High-throughput discovery metabolomics

Hyphenated mass spectrometry methods for enlarged capacity data storage systems based on chemical mixtures

Encoding abstract information in chemical mixtures uses the selective presence or absence of specific analytes, creating a binary-based framework for data storage. Data storage capacity (C in bits) can be maximized by encoding with large analyte libraries (M) at distinguishable concentration levels (L), where C = M·log2L. However, roboust decoding of such complex libraries remains challenging for practical applications. This study introduces hyphenated mass spectrometry (MS) methods, liquid chromatography (LC) and flow injection analysis (FIA) that meet the dual requirements of high analyte coverage and precise quantitation to maximize data storage capacity. Encoding and decoding use plant metabolite libraries to create specific mixtures. Using LC-MS, it is feasible to encode and decode up to 200 bits per mixture, with scalability reaching 103-104 bits at the cost of low decoding rates (ca. 0.5 bits per sec). FIA-MS offers a high-throughput alternative, handling 100 bits at faster rates (ca. 3 bits per sec). The data storage capacity can be three-fold expanded by incorporating up to eight quantitation levels, supporting binary, quaternary, or octal encoding schemes. To demonstrate the practical application of these methods, we encode and decode various digital file formats such as texts and multicolor images.

Integrated metabolomics and network pharmacology reveal the mechanisms of Xuebijing in counteracting sepsis-induced myocardial dysfunction

ETHNOPHARMACOLOGICAL RELEVANCE

Xuebijing (XBJ) injection is a Traditional Chinese medicine (TCM) injection extracted and prepared using modern TCM formulation techniques. As a widely used treatment for critically ill patients, XBJ injection has shown significant therapeutic effects in clinical applications in China. It plays an indispensable role in sepsis-induced myocardial dysfunction (SIMD). However, its underlying mechanisms require further investigation.

OBJECTIVE

This study aims to investigate the cardioprotective effects of XBJ in sepsis and to elucidate its underlying mechanisms.

METHODS

Network pharmacology was used to predict the potential active components and core targets of XBJ against SIMD. Furthermore, animal models were used to verify its pharmacodynamics. Metabolomics was integrated to track the myocardial tissue metabolites from septic rats. Molecular docking, qRT-PCR, Western blot, and immunofluorescence were performed to investigate the mechanisms of action.

RESULTS

Network pharmacology predicted that the efficacy of XBJ is attributed to 104 active components and 178 targets. Metabolomics of myocardial tissue from CLP rats revealed that the key metabolic pathways included the tricarboxylic acid (TCA) cycle, pyrimidine metabolism, and purine metabolism. Five core active components of XBJ (quercetin, luteolin, rutin, β-sitosterol, and cryptotanshinone) can modulate interleukin-6 (IL-6), epidermal growth factor receptor (EGFR), B-cell lymphoma 2 (BCL2), peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α), and hypoxia-inducible factor 1-alpha (HIF-1α). Molecular docking analysis confirmed that the core components of XBJ have a strong affinity for these key targets. Additionally, qRT-PCR, Western blotting, and immunofluorescence results indicated that XBJ can reverse the expression of these targets, ameliorated energy metabolism dysregulation, and alleviated SIMD.

CONCLUSION

XBJ exerts protective effects in a rat model of sepsis-induced myocardial injury, by modulating energy metabolism pathways that regulate key SIMD-related targets (IL-6, EGFR, BCL2, PGC-1α, and HIF-1α), thereby improving myocardial energy metabolism and alleviating inflammatory responses.

Bacteria-derived 3-hydroxydodecanoic acid induces a potent anti-tumor immune response via the GPR84 receptor

Despite advances in cancer treatment, the development of effective therapies remains an urgent unmet need. Here, we investigate the potential of bacteria-derived metabolites as a therapeutic alternative for the treatment of cancer. We detect 3-hydroxydodecanedioic acid in the serum of tumor-bearing mice treated with serum from mice previously supplemented with a mix of Clostridiales bacteria. Further, 3-hydroxydodecanoic acid, an intermediate derivative between dodecanoic and 3-hydroxydodecanedioic acids, exhibits a strong anti-tumor response via GPR84 receptor signaling and enhances CD8+ T cell infiltration and cytotoxicity within tumor tissue in multiple cancer models. Metabolomics analysis of colorectal cancer patient serum reveals an inverse correlation between the abundance of these metabolites and advanced disease stages. Our findings provide a strong rationale for 3-hydroxydodecanoic acid and the GPR84 receptor to be considered as promising therapeutic targets for cancer treatment.

Precision Dietary Intervention: Gut Microbiome and Meta-metabolome as Functional Readouts

Gut microbiome, the group of commensals residing within the intestinal tract, is closely associated with dietary patterns by interacting with food components. The gut microbiome is modifiable by the diet, and in turn, it utilizes the undigested food components as substrates and generates a group of small molecule-metabolites that addressed as "meta-metabolome" in this review. Profiling and mapping of meta-metabolome could yield insightful information at higher resolution and serve as functional readouts for precision nutrition and formation of personalized dietary strategies. For assessing the meta-metabolome, sample preparation is important, and it should aim for retrieval of gut microbial metabolites as intact as possible. The meta-metabolome can be investigated via untargeted and targeted meta-metabolomics with analytical platforms such as nuclear magnetic resonance spectroscopy and mass spectrometry. Employing flux analysis with meta-metabolomics using available database could further elucidate metabolic pathways that lead to biomarker discovery. In conclusion, integration of gut microbiome and meta-metabolomics is a promising supplementary approach to tailor precision dietary intervention. In this review, relationships among diet, gut microbiome, and meta-metabolome are elucidated, with an emphasis on recent advances in alternative analysis techniques proposed for nutritional research. We hope that this review will provide information for establishing pipelines complementary to traditional approaches for achieving precision dietary intervention.

