Highly Accurate Chemical Formula Prediction Tool Utilizing High-Resolution Mass Spectra, MS/MS Fragmentation, Heuristic Rules, and Isotope Pattern Matching

Distinguishing Artifactual Fatty Acid Dimers from Fatty Acid Esters of Hydroxy Fatty Acids in Untargeted LC-MS Pipelines

Untargeted metabolomics is a powerful profiling tool for the discovery of possible biomarkers of disease onset and progression. Analytical pipelines applying liquid chromatography (LC) and mass spectrometry (MS)-based methods are widely used to survey a broad range of metabolites within various metabolic pathways, including organic acids, amino acids, nucleosides, and lipids. Accurate and complete identification of putative metabolites is an ongoing challenge in untargeted metabolomics studies. Highly sensitive instrumentation can result in the detection of adduct and fragment ions that form reproducibly and contain identifiable ions that are difficult to distinguish from metabolic pathway intermediates, which may result in false-positive identification. At concentrations as low as 10 μM, free fatty acids have been found to form homo- and heterodimers in untargeted metabolomics pipelines that resemble the lipid class fatty acid esters of hydroxy fatty acids (FAHFAs), resulting in misidentification. This chapter details a protocol for LC-MS-based untargeted metabolomics using hydrophilic interaction chromatography (HILIC) that specifically aids in distinguishing artifactual fatty acid dimers from endogenous FAHFAs.

Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence─the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in the context of the chemical space being considered. Neither are they easily automated nor transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a database that matches an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multiproperty reference libraries constructed from a subset of the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

A Semi-Quantitative Approach to Nontarget Compositional Analysis of Complex Samples

Nontarget analysis (NTA) by liquid chromatography coupled to high-resolution mass spectrometry improves the capacity to comprehend the molecular composition of complex mixtures compared to targeted analysis techniques. However, the detection of unknown compounds means that quantification in NTA is challenging. This study proposes a new semi-quantitative methodology for use in the NTA of organic aerosol. Quantification of unknowns is achieved using the average ionization efficiency of multiple quantification standards which elute within the same retention time window as the unknown analytes. In total, 110 authentic standards constructed 25 retention time windows for the quantification of oxygenated (CHO) and organonitrogen (CHON) species. The method was validated on extracts of biomass burning organic aerosol (BBOA) and compared to quantification with authentic standards and had an average prediction error of 1.52 times. Furthermore, 70% of concentrations were estimated within a factor of 2 (prediction errors between 0.5 and 2 times) from the authentic standard quantification. The semi-quantification method also showed good agreement for the quantification of CHO compounds compared to predictive ionization efficiency-based methods, whereas for CHON species, the prediction error of the semi-quantification method (1.63) was significantly lower than the predictive ionization efficiency approach (14.94). Application to BBOA for the derivation of relative abundances of CHO and CHON species showed that using peak area underestimated the relative abundance of CHO by 19% and overestimated that of CHON by 11% compared to the semi-quantification method. These differences could lead to significant misinterpretations of source apportionment in complex samples, highlighting the need to account for ionization differences in NTA approaches.

Deciphering Isotopic Fine Structures of Silylated Compounds in Gas Chromatography-Vacuum Photoionization Orbitrap Mass Spectrometry of Bio-Oils

We introduce vacuum resonance-enhanced multiphoton ionization (REMPI) with high-resolution Orbitrap Fourier transform mass spectrometry (FTMS) for analyzing silylated polar compounds. UV laser radiation at 248 nm sensitively and selectively targets aromatic constituents, while high-resolution mass spectrometry (HRMS) enables high-performance mass spectrometric detection. This workflow enhances the detection confidence of polar constituents by identifying unique isotopologue patterns, including at the isotopic fine structure (IFS) level, in analytical standards and complex bio-oils. A direct and derivatized gas chromatography (GC) procedure on a polar standard component mixture allowed us to explore the general ionization and detection characteristics of REMPI FTMS. HRMS enabled the examination of the complex isotopologue profiles, revealing distinct patterns for the CHOxSiy-class compounds. Particularly in complex mixtures, this isobaric/isonucleonic complexity exceeded the classical mass resolution capabilities of the employed Orbitrap D30 series and prompted the usage of prolonged transients via an external data acquisition system. This procedure substantially improved mass spectrometric results by recording the unreduced time-domain transient data for up to 2 s. Notably, the ability to distinguish diagnostic isotopic pairs, such as 12C/29Si vs 13C/28Si with a mass split of ∼3.79 mDa and 13C12C/28Si29Si vs 13C2/28Si2, with an approximate mass difference of ∼3.32 mDa, demonstrates a significant and expected performance improvement. Finally, we benchmark the GC HRMS methodology to identify silylated oxygenated and nitrogen-containing constituents in ultracomplex bio-oil samples. The presented approach of utilizing the silicon isotope pattern and unique isotopologue mass splits for increasing attribution confidence can be applied beyond bio-oils toward the general GC analyses of polar oxygenates.

Non-target screening reveals 124 PFAS at an AFFF-impacted field site in Germany specified by novel systematic terminology

The uncontrolled release of aqueous film-forming foam (AFFF) ingredients during a major fire incident in Reilingen, Germany, in 2008 led to significant soil and groundwater contamination. As the identity of fluorochemical surfactants in AFFF are often veiled due to company secrets, it is important to characterize AFFF contaminations and their impact on the environment comprehensively. In this study, we adapted a systematic approach combining a suitable extraction method with liquid chromatography high-resolution quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) for an extensive non-targeted analysis. Our analysis identified 124 per- and polyfluoroalkyl substances (PFAS) from 42 subclasses in the contaminated soil (confidence levels of identification between 1 and 3). Typical for AFFF-impacted field sites, these included anionic, cationic, and zwitterionic substances with perfluoroalkyl chains spanning from 3 to 14 carbon atoms. Furthermore, we identified 1 previously unreported substance, and detected 9 PFAS subclasses for the first time in soil. AFFFs have long been employed to extinguish large hydrocarbon fires, yet their environmental consequences remain a concern. This study sheds light on the complex composition of AFFFs at this particularly contaminated area, emphasizing the necessity for extensive contaminant characterization as sound basis for informed management strategies to mitigate their adverse effects. AFFF PFAS are often named differently in the literature, leading to inconsistency in terminology. To address this issue, we introduced partially new terminology for AFFF-related PFAS to establish consistent terminology, to facilitate communication of identified compounds, and to ensure that the chemical structure can be directly derived from acronyms.

