Promises and pitfalls of untargeted metabolomics

Sansevieria trifasciata's specific metabolite improves tolerance and efficiency for particulate matter and volatile organic compound removal

Phytoremediation has become famous for removing particulate matter (PM) and volatile organic compounds (VOCs), but the ability is affected by plant health. Lately, the priming technique was a simple approach to studying improving plant tolerance against abiotic stress by specific metabolites that accumulated, known as "memory", but the mechanism underlying this mechanism and how long this "memory" was retained in the plant was a lack of study. Sansevieria trifasciata was primed for one week for PM and VOC stress to improve plant efficiency on PM and VOC. After that, the plant was recovered for two- or five-weeks, then re-exposed to the same stress with similar PM and VOC concentrations from cigarette smoke. Primed S. trifasciata showed improved removal of PMs entirely within 2 h and VOC within 24 h. The primed plant can maintain a malondialdehyde (MDA) level and retain the "memory" for two weeks. Metabolomics analysis showed that an ornithine-related compound was accumulated as a responsive metabolite under exposure to PM and VOC stress. Exogenous ornithine can maintain plant efficiency and prevent stress by increasing proline and antioxidant enzymes. This study is the first to demonstrate plant "memory" mechanisms under PM and VOC stress.

Mass spectrometry-based proteomic approaches for salivary protein biomarkers discovery and dental caries diagnosis: A critical review

Dental caries is a multifactorial chronic disease resulting from the intricate interplay among acid-generating bacteria, fermentable carbohydrates, and several host factors such as saliva. Saliva comprises several proteins which could be utilized as biomarkers for caries prevention, diagnosis, and prognosis. Mass spectrometry-based salivary proteomics approaches, owing to their sensitivity, provide the opportunity to investigate and unveil crucial cariogenic pathogen activity and host indicators and may demonstrate clinically relevant biomarkers to improve caries diagnosis and management. The present review outlines the published literature of human clinical proteomics investigations on caries and extensively elucidates frequently reported salivary proteins as biomarkers. This review also discusses important aspects while designing an experimental proteomics workflow. The protein-protein interactions and the clinical relevance of salivary proteins as biomarkers for caries, together with uninvestigated domains of the discipline are also discussed critically.

Optimization and validation of metabolomics methods for feline urine and serum towards application in veterinary medicine

BACKGROUND

Metabolomics is an emerging and powerful technology that offers a comprehensive view of an organism's physiological status. Although widely applied in human medicine, it is only recently making its introduction in veterinary medicine. As a result, validated metabolomics protocols in feline medicine are lacking at the moment. Since biological interpretation of metabolomics data can be misled by the extraction method used, species and matrix-specific optimized and validated metabolomic protocols are sorely needed.

RESULTS

Systematic optimization was performed using fractional factorial experiments for both serum (n = 57) and urine (n = 24), evaluating dilution for both matrices, and aliquot and solvent volume, protein precipitation time and temperature for serum. For the targeted (n = 76) and untargeted (n = 1949) validation of serum respectively, excellent instrumental, intra-assay and inter-day precision were observed (CV ≤ 15% or 30%, respectively). Linearity deemed sufficient both targeted and untargeted (R2 ≥ 0.99 or 0.90, respectively). An appropriate targeted recovery between 70 and 130% was achieved. For the targeted (n = 69) and untargeted (n = 2348) validation of the urinary protocol, excellent instrumental and intra-assay precision were obtained (CV ≤ 15% or 30%, respectively). Subsequently, the discriminative ability of our metabolomics methods was confirmed for feline chronic kidney disease (CKD) by univariate statistics (n = 41 significant metabolites for serum, and n = 55 for urine, p-value<0.05) and validated OPLS-DA models (R2(Y) > 0.95, Q2(Y) > 0.65, p-value<0.001 for both matrices).

SIGNIFICANCE

This study is the first to present an optimized and validated wholistic metabolomics methods for feline serum and urine using ultra-high performance liquid chromatography coupled to quadrupole-Orbitrap high-resolution mass spectrometry. This robust methodology opens avenues for biomarker panel selection and a deeper understanding of feline CKD pathophysiology and other feline applications.

Bile acids and the gut microbiome are involved in the hyperthermia mediated by 3,4-methylenedioxymethamphetamine (MDMA)

Hyperthermia induced by phenethylamines, such as 3,4-methylenedioxymethamphetamine (MDMA), can lead to life-threatening complications and death. Activation of the sympathetic nervous system and subsequent release of norepinephrine and activation of uncoupling proteins have been demonstrated to be the key mediators of phenethylamine-induced hyperthermia (PIH). Recently, the gut microbiome was shown to also play a contributing role in PIH. Here, the hypothesis that bile acids (BAs) produced by the gut microbiome are essential to PIH was tested. Changes in the serum concentrations of unconjugated primary BAs cholic acid (CA) and chenodeoxycholic acid (CDCA) and secondary BA deoxycholic acid (DCA) were measured following MDMA (20 mg/kg, sc) treatment in antibiotic treated and control rats. MDMA-induced a significant hyperthermic response and reduced the serum concentrations of three BAs 60 min post-treatment. Pretreatment with antibiotics (vancomycin, bacitracin and neomycin) in the drinking water for five days resulted in the depletion of BAs and a hypothermic response to MDMA. Gut bacterial communities in the antibiotic-treated group were distinct from the MDMA or saline treatment groups, with decreased microbiome diversity and alteration in taxa. Metagenomic functions inferred using the bioinformatic tool PICRUSt2 on 16S rRNA gene sequences indicated that bacterial genes associated to BA metabolism are less abundant in the antibiotic-MDMA treated group. Overall, these findings suggest that gut bacterial produced BAs might play an important role in MDMA-induced hyperthermia.

Heavy metal exposure and metabolomics analysis: an emerging frontier in environmental health

Exposure to heavy metals in various populations can lead to extensive damage to different organs, as these metals infiltrate and bioaccumulate in the human body, causing metabolic disruptions in various organs. To comprehensively understand the metal homeostasis, inter-organ "traffic," and extensive metabolic alterations resulting from heavy metal exposure, employing complementary analytical methods is crucial. Metabolomics is pivotal in unraveling the intricacies of disease vulnerability by furnishing thorough understandings of metabolic changes linked to different metabolic diseases. This field offers exciting prospects for enhancing the disease prevention, early detection, and tailoring treatment approaches to individual needs. This article consolidates the existing knowledge on disease-linked metabolic pathways affected by the exposure of diverse heavy metals providing concise overview of the underlying impact mechanisms. The main aim is to investigate the connection between the altered metabolic pathways and long-term complex health conditions induced by heavy metals such as diabetes mellitus, cardiovascular diseases, renal disorders, inflammation, neurodegenerative diseases, reproductive risks, and organ damage. Further exploration of common pathways may unveil the shared targets for treating associated pathological conditions. In this article, the role of metabolomics in disease susceptibility is emphasized that metabolomics is expected to be routinely utilized for the diagnosis and monitoring of diseases and practical value of biomarkers derived from metabolomics, as well as determining their appropriate integration into extensive clinical settings.

Utilising a Clinical Metabolomics LC-MS Study to Determine the Integrity of Biological Samples for Statistical Modelling after Long Term -80 °C Storage: A TOFI_Asia Sub-Study

Biological samples of lipids and metabolites degrade after extensive years in -80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of -80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS-DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of -80 °C storage. With receiver operator characteristic curves, sparse PLS-DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at -80 °C when producing predictive metrics from polar metabolites, more so than lipids.

Web-based multi-omics integration using the Analyst software suite

The growing number of multi-omics studies demands clear conceptual workflows coupled with easy-to-use software tools to facilitate data analysis and interpretation. This protocol covers three key components involved in multi-omics analysis, including single-omics data analysis, knowledge-driven integration using biological networks and data-driven integration through joint dimensionality reduction. Using the dataset from a recent multi-omics study of human pancreatic islet tissue and plasma samples, the first section introduces how to perform transcriptomics/proteomics data analysis using ExpressAnalyst and lipidomics data analysis using MetaboAnalyst. On the basis of significant features detected in these workflows, the second section demonstrates how to perform knowledge-driven integration using OmicsNet. The last section illustrates how to perform data-driven integration from the normalized omics data and metadata using OmicsAnalyst. The complete protocol can be executed in ~2 h. Compared with other available options for multi-omics integration, the Analyst software suite described in this protocol enables researchers to perform a wide range of omics data analysis tasks via a user-friendly web interface.

