Recent advances in metabolomics analysis for early drug development

Green and controlled synthesis of copper-based tryptophan flower-like chiral metal-organic framework for enantioselective recognition and volatile organic compound separation

This study reports the synthesis and application of a flower-like chiral metal-organic framework (CMOF) based on copper ions and tryptophan (Cu(Trp)-CMOF). The Cu(Trp)-CMOF was successfully synthesized at room temperature through a facile method with hexadecyl trimethyl ammonium bromide (CTAB) as a structure-directing agent. The material exhibits a flower-like morphology with hierarchical porosity and well-defined crystallinity in the monoclinic P21/c space group. Comprehensive characterization revealed that different optical configurations ((R)-type, (S)-type, and (RS)-type) could be selectively obtained by controlling the chirality of tryptophan ligands. The synthesized CMOFs demonstrated excellent performance in two key applications: colorimetric recognition of amino acid enantiomers and separation of volatile organic compounds (VOCs). When used as a chromatographic stationary phase, the CMOF significantly enhanced the separation of challenging structural isomers, particularly improving the resolution (RS) of xylene isomers (RS from 1.03 to 2.62) and ethyltoluene isomers (RS from 4.83 to 9.23) compared to conventional columns. This work provides new insights into the design and application of CMOFs for chiral separation and recognition.

Saliva metabolomics: a non-invasive frontier for diagnosing and managing oral diseases

Salivary metabolomics represents a powerful noninvasive approach for diagnosing, monitoring, and managing oral diseases, providing valuable insights into the metabolic alterations associated with conditions such as oral cancer, oral precancerous lesions, periodontal diseases, and dental caries. Through the comprehensive analysis of salivary metabolites, this methodology facilitates the identification of disease-specific biomarkers reflective of underlying pathophysiological processes, including inflammation, microbial dysbiosis, and metabolic reprogramming. Despite its promising clinical potential, several significant challenges remain, notably the difficulty in establishing direct associations between specific salivary metabolites and distinct disease mechanisms, considerable inter-individual variability, and the inherent complexity of the oral microenvironment. Furthermore, issues related to data interpretation complexity, technological constraints, and the necessity for rigorous clinical validation continue to impede its broader clinical adoption. Nevertheless, ongoing advancements in analytical technologies and bioinformatics approaches hold considerable promise for addressing these limitations, positioning salivary metabolomics as a transformative tool for precision diagnosis and personalized treatment in oral health care.

Discovering a predictive metabolic signature of drug-induced structural cardiotoxicity in cardiac microtissues

Improved prediction of drug-induced structural cardiotoxicity is required to reduce attrition driven by cardiac safety concerns in drug discovery. Omics measurements are well suited to this need, offering the potential to discover molecular signatures associated with toxicological endpoints. In addition, untargeted metabolomics can simultaneously measure xenobiotic fate within the test system. We present an extensive metabolomics study to discover a predictive metabolic signature of drug-induced structural cardiotoxicity. A human-relevant in vitro cardiac model, cardiac microtissues, were exposed to twelve xenobiotics (eight clinically labelled structural cardiotoxins and four non-cardiotoxic pharmaceuticals), each at two concentrations, for 6, 24, and 48 h. The measurements were made by direct-infusion and liquid-chromatography mass spectrometry from intracellular polar and lipid extracts, and spent culture medium, respectively. Data were used to quantify levels, and reveal the metabolic fate of the xenobiotics, and to simultaneously explore their effects on the cardiac microtissues. Xenobiotic quantification revealed free concentrations to be typically lower than nominal values, whilst discovery of xenobiotic-related features evidenced the biotransformation capacity of the microtissues. Both common and condition-specific effects of the xenobiotics on the intracellular metabolome, lipidome, and metabolic footprint were discovered. Moreover, metabolic signatures with capacity to predict structural cardiotoxicity were revealed. These included features representing several ceramides, energy metabolism intermediates, e.g. creatine, purine-related metabolites, and markers of oxidative stress, e.g. glutathione.

Drug Screening for Glycolysis Pathway in Living Cancer Cells Using 19F NMR

Glycolysis is a fundamental process for the generation of cellular energy, and its dysregulation has been linked to a number of diseases, including obesity, neurological disorders, and cancer. Targeting glycolysis is therefore a promising therapeutic strategy. However, effective drugs that specifically target glycolysis are still lacking. In this study, we introduce a novel approach utilizing 19F NMR to monitor early glycolysis in living MCF-7 cells. By tracking metabolites downstream of the glucose analogue 2-fluoro-2-deoxyglucose (2-FDG), we successfully observed the activity of key glycolytic components, such as glucose transporters (GLUTs) and hexokinase (HKs). Our results reveal distinct metabolic profiles upon inhibition of these targets, advancing the understanding of glycolytic regulation. In addition, we applied this approach to screen traditional Chinese medicines for their effects on glycolysis and identified Salvia miltiorrhiza and Fructus evodiae as modulators with contrasting effects on glycolytic metabolism. This dual modulation highlights their potential as valuable tools for therapeutic intervention. Our study provides an innovative methodology for both the exploration of glycolytic pathways and the discovery of novel therapeutics, offering new perspectives for drug development targeting metabolic diseases.

Dissecting inflammation in the immunemetabolomic era

The role of immune metabolism, specific metabolites and cell-intrinsic and -extrinsic metabolic states across the time course of an inflammatory response are emerging knowledge. Targeted and untargeted metabolomic analysis is essential to understand how immune cells adapt their metabolic program throughout an immune response. In addition, metabolomic analysis can aid to identify pathophysiological patterns in inflammatory disease. Here, we discuss new metabolomic findings within the transition from inflammation to resolution, focusing on three key programs of immunity: Efferocytosis, IL-10 signaling and trained immunity. Particularly the tryptophan-derived metabolite kynurenine was identified as essential for efferocytosis and inflammation resolution as well as a potential biomarker in diverse inflammatory conditions. In summary, metabolomic analysis and integration with transcriptomic and proteomic data, high resolution imaging and spatial information is key to unravel metabolic drivers and dependencies during inflammation and progression to tissue-repair.

Neuroprotective Activities of Sertraline, Tiagabine, and Bicifadine with Autophagy-Inducing Potentials in a 6-Hydroxidopamine-Treated Parkinson's Disease Cell Model

Parkinson's disease (PD) is one of neurodegenerative diseases characterized by the progressive loss of dopaminergic neurons in the substantia nigra. The development of a neuroprotective therapy is crucial for mitigating features and progression of PD. Since autophagy induction has recently emerged as a promising neuroprotective strategy, this study aimed to identify autophagy-inducing compounds and evaluate their neuroprotective activity. Among 3,200 compounds consisting of FDA-approved drugs or are under active development, 547 compounds targeting neurological diseases were filtered in, and three compounds (sertraline, tiagabine and bicifadine) were finally identified to exhibit the autophagy-inducing activity and also demonstrated the autophagy-dependent neuroprotective action by inhibiting the mammalian target of rapamycin (mTOR) in 6-hydroxydopamine (6-OHDA)-induced neurotoxicity in PC12 cells. Furthermore, the analysis of neurochemical changes suggested that the ability of those compounds to restore the quantity of cellular neurotransmitters such as betaine, 5-hydroxyindoleacetic acid and kynurenine might be linked to their neuroprotective function. In conclusion, compounds like sertraline, tiagabine, and bicifadine that have the ability to induce autophagy and inhibit mTOR might be repurposed as PD treatment to protect the neuronal cells.

Machine learning-assisted analysis of serum metabolomics and network pharmacology reveals the effective compound from herbal formula against alcoholic liver injury

BACKGROUND

The popularity of herbal formulas is increasing worldwide. Nevertheless, the effective compound is challenging to identify due to its intricate composition and multiple targets.

