Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices

Quartet metabolite reference materials for inter-laboratory proficiency test and data integration of metabolomics profiling

BACKGROUND

Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets.

RESULTS

As part of the Quartet Project, we establish a publicly available suite of four metabolite reference materials derived from B lymphoblastoid cell lines from a family of parents and monozygotic twin daughters. We generate comprehensive LC-MS-based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. The Quartet multi-sample-based signal-to-noise ratio enables objective assessment of the reliability of intra-batch and cross-batch metabolomics profiling in detecting intrinsic biological differences among the four groups of samples. Significant variations in the reliability of the metabolomics profiling are identified across laboratories. Importantly, ratio-based metabolomics profiling, by scaling the absolute values of a study sample relative to those of a common reference sample, enables cross-laboratory quantitative data integration. Thus, we construct the ratio-based high-confidence reference datasets between two reference samples, providing "ground truth" for inter-laboratory accuracy assessment, which enables objective evaluation of quantitative metabolomics profiling using various instruments and protocols.

CONCLUSIONS

Our study provides the community with rich resources and best practices for inter-laboratory proficiency tests and data integration, ensuring reliability of large-scale and longitudinal metabolomic studies.

Identifying key soil characteristics for Francisella tularensis classification with optimized Machine learning models

Francisella tularensis (Ft) poses a significant threat to both animal and human populations, given its potential as a bioweapon. Current research on the classification of this pathogen and its relationship with soil physical-chemical characteristics often relies on traditional statistical methods. In this study, we leverage advanced machine learning models to enhance the prediction of epidemiological models for soil-based microbes. Our model employs a two-stage feature ranking process to identify crucial soil attributes and hyperparameter optimization for accurate pathogen classification using a unique soil attribute dataset. Optimization involves various classification algorithms, including Support Vector Machines (SVM), Ensemble Models (EM), and Neural Networks (NN), utilizing Bayesian and Random search techniques. Results indicate the significance of soil features such as clay, nitrogen, soluble salts, silt, organic matter, and zinc , while identifying the least significant ones as potassium, calcium, copper, sodium, iron, and phosphorus. Bayesian optimization yields the best results, achieving an accuracy of 86.5% for SVM, 81.8% for EM, and 83.8% for NN. Notably, SVM emerges as the top-performing classifier, with an accuracy of 86.5% for both Bayesian and Random Search optimizations. The insights gained from employing machine learning techniques enhance our understanding of the environmental factors influencing Ft's persistence in soil. This, in turn, reduces the risk of false classifications, contributing to better pandemic control and mitigating socio-economic impacts on communities.

GC/MS and LC/MS serum metabolomic analysis of Chinese LN patients

China, being a densely populated nation, faces a substantial economic burden due to a high incidence of lupus nephritis (LN) cases. The concealed onset of LN has resulted in many individuals have missed the optimal timing for treatment. The aim of the research is to study the serum metabolomics of Chinese LN patients using gas chromatography (GC)/mass spectrometry (MS) and liquid chromatography (LC)/MS to identify potential diagnostic markers. Fifty LN patients and fifty normal controls, matched for Body Mass Index (BMI) and age, were selected. Serum analysis was conducted using GC/MS and LC/MS, followed by multivariate statistical analysis. Various multidimensional analyses, including principal component analysis, partial least squares discrimination analysis, and orthogonal partial least squares discrimination analysis, along with one-dimensional analyses such as t-tests, were performed. Metabolites with variable importance in projection value > 1 and a p-value < 0.05 were considered critical biomarkers for LN. Furthermore, identified biomarkers delineated relevant metabolic pathways, and a metabolic pathway map was obtained from the database. Forty-one metabolites were identified as potential LN biomarkers, primarily associated with immune regulation, energy metabolism, intestinal microbial metabolism, renal damage, and oxidative stress. The potential for diagnosing LN and other diseases through metabolomics is demonstrated. Future research should explore larger sample sizes, metabolomic comparisons across different diseases and health states, and integration of metabolomics with clinical diagnostics. Such studies will enhance the understanding of metabolomics in medical diagnosis and provide robust support for its practical application.

Untargeted Metabolomics Analysis of Gingival Tissue in Patients with Severe Periodontitis

The purpose of this study was to determine potential metabolic biomarkers and therapeutic drugs in the gingival tissue of individuals with periodontitis. Liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) were used to analyze the gingival tissue samples from 20 patients with severe periodontitis and 20 healthy controls. Differential metabolites were identified using variable important in projection (VIP) values from the orthogonal partial least squares discrimination analysis (OPLS-DA) model and then verified for significance between groups using a two-tailed Student's t test. In total, 65 metabolites were enriched in 33 metabolic pathways, with 40 showing a significant increase and 25 expressing a significant decrease. In addition, it was found that patients with severe periodontitis have abnormalities in metabolic pathways, such as glucose metabolism, purine metabolism, amino acid metabolism, and so on. Furthermore, based on a multidimensional analysis, 12 different metabolites may be the potential biomarkers of severe periodontitis. The experiment's raw data have been uploaded to the MetaboLights database, and the project number is MTBLS8357. Moreover, osteogenesis differentiation characteristics were detected in the selected metabolites. The findings may provide a basis for the study of diagnostic biomarkers and therapeutic metabolites in severe periodontitis.

Cecal metabolomics of 2 divergently selected rabbit lines revealed microbial mechanisms correlated to intramuscular fat deposition

The gastrointestinal microbiota plays a key role in the host physiology and health through a complex host-microbiota co-metabolism. Metabolites produced by microbial metabolism can travel through the bloodstream to reach distal organs and affect their function, ultimately influencing the development of relevant production traits such as meat quality. Meat quality is a complex trait made up of a number of characteristics and intramuscular fat content (IMF) is considered to be one of the most important parameters. In this study, 52 rabbits from 2 lines divergently selected for IMF (high-IMF (H) and low-IMF (L) lines) were used to perform an untargeted metabolomic analysis of their cecal content, with the aim to obtain information on genetically determined microbial metabolism related to IMF. A large, correlated response to selection was found in their cecal metabolome composition. Partial least squares discriminant analysis was used to identify the pathways differentiating the lines, which showed a classification accuracy of 99%. On the other hand, 2 linear partial least squares analyses were performed, one for each line, to extract evidence on the specific pathways associated with IMF deposition within each line, which showed predictive abilities (estimated using the Q2) of approximately 60%. The most relevant pathways differentiating the lines were those related to amino acids (aromatic, branched-chain, and gamma-glutamyl), secondary bile acids, and purines. The higher content of secondary bile acids in the L-line was related to greater lipid absorption, while the differences found in purines suggested different fermentation activities, which could be related to greater nitrogen utilization and energy efficiency in the L-line. The linear analyses showed that lipid metabolism had a greater relative importance for IMF deposition in the L-line, whereas a more complex microbial metabolism was associated with the H-line. The lysophospholipids and gamma-glutamyl amino acids were associated with IMF in both lines; the nucleotide and secondary bile acid metabolisms were mostly associated in the H-line; and the long-chain and branched-chain fatty acids were mostly associated in the L-line. A metabolic signature consisting of 2 secondary bile acids and 2 protein metabolites was found with 88% classification accuracy, pointing to the interaction between lipid absorption and protein metabolism as a relevant driver of the microbiome activity influencing IMF.