Neighbour-induced changes in root exudation patterns of buckwheat results in altered root architecture of redroot pigweed

Roots are crucial in plant adaptation through the exudation of various compounds which are influenced and modified by environmental factors. Buckwheat root exudate and root system response to neighbouring plants (buckwheat or redroot pigweed) and how these exudates affect redroot pigweed was investigated. Characterising root exudates in plant-plant interactions presents challenges, therefore a split-root system which enabled the application of differential treatments to parts of a single root system and non-destructive sampling was developed. Non-targeted metabolome profiling revealed that neighbour presence and identity induces systemic changes. Buckwheat and redroot pigweed neighbour presence upregulated 64 and 46 metabolites, respectively, with an overlap of only 7 metabolites. Root morphology analysis showed that, while the presence of redroot pigweed decreased the number of root tips in buckwheat, buckwheat decreased total root length and volume, surface area, number of root tips, and forks of redroot pigweed. Treatment with exudates (from the roots of buckwheat and redroot pigweed closely interacting) on redroot pigweed decreased the total root length and number of forks of redroot pigweed seedlings when compared to controls. These findings provide understanding of how plants modify their root exudate composition in the presence of neighbours and how this impacts each other's root systems.

Electron-activated dissociation (EAD) for the complementary annotation of metabolites and lipids through data-dependent acquisition analysis and feature-based molecular networking, applied to the sentinel amphipod Gammarus fossarum

The past decades have marked the rise of metabolomics and lipidomics as the -omics sciences which reflect the most phenotypes in living systems. Mass spectrometry-based approaches are acknowledged for both quantification and identification of molecular signatures, the latter relying primarily on fragmentation spectra interpretation. However, the high structural diversity of biological small molecules poses a considerable challenge in compound annotation. Feature-based molecular networking (FBMN) combined with database searches currently sets the gold standard for annotation of large datasets. Nevertheless, FBMN is usually based on collision-induced dissociation (CID) data, which may lead to unsatisfying information. The use of alternative fragmentation methods, such as electron-activated dissociation (EAD), is undergoing a re-evaluation for the annotation of small molecules, as it gives access to additional fragmentation routes. In this study, we apply the performances of data-dependent acquisition mass spectrometry (DDA-MS) under CID and EAD fragmentation along with FBMN construction, to perform extensive compound annotation in the crude extracts of the freshwater sentinel organism Gammarus fossarum. We discuss the analytical aspects of the use of the two fragmentation modes, perform a general comparison of the information delivered, and compare the CID and EAD fragmentation pathways for specific classes of compounds, including previously unstudied species. In addition, we discuss the potential use of FBMN constructed with EAD fragmentation spectra to improve lipid annotation, compared to the classic CID-based networks. Our approach has enabled higher confidence annotations and finer structure characterization of 823 features, including both metabolites and lipids detected in G. fossarum extracts.

Functional genomics and small molecules in mitochondrial neurodevelopmental disorders

Mitochondria are critical for brain development and homeostasis. Therefore, pathogenic variation in the mitochondrial or nuclear genome which disrupts mitochondrial function frequently results in developmental disorders and neurodegeneration at the organismal level. Large-scale application of genome-wide technologies to individuals with mitochondrial diseases has dramatically accelerated identification of mitochondrial disease-gene associations in humans. Multi-omic and high-throughput studies involving transcriptomics, proteomics, metabolomics, and saturation genome editing are providing deeper insights into the functional consequence of mitochondrial genomic variation. Integration of deep phenotypic and genomic data through allelic series continues to uncover novel mitochondrial functions and permit mitochondrial gene function dissection on an unprecedented scale. Finally, mitochondrial disease-gene associations illuminate disease mechanisms and thereby direct therapeutic strategies involving small molecules and RNA-DNA therapeutics. This review summarizes progress in functional genomics and small molecule therapeutics in mitochondrial neurodevelopmental disorders.

Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types

Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available. Metabolomics has recently emerged as a promising field for developing novel tumor biomarkers. In this paper, we introduce metabolic pathways, instrument platforms, and a wide variety of sample types for lung cancer metabolomics. Specifically, we explore the strengths, limitations, and distinguishing features of various sample types employed in lung cancer metabolomics research. Additionally, we present the latest advances in lung cancer metabolomics research that utilize diverse sample types. We summarize and enumerate research studies that have investigated lung cancer metabolomics using different metabolomic sample types. Finally, we provide a perspective on the future of metabolomics research in lung cancer. Our discussion of the potential of metabolomics in developing new tumor biomarkers may inspire further study and innovation in this dynamic field.

Methylprednisolone therapy induces differential metabolic trajectories in severe COVID-19 patients

IMPORTANCE

The SARS-CoV-2 virus infection in humans induces significant inflammatory and systemic reactions and complications of which corticosteroids like methylprednisolone have been recommended as treatment. Our understanding of the metabolic and metabolomic pathway dysregulations while using intravenous corticosteroids in COVID-19 is limited. This study will help enlighten the metabolic and metabolomic pathway dysregulations underlying high daily doses of intravenous methylprednisolone in COVID-19 patients compared to those receiving placebo. The information on key metabolites and pathways identified in this study together with the crosstalk with the inflammation and biochemistry components may be used, in the future, to leverage the use of methylprednisolone in any future pandemics from the coronavirus family.

Generalized metabolic flux analysis framework provides mechanism-based predictions of ophthalmic complications in type 2 diabetes patients

Chronic metabolic diseases arise from changes in metabolic fluxes through biomolecular pathways and gene networks accumulated over the lifetime of an individual. While clinical and biochemical profiles present just real-time snapshots of the patients' health, efficient computation models of the pathological disturbance of biomolecular processes are required to achieve individualized mechanistic insights into disease progression. Here, we describe the Generalized metabolic flux analysis (GMFA) for addressing this gap. Suitably grouping individual metabolites/fluxes into pools simplifies the analysis of the resulting more coarse-grain network. We also map non-metabolic clinical modalities onto the network with additional edges. Instead of using the time coordinate, the system status (metabolite concentrations and fluxes) is quantified as function of a generalized extent variable (a coordinate in the space of generalized metabolites) that represents the system's coordinate along its evolution path and evaluates the degree of change between any two states on that path. We applied GMFA to analyze Type 2 Diabetes Mellitus (T2DM) patients from two cohorts: EVAS (289 patients from Singapore) and NHANES (517) from the USA. Personalized systems biology models (digital twins) were constructed. We deduced disease dynamics from the individually parameterized metabolic network and predicted the evolution path of the metabolic health state. For each patient, we obtained an individual description of disease dynamics and predict an evolution path of the metabolic health state. Our predictive models achieve an ROC-AUC in the range 0.79-0.95 (sensitivity 80-92%, specificity 62-94%) in identifying phenotypes at the baseline and predicting future development of diabetic retinopathy and cataract progression among T2DM patients within 3 years from the baseline. The GMFA method is a step towards realizing the ultimate goal to develop practical predictive computational models for diagnostics based on systems biology. This tool has potential use in chronic disease management in medical practice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13755-023-00218-x.

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.

Lipidomics profiles of human spermatozoa: insights into capacitation and acrosome reaction using UPLC-MS-based approach

Introduction

Lipidomics elucidates the roles of lipids in both physiological and pathological processes, intersecting with many diseases and cellular functions. The maintenance of lipid homeostasis, essential for cell health, significantly influences the survival, maturation, and functionality of sperm during fertilization. While capacitation and the acrosome reaction, key processes before fertilization, involve substantial lipidomic alterations, a comprehensive understanding of the changes in human spermatozoa's lipidomic profiles during these processes remains unknown. This study aims to explicate global lipidomic changes during capacitation and the acrosome reaction in human sperm, employing an untargeted lipidomic strategy using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS).