METHODS

An integration machine learning-assisted approach was established, whereby the particular action mechanism and direct target were obtained through the correlation of compounds, targets, and metabolites. The association between a compound and an action pathway was selected from the shortest path of the "compound-target-pathway-disease" network, which was analyzed using the Floyd-Warshall algorithm. Subsequently, an investigation was conducted into the relationship between metabolites and action pathways, as well as targets, through the analysis of serum metabolomic profiling and the selection of metabolite biomarkers by random forest. In order to accurately identify the direct acting target as well as the most effective compound, the relationship between the compounds and their targets was investigated using a feature-based prediction model conducted by AdaBoost. The binding mode of the effective compound and the direct-acting target was verified by molecular docking, dynamics simulations, and western blotting. In this study, Baiji Wuweizi Granule (BWG) was employed to elucidate the effective compound against alcoholic liver injury (ALD).

RESULTS

BWG exerted an influence on the serum metabolomic, resulting in the identification of seven potential biomarkers. Furthermore, six effective compounds and the PI3K-AKT signalling pathway were identified through a co-analysis with the shortest path from compound to ALD in the "compound-target-pathway-disease" network. It was postulated that the effective compounds would bind with key targets from the PI3K-AKT signaling pathway, as indicated by the prediction model of compound-target interaction (R2 > 0.95). The dominant bonding type for the effective compounds and key targets was hydrogen bond. These results indicated that AKT1 was the notable target for BWG, and that 2,3,4,7-tetramethoxyphenanthrene was the marker compound for BWG against ALD. The present study provides evidence that the protective effect of BWG on ALD can be mediated by the PI3K-AKT signaling pathway.

CONCLUSIONS

Our findings demonstrate the value of a machine learning-assisted approach in identifying the key compound, target and pathway that underpin the efficacy of an herbal formula. This provides a foundation for future clinical and fundamental research.

Advances in bioinformatic methods for the acceleration of the drug discovery from nature

BACKGROUND

Drug discovery from nature has a long, ethnopharmacologically-based background. Today, natural resources are undeniably vital reservoirs of active molecules or drug leads. Advances in (bio)informatics and computational biology emphasized the role of herbal medicines in the drug discovery pipeline.

PURPOSE

This review summarizes bioinformatic approaches applied in recent drug discovery from nature.

STUDY DESIGN

It examines advancements in molecular networking, pathway analysis, network pharmacology within a systems biology framework and AI for assessing the therapeutic potential of herbal preparations.

METHODS

A comprehensive literature search was conducted using Pubmed, SciFinder, and Google Database. Obtained data was analyzed and organized in subsections: AI, systems biology integrative approach, network pharmacology, pathway analysis, molecular networking, structure-based virtual screening.

RESULTS

Bioinformatic approaches is now essential for high-throughput data analysis in drug target identification, mechanism-based drug discovery, drug repurposing and side-effects prediction. Large datasets obtained from "omics" approaches require bioinformatic calculations to unveil interactions, and patterns in disease-relevant conditions. These tools enable databases annotations, pattern-matching, connections discovery, molecular relationship exploration, and data visualisation.

CONCLUSION

Despite the complexity of plant metabolites, bioinformatic approaches assist in characterization of herbal preparations and selection of bioactive molecule. It is perceived as powerful tool for uncovering multi-target effects and potential molecular mechanisms of compounds. By integrating multiple networks that connect gene-disease, drug-target and gene-drug-target, drug discovery from natural sources is experiencing a remarkable comeback.

Exploring biomarkers of regular wine consumption in human urine: Targeted and untargeted metabolomics approaches

The epidemiological assessment of wine consumption usually has been obtained using self-reporting questionnaires. In this study, two metabolomic approaches, targeted and untargeted, were applied to 24-h urine samples from a cohort of La Rioja (Spain) (aged 52-78), comparing moderate and daily wine consumers (20 males and 13 females) without diet intervention, versus non-consumers (8 males and 35 females). Results showed that the non-targeted metabolomics approach has allowed for the annotation of sixteen compounds in 24-h urine samples from regular wine-consumers that were not detected in the urine of non-wine consumers. Additionally, the targeted metabolomics approach showed a wide range of phenol metabolites, mainly hepatic phase-II conjugates, whose concentration was significantly higher in the urine of wine consumers. As a novelty, this study focuses on discovering the main urinary biomarkers of regular wine consumption involving free-living volunteers, without dietary intervention or restrictions that might alter their regular behaviors and lifestyles.

Large Scale Printing of Robust HPLC Medium via Layer-by-Layer Stereolithography

The manufacture of high-performance liquid chromatography (HPLC) medium has long been viewed as an art rather than science; this raised a great challenge in securing separation consistency, method transferability, and scaling-up in purification of biomolecules. Herein, we report a large scale layer-by-layer manufacturing strategy for a high performance chromatography medium utilizing 3D-printing technology. Combining stereolithography 3D printing and porogenic chemistry, the strategy enables parallel production of high-performance separation medium in diverse scales, shapes, and throughput. Between 1,000 printed devices, high performance consistency was demonstrated by column-to-column and batch-to-batch reproducibility (coefficient of variation of retention time, 2.04%). Fast separations of intact proteins were realized in reversed-phase chromatography: within 1 min, resolution > 1.5 was achieved, and nondenatured antibody separation was realized in hydrophobic interaction chromatography. Purification of native proteins was directly amplified by 3 orders of magnitude: 12 mg of hemeproteins was isolated in 8 min at negligible scaling-up cost, supporting liter-scale processing of fermentation within 7 h on one 20 mm i.d. printed column. With advantages in automatic and parallel production capacity, high-fidelity microstructure across dimensions, and highly efficient method transfer and scaling-up, the stereolithographically printed high performance chromatography medium may open a new path to speeding up separation and purification processes from primary analysis to mass-purification of biomolecular entities, as demanded in the biosynthesis and pharmaceutical industries.

Metabolomics as a Tool for Unraveling the Impact of Enantioselectivity in Cellular Metabolism

Metabolomics is an emerging interdisciplinary field focused on the comprehensive analysis of all metabolites within biological samples, making it valuable for areas such as drug development, and environmental analysis. Many compounds, including pharmaceuticals and agrochemicals that have been extensively studied by metabolomics are chiral. The intrinsic chirality of biological targets can lead to a selective recognition of enantiomers resulting in distinct pharmacokinetic, pharmacodynamic and/or toxicological profiles (enantioselectivity). Given that metabolomics captures an instant snapshot of an organism's metabolic state, it serves as a powerful tool to investigate chiral compounds and understand enantioselective effects. Herein, a systematic compilation of scientific literature was performed and 48 enantioselectivity studies using metabolomics were selected. These studies revealed an increasing focus on chiral pesticides (77%), the use of animal models (59%), reliance on LC-MS techniques (52%), and predominantly untargeted approaches (83%). Enantioselective effects were described in most studies. This review describes significant advances in this emerging field and highlights the use of metabolomics to unravel the role of stereochemistry in cellular metabolism by the examination of enantiomer-specific metabolic effects. Furthermore, it elucidates enantioselectivity mechanism that can be further applied to other groups of chiral compounds.

Time-Resolved Evaluation of L-Dopa Metabolism in Bacteria-Host Symbiotic System and the Effect on Parkinson's Molecular Pathology

The gut microbiome influences drug metabolism and therapeutic efficacy. Still, the lack of a general label-free approach for monitoring bacterial or host metabolic contribution hampers deeper insights. Here, a 2D nuclear magnetic resonance (NMR) approach is introduced that enables real-time monitoring of the metabolism of Levodopa (L-dopa), an anti-Parkinson drug, in both live bacteria and bacteria-host (Caenorhabditis elegans) symbiotic systems. The quantitative method reveals that discrete Enterococcus faecalis substrains produce different amounts of dopamine in live hosts, even though they are a single species and all have the Tyrosine decarboxylase (TyrDC) gene involved in L-dopa metabolism. The differential bacterial metabolic activity correlates with differing Parkinson's molecular pathology concerning alpha-synuclein aggregation as well as behavioral phenotypes. The gene's existence or expression is not an indicator of metabolic activity is also shown, underscoring the significance of quantitative metabolic estimation in vivo. This simple approach is widely adaptable to any chemical drug to elucidate pharmacomicrobiomic relationships and may help rapidly screen bacterial metabolic effects in drug development.

Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners

The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.