Sample Preparation for Global Metabolic Profiling of Vaccinia Virus-Infected Primary Human Foreskin Fibroblasts

Vaccinia virus (VACV), the prototype member of the Poxviridae family, has played a crucial role in medicine as a key component in the development of smallpox vaccines, contributing to the eradication of this deadly disease. Beyond its historical significance, VACV continues to be pivotal in researching metabolic alterations induced by viral infections. Studies have revealed that VACV can impact pathways such as glycolysis, the tricarboxylic acid (TCA) cycle, and lipid metabolism in host cells, offering valuable insights into host-virus interactions and broader cellular metabolism. The preference for primary cells, such as human foreskin fibroblasts (HFFs), over cancer cells in metabolic studies is justified for their physiological relevance, representing native cell types with genetic stability. Metabolic profiling is an ideal tool for studying virus-induced metabolic alterations, providing a comprehensive analysis of changes in cellular metabolism triggered by viral infections. This chapter outlines a protocol for extracting HFFs, culturing, infecting them with VACV, and conducting untargeted global metabolic profiling to elucidate detailed metabolic statuses of the infected cells. This protocol may be modified for understanding the intricacies of host-virus interactions at the metabolic interface for other poxviruses and non-poxviruses.

Emerging Biomarkers in Metabolomics: Advancements in Precision Health and Disease Diagnosis

Metabolomics has come to the fore as an efficient tool in the search for biomarkers that are critical for precision health approaches and improved diagnostics. This review will outline recent advances in biomarker discovery based on metabolomics, focusing on metabolomics biomarkers reported in cancer, neurodegenerative disorders, cardiovascular diseases, and metabolic health. In cancer, metabolomics provides evidence for unique oncometabolites that are important for early disease detection and monitoring of treatment responses. Metabolite profiling for conditions such as neurodegenerative and mental health disorders can offer early diagnosis and mechanisms into the disease especially in Alzheimer's and Parkinson's diseases. In addition to these, lipid biomarkers and other metabolites relating to cardiovascular and metabolic disorders are promising for patient stratification and personalized treatment. The gut microbiome and environmental exposure also feature among the influential factors in biomarker discovery because they sculpt individual metabolic profiles, impacting overall health. Further, we discuss technological advances in metabolomics, current clinical applications, and the challenges faced by metabolomics biomarker validation toward precision medicine. Finally, this review discusses future opportunities regarding the integration of metabolomics into routine healthcare to enable preventive and personalized approaches.

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.

Metabolomics for hematologic malignancies: Advances and perspective

With the use of advanced technology, metabolomics allows for a thorough examination of metabolites and other small molecules found in biological specimens, blood, and tissues. In recent years, metabolomics has been recognized that is closely related to the development of malignancies in the hematological system. Alterations in metabolomic pathways and networks are important in the pathogenesis of hematologic malignancies and can also provide a theoretical basis for early diagnosis, efficacy evaluation, accurate staging, and individualized targeted therapy. In this review, we summarize the progress of metabolomics, including glucose metabolism, amino acid metabolism, and lipid metabolism in lymphoma, myeloma, and leukemia through specific mechanisms and pathways. The research of metabolomics gives a new insight and provides therapeutic targets for the treatment of patients with hematologic malignancies.

Spatiotemporal metabolomic approaches to the cancer-immunity panorama: a methodological perspective

Metabolic reprogramming drives the development of an immunosuppressive tumor microenvironment (TME) through various pathways, contributing to cancer progression and reducing the effectiveness of anticancer immunotherapy. However, our understanding of the metabolic landscape within the tumor-immune context has been limited by conventional metabolic measurements, which have not provided comprehensive insights into the spatiotemporal heterogeneity of metabolism within TME. The emergence of single-cell, spatial, and in vivo metabolomic technologies has now enabled detailed and unbiased analysis, revealing unprecedented spatiotemporal heterogeneity that is particularly valuable in the field of cancer immunology. This review summarizes the methodologies of metabolomics and metabolic regulomics that can be applied to the study of cancer-immunity across single-cell, spatial, and in vivo dimensions, and systematically assesses their benefits and limitations.

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Hierarchical superstructure aerogels for in situ biofluid metabolomics

High-throughput biofluid metabolomics analysis for screening life-threatening diseases is urgently needed. However, the high salt content of biofluid samples, which introduces severe interference, can greatly limit the analysis throughput. Here, a new 3-D interconnected hierarchical superstructure, namely a "plasmonic gold-on-silica (Au/SiO2) double-layered aerogel", integrating distinctive features of an upper plasmonic gold aerogel with a lower inert silica aerogel was successfully developed to achieve in situ separation and storage of inorganic salts in the silica aerogel, parallel enrichment of metabolites on the surface of the functionalized gold aerogel, and direct desorption/ionization of enriched metabolites by the photo-excited gold aerogel for rapid, sensitive, and comprehensive metabolomics analysis of human serum/urine samples. By integrating all these unique advantages into the hierarchical aerogel, multifunctional properties were introduced in the SALDI substrate to enable its effective utilization in clinical metabolomics for the discovery of reliable metabolic biomarkers to achieve unambiguous differentiation of early and advanced-stage lung cancer patients from healthy individuals. This study provides insight into the design and application of superstructured nanomaterials for in situ separation, storage, and photoexcitation of multi-components in complex biofluid samples for sensitive analysis.

Evaluating the Metabolomic Profile and Anti-Pathogenic Properties of Cannabis Species

The Cannabis species is one of the potent ancient medicinal plants acclaimed for its medicinal properties and recreational purposes. The plant parts are used and exploited all over the world for several agricultural and industrial applications. For many years Cannabis spp. has proven to present a highly diverse metabolomic profile with a pool of bioactive metabolites used for numerous pharmacological purposes ranging from anti-inflammatory to antimicrobial. Cannabis sativa has since been an extensive subject of investigation, monopolizing the research. Hence, there are fewer studies with a comprehensive understanding of the composition of bioactive metabolites grown in different environmental conditions, especially C. indica and a few other Cannabis strains. These pharmacological properties are mostly attributed to a few phytocannabinoids and some phytochemicals such as terpenoids or essential oils which have been tested for antimicrobial properties. Many other discovered compounds are yet to be tested for antimicrobial properties. These phytochemicals have a series of useful properties including anti-insecticidal, anti-acaricidal, anti-nematicidal, anti-bacterial, anti-fungal, and anti-viral properties. Research studies have reported excellent antibacterial activity against Gram-positive and Gram-negative multidrug-resistant bacteria as well as methicillin-resistant Staphylococcus aureus (MRSA). Although there has been an extensive investigation on the antimicrobial properties of Cannabis, the antimicrobial properties of Cannabis on phytopathogens and aquatic animal pathogens, mostly those affecting fish, remain under-researched. Therefore, the current review intends to investigate the existing body of research on metabolomic profile and anti-microbial properties whilst trying to expand the scope of the properties of the Cannabis plant to benefit the health of other animal species and plant crops, particularly in agriculture.

