Data Processing for GC-MS- and LC-MS-Based Untargeted Metabolomics

Non-targeted metabolomics reveals the characteristics of the unique bitterness substances in quinoa

Bitterness is a key factor that affects the consumption of quinoa products, even if they are nutritious. In this study, a non-targeted metabolomics approach based on UHPLC-Orbitrap-MS was applied to comprehensively profile the characteristic metabolites of twenty-two quinoas. A total of twenty key metabolites were identified correlated with bitterness, among which, fifteen were triterpenoid saponins. In addition, these metabolites bind to the active site of the human bitter taste receptor and are the main compounds that produce the bitter taste of quinoa. Our results contribute to a deeper understanding of the origin of quinoa bitterness and provide directions for optimizing its flavor to improve market acceptance.

Comparison of reversed-phase, hydrophilic interaction, and porous graphitic carbon chromatography columns for an untargeted toxicometabolomics study in pooled human liver microsomes, rat urine, and rat plasma

INTRODUCTION

Untargeted metabolomics studies are expected to cover a wide range of compound classes with high chemical diversity and complexity. Thus, optimizing (pre-)analytical parameters such as the analytical liquid chromatography (LC) column is crucial and the selection of the column depends primarily on the study purpose.

OBJECTIVES

The current investigation aimed to compare six different analytical columns. First, by comparing the chromatographic resolution of selected compounds. Second, on the outcome of an untargeted toxicometabolomics study using pooled human liver microsomes (pHLM), rat plasma, and rat urine as matrices.

METHODS

Separation and analysis were performed using three different reversed-phase (Phenyl-Hexyl, BEH C18, and Gold C18), two hydrophilic interaction chromatography (HILIC) (ammonium-sulfonic acid and sulfobetaine), and one porous graphitic carbon (PGC) columns coupled to high-resolution mass spectrometry (HRMS). Their impact was evaluated based on the column performance and the size of feature count, amongst others.

RESULTS

All three reversed-phase columns showed a similar performance, whereas the PGC column was superior to both HILIC columns at least for polar compounds. Comparing the size of feature count across all datasets, most features were detected using the Phenyl-Hexyl or sulfobetaine column. Considering the matrices, most significant features were detected in urine and pHLM after using the sulfobetaine and in plasma after using the ammonium-sulfonic acid column.

CONCLUSION

The results underline that the outcome of this untargeted toxicometabolomic study LC-HRMS metabolomic study was highly influenced by the analytical column, with the Phenyl-Hexyl or sulfobetaine column being the most suitable. However, column selection may also depend on the investigated compounds as well as on the investigated matrix.

Metabolomics approach to understand molecular mechanisms involved in fungal pathogen-citrus pathosystems

Citrus is a crucial crop with a significant economic impact globally. However, postharvest decay caused by fungal pathogens poses a considerable threat, leading to substantial financial losses. Penicillium digitatum, Penicillium italicum, Geotrichum citri-aurantii and Phyllosticta citricarpa are the main fungal pathogens, causing green mold, blue mold, sour rot and citrus black spot diseases, respectively. The use of chemical fungicides as a control strategy in citrus raises concerns about food and environmental safety. Therefore, understanding the molecular basis of host-pathogen interactions is essential to find safer alternatives. This review highlights the potential of the metabolomics approach in the search for bioactive compounds involved in the pathogen-citrus interaction, and how the integration of metabolomics and genomics contributes to the understanding of secondary metabolites associated with fungal virulence and the fungal infection mechanisms. Our goal is to provide a pipeline combining metabolomics and genomics that can effectively guide researchers to perform studies aiming to contribute to the understanding of the fundamental chemical and biochemical aspects of pathogen-host interactions, in order to effectively develop new alternatives for fungal diseases in citrus cultivation. We intend to inspire the scientific community to question unexplored biological systems, and to employ diverse analytical approaches and metabolomics techniques to address outstanding questions about the non-studied pathosystems from a chemical biology perspective.

