Chemical derivatization in LC-MS-based metabolomics study

Bromine signature coded derivatization LC-MS for specific profiling of carboxyl or carbonyl-containing metabolites in Mycoplasma pneumoniae infection

Carboxyl or carbonyl-containing metabolites (CoCCMs) are widely distributed in biological samples. Global profiling of CoCCMs is essential for ascertaining specific functions of metabolites and their potential physiological roles in biogenic activities. However, simultaneous determination of these compounds is hampered by poor ionization efficiency, vast polarity differences, wide discrepancy of concentration ranges. Herein, a novel bromine isotope derivatization reagent 5-bromo-2- hydrazinopyridine was employed for CoCCMs profiling by liquid chromatography-mass spectrometry (LC-MS). This method enabled rapid derivatization of 44 CoCCMs under mild conditions. Enhanced separation efficiencies, detection sensitivities, and distinctive MS fragmentation characteristics were observed. Furthermore, this method was demonstrated to be efficient in revealing metabolic alternations, and abnormal serum levels of 6-keto-PGF1α, 12(S)-HHTrE, 15(S)-HEPE and N-acetyl tryptophan were disclosed for the first time in Mycoplasma pneumoniae (MP) infectious patients. Finally, based on the distinctive 2 Da differences of molecular ion peak pairs with almost 1:1 intensity ratio originated from 79Br and 81Br isotopes, an MS-DIAL and Python assisted MS1 isotope screening-MS2 fragments characterization combination strategy was developed for rapid screening, classification, and identification of detected CoCCMs. A total of 1069 CoCCMs were detected, of which 198 CoCCMs were identified in untargeted analysis. Statistical analysis revealed altered metabolic pathways, while glutamic acid, oxoglutaric acid, succinic acid, pyruvic acid, glyceric acid, and glycine were selected as potential biomarkers of MP infection. This bromine signature coded derivatization-LC-MS approach was proved to be a valuable tool for global probing of CoCCMs in biological samples with high sensitivity and broad coverage.

Advances and challenges in non-targeted analysis: An insight into sample preparation and detection by liquid chromatography-mass spectrometry

Unknown impurities, metabolites and harmful pollutants present in pharmaceutical products, biological and environmental samples, respectively are of high concern in terms of their detection and quantification. The targeted analysis aims to quantify known chemical entities, but it lacks the ability to identify unknown components present in a sample. Non-targeted analysis is an analytical approach that can be made applicable to various disciplines of science to effectively search for unknown chemical, biological, or environmental entities that can answer various baffling mysteries of research. It employs various high-end analytical techniques that can specifically screen out multiple unknown compounds from complex mixtures. Non-targeted analysis is also applicable for complex studies such as metabolomics to search unidentified metabolites of new chemical entities. This review critically discusses the current advancements in non-targeted analysis related to the analysis of pharmaceutical, biological, and environmental samples. Various steps like sample collection, handling, preparation, extraction, its analysis using advanced techniques like high-resolution mass spectrometry, liquid chromatography mass spectrometry, and lastly interpretation of the huge amounts of complex data obtained upon analysis of complex matrices have been discussed broadly in this article. Besides the advantages of non-targeted analysis over targeted analysis, limitations, bioinformatics tools, sources of error, and research gaps have been critically analyzed.

Targeted Chiral Metabolomics of D-Amino Acids: Their Emerging Role as Potential Biomarkers in Neurological Diseases with a Focus on Their Liquid Chromatography-Mass Spectrometry Analysis upon Chiral Derivatization

In neuroscience research, chiral metabolomics is an emerging field, in which D-amino acids play an important role as potential biomarkers for neurological diseases. The targeted chiral analysis of the brain metabolome, employing liquid chromatography (LC) coupled to mass spectrometry (MS), is a pivotal approach for the identification of biomarkers for neurological diseases. This review provides an overview of D-amino acids in neurological diseases and of the state-of-the-art strategies for the enantioselective analysis of chiral amino acids (AAs) in biological samples to investigate their putative role as biomarkers for neurological diseases. Fluctuations in D-amino acids (D-AAs) levels can be related to the pathology of neurological diseases, for example, through their role in the modulation of N-methyl-D-aspartate receptors and neurotransmission. Because of the trace presence of these biomolecules in mammals and the complex nature of biological matrices, highly sensitive and selective analytical methods are essential. Derivatization strategies with chiral reagents are highlighted as critical tools for enhancing detection capabilities. The latest advances in chiral derivatization reactions, coupled to LC-MS/MS analysis, have improved the enantioselective quantification of these AAs and allow the separation of several chiral metabolites in a single analytical run. The enhanced performances of these methods can provide an accurate correlation between specific D-AA profiles and disease states, allowing for a better understanding of neurological diseases and drug effects on the brain.

Discovery of Acylated Glycine and Alanine by Integrating Chemical Derivatization-Based LC-MS/MS and Knowledge-Driven Prediction

Acylated amino acids (acyl-AAs), which consist of an amino acid head and an organic acid tail, play vital roles in various biological processes. Glycine (Gly) is the most common substrate for acylation with the organic acid tails exhibiting considerable diversity. Alanine (Ala) also exists in multiple acylated forms, predominantly modified by long-chain fatty acids. However, the full scope of acylated Gly and Ala remains largely unexplored. In this study, we employed a knowledge-driven prediction approach to expand the spectrum of acylated Gly and Ala by incorporating 111 organic acids from five different classes as potential acyl donors, leading to the generation of 222 acylated Gly and Ala species. To enhance mass spectrometry (MS) response, we used a chemical derivatization-based LC-MS/MS approach, employing dimethylamino-naphthalene-1-sulfonyl piperazine (Dns-PP) and its stable isotope-labeled form (d6-Dns-PP) as labeling reagents. Moreover, in-source fragmentation (ISF) was utilized to increase the fragment diversity and utility, aiding in structure elucidation. This strategy resulted in the identification of 53 acylated Gly and Ala metabolites in rat biological matrices, including 17 novel metabolites with distinct tissue-specific distributions. Our approach offers a deeper understanding of the physiological and pathological roles of acylated Gly and Ala, while also opening avenues for the discovery of other modified metabolites.

Mass Spectrometry Probe Combined with Machine Learning to Capture the Relationship between Metabolites and Mitochondrial Complex Activity at the Whole-Cell Level

Mitochondrial complex activity controls a multitude of physiological processes by regulating the cellular metabolism. Current methods for evaluating mitochondrial complex activity mainly focus on single metabolic reactions within mitochondria. These methods often require fresh samples in large quantities for mitochondria purification or intact mitochondrial membranes for real-time monitoring. Confronting these limitations, we shifted the analytical perspective toward interactive metabolic networks at the whole-cell level to reflect mitochondrial complex activity. To this end, we compiled a panel of mitochondrial respiratory chain-mapped metabolites (MRCMs), whose perturbations theoretically provide an overall reflection on mitochondrial complex activity. By introducing N-dimethyl-p-phenylenediamine and N-methyl-p-phenylenediamine as a pair of mass spectrometry probes, an ultraperformance liquid chromatography-tandem mass spectrometry method with high sensitivity (LLOQ as low as 0.2 fmol) was developed to obtain accurate quantitative data of MRCMs. Machine learning was then combined to capture the relationship between MRCMs and mitochondrial complex activity. Using Complex I as a proof-of-concept, we identified NADH, alanine, and phosphoenolpyruvate as metabolites associated with Complex I activity based on the whole-cell level. The effectiveness of using their concentrations to reflect Complex I activity was further validated in external data sets. Hence, by capturing the relationship between metabolites and mitochondrial complex activity at the whole-cell level, this study explores a novel analytical paradigm for the interrogation of mitochondrial complex activity, offering a favorable complement to existing methods particularly when sample quantities, type, and treatment timeliness pose challenges. More importantly, it shifts the focus from individual metabolic reactions within mitochondria to a more comprehensive view of an interactive metabolic network, which should serve as a promising direction for future research into the functional architecture between mitochondrial complexes and metabolites.

