Toward Global Metabolomics Analysis with Hydrophilic Interaction Liquid Chromatography–Mass Spectrometry: Improved Metabolite Identification by Retention Time Prediction

Metabolomic characterisation of the glioblastoma invasive margin reveals a region-specific signature

Isocitrate dehydrogenase wild-type glioblastoma (GBM) is characterised by a heterogeneous genetic landscape resulting from dynamic competition between tumour subclones to survive selective pressures. Improvements in metabolite identification and metabolome coverage have led to increased interest in clinically relevant applications of metabolomics. Here, we use liquid chromatography-mass spectrometry and gene expression microarray to profile integrated intratumour metabolic heterogeneity, as a direct functional readout of adaptive responses of subclones to the tumour microenvironment. Multi-region surgical sampling was performed on five adult GBM patients based on pre-operative brain imaging and fluorescence-guided surgery. Polar and hydrophobic metabolites extracted from tumour fragments were assessed, followed by putative assignment of metabolite identifications based on retention times and molecular mass. Class discrimination between tumour regions through showed clear separation of tumour regions based on polar metabolite profiles. Metabolic pathway assignments revealed several significantly altered metabolites between the tumour core and invasive region to be associated with purine and pyrimidine metabolism. This proof-of-principle study assesses intratumour heterogeneity through mass spectrometry-based metabolite profiling of multi-region biopsies. Bioinformatic interpretation of the GBM metabolome has highlighted the invasive region to be biologically distinct compared to tumour core and revealed putative drug-targetable metabolic pathways associated with purine and pyrimidine metabolism.

Comprehensive lipidomic profiling of human milk from lactating women across varying lactation stages and gestational ages

An untargeted lipidomic analysis was conducted to investigate the lipid composition of human milk across different lactation stages and gestational ages systematically. A total of 25 lipid subclasses and 934 lipid species as well as 90 free fatty acids were identified. Dynamic changes of the lipids throughout lactation and gestational phases were highlighted. In general, lactation stages introduced more variations in the lipid composition of human milk than gestational ages. Most lipids decreased as the milk progressed from the colostral stage to the mature stage, with some reaching a peak at the transitional stage. Significant variations in lipid composition across gestational ages were predominantly evident during early lactation period. In mature milks, most of the lipids exhibited no discernible statistical differences among gestational ages. This elucidation offers valuable insights and guidance for tailoring precise nutritional strategies for infants with diverse health needs.

Enhancing lipid identification in LC-HRMS data through machine learning-based retention time prediction

The comprehensive identification of peaks in untargeted lipidomics using LC-MS/MS remains a significant challenge. Confidence in lipid annotation can be greatly improved by integrating a highly accurate machine learning-based retention time prediction model. Such an approach enables the identification of lipids for understanding pathogenic mechanisms, biomarker discovery, and drug screening. In this study, we developed a machine learning model to predict retention times and facilitate lipid peak annotations in LC-MS-based untargeted lipidomics. Our model achieved high correlation coefficients of 0.998 and 0.990, with mean absolute errors (MAE) of 0.107 min and 0.240 min for the training and test sets, respectively. External validation showed similarly strong performance, with correlations of 0.991 and 0.978, and MAE values of 0.241 min and 0.270 min. We also compared the impact of molecular descriptors and molecular fingerprints on the model's performance, finding that molecular descriptors outperformed molecular fingerprints across all datasets when using Random Forest (RF) for model construction. Notably, this retention time calibration model demonstrates robust performance across chromatographic systems with comparable gradients and flow rates. Overall, this machine learning model enhances lipid annotation accuracy and reduces errors in untargeted lipidomics, improving data analysis across multiple datasets.

The beneficial effects of modafinil administration on repeat mild traumatic brain injury (RmTBI) pathology in adolescent male rats are not dependent upon the orexinergic system

The sleep-wake cycle plays an influential role in the development and progression of repeat mild traumatic brain injury (RmTBI)-related pathology. Therefore, we first aimed to manipulate the sleep-wake cycle post-RmTBI using modafinil, a wake-promoting substance used for the treatment of narcolepsy. We hypothesized that modafinil would exacerbate RmTBI-induced deficits. Chronic behavioural analyses were completed along with a 27-plex serum cytokine array, metabolomic and proteomic analyses of cerebrospinal fluid (CSF), as well as immunohistochemical staining in structures important for sleep/wake cycles, to examine orexin, melanin-concentrating hormone, tyrosine hydroxylase, and choline acetyltransferase, in the lateral hypothalamus, locus coeruleus, and basal forebrain, respectively. Contrary to expectation, modafinil administration attenuated behavioural deficits, metabolomic changes, and neuropathological modifications. Therefore, the second aim was to determine if the beneficial effects of modafinil treatment were driven by the orexinergic system. The same experimental protocol was used; however, RmTBI rats received chronic orexin-A administration instead of modafinil. Orexin-A administration produced drastically different outcomes, exacerbating anxiety-related and motor deficits, while also significantly disrupting their metabolomic and neuropathological profiles. These results suggest that the beneficial effects of modafinil administration post-RmTBI, work independently of its wake-promoting properties, as activation of the orexinergic wake-promoting system with orexin-A was detrimental. Overall, these findings highlight the complexity of sleep-wake changes in the injured brain and showcase the potential of the arousal and sleep systems in its treatment.

Variable glucagon metabolic actions in diverse mouse models of obesity and type 2 diabetes

OBJECTIVE

The study aimed to investigate the effects of glucagon on metabolic pathways in mouse models of obesity, fatty liver disease, and type 2 diabetes (T2D) to determine the extent and variability of hepatic glucagon resistance in these conditions.

METHODS

We investigated glucagon's effects in mouse models of fatty liver disease, obesity, and type 2 diabetes (T2D), including male BKS-db/db, high-fat diet-fed, and western diet-fed C57Bl/6 mice. Glucagon tolerance tests were performed using the selective glucagon receptor agonist acyl-glucagon (IUB288). Blood glucose, serum and liver metabolites include lipids and amino acids were measured. Additionally, liver protein expression related to glucagon signalling and a comprehensive liver metabolomics were performed.

RESULTS

Western diet-fed mice displayed impaired glucagon response, with reduced blood glucose and PKA activation. In contrast, high-fat diet-fed and db/db mice maintained normal glucagon sensitivity, showing significant elevations in blood glucose and phospho-PKA motif protein expression. Acyl-glucagon treatment also lowered liver alanine and histidine levels in high-fat diet-fed mice, but not in western diet-fed mice. Additionally, some amino acids, such as methionine, were increased by acyl-glucagon only in chow diet control mice. Despite normal glucagon sensitivity in PKA signalling, db/db mice had a distinct metabolomic response, with acyl-glucagon significantly altering 90 metabolites in db/+ mice but only 42 in db/db mice, and classic glucagon-regulated metabolites, such as cyclic adenosine monophosphate (cAMP), being less responsive in db/db mice.

CONCLUSIONS

The study reveals that hepatic glucagon resistance in obesity and T2D is complex and not uniform across metabolic pathways, underscoring the complexity of glucagon action in these conditions.