GlyCombo Enables Rapid, Complete Glycan Composition Identification across Diverse Glycomic Sample Types

Glycans are sugar-based polymers found to modify biomolecules, including lipids and proteins, as well as occur unconjugated as free polysaccharides. Due to their ubiquitous cellular presentation, glycans mediate crucial biological processes and are frequently sought after as biomarkers for a wide range of diseases. Identification of glycans present in samples acquired with mass spectrometry (MS) is a cornerstone of glycomics research; thus, the ability to rapidly identify glycans in each acquisition is integral to glycomics analysis pipelines. Here we introduce GlyCombo (https://github.com/Protea-Glycosciences/GlyCombo), an open-source, freely available software tool designed to rapidly assign monosaccharide combinations to glycan precursor masses including those subjected to MS2 in LC-MS/MS experiments. GlyCombo was evaluated across six diverse data sets, demonstrating MS vendor, derivatization, and glycan-type neutrality. Compositional assignments using GlyCombo are shown to be faster than the current predominant approach, GlycoMod, a closed-source web application. Two unique features of GlyCombo, multiple adduct search and off-by-one error anticipation, reduced unassigned MS2 scans in a benchmark data set by 40%. Finally, the comprehensiveness of glycan feature identification is exhibited in Skyline, a software that requires predefined transitions that are derived from GlyCombo output files.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

UPLC-MS/MS-Based Target Screening of 90 Phosphodiesterase Type 5 Inhibitors in 5 Dietary Supplements

The aim of individuals consuming health supplements is to attain a robust state through nutritional regulation. However, some unscrupulous manufacturers, motivated by profit, fraudulently incorporate drugs or unauthorized components with therapeutic effects into the product for instant product performance enhancement. The long-term use of these products may inadvertently inflict harm on human health and fail to promote nutritive healthcare. The illegal inclusion of these substances is prevalent in kidney-tonifying and sexuality-enhancing products. Developing effective analytical methods to identify these products and screen for illegal added ingredients can effectively prevent such products from reaching and remaining on the market. A target screening method for the detection and quantification of 90 phosphodiesterase type 5 inhibitors (PDE-5is) in 5 kinds of health products was developed and validated. The type of dietary supplements varied from tablets, capsules, and protein powder to wine and beverages. Sample preparation was completed with a one-step liquid phase extraction. The screening process of 90 PDE-5is was done efficiently within 25 min by ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) using the dynamic multiple reaction monitoring (dMRM) technique. The LODs of 90 PDE-5is were detected at levels ranging from 25 to 85 ng/g or ng/mL. This novel targeting methodology was effective and can be applied to routine market supervision. Among 286 batches of samples, 8 batches were found to be positive. Three kinds of PDE-5is were first detected in healthy products. The screening method demonstrated herein will be a promising and powerful tool for rapid screening of PDE-5is.

Formulas of High MW Unknown Compounds from Accurate Mass Differences and Ranking of Best Candidates from First Principles

The number of possible candidate formulas for high molecular weight unknown compounds (e.g., 7000-8000 Da for common 20-mer oligonucleotides) by high-resolution mass spectrometry is in the order of several hundred thousand even at the highest level of experimental accuracy. In demanding analytical applications involving new chemistries and synthetic routes where little is known about the chemical nature or mechanisms of formation of the unknown compounds (e.g., impurities), the generation of a short list of the most plausible formulas would be highly desirable. Such an approach has been developed in the current work. The concept of mass difference from a reference compound is introduced to simplify the approach and greatly reduce the number of possible formulas. The approach allows for the generation of candidate formulas by both the addition and subtraction of atoms to account for all possible molecular changes from the parent compound. A reduction of 3 orders of magnitude in the number of possible formulas has been achieved by the approach. Ranking of the formulas by the product of the sums of the absolute changes in the total number of all atoms and all heteroatoms in the proposed difference formula successfully ranked the correct formula within the top 10 from a list of 200-250 best candidate formulas. There is a tendency for the impurities to be formed involving the least change in the number of atoms and heteroatoms. ΔfHo and ΔfG'o values can be used as a complementary ranking system of the top candidates. The approach is applicable to unknowns in any other systems of high MW compounds.

Molecular-Level Insights into the Relationship between Volatility of Organic Aerosol Constituents and PM2.5 Air Pollution Levels: A Study with Ultrahigh-Resolution Mass Spectrometry

Volatility of organic aerosols (OAs) significantly influences new particle formation and the occurrence of particulate air pollution. However, the relationship between the volatility of OA and the level of particulate air pollution (i.e., particulate matter concentration) is not well understood. In this study, we compared the chemical composition (identified by an ultrahigh-resolution Orbitrap mass spectrometer) and volatility (estimated based on a predeveloped parametrization method) of OAs in urban PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) samples from seven German and Chinese cities, where the PM2.5 concentration ranged from a light (14 μg m-3) to heavy (319 μg m-3) pollution level. A large fraction (71-98%) of compounds in PM2.5 samples were attributable to intermediate-volatility organic compounds (IVOCs) and semivolatile organic compounds (SVOCs). The fraction of low-volatility organic compounds (LVOCs) and extremely low-volatility organic compounds (ELVOCs) decreased from clean (28%) to heavily polluted urban regions (2%), while that of IVOCs increased from 34 to 62%. We found that the average peak area-weighted volatility of organic compounds in different cities showed a logarithmic correlation with the average PM2.5 concentration, indicating that the volatility of urban OAs increases with the increase of air pollution level. Our results provide new insights into the relationship between OA volatility and PM pollution levels and deepen the understanding of urban air pollutant evolution.

MIST-CF: Chemical Formula Inference from Tandem Mass Spectra

Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends on time-intensive, proprietary, and expert-parametrized fragmentation tree construction and scoring. In this work, we extend our previous spectrum Transformer methodology into an energy-based modeling framework, MIST-CF: Metabolite Inference with Spectrum Transformers for Chemical Formula prediction, for learning to rank chemical formula and adduct assignments given an unannotated MS/MS spectrum. Importantly, MIST-CF learns in a data-dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction. We train and evaluate our model on a large open-access database, showing an absolute improvement of 10% top 1 accuracy over other neural network architectures. We further validate our approach on the CASMI2022 challenge data set, achieving nearly equivalent performance to the winning entry within the positive mode category without any manual curation or postprocessing of our results. These results demonstrate an exciting strategy to more powerfully leverage MS2 fragment peaks for predicting MS1 precursor chemical formulas with data-driven learning.