Quantitative analysis of methylmalonic acid in human plasma by LC-MS/MS after derivatization

Methylmalonic acid (MMA) is a reverse biomarker of vitamin B12 that is increasingly utilized in clinical practice. However, its low sensitivity and susceptibility to strong interference from isomer present chromatographic challenges. We have developed a rapid derivatization method for plasma MMA at room temperature, converting it to the corresponding 2,2,2-trifluoroethylamide derivative using N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide hydrochloride (EDC) and 2,2,2-trifluoroethylamine hydrochloride (TFEA). Amidization was completed within 10 min, followed by protein precipitation extraction of the amides with trichloroacetic acid for Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. This technique notably enhanced the signal-to-noise ratio of MMA in chromatography. The derivatized MMA exhibited excellent linearity within a concentration range of 42.4-2711.9 nmol/L, with a correlation coefficient (R2) of 0.9990. The intraday and interday precision of replicate measurements ranged from 2.4 % to 4.4 % and 2.6 % to 2.8 %, respectively, while the recovery fell between 97.9 % and 100.1 %.

Pharmacometabolomics in Drug Disposition, Toxicity, and Precision Medicine

The precision medicine initiative has driven a substantial change in the way scientists and health care practitioners think about diagnosing and treating disease. While it has long been recognized that drug response is determined by the intersection of genetic, environmental, and disease factors, improvements in technology have afforded precision medicine guided dosing of drugs to improve efficacy and reduce toxicity. Pharmacometabolomics aims to evaluate small molecule metabolites in plasma and/or urine to help evaluate mechanisms that predict and/or reflect drug efficacy and toxicity. In this mini review, we provide an overview of pharmacometabolomic approaches and methodologies. Relevant examples where metabolomic techniques have been used to better understand drug efficacy and toxicity in major depressive disorder and cancer chemotherapy are discussed. In addition, the utility of metabolomics in drug development and understanding drug metabolism, transport, and pharmacokinetics is reviewed. Pharmacometabolomic approaches can help describe factors mediating drug disposition, efficacy, and toxicity. While important advancements in this area have been made, there remain several challenges that must be overcome before this approach can be fully implemented into clinical drug therapy. SIGNIFICANCE STATEMENT: Pharmacometabolomics has emerged as an approach to identify metabolites that allow for implementation of precision medicine approaches to pharmacotherapy. This review article provides an overview of pharmacometabolomics including highlights of important examples.

Differential effects of milk, yogurt, and cheese on energy homeostasis and brown adipose tissue phenotype in high-fat diet-induced obese mice

Aim: We hypothesized that milk, yogurt, and cheese have differential impacts on energy expenditure (EE) and obesity in mice fed a high-fat diet (HFD). Methods: C57BL/6 mice (n = 16 per group) were fed a HFD or a HFD supplemented with fat-free milk (MILK), fat-free plain yogurt (YOG), or reduced-fat cheddar cheese (CHE; 19 kcal% fat), each provided at 10% of the daily energy intake, for 8 weeks. EE was quantified using a metabolic chamber. Metabolic pathways related to BAT mitochondrial function and uncoupling protein 1 (UCP1) abundance were assessed. Serum lipidomic profiles were analyzed to identify potential mediators of the observed effects. Results: MILK supplementation lowered weight gain and fat accumulation and enhanced EE and BAT thermogenesis, perhaps via the SIRT1-AMPK-PGC1α axis in BAT. This led to elevated UCP1 abundance and enhanced the abundance of hormone-sensitive lipase (HSL). MILK also altered serum lipid species, indicating enhanced energy use, and promoted BAT thermogenesis and mitochondrial function pathways. YOG exhibited a similar pattern but a lower magnitude of effects than MILK on reducing weight gain and fat mass, increasing EE, and BAT thermogenic proteins, including AMPK-PGC1α-UCP1. Both MILK and YOG showed a relative increase in serum PC 15:0_15:0 and LPC 15:0. In contrast, CHE reduced weight gain and increased EE without impacting BAT thermogenesis proteins or serum lipid species. Conclusion: Our study showed that MILK, YOG, and CHE reduced weight gain in mice on a HFD by increasing EE. MILK and YOG also up-regulated BAT thermogenesis, while both additionally altered lipids involved in fat metabolism and inflammation. CHE did not affect BAT thermogenesis and lipid species compared to HFD.

Deep learning prediction of electrospray ionization tandem mass spectra of chemically derived molecules

Chemical derivatization is a powerful strategy to enhance sensitivity and selectivity of liquid chromatography-mass spectrometry for non-targeted analysis of chemicals in complex mixtures. However, it remains impossible to obtain large sets of reference spectra for chemically derived molecules (CDMs), representing a major barrier in real-world applications. Herein, we describe a deep learning approach that enables accurate prediction of electrospray ionization tandem mass spectra for CDMs (DeepCDM). DeepCDM is established by transfer learning from a generic spectrum predicting model using a small set of experimentally acquired tandem mass spectra of CDMs, which converts a generic model with low predictability for CDMs into a specialized model with high predictability. We demonstrate DeepCDM by predicting electrospray ionization tandem mass spectra of dansylated molecules. The success in establishing Dns-MS further enables the development of DnsBank, a dansylation-specialized in silico spectral library. DnsBank achieves significant increases of accurate annotation rates of dansylated molecules, facilitating discovery of new hazardous pollutants from an environmental study of leather industrial wastewater. DeepCDM is also highly versatile for other classes of CDMs. Therefore, we envision that DeepCDM will pave a way for high-throughput identification of CDMs in non-targeted analysis to dig unknowns with potential health impacts from emerging anthropogenic chemicals.

Nitrophenols in the environment: An update on pretreatment and analysis techniques since 2017

Nitrophenols, a versatile intermediate, have been widely used in leather, medicine, chemical synthesis, and other fields. Because these components are widely applied, they can enter the environment through various routes, leading to many hazards and toxicities. There has been a recent surge in the development of simple, rapid, environmentally friendly, and effective techniques for determining these environmental pollutants. This review provides a comprehensive overview of the latest research progress on the pretreatment and analysis methods of nitrophenols since 2017, with a focus on environmental samples. Pretreatment methods include liquid-liquid extraction, solid-phase extraction, dispersive extraction, and microextraction methods. Analysis methods mainly include liquid chromatography-based methods, gas chromatography-based methods, supercritical fluid chromatography. In addition, this review also discusses and compares the advantages/disadvantages and development prospects of different pretreatment and analysis methods to provide a reference for further research.