Methods

Twelve semen specimens, exceeding the WHO reference values for semen parameters, were collected. After discontinuous density gradient separation, sperm concentration was adjusted to 2 x 106 cells/ml and divided into three groups: uncapacitated, capacitated, and acrosome-reacted. UPLC-MS analysis was performed after lipid extraction from these groups. Spectral peak alignment and statistical analysis, using unsupervised principal component analysis (PCA), bidirectional orthogonal partial least squares discriminant analysis (O2PLS-DA) analysis, and supervised partial least-squares-latent structure discriminate analysis (PLS-DA), were employed to identify the most discriminative lipids.

Results

The 1176 lipid peaks overlapped across the twelve individuals in the uncapacitated, capacitated, and acrosome-reacted groups: 1180 peaks between the uncapacitated and capacitated groups, 1184 peaks between the uncapacitated and acrosome-reacted groups, and 1178 peaks between the capacitated and acrosome-reacted groups. The count of overlapping peaks varied among individuals, ranging from 739 to 963 across sperm samples. Moreover, 137 lipids had VIP values > 1.0 and twenty-two lipids had VIP > 1.5, based on the O2PLS-DA model. Furthermore, the identified twelve lipids encompassed increases in PI 44:10, LPS 20:4, LPA 20:5, and LPE 20:4, and decreases in 16-phenyl-tetranor-PGE2, PC 40:6, PS 35:4, PA 29:1, 20-carboxy-LTB4, and 2-oxo-4-methylthio-butanoic acid.

Discussion

This study has been the first time to investigate the lipidomics profiles associated with acrosome reaction and capacitation in human sperm, utilizing UPLC-MS in conjunction with multivariate data analysis. These findings corroborate earlier discoveries on lipids during the acrosome reaction and unveil new metabolites. Furthermore, this research highlights the effective utility of UPLC-MS-based lipidomics for exploring diverse physiological states in sperm. This study offers novel insights into lipidomic changes associated with capacitation and the acrosome reaction in human sperm, which are closely related to male reproduction.

Optimising a urinary extraction method for non-targeted GC-MS metabolomics

Urine is ideal for non-targeted metabolomics, providing valuable insights into normal and pathological cellular processes. Optimal extraction is critical since non-targeted metabolomics aims to analyse various compound classes. Here, we optimised a low-volume urine preparation procedure for non-targeted GC-MS. Five extraction methods (four organic acid [OA] extraction variations and a "direct analysis" [DA] approach) were assessed based on repeatability, metabolome coverage, and metabolite recovery. The DA method exhibited superior repeatability, and achieved the highest metabolome coverage, detecting 91 unique metabolites from multiple compound classes comparatively. Conversely, OA methods may not be suitable for all non-targeted metabolomics applications due to their bias toward a specific compound class. In accordance, the OA methods demonstrated limitations, with lower compound recovery and a higher percentage of undetected compounds. The DA method was further improved by incorporating an additional drying step between two-step derivatization but did not benefit from urease sample pre-treatment. Overall, this study establishes an improved low-volume urine preparation approach for future non-targeted urine metabolomics applications using GC-MS. Our findings contribute to advancing the field of metabolomics and enable efficient, comprehensive analysis of urinary metabolites, which could facilitate more accurate disease diagnosis or biomarker discovery.

Spoilage markers for freshwater fish: A comprehensive workflow for non-targeted analysis of VOCs using DHS-GC-HRMS

Changes of volatile organic compounds (VOCs) patterns during 6 days of storage at +4 °C were investigated in different freshwater fish species, namely carp and trout, using dynamic headspace gas chromatography time-of-flight mass spectrometry (DHS-GC-TOFMS). DHS parameters were systematically optimized to establish optimum extraction and pre-concentration of VOCs. Moreover, different sample preparation methods were tested: mincing with a manual meat grinder, as well as mincing plus homogenization with a handheld homogenizer both without and with water addition. The addition of water during sample preparation led to pronounced changes of the volatile profiles, depending on the molecular structure and lipophilicity of the analytes, resulting in losses of up to 98 % of more lipophilic compounds (logP > 3). The optimized method was applied to trout and carp. Trout samples of different storage days were compared using univariate (Mann-Whitney U test, fold change calculation) and multivariate (OPLS-DA) statistics. 37 potential spoilage markers were selected; for 11 compounds identity could be confirmed via measurement of authentic standards and 10 compounds were identified by library spectrum match. 22 compounds were also found to be statistically significant spoilage markers in carp. Merging results of the different statistical approaches, the list of 37 compounds could be narrowed down to the 14 most suitable for trout spoilage assessment. This study comprises a systematic evaluation of the capabilities of DHS-GC coupled to high-resolution (HR) MS for studying spoilage in different freshwater fish species, including a comprehensive data evaluation workflow.

Metabolomic profiling between vitiligo patients and healthy subjects in plateau exhibited significant differences with those in plain

Vitiligo is the most common disorder of depigmentation, which is caused by multiple factors like metabolic abnormality, oxidative stress and the disorders of immune. In recent years, several studies have used untargeted metabolomics to analyze differential metabolites in patients with vitiligo, however, the subjects in these studies were all in plain area. In our study, multivariate analysis indicated a distinct separation between the healthy subjects from plateau and plain areas in electrospray positive and negative ions modes, respectively. Similarly, a distinct separation between vitiligo patients and healthy controls from plateau and plain areas was detected in the two ions modes. Among the identified metabolites, the serum levels of sphingosine 1-phosphate (S1P) were markedly higher in vitiligo patients compare to healthy subjects in plain and markedly higher in healthy subjects in plateau compare to those in plain. There are significant differences in serum metabolome between vitiligo patients and healthy subjects in both plateau and plain areas, as well as in healthy subjects from plateau and plain areas. S1P metabolism alteration may be involved in the pathogenesis of vitiligo.

Seminal plasma untargeted metabolomic and lipidomic profiling for the identification of a novel panel of biomarkers and therapeutic targets related to male infertility

Male infertility occurs approximately in about 50% of all infertility cases and represents a serious concern worldwide. Traditional semen analysis alone is insufficient to diagnose male infertility. Over the past two decades, advances in omics technologies have led to the widespread application of metabolomics profiling as a valuable diagnostic tool for various diseases and disorders. Seminal plasma represents a rich and easily accessible source of metabolites surrounding spermatozoa, a milieu that provides several indispensable nutrients to sustain sperm motility and fertilization. Changes of metabolic profiles in seminal plasma reflect male reproductive tract disorders. Here, we performed seminal plasma metabolomics and lipidomics profiling to identify a new pattern of biomarkers of male infertility. Seminal plasma samples from unfertile subjects (n = 31) and fertile controls (n = 19) were analyzed using an untargeted metabolomics/lipidomics integrated approach, based on Ultra-High-Pressure Liquid Chromatography-tandem Mass Spectrometry. Partial Least Squares-Discriminant Analysis showed a distinct separation between healthy fertile men and infertile subjects. Among the 15 selected candidate biomarkers based on Variable Importance in Projection scores, phosphatidylethanolamine (PE) (18:1; 18:1) resulted with the highest score. In total, 40 molecular species showed statistically significant variations between fertile and infertile men. Heat-map and volcano plot analysis indicated that acylcarnitines, phosphatidylserine (PS) (40:2) and lactate were decreased, while PE (18:1; 18:1), Phosphatidic acid (PA) (O-19:2; 18:1), Lysophosphatidylethanolamine (LPE) (O-16:1) and Phosphatidylcholine (PC) (O-16:2; 18:1)-CH3 were increased in the infertile group. The present study is the first one to analyze the metabolomics/lipidomics dysregulation in seminal plasma between fertile and infertile individuals regardless of sub-infertility condition. Association of several metabolites/lipids dysregulation with male infertility reinforced data of previous studies performed with different approaches. In particular, we confirmed significantly decreased levels of PS and carnitines in infertile patients as well as the positive correlation with sperm motility and morphology. If validated on a larger prospective cohort, the metabolite biomarkers of infertility in seminal plasma we identified in the present study might inform novel strategies for diagnosis and interventions to overcome male infertility.