L-cysteine contributes to destructive activities of odontogenic cysts/tumor

BACKGROUND

Odontogenic cysts/tumor can cause severe bone destruction, which affects maxillofacial function and aesthetics. Meanwhile, metabolic reprogramming is an important hallmark of diseases. Changes in metabolic flow affect all aspects of disease, especially bone-related diseases. At present, the researches on pathogenesis of odontogenic cysts/tumor are mainly focused on the level of gene regulation, but the effects of metabolic alterations on odontogenic cysts/tumor have still underexplored.

MATERIALS AND METHODS

Imaging analysis was used to evaluate the lesion size of different odontogenic lesions. Tartrate resistant acid phosphatase (TRAP) and immunohistochemistry (IHC) assays were utilized to detect the differences in bone destruction activity in odontogenic cysts and tumors. Furthermore, metabolomics and weighted gene co-expression network analysis (WGCNA) were conducted for the metabolomic features and key metabolite screening, respectively. The effect of ferroptosis inhibition on bone destruction was confirmed by IHC, immunofluorescence, and malondialdehyde colorimetric assay.

RESULTS

The bone destruction activity of ameloblastoma (AM) was the strongest and the weakest in odontogenic cysts (OC). High-throughput targeted metabolomics was used to map the metabolomic profiles of OC, odontogenic keratocyst (OKC) and AM. WGCNA and differential analysis identified L-cysteine in OKC and AM. Cystathionine γ-lyase (CTH) was further screened by Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The functions of L-cysteine were further validated. Finally, we confirmed that CTH affected destructive activities by regulating the sensitivity of epithelial cells to ferroptosis.

CONCLUSION

High-throughput targeted metabolomics performed on diseased tissue confirmed the unique alteration of metabolic profiles in OKC and AM. CTH and its metabolite L-cysteine are the key factors regulating destructive activities.

Breaking barriers in trauma research: A narrative review of opportunities to leverage veterinary trauma for accelerated translation to clinical solutions for pets and people

Trauma is a common cause of morbidity and mortality in humans and companion animals. Recent efforts in procedural development, training, quality systems, data collection, and research have positively impacted patient outcomes; however, significant unmet need still exists. Coordinated efforts by collaborative, translational, multidisciplinary teams to advance trauma care and improve outcomes have the potential to benefit both human and veterinary patient populations. Strategic use of veterinary clinical trials informed by expertise along the research spectrum (i.e., benchtop discovery, applied science and engineering, large laboratory animal models, clinical veterinary studies, and human randomized trials) can lead to increased therapeutic options for animals while accelerating and enhancing translation by providing early data to reduce the cost and the risk of failed human clinical trials. Active topics of collaboration across the translational continuum include advancements in resuscitation (including austere environments), acute traumatic coagulopathy, trauma-induced coagulopathy, traumatic brain injury, systems biology, and trauma immunology. Mechanisms to improve funding and support innovative team science approaches to current problems in trauma care can accelerate needed, sustainable, and impactful progress in the field. This review article summarizes our current understanding of veterinary and human trauma, thereby identifying knowledge gaps and opportunities for collaborative, translational research to improve multispecies outcomes. This translational trauma group of MDs, PhDs, and DVMs posit that a common understanding of injury patterns and resulting cellular dysregulation in humans and companion animals has the potential to accelerate translation of research findings into clinical solutions.

Comparing the metabolic pathways of different clinical phases of bipolar disorder through metabolomics studies

This study identified the metabolic biomarkers for different clinical phases of bipolar disorder (BD) through metabolomics. BD patients were divided into three groups: patients with BD and depressive episodes (BE, n = 59), patients with BD and mania/hypomania episodes (BH, n = 16), patients with BD and mixed episodes (BM, n = 10), and healthy controls (HC, n = 10). Serum from participants was collected for metabolomic sequencing, biomarkers from each group were screened separately by partial least squares analysis, and metabolic pathways connected to the biomarkers were identified. Compared with the controls, 3-D-hydroxyacetic acid and N-acetyl-glycoprotein showed significant differences in the BE, BH, and BM groups. This study suggests that different clinical types of BD share the same metabolic pathways, such as pyruvate, glycolysis/gluconeogenesis, and ketone body metabolisms. In particular, abnormal glycine, serine, and threonine metabolism was specific to BM; β-glucose, glycerol, lipids, lactate, and acetoacetate metabolites were specific to depressive episodes; the guanidine acetic acid metabolites specific to BH; and the acetic and ascorbic acids were metabolites specific to manic and BM. We screened potential biomarkers for different clinical phases of BD, which aids in BD typing and provides a theoretical basis for exploring the molecular mechanisms of BD.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Multiplatform untargeted metabolomics

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Integrating Metabolomics and Network Pharmacology to Explore the Mechanism of Xiao-Yao-San in the Treatment of Inflammatory Response in CUMS Mice

Depression can trigger an inflammatory response that affects the immune system, leading to the development of other diseases related to inflammation. Xiao-Yao-San (XYS) is a commonly used formula in clinical practice for treating depression. However, it remains unclear whether XYS has a modulating effect on the inflammatory response associated with depression. The objective of this study was to examine the role and mechanism of XYS in regulating the anti-inflammatory response in depression. A chronic unpredictable mild stress (CUMS) mouse model was established to evaluate the antidepressant inflammatory effects of XYS. Metabolomic assays and network pharmacology were utilized to analyze the pathways and targets associated with XYS in its antidepressant inflammatory effects. In addition, molecular docking, immunohistochemistry, Real-Time Quantitative Polymerase Chain Reaction (RT-qPCR), and Western Blot were performed to verify the expression of relevant core targets. The results showed that XYS significantly improved depressive behavior and attenuated the inflammatory response in CUMS mice. Metabolomic analysis revealed the reversible modulation of 21 differential metabolites by XYS in treating depression-related inflammation. Through the combination of liquid chromatography and network pharmacology, we identified seven active ingredients and seven key genes. Furthermore, integrating the predictions from network pharmacology and the findings from metabolomic analysis, Vascular Endothelial Growth Factor A (VEGFA) and Peroxisome Proliferator-Activated Receptor-γ (PPARG) were identified as the core targets. Molecular docking and related molecular experiments confirmed these results. The present study employed metabolomics and network pharmacology analyses to provide evidence that XYS has the ability to alleviate the inflammatory response in depression through the modulation of multiple metabolic pathways and targets.