Metabolic profiling unveils enhanced antibacterial synergy of polymyxin B and teixobactin against multi-drug resistant Acinetobacter baumannii

This untargeted metabolomics study investigated the synergistic antibacterial activity of polymyxin B and Leu10-teixobactin, a depsipeptide inhibitor of cell wall biosynthesis. Checkerboard microdilution assays revealed a significant synergy against polymyxin-susceptible and -resistant A. baumannii, excluding lipopolysaccharide-deficient variants. Time-kill assays confirmed bactericidal synergy, reducing bacterial burden by approximately 4-6-log10CFU/mL. The combination (2xMIC polymyxin B and 0.5xMIC Leu10-teixobactin) prevented bacterial regrowth after 24 h, indicating sustained efficacy against the emergence of resistant mutants. The analysis of A. baumannii ATCC™ 19606 metabolome demonstrated that the polymyxin B-Leu10-teixobactin combination produced more pronounced perturbation compared to the individual antibiotics across all time points (1, 3 and 6 h). Pathway analysis revealed that lipid metabolism, cell envelope biogenesis, and cellular respiration were predominantly impacted by the combination, and to a lesser extent by polymyxin B monotherapy. Leu10-teixobactin treatment alone had only a minor impact on the metabolome, primarily at the 6 h time point. Peptidoglycan assays confirmed the combination's concerted deleterious effects on bacterial cell envelope integrity. Electron microscopy further substantiated these findings, revealing pronounced cell envelope damage, membrane blebbing, and vacuole formation. These findings highlight the potential of the polymyxin B-Leu10-teixobactin combination as an effective treatment in preventing resistance in A. baumannii.

Artificial Intelligence in Metabolomics: A Current Review

Metabolomics and artificial intelligence (AI) form a synergistic partnership. Metabolomics generates large datasets comprising hundreds to thousands of metabolites with complex relationships. AI, aiming to mimic human intelligence through computational modeling, possesses extraordinary capabilities for big data analysis. In this review, we provide a recent overview of the methodologies and applications of AI in metabolomics studies in the context of systems biology and human health. We first introduce the AI concept, history, and key algorithms for machine learning and deep learning, summarizing their strengths and weaknesses. We then discuss studies that have successfully used AI across different aspects of metabolomic analysis, including analytical detection, data preprocessing, biomarker discovery, predictive modeling, and multi-omics data integration. Lastly, we discuss the existing challenges and future perspectives in this rapidly evolving field. Despite limitations and challenges, the combination of metabolomics and AI holds great promises for revolutionary advancements in enhancing human health.

Retention time dataset for heterogeneous molecules in reversed-phase liquid chromatography

Quantitative structure-property relationships have been extensively studied in the field of predicting retention times in liquid chromatography (LC). However, making transferable predictions is inherently complex because retention times are influenced by both the structure of the molecule and the chromatographic method used. Despite decades of development and numerous published machine learning models, the practical application of predicting small molecule retention time remains limited. The resulting models are typically limited to specific chromatographic conditions and the molecules used in their training and evaluation. Here, we have developed a comprehensive dataset comprising over 10,000 experimental retention times. These times were derived from 30 different reversed-phase liquid chromatography methods and pertain to a collection of 343 small molecules representing a wide range of chemical structures. These chromatographic methods encompass common LC setups for studying the retention behavior of small molecules. They offer a wide range of examples for modeling retention time with different LC setups.

Property and Activity Refinement of Dihydroquinazolinone-3-carboxamides as Orally Efficacious Antimalarials that Target PfATP4

To contribute to the global effort to develop new antimalarial therapies, we previously disclosed initial findings on the optimization of the dihydroquinazolinone-3-carboxamide class that targets PfATP4. Here we report on refining the aqueous solubility and metabolic stability to improve the pharmacokinetic profile and consequently in vivo efficacy. We show that the incorporation of heterocycle systems in the 8-position of the scaffold was found to provide the greatest attainable balance between parasite activity, aqueous solubility, and metabolic stability. Optimized analogs, including the frontrunner compound S-WJM992, were shown to inhibit PfATP4-associated Na+-ATPase activity, gave rise to a metabolic signature consistent with PfATP4 inhibition, and displayed altered activities against parasites with mutations in PfATP4. Finally, S-WJM992 showed appreciable efficacy in a malaria mouse model and blocked gamete development preventing transmission to mosquitoes. Importantly, further optimization of the dihydroquinazolinone class is required to deliver a candidate with improved pharmacokinetic and risk of resistance profiles.

Enhancing compound confidence in suspect and non-target screening through machine learning-based retention time prediction

The retention time (RT) of contaminants of emerging concern (CECs) in liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is crucial for database matching in non-targeted screening (NTS) analysis. In this study, we developed a machine learning (ML) model to predict RTs of CECs in NTS analysis. Using 1051 CEC standards, we evaluated Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Artificial Neural Network (ANN) with molecular fingerprints and chemical descriptors to establish an optimal model. The SVR model utilizing chemical descriptors resulted in good predictive capacity with R2ext = 0.850 and r2 = 0.925. The model was further validated through laboratory NTS compound characterization. When applied to examine CEC occurrence in a large wastewater treatment plant, we identified 40 level S1 CECs (confirmed structure by reference standard) and 234 level S2 compounds (probable structure by library spectrum match). The model predicted RTs for level S2 compounds, leading to the classification of 153 level S2 compounds with high confidence (ΔRT <2 min). The model served as a robust filtering mechanism within the analytical framework. This study emphasizes the importance of predicted RTs in NTS analysis and highlights the potential of prediction models. Our research introduces a workflow that enhances NTS analysis by utilizing RT prediction models to determine compound confidence levels.

Identifying and mathematically modeling the time-course of extracellular metabolic markers associated with resistance to ceftolozane/tazobactam in Pseudomonas aeruginosa

Extracellular bacterial metabolites have potential as markers of bacterial growth and resistance emergence but have not been evaluated in dynamic in vitro studies. We investigated the dynamic metabolomic footprint of a multidrug-resistant hypermutable Pseudomonas aeruginosa isolate exposed to ceftolozane/tazobactam as continuous infusion (4.5 g/day, 9 g/day) in a hollow-fiber infection model over 7-9 days in biological replicates (n = 5). Bacterial samples were collected at 0, 7, 23, 47, 71, 95, 143, 167, 191, and 215 h, the supernatant quenched, and extracellular metabolites extracted. Metabolites were analyzed via untargeted metabolomics, including hierarchical clustering and correlation with quantified total and resistant bacterial populations. The time-courses of five (of 1,921 detected) metabolites from enriched pathways were mathematically modeled. Absorbed L-arginine and secreted L-ornithine were highly correlated with the total bacterial population (r -0.79 and 0.82, respectively, P<0.0001). Ribose-5-phosphate, sedoheptulose-7-phosphate, and trehalose-6-phosphate correlated with the resistant subpopulation (0.64, 0.64, and 0.67, respectively, P<0.0001) and were likely secreted due to resistant growth overcoming oxidative and osmotic stress induced by ceftolozane/tazobactam. Using pharmacokinetic/pharmacodynamic-based transduction models, these metabolites were successfully modeled based on the total or resistant bacterial populations. The models well described the abundance of each metabolite across the differing time-course profiles of biological replicates, based on bacterial killing and, importantly, resistant regrowth. These proof-of-concept studies suggest that further exploration is warranted to determine the generalizability of these findings. The metabolites modeled here are not exclusive to bacteria. Future studies may use this approach to identify bacteria-specific metabolites correlating with resistance, which would ultimately be extremely useful for clinical translation.

Identifying potential dietary treatments for inherited metabolic disorders using Drosophila nutrigenomics

Inherited metabolic disorders are a group of genetic conditions that can cause severe neurological impairment and child mortality. Uniquely, these disorders respond to dietary treatment; however, this option remains largely unexplored because of low disorder prevalence and the lack of a suitable paradigm for testing diets. Here, we screened 35 Drosophila amino acid disorder models for disease-diet interactions and found 26 with diet-altered development and/or survival. Using a targeted multi-nutrient array, we examine the interaction in a model of isolated sulfite oxidase deficiency, an infant-lethal disorder. We show that dietary cysteine depletion normalizes their metabolic profile and rescues development, neurophysiology, behavior, and lifelong fly survival, thus providing a basis for further study into the pathogenic mechanisms involved in this disorder. Our work highlights the diet-sensitive nature of metabolic disorders and establishes Drosophila as a valuable tool for nutrigenomic studies for informing potential dietary therapies.