Integrated multi-omics analyses of microbial communities: a review of the current state and future directions

Integrated multi-omics analyses of microbiomes have become increasingly common in recent years as the emerging omics technologies provide an unprecedented opportunity to better understand the structural and functional properties of microbial communities. Consequently, there is a growing need for and interest in the concepts, approaches, considerations, and available tools for investigating diverse environmental and host-associated microbial communities in an integrative manner. In this review, we first provide a general overview of each omics analysis type, including a brief history, typical workflow, primary applications, strengths, and limitations. Then, we inform on both experimental design and bioinformatics analysis considerations in integrated multi-omics analyses, elaborate on the current approaches and commonly used tools, and highlight the current challenges. Finally, we discuss the expected key advances, emerging trends, potential implications on various fields from human health to biotechnology, and future directions.

Metabolomic profiling of a neurodegenerative retina following optic nerve transection

The degeneration of retinal ganglion cells (RGCs) often causes irreversible vision impairment. Prevention of RGC degeneration can prevent or delay the deterioration of visual function. The present study aimed to investigate retinal metabolic profiles following optic nerve transection (ONT) injury and identify the potential metabolic targets for the prevention of RGC degeneration. Retinal samples were dissected from ONT group and non‑ONT group. The untargeted metabolomics were carried out using liquid chromatography‑tandem mass spectrometry. The involved pathways and biomarkers were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and MetaboAnalyst 5.0. In the ONT group, 689 disparate metabolites were detected, including lipids and lipid‑like molecules. A total of 122 metabolites were successfully annotated and enriched in 50 KEGG pathways. Among them, 'sphingolipid metabolism' and 'primary bile acid biosynthesis' were identified involved in RGC degeneration. A total of five metabolites were selected as the candidate biomarkers for detecting RGC degeneration with an AUC value of 1. The present study revealed that lipid‑related metabolism was involved in the pathogenesis of retinal neurodegeneration. Taurine, taurochenodesoxycholic acid, taurocholic acid (TCA), sphingosine, and galabiosylceramide are shown as the promising biomarkers for the diagnosis of RGC degeneration.

An epidemiological introduction to human metabolomic investigations

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Exploring the mechanisms of clozapine-induced blood-brain barrier dysfunction using untargeted metabolomics and cellular metabolism analysis

Brain microvascular endothelial cells (BMVECs) from the blood- brain barrier form a highly selective membrane that protects the brain from circulating blood and maintains a stable microenvironment for the central nervous system. BMVEC dysfunction has been implicated in a variety of neurological and psychiatric disorders. Clozapine, a widely used antipsychotics, has been demonstrated to alter the permeability of BMVECs, but the underlying mechanisms of this effect are not fully understood. In this study, we investigated the effects of clozapine in BMVECs using untargeted metabolomics analysis. Our results illustrated that treatment with clozapine led to significant changes in the metabolic profile of BMVECs, including alterations in amino acid and energy metabolism. These findings suggest that clozapine affects BMVEC permeability through its effects on cellular metabolism. Our study could inform the development of more targeted and effective treatments for understanding the relationships among clozapine, cellular metabolism, and BMVECs in more detail.

Integrated mass spectrometry imaging and metabolomics reveals sublethal effects of indoxacarb on the red fire ant Solenopsis invicta

BACKGROUND

Indoxacarb, representing an efficient insecticide, is normally made into a bait to spread the poison among red fire ants so that it can be widely applied in the prevention and control of Solenopsis invicta. However, the potential toxicity mechanism of S. invicta in response to indoxacarb remains to be explored. In this study, we integrated mass spectrometry imaging (MSI) and untargeted metabolomics methods to reveal disturbed metabolic expression levels and spatial distribution within the whole-body tissue of S. invicta treated with indoxacarb.

RESULTS

Metabolomics results showed a significantly altered level of metabolites after indoxacarb treatment, such as carbohydrates, amino acids and pyrimidine and derivatives. Additionally, the spatial distribution and regulation of several crucial metabolites resulting from the metabolic pathway and lipids can be visualized using label-free MSI methods. Specifically, xylitol, aspartate, and uracil were distributed throughout the whole body of S. invicta, while sucrose-6'-phosphate and glycerol were mainly distributed in the abdomen of S. invicta, and thymine was distributed in the head and chest of S. invicta. Taken together, the integrated MSI and metabolomics results indicated that the toxicity mechanism of indoxacarb in S. invicta is closely associated with the disturbance in several key metabolic pathways, such as pyrimidine metabolism, aspartate metabolism, pentose and glucuronate interconversions, and inhibited energy synthesis.

CONCLUSION

Collectively, these findings provide a new perspective for the understanding of toxicity assessment between targeted organisms S. invicta and pesticides. © 2023 Society of Chemical Industry.

Molecular Interplay between Non-Host Resistance, Pathogens and Basal Immunity as a Background for Fatal Yellowing in Oil Palm (Elaeis guineensis Jacq.) Plants

An oil palm (Elaeis guineensis Jacq.) bud rod disorder of unknown etiology, named Fatal Yellowing (FY) disease, is regarded as one of the top constraints with respect to the growth of the palm oil industry in Brazil. FY etiology has been a challenge embraced by several research groups in plant pathology throughout the last 50 years in Brazil, with no success in completing Koch's postulates. Most recently, the hypothesis of having an abiotic stressor as the initial cause of FY has gained ground, and oxygen deficiency (hypoxia) damaging the root system has become a candidate for stress. Here, a comprehensive, large-scale, single- and multi-omics integration analysis of the metabolome and transcriptome profiles on the leaves of oil palm plants contrasting in terms of FY symptomatology-asymptomatic and symptomatic-and collected in two distinct seasons-dry and rainy-is reported. The changes observed in the physicochemical attributes of the soil and the chemical attributes and metabolome profiles of the leaves did not allow the discrimination of plants which were asymptomatic or symptomatic for this disease, not even in the rainy season, when the soil became waterlogged. However, the multi-omics integration analysis of enzymes and metabolites differentially expressed in asymptomatic and/or symptomatic plants in the rainy season compared to the dry season allowed the identification of the metabolic pathways most affected by the changes in the environment, opening an opportunity for additional characterization of the role of hypoxia in FY symptom intensification. Finally, the initial analysis of a set of 56 proteins/genes differentially expressed in symptomatic plants compared to the asymptomatic ones, independent of the season, has presented pieces of evidence suggesting that breaks in the non-host resistance to non-adapted pathogens and the basal immunity to adapted pathogens, caused by the anaerobic conditions experienced by the plants, might be linked to the onset of this disease. This set of genes might offer the opportunity to develop biomarkers for selecting oil palm plants resistant to this disease and to help pave the way to employing strategies to keep the safety barriers raised and strong.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Disruption of fish gut microbiota composition and holobiont's metabolome during a simulated Microcystis aeruginosa (Cyanobacteria) bloom

BACKGROUND

Cyanobacterial blooms are one of the most common stressors encountered by metazoans living in freshwater lentic systems such as lakes and ponds. Blooms reportedly impair fish health, notably through oxygen depletion and production of bioactive compounds including cyanotoxins. However, in the times of the "microbiome revolution", it is surprising that so little is still known regarding the influence of blooms on fish microbiota. In this study, an experimental approach is used to demonstrate that blooms affect fish microbiome composition and functions, as well as the metabolome of holobionts. To this end, the model teleost Oryzias latipes is exposed to simulated Microcystis aeruginosa blooms of various intensities in a microcosm setting, and the response of bacterial gut communities is evaluated in terms of composition and metabolome profiling. Metagenome-encoded functions are compared after 28 days between control individuals and those exposed to highest bloom level.

RESULTS

The gut bacterial community of O. latipes exhibits marked responses to the presence of M. aeruginosa blooms in a dose-dependent manner. Notably, abundant gut-associated Firmicutes almost disappear, while potential opportunists increase. The holobiont's gut metabolome displays major changes, while functions encoded in the metagenome of bacterial partners are more marginally affected. Bacterial communities tend to return to original composition after the end of the bloom and remain sensitive in case of a second bloom, reflecting a highly reactive gut community.

