Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Metabolic features of adolescent major depressive disorder: A comparative study between treatment-resistant depression and first-episode drug-naive depression

Major depressive disorder (MDD) is a psychiatric illness that can jeopardize the normal growth and development of adolescents. Approximately 40% of adolescent patients with MDD exhibit resistance to conventional antidepressants, leading to the development of Treatment-Resistant Depression (TRD). TRD is associated with severe impairments in social functioning and learning ability and an elevated risk of suicide, thereby imposing an additional societal burden. In this study, we conducted plasma metabolomic analysis on 53 adolescents diagnosed with first-episode drug-naïve MDD (FEDN-MDD), 53 adolescents with TRD, and 56 healthy controls (HCs) using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) and reversed-phase liquid chromatography-mass spectrometry (RPLC-MS). We established a diagnostic model by identifying differentially expressed metabolites and applying cluster analysis, metabolic pathway analysis, and multivariate linear support vector machine (SVM) algorithms. Our findings suggest that adolescent TRD shares similarities with FEDN-MDD in five amino acid metabolic pathways and exhibits distinct metabolic characteristics, particularly tyrosine and glycerophospholipid metabolism. Furthermore, through multivariate receiver operating characteristic (ROC) analysis, we optimized the area under the curve (AUC) and achieved the highest predictive accuracy, obtaining an AUC of 0.903 when comparing FEDN-MDD patients with HCs and an AUC of 0.968 when comparing TRD patients with HCs. This study provides new evidence for the identification of adolescent TRD and sheds light on different pathophysiologies by delineating the distinct plasma metabolic profiles of adolescent TRD and FEDN-MDD.

Dysregulations of amino acid metabolism and lipid metabolism in urine of children and adolescents with major depressive disorder: a case-control study

RATIONALE

The mechanisms underlying major depressive disorder (MDD) in children and adolescents are unclear. Metabolomics has been utilized to capture metabolic signatures of various psychiatric disorders; however, urinary metabolic profile of MDD in children and adolescents has not been studied.

OBJECTIVES

We analyzed urinary metabolites in children and adolescents with MDD to identify potential biomarkers and metabolic signatures.

METHODS

Here, liquid chromatography-mass spectrometry was used to profile metabolites in urine samples from 192 subjects, comprising 80 individuals with antidepressant-naïve MDD (AN-MDD), 37 with antidepressant-treated MDD (AT-MDD) and 75 healthy controls (HC). We performed orthogonal partial least squares discriminant analysis to identify differential metabolites and employed logistic regression and receiver operating characteristic analysis to establish a diagnostic panel.

RESULTS

In total, 143 and 71 differential metabolites were identified in AN-MDD and AT-MDD, respectively. These were primarily linked to lipid metabolism, molecular transport, and small molecule biochemistry. AN-MDD additionally exhibited dysregulated amino acid metabolism. Compared to HC, a diagnostic panel of seven metabolites displayed area under the receiver operating characteristic curves of 0.792 for AN-MDD, 0.828 for AT-MDD, and 0.799 for all MDD. Furthermore, the urinary metabolic profiles of children and adolescents with MDD significantly differed from those of adult MDD.

CONCLUSIONS

Our research suggests dysregulated amino acid metabolism and lipid metabolism in the urine of children and adolescents with MDD, similar to results in plasma metabolomics studies. This contributes to the comprehension of mechanisms underlying children and adolescents with MDD.

Metabolomic profiling reveals biomarkers for diverse flesh colors in jelly fungi (Auricularia cornea)

Auricularia cornea has garnered attention due to its nutrition, culinary applications, and promising commercial prospects. However, there is little information available regarding the metabolic profiling of various colors strains. In this study, 642 metabolites across 64 classes were identified by LC-MS/MS to understand the metabolic variations between white, pink and dark brown strains. Notably, prenol lipids, carboxylic acids and fatty acyls accounted for 46.8 % of the total. Comparative analysis revealed 17 shared differential metabolites (DMs) among them. ACP vs ACW exhibited 17 unique metabolites, including d-arginine and maleic acid, etc. ACP vs ACB showed 5 unique metabolites, with only PS(18:1(9Z)/0:0) demonstrating up-regulation. ACB vs ACW showed 8 unique metabolites, including 4-hydroxymandelic acid and 5'-methylthioadenosine, etc. KEGG enrichment analysis highlighted pathway variations, and MetPA analysis identified key-pathways influencing DMs accumulation in A. cornea. This pioneering metabolomics study offers insights into A. cornea metabolic profiling, potential applications, and guides further research.

Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease

Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.

A ternary correlation multi-symptom network strategy based on in vivo chemical profile identification and metabolomics to explore the molecular basis of Ephedra herb against viral pneumonia

Ephedra herb (EH), an important medicine prescribed in herbal formulas by Traditional Chinese Medicine practitioners, has been widely used in the treatment of viral pneumonia in China. However, the molecular basis of EH in viral pneumonia remains unclear. In this study, a ternary correlation multi-symptom network strategy was established based on in vivo chemical profile identification and metabolomics to explore the molecular basis of EH against viral pneumonia. Results showed that 143 compounds of EH and 70 prototype components were identified in vivo. EH could reduce alveolar-capillary barrier disruption in rats with viral pneumonia and significantly downregulate the expression of inflammatory factors and bronchoalveolar lavage fluid. Plasma metabolomics revealed that EH may be involved in the regulation of arachidonic acid, tryptophan, tyrosine, nicotinate, and nicotinamide metabolism. The multi-symptom network showed that 12 compounds have an integral function in the treatment of viral pneumonia by intervening in many pathways related to viruses, immunity and inflammation, and lung injury. Further verification demonstrated that sinapic acid and frambinone can regulate the expression of related genes. It has been shown to be a promising representative of the pharmacological constituents of ephedra.

Untargeted metabolomics reveals potential plasma biomarkers for diagnosis of primary aldosteronism using liquid chromatography-mass spectrometry

Metabolite profiling has the potential to comprehensively bridge phenotypes and complex heterogeneous physiological and pathological states. We performed a metabolomics study using parallel liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis to screen for biomarkers of primary aldosteronism (PA) from a cohort of 111 PA patients and 218 primary hypertension (PH) patients. Hydrophilic interaction chromatography and reversed-phase liquid chromatography separations were employed to obtain a global plasma metabolome of endogenous metabolites. The satisfactory classification between PA and PH patients was obtained using the MVDA model. A total of 35 differential metabolites were screened out and identified. A diagnostic biomarker panel was established using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model and receiver operating characteristic analysis. Joint analysis with clinical indicators, including plasma supine aldosterone level, plasma orthostatic aldosterone level, body mass index, and blood potassium, revealed that the combination of metabolite biomarker panel and plasma supine aldosterone has the best clinical diagnostic efficacy.

Enzyme-Driven LC-HRMS Approach for Specific Recognition of 12α-Hydroxy Bile Acids

The synthesis of 12α-hydroxylated bile acids (12HBAs) and non-12α-hydroxylated bile acids (non-12HBAs) occurs via classical and alternative pathways, respectively. The composition of these BAs is a crucial index for pathophysiologic assessment. However, accurately differentiating 12HBAs and non-12HBAs is highly challenging due to the limited standard substances. Here, we innovatively introduce 12α-hydroxysteroid dehydrogenase (12α-HSDH) as an enzymatic probe synthesized by heterologous expression in Escherichia coli, which can specifically and efficiently convert 12HBAs in vitro under mild conditions. Coupled to the conversion rate determined by liquid chromatography-high resolution mass spectrometry (LC-HRMS), this enzymatic probe allows for the straightforward distinguishing of 210 12HBAs and 312 non-12HBAs from complex biological matrices, resulting in a BAs profile with a well-defined hydroxyl feature at the C12 site. Notably, this enzyme-driven LC-HRMS approach can be extended to any molecule with explicit knowledge of enzymatic transformation. We demonstrate the practicality of this BAs profile in terms of both revealing cross-species BAs heterogeneity and monitoring the alterations of 12HBAs and non-12HBAs under asthma disease. We envisage that this work will provide a novel pattern to recognize the shift of BA metabolism from classical to alternative synthesis pathways in different pathophysiological states, thereby offering valuable insights into the management of related diseases.