Root-associated bacterial communities and root metabolite composition are linked to nitrogen use efficiency in sorghum

The development of cereal crops with high nitrogen use efficiency (NUE) is a priority for worldwide agriculture. In addition to conventional plant breeding and genetic engineering, the use of the plant microbiome offers another approach to improving crop NUE. To gain insight into the bacterial communities associated with sorghum lines that differ in NUE, a field experiment was designed comparing 24 diverse Sorghum bicolor lines under sufficient and deficient nitrogen (N). Amplicon sequencing and untargeted gas chromatography-mass spectrometry were used to characterize the bacterial communities and the root metabolome associated with sorghum genotypes varying in sensitivity to low N. We demonstrated that N stress and sorghum type (energy, sweet, and grain sorghum) significantly impacted the root-associated bacterial communities and root metabolite composition of sorghum. We found a positive correlation between sorghum NUE and bacterial richness and diversity in the rhizosphere. The greater alpha diversity in high NUE lines was associated with the decreased abundance of a dominant bacterial taxon, Pseudomonas. Multiple strong correlations were detected between root metabolites and rhizosphere bacterial communities in response to low N stress. This indicates that the shift in the sorghum microbiome due to low N is associated with the root metabolites of the host plant. Taken together, our findings suggest that host genetic regulation of root metabolites plays a role in defining the root-associated microbiome of sorghum genotypes differing in NUE and tolerance to low N stress.IMPORTANCEThe development of crops that are more nitrogen use-efficient (NUE) is critical for the future of the enhanced sustainability of agriculture worldwide. This objective has been pursued mainly through plant breeding and plant molecular engineering, but these approaches have had only limited success. Therefore, a different strategy that leverages soil microbes needs to be fully explored because it is known that soil microbes improve plant growth through multiple mechanisms. To design approaches that use the soil microbiome to increase NUE, it will first be essential to understand the relationship among soil microbes, root metabolites, and crop productivity. Using this approach, we demonstrated that certain key metabolites and specific microbes are associated with high and low sorghum NUE in a field study. This important information provides a new path forward for developing crop genotypes that have increased NUE through the positive contribution of soil microbes.

Antifungal Effect of Bacillus velezensis ZN-S10 against Plant Pathogen Colletotrichum changpingense and Its Inhibition Mechanism

In order to optimize crop production and mitigate the adverse impacts associated with the utilization of chemical agents, it is necessary to explore new biocontrol agents. Bacillus velezensis has been widely studied as a biocontrol agent because of its efficient and ecofriendly plant disease control mechanisms. This study shows that the strain ZN-S10 effectively reduces the area of leaf spots caused by the pathogen Colletotrichum changpingense ZAFU0163-1, which affects conidia production and germination, inhibits mycelium growth, and induces mycelium deformation. In antifungal experiments with crude extracts, we observed a delay in the cell cycle of conidia, which may be responsible for the inhibition of conidial germination. Among the bioactive metabolites detected through integrated LC-MS- and GC-MS-based untargeted metabolomics, 7-O-Succinyl macrolactin A, telocinobufagin, and surfactin A may be the main antifungal metabolites of strain ZN-S10. The presence of 7-O-Succinyl macrolactin A could explain the cell damage in germ tubes. This is the first report of telocinobufagin detected in B. velezensis. These results are significant for understanding the inhibitory mechanisms employed by B. velezensis and should serve as a reference in the production of biocontrol agents.

Untargeted metabolomic analysis of metabolites related to body dysmorphic disorder (BDD)