Development and evaluation of a liquid chromatography-tandem mass spectrometry method for simultaneous measurement of toxic aldehydes from brain tissue

Reactive aldehydes are a class of electrophilic low molecular weight compounds that play an essential role in physiological function and lipid peroxidation. These molecules are implicated in many diseases, especially cardiovascular and neurodegenerative diseases, and are potential endogenous markers of lipid peroxidation. However, there are limited options to accurately quantify multiple reactive aldehydes in brain tissue. This study developed and validated a 3-nitrophenylhydrazine derivatization-based LC-MS/MS method to quantify four reactive aldehydes: malondialdehyde, acrolein, 4-hydroxy-2-hexenal and 4-hydroxy-2-nonenal. Method development involved comparing the sensitivity of detection between widely used derivatization reagents: 2,4-dinitrophenylhydrazine and 3-nitrophenylhydrazine. Our data showed that 3-nitrophenylhydrazine resulted in greater sensitivity. Additional method development included evaluation of hydrolysis sample pretreatment, selection of protein precipitation reagent, and optimization of derivatization conditions. The optimized conditions included no hydrolysis and use of 20 % trichloroacetic acid as the protein precipitation reagent. The optimized derivatization condition was 25 mM 3-nitrophenylhydrazine reacted at 20 °C for 30 min. The chromatographic conditions were optimized to reduce matrix effects, ion suppression, and efficient analysis time (<7-minute analytical run). The four aldehyde species were accurately quantified in brain tissue using stable-labeled internal standards. Application of this assay to a traumatic brain injury mouse model revealed significant accumulation of acrolein, 4-hydroxy-2-hexenal, and 4-hydroxy-2-nonenal at 28 days post injury. Overall, a validated method was developed to rapidly quantify the most prominent reactive aldehydes associated with lipid peroxidation during injury progression following acute brain trauma.

Targeted Quantification of Glutathione/Arginine Redox Metabolism Based on a Novel Paired Mass Spectrometry Probe Approach for the Functional Assessment of Redox Status

Glutathione (GSH) redox control and arginine metabolism are critical in regulating the physiological response to injury and oxidative stress. Quantification assessment of the GSH/arginine redox metabolism supports monitoring metabolic pathway shifts during pathological processes and their linkages to redox regulation. However, assessing the redox status of organisms with complex matrices is challenging, and single redox molecule analysis may not be accurate for interrogating the redox status in cells and in vivo. Herein, guided by a paired derivatization strategy, we present a new ultraperformance liquid chromatography tandem mass spectrometry (UPLC-MS/MS)-based approach for the functional assessment of biological redox status. Two structurally analogous probes, 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) and newly synthesized 2-methyl-6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (MeAQC), were set for paired derivatization. The developed approach was successfully applied to LPS-stimulated RAW 264.7 cells and HDM-induced asthma mice to obtain quantitative information on GSH/arginine redox metabolism. The results suggest that the redox status was remarkably altered upon LPS and HDM stimulation. We expect that this approach will be of good use in a clinical biomarker assay and potential drug screening associated with redox metabolism, oxidative damage, and redox signaling.

Benzoyl Chloride Derivatization Advances the Quantification of Dissolved Polar Metabolites on Coral Reefs

Extracellular chemical cues constitute much of the language of life among marine organisms, from microbes to mammals. Changes in this chemical pool serve as invisible signals of overall ecosystem health and disruption to this finely tuned equilibrium. In coral reefs, the scope and magnitude of the chemicals involved in maintaining reef equilibria are largely unknown. Processes involving small, polar molecules, which form the majority components of labile dissolved organic carbon, are often poorly captured using traditional techniques. We employed chemical derivatization with mass spectrometry-based targeted exometabolomics to quantify polar dissolved phase metabolites on five coral reefs in the U.S. Virgin Islands. We quantified 45 polar exometabolites, demonstrated their spatial variability, and contextualized these findings in terms of geographic and benthic cover differences. By comparing our results to previously published coral reef exometabolomes, we show the novel quantification of 23 metabolites, including central carbon metabolism compounds (e.g., glutamate) and novel metabolites such as homoserine betaine. We highlight the immense potential of chemical derivatization-based exometabolomics for quantifying labile chemical cues on coral reefs and measuring molecular level responses to environmental stressors. Overall, improving our understanding of the composition and dynamics of reef exometabolites is vital for effective ecosystem monitoring and management strategies.

Challenges and recent advances in quantitative mass spectrometry-based metabolomics

The field of metabolomics has gained tremendous interest in recent years. Whether the goal is to discover biomarkers related to certain pathologies or to better understand the impact of a drug or contaminant, numerous studies have demonstrated how crucial it is to understand variations in metabolism. Detailed knowledge of metabolic variabilities can lead to more effective treatments, as well as faster or less invasive diagnostics. Exploratory approaches are often employed in metabolomics, using relative quantitation to look at perturbations between groups of samples. Most metabolomics studies have been based on metabolite profiling using relative quantitation, with very few studies using an approach for absolute quantitation. Using accurate quantitation facilitates the comparison between different studies, as well as enabling longitudinal studies. In this review, we discuss the most widely used techniques for quantitative metabolomics using mass spectrometry (MS). Various aspects will be addressed, such as the use of external and/or internal standards, derivatization techniques, in vivo isotopic labelling, or quantitative MS imaging. The principles, as well as the associated limitations and challenges, will be described for each approach.

Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

Derivatization of carboxylic groups prior to their LC analysis - A review

Carboxylic acids (CAs) represent a large group of important molecules participating in various biologically significant processes. Analytical study of these compounds is typically performed by liquid chromatography (LC) combined with various types of detection. However, their analysis is often accompanied by a wide variety of problems depending on used separation system or detection method. The dominant ones are: i) poor chromatographic behavior of the CAs in reversed-phase LC; ii) absence of a chromophore (or fluorophore); iii) weak ionization in mass spectrometry (MS). To overcome these problems, targeted chemical modification, and derivatization, come into play. Therefore, derivatization still plays an important and, in many cases, irreplaceable role in sample preparation, and new derivatization methods of CAs are constantly being developed. The most commonly used type of reaction for CAs derivatization is amidation. In recent years, an increased interest in the isotopic labeling derivatization method has been observed. In this review, we comprehensively summarize the possibilities and actual trends in the derivatization of CAs that have been published over the past decade.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates, such as fatty acids, carbohydrates, lipids, and amino acids, to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities, which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection: direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF-MS/MS). Using DI-FTICR MS, 9644 metabolic features were detected where 7156 were assigned a molecular formula and 1107 were annotated by accurate mass assignment. Using LC-Q-TOF-MS/MS, 21,428 metabolic features were detected where 285 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 1340 heart metabolites were identified in this study, which span a wide range of polarities including polar (benzenoids, carbohydrates, and nucleosides) as well as nonpolar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results from this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

Enhanced Compound Analysis Using Reactive Paper Spray Mass Spectrometry: Leveraging Schiff Base Reaction for Amino Acid Detection

Paper spray mass spectrometry (PS-MS) has evolved into a promising tool for monitoring reactions in thin films and microdroplets, known as reactive PS, alongside its established role in ambient and direct ionization. This study addresses the need for rapid, cost-effective methods to improve analyte identification in biofluids by leveraging reactive PS-MS in clinical chemistry environments. The technique has proven effective in derivatizing target analytes, altering hydrophobicity to enhance elution and ionization efficiency, and refining detection through thin-film reactions on paper, significantly expediting reaction rates by using amino acids (AAs) as model analytes. These molecules are prone to interacting with substrates like paper, impeding elution and detection. Additionally, highly abundant species in biofluids, such as lipids, often suppress AA ionization. This study employs the Schiff base (SB) reaction utilizing aromatic aldehydes for AA derivatization to optimize reaction conditions time, temperature, and catalyst presence and dramatically increasing the conversion ratio (CR) of formed SB. For instance, using leucine as a model AA, the CR surged from 57% at room temperature to 89% at 70 °C, with added pyridine during and after 7.5 min, displaying a 43% CR compared to the bulk reaction. Evaluation of various aromatic aldehydes as derivatization agents highlighted the importance of specific oxygen substituents for achieving higher conversion rates. Furthermore, diverse derivatization agents unveiled unique fragmentation pathways, aiding in-depth annotation of the target analyte. Successfully applied to quantify AAs in human and rat plasma, this reactive PS-MS approach showcases promising potential in efficiently detecting conventionally challenging compounds in PS-MS analysis.