A defined diet for pre-adult Drosophila melanogaster

Drosophila melanogaster is unique among animal models because it has a fully defined synthetic diet available to study nutrient-gene interactions. However, use of this diet is limited to adult studies due to impaired larval development and survival. Here, we provide an adjusted formula that reduces the developmental period, restores fat levels, enhances body mass, and fully rescues survivorship without compromise to adult lifespan. To demonstrate an application of this formula, we explored pre-adult diet compositions of therapeutic potential in a model of an inherited metabolic disorder affecting the metabolism of branched-chain amino acids. We reveal rapid, specific, and predictable nutrient effects on the disease state consistent with observations from mouse and patient studies. Together, our diet provides a powerful means with which to examine the interplay between diet and metabolism across all life stages in an animal model.

Novel aroyl guanidine anti-trypanosomal compounds that exert opposing effects on parasite energy metabolism

Human African trypanosomiasis (HAT), or sleeping sickness, is a neglected tropical disease with current treatments marred by severe side effects or delivery issues. To identify novel classes of compounds for the treatment of HAT, high throughput screening (HTS) had previously been conducted on bloodstream forms of T. b. brucei, a model organism closely related to the human pathogens T. b. gambiense and T. b. rhodesiense. This HTS had identified a number of structural classes with potent bioactivity against T. b. brucei (IC50 ≤ 10 μM) with selectivity over mammalian cell-lines (selectivity index of ≥10). One of the confirmed hits was an aroyl guanidine derivative. Deemed to be chemically tractable with attractive physicochemical properties, here we explore this class further to develop the SAR landscape. We also report the influence of the elucidated SAR on parasite metabolism, to gain insight into possible modes of action of this class. Of note, two sub-classes of analogues were identified that generated opposing metabolic responses involving disrupted energy metabolism. This knowledge may guide the future design of more potent inhibitors, while retaining the desirable physicochemical properties and an excellent selectivity profile of the current compound class.

Generic and accurate prediction of retention times in liquid chromatography by post-projection calibration

Retention time predictions from molecule structures in liquid chromatography (LC) are increasingly used in MS-based targeted and untargeted analyses, providing supplementary evidence for molecule annotation and reducing experimental measurements. Nevertheless, different LC setups (e.g., differences in gradient, column, and/or mobile phase) give rise to many prediction models that can only accurately predict retention times for a specific chromatographic method (CM). Here, a generic and accurate method is present to predict retention times across different CMs, by introducing the concept of post-projection calibration. This concept builds on the direct projections of retention times between different CMs and uses 35 external calibrants to eliminate the impact of LC setups on projection accuracy. Results showed that post-projection calibration consistently achieved a median projection error below 3.2% of the elution time. The ranking results of putative candidates reached similar levels among different CMs. This work opens up broad possibilities for coordinating retention times between different laboratories and developing extensive retention databases.

Predicting Retention Time in Unified-Hydrophilic-Interaction/Anion-Exchange Liquid Chromatography High-Resolution Tandem Mass Spectrometry (Unified-HILIC/AEX/HRMS/MS) for Comprehensive Structural Annotation of Polar Metabolome

The accuracy of the structural annotation of unidentified peaks obtained in metabolomic analysis using liquid chromatography/tandem mass spectrometry (LC/MS/MS) can be enhanced using retention time (RT) information as well as precursor and product ions. Unified-hydrophilic-interaction/anion-exchange liquid chromatography high-resolution tandem mass spectrometry (unified-HILIC/AEX/HRMS/MS) has been recently developed as an innovative method ideal for nontargeted polar metabolomics. However, the RT prediction for unified-HILIC/AEX has not been developed because of the complex separation mechanism characterized by the continuous transition of the separation modes from HILIC to AEX. In this study, we propose an RT prediction model of unified-HILIC/AEX/HRMS/MS, which enables the comprehensive structural annotation of polar metabolites. With training data for 203 polar metabolites, we ranked the feature importance using a random forest among 12,420 molecular descriptors (MDs) and constructed an RT prediction model with 26 selected MDs. The accuracy of the RT model was evaluated using test data for 51 polar metabolites, and 86.3% of the ΔRTs (difference between measured and predicted RTs) were within ±1.50 min, with a mean absolute error of 0.80 min, indicating high RT prediction accuracy. Nontargeted metabolomic data from the NIST SRM 1950-Metabolites in frozen human plasma were analyzed using the developed RT model and in silico MS/MS prediction, resulting in a successful structural estimation of 216 polar metabolites, in addition to the 62 identified based on standards. The proposed model can help accelerate the structural annotation of unknown hydrophilic metabolites, which is a key issue in metabolomic research.

Natural Products Dereplication: Databases and Analytical Methods

The development of efficient methods for dereplication has been critical in the re-emergence of the research in natural products as a source of drug leads. Current dereplication workflows rapidly identify already known bioactive secondary metabolites in the early stages of any drug discovery screening campaign based on natural extracts or enriched fractions. Two main factors have driven the evolution of natural products dereplication over the last decades. First, the availability of both commercial and public large databases of natural products containing the key annotations against which the biological and chemical data derived from the studied sample are searched for. Second, the considerable improvement achieved in analytical technologies (including instrumentation and software tools) employed to obtain robust and precise chemical information (particularly spectroscopic signatures) on the compounds present in the bioactive natural product samples. This chapter describes the main methods of dereplication, which rely on the combined use of large natural product databases and spectral libraries, alongside the information obtained from chromatographic, UV-Vis, MS, and NMR spectroscopic analyses of the samples of interest.

The African killifish: A short-lived vertebrate model to study the biology of sarcopenia and longevity

Sarcopenia, the age-related decline in muscle function, places a considerable burden on health-care systems. While the stereotypic hallmarks of sarcopenia are well characterized, their contribution to muscle wasting remains elusive, which is partly due to the limited availability of animal models. Here, we have performed cellular and molecular characterization of skeletal muscle from the African killifish-an extremely short-lived vertebrate-revealing that while many characteristics deteriorate with increasing age, supporting the use of killifish as a model for sarcopenia research, some features surprisingly reverse to an "early-life" state in the extremely old stages. This suggests that in extremely old animals, there may be mechanisms that prevent further deterioration of skeletal muscle, contributing to an extension of life span. In line with this, we report a reduction in mortality rates in extremely old killifish. To identify mechanisms for this phenomenon, we used a systems metabolomics approach, which revealed that during aging there is a striking depletion of triglycerides, mimicking a state of calorie restriction. This results in the activation of mitohormesis, increasing Sirt1 levels, which improves lipid metabolism and maintains nutrient homeostasis in extremely old animals. Pharmacological induction of Sirt1 in aged animals was sufficient to induce a late life-like metabolic profile, supporting its role in life span extension in vertebrate populations that are naturally long-lived. Collectively, our results demonstrate that killifish are not only a novel model to study the biological processes that govern sarcopenia, but they also provide a unique vertebrate system to dissect the regulation of longevity.

A conserved metabolic signature associated with response to fast-acting anti-malarial agents

IMPORTANCE

In malaria drug discovery, understanding the mode of action of lead compounds is important as it helps in predicting the potential emergence of drug resistance in the field when these drugs are eventually deployed. In this study, we have employed metabolomics technologies to characterize the potential targets of anti-malarial drug candidates in the developmental pipeline at NITD. We show that NITD fast-acting leads belonging to spiroindolone and imidazothiadiazole class induce a common biochemical theme in drug-exposed malaria parasites which is similar to another fast-acting, clinically available drug, DHA. These biochemical features which are absent in a slower acting NITD lead (GNF17) point to hemoglobin digestion and inhibition of the pyrimidine pathway as potential action points for these drugs. These biochemical themes can be used to identify and inform on the mode of action of fast drug candidates of similar profiles in future drug discovery programs.

