the analytes. Generally, the methodologies and types of instruments used for targeted analysis are different from those used for untargeted analysis, as the former requires optimal sensitivity and dynamic range, while the latter requires high-resolution and high-mass accuracy. The state of the art of analytical chemistry using high-resolution mass spectrometry permits accurate mass measurements in many fields of bioanalysis such as metabolite identification, structure elucidation and in the field of food analysis. The focus of this chapter has been put on ‘modified’ mycotoxins, a term that has been recently introduced, which encompass biologically modified and chemically modified mycotoxins. Conjugated mycotoxins in this matter consist of biologically modified mycotoxins generated in planta (masked mycotoxins), in animalia and in fungi. Their chemical structures and physicochemical characteristics cover a wide variability range, resulting in their escape during routine biomonitoring, leading to underestimation of the mycotoxin content of samples. In this chapter, the possibilities of high-resolution mass spectrometry will be clarified, and an overview of the current status of untargeted analysis of conjugated (Fusarium) mycotoxins in natural products and modified mycotoxins in human biological fluids will be pointed out.

NMR spectroscopy has become one of the preferred analytical techniques for metabolomics studies due to its inherent nondestructive nature, ability to identify and quantify metabolites simultaneously in a complex mixture, minimal sample preparation requirement, and high degree of experimental reproducibility. NMR-based metabolomics studies involve the measurement and multivariate statistical analysis of metabolites present in biological samples such as biofluids, stool/feces, intestinal content, tissue, and cell extracts by high-resolution NMR spectroscopy-the goal then is to identify and quantify metabolites and evaluate changes of metabolite concentrations in response to some perturbation. Here we describe methodologies for NMR sample preparation of biofluids (serum, saliva, and urine) and stool/feces, intestinal content, and tissues for NMR experiments including extraction of polar metabolites and application of NMR in metabolomics studies. One dimensional (1D) 1H NMR experiments with different variations such as pre-saturation, relaxation-edited, and diffusion-edited are routinely acquired for profiling and metabolite identification and quantification. 2D homonuclear 1H-1H TOCSY and COSY, 2D J-resolved, and heteronuclear 1H-13C HSQC and HMBC are also performed to assist with metabolite identification and quantification. The NMR data are then subjected to targeted and/or untargeted multivariate statistical analysis for biomarker discovery, clinical diagnosis, toxicological studies, molecular phenotyping, and functional genomics.

\n Ratoon stunting disease (RSD) is a serious disease that affects all sugarcane producing countries. The major symptom of RSD is plant growth reduction, which is only seen in ratoon plants, causing up to 80% biomass reduction depending on environmental conditions. The disease is due to Leifsonia xyli subsp. xyli (Lxx), a gram-positive and nutritionally fastidious bacterium that so far has been found to specifically colonize the xylem vessels of sugarcane. However, the successful early detection of this pathogen is currently the main challenge for RSD prevention. Breeding for resistance to RSD, although not in practice, is a viable control measure. Since sugarcane varieties differ in relation to their degree of colonization by Lxx and losses are directly related to population densities of the pathogen in the plant, a promising breeding strategy would be to select for genotypes that are resistant to bacterial multiplication. Thus, knowledge on the responses of sugarcane to RSD at the \\"omics\\" level is an essential starting step to identify key metabolic targets for breeding resistant varieties. The overall goal of this study is to determine the metabolic profiles of a susceptible (CB49-260) and resistant (SP80-3280) variety inoculated or not with Lxx and to compare the results with existing proteomic and transcriptomic data to define a core of targets (proteins, genes, and metabolites) that can be tested as markers of resistance in a collection of sugarcane varieties. Bacterial titers were quantified by Real-Time PCR (qPCR). The metabolites were profiled from the leaves and from the xylem saps collected at 30 and 120 days after inoculation (DAI). Untargeted analysis were performed with Gas Chromatography - Mass Spectrometry (GC-MS) and were carried out on leaves and sap from 120 DAI. Targeted analysis was executed with Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) on both tissues at both timepoints. To validate metabolomics results, a set of metabolites was chosen to be tested in vitro, in order to detect growth alterations caused to Lxx. qPCR confirmed the susceptibility of CB49-260 as it had higher titers than SP80-3280. Global analysis revealed that both varieties and tissues have different metabolic profiles but that those differences are more quantitative than qualitative. The targeted approach identified more amino acids, sugars, organic acids and phosphorylated compounds in the non-inoculated susceptible genotype, while the resistant one had higher abundance of phenolics. It was also shown that inoculation with Lxx results in more relative abundance of amino acids, organic acids, phosphorylated compounds and phenolics. Furthermore, a key amino acid for Lxx survival was related to inoculation on both varieties, as well as a known phenolic compound related to plant defense. Distinguished phenolics resulting from the targeted analysis were selected to evaluate their effect on Lxx growth in vitro. Although some compounds caused inhibition, further optimization of the methodology is needed to confirm these results.\n