Study on the Characteristics of Traditional Chinese Medicine Syndromes in Patients with Erosive Gastritis Based on Metabolomics

According to traditional Chinese medicine theory, tongue coatings reflect changes in the body. The goal of this study was to identify a metabolite or a set of metabolites capable of classifying characteristics of traditional Chinese medicine syndromes in erosive gastritis. In this study, we collected tongue coatings of patients with erosive gastritis with damp-heat syndrome (DHS), liver depression and qi stagnation syndrome (LDQSS), and healthy volunteers. Then, we analyzed the differences in metabolic characteristics between the two groups based on metabolomics. We identified 14 potential biomarkers related to the DHS group, and six metabolic pathways were enriched. The differential pathways included pyrimidine metabolism, pantothenate and CoA biosynthesis, citrate cycle (TCA cycle), pyruvate metabolism, glycolysis/gluconeogenesis, and purine metabolism. Similarly, in the LDQSS group, we identified 25 potential biomarkers and 18 metabolic pathways were enriched. The top five pathways were the TCA cycle, sphingolipid metabolism, fatty acid biosynthesis, pantothenate and CoA biosynthesis, and the pentose phosphate pathway. In conclusion, the DHS group and the LDQSS group have different characteristics.

Conversion of raltegravir carrying a 1,3,4-oxadiazole ring to a hydrolysis product upon pH changes decreases its antiviral activity

Raltegravir (RAL), a human immunodeficiency virus (HIV)-1 integrase inhibitor, has been administered as part of antiretroviral therapy. Studies in patients with HIV-1 have shown high variability in the pharmacokinetics of RAL, and in healthy volunteers, coadministration of proton-pump inhibitors has been shown to increase the plasma RAL concentrations. Here, we found that RAL containing a 1,3,4-oxadiazole ring is converted to a hydrolysis product (H-RAL) with a cleaved 1,3,4-oxadiazole ring at pH 1.0 and 13.0 conditions in vitro, thereby reducing the anti-HIV activity of the drug. The inclusion of cyclodextrins (beta-cyclodextrin [βCD], random methyl-βCD [RAM-βCD], and hydroxypropyl-βCD [HP-βCD]) can protect RAL from pH-induced changes. The conversion of RAL to H-RAL was detected by using various mass spectrometry analyses. The chromatogram of H-RAL increased in a time-dependent manner similar to another 1,3,4-oxadiazole-containing drug, zibotentan, using high-performance liquid chromatography. Oral bioavailability and target protein interactions of H-RAL were predicted to be lower than those of RAL. Moreover, H-RAL exhibited significantly reduced anti-HIV-1 activity, whereas combinations with βCD, RAM-βCD, and HP-βCD attenuated this effect in cell-based assays. These findings suggest that βCDs can potentially protect against the conversion of RAL to H-RAL under acidic conditions in the stomach, thereby preserving the anti-HIV-1 effect of RAL. Although clinical trials are needed for evaluation, we anticipate that protective devices such as βCDs may improve the pharmacokinetics of RAL, leading to better treatment outcomes, including reduced dosing, long-term anti-HIV-1 activity, and deeper HIV-1 suppression.

Deciphering the Metabolome under Stress: Insights from Rodent Models

Despite intensive research efforts to understand the molecular underpinnings of psychological stress and stress responses, the underlying molecular mechanisms remain largely elusive. Towards this direction, a plethora of stress rodent models have been established to investigate the effects of exposure to different stressors. To decipher affected molecular pathways in a holistic manner in these models, metabolomics approaches addressing altered, small molecule signatures upon stress exposure in a high-throughput, quantitative manner provide insightful information on stress-induced systemic changes in the brain. In this review, we discuss stress models in mice and rats, followed by mass spectrometry (MS) and nuclear magnetic resonance (NMR) metabolomics studies. We particularly focus on acute, chronic and early life stress paradigms, highlight how stress is assessed at the behavioral and molecular levels and focus on metabolomic outcomes in the brain and peripheral material such as plasma and serum. We then comment on common metabolomics patterns across different stress models and underline the need for unbiased -omics methodologies and follow-up studies of metabolomics outcomes to disentangle the complex pathobiology of stress and pertinent psychopathologies.

Comparative analysis of hepatic transcriptomes and metabolomes of Changshun green-shell laying hens based on different green eggshell color intensities

The eggshell color of avian species is an important trait that is predominantly determined by the pigments biliverdin and protoporphyrin. Various factors affect eggshell pigment deposition and coloration; however, the underlying mechanisms remain unclear. We analyzed the hepatic transcriptomes and metabolomes of Changshun green-shell hens laying dark green and light green eggs to investigate the potential role of the liver in regulating the intensity of the green eggshell color. In total, 350 differentially expressed genes and 211 differentially altered metabolites were identified. Gene set enrichment analysis revealed that the enriched pathways and Gene Ontology (GO) terms were mainly associated with energy, immunity, and nutrient metabolism. Metabolite set enrichment analysis revealed that the enriched pathways were mainly associated with amino acid, vitamin, bile acid, and lipid metabolism. Moreover, gene-metabolite interaction network analysis revealed 1 subnetwork. Most genes and metabolites in this subnetwork were determined to be related to melanin metabolism and transport. In conclusion, our results suggest that hepatic melanin metabolism and transport are critical for eggshell coloration. Six candidate genes (CDKN2B, DDC, PYCR1, ABCG5, SLC3A1, and P2RX2) and 7 candidate metabolites (serotonin, 5-hydroxyindoleacetic acid, ornithine, acetylcholine, L-tryptophan, D-ornithine, and ADP) were suggested to play important roles in this process. Meanwhile, this study suggests that changes in hepatic energy metabolism, immune status, antioxidation activity, nutrient availability, and bile acid synthesis can impair eggshell coloration.

Advances in steroid research from the pioneering neurosteroid concept to metabolomics: New insights into pregnenolone function

Advances in neuroendocrinology have led to major discoveries since the 19th century, identifying adaptive loops for maintaining homeostasis. One of the most remarkable discoveries was the concept of neurosteroids, according to which the brain is not only a target but also a source of steroid production. The identification of new membrane steroid targets now underpins the neuromodulatory effects of neurosteroids such as pregnenolone, which is involved in functions mediated by the GPCR CB1 receptor. Structural analysis of steroids is a key feature of their interactions with the phospholipid membrane, receptors and resulting activity. Therefore, mass spectrometry-based methods have been developed to elucidate the metabolic pathways of steroids, the ultimate approach being metabolomics, which allows the identification of a large number of metabolites in a single sample. This approach should enable us to make progress in understanding the role of neurosteroids in the functioning of physiological and pathological processes.