Metformin's Effects on Cognitive Function from a Biovariance Perspective: A Narrative Review

This study examines the effects of metformin on brain functions focusing on the variability of the results reported in the literature. While some studies suggest that metformin may have neuroprotective effects in diabetic patients, others report an insignificant impact of metformin on cognitive function, or even a negative effect. We propose that this inconsistency may be due to intrinsic cellular-level variability among individuals, which we term "biovariance". Biovariance persists even in demographically homogeneous samples due to complex and stochastic biological processes. Additionally, the complex metabolic actions of metformin, including its influence on neuroenergetics and neuronal survival, may produce different effects depending on individual metabolic characteristics.

The Inflammatory Mechanism of Parkinson's Disease: Gut Microbiota Metabolites Affect the Development of the Disease Through the Gut-Brain Axis

Parkinson's disease is recognized as the second most prevalent neurodegenerative disorder globally, with its incidence rate projected to increase alongside ongoing population growth. However, the precise etiology of Parkinson's disease remains elusive. This article explores the inflammatory mechanisms linking gut microbiota to Parkinson's disease, emphasizing alterations in gut microbiota and their metabolites that influence the disease's progression through the bidirectional transmission of inflammatory signals along the gut-brain axis. Building on this mechanistic framework, this article further discusses research methodologies and treatment strategies focused on gut microbiota metabolites, including metabolomics detection techniques, animal model investigations, and therapeutic approaches such as dietary interventions, probiotic treatments, and fecal transplantation. Ultimately, this article aims to elucidate the relationship between gut microbiota metabolites and the inflammatory mechanisms underlying Parkinson's disease, thereby paving the way for novel avenues in the research and treatment of this condition.

Bacterial metabolomics: current applications for human welfare and future aspects

An imbalanced microbiome is linked to several diseases, such as cancer, inflammatory bowel disease, obesity, and even neurological disorders. Bacteria and their by-products are used for various industrial and clinical purposes. The metabolites under discussion were chosen based on their biological impacts on host and gut microbiota interactions as established by metabolome research. The separation of bacterial metabolites by using statistics and machine learning analysis creates new opportunities for applications of bacteria and their metabolites in the environmental and medical sciences. Thus, the metabolite production strategies, methodologies, and importance of bacterial metabolites for human well-being are discussed in this review.

Using metabolomics to investigate the relationship between the metabolomic profile of the intestinal microbiota derivatives and mental disorders in inflammatory bowel diseases: a narrative review

Individuals with inflammatory bowel disease (IBD) are at a higher risk of developing mental disorders, such as anxiety and depression. The imbalance between the intestinal microbiota and its host, known as dysbiosis, is one of the factors, disrupting the balance of metabolite production and their signaling pathways, leading to disease progression. A metabolomics approach can help identify the role of gut microbiota in mental disorders associated with IBD by evaluating metabolites and their signaling comprehensively. This narrative review focuses on metabolomics studies that have comprehensively elucidated the altered gut microbial metabolites and their signaling pathways underlying mental disorders in IBD patients. The information was compiled by searching PubMed, Web of Science, Scopus, and Google Scholar from 2005 to 2023. The findings indicated that intestinal microbial dysbiosis in IBD patients leads to mental disorders such as anxiety and depression through disturbances in the metabolism of carbohydrates, sphingolipids, bile acids, neurotransmitters, neuroprotective, inflammatory factors, and amino acids. Furthermore, the reduction in the production of neuroprotective factors and the increase in inflammation observed in these patients can also contribute to the worsening of psychological symptoms. Analyzing the metabolite profile of the patients and comparing it with that of healthy individuals using advanced technologies like metabolomics, aids in the early diagnosis and prevention of mental disorders. This approach allows for the more precise identification of the microbes responsible for metabolite production, enabling the development of tailored dietary and pharmaceutical interventions or targeted manipulation of microbiota.

Elucidation of the mechanism of berberine against gastric mucosa injury in a rat model with chronic atrophic gastritis based on a combined strategy of multi-omics and molecular biology

Background

Berberine (BBR) is widely used to treat gastrointestinal diseases. However, the pharmacological mechanism of action of BBR in anti-chronic atrophic gastritis (CAG) remains unclear. This study aimed to investigate the mechanism of action of BBR in CAG by integration of molecular biology and multi-omics studies strategy.

Methods

The CAG model was established by alternating drinking water of 0.1% ammonia and 20 mmol/L sodium deoxycholate, accompanied by an irregular diet. Serum biochemical indices including PGI, PGII, GAS-17, IL-6, IL-1β, and TNF-α were analyzed. HE and AB-PAS staining were employed to assess pathological damage in gastric tissue. The underlying molecular mechanism of BBR in CAG treatment was explored via the integration of network pharmacology, transcriptomics, widely targeted metabolomics and intestinal flora analysis. Finally, relevant key targets and pathway were verified.

Results

The results showed that BBR exerted therapeutic effects in improving CAG via alleviating inflammation response, maintaining the gastric mucosal barrier's integrity and repairing gastric mucosal tissues. Network pharmacology showed that the treatment of CAG by BBR mainly involved in inflammatory response, apoptosis, angiogenesis and metabolic processes. Furthermore, 234 different expression genes were identified in the gastric tissue transcriptome, which were mainly involved in biological processes such as cell adhesion, angiogenesis, apoptosis, cell migration and lipids metabolism by regulating the MAPK signaling pathway. Metabolomics results showed that 125 differential metabolites were also identified, while the pathways were mainly involved in D-glutamine and D-glutamate metabolism, and tyrosine metabolism, etc. Integrating transcriptomics and metabolomics analyses indicated that BBR directly regulated Carnitine C3:0, LPC (0:0/20:3), L-Glutamic Acid and FFA (15:0) by acting on SLC25A20, PNLIPRP1, PLA2G4C, GSR, GFPT2, GCLM, CTPS1, ACSL1, ACOT4 and ACOT2. 16S rRNA sequencing revealed that BBR could restore the balance of gut microbiota dysbiosis by significantly regulating the relative abundance of unclassified_Muribaculaceae and Lactobacillus_johnsonii.

Conclusion

This study demonstrated that BBR alleviates CAG through the regulation of the MAPK signaling pathway, metabolic disorders and gut microbiota dysbiosis, thereby revealing the complex mechanism of BBR in relation to alleviating CAG from multiple levels and perspectives.

OncoSexome: the landscape of sex-based differences in oncologic diseases

The NIH policy on sex as biological variable (SABV) emphasized the importance of sex-based differences in precision oncology. Over 50% of clinically actionable oncology genes are sex-biased, indicating differences in drug efficacy. Research has identified sex differences in non-reproductive cancers, highlighting the need for comprehensive sex-based cancer data. We therefore developed OncoSexome, a multidimensional knowledge base describing sex-based differences in cancer (https://idrblab.org/OncoSexome/) across four key topics: antineoplastic drugs and responses (SDR), oncology-related biomarkers (SBM), risk factors (SRF) and microbial landscape (SML). SDR covers sex-based differences in 2051 anticancer drugs; SBM describes 12 551 sex-differential biomarkers; SRF illustrates 350 sex-dependent risk factors; SML demonstrates 1386 microbes with sex-differential abundances associated with cancer development. OncoSexome is unique in illuminating multifaceted influences of biological sex on cancer, providing both external and endogenous contributors to cancer development and describing sex-based differences for the broadest oncological classes. Given the increasing global research interest in sex-based differences, OncoSexome is expected to impact future precision oncology practices significantly.

Next-Gen Therapeutics: Pioneering Drug Discovery with iPSCs, Genomics, AI, and Clinical Trials in a Dish

In the high-stakes arena of drug discovery, the journey from bench to bedside is hindered by a daunting 92% failure rate, primarily due to unpredicted toxicities and inadequate therapeutic efficacy in clinical trials. The FDA Modernization Act 2.0 heralds a transformative approach, advocating for the integration of alternative methods to conventional animal testing, including cell-based assays that employ human induced pluripotent stem cell (iPSC)-derived organoids, and organ-on-a-chip technologies, in conjunction with sophisticated artificial intelligence (AI) methodologies. Our review explores the innovative capacity of iPSC-derived clinical trial in a dish models designed for cardiovascular disease research. We also highlight how integrating iPSC technology with AI can accelerate the identification of viable therapeutic candidates, streamline drug screening, and pave the way toward more personalized medicine. Through this, we provide a comprehensive overview of the current landscape and future implications of iPSC and AI applications being navigated by the research community and pharmaceutical industry.