Determining the parent and associated fragment formulae in mass spectrometry via the parent subformula graph

BACKGROUND

Identifying the molecular formula and fragmentation reactions of an unknown compound from its mass spectrum is crucial in areas such as natural product chemistry and metabolomics. We propose a method for identifying the correct candidate formula of an unidentified natural product from its mass spectrum. The method involves scoring the plausibility of parent candidate formulae based on a parent subformula graph (PSG), and two possible metrics relating to the number of edges in the PSG. This method is applicable to both electron-impact mass spectrometry (EI-MS) and tandem mass spectrometry (MS/MS) data. Additionally, this work introduces the two-dimensional fragmentation plot (2DFP) for visualizing PSGs.

RESULTS

Our results suggest that incorporating information regarding the edges of the PSG results in enhanced performance in correctly identifying parent formulae, in comparison to the more well-accepted "MS/MS score", on the 2016 Computational Assessment of Small Molecule Identification (CASMI 2016) data set (76.3 vs 58.9% correct formula identification) and the Research Centre for Toxic Compounds in the Environment (RECETOX) data set (66.2% vs 59.4% correct formula identification). In the extension of our method to identify the correct candidate formula from complex EI-MS data of semiochemicals, our method again performed better (correct formula appearing in the top 4 candidates in 20/23 vs 7/23 cases) than the MS/MS score, and enables the rapid identification of both the correct parent ion mass and the correct parent formula with minimal expert intervention.

CONCLUSION

Our method reliably identifies the correct parent formula even when the mass information is ambiguous. Furthermore, should parent formula identification be successful, the majority of associated fragment formulae can also be correctly identified. Our method can also identify the parent ion and its associated fragments in EI-MS spectra where the identity of the parent ion is unclear due to low quantities and overlapping compounds. Finally, our method does not inherently require empirical fitting of parameters or statistical learning, meaning it is easy to implement and extend upon.

SCIENTIFIC CONTRIBUTION

Developed, implemented and tested new metrics for assessing plausibility of candidate molecular formulae obtained from HR-MS data.

Comparative polar and lipid plasma metabolomics differentiate KSHV infection and disease states

BACKGROUND

Kaposi sarcoma (KS) is a neoplastic disease etiologically associated with infection by the Kaposi sarcoma-associated herpesvirus (KSHV). KS manifests primarily as cutaneous lesions in individuals due to either age (classical KS), HIV infection (epidemic KS), or tissue rejection preventatives in transplantation (iatrogenic KS) but can also occur in individuals, predominantly in sub-Saharan Africa (SSA), lacking any obvious immune suppression (endemic KS). The high endemicity of KSHV and human immunodeficiency virus-1 (HIV) co-infection in Africa results in KS being one of the top 5 cancers there. As with most viral cancers, infection with KSHV alone is insufficient to induce tumorigenesis. Indeed, KSHV infection of primary human endothelial cell cultures, even at high levels, is rarely associated with long-term culture, transformation, or growth deregulation, yet infection in vivo is sustained for life. Investigations of immune mediators that distinguish KSHV infection, KSHV/HIV co-infection, and symptomatic KS disease have yet to reveal consistent correlates of protection against or progression to KS. In addition to viral infection, it is plausible that pathogenesis also requires an immunological and metabolic environment permissive to the abnormal endothelial cell growth evident in KS tumors. In this study, we explored whether plasma metabolomes could differentiate asymptomatic KSHV-infected individuals with or without HIV co-infection and symptomatic KS from each other.

METHODS

To investigate how metabolic changes may correlate with co-infections and tumorigenesis, plasma samples derived from KSHV seropositive sub-Saharan African subjects in three groups, (A) asymptomatic (lacking neoplastic disease) with KSHV infection only, (B) asymptomatic co-infected with KSHV and HIV, and (C) symptomatic with clinically diagnosed KS, were subjected to analysis of lipid and polar metabolite profiles RESULTS: Polar and nonpolar plasma metabolic differentials were evident in both comparisons. Integration of the metabolic findings with our previously reported KS transcriptomics data suggests dysregulation of amino acid/urea cycle and purine metabolic pathways, in concert with viral infection in KS disease progression.

CONCLUSIONS

This study is, to our knowledge, the first to report human plasma metabolic differentials between in vivo KSHV infection and co-infection with HIV, as well as differentials between co-infection and epidemic KS.

Underutilized legumes: nutrient status and advanced breeding approaches for qualitative and quantitative enhancement

Underutilized/orphan legumes provide food and nutritional security to resource-poor rural populations during periods of drought and extreme hunger, thus, saving millions of lives. The Leguminaceae, which is the third largest flowering plant family, has approximately 650 genera and 20,000 species and are distributed globally. There are various protein-rich accessible and edible legumes, such as soybean, cowpea, and others; nevertheless, their consumption rate is far higher than production, owing to ever-increasing demand. The growing global urge to switch from an animal-based protein diet to a vegetarian-based protein diet has also accelerated their demand. In this context, underutilized legumes offer significant potential for food security, nutritional requirements, and agricultural development. Many of the known legumes like Mucuna spp., Canavalia spp., Sesbania spp., Phaseolus spp., and others are reported to contain comparable amounts of protein, essential amino acids, polyunsaturated fatty acids (PUFAs), dietary fiber, essential minerals and vitamins along with other bioactive compounds. Keeping this in mind, the current review focuses on the potential of discovering underutilized legumes as a source of food, feed and pharmaceutically valuable chemicals, in order to provide baseline data for addressing malnutrition-related problems and sustaining pulse needs across the globe. There is a scarcity of information about underutilized legumes and is restricted to specific geographical zones with local or traditional significance. Around 700 genera and 20,000 species remain for domestication, improvement, and mainstreaming. Significant efforts in research, breeding, and development are required to transform existing local landraces of carefully selected, promising crops into types with broad adaptability and economic viability. Different breeding efforts and the use of biotechnological methods such as micro-propagation, molecular markers research and genetic transformation for the development of underutilized crops are offered to popularize lesser-known legume crops and help farmers diversify their agricultural systems and boost their profitability.