Studying Plant Specialized Metabolites Using Computational Metabolomics Strategies

Plant specialized metabolites have diversified vastly over the course of plant evolution, and they are considered key players in complex interactions between plants and their environment. The chemical diversity of these metabolites has been widely explored and utilized in agriculture and crop enhancement, the food industry, and drug development, among other areas. However, the immensity of the plant metabolome can make its exploration challenging. Here we describe a protocol for exploring plant specialized metabolites that combines high-resolution mass spectrometry and computational metabolomics strategies, including molecular networking, identification of structural motifs, as well as prediction of chemical structures and metabolite classes.

Moxibustion ameliorates osteoarthritis by regulating gut microbiota via impacting cAMP-related signaling pathway

BACKGROUND

Osteoarthritis (OA) is a prevalent progressive disorder. Moxibustion has found widespread use in clinical practice for OA, while its underlying mechanism remains elusive.

OBJECTIVE

To investigate whether moxibustion can ameliorate OA by influencing the metabolic processes in OA and to elucidate the specific metabolic mechanisms involved.

METHODS

C57BL/6J WT mice were randomly assigned to one of three groups: the SHAM group, the ACLT group, and the ACLT+M group. In the ACLT+M group, mice underwent moxibustion treatment at acupoints Shenshu (BL23) and Zusanli (ST36) for a continuous period of 28 days, with each session lasting 20 min. We conducted a comprehensive analysis to assess the impact of moxibustion on OA, focusing on pathological changes, intestinal flora composition, and serum metabolites.

RESULTS

Moxibustion treatment effectively mitigated OA-related pathological changes. Specifically, moxibustion treatment resulted in the amelioration of articular cartilage damage, synovial inflammation, subchondral bone sclerosis when compared to the ACLT group. Moreover, 16S rDNA sequencing analysis revealed that moxibustion treatment positively influenced the composition of the flora, making it more similar to that of the SHAM group. Notably, moxibustion treatment led to a reduction in the abundance of Ruminococcus and Proteobacteria in the intestine. In addition, non-targeted metabolomics analysis identified 254 significantly different metabolites between the groups. Based on KEGG pathway analysis and the observed impact of moxibustion on OA-related inflammation, moxibustion therapy is closely associated with the cAMP-related signaling pathway.

CONCLUSION

Moxibustion can relieve OA by regulating intestinal flora and via impacting cAMP-related signaling pathway.

Metabolomics of natural samples: A tutorial review on the latest technologies

Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early-career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early-career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.

An evaluation of untargeted metabolomics methods to characterize inborn errors of metabolism

Inborn errors of metabolism (IEMs) encompass a diverse group of disorders that can be difficult to classify due to heterogenous clinical, molecular, and biochemical manifestations. Untargeted metabolomics platforms have become a popular approach to analyze IEM patient samples because of their ability to detect many metabolites at once, accelerating discovery of novel biomarkers, and metabolic mechanisms of disease. However, there are concerns about the reproducibility of untargeted metabolomics research due to the absence of uniform reporting practices, data analyses, and experimental design guidelines. Therefore, we critically evaluated published untargeted metabolomic platforms used to characterize IEMs to summarize the strengths and areas for improvement of this technology as it progresses towards the clinical laboratory. A total of 96 distinct IEMs were collectively evaluated by the included studies. However, most of these IEMs were evaluated by a single untargeted metabolomic method, in a single study, with a limited cohort size (55/96, 57%). The goals of the included studies generally fell into two, often overlapping, categories: detecting known biomarkers from many biochemically distinct IEMs using a single platform, and detecting novel metabolites or metabolic pathways. There was notable diversity in the design of the untargeted metabolomic platforms. Importantly, the majority of studies reported adherence to quality metrics, including the use of quality control samples and internal standards in their experiments, as well as confirmation of at least some of their feature annotations with commercial reference standards. Future applications of untargeted metabolomics platforms to the study of IEMs should move beyond single-subject analyses, and evaluate reproducibility using a prospective, or validation cohort.

Cross-Species Metabolomic Analyses in the Brassicaceae Reveals Common Responses to Ultraviolet-B Exposure

Exposure to UV-B radiation, an intrinsic component of solar light, is detrimental to all living organisms as chromophore units of DNA, RNA and proteins readily absorb high-energy photons. Indirect damage to the same molecules and lipids is mediated by elevated reactive oxygen species (ROS) levels, a side effect of exposure to UV-B stress. To protect themselves from UV-B radiation, plants produce phytochemical sunscreens, among which flavonoids have shown to be particularly effective. The core aglycone of flavonoid molecules is subjected to chemical decoration, such as glycosylation and acylation, further improving sunscreen properties. In particular, acylation, which adds a phenolic ring to flavonoid molecules, enhances the spectral absorption of UV-A and UV-B rays, providing to this class of compounds exceptional shielding power. In this study, we comprehensively analyzed the responses to UV-B radiation in four Brassicaceae species, including Arabidopsis thaliana, Brassica napus, Brassica oleracea, and Brassica rapa. Our study revealed a complete reprogramming of the central metabolic pathway in response to UV-B radiation characterized by increased production of functional precursors of specialized metabolites with UV-B shielding properties, indicating a targeted effort of plant metabolism to provide increased protection. The analysis of specialized metabolites and transcripts revealed the activation of the phenylpropanoid-acetate pathway, leading to the production of specific classes of flavonoids and a cross-species increase in phenylacylated-flavonoid glucosides with synapoyl glycoside decorations. Interestingly, our analysis also revealed that acyltransferase genes of the class of serine carboxypeptidase-like (SCPLs) proteins are costitutively expressed, but downregulated in response to UV-B radiation, possibly independently of the ELONGATED HYPOCOTYL 5 (HY5) signaling pathway.

Untargeted metabolomics of the intestinal tract of DEV-infected ducks

INTRODUCTION

Duck enteritis virus (DEV) mainly causes infectious diseases characterized by intestinal haemorrhage, inflammation and parenchymal organ degeneration in ducks and other poultry. However, the mechanism by which it causes intestinal damage in ducks is not well understood. Metabolomics can provide an in-depth understanding of the full complexity of the disease.

METHODS

In this study, 24 clinically healthy green-shell ducks (weight 1.5 kg ± 20 g) were randomly divided into 2 groups (experimental group, 18; control group, 6). The experimental group was intramuscularly injected with 0.2 mL of DEV virus in solution (TCID50 3.16 × 108 PFU/mL), and the control group was injected with 0.2 mL of sterile normal saline. Duck duodenum and ileum tissue samples were collected at 66 h, 90 h and 114 h post-injection (12 h of fasting before killing), and metabolomics analysis of duck duodenum and ileum tissues at the three time points (66, 90, 114 h) was performed by liquid chromatography-mass spectrometry (LC-MS) to screen for and analyse the potential differentiated metabolites and related signalling pathways.