Investigation of antibacterial mode of action of ω-aminoalkoxylxanthones by NMR-based metabolomics and molecular docking

INTRODUCTION

The knowledge of the mode of action of an antimicrobial is essential for drug development and helps to fight against bacterial resistance. Thus, it is crucial to use analytical techniques to study the mechanism of action of substances that have potential to act as antibacterial agents OBJECTIVE: To use NMR-based metabolomics combined with chemometrics and molecular docking to identify the metabolic responses of Staphylococcus aureus following exposure to commercial antibiotics and some synthesized ω-aminoalkoxylxanthones.

METHODS

Intracellular metabolites of S. aureus were extracted after treatment with four commercial antibiotics and three synthesized ω-aminoalkoxylxanthones. NMR spectra were obtained and 1H NMR data was analyzed using both unsupervised and supervised algorithms (PCA and PLS-DA, respectively). Docking simulations on DNA topoisomerase IV protein were also performed for the ω-aminoalkoxylxanthones.

RESULTS

Through chemometric analysis, we distinguished between the control group and antibiotics with extracellular (ampicillin) and intracellular targets (kanamycin, tetracycline, and ciprofloxacin). We identified 21 metabolites, including important metabolites that differentiate the groups, such as betaine, acetamide, glutamate, lysine, alanine, isoleucine/leucine, acetate, threonine, proline, and ethanol. Regarding the xanthone-type derivatives (S6, S7 and S8), we observed a greater similarity between S7 and ciprofloxacin, which targets bacterial DNA replication. The molecular docking analysis showed high affinity of the ω-aminoalkoxylxanthones with the topoisomerase IV enzyme, as well as ciprofloxacin.

CONCLUSION

NMR-based metabolomics has shown to be an effective technique to assess the metabolic profile of S. aureus after treatment with certain antimicrobial compounds, helping the investigation of their mechanism of action.

Advancing Anticancer Drug Discovery: Leveraging Metabolomics and Machine Learning for Mode of Action Prediction by Pattern Recognition

A bottleneck in the development of new anti-cancer drugs is the recognition of their mode of action (MoA). Metabolomics combined with machine learning allowed to predict MoAs of novel anti-proliferative drug candidates, focusing on human prostate cancer cells (PC-3). As proof of concept, 38 drugs are studied with known effects on 16 key processes of cancer metabolism, profiling low molecular weight intermediates of the central carbon and cellular energy metabolism (CCEM) by LC-MS/MS. These metabolic patterns unveiled distinct MoAs, enabling accurate MoA predictions for novel agents by machine learning. The transferability of MoA predictions based on PC-3 cell treatments is validated with two other cancer cell models, i.e., breast cancer and Ewing's sarcoma, and show that correct MoA predictions for alternative cancer cells are possible, but still at some expense of prediction quality. Furthermore, metabolic profiles of treated cells yield insights into intracellular processes, exemplified for drugs inducing different types of mitochondrial dysfunction. Specifically, it is predicted that pentacyclic triterpenes inhibit oxidative phosphorylation and affect phospholipid biosynthesis, as confirmed by respiration parameters, lipidomics, and molecular docking. Using biochemical insights from individual drug treatments, this approach offers new opportunities, including the optimization of combinatorial drug applications.

Association of multilocus sequence typing, MSH2 gene mutations, and antifungal resistance in Candida glabrata: implications for clinical outcomes in Chinese hospitals

BACKGROUND

Candida glabrata is the second most common cause of invasive candidiasis worldwide. In this study, we determined the clinical characteristics and drug sensitivity of C. glabrata isolates and investigated the associations between MSH2 gene mutations, sequence types (ST), and drug resistance.

METHODS

A total of 154 C. glabrata isolates were collected from patients being treated in three hospitals in China. The antifungal sensitivity of the strains was assessed using the broth microdilution method. Multilocus sequence typing (MLST) was also performed, followed by MSH2 sequencing. The clinical features and outcomes of C. glabrata infection were analysed for a total of 49 strains, which were collected from patients with invasive Candida infection at Longhua Hospital.

RESULTS

All 154 isolates were found to be susceptible to amphotericin, 5-fluorocytosine, anidulafungin, caspofungin, and micafungin, whereas 11.7% were fluconazole-resistant, 18.8% were itraconazole non-wild type, and 35.7% were voriconazole non-wild type. ST7 (62.34%) was the most common ST genotype, followed by ST10 (16.88%) and ST15 (7.79%). The total azole resistance rates for all isolates, ST7, ST10, and other STs were 36.4, 42.7, 34.6, and 18.8%, respectively. The ST7 and ST10 isolates were characterised by a higher drug resistance rate than the other minor ST isolates. Moreover, 59.09% of isolates had one or more MSH2 non-synonymous mutations, with V239L being the most commonly detected mutation. The frequency of MSH2 mutations was significantly higher in azole-resistant isolates than in other isolates, whereas P6L or L87P mutations were associated with the highest azole resistance rates of up to 87.5% and 80%, respectively. Our results indicated that ST7 and ST15 are independent predictors of mortality caused by C. glabrata infection and revealed a higher 30-day mortality in patients infected with these strains than in those infected with other ST isolates.

CONCLUSIONS

Our findings revealed the relationships between MLST, MSH2 gene mutations, and drug resistance in the common pathogenic fungus C. glabrata, and thereby enabled us to identify strains that are associated with higher rates of mortality. These findings will contribute to enhancing our understanding of the pathogenesis of C. glabrata infection.

AI in microbiome-related healthcare

Artificial intelligence (AI) has the potential to transform clinical practice and healthcare. Following impressive advancements in fields such as computer vision and medical imaging, AI is poised to drive changes in microbiome-based healthcare while facing challenges specific to the field. This review describes the state-of-the-art use of AI in microbiome-related healthcare. It points out limitations across topics such as data handling, AI modelling and safeguarding patient privacy. Furthermore, we indicate how these current shortcomings could be overcome in the future and discuss the influence and opportunities of increasingly complex data on microbiome-based healthcare.

Metabolomics-driven approaches for identifying therapeutic targets in drug discovery

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics-based methods, such as dose-response metabolomics, stable isotope-resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose-dependent metabolite-drug interactions. Emerging techniques, including single-cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field.

Preliminary Metabolomics Study Suggests Favorable Metabolic Changes in the Plasma of Breast Cancer Patients after Surgery and Adjuvant Treatment

Background/Objectives: The management of early breast cancer (BC) includes surgery, followed by adjuvant radiotherapy, chemotherapy, hormone therapy, or immunotherapy. However, the influence of these interventions in metabolic reprogramming remains unknown. This study explored alterations in the plasma metabolome of BC patients following distinct treatments to deepen our understanding of BC pathophysiology, outcomes, and the identification of potential biomarkers. Methods: We included 52 women diagnosed with BC and candidates for surgery as primary oncological treatment. Blood samples were collected at diagnosis, two weeks post-surgery, and one month post-radiotherapy. Plasma samples from 49 healthy women served as controls. Targeted metabolomics assessed 74 metabolites spanning carbohydrates, amino acids, lipids, nucleotide pathways, energy metabolism, and xenobiotic biodegradation. Results: Before treatment, the BC patients exhibited notable changes in carbohydrate, nucleotide, lipid, and amino acid metabolism. We noticed a gradual restoration of specific metabolite levels (hypoxanthine, 3-phosphoglyceric acid, xylonic acid, and maltose) throughout different treatments, suggesting a normalization of the nucleotide and carbohydrate metabolic pathways. Moreover, we observed increased dodecanoic acid concentrations, a metabolite associated with cancer protection. These variations distinguished patients from controls with high specificity and sensitivity. Conclusions: Our preliminary study suggests that oncological treatments modify the metabolism of patients towards a favorable profile with a decrease in the pathways that favor cell proliferation and an increase in the levels of anticancer molecules. These findings emphasize the pivotal role of metabolomics in recognizing the biological pathways influenced by each cancer treatment and the resulting metabolic consequences. Furthermore, it aids in identifying potential biomarkers for disease onset and progression.