Multi-omic analysis tools for microbial metabolites prediction

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

The gut microbiota contributes to methamphetamine-induced reproductive toxicity in male mice

Methamphetamine (METH) is a psychostimulant drug belonging to the amphetamine-type stimulant class, known to exert male reproductive toxicity. Recent studies suggest that METH can disrupt the gut microbiota. Furthermore, the gut-testis axis concept has gained attention due to the potential link between gut microbiome dysfunction and reproductive health. Nonetheless, the role of the gut microbiota in mediating the impact of METH on male reproductive toxicity remains unclear. In this study, we employed a mouse model exposed to escalating doses of METH to assess sperm quality, testicular pathology, and reproductive hormone levels. The fecal microbiota transplantation method was employed to investigate the effect of gut microbiota on male reproductive toxicity. Transcriptomic, metabolomic, and microbiological analyses were conducted to explore the damage mechanism to the male reproductive system caused by METH. We found that METH exposure led to hormonal disorders, decreased sperm quality, and changes in the gut microbiota and testicular metabolome in mice. Testicular RNA sequencing revealed enrichment of several Gene Ontology terms associated with reproductive processes, as well as PI3K-Akt signaling pathways. FMT conveyed similar reproductive damage from METH-treated mice to healthy recipient mice. The aforementioned findings suggest that the gut microbiota plays a substantial role in facilitating the reproductive toxicity caused by METH, thereby highlighting a prospective avenue for therapeutic intervention in the context of METH-induced infertility.

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Computational methods for processing and interpreting mass spectrometry-based metabolomics

Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.

Comprehensive mass spectrometric metabolomic profiling of a chemically diverse collection of plants of the Celastraceae family

Natural products exhibit interesting structural features and significant biological activities. The discovery of new bioactive molecules is a complex process that requires high-quality metabolite profiling data to properly target the isolation of compounds of interest and enable their complete structural characterization. The same metabolite profiling data can also be used to better understand chemotaxonomic links between species. This Data Descriptor details a dataset resulting from the untargeted liquid chromatography-mass spectrometry metabolite profiling of 76 natural extracts of the Celastraceae family. The spectral annotation results and related chemical and taxonomic metadata are shared, along with proposed examples of data reuse. This data can be further studied by researchers exploring the chemical diversity of natural products. This can serve as a reference sample set for deep metabolome investigation of this chemically rich plant family.

Metabolite profiling and bioactivity guided fractionation of Lactobacillaceae and rice bran postbiotics for antimicrobial-resistant Salmonella Typhimurium growth suppression

Probiotic-fermented supplements (postbiotics) are becoming increasingly explored for their activity against antibiotic-resistant enteropathogens. Prebiotics are often incorporated into postbiotics to enhance their efficacy, but due to strain differences in probiotic activity, postbiotic antimicrobial effects are poorly understood. To improve postbiotic antimicrobial efficacy, we investigated and compared metabolite profiles of postbiotics prepared with three lactic acid bacteria strains (L. fermentum, L. paracasei, and L. rhamnosus) cultured with and without rice bran, a globally abundant, rich source of prebiotics. At their minimum inhibitory dose, L. fermentum and L. paracasei postbiotics + rice bran suppressed S. Typhimurium growth 42-55% more versus their respective probiotic-alone postbiotics. The global, non-targeted metabolome of these postbiotics identified 109 metabolites increased in L. fermentum and L. paracasei rice bran postbiotics, including 49 amino acids, 20 lipids, and 12 phytochemicals metabolites. To identify key metabolite contributors to postbiotic antimicrobial activity, bioactivity-guided fractionation was applied to L. fermentum and L. paracasei rice bran-fermented postbiotics. Fractionation resulted in four L. fermentum and seven L. paracasei fractions capable of suppressing S. Typhimurium growth more effectively versus the negative control. These fractions were enriched in 15 metabolites that were significantly increased in the global metabolome of postbiotics prepared with rice bran versus postbiotic alone. These metabolites included imidazole propionate (enriched in L. fermentum + rice bran, 1.61-fold increase; L. paracasei + rice bran 1.28-fold increase), dihydroferulate (L. fermentum + rice bran, 5.18-fold increase), and linoleate (L. fermentum + rice bran, 1.82-fold increase; L. paracasei + rice bran, 3.19-fold increase), suggesting that they may be key metabolite drivers of S. Typhimurium growth suppression. Here, we show distinct mechanisms by which postbiotics prepared with lactic acid bacteria and rice bran produce metabolites with antimicrobial activity capable of suppressing S. Typhimurium growth. Probiotic strain differences contributing to postbiotic antimicrobial activity attract attention as adjunctive treatments against pathogens.

Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches

Phytocompounds are a well-established source of drug discovery due to their unique chemical and functional diversities. In the area of cancer therapeutics, several phytocompounds have been used till date to design and develop new drugs. One of the desired interests of pharmaceutical companies and researchers globally is that new anti-cancer leads are discovered, for which phytocompounds can be considered a valuable source. Simultaneously, in recent years, the growth of computational approaches like virtual screening (VS), molecular dynamics (MD), pharmacophore modelling, Quantitative structure-activity relationship (QSAR), Absorption Distribution Metabolism Excretion and Toxicity (ADMET), network biology, and machine learning (ML) has gained importance due to their efficiency, reduced time-consuming nature, and cost-effectiveness. Therefore, the present review amalgamates the information on plant-based molecules identified for cancer lead discovery from in silico approaches. The mandate of this review is to discuss studies published in the last 5-6 years that aim to identify the phytomolecules as leads against cancer with the help of traditional computational approaches as well as newer techniques like network pharmacology and ML. This review also lists the databases and webservers available in the public domain for phytocompounds related information that can be harnessed for drug discovery. It is expected that the present review would be useful to pharmacologists, medicinal chemists, molecular biologists, and other researchers involved in the development of natural products (NPs) into clinically effective lead molecules.

Identifying plasma metabolic characteristics of major depressive disorder, bipolar disorder, and schizophrenia in adolescents

Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are classified as major mental disorders and together account for the second-highest global disease burden, and half of these patients experience symptom onset in adolescence. Several studies have reported both similar and unique features regarding the risk factors and clinical symptoms of these three disorders. However, it is still unclear whether these disorders have similar or unique metabolic characteristics in adolescents. We conducted a metabolomics analysis of plasma samples from adolescent healthy controls (HCs) and patients with MDD, BD, and SCZ. We identified differentially expressed metabolites between patients and HCs. Based on the differentially expressed metabolites, correlation analysis, metabolic pathway analysis, and potential diagnostic biomarker identification were conducted for disorders and HCs. Our results showed significant changes in plasma metabolism between patients with these mental disorders and HCs; the most distinct changes were observed in SCZ patients. Moreover, the metabolic differences in BD patients shared features with those in both MDD and SCZ, although the BD metabolic profile was closer to that of MDD than to SCZ. Additionally, we identified the metabolites responsible for the similar and unique metabolic characteristics in multiple metabolic pathways. The similar significant differences among the three disorders were found in fatty acid, steroid-hormone, purine, nicotinate, glutamate, tryptophan, arginine, and proline metabolism. Interestingly, we found unique characteristics of significantly altered glycolysis, glycerophospholipid, and sphingolipid metabolism in SCZ; lysine, cysteine, and methionine metabolism in MDD and BD; and phenylalanine, tyrosine, and aspartate metabolism in SCZ and BD. Finally, we identified five panels of potential diagnostic biomarkers for MDD-HC, BD-HC, SCZ-HC, MDD-SCZ, and BD-SCZ comparisons. Our findings suggest that metabolic characteristics in plasma vary across psychiatric disorders and that critical metabolites provide new clues regarding molecular mechanisms in these three psychiatric disorders.

ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis

High-resolution mass spectrometry (HRMS) is a prominent analytical tool that characterizes chlorinated disinfection byproducts (Cl-DBPs) in an unbiased manner. Due to the diversity of chemicals, complex background signals, and the inherent analytical fluctuations of HRMS, conventional isotopic pattern (37Cl/35Cl), mass defect, and direct molecular formula (MF) prediction are insufficient for accurate recognition of the diverse Cl-DBPs in real environmental samples. This work proposes a novel strategy to recognize Cl-containing chemicals based on machine learning. Our hierarchical machine learning framework has two random forest-based models: the first layer is a binary classifier to recognize Cl-containing chemicals, and the second layer is a multiclass classifier to annotate the number of Cl present. This model was trained using ∼1.4 million distinctive MFs from PubChem. Evaluated on over 14,000 unique MFs from NIST20, this machine learning model achieved 93.3% accuracy in recognizing Cl-containing MFs (Cl-MFs) and 92.9% accuracy in annotating the number of Cl for Cl-MFs. Furthermore, the trained model was integrated into ChloroDBPFinder, a standalone R package for the streamlined processing of LC-HRMS data and annotating both known and unknown Cl-containing compounds. Tested on existing Cl-DBP data sets related to aspartame chlorination in tap water, our ChloroDBPFinder efficiently extracted 159 Cl-containing DBP features and tentatively annotated the structures of 10 Cl-DBPs via molecular networking. In another application of a chlorinated humic substance, ChloroDBPFinder extracted 79 high-quality Cl-DBPs and tentatively annotated six compounds. In summary, our proposed machine learning strategy and the developed ChloroDBPFinder provide an advanced solution to identifying Cl-containing compounds in nontargeted analysis of water samples. It is freely available on GitHub (https://github.com/HuanLab/ChloroDBPFinder).

Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used in untargeted metabolomics, but large-scale and high-accuracy metabolite annotation remains a challenge due to the complex nature of biological samples. Recently introduced electron impact excitation of ions from organics (EIEIO) fragmentation can generate information-rich fragment ions. However, effective utilization of EIEIO tandem mass spectrometry (MS/MS) is hindered by the lack of reference spectral databases. Molecular networking (MN) shows great promise in large-scale metabolome annotation, but enhancing the correlation between spectral and structural similarity is essential to fully exploring the benefits of MN annotation. In this study, a novel approach was proposed to enhance metabolite annotation in untargeted metabolomics using EIEIO and MN. MS/MS spectra were acquired in EIEIO and collision-induced dissociation (CID) modes for over 400 reference metabolites. The study revealed a stronger correlation between the EIEIO spectra and metabolite structure. Moreover, the EIEIO spectral network outperformed the CID spectral network in capturing structural analogues. The annotation performance of the structural similarity network for untargeted LC-MS/MS was evaluated. For the spiked NIST SRM 1950 human plasma, the annotation coverage and accuracy were 72.94 and 74.19%, respectively. A total of 2337 metabolite features were successfully annotated in NIST SRM 1950 human plasma, which was twice that of LC-CID MS/MS. Finally, the developed method was applied to investigate prostate cancer. A total of 87 significantly differential metabolites were annotated. This study combining EIEIO and MN makes a valuable contribution to improving metabolome annotation.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.

A comprehensive insight into the transformation pathways and products of fluoxetine and venlafaxine in wastewater based on molecular networking nontarget screening

Fluoxetine (FLX) and venlafaxine (VEN) are widely used antidepressant pharmaceuticals and were frequently detected in wastewater. Despite incomplete mineralization during biological wastewater treatment processes has been revealed, little is known about their transformation products (TPs) formed in the biological systems. To fill this gap, batch reactors and molecular networking nontarget screening were employed to identify the TPs and explore the transformation pathways of FLX and VEN in wastewater. On the basis, the concentrations of the TPs in wastewater treatment plants (WWTPs) were determined and their toxicity was predicted. The removal rate constants per unit of biomass of FLX and VEN were up to 0.3192 and 0.1644 L/(gMLSS*d) in batch experiments, respectively. Subsequently, 11 TPs of VEN and 11 TPs of FLX were tentatively identified, among which 9 TPs of FLX and 5 TPs of VEN were newly reported in this study. The proposed transformation pathways provided new insights into the transformation reactions including dehydrogenation, N-formylation and hydroxylation for FLX, and formylation, epoxidation and methylation for VEN. Particularly, N-succinylation and demethylation were the dominant transformation pathways for FLX and VEN during transformation processes. The results of sampling campaigns revealed that the accumulated concentration of TPs were higher than the concentrations of VEN in effluent of WWTPs. In silico prediction results suggested that certain TPs have higher toxicity, persistence and biodegradability than their corresponding parent compounds of FLX and VEN. In addition, VEN-TP264(a) showed higher ecological risks than VEN. This study revealed the transformation processes and fate of FLX and VEN in wastewater, indicating that greater concerns should be exerted on the toxicity detection and control of the TPs of FLX and VEN in the treated wastewater.

Novel Insights into the Adverse Health Effects of per- and Polyfluoroalkyl Substances on the Kidney via Human Urine Metabolomics

Per- and polyfluoroalkyl substances (PFAS) receive significant research attention due to their potential adverse effects on human health. Evidence shows that the kidney is one of the target organs of PFAS. In occupational exposure scenarios, high PFAS concentrations may adversely affect kidney metabolism, but whether this effect is reflected in the small metabolic molecules contained in urine remains unknown. In this study, 72 matched serum and urine samples from occupational workers of a fluorochemical manufactory as well as 153 urine samples from local residents were collected, and 23 PFAS levels were quantified. The concentrations of Σ23PFAS in the serum and urine samples of workers were 5.43 ± 1.02 μg/mL and 201 ± 46.9 ng/mL, respectively, while the Σ23PFAS concentration in the urine of the residents was 6.18 ± 0.76 ng/mL. For workers, high levels of urinary PFAS were strongly correlated with levels in serum (r = 0.57-0.93), indicating that urinary PFAS can be a good indicator for serum PFAS levels. Further, a urine nontargeted metabolomics study was conducted. The results of association models, including Bayesian kernel machine regression, demonstrated positive correlations between urinary PFAS levels and key small kidney molecules. A total of eight potential biomarkers associated with PFAS exposure were identified, and all of them showed significant positive correlations with markers of kidney function. These findings provide the first evidence that urine can serve as a matrix to indicate the adverse health effects of high levels of exposure to PFAS on the kidneys.