RESULTS

Screening was performed in the positive/negative mode (Pos: Positive ion mode; the ionization of substances at the ion source with positive ions such as H+, NH4+, Na+ and K+; Neg: Negative ion mode; the ionization of substances at the ion source with negative ions such as Cl-, OAc-), and compound abundance was compared to that in the control group. The total number of differentially abundant compounds in the duodenum at 66 h, 90 h and 114 h of DEV infection gradually increased, and metabolites such as cytidine, 2'-deoxyriboside and 4-guanidinobutyric acid were differentially abundant metabolites common to all three time periods. The metabolic pathways related to inflammatory response and immune response were tryptophan acid metabolism, cysteine-methionine metabolism, histidine metabolism and other amino acid metabolism and fat metabolism. Among them, the metabolic pathways with more differentially abundant metabolites were amino acid biosynthesis, cysteine and methionine metabolism, tryptophan metabolism, unsaturated fatty acid biosynthesis and purine metabolism, and the metabolic pathways with more enrichment factors were the IgA-related intestinal immune network pathway and lysosome pathway. Compared with the control group, there were 16 differentially abundant metabolites in the ileum tissue of DEV-infected ducks at 66 h of infection, 52 at 90 h of infection, and 40 at 14 h of infection with TD114. The metabolic pathways with more enriched differentially abundant metabolites were pyrimidine metabolism, tyrosine metabolism, phenylalanine metabolism and tryptophan biosynthesis. The metabolic pathways with the most enrichment factors were the mTOR signalling pathway, ferroptosis pathway, tryptophan metabolism pathway and caffeine metabolism pathway.

CONCLUSION

Comparative analysis showed that the number of differentially abundant metabolites in the duodenum and ileum differed to some extent after DEV infection, with significantly more differentially abundant metabolites in duodenal tissues and fewer in ileal tissues; after DEV infection, the highest number of differentially abundant metabolites was obtained at 114 h of DEV infection, followed by the second highest at 90 h of infection and the lowest at 66 h of infection. The common differentially abundant metabolites in duodenal and ileal tissues were prostaglandins, arachidonic acid, and arachidonic ethanolamine. The main metabolic pathways in the duodenum were the IgA-associated intestinal immune network pathway and the lysosomal pathway, and the metabolic pathways with more enriched factors in the ileum were the mTOR signalling pathway, the ferroptosis pathway, and the tryptophan metabolism pathway.

Metabolite profiling of Trichinella spiralis adult worms and muscle larvae identifies their excretory and secretory products

Human trichinellosis is a parasitic infection caused by roundworms belonging to the genus Trichinella, especially Trichinella spiralis. Early and accurate clinical diagnoses of trichinellosis are required for efficacious prognosis and treatment. Current drug therapies are limited by antiparasitic resistance, poor absorption, and an inability to kill the encapsulating muscle-stage larvae. Therefore, reliable biomarkers and drug targets for novel diagnostic approaches and anthelmintic drugs are required. In this study, metabolite profiles of T. spiralis adult worms and muscle larvae were obtained using mass spectrometry-based metabolomics. In addition, metabolite-based biomarkers of T. spiralis excretory-secretory products and their related metabolic pathways were characterized. The metabolic profiling identified major, related metabolic pathways involving adenosine monophosphate (AMP)-dependent synthetase/ligase and glycolysis/gluconeogenesis in T. spiralis adult worms and muscle larvae, respectively. These pathways are potential drug targets for the treatment of the intestinal and muscular phases of infection. The metabolome of larva excretory-secretory products was characterized, with amino acid permease and carbohydrate kinase being identified as key metabolic pathways. Among six metabolites, decanoyl-l-carnitine and 2,3-dinor-6-keto prostaglandin F1α-d9 were identified as potential metabolite-based biomarkers that might be related to the host inflammatory processes. In summary, this study compared the relationships between the metabolic profiles of two T. spiralis growth stages. Importantly, the main metabolites and metabolic pathways identified may aid the development of novel clinical diagnostics and therapeutics for human trichinellosis and other related helminthic infections.

Synthetic biology identifies the minimal gene set required for paclitaxel biosynthesis in a plant chassis

The diterpenoid paclitaxel (Taxol) is a chemotherapy medication widely used as a first-line treatment against several types of solid cancers. The supply of paclitaxel from natural sources is limited. However, missing knowledge about the genes involved in several specific metabolic steps of paclitaxel biosynthesis has rendered it difficult to engineer the full pathway. In this study, we used a combination of transcriptomics, cell biology, metabolomics, and pathway reconstitution to identify the complete gene set required for the heterologous production of paclitaxel. We identified the missing steps from the current model of paclitaxel biosynthesis and confirmed the activity of most of the missing enzymes via heterologous expression in Nicotiana benthamiana. Notably, we identified a new C4β-C20 epoxidase that could overcome the first bottleneck of metabolic engineering. We used both previously characterized and newly identified oxomutases/epoxidases, taxane 1β-hydroxylase, taxane 9α-hydroxylase, taxane 9α-dioxygenase, and phenylalanine-CoA ligase, to successfully biosynthesize the key intermediate baccatin III and to convert baccatin III into paclitaxel in N. benthamiana. In combination, these approaches establish a metabolic route to taxoid biosynthesis and provide insights into the unique chemistry that plants use to generate complex bioactive metabolites.

Metabolic pathway analysis using stable isotopes in patients with cancer

Metabolic reprogramming is central to malignant transformation and cancer cell growth. How tumours use nutrients and the relative rates of reprogrammed pathways are areas of intense investigation. Tumour metabolism is determined by a complex and incompletely defined combination of factors intrinsic and extrinsic to cancer cells. This complexity increases the value of assessing cancer metabolism in disease-relevant microenvironments, including in patients with cancer. Stable-isotope tracing is an informative, versatile method for probing tumour metabolism in vivo. It has been used extensively in preclinical models of cancer and, with increasing frequency, in patients with cancer. In this Review, we describe approaches for using in vivo isotope tracing to define fuel preferences and pathway engagement in tumours, along with some of the principles that have emerged from this work. Stable-isotope infusions reported so far have revealed that in humans, tumours use a diverse set of nutrients to supply central metabolic pathways, including the tricarboxylic acid cycle and amino acid synthesis. Emerging data suggest that some activities detected by stable-isotope tracing correlate with poor clinical outcomes and may drive cancer progression. We also discuss current challenges in isotope tracing, including comparisons of in vivo and in vitro models, and opportunities for future discovery in tumour metabolism.