Systemic host inflammation induces stage-specific transcriptomic modification and slower maturation in malaria parasites

Maturation rates of malaria parasites within red blood cells (RBCs) can be influenced by host nutrient status and circadian rhythm; whether host inflammatory responses can also influence maturation remains less clear. Here, we observed that systemic host inflammation induced in mice by an innate immune stimulus, lipopolysaccharide (LPS), or by ongoing acute Plasmodium infection, slowed the progression of a single cohort of parasites from one generation of RBC to the next. Importantly, plasma from LPS-conditioned or acutely infected mice directly inhibited parasite maturation during in vitro culture, which was not rescued by supplementation, suggesting the emergence of inhibitory factors in plasma. Metabolomic assessments confirmed substantial alterations to the plasma of LPS-conditioned and acutely infected mice, and identified a small number of candidate inhibitory metabolites. Finally, we confirmed rapid parasite responses to systemic host inflammation in vivo using parasite scRNA-seq, noting broad impairment in transcriptional activity and translational capacity specifically in trophozoites but not rings or schizonts. Thus, we provide evidence that systemic host inflammation rapidly triggered transcriptional alterations in circulating blood-stage Plasmodium trophozoites and predict candidate inhibitory metabolites in the plasma that may impair parasite maturation in vivo. IMPORTANCE Malaria parasites cyclically invade, multiply, and burst out of red blood cells. We found that a strong inflammatory response can cause changes to the composition of host plasma, which directly slows down parasite maturation. Thus, our work highlights a new mechanism that limits malaria parasite growth in the bloodstream.

Identification of endogenous carbonyl steroids in human serum by chemical derivatization, hydrogen/deuterium exchange mass spectrometry and the quantitative structure-retention relationship

Steroids are tetracyclic aliphatic compounds, and most of them contain carbonyl groups. The disordered homeostasis of steroids is closely related to the occurrence and progression of various diseases. Due to high structural similarity, low concentrations in vivo, poor ionization efficiency, and interference from endogenous substances, it is very challenging to comprehensively and unambiguously identify endogenous steroids in biological matrix. Herein, an integrated strategy was developed for the characterization of endogenous steroids in serum based on chemical derivatization, ultra-performance liquid chromatography quadrupole Exactive mass spectrometry (UPLC-Q-Exactive-MS/MS), hydrogen/deuterium (H/D) exchange, and a quantitative structure-retention relationship (QSRR) model. To enhance the mass spectrometry (MS) response of carbonyl steroids, the ketonic carbonyl group was derivatized by Girard T (GT). Firstly, the fragmentation rules of derivatized carbonyl steroid standards by GT were summarized. Then, carbonyl steroids in serum were derivatized by GT and identified based on the fragmentation rules or by comparing retention time and MS/MS spectra with those of standards. H/D exchange MS was utilized to distinguish derivatized steroid isomers for the first time. Finally, a QSRR model was constructed to predict the retention time of the unknown steroid derivatives. With this strategy, 93 carbonyl steroids were identified from human serum, and 30 of them were determined to be dicarbonyl steroids by the charge number of characteristic ions and the number of exchangeable hrdrogen or comparing with standards. The QSRR model built by the machine learning algorithms has an excellent regression correlation, thus the accurate structures of 14 carbonyl steroids were determined, among which three steroids were reported for the first time in human serum. This study provides a new analytical method for the comprehensive and reliable identification of carbonyl steroids in biological matrix.

Retention time prediction for small samples based on integrating molecular representations and adaptive network

Retention time (RT) can provide orthogonal information different from that of mass spectrometry and contribute to identifying compounds. Many machine learning methods have been developed and applied to RT prediction. In application, the training data size is usually small in most chromatography systems. To enhance the performance of RT prediction, this study proposes a RT prediction method based on multi-data combinations and adaptive neural network (MDC-ANN). MDC-ANN establishes the RT prediction model for the target chromatographic system through transfer learning and a base deep learning model trained on a big dataset. It selects the optimal molecular representation combination from the multiple input candidates and automatically determines the neural network structure according to the determined input combination. MDC-ANN was compared with two new efficient deep learning methods, three transferring methods and four popular machine learning methods on 14 small datasets and showed advantages in MAE, MedAE, MRE and R² in most cases. The experiment results illustrated that integrating multiple molecular representations can provide more information, improve the performance of RT prediction and contribute to compound annotation, different chromatographic systems may use different molecular representation combinations to obtain good RT prediction performance. Hence, MDC-ANN which automatically determines the best combination of molecular representations for a specific system is promising for predicting RTs accurately in real applications.

Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics

Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic results. Standards of metabolites were tested using liquid chromatography coupled with high-resolution mass spectrometry. In CCSBase and QSRR predictor machine learning models, experimental results were used to generate predicted CCS and Rt of the Human Metabolome Database. From 542 standards, 266 and 301 compounds were detected in positive and negative electrospray ionization mode, respectively, corresponding to 380 different metabolites. CCS and Rt were then predicted using machine learning tools for almost 114,000 metabolites. R² score of the linear regression between predicted and measured data achieved 0.938 and 0.898 for CCS and Rt, respectively, demonstrating the models' reliability. A CCS and Rt index filter of mean error ± 2 standard deviations could remove most misidentifications. Its application to data generated from a toxicology study on tobacco cigarettes reduced hits by 76%. Regarding the volume of data produced by metabolomics, the practical workflow provided allows for the implementation of valuable large-scale databases to improve the biological interpretation of metabolomics data.

A choline-releasing glycerophosphodiesterase essential for phosphatidylcholine biosynthesis and blood stage development in the malaria parasite

The malaria parasite Plasmodium falciparum synthesizes significant amounts of phospholipids to meet the demands of replication within red blood cells. De novo phosphatidylcholine (PC) biosynthesis via the Kennedy pathway is essential, requiring choline that is primarily sourced from host serum lysophosphatidylcholine (lysoPC). LysoPC also acts as an environmental sensor to regulate parasite sexual differentiation. Despite these critical roles for host lysoPC, the enzyme(s) involved in its breakdown to free choline for PC synthesis are unknown. Here, we show that a parasite glycerophosphodiesterase (PfGDPD) is indispensable for blood stage parasite proliferation. Exogenous choline rescues growth of PfGDPD-null parasites, directly linking PfGDPD function to choline incorporation. Genetic ablation of PfGDPD reduces choline uptake from lysoPC, resulting in depletion of several PC species in the parasite, whilst purified PfGDPD releases choline from glycerophosphocholine in vitro. Our results identify PfGDPD as a choline-releasing glycerophosphodiesterase that mediates a critical step in PC biosynthesis and parasite survival.

Mass Spectrometric Methods for Non-Targeted Screening of Metabolites: A Future Perspective for the Identification of Unknown Compounds in Plant Extracts

Phyto products are widely used in natural products, such as medicines, cosmetics or as so-called “superfoods”. However, the exact metabolite composition of these products is still unknown, due to the time-consuming process of metabolite identification. Non-target screening by LC-HRMS/MS could be a technique to overcome these problems with its capacity to identify compounds based on their retention time, accurate mass and fragmentation pattern. In particular, the use of computational tools, such as deconvolution algorithms, retention time prediction, in silico fragmentation and sophisticated search algorithms, for comparison of spectra similarity with mass spectral databases facilitate researchers to conduct a more exhaustive profiling of metabolic contents. This review aims to provide an overview of various techniques and tools for non-target screening of phyto samples using LC-HRMS/MS.

Retention Time Prediction with Message-Passing Neural Networks

Retention time prediction, facilitated by advances in machine learning, has become a useful tool in untargeted LC-MS applications. State-of-the-art approaches include graph neural networks and 1D-convolutional neural networks that are trained on the METLIN small molecule retention time dataset (SMRT). These approaches demonstrate accurate predictions comparable with the experimental error for the training set. The weak point of retention time prediction approaches is the transfer of predictions to various systems. The accuracy of this step depends both on the method of mapping and on the accuracy of the general model trained on SMRT. Therefore, improvements to both parts of prediction workflows may lead to improved compound annotations. Here, we evaluate capabilities of message-passing neural networks (MPNN) that have demonstrated outstanding performance on many chemical tasks to accurately predict retention times. The model was initially trained on SMRT, providing mean and median absolute cross-validation errors of 32 and 16 s, respectively. The pretrained MPNN was further fine-tuned on five publicly available small reversed-phase retention sets in a transfer learning mode and demonstrated up to 30% improvement of prediction accuracy for these sets compared with the state-of-the-art methods. We demonstrated that filtering isomeric candidates by predicted retention with the thresholds obtained from ROC curves eliminates up to 50% of false identities.