BUDDY: molecular formula discovery via bottom-up MS/MS interrogation

A substantial fraction of metabolic features remains undetermined in mass spectrometry (MS)-based metabolomics, and molecular formula annotation is the starting point for unraveling their chemical identities. Here we present bottom-up tandem MS (MS/MS) interrogation, a method for de novo formula annotation. Our approach prioritizes MS/MS-explainable formula candidates, implements machine-learned ranking and offers false discovery rate estimation. Compared with the mathematically exhaustive formula enumeration, our approach shrinks the formula candidate space by 42.8% on average. Method benchmarking on annotation accuracy was systematically carried out on reference MS/MS libraries and real metabolomics datasets. Applied on 155,321 recurrent unidentified spectra, our approach confidently annotated >5,000 novel molecular formulae absent from chemical databases. Beyond the level of individual metabolic features, we combined bottom-up MS/MS interrogation with global optimization to refine formula annotations while revealing peak interrelationships. This approach allowed the systematic annotation of 37 fatty acid amide molecules in human fecal data. All bioinformatics pipelines are available in a standalone software, BUDDY ( https://github.com/HuanLab/BUDDY ).

Understanding the Seasonal Effect of Metabolite Production in Terminalia catappa L. Leaves through a Concatenated MS- and NMR-Based Metabolomics Approach

Terminalia catappa L. (Combretaceae) is a medicinal plant that is part of the Brazilian biodiversity; this plant is popularly used for the treatment of a wide range of diseases. To better understand the chemical composition of T. catappa in different seasons, we conducted a thorough study using LC-MS and NMR data analysis techniques. The study helped obtain a chemical profile of the plant ethanolic extracts in different seasons of the year (spring, summer, autumn, and winter). The dereplication of LC-HRMS data allowed the annotation of 90 compounds in the extracts of T. catappa (hydrolyzable tannins, ellagic acid derivatives, and glycosylated flavonoids). Triterpenes and C-glycosyl flavones were the compounds that significantly contributed to differences observed between T. catappa plant samples harvested in autumn/winter and spring, respectively. The variations observed in the compound composition of the plant leaves may be related to processes induced by environmental stress and leaf development. Data fusion applied in the metabolomic profiling study allowed us to identify metabolites with greater confidence, and provided a better understanding regarding the production of specialized metabolites in T. catappa leaves under different environmental conditions, which may be useful to establish appropriate quality criteria for the standardization of this medicinal plant.

Markers for obese and non-obese Type 2 diabetes identified using whole blood metabolomics

Definitive differences in blood metabolite profiles between obese and non-obese Type 2 diabetes (T2D) have not been established. We performed an LC-MS-based non-targeted metabolomic analysis of whole blood samples collected from subjects classified into 4 types, based on the presence or absence of obesity and T2D. Of the 125 compounds identified, 20, comprising mainly nucleobases and glucose metabolites, showed significant increases or decreases in the T2D group. These included cytidine, UDP-glucuronate, UMP, 6-phosphogluconate, and pentose-phosphate. Among those 20 compounds, 11 enriched in red blood cells (RBCs) have rarely been studied in the context of diabetes, indicating that RBC metabolism is more extensively disrupted than previously known. Correlation analysis revealed that these T2D markers include 15 HbA1c-associated and 5 irrelevant compounds that may reflect diabetic conditions by a different mechanism than that of HbA1c. In the obese group, enhanced protein and fatty acid catabolism causes increases in 13 compounds, including methylated or acetylated amino acids and short-chain carnitines. Our study, which may be considered a pilot investigation, suggests that changes in blood metabolism due to obesity and diabetes are large, but essentially independent.

Gamma radiolytic stability of the novel modified diglycolamide 2,2'-oxybis(N,N-didecylpropanamide) (mTDDGA) for grouped actinide extraction

Reprocessing of spent nuclear fuel aims at improving resource efficiency and reducing its radiotoxicity and heat production in the long term. The necessary separation of certain metal ions from the spent fuel solutions can be achieved using different solvent extraction processes. For the scenario of the EURO-GANEX process, the use of the new, modified diglycolamide 2,2′-oxybis(N,N-didecylpropanamide) (mTDDGA) was recently proposed to simplify the current solvent composition and reduce extraction of fission products. Before further developing the process based on this new ligand, its stability under ionizing radiation conditions needs to be studied. For this reason, gamma irradiation experiments were conducted followed by analyses with high performance liquid chromatography coupled to a mass spectrometer (HPLC-MS). The determined degradation rate of mTDDGA was found to be lower than that of the reference molecule N,N,N′,N′-tetra-n-octyl-diglycolamide (TODGA). Many identified degradation compounds of both molecules are analogues showing the same bond breaking, although also unreported de-methylation, double/triple de-alkylation and n-dodecane addition products were observed.

The radiolysis behavior of a new diglycolamide for solvent extraction of actinides and lanthanides was studied. The observed degradation rate was lower than for the reference molecule and 22 degradation compounds were identified.

Molecular Formula Prediction for Chemical Filtering of 3D OrbiSIMS Datasets

Modern mass spectrometry techniques produce a wealth of spectral data, and although this is an advantage in terms of the richness of the information available, the volume and complexity of data can prevent a thorough interpretation to reach useful conclusions. Application of molecular formula prediction (MFP) to produce annotated lists of ions that have been filtered by their elemental composition and considering structural double bond equivalence are widely used on high resolving power mass spectrometry datasets. However, this has not been applied to secondary ion mass spectrometry data. Here, we apply this data interpretation approach to 3D OrbiSIMS datasets, testing it for a series of increasingly complex samples. In an organic on inorganic sample, we successfully annotated the organic contaminant overlayer separately from the substrate. In a more challenging purely organic human serum sample we filtered out both proteins and lipids based on elemental compositions, 226 different lipids were identified and validated using existing databases, and we assigned amino acid sequences of abundant serum proteins including albumin, fibronectin, and transferrin. Finally, we tested the approach on depth profile data from layered carbonaceous engine deposits and annotated previously unidentified lubricating oil species. Application of an unsupervised machine learning method on filtered ions after performing MFP from this sample uniquely separated depth profiles of species, which were not observed when performing the method on the entire dataset. Overall, the chemical filtering approach using MFP has great potential in enabling full interpretation of complex 3D OrbiSIMS datasets from a plethora of material types.