Metabolic pathways engineering for drought or/and heat tolerance in cereals

Drought (D) and heat (H) are the two major abiotic stresses hindering cereal crop growth and productivity, either singly or in combination (D/+H), by imposing various negative impacts on plant physiological and biochemical processes. Consequently, this decreases overall cereal crop production and impacts global food availability and human nutrition. To achieve global food and nutrition security vis-a-vis global climate change, deployment of new strategies for enhancing crop D/+H stress tolerance and higher nutritive value in cereals is imperative. This depends on first gaining a mechanistic understanding of the mechanisms underlying D/+H stress response. Meanwhile, functional genomics has revealed several stress-related genes that have been successfully used in target-gene approach to generate stress-tolerant cultivars and sustain crop productivity over the past decades. However, the fast-changing climate, coupled with the complexity and multigenic nature of D/+H tolerance suggest that single-gene/trait targeting may not suffice in improving such traits. Hence, in this review-cum-perspective, we advance that targeted multiple-gene or metabolic pathway manipulation could represent the most effective approach for improving D/+H stress tolerance. First, we highlight the impact of D/+H stress on cereal crops, and the elaborate plant physiological and molecular responses. We then discuss how key primary metabolism- and secondary metabolism-related metabolic pathways, including carbon metabolism, starch metabolism, phenylpropanoid biosynthesis, γ-aminobutyric acid (GABA) biosynthesis, and phytohormone biosynthesis and signaling can be modified using modern molecular biotechnology approaches such as CRISPR-Cas9 system and synthetic biology (Synbio) to enhance D/+H tolerance in cereal crops. Understandably, several bottlenecks hinder metabolic pathway modification, including those related to feedback regulation, gene functional annotation, complex crosstalk between pathways, and metabolomics data and spatiotemporal gene expressions analyses. Nonetheless, recent advances in molecular biotechnology, genome-editing, single-cell metabolomics, and data annotation and analysis approaches, when integrated, offer unprecedented opportunities for pathway engineering for enhancing crop D/+H stress tolerance and improved yield. Especially, Synbio-based strategies will accelerate the development of climate resilient and nutrient-dense cereals, critical for achieving global food security and combating malnutrition.

Improving the Data Quality of Untargeted Metabolomics through a Targeted Data-Dependent Acquisition Based on an Inclusion List of Differential and Preidentified Ions

Metabolomics based on high-resolution mass spectrometry has become a powerful technique in biomedical research. The development of various analytical tools and online libraries has promoted the identification of biomarkers. However, how to make mass spectrometry collect more data information is an important but underestimated research topic. Herein, we combined full-scan and data-dependent acquisition (DDA) modes to develop a new targeted DDA based on the inclusion list of differential and preidentified ions (dpDDA). In this workflow, the MS1 datasets for statistical analysis and metabolite preidentification were first obtained using full-scan, and then, the MS/MS datasets for metabolite identification were obtained using targeted DDA of quality control samples based on the inclusion list. Compared with the current methods (DDA, data-independent acquisition, targeted DDA with time-staggered precursor ion list, and iterative exclusion DDA), dpDDA showed better stability, higher characteristic ion coverage, higher differential metabolites' MS/MS coverage, and higher quality MS/MS spectra. Moreover, the same trend was verified in the analysis of large-scale clinical samples. More surprisingly, dpDDA can distinguish patients with different severities of coronary heart disease (CHD) based on the Canadian Cardiovascular Society angina classification, which we cannot distinguish through conventional metabolomics data collection. Finally, dpDDA was employed to differentiate CHD from healthy control, and targeted metabolomics confirmed that dpDDA could identify a more complete metabolic pathway network. At the same time, four unreported potential CHD biomarkers were identified, and the area under the receiver operating characteristic curve was greater than 0.85. These results showed that dpDDA would expand the discovery of biomarkers based on metabolomics, more comprehensively explore the key metabolites and their association with diseases, and promote the development of precision medicine.