Metabolic Signatures of Surface-Modified Poly(lactic-co-glycolic acid) Nanoparticles in Differentiated THP-1 Cells Derived with Liquid Chromatography-Mass Spectrometry-based Metabolomics

Polymeric nanoparticles (NPs) are widely used in preclinical drug delivery investigations, and some formulations are now in the clinic. However, the detailed effects of many NPs at the subcellular level have not been fully investigated. In this study, we used differentiated THP-1 macrophage cells, as a model, to investigate the metabolic changes associated with the use of poly (lactic-co-glycolic acid) (PLGA) NPs with different surface coating or conjugation chemistries. Liquid chromatography-mass spectrometry-based metabolic profiling was performed on the extracts (n = 6) of the differentiated THP-1 cells treated with plain, Pluronic (F-127, F-68, and P-85)-coated and PEG-PLGA NPs and control (no treatment). Principal component analysis and orthogonal partial least squares-discriminant analysis (OPLS-DA) in conjunction with univariate and pathway analyses were performed to identify significantly changed metabolites and pathways related to exposure of the cells to NPs. OPLS-DA of each class in the study compared to the control showed clear separation and clustering with cross-validation values of R ² and Q ² > 0.5. A total of 105 metabolites and lipids were found to be significantly altered in the differentiated THP-1 cell profiles due to the NP exposure, whereas more than 20 metabolic pathways were found to be affected. These pathways included glycerophospholipid, sphingolipid, linoleic acid, arginine and proline, and alpha-linolenic acid metabolisms. PLGA NPs were found to perturb some amino acid metabolic pathways and altered membrane lipids to a different degree. The metabolic effect of the PLGA NPs on the cells were comparable to those caused by silver oxide NPs and other inorganic nanomaterials. However, PEG-PLGA NPs demonstrated a reduced impact on the cellular metabolism compared to Pluronic copolymer-coated PLGA and plain PLGA NPs.

Lipoprotein Deprivation Reveals a Cholesterol-Dependent Therapeutic Vulnerability in Diffuse Glioma Metabolism

Simple Summary

High-grade gliomas are aggressive cancers that arise in children and adults, for which there is an urgent need for more effective drug therapies. Targeting the energy requirements (‘metabolism’) of these cancer cells may offer a new avenue for therapy. Cholesterol is a fatty substance found on the surface of cancer cells. Our research shows that childhood high-grade gliomas require cholesterol for their energy needs. By repurposing a drug called LXR-623 to reduce the levels of cholesterol inside high-grade glioma cancer cells, we could impair the growth of these cells in laboratory conditions. These results provide evidence for future experiments using LXR-623 to test whether this drug is able to increase the survival of mice with similar high-grade gliomas.

Abstract

Poor outcomes associated with diffuse high-grade gliomas occur in both adults and children, despite substantial progress made in the molecular characterisation of the disease. Targeting the metabolic requirements of cancer cells represents an alternative therapeutic strategy to overcome the redundancy associated with cell signalling. Cholesterol is an integral component of cell membranes and is required by cancer cells to maintain growth and may also drive transformation. Here, we show that removal of exogenous cholesterol in the form of lipoproteins from culture medium was detrimental to the growth of two paediatric diffuse glioma cell lines, KNS42 and SF188, in association with S-phase elongation and a transcriptomic program, indicating dysregulated cholesterol homeostasis. Interrogation of metabolic perturbations under lipoprotein-deficient conditions revealed a reduced abundance of taurine-related metabolites and cholesterol ester species. Pharmacological reduction in intracellular cholesterol via decreased uptake and increased export was simulated using the liver X receptor agonist LXR-623, which reduced cellular viability in both adult and paediatric models of diffuse glioma, although the mechanism appeared to be cholesterol-independent in the latter. These results provide proof-of-principle for further assessment of liver X receptor agonists in paediatric diffuse glioma to complement the currently approved therapeutic regimens and expand the options available to clinicians to treat this highly debilitating disease.

Profiling of grazed cultures of the chlorophyte alga Dunaliella tertiolecta using an untargeted LC-MS approach

Extracellular signals are reported to mediate chemical cross-talk among pelagic microbes, including microalgal prey and predators. Water-soluble mediator compounds play a crucial role in extracellular communication which is vital for prey recognition, attraction, capture, and predator deterrence. A range of exo-metabolites including oxylipins and vitamins are released by prey in response to grazing stress. The temporal dynamics of such exo-metabolites largely remains unknown, especially in large-scale cultivation of microalgae such as closed or open ponds. In open ponds, infestation of predators is almost inevitable but highly undesirable due to the imminent threat of culture collapse. The early production of exo-metabolites emitted by microalgal prey in response to predator attack could be leveraged as diagnostic markers of possible culture collapse. This study uses an untargeted approach for temporal profiling of Dunaliella tertiolecta-specific exo-metabolites under grazing pressure from Oxyrrhis marina. We report 24 putatively identified metabolites, belonging to various classes such as short peptides, lipids, indole-derivatives, and free amino acids, as potential markers of grazing-mediated stress. In addition, this study outlines a clear methodology for screening of exo-metabolites in marine algal samples, the analysis of which is frequently hindered by high salt concentrations. In future, a chemistry-based targeted detection of these metabolites could enable a quick and on-site screening of predators in microalgal cultures.

An Integrated Multi-Omic Network Analysis Identifies Seizure-Associated Dysregulated Pathways in the GAERS Model of Absence Epilepsy

Absence epilepsy syndromes are part of the genetic generalized epilepsies, the pathogenesis of which remains poorly understood, although a polygenic architecture is presumed. Current focus on single molecule or gene identification to elucidate epileptogenic drivers is unable to fully capture the complex dysfunctional interactions occurring at a genetic/proteomic/metabolomic level. Here, we employ a multi-omic, network-based approach to characterize the molecular signature associated with absence epilepsy-like phenotype seen in a well validated rat model of genetic generalized epilepsy with absence seizures. Electroencephalographic and behavioral data was collected from Genetic Absence Epilepsy Rats from Strasbourg (GAERS, n = 6) and non-epileptic controls (NEC, n = 6), followed by proteomic and metabolomic profiling of the cortical and thalamic tissue of rats from both groups. The general framework of weighted correlation network analysis (WGCNA) was used to identify groups of highly correlated proteins and metabolites, which were then functionally annotated through joint pathway enrichment analysis. In both brain regions a large protein-metabolite module was found to be highly associated with the GAERS strain, absence seizures and associated anxiety and depressive-like phenotype. Quantitative pathway analysis indicated enrichment in oxidative pathways and a downregulation of the lysine degradation pathway in both brain regions. GSTM1 and ALDH2 were identified as central regulatory hubs of the seizure-associated module in the somatosensory cortex and thalamus, respectively. These enzymes are involved in lysine degradation and play important roles in maintaining oxidative balance. We conclude that the dysregulated pathways identified in the seizure-associated module may be involved in the aetiology and maintenance of absence seizure activity. This dysregulated activity could potentially be modulated by targeting one or both central regulatory hubs.

Rapid HILIC-Z ion mobility mass spectrometry (RHIMMS) method for untargeted metabolomics of complex biological samples

INTRODUCTION

Recent advances in high-throughput methodologies in the 'omics' and synthetic biology fields call for rapid and sensitive workflows in the metabolic phenotyping of complex biological samples.