Body dysmorphic disorder (BDD) is a disorder associated with depression and eating disorders. It often arises from minor defects in appearance or an individual imagining that he or she is defective. However, the mechanisms causing BDD remain unclear, and its pathogenesis and adjuvant treatment methods still need to be explored. Here, we employed a liquid chromatography-mass spectrometry (LC-MS)-based metabolomics approach to identify key metabolic differences in BDD versus healthy patients. We obtained plasma samples from two independent cohorts (including eight BDD patients and eight healthy control patients). Raw data were analyzed using Compound Discoverer to determine peak alignment, retention time correction, and extraction of peak areas. Metabolite structure identification was also obtained using Compound Discoverer by of accurate mass matching (< 10 ppm) and secondary spectral matching queries of compound databases. Next, multidimensional statistical analyses were performed using the ropls R package. These analyses included: unsupervised principal component analysis, supervised partial Least-Squares Discriminant Analysis, and orthogonal partial Least-Squares Discriminant Analysis. We then identified the most promising metabolic signatures associated with BDD across all metabolomic datasets. Principal component analysis showed changes in small-molecule metabolites in patients, and we also found significant differences in metabolite abundance between the BDD and normal groups. Our findings suggest that the occurrence of BDD may be related to metabolites participating in the following KEGG pathways: ABC transporters, purine metabolism, glycine, serine and threonine metabolism, pyrimidine, pyrimidine metabolism, biosynthesis of 12-, 14-, and 16-membered macrolides, microbial metabolism in diverse environments, biosynthesis of secondary metabolites, and caffeine and insect hormone biosynthesis.

Advances in experimental and computational methodologies for the study of microbial-surface interactions at different omics levels

The study of the biological response of microbial cells interacting with natural and synthetic interfaces has acquired a new dimension with the development and constant progress of advanced omics technologies. New methods allow the isolation and analysis of nucleic acids, proteins and metabolites from complex samples, of interest in diverse research areas, such as materials sciences, biomedical sciences, forensic sciences, biotechnology and archeology, among others. The study of the bacterial recognition and response to surface contact or the diagnosis and evolution of ancient pathogens contained in archeological tissues require, in many cases, the availability of specialized methods and tools. The current review describes advances in in vitro and in silico approaches to tackle existing challenges (e.g., low-quality sample, low amount, presence of inhibitors, chelators, etc.) in the isolation of high-quality samples and in the analysis of microbial cells at genomic, transcriptomic, proteomic and metabolomic levels, when present in complex interfaces. From the experimental point of view, tailored manual and automatized methodologies, commercial and in-house developed protocols, are described. The computational level focuses on the discussion of novel tools and approaches designed to solve associated issues, such as sample contamination, low quality reads, low coverage, etc. Finally, approaches to obtain a systems level understanding of these complex interactions by integrating multi omics datasets are presented.

Chemical Characterization and Non-targeted Analysis of Medical Device Extracts: A Review of Current Approaches, Gaps, and Emerging Practices

The developers of medical devices evaluate the biocompatibility of their device prior to FDA's review and subsequent introduction to the market. Chemical characterization, described in ISO 10993-18:2020, can generate information for toxicological risk assessment and is an alternative approach for addressing some biocompatibility end points (e.g., systemic toxicity, genotoxicity, carcinogenicity, reproductive/developmental toxicity) that can reduce the time and cost of testing and the need for animal testing. Additionally, chemical characterization can be used to determine whether modifications to the materials and manufacturing processes alter the chemistry of a patient-contacting device to an extent that could impact device safety. Extractables testing is one approach to chemical characterization that employs combinations of non-targeted analysis, non-targeted screening, and/or targeted analysis to establish the identities and quantities of the various chemical constituents that can be released from a device. Due to the difficulty in obtaining a priori information on all the constituents in finished devices, information generation strategies in the form of analytical chemistry testing are often used. Identified and quantified extractables are then assessed using toxicological risk assessment approaches to determine if reported quantities are sufficiently low to overcome the need for further chemical analysis, biological evaluation of select end points, or risk control. For extractables studies to be useful as a screening tool, comprehensive and reliable non-targeted methods are needed. Although non-targeted methods have been adopted by many laboratories, they are laboratory-specific and require expensive analytical instruments and advanced technical expertise to perform. In this Perspective, we describe the elements of extractables studies and provide an overview of the current practices, identified gaps, and emerging practices that may be adopted on a wider scale in the future. This Perspective is outlined according to the steps of an extractables study: information gathering, extraction, extract sample processing, system selection, qualification, quantification, and identification.