Toxicity effects of pesticides based on zebrafish (Danio rerio) models: Advances and perspectives

Pesticides inevitably enter aquatic environments, posing potential risks to organisms. The common aquatic model organism, zebrafish (Danio rerio), are widely used to evaluate the toxicity of pesticides. In this review, we searched the Web of Science database for articles published between 2012 and 2022, using the keywords "pesticide", "zebrafish", and "toxicity", retrieving 618 publications. Furthermore, we described the main pathways by which pesticides enter aquatic environments and the fate of their residues in these environments. We systematically reviewed the toxicity effects of pesticides on zebrafish, including developmental toxicity, endocrine-disrupting effects, reproductive toxicity, neurotoxicity, immunotoxicity, and genotoxicity. Importantly, we summarized the latest research progress on the toxicity mechanism of pesticides to zebrafish based on omics technologies, including transcriptomics, metabolomics, and microbiomics. Finally, we discussed future research prospects, focusing on the combined exposure of multiple pollutants including pesticides, the risk of multigenerational exposure to pesticides, and the chronic toxicity of aquatic nanopesticides. This review provides essential data support for ecological risk assessments of pesticides in aquatic environments, and has implications for water management in the context of pesticide pollution.

Mass spectrometry as a lens into molecular human nutrition and health

Mass spectrometry (MS) has developed over the last decades into the most informative and versatile analytical technology in molecular and structural biology (). The platform enables discovery, identification, and characterisation of non-volatile biomolecules, such as proteins, peptides, DNA, RNA, nutrients, metabolites, and lipids at both speed and scale and can elucidate their interactions and effects. The versatility, robustness, and throughput have rendered MS a major research and development platform in molecular human health and biomedical science. More recently, MS has also been established as the central tool for 'Molecular Nutrition', enabling comprehensive and rapid identification and characterisation of macro- and micronutrients, bioactives, and other food compounds. 'Molecular Nutrition' thereby helps understand bioaccessibility, bioavailability, and bioefficacy of macro- and micronutrients and related health effects. Hence, MS provides a lens through which the fate of nutrients can be monitored along digestion via absorption to metabolism. This in turn provides the bioanalytical foundation for 'Personalised Nutrition' or 'Precision Nutrition' in which design and development of diets and nutritional products is tailored towards consumer and patient groups sharing similar genetic and environmental predisposition, health/disease conditions and lifestyles, and/or objectives of performance and wellbeing. The next level of integrated nutrition science is now being built as 'Systems Nutrition' where public and personal health data are correlated with life condition and lifestyle factors, to establish directional relationships between nutrition, lifestyle, environment, and health, eventually translating into science-based public and personal heath recommendations and actions. This account provides a condensed summary of the contributions of MS to a precise, quantitative, and comprehensive nutrition and health science and sketches an outlook on its future role in this fascinating and relevant field.

Exploring metabolic dynamics during the fermentation of sea buckthorn beverage: comparative analysis of volatile aroma compounds and non-volatile metabolites using GC-MS and UHPLC-MS

Sea buckthorn has a high nutritional value, but its sour taste and foul odor make it unpalatable for consumers. In this study, we analyzed the metabolite changes occurring during the yeast-assisted fermentation of sea buckthorn juice using the HeadSpace Solid-Phase Microextraction Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) and Ultra-High Performance Liquid Chromatography-Mass Spectrometry (UHPLC-MS) techniques. A total of 86 volatile aroma compounds were identified during the fermentation process. The content of total volatiles in sea buckthorn juice increased by 3469.16 μg/L after 18 h of fermentation, with 22 compounds showing elevated levels. Notably, the total content of esters with fruity, floral, and sweet aromas increased by 1957.09 μg/L. We identified 379 non-volatile metabolites and observed significant increases in the relative abundance of key active ingredients during fermentation: glycerophosphorylcholine (increased by 1.54), glutathione (increased by 1.49), L-glutamic acid (increased by 2.46), and vanillin (increased by 0.19). KEGG pathway analysis revealed that amino acid metabolism and lipid metabolism were the primary metabolic pathways involved during fermentation by Saccharomyces cerevisiae. Fermentation has been shown to improve the flavor of sea buckthorn juice and increase the relative content of bioactive compounds. This study provides novel insights into the metabolic dynamics of sea buckthorn juice following yeast fermentation through metabolomics analysis. These findings could serve as a theoretical foundation for further studies on the factors influencing differences in yeast fermentation.

Simultaneous Multi-Omics Analysis by Direct Infusion Mass Spectrometry (SMAD-MS)

Combined multi-omics analysis of proteomics, polar metabolomics, and lipidomics requires separate liquid chromatography-mass spectrometry (LC-MS) platforms for each omics layer. This requirement for different platforms limits throughput and increases costs, preventing the application of mass spectrometry-based multi-omics to large scale drug discovery or clinical cohorts. Here, we present an innovative strategy for simultaneous multi-omics analysis by direct infusion (SMAD) using one single injection without liquid chromatography. SMAD allows quantification of over 9,000 metabolite m/z features and over 1,300 proteins from the same sample in less than five minutes. We validated the efficiency and reliability of this method and then present two practical applications: mouse macrophage M1/M2 polarization and high throughput drug screening in human 293T cells. Finally, we demonstrate relationships between proteomic and metabolomic data are discovered by machine learning.

'Multi-omics' data integration: applications in probiotics studies

The concept of probiotics is witnessing increasing attention due to its benefits in influencing the host microbiome and the modulation of host immunity through the strengthening of the gut barrier and stimulation of antibodies. These benefits, combined with the need for improved nutraceuticals, have resulted in the extensive characterization of probiotics leading to an outburst of data generated using several 'omics' technologies. The recent development in system biology approaches to microbial science is paving the way for integrating data generated from different omics techniques for understanding the flow of molecular information from one 'omics' level to the other with clear information on regulatory features and phenotypes. The limitations and tendencies of a 'single omics' application to ignore the influence of other molecular processes justify the need for 'multi-omics' application in probiotics selections and understanding its action on the host. Different omics techniques, including genomics, transcriptomics, proteomics, metabolomics and lipidomics, used for studying probiotics and their influence on the host and the microbiome are discussed in this review. Furthermore, the rationale for 'multi-omics' and multi-omics data integration platforms supporting probiotics and microbiome analyses was also elucidated. This review showed that multi-omics application is useful in selecting probiotics and understanding their functions on the host microbiome. Hence, recommend a multi-omics approach for holistically understanding probiotics and the microbiome.