OBJECTIVE

The objective of this research was to evaluate a straightforward to implement LC-MS metabolomics method using a commercially available chromatography column that provides increased throughput. Reducing run time can potentially impact chromatography and therefore the effects of ion mobility spectrometry to expand peak capacity were also evaluated. Additional confidence provided via collision cross section measurements for detected features was also explored.

METHODS

A rapid untargeted metabolomics workflow was developed with broad metabolome coverage, combining zwitterionic-phase hydrophilic interaction chromatography (HILIC-Z) with drift tube ion mobility-quadrupole time-of-flight (DTIM-qTOF) mass spectrometry. The analytical performance of our method was explored using extracts from complex biological samples, including a reproducibility study on chicken serum and a simple comparative study on a bacterial metabolome.

RESULTS

The method is acronymised RHIMMS for rapid HILIC-Z ion mobility mass spectrometry. We present the RHIMMS workflow starting with data acquisition, followed by data processing and analysis. RHIMMS demonstrates improved chromatographic separation for a selection of metabolites with wide physicochemical properties while maintaining reproducibility at better than 20% over 200 injections at 3.5 min per sample for the selected metabolites, and a mean of 13.9% for the top 50 metabolites by intensity. Additionally, the combination of rapid chromatographic separation with ion mobility allows improved annotation and the ability to distinguish isobaric compounds.

CONCLUSION

Our results demonstrate RHIMMS to be a rapid, reproducible, sensitive and high-resolution analytical platform that is highly applicable to the untargeted metabolomics analysis of complex samples.

A profile of research on the parasitic trypanosomatids and the diseases they cause

The parasitic trypanosomatids cause lethal and debilitating diseases, the leishmaniases, Chagas disease, and the African trypanosomiases, with major impacts on human and animal health. Sustained research has borne fruit by assisting efforts to reduce the burden of disease and by improving our understanding of fundamental molecular and cell biology. But where has the research primarily been conducted, and which research areas have received the most attention? These questions are addressed below using publication and citation data from the past few decades.

Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples

LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.

Deep Neural Network Pretrained by Weighted Autoencoders and Transfer Learning for Retention Time Prediction of Small Molecules

Retention time (RT) prediction contributes to identification of small molecules measured by high-performance liquid chromatography coupled with high-resolution mass spectrometry. Deep learning algorithms based on big data can enhance the accuracy of RT prediction. But at different chromatographic conditions, RTs of compounds are different, and the number of compounds with known RTs is small in most cases. Therefore, the transfer of big data is necessary. In this work, a strategy using a deep neural network (DNN) pretrained by weighed autoencoders and transfer learning (DNNpwa-TL) was proposed to efficiently predict RTs of compounds. The loss function in the autoencoders was calculated with features weighted by mutual information. Then, a DNN pretrained by weighted autoencoders (DNNpwa) was produced. For other specific chromatographic methods, the transfer learning model DNNpwa-TLs were built through fine-tuning the DNNpwa with the help of some compounds with known RTs to conduct the RT prediction. With the above strategy, a DNNpwa was first built with the METLIN small molecule retention time data set containing 80 038 small molecule compounds. A median relative error of 3.1% and a mean relative error of 4.9% were achieved. Then, 17 data sets from different chromatographic methods were studied, and the results showed that the performance of DNNpwa-TL was better than those of other deep learning models. Besides, DNNpwa-TL outperformed random forest, gradient boost, least absolute shrinkage and selection operator regression, and DNN for most of the 17 data sets. Therefore, DNNpwa-TL can provide an efficient method to perform RT prediction of small molecule compounds for different chromatographic methods and conditions.

Materials-driven fibronectin assembly on nanoscale topography enhances mesenchymal stem cell adhesion, protecting cells from bacterial virulence factors and preventing biofilm formation

Post-operative infection is a major complication in patients recovering from orthopaedic surgery. As such, there is a clinical need to develop biomaterials for use in regenerative surgery that can promote mesenchymal stem cell (MSC) osteospecific differentiation and that can prevent infection caused by biofilm-forming pathogens. Nanotopographical approaches to pathogen control are being identified, including in orthopaedic materials such as titanium and its alloys. These topographies use high aspect ratio nanospikes or nanowires to prevent bacterial adhesion but these features also significantly reduce MSC adhesion and activity. Here, we use a poly (ethyl acrylate) (PEA) polymer coating on titanium nanowires to spontaneously organise fibronectin (FN) and to deliver bone morphogenetic protein 2 (BMP2) to enhance MSC adhesion and osteospecific signalling. Using a novel MSC-Pseudomonas aeruginosa co-culture, we show that the coated nanotopographies protect MSCs from cytotoxic quorum sensing and signalling molecules, enhance MSC adhesion and osteoblast differentiation and reduce biofilm formation. We conclude that the PEA polymer-coated nanotopography can both support MSCs and prevent pathogens from adhering to a biomaterial surface, thus protecting from biofilm formation and bacterial infection, and supporting osteogenic repair.

DeepLC can predict retention times for peptides that carry as-yet unseen modifications

The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography-mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We present DeepLC, a deep learning peptide retention time predictor using peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides and, more importantly, accurately predicts retention times for modifications not seen during training. Moreover, we show that DeepLC's ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open search of a wide variety of proteome data.

Retention time prediction in hydrophilic interaction liquid chromatography with graph neural network and transfer learning

The combination of retention time (RT), accurate mass and tandem mass spectra can improve the structural annotation in untargeted metabolomics. However, the incorporation of RT for metabolite identification has received less attention because of the limitation of available RT data, especially for hydrophilic interaction liquid chromatography (HILIC). Here, the Graph Neural Network-based Transfer Learning (GNN-TL) is proposed to train a model for HILIC RTs prediction. The graph neural network was pre-trained using an in silico HILIC RT dataset (pseudo-labeling dataset) with ∼306 K molecules. Then, the weights of dense layers in the pre-trained GNN (pre-GNN) model were fine-tuned by transfer learning using a small number of experimental HILIC RTs from the target chromatographic system. The GNN-TL outperformed the methods in Retip, including the Random Forest (RF), Bayesian-regularized neural network (BRNN), XGBoost, light gradient-boosting machine (LightGBM), and Keras. It achieved the lowest mean absolute error (MAE) of 38.6 s on the test set and 33.4 s on an additional test set. It has the best ability to generalize with a small performance difference between training, test, and additional test sets. Furthermore, the predicted RTs can filter out nearly 60% false positive candidates on average, which is valuable for the identification of compounds complementary to mass spectrometry.

Polymyxin-Induced Metabolic Perturbations in Human Lung Epithelial Cells

Inhaled polymyxins are associated with toxicity in human lung epithelial cells that involves multiple apoptotic pathways. However, the mechanism of polymyxin-induced pulmonary toxicity remains unclear. This study aims to investigate polymyxin-induced metabolomic perturbations in human lung epithelial A549 cells. A549 cells were treated with 0.5 or 1.0 mM polymyxin B or colistin for 1, 4, and 24 h. Cellular metabolites were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS), and significantly perturbed metabolites (log₂ fold change [log₂FC] ≥ 1; false-discovery rate [FDR] ≤ 0.2) and key pathways were identified relative to untreated control samples. At 1 and 4 h, very few significant changes in metabolites were observed relative to the untreated control cells. At 24 h, taurine (log₂FC = -1.34 ± 0.64) and hypotaurine (log₂FC = -1.20 ± 0.27) were significantly decreased by 1.0 mM polymyxin B. The reduced form of glutathione (GSH) was significantly depleted by 1.0 mM polymyxin B at 24 h (log₂FC = -1.80 ± 0.42). Conversely, oxidized glutathione (GSSG) was significantly increased by 1.0 mM both polymyxin B (log₂FC = 1.38 ± 0.13 at 4 h and 2.09 ± 0.20 at 24 h) and colistin (log₂FC = 1.33 ± 0.24 at 24 h). l-Carnitine was significantly decreased by 1.0 mM of both polymyxins at 24 h, as were several key metabolites involved in biosynthesis and degradation of choline and ethanolamine (log₂FC ≤ -1); several phosphatidylserines were also increased (log₂FC ≥ 1). Polymyxins perturbed key metabolic pathways that maintain cellular redox balance, mitochondrial β-oxidation, and membrane lipid biogenesis. These mechanistic findings may assist in developing new pharmacokinetic/pharmacodynamic strategies to attenuate the pulmonary toxicities of inhaled polymyxins and in the discovery of new-generation polymyxins.