A literature search from 2007 to 2014 was conducted to identify publications where principally LC-Orbitrap™-high-resolution mass spectrometry (HRMS) has been employed in food analysis. Of a total of 212 relevant references, only 22 papers were from 2007–10, but in subsequent years there has been a steady growth in publications with 38–55 relevant papers being published each year from 2011 to 2014. In the food safety area, over 50% of the published papers were equally divided between pesticides, veterinary drug residues and natural toxins (including mycotoxins) focused primarily on multi-analyte target analysis. LC-Orbitrap-HRMS was also found to be increasingly important for the analysis of bioactive substances, principally phenolic compounds in foods. A number of studies reported for the first time the identification of new fungal metabolites, predominantly various conjugated forms of known mycotoxins. Novel process contaminants were also identified by LC-Orbitrap-HRMS, as were various substances used for food adulteration and bioactive substances in herbal products and dietary supplements. Untargeted analysis is seen as a major future trend where HRMS plays a significant role. Retrospective analysis of scanned high-resolution mass spectra in conjunction with relevant databases can provide new insights. Metabolomics is also being increasingly used where foods are being profiled through fingerprinting using HRMS. All evidence points towards future growth in the number of applications of HRMS in food safety and quality, as the power of this technique gains wider recognition.

ABSTRACTMetabolites have essential roles in microbial communities, including as mediators of nutrient and energy exchange, cell-to-cell communication, and antibiosis. However, detecting and quantifying metabolites and other chemicals in samples having extremes in salt or mineral content using liquid chromatography-mass spectrometry (LC-MS)-based methods remains a significant challenge. Here, we report a facile method based on in situ chemical derivatization followed by extraction for analysis of metabolites and other chemicals in hypersaline samples, enabling for the first time direct LC-MS-based exometabolomics analysis in sample matrices containing up to 2 M total dissolved salts. The method, MetFish, is applicable to molecules containing amine, carboxylic acid, carbonyl, or hydroxyl functional groups, and it can be integrated into either targeted or untargeted analysis pipelines. In targeted analyses, MetFish provided limits of quantification as low as 1 nM, broad linear dynamic ranges (up to 5 to 6 orders of magnitude) with excellent linearity, and low median interday reproducibility (e.g., 2.6%). MetFish was successfully applied in targeted and untargeted exometabolomics analyses of microbial consortia, quantifying amino acid dynamics in the exometabolome during community succession; in situ in a native prairie soil, whose exometabolome was isolated using a hypersaline extraction; and in input and produced fluids from a hydraulically fractured well, identifying dramatic changes in the exometabolome over time in the well.IMPORTANCE The identification and accurate quantification of metabolites using electrospray ionization-mass spectrometry (ESI-MS) in hypersaline samples is a challenge due to matrix effects. Clean-up and desalting strategies that typically work well for samples with lower salt concentrations are often ineffective in hypersaline samples. To address this gap, we developed and demonstrated a simple yet sensitive and accurate method—MetFish—using chemical derivatization to enable mass spectrometry-based metabolomics in a variety of hypersaline samples from varied ecosystems and containing up to 2 M dissolved salts.

Exposure to green spaces has been linked to numerous health benefits, including a reduction in the risk of cardiovascular disease and lower mortality rates. However, to better understand how green spaces influence human health, it is essential to have valid, quantitative measures of greenness exposure. Building on our previous identification of urinary limonene metabolites as potential biomarkers of exposure, we now investigate α-pinene, another abundant plant-emitted monoterpene, to identify and quantify its urinary metabolites and evaluate their suitability as biomarkers. We used liquid chromatography-high resolution mass spectrometry (LC-HRMS) to analyze samples of human urine collected following either controlled α-pinene inhalation or real-world greenness exposure. Through a combination of pseudo-targeted and untargeted analyses, we discovered 22 α-pinene metabolites post-inhalation including nine novel structures, with two confirmed against synthetic standards. Relative quantitation of the urinary levels of the metabolites was used to estimate their kinetic parameters. A 4 h exposure to a forest environment resulted in significant increases in the most abundant metabolites. This suggests that α-pinene metabolites, specifically myrtenic acid glucuronide and dihydromyrtenic acid glucuronide, may serve as valid biomarkers when assessing individual exposure to green environments. When combined with other subjective and objective measures, these novel urinary biomarkers promote a more comprehensive assessment of exposure to greenness.

Degradation of dimethachlor was evaluated in soils and water, detecting metabolites in incurred samples. Putative elucidation was performed using HRMS and software tools, detecting new possible metabolites of dimethachlor.

This work introduces an alternative experimental approach by integrating high-resolution mass spectrometry (HRMS) with multivariate statistical analysis for metabolite detection and identification. The integration of these tools maximizes information extraction from data, improving accuracy and reducing the risk of false identifications. Seven volunteers' urine samples were collected before and after oral administration of 10 mg of methylclostebol (4-chloro-17β-hydroxy-17α-methylandrost-4-en-3-one, ClMT) and assigned to three excretion time intervals. Analyses were carried out on a GC-HRMS system (Agilent 8890 GC coupled with 7250 GC/QTOF), utilizing low-energy electron ionization (< 18 eV) to preserve the native molecular skeleton, thereby simplifying mass spectrum interpretation, with acquisition in full scan mode. Raw data were then processed and subjected to multivariate analysis. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was employed to emphasize differences among specific sample conditions, and features that significantly contribute to classification in the OPLS-DA can be identified as important biomarkers. Samples from the three excretion intervals demonstrated clear separations, occupying distinct areas within the model's defined space. From this approach, the S-plot displayed seven features identified as biomarkers related to methylclostebol ingestion, comparing their mass spectra with an in-house library of LE-EI mass spectra. The application of this approach is demonstrated to enhance the identification of new markers related to the intake of prohibited substances in the anti-doping field, such as methylclostebol. Its application proved to be an alternative strategy that allows for gathering a more comprehensive range of information in the antidoping field.