Metabolomics and its applications in assisted reproductive technology

Metabolomics, an emerging omics technology developed in the post-gene age, is an important part of systems biology. It interprets the pathophysiological state of the subject by quantitatively describing the dynamic changes of metabolites through analytical methods, mainly mass spectrometry (MS) and nuclear magnetic resonance (NMR). Assisted reproductive technology (ART) is a method used to manipulate sperm, oocytes, and embryos to achieve conception. Recently, several studies have reported that metabolomics methods can be used to measure metabolites in ART samples; these metabolites can be used to evaluate the quality of gametes and embryos. This article reviews the progress of research on metabolomics and the application of this technology in the field of ART, thus providing a reference for research and development directions in the future.

High-throughput Saccharomyces cerevisiae cultivation method for credentialing-based untargeted metabolomics

Identifying metabolites in model organisms is critical for many areas of biology, including unravelling disease aetiology or elucidating functions of putative enzymes. Even now, hundreds of predicted metabolic genes in Saccharomyces cerevisiae remain uncharacterized, indicating that our understanding of metabolism is far from complete even in well-characterized organisms. While untargeted high-resolution mass spectrometry (HRMS) enables the detection of thousands of features per analysis, many of these have a non-biological origin. Stable isotope labelling (SIL) approaches can serve as credentialing strategies to distinguish biologically relevant features from background signals, but implementing these experiments at large scale remains challenging. Here, we developed a SIL-based approach for high-throughput untargeted metabolomics in S. cerevisiae, including deep-48 well format-based cultivation and metabolite extraction, building on the peak annotation and verification engine (PAVE) tool. Aqueous and nonpolar extracts were analysed using HILIC and RP liquid chromatography, respectively, coupled to Orbitrap Q Exactive HF mass spectrometry. Of the approximately 37,000 total detected features, only 3-7% of the features were credentialed and used for data analysis with open-source software such as MS-DIAL, MetFrag, Shinyscreen, SIRIUS CSI:FingerID, and MetaboAnalyst, leading to the successful annotation of 198 metabolites using MS² database matching. Comparable metabolic profiles were observed for wild-type and sdh1Δ yeast strains grown in deep-48 well plates versus the classical shake flask format, including the expected increase in intracellular succinate concentration in the sdh1Δ strain. The described approach enables high-throughput yeast cultivation and credentialing-based untargeted metabolomics, providing a means to efficiently perform molecular phenotypic screens and help complete metabolic networks.

UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC-MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets.

Qualitative Analysis of Polyphenols in Glycerol Plant Extracts Using Untargeted Metabolomics

Glycerol is a reliable solvent for extracting polyphenols from food and waste products. There has been an increase in the application of glycerol over benchmark alcoholic solvents such as ethanol and methanol for natural product generation because of its non-toxic nature and high extraction efficiency. However, plant extracts containing a high glycerol concentration are unsuitable for mass spectrometry-based investigation utilising electrospray ionization, inhibiting the ability to analyse compounds of interest. In this investigation, a solid phase extraction protocol is outlined for removing glycerol from plant extracts containing a high concentration of glycerol and their subsequent analysis of polyphenols using ultra-performance liquid chromatography coupled with quadrupole time of flight tandem mass spectrometry. Using this method, glycerol-based extracts of Queen Garnet Plum (Prunus salicina) were investigated and compared to ethanolic extracts. Anthocyanins and flavonoids in high abundance were found in both glycerol and ethanol extracts. The polyphenol metabolome of Queen Garnet Plum was 53% polyphenol glycoside derivatives and 47% polyphenols in their aglycone forms. Furthermore, 56% of the flavonoid derivates were found to be flavonoid glycosides, and 44% were flavonoid aglycones. In addition, two flavonoid glycosides not previously found in Queen Garnet Plum were putatively identified: Quercetin-3-O-xyloside and Quercetin-3-O-rhamnoside.

A Strategy for Uncovering the Serum Metabolome by Direct-Infusion High-Resolution Mass Spectrometry

Direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) is a promising tool for high-throughput metabolomics analysis. However, metabolite assignment is limited by the inadequate mass accuracy and chemical space of the metabolome database. Here, a serum metabolome characterization method was proposed to make full use of the potential of DI-nESI-HRMS. Different from the widely used database search approach, unambiguous formula assignments were achieved by a reaction network combined with mass accuracy and isotopic patterns filter. To provide enough initial known nodes, an initial network was directly constructed by known metabolite formulas. Then experimental formula candidates were screened by the predefined reaction with the network. The effects of sources and scales of networks on assignment performance were investigated. Further, a scoring rule for filtering unambiguous formula candidates was proposed. The developed approach was validated by a pooled serum sample spiked with reference standards. The coverage and accuracy rates for the spiked standards were 98.9% and 93.6%, respectively. A total of 1958 monoisotopic features were assigned with unique formula candidates for the pooled serum, which is twice more than the database search. Finally, a case study of serum metabolomics in diabetes was carried out using the developed method.

Metabolite profiling and identification in living cells by coupling stable isotope tracing and induced electrospray mass spectrometry

Direct observation of metabolites in living cells by mass spectrometry offers a bright future for biological studies but also suffers a severe challenge to untargeted peak assignment to tentative metabolite candidates. In this study, we developed a method combining stable isotope tracing and induced electrospray mass spectrometry for living-cells metabolite measurement and identification. By using 13C6-glucose and ammonium chloride-15N as the sole carbon and nitrogen sources for cell culture, Escherichia coli synthesized metabolites with 15N and 13C elements. Tracing the number of carbon and nitrogen atoms could offer a complementary dimension for candidate peak searching. As a result, the identification confidence of metabolites achieved a universal improvement based on carbon/nitrogen labelling and filtration.

Data Processing and Analysis in Mass Spectrometry-Based Metabolomics

Metabolomics is the latest of the omics sciences. It attempts to measure and characterize metabolites-small chemical compounds <1500 Da-on cells, tissue, or biofluids, which are usually products of biological reactions. As metabolic reactions are closer to the phenotype, metabolomics has emerged as an attractive science for various areas of research, including personalized medicine. However, due to the complexity of data obtained and the absence of curated databases for metabolite identification, data processing is the major bottleneck in this area since most technicians lack the required bioinformatics expertise to process datasets in a reliable and fast manner. The aim of this chapter is to describe the available tools for data processing that makes an inexperienced researcher capable of obtaining reliable results without having to undergo through huge parametrization steps.

