Improving predictions of compound amenability for liquid chromatography-mass spectrometry to enhance non-targeted analysis

Mass-spectrometry-based non-targeted analysis (NTA), in which mass spectrometric signals are assigned chemical identities based on a systematic collation of evidence, is a growing area of interest for toxicological risk assessment. Successful NTA results in better identification of potentially hazardous pollutants within the environment, facilitating the development of targeted analytical strategies to best characterize risks to human and ecological health. A supporting component of the NTA process involves assessing whether suspected chemicals are amenable to the mass spectrometric method, which is necessary in order to assign an observed signal to the chemical structure. Prior work from this group involved the development of a random forest model for predicting the amenability of 5517 unique chemical structures to liquid chromatography-mass spectrometry (LC-MS). This work improves the interpretability of the group's prior model of the same endpoint, as well as integrating 1348 more data points across negative and positive ionization modes. We enhance interpretability by feature engineering, a machine learning practice that reduces the input dimensionality while attempting to preserve performance statistics. We emphasize the importance of interpretable machine learning models within the context of building confidence in NTA identification. The novel data were curated by the labeling of compounds as amenable or unamenable by expert curators, resulting in an enhanced set of chemical compounds to expand the applicability domain of the prior model. The balanced accuracy benchmark of the newly developed model is comparable to performance previously reported (mean CV BA is 0.84 vs. 0.82 in positive mode, and 0.85 vs. 0.82 in negative mode), while on a novel external set, derived from this work's data, the Matthews correlation coefficients (MCC) for the novel models are 0.66 and 0.68 for positive and negative mode, respectively. Our group's prior published models scored MCC of 0.55 and 0.54 on the same external sets. This demonstrates appreciable improvement over the chemical space captured by the expanded dataset. This work forms part of our ongoing efforts to develop models with higher interpretability and higher performance to support NTA efforts.

Ultrahigh performance LC/FT-MS non-targeted screening for biomass burning organic aerosol with MZmine2 and MFAssignR

In recent years, ultrahigh performance liquid chromatography Fourier transform mass spectrometry (LC/FT-MS) based non-targeted screening (NTS) methods have become increasingly popular for comprehensive analysis of complex organic mixtures. However, applying these methods for environmental complex mixture analysis is challenging due to the extreme complexity of natural samples and a lack of standard samples or surrogates for environmental complex mixtures. Furthermore, limited molecular markers in the databases and insufficient data processing software workflows make the application of these methods more challenging for environmental complex mixtures. In this work, we implement a new NTS data processing workflow to process data collected from ultrahigh performance liquid chromatography and Fourier transform Orbitrap Elite Mass Spectrometry (LC/FT-MS) by combining MZmine2 and MFAssignR, two opensource data processing tools and commercial Mesquite liquid smoke as a surrogate for biomass burning organic aerosol. MZmine2.53 data extraction followed MFAssignR molecular formula assignment offered noise free and highly accurate 1733 individual molecular formulas presented in liquid smoke with 4906 molecular species, including isomers. The results of this new approach were consistent with the results of direct infusion FT-MS analysis confirming its reliability. Over 90% of the molecular formulas presented in mesquite liquid smoke were matched with the molecular formulas of ambient biomass burning organic aerosol. This suggests the potential use of commercial liquid smoke is an acceptable surrogate for biomass burning organic aerosol research. The presented method significantly improves the identification of the molecular composition of biomass burning organic aerosol by successfully addressing some of the limitations related to the data analysis and giving a semi quantitative insight into the analysis.

Predicting Preferences for Adduct Formation in Electrospray Ionization: The Case Study of Succinic Acid

A simple theoretical approach is developed that can be used to predict the preference of ion adduct formation (with alkali Li⁺, Na⁺, K⁺ and alkaline earth Ca²⁺, Mg²⁺ metals) in electrospray ionization mass spectrometry (ESI-MS) of succinic acid, associated with several protonation/deprotonation equilibria. The applied strategy consists of using a vacuum environment as well as both implicit and explicit solvation of reactive sites and density functional theory as the method of choice. These distinct levels of theory mimic the smooth transition between the condensed environment and free ion in the gas phase. Good correlation between the Gibbs free energies for protonation/adduct formation processes with peak observation in the obtained mass spectra provide insight into the physical basis behind adduct preference and selectivity. This signifies the relationship between microscopic interactions, ionization efficiency, and types of ions that reach the detector.