Intratumour heterogeneity in microRNAs expression regulates glioblastoma metabolism

While specific microRNA (miRNA) signatures have been identified in glioblastoma (GBM), the intratumour heterogeneity in miRNA expression has not yet been characterised. In this study, we reveal significant alterations in miRNA expression across three GBM tumour regions: the core, rim, and invasive margin. Our miRNA profiling analysis showed that miR-330-5p and miR-215-5p were upregulated in the invasive margin relative to the core and the rim regions, while miR-619-5p, miR-4440 and miR-4793-3p were downregulated. Functional analysis of newly identified miRNAs suggests their involvement in regulating lipid metabolic pathways. Subsequent liquid chromatography-mass spectrometry (LC-MS) and tandem mass spectroscopy (LC-MS/MS) profiling of the intracellular metabolome and the lipidome of GBM cells with dysregulated miRNA expression confirmed the alteration in the metabolite levels associated with lipid metabolism. The identification of regional miRNA expression signatures may underlie the metabolic heterogeneity within the GBM tumour and understanding this relationship may open new avenues for the GBM treatment.

Current Challenges and Recent Developments in Mass Spectrometry-Based Metabolomics

High-resolution mass spectrometry (MS) has advanced the study of metabolism in living systems by allowing many metabolites to be measured in a single experiment. Although improvements in mass detector sensitivity have facilitated the detection of greater numbers of analytes, compound identification strategies, feature reduction software, and data sharing have not kept up with the influx of MS data. Here, we discuss the ongoing challenges with MS-based metabolomics, including de novo metabolite identification from mass spectra, differentiation of metabolites from environmental contamination, chromatographic separation of isomers, and incomplete MS databases. Because of their popularity and sensitive detection of small molecules, this review focuses on the challenges of liquid chromatography-mass spectrometry-based methods. We then highlight important instrumentational, experimental, and computational tools that have been created to address these challenges and how they have enabled the advancement of metabolomics research.

Divergent metabolism between Trypanosoma congolense and Trypanosoma brucei results in differential sensitivity to metabolic inhibition

Animal African Trypanosomiasis (AAT) is a debilitating livestock disease prevalent across sub-Saharan Africa, a main cause of which is the protozoan parasite Trypanosoma congolense. In comparison to the well-studied T. brucei, there is a major paucity of knowledge regarding the biology of T. congolense. Here, we use a combination of omics technologies and novel genetic tools to characterise core metabolism in T. congolense mammalian-infective bloodstream-form parasites, and test whether metabolic differences compared to T. brucei impact upon sensitivity to metabolic inhibition. Like the bloodstream stage of T. brucei, glycolysis plays a major part in T. congolense energy metabolism. However, the rate of glucose uptake is significantly lower in bloodstream stage T. congolense, with cells remaining viable when cultured in concentrations as low as 2 mM. Instead of pyruvate, the primary glycolytic endpoints are succinate, malate and acetate. Transcriptomics analysis showed higher levels of transcripts associated with the mitochondrial pyruvate dehydrogenase complex, acetate generation, and the glycosomal succinate shunt in T. congolense, compared to T. brucei. Stable-isotope labelling of glucose enabled the comparison of carbon usage between T. brucei and T. congolense, highlighting differences in nucleotide and saturated fatty acid metabolism. To validate the metabolic similarities and differences, both species were treated with metabolic inhibitors, confirming that electron transport chain activity is not essential in T. congolense. However, the parasite exhibits increased sensitivity to inhibition of mitochondrial pyruvate import, compared to T. brucei. Strikingly, T. congolense exhibited significant resistance to inhibitors of fatty acid synthesis, including a 780-fold higher EC50 for the lipase and fatty acid synthase inhibitor Orlistat, compared to T. brucei. These data highlight that bloodstream form T. congolense diverges from T. brucei in key areas of metabolism, with several features that are intermediate between bloodstream- and insect-stage T. brucei. These results have implications for drug development, mechanisms of drug resistance and host-pathogen interactions.

Genetic and pharmacological evidence for kinetic competition between alternative poly(A) sites in yeast

Most eukaryotic mRNAs accommodate alternative sites of poly(A) addition in the 3’ untranslated region in order to regulate mRNA function. Here, we present a systematic analysis of 3’ end formation factors, which revealed 3’UTR lengthening in response to a loss of the core machinery, whereas a loss of the Sen1 helicase resulted in shorter 3’UTRs. We show that the anti-cancer drug cordycepin, 3’ deoxyadenosine, caused nucleotide accumulation and the usage of distal poly(A) sites. Mycophenolic acid, a drug which reduces GTP levels and impairs RNA polymerase II (RNAP II) transcription elongation, promoted the usage of proximal sites and reversed the effects of cordycepin on alternative polyadenylation. Moreover, cordycepin-mediated usage of distal sites was associated with a permissive chromatin template and was suppressed in the presence of an rpb1 mutation, which slows RNAP II elongation rate. We propose that alternative polyadenylation is governed by temporal coordination of RNAP II transcription and 3’ end processing and controlled by the availability of 3’ end factors, nucleotide levels and chromatin landscape.

The elucidation of the biodegradation of nitrobenzene and p-nitrophenol of nitroreductase from Antarctic psychrophile Psychrobacter sp. ANT206 under low temperature

Psychrobacter is one important typical strain in the Antarctic environment. In our previous study, Psychrobacter sp. ANT206 from Antarctica with novel cold-adapted nitroreductase (PsNTR) could biodegrade nitrobenzene and p-nitrophenol in low temperature environment. In this study, the in-frame deletion mutant of psntr (Δpsntr-ANT206) that displayed well genetic stability and kanamycin resistance stability was constructed using allelic replacement method. Additionally, Δpsntr-ANT206 was more sensitive to nitrobenzene and p-nitrophenol in the comparison of heat and hyperosmolarity, suggesting that psntr gene participated in the regulation of the tolerance against nitro-aromatic compounds (NACs). Further analysis was conducted by integrated gas chromatography-mass spectrometry (GC-MS), and several metabolites were identified. Among them, ethylbenzene, L-Alanine, citric acid, aniline, 4-aminophenol and other metabolites were different between the wild-type strain and Δpsntr-ANT206 under nitrobenzene and p-nitrophenol stress at different time periods under low temperature, respectively. These data could increase the knowledge of the construction of deletion mutant strains and biodegradation mechanism of NACs of typical strains Psychrobacter from Antarctica, which would also provide the basis of the molecular technique on the regulation of bioremediation of the contaminants under low temperature in the future.