Operationalizing the Exposome Using Passive Silicone Samplers

The exposome, which is defined as the cumulative effect of environmental exposures and corresponding biological responses, aims to provide a comprehensive measure for evaluating non-genetic causes of disease. Operationalization of the exposome for environmental health and precision medicine has been limited by the lack of a universal approach for characterizing complex exposures, particularly as they vary temporally and geographically. To overcome these challenges, passive sampling devices (PSDs) provide a key measurement strategy for deep exposome phenotyping, which aims to provide comprehensive chemical assessment using untargeted high-resolution mass spectrometry for exposome-wide association studies. To highlight the advantages of silicone PSDs, we review their use in population studies and evaluate the broad range of applications and chemical classes characterized using these samplers. We assess key aspects of incorporating PSDs within observational studies, including the need to preclean samplers prior to use to remove impurities that interfere with compound detection, analytical considerations, and cost. We close with strategies on how to incorporate measures of the external exposome using PSDs, and their advantages for reducing variability in exposure measures and providing a more thorough accounting of the exposome. Continued development and application of silicone PSDs will facilitate greater understanding of how environmental exposures drive disease risk, while providing a feasible strategy for incorporating untargeted, high-resolution characterization of the external exposome in human studies.

Defining Blood Plasma and Serum Metabolome by GC-MS

Metabolomics uses advanced analytical chemistry methods to analyze metabolites in biological samples. The most intensively studied samples are blood and its liquid components: plasma and serum. Armed with advanced equipment and progressive software solutions, the scientific community has shown that small molecules’ roles in living systems are not limited to traditional “building blocks” or “just fuel” for cellular energy. As a result, the conclusions based on studying the metabolome are finding practical reflection in molecular medicine and a better understanding of fundamental biochemical processes in living systems. This review is not a detailed protocol of metabolomic analysis. However, it should support the reader with information about the achievements in the whole process of metabolic exploration of human plasma and serum using mass spectrometry combined with gas chromatography.

The impact of extraction protocol on the chemical profile of cannabis extracts from a single cultivar

The last two decades have seen a dramatic shift in cannabis legislation around the world. Cannabis products are now widely available and commercial production and use of phytocannabinoid products is rapidly growing. However, this growth is outpacing the research needed to elucidate the therapeutic efficacy of the myriad of chemical compounds found primarily in the flower of the female cannabis plant. This lack of research and corresponding regulation has resulted in processing methods, products, and terminology that are variable and confusing for consumers. Importantly, the impact of processing methods on the resulting chemical profile of full spectrum cannabis extracts is not well understood. As a first step in addressing this knowledge gap we have utilized a combination of analytical approaches to characterize the broad chemical composition of a single cannabis cultivar that was processed using previously optimized and commonly used commercial extraction protocols including alcoholic solvents and super critical carbon dioxide. Significant variation in the bioactive chemical profile was observed in the extracts resulting from the different protocols demonstrating the need for further research regarding the influence of processing on therapeutic efficacy as well as the importance of labeling in the marketing of multi-component cannabis products.

Evaluation of ambient mass spectrometry tools for assessing inherent postharvest pepper quality

Horticulturists are interested in evaluating how cultivar, environment, or production system inputs can affect postharvest quality. Ambient mass spectrometry approaches enable analysis of minimally processed samples under ambient conditions and offer an attractive high-throughput alternative for assessing quality characteristics in plant products. Here, we evaluate direct analysis in real time (DART-MS) mass spectrometry and rapid evaporative ionization-mass spectrometry (REIMS) to assess quality characteristics in various pepper (Capsicum annuum L.) cultivars. DART-MS exhibited the ability to discriminate between pod colors and pungency based on chemical fingerprints, while REIMS could distinguish pepper market class (e.g., bell, lunchbox, and popper). Furthermore, DART-MS analysis resulted in the putative detection of important bioactive compounds in human diet such as vitamin C, p-coumaric acid, and capsaicin. The results of this study demonstrate the potential for these approaches as accessible and reliable tools for high throughput screening of pepper quality.