A Structure-Guided Molecular Network Strategy for Global Untargeted Metabolomics Data Annotation

Large-scale metabolite annotation is a bottleneck in untargeted metabolomics. Here, we present a structure-guided molecular network strategy (SGMNS) for deep annotation of untargeted ultra-performance liquid chromatography-high resolution mass spectrometry (MS) metabolomics data. Different from the current network-based metabolite annotation method, SGMNS is based on a global connectivity molecular network (GCMN), which was constructed by molecular fingerprint similarity of chemical structures in metabolome databases. Neighbor metabolites with similar structures in GCMN are expected to produce similar spectra. Network annotation propagation of SGMNS is performed using known metabolites as seeds. The experimental MS/MS spectra of seeds are assigned to corresponding neighbor metabolites in GCMN as their "pseudo" spectra; the propagation is done by searching predicted retention times, MS1, and "pseudo" spectra against metabolite features in untargeted metabolomics data. Then, the annotated metabolite features were used as new seeds for annotation propagation again. Performance evaluation of SGMNS showed its unique advantages for metabolome annotation. The developed method was applied to annotate six typical biological samples; a total of 701, 1557, 1147, 1095, 1237, and 2041 metabolites were annotated from the cell, feces, plasma (NIST SRM 1950), tissue, urine, and their pooled sample, respectively, and the annotation accuracy was >83% with RSD <2%. The results show that SGMNS fully exploits the chemical space of the existing metabolomes for metabolite deep annotation and overcomes the shortcoming of insufficient reference MS/MS spectra.

Metabolomics in drug research and development: The recent advances in technologies and applications

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding of metabolism in biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. More importantly, metabolomics greatly accelerates the drug development process by predicting pharmacokinetics, pharmacodynamics, and drug response. In addition, metabolomics facilitates the exploration of drug repurposing and drug-drug interactions, as well as the development of personalized treatment strategies. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

An integrated deep learning framework for the interpretation of untargeted metabolomics data

Untargeted metabolomics is gaining widespread applications. The key aspects of the data analysis include modeling complex activities of the metabolic network, selecting metabolites associated with clinical outcome and finding critical metabolic pathways to reveal biological mechanisms. One of the key roadblocks in data analysis is not well-addressed, which is the problem of matching uncertainty between data features and known metabolites. Given the limitations of the experimental technology, the identities of data features cannot be directly revealed in the data. The predominant approach for mapping features to metabolites is to match the mass-to-charge ratio (m/z) of data features to those derived from theoretical values of known metabolites. The relationship between features and metabolites is not one-to-one since some metabolites share molecular composition, and various adduct ions can be derived from the same metabolite. This matching uncertainty causes unreliable metabolite selection and functional analysis results. Here we introduce an integrated deep learning framework for metabolomics data that take matching uncertainty into consideration. The model is devised with a gradual sparsification neural network based on the known metabolic network and the annotation relationship between features and metabolites. This architecture characterizes metabolomics data and reflects the modular structure of biological system. Three goals can be achieved simultaneously without requiring much complex inference and additional assumptions: (1) evaluate metabolite importance, (2) infer feature-metabolite matching likelihood and (3) select disease sub-networks. When applied to a COVID metabolomics dataset and an aging mouse brain dataset, our method found metabolic sub-networks that were easily interpretable.

Statistical methods and resources for biomarker discovery using metabolomics

Metabolomics is a dynamic tool for elucidating biochemical changes in human health and disease. Metabolic profiles provide a close insight into physiological states and are highly volatile to genetic and environmental perturbations. Variation in metabolic profiles can inform mechanisms of pathology, providing potential biomarkers for diagnosis and assessment of the risk of contracting a disease. With the advancement of high-throughput technologies, large-scale metabolomics data sources have become abundant. As such, careful statistical analysis of intricate metabolomics data is essential for deriving relevant and robust results that can be deployed in real-life clinical settings. Multiple tools have been developed for both data analysis and interpretations. In this review, we survey statistical approaches and corresponding statistical tools that are available for discovery of biomarkers using metabolomics.