Abundance of organosulfates derived from biogenic volatile organic compounds: Seasonal and spatial contrasts at four sites in China

Atmospheric organosulfates (OSs) derived from biogenic volatile organic compounds (BVOCs) encode chemical interaction strength between anthroposphere and biosphere. We report BVOC-derived OSs in the summer of 2016 and the winter of 2017 at four locations in China (i.e., Hong Kong (HK), Guangzhou (GZ), Shanghai (SH), and Beijing (BJ)). The spatial coverage of three climatic zones from the south to the north in China is accompanied with a wide range of aerosol inorganic sulfate (4.9-13.8 μg/m³). We employed a combined targeted and untargeted approach using high-performance liquid chromatography-Orbitrap mass spectrometry to quantify/semi-quantify ~200 OSs and nitrooxy OSs derived from four types of precursors, namely C₂-C₃ oxygenated VOCs, isoprene, monoterpenes (MT), and sesquiterpenes (ST). The seasonal averages of the total quantified OSs across the four sites are in the range of 201-545 (summer) and 123-234 ng/m³ (winter), with the isoprene-derived OSs accounting for more than 80% (summer) and 57% (winter). The C_2-3 OSs and isoprene-derived OSs share the same seasonality (summer >winter) and the same south-north spatial gradient as those of isoprene emissions. In contrast, the MT- and ST-derived OSs are of either comparable abundance or slightly higher abundance in winter at the four sites. The spatial contrasts for MT- and ST-derived OSs are not clearly discernable among GZ, SH, and BJ. HK is noted to have invariably lower abundances of all groups of OSs, in line with its aerosol inorganic sulfate being the lowest. These results indicate that BVOC emissions are the driving factor regulating the formation of C_2-3 OSs and isoprene-derived OSs. Other factors, such as sulfate abundance, however, play a more important role in the formation of MT- and ST-derived OSs. This in turn suggests that the formation kinetics and/or pathways differ between these two sub-groups of BVOCs-derived OSs.

Evaluation of Sample Preparation Methods for Non-Target Screening of Organic Micropollutants in Urban Waters Using High-Resolution Mass Spectrometry

Non-target screening (NTS) has gained interest in recent years for environmental monitoring purposes because it enables the analysis of a large number of pollutants without predefined lists of molecules. However, sample preparation methods are diverse, and few have been systematically compared in terms of the amount and relevance of the information obtained by subsequent NTS analysis. The goal of this work was to compare a large number of sample extraction methods for the unknown screening of urban waters. Various phases were tested for the solid-phase extraction of micropollutants from these waters. The evaluation of the different phases was assessed by statistical analysis based on the number of detected molecules, their range, and physicochemical properties (molecular weight, standard recoveries, polarity, and optical properties). Though each cartridge provided its own advantages, a multilayer cartridge combining several phases gathered more information in one single extraction by benefiting from the specificity of each one of its layers.

Comparison of High-Resolution Fourier Transform Mass Spectrometry Platforms for Putative Metabolite Annotation

Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap mass spectrometry (MS) are among the highest-performing analytical platforms used in metabolomics. Non-targeted metabolomics experiments, however, yield extremely complex datasets that make metabolite annotation very challenging and sometimes impossible. The high-resolution accurate mass measurements of the leading MS platforms greatly facilitate this process by reducing mass errors and spectral overlaps. When high resolution is combined with relative isotopic abundance (RIA) measurements, heuristic rules, and constraints during searches, the number of candidate elemental formula(s) can be significantly reduced. Here, we evaluate the performance of Orbitrap ID-X and 12T solariX FT-ICR mass spectrometers in terms of mass accuracy and RIA measurements and how these factors affect the assignment of the correct elemental formulas in the metabolite annotation pipeline. Quality of the mass measurements was evaluated under various experimental conditions (resolution: 120, 240, 500 K; automatic gain control: 5 × 104, 1 × 105, 5 × 105) for the Orbitrap MS platform. High average mass accuracy (<1 ppm for UPLC-Orbitrap MS and <0.2 ppm for direct infusion FT-ICR MS) was achieved and allowed the assignment of correct elemental formulas for over 90% (m/z 75-466) of the 104 investigated metabolites. 13C1 and 18O1 RIA measurements further improved annotation certainty by reducing the number of candidates. Overall, our study provides a systematic evaluation for two leading Fourier transform (FT)-based MS platforms utilized in metabolite annotation and provides the basis for applying these, individually or in combination, to metabolomics studies of biological systems.

Human age-declined saliva metabolic markers determined by LC-MS

Metabolites in human biofluids reflect individual physiological states influenced by various factors. Using liquid chromatography-mass spectrometry (LC–MS), we conducted non-targeted, non-invasive metabolomics using saliva of 27 healthy volunteers in Okinawa, comprising 13 young (30 ± 3 year) and 14 elderly (76 ± 4 year) subjects. Few studies have comprehensively identified age-dependent changes in salivary metabolites. Among 99 salivary metabolites, 21 were statistically age-related. All of the latter decline in abundance with advancing age, except ATP, which increased 1.96-fold in the elderly, possibly due to reduced ATP consumption. Fourteen age-linked and highly correlated compounds function in a metabolic network involving the pentose-phosphate pathway, glycolysis/gluconeogenesis, amino acids, and purines/pyrimidines nucleobases. The remaining seven less strongly correlated metabolites, include ATP, anti-oxidation-related glutathione disulfide, muscle-related acetyl-carnosine, N-methyl-histidine, creatinine, RNA-related dimethyl-xanthine and N-methyl-adenosine. In addition, glutamate and N-methyl-histidine are related to taste, so their decline suggests that the elderly lose some ability to taste. Reduced redox metabolism and muscle activity are suggested by changes in glutathione and acetyl-carnosine. These age-linked salivary metabolites together illuminate a metabolic network that reflects a decline of oral functions during human aging.

Analytical methods for organosulfate detection in aerosol particles: Current status and future perspectives

Organosulfates (OSs) are well-known water-soluble constituents of atmospheric aerosol particles. They are formed from multiphase reactions between volatile organic compounds (VOCs) and their photooxidation products, and acidic sulfate originating from biogenic and anthropogenic sources in the atmosphere. Although the analytical procedures used to measure OSs, including sampling, pre-treatment, and instrumental detection, have advanced substantially in the last decade, there is still a need for accurate and standardized analysis procedures for the identification, quantification, and comparison of OSs in different regions. Additionally, there has no study focused on the health effects of OSs. This review outlines the analytical methods developed for OS detection during the last decade, highlighting both improvements and drawbacks. It also considers the future development of analytical methods for OS detection, and proposes the establishment of OSs screening method from the perspective of health effects to solve the problem of unknown health related OSs identification.