Comparative metabolomics profiling reveals the unique bioactive compounds and astringent taste formation of rosehips

Rosehips are a prominent source of numerous bioactive compounds. However, despite their extensive potential, the metabolic profiles among different rosehip species have not been fully elucidated. In this study, 523 secondary metabolites from rosehips of 12 Rosa species were identified using ultra-high-performance liquid chromatography-tandem mass spectrometry. They were primarily composed of flavonoids and phenolic acids. A K-means analysis revealed the characteristic metabolites in different rosehips. For example, R. persica contained a more abundant supply of phenolic acids, while R. roxburghii harbored a richer array of terpenoids. A total of 73 key active ingredients were screened from traditional Chinese medicine databases, and they indicated that R. persica is more promising for use in functional foods or health supplements compared with the other fruits. Moreover, a differential analysis identified 47 compounds as potential contributors to the astringent taste of rosehips, including ellagic acid 4-O-glucoside and cadaverine. This study provides valuable information to develop new functional foods of rosehips and improve the quality of their fruits.

Metabolomics studies in common multifactorial eye disorders: a review of biomarker discovery for age-related macular degeneration, glaucoma, diabetic retinopathy and myopia

Introduction

Multifactorial Eye disorders are a significant public health concern and have a huge impact on quality of life. The pathophysiological mechanisms underlying these eye disorders were not completely understood since functional and low-throughput biological tests were used. By identifying biomarkers linked to eye disorders, metabolomics enables early identification, tracking of the course of the disease, and personalized treatment.

Methods

The electronic databases of PubMed, Scopus, PsycINFO, and Web of Science were searched for research related to Age-Related macular degeneration (AMD), glaucoma, myopia, and diabetic retinopathy (DR). The search was conducted in August 2023. The number of cases and controls, the study's design, the analytical methods used, and the results of the metabolomics analysis were all extracted. Using the QUADOMICS tool, the quality of the studies included was evaluated, and metabolic pathways were examined for distinct metabolic profiles. We used MetaboAnalyst 5.0 to undertake pathway analysis of differential metabolites.

Results

Metabolomics studies included in this review consisted of 36 human studies (5 Age-related macular degeneration, 10 Glaucoma, 13 Diabetic retinopathy, and 8 Myopia). The most networked metabolites in AMD include glycine and adenosine monophosphate, while methionine, lysine, alanine, glyoxylic acid, and cysteine were identified in glaucoma. Furthermore, in myopia, glycerol, glutamic acid, pyruvic acid, glycine, cysteine, and oxoglutaric acid constituted significant metabolites, while glycerol, glutamic acid, lysine, citric acid, alanine, and serotonin are highly networked metabolites in cases of diabetic retinopathy. The common top metabolic pathways significantly enriched and associated with AMD, glaucoma, DR, and myopia were arginine and proline metabolism, methionine metabolism, glycine and serine metabolism, urea cycle metabolism, and purine metabolism.

Conclusion

This review recapitulates potential metabolic biomarkers, networks and pathways in AMD, glaucoma, DR, and myopia, providing new clues to elucidate disease mechanisms and therapeutic targets. The emergence of advanced metabolomics techniques has significantly enhanced the capability of metabolic profiling and provides novel perspectives on the metabolism and underlying pathogenesis of these multifactorial eye conditions. The advancement of metabolomics is anticipated to foster a deeper comprehension of disease etiology, facilitate the identification of novel therapeutic targets, and usher in an era of personalized medicine in eye research.

Multiscale metabolomics techniques: Insights into neuroscience research

The field of metabolomics examines the overall composition and dynamic patterns of metabolites in living organisms. The primary methods used in metabolomics include liquid chromatography (LC), nuclear magnetic resonance (NMR), and mass spectrometry (MS) analysis. These methods enable the identification and examination of metabolite types and contents within organisms, as well as modifications to metabolic pathways and their connection to the emergence of diseases. Research in metabolomics has extensive value in basic and applied sciences. The field of metabolomics is growing quickly, with the majority of studies concentrating on biomedicine, particularly early disease diagnosis, therapeutic management of human diseases, and mechanistic knowledge of biochemical processes. Multiscale metabolomics is an approach that integrates metabolomics techniques at various scales, including the holistic, tissue, cellular, and organelle scales, to enable more thorough and in-depth studies of metabolic processes in organisms. Multiscale metabolomics can be combined with methods from systems biology and bioinformatics. In recent years, multiscale metabolomics approaches have become increasingly important in neuroscience research due to the nervous system's high metabolic demands. Multiscale metabolomics can offer novel concepts and approaches for the diagnosis, treatment, and development of medication for neurological illnesses in addition to a more thorough understanding of brain metabolism and nervous system function. In this review, we summarize the use of multiscale metabolomics techniques in neuroscience, address the promise and constraints of these techniques, and provide an overview of the metabolome and its applications in neuroscience.

Targeted metabolomics in human and animal biofluids and tissues using liquid chromatography coupled with tandem mass spectrometry

Here, we present a targeted polar metabolomics protocol for the analysis of biofluids and frozen tissue biopsies using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We describe steps for sample pretreatment, liquid-liquid extraction, and isolation of polar metabolites. We then detail procedures for target LC-MS/MS analysis. In this protocol, we focus on the analysis of plasma and serum samples. We also provide brief instructions on how to process other biological matrices as supplemental information. For complete details on the use and execution of this protocol, please refer to Coskun et al. (2022).1.

Establishing a framework for best practices for quality assurance and quality control in untargeted metabolomics

BACKGROUND

Quality assurance (QA) and quality control (QC) practices are key tenets that facilitate study and data quality across all applications of untargeted metabolomics. These important practices will strengthen this field and accelerate its success. The Best Practices Working Group (WG) within the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) focuses on community use of QA/QC practices and protocols and aims to identify, catalogue, harmonize, and disseminate current best practices in untargeted metabolomics through community-driven activities.

AIM OF REVIEW

A present goal of the Best Practices WG is to develop a working strategy, or roadmap, that guides the actions of practitioners and progress in the field. The framework in which mQACC operates promotes the harmonization and dissemination of current best QA/QC practice guidance and encourages widespread adoption of these essential QA/QC activities for liquid chromatography-mass spectrometry.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Community engagement and QA/QC information gathering activities have been occurring through conference workshops, virtual and in-person interactive forum discussions, and community surveys. Seven principal QC stages prioritized by internal discussions of the Best Practices WG have received participant input, feedback and discussion. We outline these stages, each involving a multitude of activities, as the framework for identifying QA/QC best practices. The ultimate planned product of these endeavors is a "living guidance" document of current QA/QC best practices for untargeted metabolomics that will grow and change with the evolution of the field.

Evaluation of normalization strategies for GC-based metabolomics

INTRODUCTION

For many samples studied by GC-based metabolomics applications, extensive sample preparation involving extraction followed by a two-step derivatization procedure of methoximation and trimethylsilylation (TMS) is typically required to expand the metabolome coverage. Performing normalization is critical to correct for variations present in samples and any biases added during the sample preparation steps and analytical runs. Addressing the totality of variations with an adequate normalization method increases the reliability of the downstream data analysis and interpretation of the results.

OBJECTIVES

Normalizing to sample mass is one of the most commonly employed strategies, while the total peak area (TPA) as a normalization factor is also frequently used as a post-acquisition technique. Here, we present a new normalization approach, total derivatized peak area (TDPA), where data are normalized to the intensity of all derivatized compounds. TDPA relies on the benefits of silylation as a universal derivatization method for GC-based metabolomics studies.

METHODS

Two sample classes consisting of systematically incremented sample mass were simulated, with the only difference between the groups being the added amino acid concentrations. The samples were TMS derivatized and analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). The performance of five normalization strategies (no normalization, normalized to sample mass, TPA, total useful peak area (TUPA), and TDPA) were evaluated on the acquired data.