Narrative review of metabolomics in cardiovascular disease

Cardiovascular diseases are accompanied by disorders in the cardiac metabolism. Furthermore, comorbidities often associated with cardiovascular disease can alter systemic and myocardial metabolism contributing to worsening of cardiac performance and health status. Biomarkers such as natriuretic peptides or troponins already support diagnosis, prognosis and treatment of patients with cardiovascular diseases and are represented in international guidelines. However, as cardiovascular diseases affect various pathophysiological pathways, a single biomarker approach cannot be regarded as ideal to reveal optimal clinical application. Emerging metabolomics technology allows the measurement of hundreds of metabolites in biological fluids or biopsies and thus to characterize each patient by its own metabolic fingerprint, improving our understanding of complex diseases, significantly altering the management of cardiovascular diseases and possibly personalizing medicine. This review outlines current knowledge, perspectives as well as limitations of metabolomics for diagnosis, prognosis and treatment of cardiovascular diseases such as heart failure, atherosclerosis, ischemic and non-ischemic cardiomyopathy. Furthermore, an ongoing research project tackling current inconsistencies as well as clinical applications of metabolomics will be discussed. Taken together, the application of metabolomics will enable us to gain more insights into pathophysiological interactions of metabolites and disease states as well as improving therapies of patients with cardiovascular diseases in the future.

Label-Free SERS Analysis of Urine Using a 3D-Stacked AgNW-Glass Fiber Filter Sensor for the Diagnosis of Pancreatic Cancer and Prostate Cancer

Metabolomics shows tremendous potential for the early diagnosis and screening of cancer. For clinical application as an effective diagnostic tool, however, improved analytical methods for complex biological fluids are required. Here, we developed a reliable rapid urine analysis system based on surface-enhanced Raman spectroscopy (SERS) using 3D-stacked silver nanowires (AgNWs) on a glass fiber filter (GFF) sensor and applied it to the diagnosis of pancreatic cancer and prostate cancer. Urine samples were pretreated with centrifugation to remove large debris and with calcium ion addition to improve the binding of metabolites to AgNWs. The label-free urine-SERS detection using the AgNW-GFF SERS sensor showed different spectral patterns and distinguishable specific peaks in three groups: normal control (n = 30), pancreatic cancer (n = 22), and prostate cancer (n = 22). Multivariate analyses of SERS spectra using unsupervised principal component analysis and supervised orthogonal partial least-squares discriminant analysis showed excellent discrimination between the pancreatic cancer group and the prostate cancer group as well as between the normal control group and the combined cancer groups. The results demonstrate the great potential of the urine-SERS analysis system using the AgNW-GFF SERS sensor for the noninvasive diagnosis and screening of cancers.

Metabolites from Wild Potato Inhibit Virulence Factors of the Soft Rot and Blackleg Pathogen Pectobacterium brasiliense

Potato (Solanum tuberosum L.) is the primary vegetable crop consumed worldwide and is largely affected by bacterial pathogens that can cause soft rot and blackleg disease. Recently, resistance to these diseases has been identified in the wild potato S. chacoense, and the mechanism of resistance is unknown. Here, it was hypothesized that S. chacoense stems or tubers have unique chemistry that confers resistance to the pathogen Pectobacterium brasiliense through bactericidal, bacteriostatic, or antivirulence activity. Stem and tuber metabolite extracts were collected from S. chacoense and tested for effects on Pectobacterium bacterial multiplication rates, and activity and expression of known exoenzymes and virulence genes using S. tuberosum extracts as a comparative control. Comparatively, the S. chacoense extracts did not affect bacterial multiplication rate; however, they did reduce pectinase, cellulase, and protease activities. The chemical extracts were profiled using a bioassay-guided fractionation, and a nontargeted metabolomics comparison of S. chacoense and S. tuberosum stems and tubers was performed. The data showed that selected alkaloids, phenolic amines, phenols, amines, and peptides are integrative chemical sources of resistance against the bacteria.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data