Using Porcine Jejunum Ex Vivo to Study Absorption and Biotransformation of Natural Products in Plant Extracts: Pueraria lobata as a Case Study

Herbal preparations (HPs) used in folk medicine are complex mixtures of natural products (NPs). Their efficacy in vivo after ingestion depends on the uptake of the active ingredient, and, in some cases, their metabolites, in the gastrointestinal tract. Thus, correlating bioactivities measured in vitro and efficacy in vivo is a challenge. An extract of Pueraria lobata rich in different types of isoflavones was used to evaluate the capacity of viable porcine small intestine ex vivo to elucidate the absorption of HP constituents, and, in some cases, their metabolites. The identification and transport of permeants across the jejunum was monitored by liquid chromatography-mass spectrometry (LC-MS), combining targeted and untargeted metabolite profiling approaches. It was observed that the C-glycoside isoflavones were stable and crossed the intestinal membrane, while various O-glycoside isoflavones were metabolized into their corresponding aglycones, which were then absorbed. These results are consistent with human data, highlighting the potential of using this approach. A thorough investigation of the impact of absorption and biotransformation was obtained without in vivo studies. The combination of qualitative untargeted and quantitative targeted LC-MS methods effectively monitored a large number of NPs and their metabolites, which is essential for research on HPs.

UHPLC-(ESI)-HRMS and NMR-Based Metabolomics Approach to Access the Seasonality of Byrsonima intermedia and Serjania marginata From Brazilian Cerrado Flora Diversity

Seasonality is one of the major environmental factors that exert influence over the synthesis and accumulation of secondary metabolites in medicinal plants. The application of the metabolomics approach for quality control of plant extracts is essentially important because it helps one to establish a standard metabolite profile and to analyze factors that affect the effectiveness of the medicinal plants. The Brazilian Cerrado flora is characterized by a rich diversity of native plant species, and a number of these plant species have been found to have suitable medicinal properties. Some of these plant species include Byrsonima intermedia and Serjania marginata. To better understand the chemical composition of these plant species, we conducted a study using the state-of-the-art techniques including the HPLC system coupled to an Exactive-Orbitrap high resolution mass spectrometer with electrospray ionization interface UHPLC-(ESI)-HRMS and by NMR being performed 2D J-resolved and proton NMR spectroscopy. For the analysis, samples were harvested bimonthly during two consecutive years. UHPLC-(ESI)-HRMS data were preprocessed and the output data uploaded into an in-house Excel macro for peak dereplication. MS and NMR data were concatenated using the data fusion method and submitted to multivariate statistical analysis. The dereplication of LC-HRMS data helped in the annotation of the major compounds present in the extracts of the three plant species investigated allowing the annotation of 68 compounds in the extracts of B. intermedia (cinnamic acids, phenolic acids derived from galloyl quinic and shikimic acid, proanthocyanidins, glycosylated flavonoids, triterpenes and other phenols) and 81 compounds in the extracts of S. marginata (phenolic acids, saponins, proanthocyanidins, glycosylated flavonoids among other compounds). For a better assessment of the great number of responses, the significance of the chemical variables for the differentiation and correlation of the seasons was determined using the variable importance on projection (VIP) technique and through the application of the false discovery rate (FDR) estimation. The statistical data obtained showed that seasonal factors played an important role on the production of metabolites in each plant species. Temperature conditions, drought and solar radiation were found to be the main factors that affected the variability of phenolic compounds in each species.

Extension of Diagnostic Fragmentation Filtering for Automated Discovery in DNA Adductomics

Development of high-resolution/accurate mass liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) methodology enables the characterization of covalently modified DNA induced by interaction with genotoxic agents in complex biological samples. Constant neutral loss monitoring of 2'-deoxyribose or the nucleobases using data-dependent acquisition represents a powerful approach for the unbiased detection of DNA modifications (adducts). The lack of available bioinformatics tools necessitates manual processing of acquired spectral data and hampers high throughput application of these techniques. To address this limitation, we present an automated workflow for the detection and curation of putative DNA adducts by using diagnostic fragmentation filtering of LC-MS/MS experiments within the open-source software MZmine. The workflow utilizes a new feature detection algorithm, DFBuilder, which employs diagnostic fragmentation filtering using a user-defined list of fragmentation patterns to reproducibly generate feature lists for precursor ions of interest. The DFBuilder feature detection approach readily fits into a complete small-molecule discovery workflow and drastically reduces the processing time associated with analyzing DNA adductomics results. We validate our workflow using a mixture of authentic DNA adduct standards and demonstrate the effectiveness of our approach by reproducing and expanding the results of a previously published study of colibactin-induced DNA adducts. The reported workflow serves as a technique to assess the diagnostic potential of novel fragmentation pattern combinations for the unbiased detection of chemical classes of interest.

A simplified strategy for molecular formula determination of chemical constituents in traditional Chinese medicines based on accurate mass, A + 1 and A + 2 isotopic peaks using Fourier transform ion cyclotron resonance mass spectrometry

RATIONALE

Recently, isotopic fine structures derived from Fourier transform ion cyclotron resonance mass spectrometry have been used to determine the molecular formula for unknown compounds in many complex systems. However, a simplified strategy for molecular formula determination of chemical constituents in traditional Chinese medicines (TCMs) based on accurate mass, A + 1 and A + 2 isotopic peaks is necessary.

METHODS

Salviae miltiorrhizae was selected as a representative species. First, the chemical constituents were chromatographically separated and their accurate masses were obtained. The A + 1 and A + 2 isotopic peaks of all chemical constituents were then also acquired. Finally, the chemical formulae of the chemical constituents were determined.

RESULTS

In the sample of Salviae miltiorrhizae, the formulae of 38 CHO-containing chemical constituents were quickly determined, and all chemical constituents were identified using their tandem mass spectrometric data. Moreover, the method was validated by comparison of the A + 1 and A + 2 isotopic peaks, their fragmentation patterns and the retention times of six selected standard substances.

CONCLUSIONS

The results demonstrate that the described strategy performs well for molecular formula determination of chemical constituents in TCMs. This also indicates that this method will be meaningful for the structural identification of chemical constituents of TCMs.