CONCLUSION

Gut-associated bacterial communities and holobiont functioning are affected by both short and long exposure to M. aeruginosa, and show evidence of post-bloom resilience. These findings point to the significance of bloom events to fish health and fitness, including survival and reproduction, through microbiome-related effects. In the context of increasingly frequent and intense blooms worldwide, potential outcomes relevant to conservation biology as well as aquaculture warrant further investigation. Video Abstract.

Comparison of Phytochemical Composition and Untargeted Metabolomic Analysis of an Extract from Cnidoscolus aconitifolius (Mill.) I. I. Johnst and Porophyllum ruderale (Jacq.) Cass. and Biological Cytotoxic and Antiproliferative Activity In Vitro

Cnidoscolus aconitifolius (CA) and Porophyllum ruderale (PR) are representative edible plants that are a traditional food source in Mexico. This research aimed to analyze the phytochemical composition and untargeted metabolomics analysis of CA and PR and evaluate their antiproliferative effect in vitro. The phytochemical composition (UPLC-DAD-QToF/MS-ESI) identified up to 38 polyphenols and selected organic acids that were clustered by the untargeted metabolomics in functional activities linked to indolizidines, pyridines, and organic acids. Compared with PR, CA displayed a higher reduction in the metabolic activity of human SW480 colon adenocarcinoma cells (LC50: 10.65 mg/mL), and both extracts increased the total apoptotic cells and arrested cell cycle at G0/G1 phase. PR increased mRNA Apc gene expression, whereas both extracts reduced mRNA Kras expression. Rutin/epigallocatechin gallate displayed the highest affinity to APC and K-RAS proteins in silico. Further research is needed to experiment on other cell lines. Results suggested that CA and PR are polyphenol-rich plant sources exhibiting antiproliferative effects in vitro.

Mass spectrometry-based techniques for identification of compounds in milk and meat matrix

Food including milk and meat is often viewed as the mixture of different components such as fat, protein, carbohydrates, moisture and ash, which are estimated using well-established protocols and techniques. However, with the advent of metabolomics, low-molecular weight substances, also known as metabolites, have been recognized as one of the major factors influencing the production, quality and processing. Therefore, different separation and detection techniques have been developed for the rapid, robust and reproducible separation and identification of compounds for efficient control in milk and meat production and supply chain. Mass-spectrometry based techniques such as GC-MS and LC-MS and nuclear magnetic resonance spectroscopy techniques have been proven successful in the detailed food component analysis owing to their associated benefits. Different metabolites extraction protocols, derivatization, spectra generated, data processing followed by data interpretation are the major sequential steps for these analytical techniques. This chapter deals with not only the detailed discussion of these analytical techniques but also sheds light on various applications of these analytical techniques in milk and meat products.

Chemical profiling of Cannabis sativa from eleven Tanzanian regions

The aim of this research was to investigate the chemical profiles of Cannabis sativa from 11 Tanzanian regions using preliminary tests as well as instrumental analyses with GC-MS and LC-MS. Generally, all the seized samples tested positive for the presence of (Δ⁹-THC. The preliminary test with Duquenois method followed by chloroform addition revealed the presence of Δ⁹-tetrahydrocannabinol (Δ⁹-THC) in all the samples. GC-MS analyses of the samples revealed the presence of nine cannabinoids including Δ⁹-THC, Δ⁸-THC, cannabidivarol, cannabidiol, Δ⁹-tetrahydrocannabivarin (Δ⁹-THCV), cannabichromene, cannabinol, caryophyllene, and cannabicouramaronone, whereas LC-MS chemical profiling revealed the presence 24 chemical substances, including 4 cannabinoids, 15 different types of drugs and 5 amino acids. The Pwani region had the highest percentage composition of Δ⁹-THC (13.45%), the main psychoactive ingredient of Cannabis sativa, followed by Arusha (10.92%) and Singida (10.08%). The sample from Kilimanjaro had the lowest percentage of Δ⁹-THC (6.72%). Apart from cannabinoids, the majority of other chemical substances were found in the Dar es Salaam region sample, which could be attributed to the fact that the city is the epicenter of business rather than the cultivation area, implying that the samples were obtained from different sources and blended as a single package.

A State-of-the-Science Review on High-Resolution Metabolomics Application in Air Pollution Health Research: Current Progress, Analytical Challenges, and Recommendations for Future Direction

BACKGROUND

Understanding the mechanistic basis of air pollution toxicity is dependent on accurately characterizing both exposure and biological responses. Untargeted metabolomics, an analysis of small-molecule metabolic phenotypes, may offer improved estimation of exposures and corresponding health responses to complex environmental mixtures such as air pollution. The field remains nascent, however, with questions concerning the coherence and generalizability of findings across studies, study designs and analytical platforms.

OBJECTIVES

We aimed to review the state of air pollution research from studies using untargeted high-resolution metabolomics (HRM), highlight the areas of concordance and dissimilarity in methodological approaches and reported findings, and discuss a path forward for future use of this analytical platform in air pollution research.

METHODS

We conducted a state-of-the-science review to a) summarize recent research of air pollution studies using untargeted metabolomics and b) identify gaps in the peer-reviewed literature and opportunities for addressing these gaps in future designs. We screened articles published within Pubmed and Web of Science between 1 January 2005 and 31 March 2022. Two reviewers independently screened 2,065 abstracts, with discrepancies resolved by a third reviewer.

RESULTS

We identified 47 articles that applied untargeted metabolomics on serum, plasma, whole blood, urine, saliva, or other biospecimens to investigate the impact of air pollution exposures on the human metabolome. Eight hundred sixteen unique features confirmed with level-1 or -2 evidence were reported to be associated with at least one or more air pollutants. Hypoxanthine, histidine, serine, aspartate, and glutamate were among the 35 metabolites consistently exhibiting associations with multiple air pollutants in at least 5 independent studies. Oxidative stress and inflammation-related pathways-including glycerophospholipid metabolism, pyrimidine metabolism, methionine and cysteine metabolism, tyrosine metabolism, and tryptophan metabolism-were the most commonly perturbed pathways reported in > 70 % of studies. More than 80% of the reported features were not chemically annotated, limiting the interpretability and generalizability of the findings.

CONCLUSIONS

Numerous investigations have demonstrated the feasibility of using untargeted metabolomics as a platform linking exposure to internal dose and biological response. Our review of the 47 existing untargeted HRM-air pollution studies points to an underlying coherence and consistency across a range of sample analytical quantitation methods, extraction algorithms, and statistical modeling approaches. Future directions should focus on validation of these findings via hypothesis-driven protocols and technical advances in metabolic annotation and quantification. https://doi.org/10.1289/EHP11851.

Elevated serum β-hydroxybutyrate, a circulating ketone metabolite, accelerates colorectal cancer proliferation and metastasis via ACAT1

Colorectal cancer (CRC) ranks third in incidence and second in mortality worldwide. Metabolic disorders are known to be closely associated with CRC. Functional metabolomics aims to translate metabolomics-derived biomarkers to disease mechanisms. Previous work based on untargeted liquid chromatography identified 30 differential metabolites of CRC. Among them, only β-hydroxybutyrate (BHB) was elevated in CRC. Here, we first confirm the increased level of β-hydroxybutyrate by targeted metabolomic analysis using an independent cohort of 400 serum samples by UPLC-QQQ-MS/MS analysis. Using appropriate cell and animal models, we find that treatment with pathological levels of β-hydroxybutyrate expedites CRC proliferation and metastasis. Out of four major rate-limiting enzymes of ketolysis, only acetyl-coenzyme A acetyltransferase1 (ACAT1) expression is increased in paired human CRC tissues. These findings suggest probable clinical relevance for the functional implications of β-hydroxybutyrate in CRC. We demonstrate that β-hydroxybutyrate may exert its tumorigenic effects via regulation of ACAT1, due to induction of downstream isocitrate dehydrogenase1 (IDH1) acetylation. Genetic silencing of ACAT1 significantly suppresses the progression of CRC and abrogates the effects of β-hydroxybutyrate both in vitro and in vivo. Overall, this study suggests that targeting β-hydroxybutyrate and its major rate-limiting enzyme ACAT1 may provide a new avenue for therapeutic intervention in CRC.