Chemical derivatization strategies for enhancing the HPLC analytical performance of natural active triterpenoids

Triterpenoids widely exist in nature, displaying a variety of pharmacological activities. Determining triterpenoids in different matrices, especially in biological samples holds great significance. High-performance liquid chromatography (HPLC) has become the predominant method for triterpenoids analysis due to its exceptional analytical performance. However, due to the structural similarities among botanical samples, achieving effective separation of each triterpenoid proves challenging, necessitating significant improvements in analytical methods. Additionally, triterpenoids are characterized by a lack of ultraviolet (UV) absorption groups and chromophores, along with low ionization efficiency in mass spectrometry. Consequently, routine HPLC analysis suffers from poor sensitivity. Chemical derivatization emerges as an indispensable technique in HPLC analysis to enhance its performance. Considering the structural characteristics of triterpenoids, various derivatization reagents such as acid chlorides, rhodamines, isocyanates, sulfonic esters, and amines have been employed for the derivatization analysis of triterpenoids. This review comprehensively summarized the research progress made in derivatization strategies for HPLC detection of triterpenoids. Moreover, the limitations and challenges encountered in previous studies are discussed, and future research directions are proposed to develop more effective derivatization methods.

Photocatalytic inactivation mechanism of nano-BiPO4 against Vibrio parahaemolyticus and its application in abalone

Vibrio parahaemolyticus (V. parahaemolyticus) is the main pathogenic bacteria in seafood that can cause serious food-borne illness. The annual incidence of V. parahaemolyticus infection in the United States exceeds 45,000 cases, indicating there are potential shortcomings in seafood sterilization techniques. Meanwhile, the ongoing emergence of antibiotic-resistant strains highlights the urgent need for novel bacteriostatic strategies to eliminate V. parahaemolyticus. Nano-BiPO4 is a semiconductor with high H2O2 production efficiency and has potential for photocatalytic bacterial inactivation. But the effectiveness and mechanism of BiPO4 photocatalytic inactivation of V. parahaemolyticus has not been reported. In this study, nano-BiPO4 synthesized in pure water (P1) was found to exhibit optimal H2O2 production efficiency (1203 μmol h-1g-1) and antibacterial activity (in 0.8 g/L). Under UV light irradiation, P1 induced alterations in bacterial cell morphology, elevation in intracellular levels of ROS, H2O2, O2-, GSSG and MDA, and reduction in GSH level. Meanwhile, metabolomic analysis revealed that P1 stimulates the arginine biosynthesis, TCA cycle and alanine, aspartate and glutamate metabolism. These abnormal changes in the oxidative stress indicators and metabolic pathways proved that the bacterial damage was related to the H2O2 produced by nano-BiPO4 photocatalysis. Moreover, sliced abalone and hemolysis assay were used to demonstrate the applicability and biosafety of P1. This study provides theoretical support for exploring nano-BiPO4 as a bacterial inhibitor against V. parahaemolyticus.

Improved quantification of carbonyl sub-metabolome by liquid chromatography mass spectrometry using a fragment controlled multiplexed isotopic tag

BACKGROUND

Carbonyl-containing metabolites are a class of key intermediate in metabolism, which has potentials to be biomarkers. Since their poor ionization, derivatization reagents, such as dansylhydrazine, are usually used to improve the sensitivity and/or to facilitate quantification. However, most current carbonyl derivatization reagents only have two channels, one is isotopically labeled and the other one is non-labeled. To quantify more samples in a run and using data-independent acquisition (DIA) mode to get comprehensive and unbiased mass fragmentation, we proposed a fragment-controlled isotopic tag, called DiMe-FP-NHNH2 (FP) which has five channels: Δ0, Δ3, Δ6, Δ9, and Δ12, thus up to 5 samples can be analyzed in a run.

RESULTS

The most important improvement is that the FP tag can produce multiple characteristic signals in tandem mass, diagnostic ions and neutral losses, which helps to selectively detect aldehydes/ketones for targeted and untargeted analysis. To exhibit all capabilities of the FP tag, we mimicked an untargeted metabolomics experiment, which comprises two steps. First, discovery step, using Data-Independent Analysis (SWATH-MS) and the labeling of two channels (Δ0 and Δ3), we picked out aldehyde/ketone from the pooled urine samples based on three characteristic signals, including isotope patterns, diagnostic ions, and neutral losses. Second, five-plex quantification, relative and absolute quantification were achieved in a single LC-MS analysis. Notably, because of different nominal masses, the FP tag can be used on any low or high resolution mass spectrometers.

SIGNIFICANCE

The benefits and performance of the FP tag are demonstrated by the analysis of urine samples collected from patients from a prostate cancer study, in which more than a thousand features were found based on MS1 fingerprint, but only around 120 aldehyde/ketone candidates were confirmed with characteristic signals and nine of which were quantified showing significant differences from healthy and reference urine samples.

Strategy for characterization and quantification of fatty acids in plasma by parallel d0/d6-dansylhydrazine derivatization combined with UPLC-MS/MS

Fatty acids (FAs) play a vital physiological role in lipid metabolism, which is reported as potential diagnostic biomarker for various diseases. Thus, it is urgent to develop a credible method that can profile FA metabolism with a holistic view. Here, a targeted strategy to screen FAs was developed by parallel labeling with d0/d6-dansylhydrazine (d0/d6-DnsHz) and using ultra-high performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-MS/MS) in data-dependent MS/MS (ddMS2) mode. The simple and mild derivatization procedure within 3 h allowed for a significant improvement in sensitivity. Additionally, the characteristic product ions introduced by the derivatization reagent assist to identify the unknown FA species. A quantitation method was established by multiple reaction monitoring (MRM) and the d6-DnsHz tagged standards for each analyte were used as internal standards to overcome the matrix effects. By applying the method to determine FA levels in plasma collected from the esophageal squamous cell carcinoma (ESCC) patients and healthy controls, 65 FA metabolites were characterized and six FAs were found to be altered by the invasion of tumors. The parallel derivatization strategy provides insights into the identification of unknown FAs and paves a new way for targeted metabolomics. Also, this novel method is a powerful tool for characterization and quantification of FAs in biological samples, which shows a great potential application in clinical diagnosis and investigation of disease mechanisms.

Increasing coverage of the urinary polar metabolome using ultra high-performance hydrophobic interaction liquid chromatography combined with linear and cyclic travelling wave ion mobility and mass spectrometry

The use of HILIC-based separations for the analysis of polar metabolites in metabolic phenotyping studies is well established. Here, we demonstrate the increased coverage of the polar metabolome obtained by travelling wave (TW) ion mobility (IM) instruments combined with HILIC and mass spectrometry (MS) for metabotyping rat and mouse urine samples. Profiling was performed using either a linear TW IM-MS based instrument with a path length of 40 cm or an instrument with a cyclic travelling wave analyser (cIM) with a path length of 95 cm. Due to the added resolution afforded by using both the linear and cyclic IM geometries with MS detection (IM-MS) significant increases in feature count (m/z-tR pairs) were generally obtained compared to HILIC-MS alone. In addition, the use of both linear and cyclic IM-MS improved the quality of the mass spectra obtained as a result of the separation of co-eluting analytes. As would be expected from the increased path length of the cyclic IM-MS instrument compared to the linear device, the largest gains in feature detection were obtained for the HILIC-cIM-MS combination. By increasing the resolution of coeluting components, the cyclic IM-MS instrumentation also provided the largest improvement in the quality of the mass spectral data obtained. When applied to mouse urines obtained from both control and gefitinib-dosed mice, time-related changes were detected in those obtained from the treated animals that were not seen in the controls. Polar metabolites affected by drug administration included, but were not limited to, hypoxanthine, 1,3-dimethyluracil and acetylcarnitine. The changes seen in the relative concentrations of these endogenous metabolites appeared to be related to drug concentrations in the plasma and urine suggesting a pharmacometabodynamic link.