EASY-FIA: A Readably Usable Standalone Tool for High-Resolution Mass Spectrometry Metabolomics Data Pre-Processing

Flow injection analysis coupled with high-resolution mass spectrometry (FIA-HRMS) is a fair trade-off between resolution and speed. However, free software available for data pre-processing is few, web-based, and often requires advanced user specialization. These tools rarely embedded blank and noise evaluation strategies, and direct feature annotation. We developed EASY-FIA, a free standalone application that can be employed for FIA-HRMS metabolomic data pre-processing by users with no bioinformatics/programming skills. We validated the tool's performance and applicability in two clinical metabolomics case studies. The main functions of our application are blank subtraction, alignment of the metabolites, and direct feature annotation by means of the Human Metabolome Database (HMDB) using a minimum number of mass spectrometry parameters. In a scenario where FIA-HRMS is increasingly recognized as a reliable strategy for fast metabolomics analysis, EASY-FIA could become a standardized and feasible tool easily usable by all scientists dealing with MS-based metabolomics. EASY-FIA was implemented in MATLAB with the App Designer tool and it is freely available for download.

Machine learning alternative to systems biology should not solely depend on data

In recent years, artificial intelligence (AI)/machine learning has emerged as a plausible alternative to systems biology for the elucidation of biological phenomena and in attaining specified design objective in synthetic biology. Although considered highly disruptive with numerous notable successes so far, we seek to bring attention to both the fundamental and practical pitfalls of their usage, especially in illuminating emergent behaviors from chaotic or stochastic systems in biology. Without deliberating on their suitability and the required data qualities and pre-processing approaches beforehand, the research and development community could experience similar 'AI winters' that had plagued other fields. Instead, we anticipate the integration or combination of the two approaches, where appropriate, moving forward.

A Direct Infusion Probe for Rapid Metabolomics of Low-Volume Samples

Targeted and nontargeted metabolomics has the potential to evaluate and detect global metabolite changes in biological systems. Direct infusion mass spectrometric analysis enables detection of all ionizable small molecules, thus simultaneously providing information on both metabolites and lipids in chemically complex samples. However, to unravel the heterogeneity of the metabolic status of cells in culture and tissue a low number of cells per sample should be analyzed with high sensitivity, which requires low sample volumes. Here, we present the design and characterization of the direct infusion probe, DIP. The DIP is simple to build and position directly in front of a mass spectrometer for rapid metabolomics of chemically complex biological samples using pneumatically assisted electrospray ionization at 1 μL/min flow rate. The resulting data is acquired in a square wave profile with minimal carryover between samples that enhances throughput and enables several minutes of uniform MS signal from 5 μL sample volumes. The DIP was applied to study the intracellular metabolism of insulin secreting INS-1 cells and the results show that exposure to 20 mM glucose for 15 min significantly alters the abundance of several small metabolites, amino acids, and lipids.

Metabolomics as a Prospective Tool for Soybean (Glycine max) Crop Improvement

Global demand for soybean and its products has stimulated research into the production of novel genotypes with higher yields, greater drought and disease tolerance, and shorter growth times. Genetic research may be the most effective way to continue developing high-performing cultivars with desirable agronomic features and improved nutritional content and seed performance. Metabolomics, which predicts the metabolic marker for plant performance under stressful conditions, is rapidly gaining interest in plant breeding and has emerged as a powerful tool for driving crop improvement. The development of increasingly sensitive, automated, and high-throughput analytical technologies, paired with improved bioinformatics and other omics techniques, has paved the way for wide characterization of genetic characteristics for crop improvement. The combination of chromatography (liquid and gas-based) with mass spectrometry has also proven to be an indisputable efficient platform for metabolomic studies, notably plant metabolic fingerprinting investigations. Nevertheless, there has been significant progress in the use of nuclear magnetic resonance (NMR), capillary electrophoresis, and Fourier-transform infrared spectroscopy (FTIR), each with its own set of benefits and drawbacks. Furthermore, utilizing multivariate analysis, principal components analysis (PCA), discriminant analysis, and projection to latent structures (PLS), it is possible to identify and differentiate various groups. The researched soybean varieties may be correctly classified by using the PCA and PLS multivariate analyses. As metabolomics is an effective method for evaluating and selecting wild specimens with desirable features for the breeding of improved new cultivars, plant breeders can benefit from the identification of metabolite biomarkers and key metabolic pathways to develop new genotypes with value-added features.

Multi-Omics Approaches to Study Molecular Mechanisms in Cannabis sativa

Cannabis (Cannabis sativa L.), also known as hemp, is one of the oldest cultivated crops, grown for both its use in textile and cordage production, and its unique chemical properties. However, due to the legislation regulating cannabis cultivation, it is not a well characterized crop, especially regarding molecular and genetic pathways. Only recently have regulations begun to ease enough to allow more widespread cannabis research, which, coupled with the availability of cannabis genome sequences, is fuelling the interest of the scientific community. In this review, we provide a summary of cannabis molecular resources focusing on the most recent and relevant genomics, transcriptomics and metabolomics approaches and investigations. Multi-omics methods are discussed, with this combined approach being a powerful tool to identify correlations between biological processes and metabolic pathways across diverse omics layers, and to better elucidate the relationships between cannabis sub-species. The correlations between genotypes and phenotypes, as well as novel metabolites with therapeutic potential are also explored in the context of cannabis breeding programs. However, further studies are needed to fully elucidate the complex metabolomic matrix of this crop. For this reason, some key points for future research activities are discussed, relying on multi-omics approaches.

Evaluating and minimizing batch effects in metabolomics

Determining metabolomic differences among samples of different phenotypes is a critical component of metabolomics research. With the rapid advances in analytical tools such as ultrahigh-resolution chromatography and mass spectrometry, an increasing number of metabolites can now be profiled with high quantification accuracy. The increased detectability and accuracy raise the level of stringiness required to reduce or control any experimental artifacts that can interfere with the measurement of phenotype-related metabolome changes. One of the artifacts is the batch effect that can be caused by multiple sources. In this review, we discuss the origins of batch effects, approaches to detect interbatch variations, and methods to correct unwanted data variability due to batch effects. We recognize that minimizing batch effects is currently an active research area, yet a very challenging task from both experimental and data processing perspectives. Thus, we try to be critical in describing the performance of a reported method with the hope of stimulating further studies for improving existing methods or developing new methods.