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 Metabolomics-Based Study on the Discriminative Classification Models and Toxicological Mechanism of Estazolam Fatal Intoxication

Fatal intoxication with sedative-hypnotic drugs is increasing yearly. However, the plasma drug concentration data for fatal intoxication involving these substances are not systematic and even overlap with the intoxication group. Therefore, developing a more precise and trustworthy approach to determining the cause of death is necessary. This study analyzed mice plasma and brainstem samples using the liquid chromatography-high resolution tandem mass spectrometry (LC-HR MS/MS)-based metabolomics method to create discriminative classification models for estazolam fatal intoxication (EFI). The most perturbed metabolic pathway between the EFI and EIND (estazolam intoxication non-death) was examined, Both EIND and EFI groups were administered 500 mg of estazolam per 100 g of body weight. Mice that did not die beyond 8 hours were treated with cervical dislocation and were classified into the EIND groups; the lysine degradation pathway was verified by qPCR (Quantitative Polymerase Chain Reaction), metabolite quantitative and TEM (transmission electron microscopy) analysis. Non-targeted metabolomics analysis with EFI were the experimental group and four hypoxia-related non-drug-related deaths (NDRDs) were the control group. Mass spectrometry data were analyzed with Compound Discoverer (CD) 3.1 software and multivariate statistical analyses were performed using the online software MetaboAnalyst 5.0. After a series of analyses, the results showed the discriminative classification model in plasma was composed of three endogenous metabolites: phenylacetylglycine, creatine and indole-3-lactic acid, and in the brainstem was composed of palmitic acid, creatine, and indole-3-lactic acid. The specificity validation results showed that both classification models distinguished between the other four sedatives-hypnotics, with an area under ROC curve (AUC) of 0.991, and the classification models had an extremely high specificity. When comparing different doses of estazolam, the AUC value of each group was larger than 0.80, and the sensitivity was also high. Moreover, the stability results showed that the AUC value was equal to or very close to 1 in plasma samples stored at 4 °C for 0, 1, 5, 10 and 15 days; the predictive power of the classification model was stable within 15 days. The results of lysine degradation pathway validation revealed that the EFI group had the highest lysine and saccharopine concentrations (mean (ng/mg) = 1.089 and 1.2526, respectively) when compared to the EIND and control group, while the relative expression of SDH (saccharopine dehydrogenase) showed significantly lower in the EFI group (mean = 1.206). Both of these results were statistically significant. Furthermore, TEM analysis showed that the EFI group had the more severely damaged mitochondria. This work gives fresh insights into the toxicological processes of estazolam and a new method for identifying EFI-related causes of mortality.

Multimodal plasma metabolomics and lipidomics in elucidating metabolic perturbations in tuberculosis patients with concurrent type 2 diabetes

Type 2 diabetes mellitus (DM) poses a major burden for the treatment and control of tuberculosis (TB). Characterization of the underlying metabolic perturbations in DM patients with TB infection would yield insights into the pathophysiology of TB-DM, thus potentially leading to improvements in TB treatment. In this study, a multimodal metabolomics and lipidomics workflow was applied to investigate plasma metabolic profiles of patients with TB and TB-DM. Significantly different biological processes and biomarkers in TB-DM vs. TB were identified using a data-driven, knowledge-based framework. Changes in metabolic and signaling pathways related to carbohydrate and amino acid metabolism were mainly captured by amide HILIC column metabolomics analysis, while perturbations in lipid metabolism were identified by the C18 metabolomics and lipidomics analysis. Compared to TB, TB-DM exhibited elevated levels of bile acids and molecules related to carbohydrate metabolism, as well as the depletion of glutamine, retinol, lysophosphatidylcholine, and phosphatidylcholine. Moreover, arachidonic acid metabolism was determined as a potential important factor in the interaction between TB and DM pathophysiology. In a correlation network of the significantly altered molecules, among the central nodes, chenodeoxycholic acid was robustly associated with TB and DM. Fatty acid (22:4) was a component of all significant modules. In conclusion, the integration of multimodal metabolomics and lipidomics provides a thorough picture of the metabolic changes associated with TB-DM. The results obtained from this comprehensive profiling of TB patients with DM advance the current understanding of DM comorbidity in TB infection and contribute to the development of more effective treatment.

Simultaneous measurement of kynurenine metabolites and explorative metabolomics using liquid chromatography-mass spectrometry: A novel accurate method applied to serum and plasma samples from a large healthy cohort

Kynurenine metabolites are emerging as promising clinical biomarkers in several diseases, especially within psychiatry. Unfortunately, they are difficult to detect, particularly the challenging neurotoxic metabolite quinolinic acid (QUIN). The aim of this study was twofold: First, to develop a liquid chromatography-mass spectrometry method (LC-MS) for simultaneous targeted quantification of key kynurenine metabolites together with untargeted metabolomics, and second, to demonstrate the feasibility of the method by exploring serum/plasma and gender differences in 120 healthy young adults between 18 and 30 years of age. A range of analytical columns (C18 and biphenyl columns) and mobile phases (acidic and alkaline) were systematically evaluated. The optimized LC-MS method was based on a biphenyl column, a water-methanol gradient with 0.2% formic acid, and authentic isotope-labeled standards for each kynurenine metabolite. Precision and accuracy of targeted quantification of the key kynurenine metabolites tryptophan (TRP), kynurenine (KYN), kynurenic acid (KYNA), 3-hydroxykynurenine (3-HK), and QUIN were excellent, far exceeding the acceptance criteria specified by international guidelines. Median inter- and intra-day precision were < 6% in serum and plasma; the median accuracy was 2.4% in serum and 8% in plasma. Serum concentrations were ≤ 10% different from the corresponding concentrations in plasma for all kynurenine metabolites in healthy young adults. Men had higher levels (8-18%) of TRP, KYN, and KYNA than women (p ≤ 0.009), while no differences were observed for 3-HK and QUIN (p > 0.70). Incurred sample reanalysis of 10% of the samples yielded a median difference < 5% from the initial measurement, demonstrating the robustness of the method. Besides the targeted quantification of key kynurenine metabolites, our method was found to be suitable for simultaneous untargeted metabolomics analyses of hundreds of metabolites. A range of compound classes could be detected including amino acids, nucleic acids, dipeptides, antioxidants, and acylcarnitines, making explorative studies highly feasible. For example, we identified an additional kynurenine metabolite, 2-Quinolinecarboxylic acid, which was 47% higher in males than females (adjusted p-value = 0.001). In conclusion, in this study, we present a reliable and robust LC-MS method for simultaneous targeted and untargeted metabolomics ready for both research and clinical use. We show that both serum and plasma can be used for kynurenine studies, and the reported gender differences are in accordance with the literature. Future studies should consider using biphenyl-based LC-MS columns to successfully detect QUIN.

Metabolomic Profiling of Lungs from Mice Reveals the Variability of Metabolites in Pneumocystis Infection and the Metabolic Abnormalities in BAFF-R-Deficient Mice

Purpose

The incidence of Pneumocystis pneumonia (PCP) in patients without human immunodeficiency virus (HIV) has been increasing. In this study, we aimed to investigate the metabolic changes in Pneumocystis infection and the metabolic abnormalities in B-cell-activating factor receptor (BAFF-R)-deficient mice with Pneumocystis infection.

Methods

The important function of B cells during Pneumocystis infection is increasingly recognized. In this study, a Pneumocystis-infected mouse model was constructed in BAFF-R^-/- mice and wild-type (WT) mice. Lungs of uninfected WT C57BL/6, WT Pneumocystis-infected, and BAFF-R^-/- Pneumocystis-infected mice were used for metabolomic analyses to compare the metabolomic profiles among the groups, with the aim of exploring the metabolic influence of Pneumocystis infection and the influence of mature B-cell deficiency during infection.