Unravelling the brain metabolome: A review of liquid chromatography - mass spectrometry strategies for extracellular brain metabolomics

The analysis of the brain extracellular metabolome is of interest for numerous subdomains within neuroscience. Not only does it provide information about normal physiological functions, it is even more of interest for biomarker discovery and target discovery in disease. The extracellular analysis of the brain is particularly interesting as it provides information about the release of mediators in the brain extracellular fluid to look at cellular signaling and metabolic pathways through the release, diffusion and re-uptake of neurochemicals. In vivo samples are obtained through microdialysis, cerebral open-flow microperfusion or solid-phase microextraction. The analytes of potential interest are typically low in concentration and can have a wide range of physicochemical properties. Liquid chromatography coupled to mass spectrometry has proven its usefulness in brain metabolomics. It allows sensitive and specific analysis of low sample volumes, obtained through different approaches. Several strategies for the analysis of the extracellular fluid have been proposed. The most widely used approaches apply sample derivatization, specific stationary phases and/or hydrophilic interaction liquid chromatography. Miniaturization of these methods allows an even higher sensitivity. The development of chiral metabolomics is indispensable, as it allows to compare the enantiomeric ratio of compounds and provides even more challenges. Some limitations continue to exist for the previously developed methods and the development of new, more sensitive methods remains needed. This review provides an overview of the methods developed for sampling and liquid chromatography-mass spectrometry analysis of the extracellular metabolome.

Determination of vitamin D metabolites in various biological samples through an improved chemical derivatization assisted liquid chromatography-tandem mass spectrometry approach

Vitamin D (VD) metabolites are involved in a variety of important metabolic processes and physiological effects in organisms. Profiling of VD metabolites favors a deep understanding of the physiological role of VD. However, VD metabolites are difficult to detect due to their high chemical structural rigidity, structural similarity, and low sensitivities under liquid chromatography-tandem mass spectrometry (LC-MS). Herein, we present a chemical derivatization assisted LC-MS/MS strategy for the detection of VDs, in which 4-phenyl-1,2,4-triazoline-3,5-dione (PTAD) is employed to derivatize the conjugated diene of VD metabolites and provides sensitizing reporters for MS detection. After PTAD derivatization, the sensitivities of seven VD metabolites increased by 24-276 folds, with the limits of detection ranging from 3 to 20 pg mL-1. Using this method, we achieved a sensitive and accurate quantification of 7 VD metabolites (vitamin D2, vitamin D3, 25-hydroxyvitamin D2, 25-hydroxyvitamin D3, 1,25-dihydroxyvitamin D2, 1,25-dihydroxyvitamin D3, and 1,24,25-trihydroxyvitamin D3) of the VD metabolic pathway in different trace biological samples, including human serum, mouse tissues (namely liver, kidney, lung, and spleen), and cells. We believe that the present method can provide a promising tool for an in-depth analysis of VD metabolism.

DMMIC derivatization-assisted liquid chromatography-mass spectrometry method for metabolite profiling of the glutathione anabolic pathway in esophageal cancer tissues and cells

In this work, a new pyrylium derivatization-assisted liquid chromatography-mass spectrometry (LC-MS) method was developed for metabolite profiling of the glutathione anabolic pathway (GAP) in cancer tissues and cells. The pyrylium salt of 6,7-dimethoxy-3-methyl isochromenylium tetrafluoroborate (DMMIC) was used to label the amino group of metabolites, and a reductant of dithiothreitol (DTT) was employed to stabilize the thiol group. By combining DMMIC derivatization with LC-MS, it was feasible to quantify the 13 main metabolites on the GAP in complex biological samples, which had good linearity (R2 = 0.9981-0.9999), precision (interday precision of 1.6%-19.0% and intraday precision of 1.4%-19.8%) and accuracy (83.4%-115.7%). Moreover, the recovery assessments in tissues (82.5%-107.3%) and in cells (98.1%-118.9%) with GSH-13C2, 15N, and Cys-15N demonstrated the reliability of the method in detecting tissues and cells. Following a methodological evaluation, the method was applied successfully to investigate difference in the GAP between the carcinoma and para-carcinoma tissues of esophageal squamous cell carcinoma (ESCC) and the effect of p-hydroxycinnamaldehyde (CMSP) on the GAP in KYSE-150 esophageal cancer cells. The results demonstrate that the developed method provides a promising new tool to elucidate the roles of GAP in physiological and pathological processes, which can contribute to research on drugs and diseases.

Polarity-regulated derivatization-assisted LC-MS method for amino-containing metabolites profiling in gastric cancer

Amino-containing compounds, including amino acids, aliphatic amines, aromatic amines, small peptides and catecholamines, are involved in various biological processes and play vital roles in multiple metabolic pathways. Previous studies indicated that some amino-containing metabolites are significant diagnostic and prognostic biomarkers of gastric cancer. However, the discovery of precise biomarkers for the preoperative diagnosis of gastric cancer is still in an urgent need. Herein, we established a polarity-regulated derivatization method coupled with liquid chromatography-mass spectrometry (LC-MS) for amino-containing metabolites profiling in the serum samples of patients with gastric cancer and healthy controls, based on our newly designed and synthesized derivatization reagent (S)-3-(1-(diisopropoxyphosphoryl) pyrrolidine-2-carboxamido)-N-hydroxysuccinimidyl ester (3-DP-NHS). Enhanced separation efficiency and detection sensitivity for amino-containing metabolites were achieved after derivatization. This method exhibited good linearity, recovery, intra- and inter-day precision and accuracy. Only 5 μL serum is needed for untargeted analysis, enabling 202 amino-containing metabolites to be detected. Statistical analysis revealed altered amino acid metabolisms in patients with gastric cancer. Furthermore, ultra high performance liquid chromatography coupled with mass spectrometry (UHPLC-MS/MS) analysis quantification revealed increased serum levels of tryptamine and decreased concentrations of arginine and tryptophan in patients with gastric cancer. Receiver operating characteristic (ROC) curves indicated that an increased tryptamine/tryptophan ratio could serve as a potential biomarker for gastric cancer diagnosis. This study demostrated the possibility of using serum amino acid biomarkers for gastric cancer diagnosis, providing new avenues for the treatment of gastric cancer.