Plant response to heavy metal stress toxicity: the role of metabolomics and other omics tools

Metabolomic investigations offers a significant foundation for improved comprehension of the adaptability of plants to reconfigure the key metabolic pathways and their response to changing climatic conditions. Their application to ecophysiology and ecotoxicology help to assess potential risks caused by the contaminants, their modes of action and the elucidation of metabolic pathways associated with stress responses. Heavy metal stress is one of the most significant environmental hazards affecting the physiological and biochemical processes in plants. Metabolomic tools have been widely utilised in the massive characterisation of the molecular structure of plants at various stages for understanding the diverse aspects of the cellular functioning underlying heavy metal stress-responsive mechanisms. This review emphasises on the recent progressions in metabolomics in plants subjected to heavy metal stresses. Also, it discusses the possibility of facilitating effective management strategies concerning metabolites for mitigating the negative impacts of heavy metal contaminants on the growth and productivity of plants.

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

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

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.

Differences in the metabolites of brown and milled rice grains of semiwaxy and conventional japonica varieties

In this study, we analyzed the differences in metabolites between semiwaxy japonica rice (Yangnongxiang 28 [YNX28]) and conventional japonica rice (Hongyang 5 [HY5]) before and after brown rice milling. The metabolites of brown and milled rice grains from the two rice varieties were analyzed by LC-MS-based nontargeted metabolomics. A total of 266 differentially abundant metabolites (DMs) were tentatively identified in brown rice grains of YNX28 (YNX28B) compared with milled rice grains of YNX28 (YNX28H), and these included 248 upregulated and 12 downregulated DMs. A total of 273 (234 upregulated and 39 downregulated) DMs were tentatively identified in brown rice grains of HY5 (HY5B) compared with milled rice grains of this variety (HY5H). Kyoto Encyclopedia of Genes and Genomes pathway involved and enrichment analyses revealed that 53 and 7 metabolite pathways were enriched and significantly enriched (p < 0.05), respectively, in the DMs identified in YNX28B compared with YNX28H, and the main enriched pathways were related to starch and sucrose metabolism, glycerol phospholipid metabolism, arginine and proline metabolism, and glycine, serine and threonine metabolism. Forty-six metabolite pathways were enriched in DMs identified in HY5B compared with HY5H, and these included 16 pathways that were significantly enriched (p < 0.05); in addition, the main enriched pathways were related to starch and sucrose metabolism, glycerol phospholipid metabolism, arginine and proline metabolism, and glycine, serine and threonine metabolism. This study provides a theoretical reference for further on the changes in metabolites during rice processing and provides a basis for improving the nutritional quality in rice. PRACTICAL APPLICATION: Original data were obtained regarding the changes of different metabolites in semiwaxy japonica rice and conventional japonica rice before and after processing. The purpose of this study was to investigate the difference of metabolite loss in two rice varieties before and after processing. This paper reports on the differences of metabolites between the two types of japonica rice before and after processing, as well as the changes of key metabolites before and after processing, it also provides important theoretical basis for developing new rice varieties with good nutritional quality.

Multinomial machine learning identifies independent biomarkers by integrated metabolic analysis of acute coronary syndrome

A multi-class classification model for acute coronary syndrome (ACS) remains to be constructed based on multi-fluid metabolomics. Major confounders may exert spurious effects on the relationship between metabolism and ACS. The study aims to identify an independent biomarker panel for the multiclassification of HC, UA, and AMI by integrating serum and urinary metabolomics. We performed a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics study on 300 serum and urine samples from 44 patients with unstable angina (UA), 77 with acute myocardial infarction (AMI), and 29 healthy controls (HC). Multinomial machine learning approaches, including multinomial adaptive least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), and assessment of the confounders were applied to integrate a multi-class classification biomarker panel for HC, UA and AMI. Different metabolic landscapes were portrayed during the transition from HC to UA and then to AMI. Glycerophospholipid metabolism and arginine biosynthesis were predominant during the progression from HC to UA and then to AMI. The multiclass metabolic diagnostic model (MDM) dependent on ACS, including 2-ketobutyric acid, LysoPC(18:2(9Z,12Z)), argininosuccinic acid, and cyclic GMP, demarcated HC, UA, and AMI, providing a C-index of 0.84 (HC vs. UA), 0.98 (HC vs. AMI), and 0.89 (UA vs. AMI). The diagnostic value of MDM largely derives from the contribution of 2-ketobutyric acid, and LysoPC(18:2(9Z,12Z)) in serum. Higher 2-ketobutyric acid and cyclic GMP levels were positively correlated with ACS risk and atherosclerosis plaque burden, while LysoPC(18:2(9Z,12Z)) and argininosuccinic acid showed the reverse relationship. An independent multiclass biomarker panel for HC, UA, and AMI was constructed using the multinomial machine learning methods based on serum and urinary metabolite signatures.

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

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

A Metabolites Merging Strategy (MMS): Harmonization to Enable Studies' Intercomparison

Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps. Step 1: Translation and merging of the different datasets by employing InChIKeys for data integration, encompassing the translation of metabolite names (if needed). Followed by Step 2: Attributes' retrieval from the InChIkey, including descriptors of name (title name from PubChem and RefMet name from Metabolomics Workbench), and chemical properties (molecular weight and molecular formula), both systematic (InChI, InChIKey, SMILES) and non-systematic identifiers (PubChem, CheBI, HMDB, KEGG, LipidMaps, DrugBank, Bin ID and CAS number), and their ontology. Finally, a meticulous three-step curation process is used to rectify disparities for conjugated base/acid compounds (optional step), missing attributes, and synonym checking (duplicated information). The MMS procedure is exemplified through a case study of urinary asthma metabolites, where MMS facilitated the identification of significant pathways hidden when no dataset merging strategy was followed. This study highlights the need for standardized and unified metabolite datasets to enhance the reproducibility and comparability of metabolomics studies.

Multi-year field trials provide a massive repository of trait data on a highly diverse population of tomato and uncover novel determinants of tomato productivity

Tomato (Solanum lycopersicum) is a prominent fruit with rich genetic resources for crop improvement. By using a phenotype-guided screen of over 7900 tomato accessions from around the world, we identified new associations for complex traits such as fruit weight and total soluble solids (Brix). Here, we present the phenotypic data from several years of trials. To illustrate the power of this dataset we use two case studies. First, evaluation of color revealed allelic variation in phytoene synthase 1 that resulted in differently colored or even bicolored fruit. Secondly, in view of the negative relationship between fruit weight and Brix, we pre-selected a subset of the collection that includes high and low Brix values in each category of fruit size. Genome-wide association analysis allowed us to detect novel loci associated with total soluble solid content and fruit weight. In addition, we developed eight F2 biparental intraspecific populations. Furthermore, by taking a phenotype-guided approach we were able to isolate individuals with high Brix values that were not compromised in terms of yield. In addition, the demonstration of novel results despite the high number of previous genome-wide association studies of these traits in tomato suggests that adoption of a phenotype-guided pre-selection of germplasm may represent a useful strategy for finding target genes for breeding.