Detoxification of monoterpenes by a family of plant glycosyltransferases

Plant monoterpenes are challenging compounds, since they often act as solvents, and thus have both phytotoxic and antimicrobial properties. In this study an approach is developed to identify and characterize enzymes that can detoxify monoterpenoids, and thus would protect both plants and microbial production systems from these compounds. Plants respond to the presence of monoterpenes by expressing glycosyltransferases (UGTs), which conjugate the monoterpenoids into glycosides. By identifying these enzymes in a transcriptomics approach using Mentha × piperita, a family of UGTs was identified which is active on cyclic monoterpenoids such as menthol, and on acyclic monoterpenoids such as geranic acid. Other members of this family, from tomato, were also shown to be active on these monoterpenoids. In vitro and in vivo activity of different UGTs were tested with different substrates. We found that some glycosyltransferases significantly affect the toxicity of selected monoterpenoids in Escherichia coli, suggesting that glycosyltransferases can protect cells from monoterpenoid toxicity.

Natural variation meets synthetic biology: Promiscuous trichome-expressed acyltransferases from Nicotiana

Acylsugars are defensive, trichome-synthesized sugar esters produced in plants across the Solanaceae (nightshade) family. Although assembled from simple metabolites and synthesized by a relatively short core biosynthetic pathway, tremendous within- and across-species acylsugar structural variation is documented across the family. To advance our understanding of the diversity and the synthesis of acylsugars within the Nicotiana genus, trichome extracts were profiled across the genus coupled with transcriptomics-guided enzyme discovery and in vivo and in vitro analysis. Differences in the types of sugar cores, numbers of acylations, and acyl chain structures contributed to over 300 unique annotated acylsugars throughout Nicotiana. Placement of acyl chain length into a phylogenetic context revealed that an unsaturated acyl chain type was detected in a few closely related species. A comparative transcriptomics approach identified trichome-enriched Nicotiana acuminata acylsugar biosynthetic candidate enzymes. More than 25 acylsugar variants could be produced in a single enzyme assay with four N. acuminata acylsugar acyltransferases (NacASAT1-4) together with structurally diverse acyl-CoAs and sucrose. Liquid chromatography coupled with mass spectrometry screening of in vitro products revealed the ability of these enzymes to make acylsugars not present in Nicotiana plant extracts. In vitro acylsugar production also provided insights into acyltransferase acyl donor promiscuity and acyl acceptor specificity as well as regiospecificity of some ASATs. This study suggests that promiscuous Nicotiana acyltransferases can be used as synthetic biology tools to produce novel and potentially useful metabolites.

Quantitative non-targeted analysis: Bridging the gap between contaminant discovery and risk characterization

Chemical risk assessments follow a long-standing paradigm that integrates hazard, dose-response, and exposure information to facilitate quantitative risk characterization. Targeted analytical measurement data directly support risk assessment activities, as well as downstream risk management and compliance monitoring efforts. Yet, targeted methods have struggled to keep pace with the demands for data regarding the vast, and growing, number of known chemicals. Many contemporary monitoring studies therefore utilize non-targeted analysis (NTA) methods to screen for known chemicals with limited risk information. Qualitative NTA data has enabled identification of previously unknown compounds and characterization of data-poor compounds in support of hazard identification and exposure assessment efforts. In spite of this, NTA data have seen limited use in risk-based decision making due to uncertainties surrounding their quantitative interpretation. Significant efforts have been made in recent years to bridge this quantitative gap. Based on these advancements, quantitative NTA data, when coupled with other high-throughput data streams and predictive models, are poised to directly support 21st-century risk-based decisions. This article highlights components of the chemical risk assessment process that are influenced by NTA data, surveys the existing literature for approaches to derive quantitative estimates of chemicals from NTA measurements, and presents a conceptual framework for incorporating NTA data into contemporary risk assessment frameworks.

30 Years of research on ESI/MS response: Trends, contradictions and applications

The variation of ionization efficiency for different compounds has puzzled researchers since the invention of the electrospray mass spectrometry (ESI/MS). Ionization depends on the properties of the compound, eluent, matrix, and instrument. Despite significant research, some aspects have remained unclear. For example, research groups have reached contradicting conclusions regarding the ionization processes. One of the best-known is the significance of the logP value for predicting the ionization efficiency. In this tutorial review, we analyse the methodology used for ionization efficiency measurements as well as the most important trends observed in the data. Additionally, we give suggestions regarding the measurement methodology and modelling strategies to yield meaningful and consistent ionization efficiency data. Finally, we have collected a wide range of ionization efficiency values from the literature and evaluated the consistency of these data. We also make this collection available for everyone for downloading as well as for uploading additional and new ionization efficiency data. We hope this GitHub based ionization efficiency repository will allow a joined community effort to collect and unify the current knowledge about the ionization processes.

Strategies for Drawing Quantitative Conclusions from Nontargeted Liquid Chromatography-High-Resolution Mass Spectrometry Analysis

This Feature aims at giving an overview of different possibilities for quantitatively comparing the results obtained from LC-HRMS-based nontargeted analysis. More specifically, quantification via structurally similar internal standards, different isotope labeling strategies, radiolabeling, and predicted ionization efficiencies are reviewed.