Study on toxicity effects of environmental pollutants based on metabolomics: A review

In the past few decades, the toxic effects of environmental pollutants on non-target organisms have received more and more attention. As a new omics technology, metabolomics can clarify the metabolic homeostasis of the organism at the overall level by studying the changes in the relative contents of endogenous metabolites in the organism. Recently, a large number of studies have used metabolomics technology to study the toxic effects of environmental pollutants on organisms. In this review, we reviewed the analysis processes and data processes of metabolomics and its application in the study of the toxic effects of environmental pollutants including heavy metals, pesticides, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, polybrominated diphenyl ethers and microplastics. In addition, we emphasized that the combination of metabolomics and other omics technologies will help to explore the toxic mechanism of environmental pollutants and provide new research ideas for the toxicological evaluation of environmental pollutants.

Dimethylcysteine (DiCys)/o-Phthalaldehyde Derivatization for Chiral Metabolite Analyses: Cross-Comparison of Six Chiral Thiols

Metabolomics profiling using liquid chromatography-mass spectrometry (LC-MS) has become an important tool in biomedical research. However, resolving enantiomers still represents a significant challenge in the metabolomics study of complex samples. Here, we introduced N,N-dimethyl-l-cysteine (dimethylcysteine, DiCys), a chiral thiol, for the o-phthalaldehyde (OPA) derivatization of enantiomeric amine metabolites. We took interest in DiCys because of its potential for multiplex isotope-tagged quantification. Here, we characterized the usefulness of DiCys in reversed-phase LC-MS analyses of chiral metabolites, compared against five commonly used chiral thiols: N-acetyl-l-cysteine (NAC); N-acetyl-d-penicillamine (NAP); isobutyryl-l-cysteine (IBLC); N-(tert-butoxycarbonyl)-l-cysteine methyl ester (NBC); and N-(tert-butylthiocarbamoyl)-l-cysteine ethyl ester (BTCC). DiCys and IBLC showed the best overall performance in terms of chiral separation, fluorescence intensity, and ionization efficiency. For chiral separation of amino acids, DiCys/OPA also outperformed Marfey’s reagents: 1-fluoro-2-4-dinitrophenyl-5-l-valine amide (FDVA) and 1-fluoro-2-4-dinitrophenyl-5-l-alanine amide (FDAA). As proof of principle, we compared DiCys and IBLC for detecting chiral metabolites in aqueous extracts of rice. By LC–MS analyses, both methods detected twenty proteinogenic l-amino acids and seven d-amino acids (Ala, Arg, Lys, Phe, Ser, Tyr, and Val), but DiCys showed better analyte separation. We conclude that DiCys/OPA is an excellent amine-derivatization method for enantiomeric metabolite detection in LC-MS analyses.

Exploration study on serum metabolic profiles of Chinese male patients with artificial stone silicosis, silicosis, and coal worker's pneumoconiosis

Long-term exposure to inhaled silica dust induces pneumoconiosis, which remains a heavy burden in developing countries. Modern industry provides new resources of occupational SiO₂ leading to artificial stone silicosis especially in developed countries. This study aimed to characterize the serum metabolic profile of pneumoconiosis and artificial stone silicosis patients. Our case-control study recruited 46 pairs of pneumoconiosis patients and dust-exposed workers. Nontargeted metabolomics and lipidomics by ultra-high-performance liquid chromatography-tandem mass spectrometry platform were conducted to characterize serum metabolic profile in propensity score-matched (PSM) pilot study. 54 differential metabolites were screened, 24 of which showed good screening efficiency through receiver operating characteristics (ROC) in pilot study and validation study (both AUC > 0.75). 4 of the 24 metabolites can predict pneumoconiosis stages, which are 1,2-dioctanoylthiophosphatidylcholine, phosphatidylcholine(O-18:1/20:1), indole-3-acetamide and l-homoarginine. Kynurenine, N-tetradecanoylsphingosine 1-phosphate, 5-methoxytryptophol and phosphatidylethanolamine(22:6/18:1) displayed the potential as specific biomarkers for artificial stone silicosis. Taken together, our results confirmed that tryptophan metabolism is closely related to pneumoconiosis and may be related to disease progression. Hopefully, our results could supplement the biomarkers of pneumoconiosis and provide evidence for the discovery of artificial stone silicosis-specific biomarkers.

New Advances in Tissue Metabolomics: A Review

Metabolomics offers a hypothesis-generating approach for biomarker discovery in clinical medicine while also providing better understanding of the underlying mechanisms of chronic diseases. Clinical metabolomic studies largely rely on human biofluids (e.g., plasma, urine) as a more convenient specimen type for investigation. However, biofluids are non-organ specific reflecting complex biochemical processes throughout the body, which may complicate biochemical interpretations. For these reasons, tissue metabolomic studies enable deeper insights into aberrant metabolism occurring at the direct site of disease pathogenesis. This review highlights new advances in metabolomics for ex vivo analysis, as well as in situ imaging of tissue specimens, including diverse tissue types from animal models and human participants. Moreover, we discuss key pre-analytical and post-analytical challenges in tissue metabolomics for robust biomarker discovery with a focus on new methodological advances introduced over the past six years, including innovative clinical applications for improved screening, diagnostic testing, and therapeutic interventions for cancer.

Metabolomics Approaches for the Comprehensive Evaluation of Fermented Foods: A Review

Fermentation is an important process that can provide new flavors and nutritional and functional foods, to deal with changing consumer preferences. Fermented foods have complex chemical components that can modulate unique qualitative properties. Consequently, monitoring the small molecular metabolites in fermented food is critical to clarify its qualitative properties and help deliver personalized nutrition. In recent years, the application of metabolomics to nutrition research of fermented foods has expanded. In this review, we examine the application of metabolomics technologies in food, with a primary focus on the different analytical approaches suitable for food metabolomics and discuss the advantages and disadvantages of these approaches. In addition, we summarize emerging studies applying metabolomics in the comprehensive analysis of the flavor, nutrition, function, and safety of fermented foods, as well as emphasize the applicability of metabolomics in characterizing the qualitative properties of fermented foods.