Results

The results indicated that many metabolites, mainly lipids and lipid-like molecules, were dysregulated in Pneumocystis-infected WT mice compared with uninfected WT C57BL/6 mice. The data also demonstrated significant changes in tryptophan metabolism, and the expression levels of key enzymes of tryptophan metabolism, such as indoleamine 2,3-dioxygenase 1 (IDO1), were significantly upregulated. In addition, B-cell development and function might be associated with lipid metabolism. We found a lower level of alitretinoin and the abnormalities of fatty acid metabolism in BAFF-R^-/- Pneumocystis-infected mice. The mRNA levels of enzymes associated with fatty acid metabolism in the lung were upregulated in BAFF-R^-/- Pneumocystis-infected mice and positively correlated with the level of IL17A, thus suggesting that the abnormalities of fatty acid metabolism may be associated with greater inflammatory cell infiltration in the lung tissue of BAFF-R^-/- Pneumocystis-infected mice compared with the WT Pneumocystis-infected mice.

Conclusion

Our data revealed the variability of metabolites in Pneumocystis-infected mice, suggesting that the metabolism plays a vital role in the immune response to Pneumocystis infection.

Metabolomics of asthma, COPD, and asthma-COPD overlap: an overview

The two common progressive lung diseases, asthma and chronic obstructive pulmonary disease (COPD), are the leading causes of morbidity and mortality worldwide. Asthma-COPD overlap, referred to as ACO, is another complex pulmonary disease that manifests itself with features of both asthma and COPD. The disease has no clear diagnostic or therapeutic guidelines, thereby making both diagnosis and treatment challenging. Though a number of studies on ACO have been documented, gaps in knowledge regarding the pathophysiologic mechanism of this disorder exist. Addressing this issue is an urgent need for improved diagnostic and therapeutic management of the disease. Metabolomics, an increasingly popular technique, reveals the pathogenesis of complex diseases and holds promise in biomarker discovery. This comprehensive narrative review, comprising 99 original research articles in the last five years (2017-2022), summarizes the scientific advances in terms of metabolic alterations in patients with asthma, COPD, and ACO. The analytical tools, nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS), commonly used to study the expression of the metabolome, are discussed. Challenges frequently encountered during metabolite identification and quality assessment are highlighted. Bridging the gap between phenotype and metabotype is envisioned in the future.

A proposed framework to evaluate the quality and reliability of targeted metabolomics assays from the UK Consortium on Metabolic Phenotyping (MAP/UK)

Targeted metabolite assays that measure tens or hundreds of pre-selected metabolites, typically using liquid chromatography-mass spectrometry, are increasingly being developed and applied to metabolic phenotyping studies. These are used both as standalone phenotyping methods and for the validation of putative metabolic biomarkers obtained from untargeted metabolomics studies. However, there are no widely accepted standards in the scientific community for ensuring reliability of the development and validation of targeted metabolite assays (referred to here as 'targeted metabolomics'). Most current practices attempt to adopt, with modifications, the strict guidance provided by drug regulatory authorities for analytical methods designed largely for measuring drugs and other xenobiotic analytes. Here, the regulatory guidance provided by the European Medicines Agency, US Food and Drug Administration and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use are summarized. In this Perspective, we have adapted these guidelines and propose a less onerous 'tiered' approach to evaluate the reliability of a wide range of metabolomics analyses, addressing the need for community-accepted, harmonized guidelines for tiers other than full validation. This 'fit-for-purpose' tiered approach comprises four levels-discovery, screening, qualification and validation-and is discussed in the context of a range of targeted and untargeted metabolomics assays. Issues arising with targeted multiplexed metabolomics assays, and how these might be addressed, are considered. Furthermore, guidance is provided to assist the community with selecting the appropriate degree of reliability for a series of well-defined applications of metabolomics.

Comprehensive lipid profiles investigation reveals host metabolic and immune alterations during anti-tuberculosis treatment: Implications for therapeutic monitoring

In this study, we investigated the lipidome of tuberculosis patients during standard chemotherapy to discover biosignatures that could aid therapeutic monitoring. UPLC-QToF MS was used to analyze 82 baseline and treatment plasma samples of patients with pulmonary tuberculosis. Subsequently, a data-driven and knowledge-based workflow, including robust annotation, statistical analysis, and functional analysis, was applied to assess lipid profiles during treatment. Overall, the lipids species from 17 lipid subclasses were significantly altered by anti-tuberculosis chemotherapy. Cholesterol ester (CE), monoacylglycerols, and phosphatidylcholine (PC) were upregulated, whereas triacylglycerols, sphingomyelin, and ether-linked phosphatidylethanolamines (PE O-) were downregulated. Notably, PCs demonstrated a clear upward expression pattern during tuberculosis treatment. Several lipid species were identified as potential biomarkers for therapeutic monitoring, such as PC(42:6), PE(O-40:5), CE(24:6), and dihexosylceramide Hex2Cer(34:2;2 O). Functional and lipid gene enrichment analysis revealed alterations in pathways related to lipid metabolism and host immune responses. In conclusion, this study provides a foundation for the use of lipids as biomarkers for clinical management of tuberculosis.

Multi-omics analyses from a single sample: prior metabolite extraction does not alter the 16S rRNA-based characterization of prokaryotic community in a diversity of sample types

Massive sequencing of the 16S rRNA gene has become a standard first step to describe and compare microbial communities from various samples. Parallel analysis of high numbers of samples makes it relevant to the statistical testing of the influence of natural or experimental factors and variables. However, these descriptions fail to document changes in community or ecosystem functioning. Nontargeted metabolomics are a suitable tool to bridge this gap, yet extraction protocols are different. In this study, prokaryotic community compositions are documented by 16S rRNA gene sequencing after direct DNA extraction or after metabolites extraction followed by DNA extraction. Results obtained using the V3-V4 region on nonaxenic cultures of cyanobacteria, lake water column, biofilm, and gut of wild and lab-reared fish indicate that prior extraction of metabolites does not influence the obtained image of prokaryotic communities. This validates sequential extraction of metabolites followed by DNA as a way to combine 16S rRNA sequencing with metabolome characterization from a single sample. This approach has the potential to complement community structure characterization with a proxy of their functioning, without the uncertainties associated with the use of separate samples.

Mass Spectrometry-Based Untargeted Metabolomics and Lipidomics Platforms to Analyze Cell Culture Extracts

Metabolites represent the most downstream level of the cellular organization. Hence, an in vitro untargeted metabolomics approach is extremely valuable to deepen the understanding of how endogenous metabolites in cells are altered under a given biological condition. This chapter describes a robust liquid chromatography-high-resolution mass spectrometry-based metabolomics and lipidomics platform applied to cell culture extracts. The analytical workflow includes an optimized sample preparation procedure to cover a wide range of metabolites using liquid-liquid extraction and validated instrumental operation procedures with the implementation of comprehensive quality assurance and quality control measures to ensure high reproducibility. The lipidomics platform is based on reversed-phase liquid chromatography for the separation of slightly polar to apolar metabolites and covers a broad range of lipid classes, while the metabolomics platform makes use of two hydrophilic interaction liquid chromatography methods for the separation of polar metabolites, such as organic acids, amino acids, and sugars. The chapter focuses on the analysis of cultured HepaRG cells that are derived from a human hepatocellular carcinoma; however, the sample preparation and analytical platforms can easily be adapted for other types of cells.