The evaluation of liquid chromatography high-resolution mass spectrometry (LC-HRMS) raw data is a crucial step in untargeted metabolomics studies to minimize false positive findings. A variety of commercial or open source software solutions are available for such data processing. This study aims to compare three different data processing workflows (Compound Discoverer 3.1, XCMS Online combined with MetaboAnalyst 4.0, and a manually programmed tool using R) to investigate LC-HRMS data of an untargeted metabolomics study. Simple but highly standardized datasets for evaluation were prepared by incubating pHLM (pooled human liver microsomes) with the synthetic cannabinoid A-CHMINACA. LC-HRMS analysis was performed using normal- and reversed-phase chromatography followed by full scan MS in positive and negative mode. MS/MS spectra of significant features were subsequently recorded in a separate run. The outcome of each workflow was evaluated by its number of significant features, peak shape quality, and the results of the multivariate statistics. Compound Discoverer as an all-in-one solution is characterized by its ease of use and seems, therefore, suitable for simple and small metabolomic studies. The two open source solutions allowed extensive customization but particularly, in the case of R, made advanced programming skills necessary. Nevertheless, both provided high flexibility and may be suitable for more complex studies and questions.

An improved detection and identification strategy for untargeted metabolomics based on UPLC-MS

Untargeted metabolomics provides a comprehensive investigation of metabolites and enables the discovery of biomarkers. Improvements in sample preparation, chromatographic separation and raw data processing procedure greatly enhance the metabolome coverage. In addition, database-dependent software identification is also essential, upon which enhances the identification confidence and benefits downstream biological analysis. Herein, we developed an improved detection and identification strategy for untargeted metabolomics based on UPLC-MS. In this work, sample preparation was optimized by considering chemical properties of different metabolites. Chromatographic separation was done by two different columns and MS detection was performed under positive and negative ion modes regarding to the different polarities of metabolites. According to the characteristics of the collected data, an improved identification and evaluation strategy was developed involving fragment simulation and MS/MS library search based on two commonly used databases, HMDB and METLIN. Such combination integrated information from different databases and was aimed to enhance identification confidence by considering the rationality of fragmentation, biological sources and functions comprehensively. In addition, decision tree analysis and lab-developed database were also introduced to assist the data processing and enhance the identification confidence. Finally, the feasibility of the developed strategy was validated by liver samples of obesity mice and controls. 238 metabolites were accurately detected, which was beneficial for the subsequent biomarker discovery and downstream pathway analysis. Therefore, the developed strategy remarkably facilitated the identification accuracy and the confirmation of metabolites in untargeted metabolomics.

Pulse Crop Effects on Gut Microbial Populations, Intestinal Function, and Adiposity in a Mouse Model of Diet-Induced Obesity

The dietary fiber gap that is present in many countries co-exists with a low intake of grain legumes (pulses) that have 2-3 times more dietary fiber than cereal grains that are commonly recommended to increase fiber intake. Given the relationships among dietary fiber, gut health and chronic disease risk, a study was undertaken in a preclinical mouse model for obesity to examine how commonly consumed pulses, i.e., chickpea, common bean, dry pea and lentil, would impact gut microbes, intestinal function, and adiposity. Pulses were fed to C57BL/6 mice at similar levels of protein and fiber. Bacterial count in the cecum was elevated 3-fold by pulse consumption. At the phylum level, a 2.2- to 5-fold increase in Bacteriodetes relative to Firmicutes was observed. For Akkermansia muciniphila, a health-beneficial bacterium, differential effects were detected among pulses ranging from no effect to a 49-fold increase. Significant differences among pulses in biomarkers of intestinal function were not observed. Pulses reduced accumulation of lipid in adipose tissue with a greater reduction in the subcutaneous versus visceral depots. Metabolomics analysis indicated that 108 metabolites were highly different among pulse types, and several compounds are hypothesized to influence the microbiome. These results support recent recommendations to increase consumption of pulse-based foods for improved health, although all pulses were not equal in their effects.