A Mass Spectrometry Based Metabolite Profiling Workflow for Selecting Abundant Specific Markers and Their Structurally Related Multi-Component Signatures in Traditional Chinese Medicine Multi-Herb Formulae

In Traditional Chinese Medicine (TCM), herbal preparations often consist of a mixture of herbs. Their quality control is challenging because every single herb contains hundreds of components (secondary metabolites). A typical 10 herb TCM formula was selected to develop an innovative strategy for its comprehensive chemical characterization and to study the specific contribution of each herb to the formula in an exploratory manner. Metabolite profiling of the TCM formula and the extract of each single herb were acquired with liquid chromatography coupled to high-resolution mass spectrometry for qualitative analyses, and to evaporative light scattering detection (ELSD) for semi-quantitative evaluation. The acquired data were organized as a feature-based molecular network (FBMN) which provided a comprehensive view of all types of secondary metabolites and their occurrence in the formula and all single herbs. These features were annotated by combining MS/MS-based in silico spectral match, manual evaluation of the structural consistency in the FBMN clusters, and taxonomy information. ELSD detection was used as a filter to select the most abundant features. At least one marker per herb was highlighted based on its specificity and abundance. A single large-scale fractionation from the enriched formula enabled the isolation and formal identification of most of them. The obtained markers allowed an improved annotation of associated features by manually propagating this information through the FBMN. These data were incorporated in the high-resolution metabolite profiling of the formula, which highlighted specific series of related components to each individual herb markers. These series of components, named multi-component signatures, may serve to improve the traceability of each herb in the formula. Altogether, the strategy provided highly informative compositional data of the TCM formula and detailed visualizations of the contribution of each herb by FBMN, filtered feature maps, and reconstituted chromatogram traces of all components linked to each specific marker. This comprehensive MS-based analytical workflow allowed a generic and unbiased selection of specific and abundant markers and the identification of multiple related sub-markers. This exploratory approach could serve as a starting point to develop more simple and targeted quality control methods with adapted marker specificity selection criteria to given TCM formula.

Aging markers in human urine: A comprehensive, non-targeted LC-MS study

Metabolites in human biofluids document the physiological status of individuals. We conducted comprehensive, non-targeted, non-invasive metabolomic analysis of urine from 27 healthy human subjects, comprising 13 young adults (30 ± 3 years) and 14 seniors (76 ± 4 years). Quantitative analysis of 99 metabolites revealed 55 that displayed significant differences in abundance between the two groups. Forty-four did not show a statistically significant relationship with age. These include 13 standard amino acids, 5 methylated, 4 acetylated, and 9 other amino acids, 6 nucleosides, nucleobases, and derivatives, 4 sugar derivatives, 5 sugar phosphates, 4 carnitines, 2 hydroxybutyrates, 1 choline, and 1 ethanolamine derivative, and glutathione disulfide. Abundances of 53 compounds decreased, while 2 (glutathione disulfide, myo-inositol) increased in elderly people. The great majority of age-linked markers were highly correlated with creatinine. In contrast, 44 other urinary metabolites, including urate, carnitine, hippurate, and betaine, were not age-linked, neither declining nor increasing in elderly subjects. As metabolite profiles of urine and blood are quite different, age-related information in urine offers additional valuable insights into aging mechanisms of endocrine system. Correlation analysis of urinary metabolites revealed distinctly inter-related groups of compounds.

Visible Post-Data Analysis Protocol for Natural Mycotoxin Production

Fungal natural products are routinely analyzed using target detection protocols by comparing to commercial standards. However, discovery of new products suffers from a lack of high-throughput analytical techniques. Post-data process techniques have become popular tools for natural product confirmations and mycotoxin family analysis. In this work, a visible post-data process procedure with MZmine, GNPS, and Xcalibur was used for efficient analysis of high-resolution mass spectrometry. Conjugated products were screened with an optimized diagnostic fragmentation filtering module in MZmine and further confirmed with Xcalibur by comparing to unconjugated commercial standards. MS/MS spectral data were processed and used to establish a feature based on a molecular networking map in GNPS (Global Natural Products Social Molecular Networking; https://gnps.ucsd.edu), for visualization of fungal natural product families. The results demonstrate the potential of combining MZmine-, GNPS-, and Xcalibur-based methods for visible analysis of fungal natural products.

An Ancient Residue Metabolomics-Based Method to Distinguish Use of Closely Related Plant Species in Ancient Pipes

Residues from ancient artifacts can help identify which plant species were used for their psychoactive properties, providing important information regarding the deep-time co-evolutionary relationship between plants and humans. However, relying on the presence or absence of one or several biomarkers has limited the ability to confidently connect residues to particular plants. We describe a comprehensive metabolomics-based approach that can distinguish closely related species and provide greater confidence in species use determinations. An ~1430-year-old pipe from central Washington State not only contained nicotine, but also had strong evidence for the smoking of Nicotiana quadrivalvis and Rhus glabra, as opposed to several other species in this pre-contact pipe. Analysis of a post-contact pipe suggested use of different plants, including the introduced trade tobacco, Nicotiana rustica. Ancient residue metabolomics provides a new frontier in archaeo-chemistry, with greater precision to investigate the evolution of drug use and similar plant-human co-evolutionary dynamics.

Methods and Challenges for Computational Data Analysis for DNA Adductomics

Frequent exposure to chemicals in the environment, diet, and endogenous electrophiles leads to chemical modification of DNA and the formation of DNA adducts. Some DNA adducts can induce mutations during cell division and, when occurring in critical regions of the genome, can lead to the onset of disease, including cancer. The targeted analysis of DNA adducts over the past 30 years has revealed that the human genome contains many types of DNA damages. However, a long-standing limitation in conducting DNA adduct measurements has been the inability to screen for the total complement of DNA adducts derived from a wide range of chemicals in a single assay. With the advancement of high-resolution mass spectrometry (MS) instrumentation and new scanning technologies, nontargeted "omics" approaches employing data-dependent acquisition and data-independent acquisition methods have been established to simultaneously screen for multiple DNA adducts, a technique known as DNA adductomics. However, notable challenges in data processing must be overcome for DNA adductomics to become a mature technology. DNA adducts occur at low abundance in humans, and current softwares do not reliably detect them when using common MS data acquisition methods. In this perspective, we discuss contemporary computational tools developed for feature finding of MS data widely utilized in the disciplines of proteomics and metabolomics and highlight their limitations for conducting nontargeted DNA-adduct biomarker discovery. Improvements to existing MS data processing software and new algorithms for adduct detection are needed to develop DNA adductomics into a powerful tool for the nontargeted identification of potential cancer-causing agents.