Identification of Koala (Phascolarctos cinereus) Faecal Cortisol Metabolites Using Liquid Chromatography-Mass Spectrometry and Enzyme Immunoassays

The koala (Phascolarctos cinereus) is an arboreal folivorous marsupial endemic to Australia. Anthropogenic activities and climate change are threats to this species' survival and are potential stressors. A suitable non-invasive method is needed to objectively detect stress in koalas. Under conditions of stress, the concentration of the hormone cortisol in plasma or in saliva is elevated, and this would provide a convenient measure; however, collecting blood or saliva from wild animals is both practically difficult and stressful, and so likely to confound any measurement. In contrast, measurement of cortisol metabolites in faeces provides a practical and non-invasive method to objectively measure stress in koalas. Unfortunately, the identity of the main faecal cortisol metabolites of koalas is unknown. In this study, we have used both untargeted liquid chromatography-mass spectrometry (LC-MS) and enzyme immunoassays (EIAs) to identify several faecal cortisol metabolites in two koalas, one female (18 months old, 4.1 kg) and one male (4 years old, 6.95 kg) upon administration of hydrocortisone (cortisol) sodium succinate. The LC-MS analysis identified tetrahydrocortisol along with several other isomers as cortisol metabolites. After a survey of five enzyme immunoassays, we found that two metabolites, tetrahydrocortisol and 3β-allotetrahydrocortisol, could be detected by EIAs that used antibodies that were raised against their structurally similar corticosterone counterparts, tetrahydrocorticosterone and 3β-allotetrahydrocorticosterone, respectively. While the 3β-allotetrahydrocortisol metabolite was detected in the faeces of only one of the two animals studied, tetrahydrocortisol was detected in both. These results ultimately indicate that tetrahydrocortisol is likely the main faecal cortisol metabolite in koalas, and we demonstrate that it can be measured by an EIA (50c) that was originally developed to measure tetrahydrocorticosterone.

Synergy of the Polymyxin-Chloramphenicol Combination against New Delhi Metallo-β-Lactamase-Producing Klebsiella pneumoniae Is Predominately Driven by Chloramphenicol

Carbapenem-resistant Klebsiella pneumoniae has been classified as an Urgent Threat by the Centers for Disease Control and Prevention (CDC). The combination of two "old" antibiotics, polymyxin and chloramphenicol, displays synergistic killing against New Delhi metallo-β-lactamase (NDM)-producing K. pneumoniae. However, the mechanism(s) underpinning their synergistic killing are not well studied. We employed an in vitro pharmacokinetic/pharmacodynamic model to mimic the pharmacokinetics of the antibiotics in patients and examined bacterial killing against NDM-producing K. pneumoniae using a metabolomic approach. Metabolomic analysis was integrated with an isolate-specific genome-scale metabolic network (GSMN). Our results show that metabolic responses to polymyxin B and/or chloramphenicol against NDM-producing K. pneumoniae involved the inhibition of cell envelope biogenesis, metabolism of arginine and nucleotides, glycolysis, and pentose phosphate pathways. Our metabolomic and GSMN modeling results highlight the novel mechanisms of a synergistic antibiotic combination at the network level and may have a significant potential in developing precision antimicrobial chemotherapy in patients.

β-Adrenoceptor regulation of metabolism in U937 derived macrophages

Macrophages have important roles in the immune system including clearing pathogens and wound healing. Metabolic phenotypes in macrophages have been associated with functional phenotypes, where pro-inflammatory macrophages have an increased rate of glycolysis and anti-inflammatory macrophages primarily use oxidative phosphorylation. β-adrenoceptor (βAR) signalling in macrophages has been implicated in disease states such as cancer, atherosclerosis and rheumatoid arthritis. The impact of βAR signalling on macrophage metabolism has not been defined. Using metabolomics and proteomics, we describe the impact of βAR signalling on macrophages treated with isoprenaline. We found that βAR signalling alters proteins involved in cytoskeletal rearrangement and redox homeostasis of the cell. We showed that βAR signalling in macrophages shifts glucose metabolism from glycolysis towards the tricarboxylic acid cycle and pentose phosphate pathways. We also show that βAR signalling perturbs purine metabolism by accumulating adenylate and guanylate pools. Taken together, these results indicate that βAR signalling shifts metabolism to support redox processes and upregulates proteins involved in cytoskeletal changes, which may contribute to βAR effects on macrophage function.

Global-Scale Metabolomic Profiling of Human Hair for Simultaneous Monitoring of Endogenous Metabolome, Short- and Long-Term Exposome

Hair is a unique biological matrix that adsorbs short-term exposures (e. g., environmental contaminants and personal care products) on its surface and also embeds endogenous metabolites and long-term exposures in its matrix. In this work, we developed an untargeted metabolomics workflow to profile both temporal exposure chemicals and endogenous metabolites in the same hair sample. This analytical workflow begins with the extraction of short-term exposures from hair surfaces through washing. Further development of mechanical homogenization extracts endogenous metabolites and long-term exposures from the cleaned hair. Both solutions of hair wash and hair extract were analyzed using ultra-high-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics for global-scale metabolic profiling. After analysis, raw data were processed using bioinformatic programs recently developed specifically for exposome research. Using optimized experimental conditions, we detected a total of 10,005 and 9,584 metabolic features from hair wash and extraction samples, respectively. Among them, 274 and 276 features can be definitively confirmed by MS² spectral matching against spectral library, and an additional 3,356 and 3,079 features were tentatively confirmed as biotransformation metabolites. To demonstrate the performance of our hair metabolomics, we collected hair samples from three female volunteers and tested their hair metabolic changes before and after a 2-day exposure exercise. Our results show that 645 features from wash and 89 features from extract were significantly changed from the 2-day exposure. Altogether, this work provides a novel analytical approach to study the hair metabolome and exposome at a global scale, which can be implemented in a wide range of biological applications for a deeper understanding of the impact of environmental and genetic factors on human health.

Suspect screening of environmental contaminants by UHPLC-HRMS and transposable Quantitative Structure-Retention Relationship modelling

A Quantitative Structure-Retention Relationship (QSRR) model is proposed and aims at increasing the confidence level associated to the identification of organic contaminants by Ultra-High Performance Liquid Chromatography hyphenated to High Resolution Mass Spectrometry (UHPLC-HRMS) in environmental samples under a suspect screening approach. The model was built from a selection of 8 easily accessible physicochemical descriptors, and was validated from a set of 274 organic compounds commonly found in environmental samples. The proposed predictive figure approach is based on the mobile phase composition at solute elution (expressed as % acetonitrile), that has the major advantage of making the model reusable by other laboratories, since the elution composition is independent of both the column geometry and the UHPLC-system. The model quality was assessed and was altered neither by the columns from different lots, nor by the complex matrices of environmental water samples. Then, the solute retention of any organic compound present in water samples is expected to be predicted within ± 14.3% acetonitrile by our model. Solute retention can therefore be used as a supplementary tool for the identification of environmental contaminants by UHPLC-HRMS, in addition to mass spectrometry data already used in the suspect screening approach.

Integrated Metabolomics and Transcriptomics Using an Optimised Dual Extraction Process to Study Human Brain Cancer Cells and Tissues

The integration of untargeted metabolomics and transcriptomics from the same population of cells or tissue enhances the confidence in the identified metabolic pathways and understanding of the enzyme–metabolite relationship. Here, we optimised a simultaneous extraction method of metabolites/lipids and RNA from ependymoma cells (BXD-1425). Relative to established RNA (mirVana kit) or metabolite (sequential solvent addition and shaking) single extraction methods, four dual-extraction techniques were evaluated and compared (methanol:water:chloroform ratios): cryomill/mirVana (1:1:2); cryomill-wash/Econospin (5:1:2); rotation/phenol-chloroform (9:10:1); Sequential/mirVana (1:1:3). All methods extracted the same metabolites, yet rotation/phenol-chloroform did not extract lipids. Cryomill/mirVana and sequential/mirVana recovered the highest amounts of RNA, at 70 and 68% of that recovered with mirVana kit alone. sequential/mirVana, involving RNA extraction from the interphase of our established sequential solvent addition and shaking metabolomics-lipidomics extraction method, was the most efficient approach overall. Sequential/mirVana was applied to study a) the biological effect caused by acute serum starvation in BXD-1425 cells and b) primary ependymoma tumour tissue. We found (a) 64 differentially abundant metabolites and 28 differentially expressed metabolic genes, discovering four gene-metabolite interactions, and (b) all metabolites and 62% lipids were above the limit of detection, and RNA yield was sufficient for transcriptomics, in just 10 mg of tissue.