Solid-phase microextraction of endogenous metabolites from intact tissue validated using a Biocrates standard reference method kit

Improved analytical methods for the metabolomic profiling of tissue samples are constantly needed. Currently, conventional sample preparation methods often involve tissue biopsy and/or homogenization, which disrupts the endogenous metabolome. In this study, solid-phase microextraction (SPME) fibers were used to monitor changes in endogenous compounds in homogenized and intact ovine lung tissue. Following SPME, a Biocrates AbsoluteIDQ assay was applied to make a downstream targeted metabolomics analysis and confirm the advantages of in vivo SPME metabolomics. The AbsoluteIDQ kit enabled the targeted analysis of over 100 metabolites via solid-liquid extraction and SPME. Statistical analysis revealed significant differences between conventional liquid extractions from homogenized tissue and SPME results for both homogenized and intact tissue samples. In addition, principal component analysis revealed separated clustering among all the three sample groups, indicating changes in the metabolome due to tissue homogenization and the chosen sample preparation method. Furthermore, clear differences in free metabolites were observed when extractions were performed on the intact and homogenized tissue using identical SPME procedures. Specifically, a direct comparison showed that 47 statistically distinct metabolites were detected between the homogenized and intact lung tissue samples (P < 0.05) using mixed-mode SPME fibers. These changes were probably due to the disruptive homogenization of the tissue. This study's findings highlight both the importance of sample preparation in tissue-based metabolomics studies and SPME's unique ability to perform minimally invasive extractions without tissue biopsy or homogenization while providing broad metabolite coverage.

Unveiling Microbial Chemical Interactions Based on Metabolomics Approaches

Microorganisms are ubiquitous in diverse habitats and studying their chemical interactions with the environment and comprehend its complex relations with both hosts and environment, are crucial for the development of strategies to control microbial diseases. This chapter discusses the importance of studying microorganisms with agricultural benefits, using specialized metabolites as examples. Herein we highlight the challenges and opportunities in utilizing microorganisms as alternatives to synthetic pesticides and fertilizers in agriculture. Genome-guided investigations and improved analytical methodologies are necessary to characterize diverse and complex biomolecules produced by microorganisms. Predicting and isolating bioproducts based on genetic information have become a focus for researchers, aided by tools like antiSMASH, BiG-SCAPE, PRISM, and others. However, translating genomic data into practical applications can be complex. Therefore, integrating genomics, transcriptomics, and metabolomics enhances chemical characterization, aiding in discovering new metabolic pathways and specialized metabolites. Additionally, elicitation is one promising strategy to enhance beneficial metabolite production. Finally, identify and characterize microbial secondary metabolites remain challenging due to their low production, complex chemical structure characterization and different environmental factors necessary for metabolite in vitro production.

Targeted metabolomics reveals serum changes of amino acids in mild to moderate ischemic stroke and stroke mimics

Background

The pathophysiological processes linked to an acute ischemic stroke (IS) can be reflected in the circulating metabolome. Amino acids (AAs) have been demonstrated to be one of the most significant metabolites that can undergo significant alteration after a stroke.

Methods

We sought to identify the potential biomarkers for the early detection of IS using an extensive targeted technique for reliable quantification of 27 different AAs based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). A cohort with 216 participants was enrolled, including 70 mild to moderate ischemic stroke patients (National Institutes of Health Stroke Scale < 15, MB group), 76 stroke mimics (MM group) and 70 healthy controls (NC group).

Results

It was found that upon comparing MB and MM to control patients, AAs shifts were detected via partial least squares discrimination analysis (PLS-DA) and pathway analysis. Interestingly, MB and MM exhibited similar AAs pattern. Moreover, ornithine, asparagine, valine, citrulline, and cysteine were identified for inclusion in a biomarker panel for early-stage stroke detection based upon an AUC of 0.968 (95% CI 0.924-0.998). Levels of ornithine were positively associated with infract volume, 3 months mRS score, and National Institutes of Health Stroke Scale (NIHSS) score in MB. In addition, a metabolites biomarker panel, including ornithine, taurine, phenylalanine, citrulline, cysteine, yielded an AUC of 0.99 (95% CI 0.966-1) which can be employed to effectively discriminate MM patients from control.

Conclusion

Overall, alternations in serum AAs are characteristic metabolic features of MB and MM. AAs could serve as promising biomarkers for the early diagnosis of MB patients since mild to moderate IS patients were enrolled in the study. The metabolism of AAs can be considered as a key indicator for both the prevention and treatment of IS.

Optimization of the growth conditions through response surface methodology and metabolomics for maximizing the auxin production by Pantoea agglomerans C1

Introduction

The fermentative production of auxin/indole 3-acetate (IAA) using selected Pantoea agglomerans strains can be a promising approach to developing novel plant biostimulants for agriculture use.

Methods

By integrating metabolomics and fermentation technologies, this study aimed to define the optimal culture conditions to obtain auxin/IAA-enriched plant postbiotics using P. agglomerans strain C1. Metabolomics analysis allowed us to demonstrate that the production of a selected.

Results and discussion

Array of compounds with plant growth-promoting- (IAA and hypoxanthine) and biocontrol activity (NS-5, cyclohexanone, homo-L-arginine, methyl hexadecenoic acid, and indole-3-carbinol) can be stimulated by cultivating this strain on minimal saline medium amended with sucrose as a carbon source. We applied a three-level-two-factor central composite design (CCD) based response surface methodology (RSM) to explore the impact of the independent variables (rotation speed and medium liquid-to-flask volume ratio) on the production of IAA and IAA precursors. The ANOVA component of the CCD indicated that all the process-independent variables investigated significantly impacted the auxin/IAA production by P. agglomerans strain C1. The optimum values of variables were a rotation speed of 180 rpm and a medium liquid-to-flask volume ratio of 1:10. Using the CCD-RSM method, we obtained a maximum indole auxin production of 208.3 ± 0.4  mg IAA_equ/L, which was a 40% increase compared to the growth conditions used in previous studies. Targeted metabolomics allowed us to demonstrate that the IAA product selectivity and the accumulation of the IAA precursor indole-3-pyruvic acid were significantly affected by the increase in the rotation speed and the aeration efficiency.

Applying multi-omics techniques to the discovery of biomarkers for acute aortic dissection

Acute aortic dissection (AAD) is a cardiovascular disease that manifests suddenly and fatally. Due to the lack of specific early symptoms, many patients with AAD are often overlooked or misdiagnosed, which is undoubtedly catastrophic for patients. The particular pathogenic mechanism of AAD is yet unknown, which makes clinical pharmacological therapy extremely difficult. Therefore, it is necessary and crucial to find and employ unique biomarkers for Acute aortic dissection (AAD) as soon as possible in clinical practice and research. This will aid in the early detection of AAD and give clear guidelines for the creation of focused treatment agents. This goal has been made attainable over the past 20 years by the quick advancement of omics technologies and the development of high-throughput tissue specimen biomarker screening. The primary histology data support and add to one another to create a more thorough and three-dimensional picture of the disease. Based on the introduction of the main histology technologies, in this review, we summarize the current situation and most recent developments in the application of multi-omics technologies to AAD biomarker discovery and emphasize the significance of concentrating on integration concepts for integrating multi-omics data. In this context, we seek to offer fresh concepts and recommendations for fundamental investigation, perspective innovation, and therapeutic development in AAD.

Progress with Metabolomic Blood Tests for Gastrointestinal Cancer Diagnosis-An Assessment of Biomarker Translation

There is an urgent need for cost-effective, non-invasive tools to detect early stages of gastrointestinal cancer (colorectal, gastric, and esophageal cancers). Despite many publications suggesting circulating metabolites acting as accurate cancer biomarkers, few have reached the clinic. In upper gastrointestinal cancer this is critically important, as there is no test to complement gold-standard endoscopic evaluation in patients with mild symptoms that do not meet referral criteria. Therefore, this study aimed to describe and solve this translational gap. Studies reporting diagnostic accuracy of metabolomic blood-based gastrointestinal cancer biomarkers from 2007 to 2020 were systematically reviewed and progress of each biomarker along the discovery-validation-adoption pathway was mapped. Successful biomarker translation was defined as a composite endpoint, including patent protection/FDA approval/recommendation in national guidelines. The review found 77 biomarker panels of gastrointestinal cancer, including 25 with an AUROC >0.9. All but one was stalled at the discovery phase, 9.09% were patented and none were clinically approved, confirming the extent of biomarker translational gap. In addition, there were numerous "re-discoveries," including histidine, discovered in 7 colorectal studies. Finally, this study quantitatively supports the presence of a translational gap between discovery and clinical adoption, despite clear evidence of highly performing biomarkers with significant potential clinical value.

Measuring biological age using omics data

Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.