A Comprehensive Nontarget Analysis for the Molecular Reconstruction of Organic Aerosol Composition from Glacier Ice Cores

Ice cores are climate archives suitable for the reconstruction of past atmospheric composition changes. Ice core analysis provides valuable insight into the chemical nature of aerosols and enables constraining emission inventories of primary emissions and of gas-phase precursors. Changes in the emissions of volatile organic compounds (VOCs) can affect formation rates and mechanisms as well as chemical composition of aerosols during the preindustrial era, key information for understanding aerosol climate effects. Here, we present an analytical method for the reconstruction of organic aerosol composition preserved in glacier ice cores. A solid-phase-extraction method, optimized toward oxidation products of biogenic VOCs, provides an enrichment factor of ∼200 and quantitative recovery for compounds of interest. We applied the preconcentration method on ice core samples from the high-alpine Fiescherhorn glacier (Swiss Alps), and used high-performance liquid chromatography coupled to high-resolution mass spectrometry as a sensitive detection method. We describe a nontarget analysis that screens for organic molecules in the ice core samples. We evaluate the atmospheric origin of the detected compounds in the ice by molecular-resolved comparison with airborne particulate matter samples from the nearby high-alpine research station Jungfraujoch. The presented method is able to shed light upon the history of the evolution of organic aerosol composition in the anthropocene, a research field in paleoclimatology with considerable potential.

Metabolite signatures of grasspea suspension-cultured cells illustrate the complexity of dehydration response

This represents the first report deciphering the dehydration response of suspension-cultured cells of a crop species, highlighting unique and shared pathways, and adaptive mechanisms via profiling of 330 metabolites. Grasspea, being a hardy legume, is an ideal model system to study stress tolerance mechanisms in plants. In this study, we investigated the dehydration-responsive metabolome in grasspea suspension-cultured cells (SCCs) to identify the unique and shared metabolites crucial in imparting dehydration tolerance. To reveal the dehydration-induced metabolite signatures, SCCs of grasspea were exposed to 10% PEG, followed by metabolomic profiling. Chromatographic separation by HPLC coupled with MRM-MS led to the identification of 330 metabolites, designated dehydration-responsive metabolites (DRMs), which belonged to 28 varied functional classes. The metabolome was found to be constituted by carboxylic acids (17%), amino acids (13.5%), flavonoids (10.9%) and plant growth regulators (10%), among others. Pathway enrichment analysis revealed predominance of metabolites involved in phytohormone biosynthesis, secondary metabolism and osmotic adjustment. Exogenous application of DRMs, arbutin and acetylcholine, displayed improved physiological status in stress-resilient grasspea as well as hypersensitive pea, while administration of lauric acid imparted detrimental effects. This represents the first report on stress-induced metabolomic landscape of a crop species via a suspension culture system, which would provide new insights into the molecular mechanism of stress responses and adaptation in crop species.

Selenium-isotopic signature toward mass spectrometric identification and enzyme activity assay

The unraveling of enzymatic reactions, especially identification of enzymatic substrates or products, is important to elucidate biological processes. Here a selenium-isotopic signature for mass spectrometric identification of enzymatic-related species is demonstrated by using selenium-containing peptides (SePeps) as substrates. Thus a strategy is proposed for rapid and precise assay of multiple enzyme activity. These SePeps can be synthesized by introduction of one selenomethionine residue in the sequence and simply identified in the full-scan mode with the feature of distinctive selenium-isotopic distribution without MS/MS verifications, which proposes a novel solution to the specific identification of enzyme-related species, allows to exclude the interferences of species with tiny mass differences in bio-samples, and meanwhile can offer a judgement on data accuracy for the analysis of enzyme activities. As a proof-of-concept, a method for multiple analysis of two representative enzymes in MCF-7 cell lysate has been developed with the isotopic peak areas of either SePep substrates or enzymatic products with the top intensities. These results could be the foundation to extend the method for more complicated enzyme systems. The selenium-isotopic signature provides a powerful protocol for high-throughput assays of peptide-metabolizing enzymes with enhanced confidence and can be extended to screen enzymatic reaction-related substrates.

Elemental Composition Determinations Using the Abundant Isotope

Elemental compositions are commonly determined from the exact m/z of the monoisotopic peak, which is often the lightest isotope. However, the lightest isotope peak is often weak or absent and the monoisotopic peak can be difficult to identify for organometallics, polyhalogenated compounds, or large molecules. An alternative approach using the abundant isotope for elemental composition determinations is presented here.

Challenges in Identifying the Dark Molecules of Life

Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.

SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information

Mass spectrometry is a predominant experimental technique in metabolomics and related fields, but metabolite structural elucidation remains highly challenging. We report SIRIUS 4 (https://bio.informatik.uni-jena.de/sirius/), which provides a fast computational approach for molecular structure identification. SIRIUS 4 integrates CSI:FingerID for searching in molecular structure databases. Using SIRIUS 4, we achieved identification rates of more than 70% on challenging metabolomics datasets.

Diverse metabolic reactions activated during 58-hr fasting are revealed by non-targeted metabolomic analysis of human blood

During human fasting, metabolic markers, including butyrates, carnitines, and branched-chain amino acids, are upregulated for energy substitution through gluconeogenesis and use of stored lipids. We performed non-targeted, accurate semiquantitative metabolomic analysis of human whole blood, plasma, and red blood cells during 34–58 hr fasting of four volunteers. During this period, 44 of ~130 metabolites increased 1.5~60-fold. Consistently fourteen were previously reported. However, we identified another 30 elevated metabolites, implicating hitherto unrecognized metabolic mechanisms induced by fasting. Metabolites in pentose phosphate pathway are abundant, probably due to demand for antioxidants, NADPH, gluconeogenesis and anabolic metabolism. Global increases of TCA cycle-related compounds reflect enhanced mitochondrial activity in tissues during fasting. Enhanced purine/pyrimidine metabolites support RNA/protein synthesis and transcriptional reprogramming, which is promoted also by some fasting-related metabolites, possibly via epigenetic modulations. Thus diverse, pronounced metabolite increases result from greatly activated catabolism and anabolism stimulated by fasting. Anti-oxidation may be a principal response to fasting.

Contribution of Untargeted Metabolomics for Future Assessment of Biotech Crops

The nutritional value and safety of food crops are ultimately determined by their chemical composition. Recent developments in the field of metabolomics have made it possible to characterize the metabolic profile of crops in a comprehensive and high-throughput manner. Here, we propose that state-of-the-art untargeted metabolomics technology should be leveraged for safety assessment of new crop products. We suggest generally applicable experimental design principles that facilitate the efficient and rigorous identification of both intended and unintended metabolic alterations associated with a newly engineered trait. Our proposition could contribute to increased transparency of the safety assessment process for new biotech crops.