RESULTS

Of the five normalization techniques compared, TUPA and TDPA were the most effective. On PCA score space, they offered a clear separation between the two classes.

CONCLUSION

TUPA and TDPA carry different strengths: TUPA requires peak alignment across all samples, which depends upon the completion of the study, while TDPA is free from the requirement of alignment. The findings of the study would enhance the convenient and effective use of data normalization strategies and contribute to overcoming the data normalization challenges that currently exist in the metabolomics community.

Real-Time NMR-Based Drug Discovery to Identify Inhibitors against Fatty Acid Synthesis in Living Cancer Cells

Abnormal fatty acid metabolism is recognized as a key driver of tumor development and progression. Although numerous inhibitors have been developed to target this pathway, finding drugs with high specificity that do not disrupt normal cellular metabolism remains a formidable challenge. In this paper, we introduced a novel real-time NMR-based drug screening technique that operates within living cells. This technique provides a direct way to putatively identify molecular targets involved in specific metabolic processes, making it a powerful tool for cell-based drug screening. Using 2-13C acetate as a tracer, combined with 3D cell clusters and a bioreactor system, our approach enables real-time detection of inhibitors that target fatty acid metabolism within living cells. As a result, we successfully demonstrated the initial application of this method in the discovery of traditional Chinese medicines that specifically target fatty acid metabolism. Elucidating the mechanisms behind herbal medicines remains challenging due to the complex nature of their compounds and the presence of multiple targets. Remarkably, our findings demonstrate the significant inhibitory effect of P. cocos on fatty acid synthesis within cells, illustrating the potential of this approach in analyzing fatty acid metabolism events and identifying drug candidates that selectively inhibit fatty acid synthesis at the cellular level. Moreover, this systematic approach represents a valuable strategy for discovering the intricate effects of herbal medicine.

MARS: A Multipurpose Software for Untargeted LC-MS-Based Metabolomics and Exposomics

Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis.

LC-MS/MS-based targeted amino acid metabolic profile of Auricularia cornea grown on pinecone substrate

Pinecone substrate offers an eco-friendly and cost-effective alternative for cultivating edible fungi. This pioneering study explores the 94 amino acids metabolic profiles of Auricularia cornea grown on various pinecone substrates. To our knowledge, this is the first study of quantify A. cornea on an oleaginous substrate (pinecone) using targeted LC-MS /MS-based metabolomics approaches. Five different pinecone substrate percentages (0%, 7%, 14%, 21%, and 28% respectively) were used for A. cornea culture, and the resulting fruiting bodies were analyzed for amino acids metabolic profiles. Detected 79 amino acids metabolites, 15 undetected. High contents of succinic-acid and γ-aminobutyric acid. Thirty-three amino acid metabolites showed significant differences between groups, primarily related to protein synthesis. KEGG analysis revealed that seven major metabolic pathways were significantly enriched. The findings provide valuable insights into the metabolite composition of A. cornea grown on a pinecone substrate, potentially contribute to the understanding of its nutritional and medicinal properties.

Comprehensive Single-Platform Lipidomic/Metabolomic Analysis Using Supercritical Fluid Chromatography-Mass Spectrometry

Supercritical fluid chromatography (SFC) is a rapidly expanding technique in the analysis of nonpolar to moderately polar substances and, more recently, also in the analysis of compounds with higher polarity. Herein, we demonstrate a proof of concept for the application of a commercial SFC instrument with electrospray ionization-mass spectrometry (MS) detection as a platform for the comprehensive analysis of metabolites with the full range of polarities, from nonpolar lipids up to highly polar metabolites. The developed single-platform SFC-MS lipidomic/metabolomic method is based on two consecutive injections of lipid and polar metabolite extracts from biphase methyl tert-butyl ether extraction using a diol column and two different gradient programs of methanol-water-ammonium formate modifier. Detailed development of the method focused mainly on the pressure limits of the system, the long-term repeatability of results, and the chromatographic performance, including optimization of the flow rate program, modifier composition and gradient, and injection solvent selection. The developed method enabled fast and comprehensive analysis of lipids and polar metabolites from plasma within a 24 min cycle with two injections using a simple analytical platform based on a single instrument, column, and mobile phase. Finally, the results from SFC-MS analysis of polar metabolites were compared with widely established liquid chromatography MS analysis in metabolomics. The comparison showed different separation selectivity of metabolites using both methods and overall lower sensitivity of the SFC-MS due to the higher flow rate and worse chromatographic performance.

Early Metabolic Disruption and Predictive Biomarkers of Delayed-Cerebral Ischemia in Aneurysmal Subarachnoid Hemorrhage

Delayed cerebral ischemia (DCI) following aneurysmal subarachnoid hemorrhage (aSAH) is a major cause of complications and death. Here, we set out to identify high-performance predictive biomarkers of DCI and its underlying metabolic disruptions using metabolomics and lipidomics approaches. This single-center prospective observational study enrolled 61 consecutive patients with severe aSAH; among them, 22 experienced a DCI. Nine patients without aSAH were included as validation controls. Blood and cerebrospinal fluid (CSF) were sampled within the first 24 h after admission. We identified a panel of 20 metabolites that, together, showed high predictive performance for DCI. This panel of metabolites included lactate, cotinine, salicylate, 6 phosphatidylcholines, and 4 sphingomyelins. The interplay of the metabolome and the lipidome found between CSF and plasma in our patients underscores that aSAH and its associated DCI complications can extend beyond cerebral implications, with a peripheral dimension as well. As an illustration, early biological disruptions that might explain the subsequent DCI found systemic hypoxia driven mainly by higher blood lactate, arginine, and proline metabolism likely associated with vascular NO and disrupted ceramide/sphingolipid metabolism. We conclude that targeting early peripheral hypoxia preceding DCI could provide an interesting strategy for the prevention of vascular dysfunction.

An integrated approach for studying the metabolic profiling of herbal medicine in mice using high-resolution mass spectrometry and metabolomics data processing tools

Analysis of exposure to traditional Chinese medicine (TCM) in vivo based on mass spectrometry is helpful for the screening of effective ingredients of TCM and the development of new drugs. The method of screening biomarkers through metabolomics technology is a nontargeted research method to explore the differential components between two sets of biological samples. By taking this advantage, this study aims to takes Forsythia suspensa, which is a TCM also known as Lian Qiao (LQ), as the research object and to study its in vivo exposure by using metabolomics technology. By comparing the significant differences between biological samples before and after administration, it could be focused on the components that were significantly upregulated, where a complete set of analysis strategies for nontargeted TCM in vivo exposure mass spectrometry was established. Furthermore, the threshold parameters for peak extraction, parameter selection during statistical data analysis, and sample concentration multiples in this method have also been optimized. More interestingly, by using the established analysis strategy, we found 393 LQ-related chemical components in mice after administration, including 102 prototypes and 291 LQ-related metabolites, and plotted their metabolic profiles in vivo. In short, this study has obtained a complete mass spectrum of LQ exposure in mice in vivo for the first time, which provides a reference for research on the active ingredients of LQ in vivo. More importantly, compared with other methods, the analysis strategy of nontargeted exposure of TCM in vivo-based mass spectrometry, constructed by using this research method, has good universality and does not require self-developed postprocessing software. It is worth mentioning that, for the identification and characterization of trace amounts of metabolites in vivo, this analysis strategy has no discrimination and has a detection capability similar to that of highly exposed components.

High-Throughput Lipidomic and Metabolomic Profiling for Brain Tissue and Biofluid Samples in Neurodegenerative Disorders

Recent research has revealed the potential of lipidomics and metabolomics in identifying new biomarkers and mechanistic insights for neurodegenerative disorders. To contribute to this promising area, we present a detailed protocol for conducting an integrated lipidomic and metabolomic profiling of brain tissue and biofluid samples. In this method, a single-phase methanol extraction is employed for extracting both nonpolar and highly polar lipids and metabolites from each biological sample. The extracted samples are then subjected to liquid chromatography-mass spectrometry-based assays to provide relative or semiquantitative measurements for hundreds of selected lipids and metabolites per sample. This high-throughput approach enables the generation of new hypotheses regarding the mechanistic and functional significance of lipid and metabolite alterations in neurodegenerative disorders while also facilitating the discovery of new biomarkers to support drug development.