Serum metabolomic analysis reveals disorder of steroid hormone biosynthesis in patients with idiopathic inflammatory myopathy

Idiopathic inflammatory myopathy (IIM) is a heterogeneous group of autoimmune diseases with various clinical manifestations, treatment responses, and prognoses. According to the clinical manifestations and presence of different myositis-specific autoantibodies (MSAs), IIM is classified into several major subgroups, including PM, DM, IBM, ASS, IMNM, and CADM. However, the pathogenic mechanisms of these subgroups remain unclear and need to be investigated. Here, we applied MALDI-TOF-MS to examine the serum metabolome of 144 patients with IIM and analyze differentially expressed metabolites among IIM subgroups or MSA groups. The results showed that the DM subgroup had lower activation of the steroid hormone biosynthesis pathway, while the non-MDA5 MSA group had higher activation of the arachidonic acid metabolism pathway. Our study may provide some insights into the heterogeneous mechanisms of IIM subgroups, potential biomarkers, and management of IIM.

A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics

Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.

Diagnostic, Prognostic and Mechanistic Biomarkers of COVID-19 Identified by Mass Spectrometric Metabolomics

A number of studies have assessed the impact of SARS-CoV-2 infection and COVID-19 severity on the metabolome of exhaled air, saliva, plasma, and urine to identify diagnostic and prognostic biomarkers. In spite of the richness of the literature, there is no consensus about the utility of metabolomic analyses for the management of COVID-19, calling for a critical assessment of the literature. We identified mass spectrometric metabolomic studies on specimens from SARS-CoV2-infected patients and subjected them to a cross-study comparison. We compared the clinical design, technical aspects, and statistical analyses of published studies with the purpose to identify the most relevant biomarkers. Several among the metabolites that are under- or overrepresented in the plasma from patients with COVID-19 may directly contribute to excessive inflammatory reactions and deficient immune control of SARS-CoV2, hence unraveling important mechanistic connections between whole-body metabolism and the course of the disease. Altogether, it appears that mass spectrometric approaches have a high potential for biomarker discovery, especially if they are subjected to methodological standardization.

Plant-Derived Products with Therapeutic Potential against Gastrointestinal Bacteria

The rising burden of antimicrobial resistance and increasing infectious disease outbreaks, including the recent COVID-19 pandemic, has led to a growing demand for the development of natural products as a valuable source of leading medicinal compounds. There is a wide variety of active constituents found in plants, making them an excellent source of antimicrobial agents with therapeutic potential as alternatives or potentiators of antibiotics. The structural diversity of phytochemicals enables them to act through a variety of mechanisms, targeting multiple biochemical pathways, in contrast to traditional antimicrobials. Moreover, the bioactivity of the herbal extracts can be explained by various metabolites working in synergism, where hundreds to thousands of metabolites make up the extract. Although a vast amount of literature is available regarding the use of these herbal extracts against bacterial and viral infections, critical assessments of their quality are lacking. This review aims to explore the efficacy and antimicrobial effects of herbal extracts against clinically relevant gastrointestinal infections including pathogenic Escherichia coli, toxigenic Clostridioides difficile, Campylobacter and Salmonella species. The review will discuss research gaps and propose future approaches to the translational development of plant-derived products for drug discovery purposes for the treatment and prevention of gastrointestinal infectious diseases.

Metabolome and transcriptome signatures shed light on the anti-obesity effect of Polygonatum sibiricum

Obesity has become one of the major threats to human health across the globe. The rhizomes of Polygonatum sibiricum have shown promising anti-obesity effect. However, the metabolic and genetic basis mediating this beneficial effect are not fully resolved. It is well known that older rhizomes of P. sibiricum exert stronger pharmacological effects. Here, we performed high-resolution metabolome profiling of P. sibiricum rhizomes at different growth stages, and identified that three candidate anti-obesity metabolites, namely phloretin, linoleic acid and α-linolenic acid, accumulated more in adult rhizomes. To elucidate the genetic basis controlling the accumulation of these metabolites, we performed transcriptome profiling of rhizomes from juvenile and adult P. sibiricum. Through third-generation long-read sequencing, we built a high-quality transcript pool of P. sibiricum, and resolved the genetic pathways involved in the biosynthesis and metabolism of phloretin, linoleic acid and α-linolenic acid. Comparative transcriptome analysis revealed altered expression of the genetic pathways in adult rhizomes, which likely lead to higher accumulation of these candidate metabolites. Overall, we identified several metabolic and genetic signatures related to the anti-obesity effect of P. sibiricum. The metabolic and transcriptional datasets generated in this work could also facilitate future research on other beneficial effects of this medicinal plant.