Metabolomic analyses can reveal associations between an organism's metabolome and further aspects of its phenotypic state, an attractive prospect for many life‐sciences researchers. The metabolomic approach has been employed in some, but not many, insect study systems, starting in 1990 with the evaluation of the metabolic effects of parasitism on moth larvae. Metabolomics has now been applied to a variety of aspects of insect biology, including behaviour, infection, temperature stress responses, CO2 sedation, and bacteria–insect symbiosis. From a technical and reporting standpoint, these studies have adopted a range of approaches utilising established experimental methodologies. Here, we review current literature and evaluate the metabolomic approaches typically utilised by entomologists. We suggest that improvements can be made in several areas, including sampling procedures, the reduction in sampling and equipment variation, the use of sample extracts, statistical analyses, confirmation, and metabolite identification. Overall, it is clear that metabolomics can identify correlations between phenotypic states and underlying cellular metabolism that previous, more targeted, approaches are incapable of measuring. The unique combination of untargeted global analyses with high‐resolution quantitative analyses results in a tool with great potential for future entomological investigations.

There are numerous articles published for geographical discrimination of tea. However, few research works focused on the authentication and traceability of Westlake Longjing green tea from the first- and second-grade producing regions because the tea trees are planted in a limited growing zone with identical cultivate condition. In this work, a comprehensive analytical strategy was proposed by ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry-based untargeted metabolomics coupled with chemometrics. The automatic untargeted data analysis strategy was introduced to screen metabolites that expressed significantly among different regions. Chromatographic features of metabolites can be automatically and efficiently extracted and registered. Meanwhile, those that were valuable for geographical origin discrimination were screened based on statistical analysis and contents in samples. Metabolite identification was performed based on high-resolution mass values and tandem mass spectra of screened peaks. Twenty metabolites were identified, based on which the two-way encoding partial least squares discrimination analysis was built for geographical origin prediction. Monte Caro simulation results indicated that prediction accuracy was up to 99%. Our strategy can be applicable for practical applications in the quality control of Westlake Longjing green tea.

The proposed metabolomic workflow, based on coupling high-resolution mass spectrometry with computational tools, can be an alternative strategy for metabolite detection and identification. This approach allows the extension of the investigation field to chemically different compounds, maximizing the information obtainable from the data and minimizing the time and resources required. Urine samples were collected from 5 healthy volunteers before and after oral administration of 3β-hydroxyandrost-5-ene-7,17-dione as a model compound and defining three excretion time intervals. Raw data were acquired in both positive and negative ionization modes using an Agilent Technologies 1290 Infinity II series HPLC coupled to a 6545 Accurate-Mass Quadrupole Time-of-Flight. They were then processed to align peak retention times with the same accurate mass, and the resulting data matrix was subjected to multivariate analysis. Multivariate analysis (PCA and PLS-DA models) demonstrated high similarity between samples belonging to the same collection time interval and clear discrimination between different excretion intervals. The blank and long excretion groups were distinguished suggesting the presence of long excretion markers, which are of remarkable interest in anti-doping analyses. The correspondence of some significant features with metabolites reported in the literature confirmed the rationale and usefulness of the proposed metabolomic approach. The presented study proposes a metabolomics workflow for the early detection and characterization of drug metabolites by untargeted urinary analysis to reduce the range of substances still excluded from routine screening. Its application has detected minor steroid metabolites, as well as unexpected endogenous alterations, proving to be an alternative strategy that can allow gathering a more complete range of information in the antidoping field.

Automatic untargeted metabolic profiling analysis coupled with Chemometrics for improving metabolite identification quality to enhance geographical origin discrimination capability

After 2 months from the infestation of tomato plants with the root-knot nematode (RKN) Meloidogyne incognita, we performed a gas chromatography-mass spectrometry untargeted fingerprint analysis for the identification of characteristic metabolites and biomarkers. Principal component analysis, and orthogonal projections to latent structures discriminant analysis suggested dramatic local changes of the plant metabolome. In the case of tomato leaves, β-alanine, phenylalanine, and melibiose were induced in response to RKN stimuli, while ribose, glycerol, myristic acid, and palmitic acid were reduced. For tomato stems, upregulated metabolites were ribose, sucrose, fructose, and glucose, while fumaric acid and glycine were downregulated. The variation in molecular strategies to the infestation of RKNs may play an important role in how Solanum lycopersicum and other plants adapt to nematode parasitic stress.