Automated sample preparation with 6-Aminoquinolyl-N-hydroxysuccinimidyl carbamate and iodoacetamide derivatization reagents for enantioselective liquid chromatography tandem mass spectrometry amino acid analysis

Enantioselective amino acid analysis is gaining increasing importance in pharmaceutical, biomedical and food sciences. While there are many methods available for enantiomer separation of amino acids, the simultaneous analysis of all chiral proteinogenic amino acids by a single method with one column and a single condition is still challenging. Herein, we report an enantioselective high-performance liquid chromatography-tandem mass spectrometry (LC-MS) assay using Chiralpak QN-AX as chiral column. With 6-aminoquinolyl-N-hydrosysuccinimidyl carbamate (AQC) as derivatization reagent, efficient enantioselective separation of D- and L-amino acids using HPLC has become possible. Thiol-containing amino acids like Cys are alkylated prior to AQC-labelling. A protocol for automated sample preparation including both derivatization step and calibrator preparation is presented. For compensating matrix effects, u-13C15N-labelled internal standards (IS) were employed. The method was validated and applied to the enantioselective analysis of amino acids in a bacterial fermentation broth.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates such as fatty acids, carbohydrates, lipids, and amino acids to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic extractions) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection - direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF MS/MS). Using DI-FTICR MS, 9,521 metabolic features were detected where 7,699 were assigned a chemical formula and 1,756 were assigned an annotated by accurate mass assignment. Using LC-Q-TOF MS/MS, 21,428 metabolic features were detected where 626 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 2276 heart metabolites were identified in this study which span a wide range of polarities including polar (benzenoids, alkaloids and derivatives and nucleosides) as well as non-polar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results of this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

Intensity-dependent mass search for improving metabolite database matches in chemical isotope labeling LC-QTOF-MS-based metabolomics

Liquid chromatography mass spectrometry (LC-MS) has been increasingly used for metabolome analysis. One of the critical steps in the LC-MS metabolome analysis workflow is related to metabolite identification. Among the measured parameters, peak mass is commonly used to search against a database for potential metabolite matches. Higher accuracy mass measurement allows the use of a narrower mass tolerance window for mass search. While various types of mass analyzers can routinely measure a peak mass with an error of less than a few ppm, mass measurement accuracy is not uniform for peaks with different intensities, particularly for quadrupole time-of-flight (QTOF) MS. Herein we present a simple and convenient method to determine the relation between peak intensity and mass error in LC-QTOF-MS-based metabolome analysis, followed by intensity-dependent mass search (IDMS) of a database for metabolite matches. This method is based on running a series of sodium formate mass calibrants, as part of the standard operating procedure (SOP) in LC-MS data acquisition, and then curve-fitting the measured mass errors and peak intensities. We show that, in two different quadrupole time-of-flight (QTOF) mass analyzers, mass accuracy is generally reduced as peak intensity decreases, which is independent of m/z values in the range commonly used for metabolite detection (e.g., m/z < 1000). We demonstrate the improvement in metabolite matches using IDMS in the analyses of dansyl labeled standards and human urine samples. We have implemented the IDMS method in the freely available MCID database at www.mycompoundid.org, which is composed of 8021 known human endogenous metabolites and their predicted metabolic products (375,809 compounds from one metabolic reaction and 10,583,901 compounds from two reactions).

An alternative approach to validation of liquid chromatography-mass spectrometry methods for the quantification of endogenous compounds

Despite the progress in the quantification of xenobiotics, the development and validation of methods designed for endogenous substances still remain challenging due to the natural presence of the analytes in a biological matrix, leading to the inability to obtain a blank sample. Several generally recognized procedures are described to solve this issue, like using surrogate or analyte-depleted matrices or surrogate analytes. However, the workflows used do not always meet the requirements for developing a reliable analytical method or are cost-intensive. This study aimed to design an alternative approach for preparing validation reference samples using authentic analytical standards while preserving the nature of the biological matrix and solving the problem with the inherent presence of analyzed compounds in a studied matrix. The methodology used is based on the standard-addition type procedure. However, unlike the original method, the addition is modified according to a previously measured basal concentration of monitored substances in the pooled biological sample to obtain a predefined concentration in reference samples according to the European Medicines Agency (EMA) validation guideline. The study shows the advantages of described approach on an example of LC-MS/MS analysis of 15 bile acids in human plasma and compares it with other methods commonly used in this field. The method was successfully validated according to the EMA guideline with lower limit of quantification of 5 nmol/L and linearity in the range of 5 - 2000 nmol/L. Finally, the method was used in a metabolomic study on a cohort of pregnant women (n = 28) to confirm intrahepatic cholestasis, the major liver disease observed in pregnancy.

Approach strategies and application of metabolomics to biotechnology in plants

Metabolomics refers to the technology for the comprehensive analysis of metabolites and low-molecular-weight compounds in a biological system, such as cells or tissues. Metabolites play an important role in biological phenomena through their direct involvement in the regulation of physiological mechanisms, such as maintaining cell homeostasis or signal transmission through protein-protein interactions. The current review aims provide a framework for how the integrated analysis of metabolites, their functional actions and inherent biological information can be used to understand biological phenomena related to the regulation of metabolites and how this information can be applied to safety assessments of crops created using biotechnology. Advancement in technology and analytical instrumentation have led new ways to examine the convergence between biology and chemistry, which has yielded a deeper understanding of complex biological phenomena. Metabolomics can be utilized and applied to safety assessments of biotechnology products through a systematic approach using metabolite-level data processing algorithms, statistical techniques, and database development. The integration of metabolomics data with sequencing data is a key step towards improving additional phenotypical evidence to elucidate the degree of environmental affects for variants found in genome associated with metabolic processes. Moreover, information analysis technology such as big data, machine learning, and IT investment must be introduced to establish a system for data extraction, selection, and metabolomic data analysis for the interpretation of biological implications of biotechnology innovations. This review outlines the integrity of metabolomics assessments in determining the consequences of genetic engineering and biotechnology in plants.

Antarctic Soil Metabolomics: A Pilot Study

In Antarctica, ice-free areas can be found along the coast, on mountain peaks, and in the McMurdo Dry Valleys, where microorganisms well-adapted to harsh conditions can survive and reproduce. Metabolic analyses can shed light on the survival mechanisms of Antarctic soil communities from both coastal sites, under different plant coverage stages, and inner sites where slow-growing or dormant microorganisms, low water availability, salt accumulation, and a limited number of primary producers make metabolomic profiling difficult. Here, we report, for the first time, an efficient protocol for the extraction and the metabolic profiling of Antarctic soils based on the combination of NMR spectroscopy and mass spectrometry (MS). This approach was set up on samples harvested along different localities of Victoria Land, in continental Antarctica, devoid of or covered by differently developed biological crusts. NMR allowed for the identification of thirty metabolites (mainly sugars, amino acids, and organic acids) and the quantification of just over twenty of them. UPLC-MS analysis identified more than twenty other metabolites, in particular flavonoids, medium- and long-chain fatty acids, benzoic acid derivatives, anthracenes, and quinones. Our results highlighted the complementarity of the two analytical techniques. Moreover, we demonstrated that their combined use represents the "gold standard" for the qualitative and quantitative analysis of little-explored samples, such as those collected from Antarctic soils.

An Integrated Multi-OMICS Approach Highlights Elevated Non-Esterified Fatty Acids Impact BeWo Trophoblast Metabolism and Lipid Processing

Maternal obesity and gestational diabetes mellitus (GDM) are linked with impaired placental function and early onset of non-communicable cardiometabolic diseases in offspring. Previous studies have highlighted that the dietary non-esterified fatty acids (NEFAs) palmitate (PA) and oleate (OA), key dietary metabolites associated with maternal obesity and GDM, are potential modulators of placental lipid processing. Using the BeWo cell line model, the current study integrated transcriptomic (mRNA microarray), metabolomic, and lipidomic readouts to characterize the underlying impacts of exogenous PA and OA on placental villous trophoblast cell metabolism. Targeted gas chromatography and thin-layer chromatography highlighted that saturated and monounsaturated NEFAs differentially impact BeWo cell lipid profiles. Furthermore, cellular lipid profiles differed when exposed to single and multiple NEFA species. Additional multi-omic analyses suggested that PA exposure is associated with enrichment in β-oxidation pathways, while OA exposure is associated with enrichment in anti-inflammatory and antioxidant pathways. Overall, this study further demonstrated that dietary PA and OA are important regulators of placental lipid metabolism. Encouraging appropriate dietary advice and implementing dietary interventions to maintain appropriate placental function by limiting excessive exposure to saturated NEFAs remain crucial in managing at-risk obese and GDM pregnancies.