Semi-quantitative non-target analysis of water with liquid chromatography/high-resolution mass spectrometry: How far are we?

Combining high-resolution mass spectrometry (HRMS) with liquid chromatography (LC) has considerably increased the capability of analytical chemistry. Among others, it has stimulated the growth of the non-target analysis, which aims at identifying compounds without their preceding selection. This approach is already widely applied in various fields, such as metabolomics, proteomics, etc. The applicability of LC/HRMS-based non-target analysis in environmental analyses, such as water studies, would be beneficial for understanding the environmental fate of polar pollutants and evaluating the health risks exposed by the new emerging contaminants. During the last five to seven years the use of LC/HRMS-based non-target analysis has grown rapidly. However, routine non-target analysis is still uncommon for most environmental monitoring agencies and environmental scientists. The main reasons are the complicated data processing and the inability to provide quantitative information about identified compounds. The latter shortcoming follows from the lack of standard substances, considered so far as the soul of each quantitative analysis for the newly discovered pollutants. To overcome this, non-target analyses could be combined with semi-quantitation. This Perspective aims at describing the current methods for non-target analysis, the possibilities and challenges of standard substance-free semi-quantitative analysis, and proposes tools to join these two fields together.

Relative molar response of lipophilic marine algal toxins in liquid chromatography/electrospray ionization mass spectrometry

RATIONALE

Accurate quantitative analysis of lipophilic toxins by liquid chromatography/mass spectrometry (LC/MS) requires calibration solution reference materials (RMs) for individual toxin analogs. Untargeted analysis is aimed at identifying a vast number of compounds and thus validation of fully quantitative untargeted methods is not feasible. However, a semi-quantitative approach allowing for profiling is still required and will be strengthened by knowledge of the relative molar response (RMR) of analogs in LC/MS with electrospray ionization (ESI).

METHODS

RMR factors were evaluated for toxins from the okadaic acid (OA/DTXs), yessotoxin (YTX), pectenotoxin (PTX), azaspiracid (AZA) and cyclic imine (CI) toxin groups, in both solvent standards and environmental sample extracts. Since compound ionization and fragmentation influences the MS response of toxins, RMRs were assessed under different chromatographic conditions (gradient, isocratic) and MS acquisition modes (SIM, SRM, All-ion, target MS/MS) on low- and high-resolution mass spectrometers.

RESULTS

In general, RMRs were not significantly impacted by chromatographic conditions (isocratic vs gradient), with the exception of DTX1. MS acquisition modes had a more significant impact, with PnTX-G and SPX differing notably. For a given toxin group, response factors were generally in the range of 0.5 to 2. The cyclic imines were an exception.

CONCLUSIONS

Differences in RMRs between toxins of a same chemical base structure were not significant enough to indicate major issues for non-targeted semi-quantitative analysis, where there is limited or no availability of standards for many compounds, and where high degrees of accuracy are not required. Differences in RMRs should be considered when developing methods that use a standard of a single analogue to quantitate other toxins from the same group.

Adduct Formation in ESI/MS by Mobile Phase Additives

Adduct formation is a common ionization method in electrospray ionization mass spectrometry (ESI/MS). However, this process is poorly understood and complicated to control. We demonstrate possibilities to control adduct formation via mobile phase additives in ESI positive mode for 17 oxygen and nitrogen bases. Mobile phase additives were found to be a very effective measure for manipulating the formation efficiencies of adducts. An appropriate choice of additive may increase sensitivity by up to three orders of magnitude. In general, sodium adduct [M + Na]⁺ and protonated molecule [M + H]⁺ formation efficiencies were found to be in good correlation; however, the former were significantly more influenced by mobile phase properties. Although the highest formation efficiencies for both species were observed in water/acetonitrile mixtures not containing additives, the repeatability of the formation efficiencies was found to be improved by additives. It is concluded that mobile phase additives are powerful, yet not limiting factors, for altering adduct formation. Graphical Abstract ᅟ.

Predicting ESI/MS Signal Change for Anions in Different Solvents

LC/ESI/MS is a technique widely used for qualitative and quantitative analysis in various fields. However, quantification is currently possible only for compounds for which the standard substances are available, as the ionization efficiency of different compounds in ESI source differs by orders of magnitude. In this paper we present an approach for quantitative LC/ESI/MS analysis without standard substances. This approach relies on accurately predicting the ionization efficiencies in ESI source based on a model, which uses physicochemical parameters of analytes. Furthermore, the model has been made transferable between different mobile phases and instrument setups by using a suitable set of calibration compounds. This approach has been validated both in flow injection and chromatographic mode with gradient elution.