Recent Advances and Techniques for Identifying Novel Antibacterial Targets

BACKGROUND

With the emergence of drug-resistant bacteria, the development of new antibiotics is urgently required. Target-based drug discovery is the most frequently employed approach for the drug development process. However, traditional drug target identification techniques are costly and time-consuming. As research continues, innovative approaches for antibacterial target identification have been developed which enabled us to discover drug targets more easily and quickly.

METHODS

In this review, methods for finding drug targets from omics databases have been discussed in detail including principles, procedures, advantages, and potential limitations. The role of phage-driven and bacterial cytological profiling approaches is also discussed. Moreover, current article demonstrates the advancements being made in the establishment of computational tools, machine learning algorithms, and databases for antibacterial target identification.

RESULTS

Bacterial drug targets successfully identified by employing these aforementioned techniques are described as well.

CONCLUSION

The goal of this review is to attract the interest of synthetic chemists, biologists, and computational researchers to discuss and improve these methods for easier and quicker development of new drugs.

Push forward LC-MS-based therapeutic drug monitoring and pharmacometabolomics for anti-tuberculosis precision dosing and comprehensive clinical management

The spread of tuberculosis (TB), especially multidrug-resistant TB and extensively drug-resistant TB, has strongly motivated the research and development of new anti-TB drugs. New strategies to facilitate drug combinations, including pharmacokinetics-guided dose optimization and toxicology studies of first- and second-line anti-TB drugs have also been introduced and recommended. Liquid chromatography-mass spectrometry (LC-MS) has arguably become the gold standard in the analysis of both endo- and exo-genous compounds. This technique has been applied successfully not only for therapeutic drug monitoring (TDM) but also for pharmacometabolomics analysis. TDM improves the effectiveness of treatment, reduces adverse drug reactions, and the likelihood of drug resistance development in TB patients by determining dosage regimens that produce concentrations within the therapeutic target window. Based on TDM, the dose would be optimized individually to achieve favorable outcomes. Pharmacometabolomics is essential in generating and validating hypotheses regarding the metabolism of anti-TB drugs, aiding in the discovery of potential biomarkers for TB diagnostics, treatment monitoring, and outcome evaluation. This article highlighted the current progresses in TDM of anti-TB drugs based on LC-MS bioassay in the last two decades. Besides, we discussed the advantages and disadvantages of this technique in practical use. The pressing need for non-invasive sampling approaches and stability studies of anti-TB drugs was highlighted. Lastly, we provided perspectives on the prospects of combining LC-MS-based TDM and pharmacometabolomics with other advanced strategies (pharmacometrics, drug and vaccine developments, machine learning/artificial intelligence, among others) to encapsulate in an all-inclusive approach to improve treatment outcomes of TB patients.

MSdeCIpher: A Tool to Link Data from Complementary Ionization Techniques in High-Resolution GC-MS to Identify Molecular Ions

Electron ionization (EI) and molecular ion-generating techniques like chemical ionization (CI) are complementary ionization methods in gas chromatography (GC)-mass spectrometry (MS). However, manual curation effort and expert knowledge are required to correctly assign molecular ions to fragment spectra. MSdeCIpher is a software tool that enables the combination of two separate datasets from fragment-rich spectra, like EI-spectra, and soft ionization spectra containing molecular ion candidates. Using high-resolution GC-MS data, it identifies and assigns molecular ions based on retention time matching, user-defined adduct/neutral loss criteria, and sum formula matching. To our knowledge, no other freely available or vendor tool is currently capable of combining fragment-rich and soft ionization datasets in this manner. The tool's performance was evaluated on three test datasets. When molecular ions are present, MSdeCIpher consistently ranks the correct molecular ion for each fragment spectrum in one of the top positions, with average ranks of 1.5, 1, and 1.2 in the three datasets, respectively. MSdeCIpher effectively reduces candidate molecular ions for each fragment spectrum and thus enables the usage of compound identification tools that require molecular masses as input. It paves the way towards rapid annotations in untargeted analysis with high-resolution GC-MS.

Metabolome Profiling of Marrubium peregrinum L. and Marrubium friwaldskyanum Boiss Reveals Their Potential as Sources of Plant-Based Pharmaceuticals

Marrubium species have been used since ancient times as food additives and curative treatments. Their phytochemical composition and various pharmacological activities were the focus of a number of scientific investigations but no comprehensive metabolome profiling to identify the numerous primary and secondary metabolites has been performed so far. This study aimed to generate a comprehensive picture of the total metabolite content of two Marrubium species-M. peregrinum and M. friwaldskyanum-to provide detailed information about the main primary and secondary metabolites. In addition, the elemental composition was also evaluated. For this purpose, non-targeted metabolomic analyses were conducted using GC-MS, UPLC-MS/MS and ICP-MS approaches. Nearly 500 compounds and 12 elements were detected and described. The results showed a strong presence of phenolic acids, flavonoids and their glucosides, which are generally of great interest due to their various pharmacological activities. Furthermore, tissue-specific analyses for M. friwaldskyanum stem, leaves and flowers were carried out in order to outline the sources of potentially important bioactive molecules. The results generated from this study depict the Marrubium metabolome and reveal its dual scientific importance-from one side, providing information about the metabolites that is fundamental and vital for the survival of these species, and from the other side, defining the large diversity of secondary substances that are a potential source of phytotherapeutic agents.

Preclinical pharmacokinetic exploration of a novel osteoporotic quinazolinone-benzopyran-indole hybrid (S019-0385) using LC-MS/MS

1. The current investigation was to develop and validate the LC-MS/MS method in order to analyse the various pharmacokinetic parameters of S019-0385. A sensitive, selective, and robust LC-MS/MS approach was established and validated for measuring S019-0385 in female mice plasma and tissue, using optimal multiple reaction monitoring (MRM) transition m/z 488.25/329.12 on positive mode. On a Waters Symmetry Shield C18 column, the analyte was separated using acetonitrile and deionised water with formic acid within 6 min at 0.7 mL/min. Linearity (R2 ≥ 0.99) was observed across 0.195-100 ng/mL concentration range using linear least-squares regression.2. Blood-to-plasma ratio and plasma protein drug binding (%) in mice and human was assessed and found to be less than 1 and >83%, respectively. Absolute bioavailability (%F) of S019-0385 in female Swiss mice was exhibited to be 6.90%. Percent dose excreted S019-0385 in unchanged form through urine and faecal was found to be less than 2% and 0.5%, respectively.3. Following oral administration at 5 mg/kg, the concentration of S019-0385 in tissue distribution was found to be in the order of C small intestine > C bone > C lung > C spleen > C kidney > C liver > C heart > C brain.

Comprehensive Profiling of Amine-Containing Metabolite Isomers with Chiral Phosphorus Reagents

Metabolite isomers play diverse and crucial roles in various metabolic processes. However, in untargeted metabolomics analysis, it remains a great challenge to distinguish between the constitutional isomers and enantiomers of amine-containing metabolites due to their similar chemical structures and physicochemical properties. In this work, the triplex stable isotope N-phosphoryl amino acids labeling (SIPAL) is developed to identify and relatively quantify the amine-containing metabolites and their isomers by using chiral phosphorus reagents coupled with high-resolution tandem mass spectroscopy. The constitutional isomers could be effectively distinguished with stereo isomers by using the diagnosis ions in MS/MS spectra. The in-house software MS-Isomerism has been parallelly developed for high-throughput screening and quantification. The proposed strategy enables the unbiased detection and relative quantification of isomers of amine-containing metabolites. Based on the characteristic triplet peaks with SIPAL tags, a total of 854 feature peaks with 154 isomer groups are successfully recognized as amine-containing metabolites in liver cells, in which 37 amine-containing metabolites, including amino acids, polyamines, and small peptides, are found to be significantly different between liver cancer cells and normal cells. Notably, it is the first time to identify S-acetyl-glutathione as an endogenous metabolite in liver cells. The SIPAL strategy could provide spectacular insight into the chemical structures and biological functions of the fascinating amine-containing metabolite isomers. The feasibility of SIPAL in isomeric metabolomics analysis may reach a deeper understanding of the mirror-chemistry in life and further advance the discovery of novel biomarkers for disease diagnosis.