Quantifying the impact of dynamic plant-environment interactions on metabolic regulation

A plant's genome encodes enzymes, transporters and many other proteins which constitute metabolism. Interactions of plants with their environment shape their growth, development and resilience towards adverse conditions. Although genome sequencing technologies and applications have experienced triumphantly rapid development during the last decades, enabling nowadays a fast and cheap sequencing of full genomes, prediction of metabolic phenotypes from genotype × environment interactions remains, at best, very incomplete. The main reasons are a lack of understanding of how different levels of molecular organisation depend on each other, and how they are constituted and expressed within a setup of growth conditions. Phenotypic plasticity, e.g., of the genetic model plant Arabidopsis thaliana, has provided important insights into plant-environment interactions and the resulting genotype x phenotype relationships. Here, we summarize previous and current findings about plant development in a changing environment and how this might be shaped and reflected in metabolism and its regulation. We identify current challenges in the study of plant development and metabolic regulation and provide an outlook of how methodological workflows might support the application of findings made in model systems to crops and their cultivation.

A comprehensive metabolomics and lipidomics atlas for the legumes common bean, chickpea, lentil and lupin

Legumes represent an important component of human and livestock diets; they are rich in macro- and micronutrients such as proteins, dietary fibers and polyunsaturated fatty acids. Whilst several health-promoting and anti-nutritional properties have been associated with grain content, in-depth metabolomics characterization of major legume species remains elusive. In this article, we used both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) to assess the metabolic diversity in the five legume species commonly grown in Europe, including common bean (Phaseolus vulgaris), chickpea (Cicer arietinum), lentil (Lens culinaris), white lupin (Lupinus albus) and pearl lupin (Lupinus mutabilis), at the tissue level. We were able to detect and quantify over 3400 metabolites covering major nutritional and anti-nutritional compounds. Specifically, the metabolomics atlas includes 224 derivatized metabolites, 2283 specialized metabolites and 923 lipids. The data generated here will serve the community as a basis for future integration to metabolomics-assisted crop breeding and facilitate metabolite-based genome-wide association studies to dissect the genetic and biochemical bases of metabolism in legume species.

Intracellular metabolic profiling of drug resistant cells by surface enhanced Raman scattering

BACKGROUND

Intracellular metabolic profiling reveals real-time metabolic information useful for the study of underlying mechanisms of cells in particular conditions such as drug resistance. However, mass spectrometry (MS), one of the leading metabolomics technologies, usually requires a large number of cells and complex pretreatments. Surface enhanced Raman scattering (SERS) has an ultrahigh detection sensitivity and specificity, favorable for metabolomics analysis. However, some targeted SERS methods focus on very limited metabolite without global bioprofiling, and some label-free approaches try to fingerprint the metabolic response based on whole SERS spectral classification, but comprehensive interpretation of biological mechanisms was lacking. (95) RESULTS: We proposed a label-free SERS technique for intracellular metabolic profiling in complex cellular lysates within 3 min. We first compared three kinds of cellular lysis methods and sonication lysis shows the highest extraction efficiency of metabolites. To obtain comprehensive metabolic information, we collected a spectral set for each sample and further qualified them by the Pearson correlation coefficient (PCC) to calculate how many spectra should be acquired at least to gain the adequate information from a statistical and global view. In addition, according to our measurements with 10 pure metabolites, we can understand the spectra acquired from complex cellular lysates of different cell lines more precisely. Finally, we further disclosed the variations of 22 SERS bands in enzalutamide-resistant prostate cancer cells and some are associated with the androgen receptor signaling activity and the methionine salvage pathway in the drug resistance process, which shows the same metabolic trends as MS. (149) SIGNIFICANCE: Our technique has the capability to capture the intracellular metabolic fingerprinting with the optimized lysis approach and spectral set collection, showing high potential in rapid, sensitive and global metabolic profiling in complex biosamples and clinical liquid biopsy. This gives a new perspective to the study of SERS in insightful understanding of relevant biological mechanisms. (54).

A large-scale, targeted metabolomics method for the analysis and quantification of metabolites in human plasma via liquid chromatography-mass spectrometry

Metabolomics is the study of small molecules, primarily metabolites, that are produced during metabolic processes. Analysis of the composition of an organism's metabolome can yield useful information about an individual's health status at any given time. In recent years, the development of large-scale, targeted metabolomic methods has allowed for the analysis of biological samples using analytical techniques such as LC-MS/MS. This paper presents a large-scale metabolomics method for analysis of biological samples, with a focus on quantification of metabolites found in blood plasma. The method comprises a 10-min chromatographic separation using HILIC and RP stationary phases combined with positive and negative electrospray ionization in order to maximize metabolome coverage. Complete analysis of a single sample can be achieved in as little as 40 min using the two columns and dual modes of ionization. With 540 metabolites and the inclusion of over 200 analytical standards, this method is comprehensive and quantitatively robust when compared to current targeted metabolomics methods. This study uses a large-scale evaluation of metabolite recovery from plasma that enables absolute quantification of metabolites by correcting for analyte loss throughout processes such as extraction, handling, or storage. In addition, the method was applied to plasma collected from adjuvant breast cancer patients to confirm the suitability of the method to clinical samples.

UHPLC/MS-Based Untargeted Metabolomics Reveals Metabolic Characteristics of Clinical Strain of Mycoplasma bovis

Mycoplasma bovis is a global concern for the cattle industry owing to its high rates of infection and resulting morbidity, but its pathogenesis remains poorly understood. Metabolic pathways and characteristics of M. bovis clinical strain were elucidated by comparing the differential expression of metabolites between M. bovis clinical strain NX114 and M. bovis international reference strain PG45. Metabolites of M. bovis in the logarithmic stage were analyzed based on the non-targeted metabolomic technology of ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS). We found 596 metabolites with variable expression, of which, 190 had substantial differences. Differential metabolite analysis of M. bovis NX114 showed organic acids and their derivatives, nucleosides, and nucleotide analogs as important components. We found O-Phospho-L-serine (SEP) as a potential signature metabolite and indicator of pathogenicity. The difference in nucleic acid metabolites reflects the difference in growth phenotypes between both strains of M. bovis. According to KEGG enrichment analysis, the ABC transporter synthesis route had the most differential metabolites of the first 15 differential enrichment pathways. This study reflects the species-specific differences between two strains of M. bovis and further enriches our understanding of its metabolism, paving the way for further research into its pathogenesis.