Uncovering the Carboxylated Metabolome in Gut Microbiota-Host Co-metabolism: A Chemical Derivatization-Molecular Networking Approach

Gut microbiota-host co-metabolites serve as essential mediators of communication between the host and gut microbiota. They provide nutrient sources for host cells and regulate gut microenvironment, which are associated with a variety of diseases. Analysis of gut microbiota-host co-metabolites is of great significance to explore the host-gut microbiota interaction. In this study, we integrated chemical derivatization, liquid chromatography-mass spectrometry, and molecular networking (MN) to establish a novel CD-MN strategy for the analysis of carboxylated metabolites in gut microbial-host co-metabolism. Using this strategy, 261 carboxylated metabolites from mouse feces were detected, which grouped to various classes including fatty acids, bile acids, N-acyl amino acids, benzoheterocyclic acids, aromatic acids, and other unknown small-scale molecular clusters in MN. Based on the interpretation of the bile acid cluster, a novel type of phenylacetylated conjugates of host bile acids was identified, which were mediated by gut microbiota and exhibited a strong binding ability to Farnesoid X receptor and Takeda G protein-coupled receptor 5. Our proposed strategy offers a promising platform for uncovering carboxylated metabolites in gut microbial-host co-metabolism.

Isotope-coded derivatization with designed Girard-type reagent as charged isobaric mass tags for non-targeted profiling and discovery of natural aldehydes by liquid chromatography-tandem mass spectrometry

Aldehyde-containing metabolites are reactive electrophiles that have attracted extensive attention due to their widespread occurrence in organisms and natural foods. Herein we described a newly-designed Girard's reagent, 1-(4-hydrazinyl-4-oxobutyl)pyridin-1-ium bromide (HBP), as charged tandem mass (MS/MS) tags to facilitate selective capture, sensitive detection and semi-targeted discovery of aldehyde metabolites via hydrazone formation. After HBP labeling, the detection signals of the test aldehydes were increased by 21-2856 times, with the limits of detection were 2.5-7 nM. Upon isotope-coded derivatization with a pair of labeling reagents, HBP-d₀ and its deuterium-labeled counterpart HBP-d₅, the aldehyde analytes were converted to hydrazone derivatives, which generated characteristic neutral fragments of 79 Da and 84 Da, respectively. The isobaric HBP-d₀/HBP-d₅ labeling based LC-MS/MS method was validated by relative quantification of human urinary aldehydes (slope=0.999, R² > 0.99, RSDs ≤ 8.5%) and discrimination analysis between diabetic and control samples. The unique isotopic doubles (Δm/z = 5 Da) by dual neutral loss scanning (dNLS) provided a generic reactivity-based screening strategy that allowed non-targeted profiling and identification of endogenous aldehydes even amidst noisy data. The LC-dNLS-MS/MS screening of cinnamon extracts led to finding 61 possible natural aldehydes and guided discovery of 10 previously undetected congeners in this medicinal plant.

Simultaneous and dynamic measurement of Schisandrol A changes in rat blood and brain and its comparative pharmacokinetic study in control and Parkinson's disease rats by dual-probe in vivo microdialysis

Schisandrol A (SchA) is the main active ingredient of Schisandra chinensis (Turcz.) Baill., which is a famous traditional Chinese herbal medicine. SchA can penetrate the blood-brain barrier and has a significant neuroprotective effect. A group of multiplexed stable isotope mass tags (MSIMTs, m/z 332, 338, 346, 349, 351, 354, 360, 363, 374 and 377) were synthesized to perform multiplexed stable isotope labeling derivatization (MSILD) of SchA in rat microdialysates and standards. A new magnetic molecularly imprinted polymer was prepared using MSIMT-375-SchA as dummy template. All the 10-plexed derivatives of MSIMTs-SchA can be efficiently and selectively enriched and purified using this adsorbent by magnetic dispersive solid phase extraction (MDSPE) before ultra high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) analysis. It should be pointed out that the MSIMT-346-SchA standard derivative was used as internal standard in the process of MDSPE and UHPLC-MS/MS. On these bases, 9 different rat microdialysate samples can be determined by UHPLC-MS/MS in a single run. The utilization of MSIMTs significantly increased the sensitivity, accuracy, selectivity and analysis throughput. Under the optimized conditions, satisfactory linearity (R²> 0.987), limit of detection (LODs, 0.15-0.26 pg/mL) and lower limit of quantitative (LLOQ, 0.8-2.0 pg/mL) were obtained. Intra- and inter-day precisions were in the range of 2.2% -12.5%, and recoveries 94.2% -106.2%. The matrix effects were very low, and the average derivatization efficiency of 10-plex MSIMTs to SchA was as high as 97.8%. Using the developed dual-probe in vivo microdialysis sampling technique, the proposed analytical method has been applied for comparative pharmacokinetics of SchA in the brain and blood of control and Parkinson's disease (PD) rats.

Metabolomics-Based Mechanistic Insights into Revealing the Adverse Effects of Pesticides on Plants: An Interactive Review

In plant biology, metabolomics is often used to quantitatively assess small molecules, metabolites, and their intermediates in plants. Metabolomics has frequently been applied to detect metabolic alterations in plants exposed to various biotic and abiotic stresses, including pesticides. The widespread use of pesticides and agrochemicals in intensive crop production systems is a serious threat to the functionality and sustainability of agroecosystems. Pesticide accumulation in soil may disrupt soil-plant relationships, thereby posing a pollution risk to agricultural output. Application of metabolomic techniques in the assessment of the biological consequences of pesticides at the molecular level has emerged as a crucial technique in exposome investigations. State-of-the-art metabolomic approaches such as GC-MS, LC-MS/MS UHPLC, UPLC-IMS-QToF, GC/EI/MS, MALDI-TOF MS, and ¹H-HR-MAS NMR, etc., investigating the harmful effects of agricultural pesticides have been reviewed. This updated review seeks to outline the key uses of metabolomics related to the evaluation of the toxicological impacts of pesticides on agronomically important crops in exposome assays as well as bench-scale studies. Overall, this review describes the potential uses of metabolomics as a method for evaluating the safety of agricultural chemicals for regulatory applications. Additionally, the most recent developments in metabolomic tools applied to pesticide toxicology and also the difficulties in utilizing this approach are discussed.

Ocean acidificationf affects the bioenergetics of marine mussels as revealed by high-coverage quantitative metabolomics

Ocean acidification has become a major ecological and environmental problem in the world, whereas the impact mechanism of ocean acidification in marine bivalves is not fully understood. Cellular energy allocation (CEA) approach and high-coverage metabolomic techniques were used to investigate the acidification effects on the energy metabolism of mussels. The thick shell mussels Mytilus coruscus were exposed to seawater pH 8.1 (control) and pH 7.7 (acidification) for 14 days and allowed to recover at pH 8.1 for 7 days. The levels of carbohydrates, lipids and proteins significantly decreased in the digestive glands of the mussels exposed to acidification. The 14-day acidification exposure increased the energy demands of mussels, resulting in increased electron transport system (ETS) activity and decreased cellular energy allocation (CEA). Significant carry-over effects were observed on all cellular energy parameters except the concentration of carbohydrates and cellular energy demand (Ec) after 7 days of recovery. Metabolomic analysis showed that acidification affected the phenylalanine, tyrosine and tryptophan biosynthesis, taurine and hypotaurine metabolism, and glycine, serine and threonine metabolism. Correlation analysis showed that mussel cell energy parameters (carbohydrates, lipids, proteins, CEA) were negatively/positively correlated with certain differentially abundant metabolites. Overall, the integrated biochemical and metabolomics analyses demonstrated the negative effects of acidification on energy metabolism at the cellular level and implicated the alteration of biosynthesis and metabolism of amino acids as a mechanism of metabolic perturbation caused by acidification in mussels.