Metabolomics and mitochondrial dysfunction in cardiometabolic disease

Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.

Bioprospecting-based untargeted metabolomics identifies alkaloids as potential anti-inflammatory bioactive markers of Ocotea species (Lauraceae)

BACKGROUND

Species within the Ocotea genus (Lauraceae), have demonstrated an interesting profile of bioactivities. Renowned for their diverse morphology and intricate specialized metabolite composition, Ocotea species have re-emerged as compelling candidates for bioprospecting in drug discovery research. However, it is a genus insufficiently studied, particularly regarding anti-inflammatory activity.

PURPOSE

To investigate the anti-inflammatory activity of Ocotea spp. extracts and determine the major markers in this genus.

METHODS

Extracts of 60 different Ocotea spp. were analysed by an ex vivo anti-inflammatory assay in human whole blood. The experiment estimates the prostaglandin E2 levels, which is one of the main mediators of the inflammatory cascade, responsible for the classical symptoms of fever, pain, and other common effects of the inflammatory process. Untargeted metabolomics analysis through liquid chromatography coupled with high-resolution mass spectrometry was performed, along with statistical analysis, to investigate which Ocotea metabolites are correlated with their anti-inflammatory activity.

RESULTS

The anti-inflammatory screening indicated that 49 out of 60 Ocotea spp. extracts exhibited significant inhibition of PGE2 release compared to the vehicle (p < 0.05). Furthermore, 10 of these extracts showed statistical similarity to the reference drugs. The bioactive markers were accurately identified using multivariate statistics combined with a fold change (> 1.5) and adjusted false discovery rate analysis as unknown compounds and alkaloids, with a majority of aporphine and benzylisoquinolines. These alkaloids were annotated with an increased level of confidence since MSE spectra were compared with comprehensive databases.

CONCLUSION

This study represents the first bioprospecting report revealing the anti-inflammatory potential of several Ocotea spp. The determination of their anti-inflammatory markers could contribute to drug discovery and the chemical knowledge of the Ocotea genus.

Plasma metabolomic profiling of patients with transient ischemic attack reveals positive role of neutrophils in ischemic tolerance

BACKGROUND

Transient ischemic attack (TIA) induces ischemic tolerance that can reduce the subsequent ischemic damage and improve prognosis of patients with stroke. However, the underlying mechanisms remain elusive. Recent advances in plasma metabolomics analysis have made it a powerful tool to investigate human pathophysiological phenotypes and mechanisms of diseases. In this study, we aimed to identify the bioactive metabolites from the plasma of patients with TIA for determination of their prophylactic and therapeutic effects on protection against cerebral ischemic stroke, and the mechanism of TIA-induced ischemic tolerance against subsequent stroke.

METHODS

Metabolomic profiling using liquid chromatography-mass spectrometry was performed to identify the TIA-induced differential bioactive metabolites in the plasma samples of 20 patients at day 1 (time for basal metabolites) and day 7 (time for established chronic ischemic tolerance-associated metabolites) after onset of TIA. Mouse middle cerebral artery occlusion (MCAO)-induced stroke model was used to verify their prophylactic and therapeutic potentials. Transcriptomics changes in circulating neutrophils of patients with TIA were determined by RNA-sequencing. Multivariate statistics and integrative analysis of metabolomics and transcriptomics were performed to elucidate the potential mechanism of TIA-induced ischemic tolerance.

FINDINGS

Plasma metabolomics analysis identified five differentially upregulated metabolites associated with potentially TIA-induced ischemic tolerance, namely all-trans 13,14 dihydroretinol (atDR), 20-carboxyleukotriene B4, prostaglandin B2, cortisol and 9-KODE. They were associated with the metabolic pathways of retinol, arachidonic acid, and neuroactive ligand-receptor interaction. Prophylactic treatment of MCAO mice with these five metabolites significantly improved neurological functions. Additionally, post-stroke treatment with atDR or 9-KODE significantly reduced the cerebral infarct size and enhanced sensorimotor functions, demonstrating the therapeutic potential of these bioactive metabolites. Mechanistically, we found in patients with TIA that these metabolites were positively correlated with circulating neutrophil counts. Integrative analysis of plasma metabolomics and neutrophil transcriptomics further revealed that TIA-induced metabolites are significantly correlated with specific gene expression in circulating neutrophils which showed prominent enrichment in FoxO signaling pathway and upregulation of the anti-inflammatory cytokine IL-10. Finally, we demonstrated that the protective effect of atDR-pretreatment on MCAO mice was abolished when circulating neutrophils were depleted.

INTERPRETATION

TIA-induced potential ischemic tolerance is associated with upregulation of plasma bioactive metabolites which can protect against cerebral ischemic damage and improve neurological functions through a positive role of circulating neutrophils.

FUNDING

National Natural Science Foundation of China (81974210), Science and Technology Planning Project of Guangdong Province, China (2020A0505100045), Natural Science Foundation of Guangdong Province (2019A1515010671), Science and Technology Program of Guangzhou, China (2023A03J0577), and Natural Science Foundation of Jiangxi, China（20224BAB216043).

Non-targeted metabolomics aids in sex pheromone identification: a proof-of-concept study with the triangulate cobweb spider, Steatoda triangulosa

Targeted metabolomics has been widely used in pheromone research but may miss pheromone components in study organisms that produce pheromones in trace amount and/or lack bio-detectors (e.g., antennae) to readily locate them in complex samples. Here, we used non-targeted metabolomics-together with high-performance liquid chromatography-mass spectrometry (HPLC-MS), gas chromatography-MS, and behavioral bioassays-to unravel the sex pheromone of the triangulate cobweb spider, Steatoda triangulosa. A ternary blend of three contact pheromone components [N-4-methylvaleroyl-O-isobutyroyl-L-serine (5), N-3-methylbutyryl-O-isobutyroyl-L-serine (11), and N-3-methylbutyryl-O-butyroyl-L-serine (12)] elicited courtship by S. triangulosa males as effectively as female web extract. Hydrolysis of 5, 11 and 12 at the ester bond gave rise to two mate-attractant pheromone components [butyric acid (7) and isobutyric acid (8)] which attracted S. triangulosa males as effectively as female webs. Pheromone components 11 and 12 are reported in spiders for the first time, and were discovered only through the use of non-targeted metabolomics and GC-MS. All compounds resemble pheromone components previously identified in widow spiders. Our study provides impetus to apply non-targeted metabolomics for pheromone research in a wide range of animal taxa.

Comparative analysis between 2D and 3D colorectal cancer culture models for insights into cellular morphological and transcriptomic variations

Drug development is a time-consuming and expensive process, given the low success rate of clinical trials. Now, anticancer drug developments have shifted to three-dimensional (3D) models which are more likely to mimic tumor behavior compared to traditional two-dimensional (2D) cultures. A comparative study among different aspects was conducted between 2D and 3D cultures using colorectal cancer (CRC) cell lines, in addition, Formalin-Fixed Paraffin-Embedded (FFPE) block samples of patients with CRC were used for evaluation. Compared to the 2D culture, cells grown in 3D displayed significant (p < 0.01) differences in the pattern of cell proliferation over time, cell death phase profile, expression of tumorgenicity-related genes, and responsiveness to 5-fluorouracil, cisplatin, and doxorubicin. Epigenetically, 3D cultures and FFPE shared the same methylation pattern and microRNA expression, while 2D cells showed elevation in methylation rate and altered microRNA expression. Lastly, transcriptomic study depending on RNA sequencing and thorough bioinformatic analyses showed significant (p-adj < 0.05) dissimilarity in gene expression profile between 2D and 3D cultures involving thousands of genes (up/down-regulated) of multiple pathways for each cell line. Taken together, the study provides insights into variations in cellular morphologies between cells cultured in 2D and 3D models.