Proteomic and Low-Polar Metabolite Profiling Reveal Unique Dynamics in Fatty Acid Metabolism during Flower and Berry Development of Table Grapes

Grapevine development and ripening are complex processes that involve several biochemical pathways, including fatty acid and lipid metabolism. Fatty acids are essential components of lipids, which play crucial roles in fruit maturation and flavor development. However, the dynamics of fatty acid metabolism in grape flowers and berries are poorly understood. In this study, we present those dynamics and investigate the mechanisms of fatty acid homeostasis on 'Thompson Seedless' berries using metabolomic and proteomic analyses. Low-polar metabolite profiling indicated a higher abundance of fatty acids at the pre-flowering and pre-veraison stages. Proteomic analyses revealed that grape flowers and berries display unique profiles of proteins involved in fatty acid biosynthesis, triacylglycerol assembly, fatty acid β-oxidation, and lipid signaling. These findings show, for the first time, that fatty acid metabolism also plays an important role in the development of non-oil-rich tissues, opening new perspectives about lipid function and its relation to berry quality.

Metabolomics Analysis of Sporulation-Associated Metabolites of Metarhizium anisopliae Based on Gas Chromatography-Mass Spectrometry

Metarhizium anisopliae, an entomopathogenic fungus, has been widely used for the control of agricultural and forestry pests. However, sporulation degeneration occurs frequently during the process of successive culture, and we currently lack a clear understanding of the underlying mechanisms. In this study, the metabolic profiles of M. anisopliae were comparatively analyzed based on the metabolomics approach of gas chromatography-mass spectrometry (GC-MS). A total of 74 metabolites were detected in both normal and degenerate strains, with 40 differential metabolites contributing significantly to the model. Principal component analysis (PCA) and potential structure discriminant analysis (PLS-DA) showed a clear distinction between the sporulation of normal strains and degenerate strains. Specifically, 23 metabolites were down-regulated and 17 metabolites were up-regulated in degenerate strains compared to normal strains. The KEGG enrichment analysis identified 47 significant pathways. Among them, the alanine, aspartate and glutamate metabolic pathways and the glycine, serine and threonine metabolism had the most significant effects on sporulation, which revealed that significant changes occur in the metabolic phenotypes of strains during sporulation and degeneration processes. Furthermore, our subsequent experiments have substantiated that the addition of amino acids could improve M. anisopliae's spore production. Our study shows that metabolites, especially amino acids, which are significantly up-regulated or down-regulated during the sporulation and degeneration of M. anisopliae, may be involved in the sporulation process of M. anisopliae, and amino acid metabolism (especially glutamate, aspartate, serine, glycine, arginine and leucine) may be an important part of the sporulation mechanism of M. anisopliae. This study provides a foundation and technical support for rejuvenation and production improvement strategies for M. anisopliae.

Acidic Methanol Treatment Facilitates Matrix-Assisted Laser Desorption Ionization-Mass Spectrometry Imaging of Energy Metabolism

Detection of small molecule metabolites (SMM), particularly those involved in energy metabolism using MALDI-mass spectrometry imaging (MSI), is challenging due to factors including ion suppression from other analytes present (e.g., proteins and lipids). One potential solution to enhance SMM detection is to remove analytes that cause ion suppression from tissue sections before matrix deposition through solvent washes. Here, we systematically investigated solvent treatment conditions to improve SMM signal and preserve metabolite localization. Washing with acidic methanol significantly enhances the detection of phosphate-containing metabolites involved in energy metabolism. The improved detection is due to removing lipids and highly polar metabolites that cause ion suppression and denaturing proteins that release bound phosphate-containing metabolites. Stable isotope infusions of [13C6]nicotinamide coupled to MALDI-MSI ("Iso-imaging") in the kidney reveal patterns that indicate blood vessels, medulla, outer stripe, and cortex. We also observed different ATP:ADP raw signals across mouse kidney regions, consistent with regional differences in glucose metabolism favoring either gluconeogenesis or glycolysis. In mouse muscle, Iso-imaging using [13C6]glucose shows high glycolytic flux from infused circulating glucose in type 1 and 2a fibers (soleus) and relatively lower glycolytic flux in type 2b fiber type (gastrocnemius). Thus, improved detection of phosphate-containing metabolites due to acidic methanol treatment combined with isotope tracing provides an improved way to probe energy metabolism with spatial resolution in vivo.

Characterization of color variation in bamboo sheath of Chimonobambusa hejiangensis by UPLC-ESI-MS/MS and RNA sequencing

BACKGROUND

Chimonobambusa hejiangensis (C.hejiangensis) is a high-quality bamboo species native to China, known for its shoots that are a popular nutritional food. Three C.hejiangensis cultivars exhibit unique color variation in their shoot sheaths, however, the molecular mechanism behind this color change remains unclear.

METHODS

We investigated flavonoid accumulation in the three bamboo cultivar sheaths using metabolomics and transcriptomics.

RESULTS

UPLC-MS/MS identified 969 metabolites, with 187, 103, and 132 having differential accumulation in the yellow-sheath (YShe) vs. spot-sheath (SShe)/black-sheath (BShe) and SShe vs. BShe comparison groups. Flavonoids were the major metabolites that determined bamboo sheath color through differential accumulation of metabolites (DAMs) analysis. Additionally, there were 33 significantly differentially expressed flavonoid structural genes involved in the anthocyanin synthesis pathway based on transcriptome data. We conducted a KEGG analysis on DEGs and DAMs, revealing significant enrichment of phenylpropanoid and flavonoid biosynthetic pathways. Using gene co-expression network analysis, we identified nine structural genes and 29 transcription factors strongly linked to anthocyanin biosynthesis.

CONCLUSION

We identified a comprehensive regulatory network for flavonoid biosynthesis which should improve our comprehension of the molecular mechanisms responsible for color variation and flavonoid biosynthesis in bamboo sheaths.

Efficient sugar utilization and transition from oxidative to substrate-level phosphorylation in high starch storage roots of African cassava genotypes

Cassava's storage roots represent one of the most important sources of nutritional carbohydrates worldwide. Particularly, smallholder farmers in sub-Saharan Africa depend on this crop plant, where resilient and yield-improved varieties are of vital importance to support steadily increasing populations. Aided by a growing understanding of the plant's metabolism and physiology, targeted improvement concepts already led to visible gains in recent years. To expand our knowledge and to contribute to these successes, we investigated storage roots of eight cassava genotypes with differential dry matter content from three successive field trials for their proteomic and metabolic profiles. At large, the metabolic focus in storage roots transitioned from cellular growth processes toward carbohydrate and nitrogen storage with increasing dry matter content. This is reflected in higher abundance of proteins related to nucleotide synthesis, protein turnover, and vacuolar energization in low starch genotypes, while proteins involved in sugar conversion and glycolysis were more prevalent in high dry matter genotypes. This shift in metabolic orientation was underlined by a clear transition from oxidative- to substrate-level phosphorylation in high dry matter genotypes. Our analyses highlight metabolic patterns that are consistently and quantitatively associated with high dry matter accumulation in cassava storage roots, providing fundamental understanding of cassava's metabolism as well as a data resource for targeted genetic improvement.