The impact of hyperglycemia upon BeWo trophoblast cell metabolic function: A multi-OMICS and functional metabolic analysis

Pre-existing and gestationally-developed diabetes mellitus have been linked with impairments in placental villous trophoblast cell metabolic function, that are thought to underlie the development of metabolic diseases early in the lives of the exposed offspring. Previous research using placental cell lines and ex vivo trophoblast preparations have highlighted hyperglycemia is an important independent regulator of placental function. However, it is poorly understood if hyperglycemia directly influences aspects of placental metabolic function, including nutrient storage and mitochondrial respiration, that are altered in term diabetic placentae. The current study examined metabolic and mitochondrial function as well as nutrient storage in both undifferentiated cytotrophoblast and differentiated syncytiotrophoblast BeWo cells cultured under hyperglycemia conditions (25 mM glucose) for 72 hours to further characterize the direct impacts of placental hyperglycemic exposure. Hyperglycemic-exposed BeWo trophoblasts displayed increased glycogen and triglyceride nutrient stores, but real-time functional readouts of metabolic enzyme activity and mitochondrial respiratory activity were not altered. However, specific investigation into mitochondrial dynamics highlighted increased expression of markers associated with mitochondrial fission that could indicate high glucose-exposed trophoblasts are transitioning towards mitochondrial dysfunction. To further characterize the impacts of independent hyperglycemia, the current study subsequently utilized a multi-omics approach and evaluated the transcriptomic and metabolomic signatures of BeWo cytotrophoblasts. BeWo cytotrophoblasts exposed to hyperglycemia displayed increased mRNA expression of ACSL1, HSD11B2, RPS6KA5, and LAP3 and reduced mRNA expression of CYP2F1, and HK2, concomitant with increased levels of: lactate, malonate, and riboflavin metabolites. These changes highlighted important underlying alterations to glucose, glutathione, fatty acid, and glucocorticoid metabolism in BeWo trophoblasts exposed to hyperglycemia. Overall, these results demonstrate that hyperglycemia is an important independent regulator of key areas of placental metabolism, nutrient storage, and mitochondrial function, and these data continue to expand our knowledge on mechanisms governing the development of placental dysfunction.

Chemical derivatization strategy for mass spectrometry-based lipidomics

Lipids, serving as the structural components of cellular membranes, energy storage, and signaling molecules, play the essential and multiple roles in biological functions of mammals. Mass spectrometry (MS) is widely accepted as the first choice for lipid analysis, offering good performance in sensitivity, accuracy, and structural characterization. However, the untargeted qualitative profiling and absolute quantitation of lipids are still challenged by great structural diversity and high structural similarity. In recent decade, chemical derivatization mainly targeting carboxyl group and carbon-carbon double bond of lipids have been developed for lipidomic analysis with diverse advantages: (i) offering more characteristic structural information; (ii) improving the analytical performance, including chromatographic separation and MS sensitivity; (iii) providing one-to-one chemical isotope labeling internal standards based on the isotope derivatization regent in quantitative analysis. Moreover, the chemical derivatization strategy has shown great potential in combination with ion mobility mass spectrometry and ambient mass spectrometry. Herein, we summarized the current states and advances in chemical derivatization-assisted MS techniques for lipidomic analysis, and their strengths and challenges are also given. In summary, the chemical derivatization-based lipidomic approach has become a promising and reliable technique for the analysis of lipidome in complex biological samples.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

Derivatization Strategies in Flavor Analysis: An Overview over the Wine and Beer Scenario

Wine and beer are the most appreciated and consumed beverages in the world. This success is mainly due to their characteristic taste, smell, and aroma, which can delight consumer’s palates. These olfactory characteristics are produced from specific classes of volatile compounds called “volatile odor-active compounds” linked to different factors such as age and production. Given the vast market of drinking beverages, the characterization of these odor compounds is increasingly important. However, the chemical complexity of these beverages has led the scientific community to develop several analytical techniques for extracting and quantifying these molecules. Even though the recent “green-oriented” trend is directed towards direct preparation-free procedures, for some class of analytes a conventional step like derivatization is unavoidable. This review is a snapshot of the most used derivatization strategies developed in the last 15 years for VOAs’ determination in wine and beer, the most consumed fermented beverages worldwide and among the most complex ones. A comprehensive overview is provided for every method, whereas pros and cons are critically analyzed and discussed. Emphasis was given to miniaturized methods which are more consistent with the principles of “green analytical chemistry”.

Camel (Camelus spp.) Urine Bioactivity and Metabolome: A Systematic Review of Knowledge Gaps, Advances, and Directions for Future Research

Up to the present day, studies on the therapeutic properties of camel (Camelus spp.) urine and the detailed characterization of its metabolomic profile are scarce and often unrelated. Information on inter individual variability is noticeably limited, and there is a wide divergence across studies regarding the methods for sample storage, pre-processing, and extract derivatization for metabolomic analysis. Additionally, medium osmolarity is not experimentally adjusted prior to bioactivity assays. In this scenario, the methodological standardization and interdisciplinary approach of such processes will strengthen the interpretation, repeatability, and replicability of the empirical results on the compounds with bioactive properties present in camel urine. Furthermore, sample enlargement would also permit the evaluation of camel urine's intra- and interindividual variability in terms of chemical composition, bioactive effects, and efficacy, while it may also permit researchers to discriminate potential animal-intrinsic and extrinsic conditioning factors. Altogether, the results would help to evaluate the role of camel urine as a natural source for the identification and extraction of specific novel bioactive substances that may deserve isolated chemical and pharmacognostic investigations through preclinical tests to determine their biological activity and the suitability of their safety profile for their potential inclusion in therapeutic formulas for improving human and animal health.

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.

Isotopic Tracer for Absolute Quantification of Metabolites of the Pentose Phosphate Pathway in Bacteria

The pentose phosphate pathway (PPP) plays a key role in many metabolic functions, including the generation of NADPH, biosynthesis of nucleotides, and carbon homeostasis. In particular, the intermediates of PPP have been found to be significantly perturbed in bacterial metabolomic studies. Nonetheless, detailed analysis to gain mechanistic information of PPP metabolism remains limited as most studies are unable to report on the absolute levels of the metabolites. Absolute quantification of metabolites is a prerequisite to study the details of fluxes and its regulations. Isotope tracer or labeling studies are conducted in vivo and in vitro and have significantly improved the analysis and understanding of PPP. Due to the laborious procedure and limitations in the in vivo method, an in vitro approach known as Group Specific Internal Standard Technology (GSIST) has been successfully developed to measure the absolute levels of central carbon metabolism, including PPP. The technique adopts derivatization of an experimental sample and a corresponding internal standard with isotope-coded reagents to provide better precision for accurate identification and absolute quantification. In this review, we highlight bacterial studies that employed isotopic tracers as the tagging agents used for the absolute quantification analysis of PPP metabolites.