Current progress in metabolomics of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders of pregnancy and can cause short- and long-term adverse effects in both pregnant women and their offspring. However, the etiology and pathogenesis of GDM are still unclear. As a metabolic disease, GDM is well suited to metabolomics study, which can monitor the changes in small molecular metabolites induced by maternal stimuli or perturbations in real time. The application of metabolomics in GDM can be used to discover diagnostic biomarkers, evaluate the prognosis of the disease, guide the application of diet or drugs, evaluate the curative effect, and explore the mechanism. This review provides comprehensive documentation of metabolomics research methods and techniques as well as the current progress in GDM research. We anticipate that the review will contribute to identifying gaps in the current knowledge or metabolomics technology, provide evidence-based information, and inform future research directions in GDM.

Metabolomics-based predictive biomarkers of radiation injury and countermeasure efficacy: current status and future perspectives

INTRODUCTION

There is an urgent need for specific and sensitive bioassays to augment biodosimetric assessments of unwanted and excessive radiation exposures that originate from unexpected nuclear/radiological events, including nuclear accidents, acts of terrorism, or the use of a radiological dispersal device. If sufficiently intense, such ionizing radiation exposures are likely to impact normal metabolic processes within the cells and organs of the body, thus inducing multifaceted biological responses.

AREAS COVERED

This review covers the application of metabolomics, an emerging and promising technology based on quantitative and qualitative determinations of small molecules in biological samples for the rapid assessment of an individual's exposure to ionizing radiation. Recent advancements in the analytics of high-resolution chromatography, mass spectrometry, and bioinformatics have led to untargeted (global) and targeted (quantitative phase) approaches to identify biomarkers of radiation injury and countermeasure efficacy. Biomarkers are deemed essential for both assessing the radiation exposure levels and for extrapolative processes involved in determining scaling factors of a given radiation countering medicinal between experimental animals and humans.

EXPERT OPINION

The discipline of metabolomics appears to be highly informative in assessing radiation exposure levels and for identifying biomarkers of radiation injury and countermeasure efficacy.

Untargeted and targeted gingival metabolome in rodents reveal metabolic links between high-fat diet-induced obesity and periodontitis

AIM

To characterize gingival metabolome in high-fat diet (HFD)-induced obesity in mice with/without periodontitis.

METHODS

HFD-induced obesity mouse model was established by 16-week feeding, and a lean control group was fed with low-fat diet (n = 21/group). Both models were induced for periodontitis on the left sides by molar ligation for 10 days, whereas the right sides were used as controls. Gingival metabolome and arginine metabolism were analysed by non-targeted/targeted liquid chromatography-mass spectrometry.

RESULTS

Of 2247 reference features, presence of periodontitis altered 165 in lean versus 885 in HFD mice; and HFD altered 525 in absence versus 1435 in presence of periodontitis. Compared with healthy condition, periodontitis and HFD had distinct effects on gingival metabolome. Metabolomic impacts of periodontitis were generally greater in HFD mice versus lean controls. K-medoids clustering showed that HFD amplified the impacts of periodontitis on gingival metabolome in both intensity and extensity. Ten metabolic pathways were enriched, including 2 specific to periodontitis, 5 specific to HFD and 3 shared ones. Targeted validation on arginine metabolism confirmed the additive effects between HFD and periodontitis.

CONCLUSION

The obese population consuming excessive HFD display amplified metabolic response to periodontitis, presenting a metabolic susceptibility to exacerbated periodontal destruction.

Technologies for High-Throughput Identification of Antibiotic Mechanism of Action

There are two main strategies for antibiotic discovery: target-based and phenotypic screening. The latter has been much more successful in delivering first-in-class antibiotics, despite the major bottleneck of delayed Mechanism-of-Action (MOA) identification. Although finding new antimicrobial compounds is a very challenging task, identifying their MOA has proven equally challenging. MOA identification is important because it is a great facilitator of lead optimization and improves the chances of commercialization. Moreover, the ability to rapidly detect MOA could enable a shift from an activity-based discovery paradigm towards a mechanism-based approach. This would allow to probe the grey chemical matter, an underexplored source of structural novelty. In this study we review techniques with throughput suitable to screen large libraries and sufficient sensitivity to distinguish MOA. In particular, the techniques used in chemical genetics (e.g., based on overexpression and knockout/knockdown collections), promoter-reporter libraries, transcriptomics (e.g., using microarrays and RNA sequencing), proteomics (e.g., either gel-based or gel-free techniques), metabolomics (e.g., resourcing to nuclear magnetic resonance or mass spectrometry techniques), bacterial cytological profiling, and vibrational spectroscopy (e.g., Fourier-transform infrared or Raman scattering spectroscopy) were discussed. Ultimately, new and reinvigorated phenotypic assays bring renewed hope in the discovery of a new generation of antibiotics.

The maternal serum metabolome by multisegment injection-capillary electrophoresis-mass spectrometry: a high-throughput platform and standardized data workflow for large-scale epidemiological studies

A standardized data workflow is described for large-scale serum metabolomic studies using multisegment injection-capillary electrophoresis-mass spectrometry. Multiplexed separations increase throughput (<4 min/sample) for quantitative determination of 66 polar/ionic metabolites in serum filtrates consistently detected (coefficient of variance (CV) <30%) with high frequency (>75%) from a multi-ethnic cohort of pregnant women (n = 1,004). We outline a validated protocol implemented in four batches over a 7-month period that includes details on preventive maintenance, sample workup, data preprocessing and metabolite authentication. We achieve stringent quality control (QC) and robust batch correction of long-term signal drift with good mutual agreement for a wide range of metabolites, including serum glucose as compared to a clinical chemistry analyzer (mean bias = 11%, n = 668). Control charts for a recovery standard (mean CV = 12%, n = 2,412) and serum metabolites in QC samples (median CV = 13%, n = 202) demonstrate acceptable intermediate precision with a median intraclass coefficient of 0.87. We also report reference intervals for 53 serum metabolites from a diverse population of women in their second trimester of pregnancy.

Methods for Root Exudate Collection and Analysis

Plant root exudation has long been recognized as a vital communication system between plants and microbial communities populating the rhizosphere. Due to the high complexity of the collection process and analysis, a variety of techniques have been developed to mimic natural exudation conditions. In addition, significant progress improving existing techniques and developing new methodologies of root exudate collection and analysis have been made. However, optimal standard methods that compare closely with environmental soil conditions are not yet available. In this review, we provide an overview of all those topics and provide suggestions for improvement.

DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.