Abscisic acid promotes plant acclimation to the combination of salinity and high light stress

Plants encounter combinations of different abiotic stresses such as salinity (S) and high light (HL). These environmental conditions have a detrimental effect on plant growth and development, posing a threat to agricultural production. Metabolic changes play a crucial role in enabling plants to adapt to fluctuations in their environment. Furthermore, hormones such as abscisic acid (ABA), jasmonic acid (JA) and salicylic acid (SA) have been previously identified as regulators of plant responses to different abiotic stresses. Here we studied the response of Arabidopsis wild type (Col and Ler) plants and mutants impaired in hormone biosynthesis (aba2-11 and aba1-1 in ABA, aos in JA and sid2 in SA) to the combination of S and HL (S + HL). Our findings showed that aba2-11 plants displayed reduced growth, impaired photosystem II (PSII) function, increased leaf damage, and decreased survival compared to Col when subjected to stress combination. However, aos and sid2 mutants did not display significant changes in response to S + HL compared to Col, indicating a key role for ABA in promoting plant tolerance to S + HL and suggesting a marginal role for JA and SA in this process. In addition, we revealed differences in the metabolic response of plants to S + HL compared to S or HL. The analysis of altered metabolic pathways under S + HL suggested that the accumulation of flavonoids is ABA-dependent, whereas the accumulation of branched-chain amino acids (BCAAs) and proline is ABA-independent. Therefore, our study uncovered a key function for ABA in regulating the accumulation of different flavonoids in plants during S + HL.

Eco-Metabolomics Applied to the Chemical Ecology of Poison Frogs (Dendrobatoidea)

Amphibians are one of the most remarkable sources of unique natural products. Biogenic amines, peptides, bufodienolides, alkaloids, and volatile organic compounds have been characterized in different species. The superfamily Dendrobatoidea represents one of the most enigmatic cases of study in chemical ecology because their skin secretome is composed by a complex mixture (i.e. cocktail) of highly lethal and noxious unique alkaloid structures. While chemical defences from dendrobatoids (families Dendrobatidae and Aromobatidae) have been investigated employing ecological, behavioral, phylogenetic and evolutionary perspectives, studies about the analytical techniques needed to perform the chemical characterization have been neglected for many years. Therefore, our aim is to summarize the current methods applied for the characterization of chemical profiles in dendrobatoids and to illustrate innovative Eco-metabolomics strategies that could be translated to this study model. This approach could be extended to natural products other than alkaloids and implemented for the chemical analysis of different species of dendrobatoids employing both low- and high-resolution mass spectrometers. Here, we overview important biological features to be considered, procedures that could be applied to perform the chemical characterization, steps and tools to perform an Eco-metabolomic analysis, and a final discussion about future perspectives.

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.

A metabolome and transcriptome survey to tap the dynamics of fruit prolonged shelf-life and improved quality within Greek tomato germplasm

Introduction

Tomato is a high economic value crop worldwide with recognized nutritional properties and diverse postharvest potential. Nowadays, there is an emerging awareness about the exploitation and utilization of underutilized traditional germplasm in modern breeding programs. In this context, the existing diversity among Greek accessions in terms of their postharvest life and nutritional value remains largely unexplored.

Methods

Herein, a detailed evaluation of 130 tomato Greek accessions for postharvest and nutritional characteristics was performed, using metabolomics and transcriptomics, leading to the selection of accessions with these interesting traits.

Results

The results showed remarkable differences among tomato Greek accessions for overall ripening parameters (color, firmness) and weight loss. On the basis of their postharvest performance, a balance between short shelf life (SSL) and long shelf life (LSL) accessions was revealed. Metabolome analysis performed on 14 selected accessions with contrasting shelf-life potential identified a total of 206 phytonutrients and volatile compounds. In turn, transcriptome analysis in fruits from the best SSL and the best LSL accessions revealed remarkable differences in the expression profiles of transcripts involved in key metabolic pathways related to fruit quality and postharvest potential.

Discussion

The pathways towards cell wall synthesis, polyamine synthesis, ABA catabolism, and steroidal alkaloids synthesis were mostly induced in the LSL accession, whereas those related to ethylene biosynthesis, cell wall degradation, isoprenoids, phenylpropanoids, ascorbic acid and aroma (TomloxC) were stimulated in the SSL accession. Overall, these data would provide valuable insights into the molecular mechanism towards enhancing shelf-life and improving flavor and aroma of modern tomato cultivars.

Gut Microbiota Combined with Serum Metabolomics to Investigate the Hypoglycemic Effect of Actinidia arguta Leaves

Actinidia arguta leaves (AAL) are an excellent source of bioactive components for the food industry and possess many functional properties. However, the hypoglycemic effect and mechanism of AAL remain unclear. The aim of this work was to investigate the potential hypoglycemic effect of AAL and explore its possible mechanism using 16S rRNA sequencing and serum metabolomics in diabetic mice induced by high-fat feeding in combination with streptozotocin injection. A total of 25 flavonoids from AAL were isolated and characterized, and the contents of the extract from the AAL ranged from 0.14 mg/g DW to 8.97 mg/g DW. The compound quercetin (2) had the highest content of 8.97 ± 0.09 mg/g DW, and the compound kaempferol-3-O-(2'-O-D-glucopyl)-β-D-rutinoside (12) had the lowest content of 0.14 ± 0.01 mg/g DW. In vivo experimental studies showed that AAL reduced blood glucose and cholesterol levels, improved insulin sensitivity, and ameliorated oxidative stress and liver and kidney pathological damage. In addition, gut microbiota analysis found that AAL significantly reduced the F/B ratio, enriched the beneficial bacteria Bacteroides and Bifidobacterium, and inhibited the harmful bacteria Lactobacillus and Desulfovibrio, thereby playing an active role in intestinal imbalance. In addition, metabolomics analysis showed that AAL could improve amino acid metabolism and arachidonic acid metabolism, thereby exerting a hypoglycemic effect. This study confirmed that AAL can alleviate type 2 diabetes mellitus (T2DM) by regulating intestinal flora and interfering with related metabolic pathways, providing a scientific basis for its use as a dietary supplement and for further exploration of the mechanism of AAL against T2DM.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research

The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.

Mussel mass mortality in the Clinch River, USA: metabolomics detects affected pathways and biomarkers of stress

Biologists monitoring freshwater mussel (order Unionida) populations rely on behavioral, often subjective, signs to identify moribund ("sick") or stressed mussels, such as gaping valves and slow response to probing, and they lack clinical indicators to support a diagnosis. As part of a multi-year study to investigate causes of reoccurring mortality of pheasantshell (Ortmanniana pectorosa; synonym Actinonaias pectorosa) in the Clinch River, Virginia and Tennessee, USA, we analyzed the hemolymph metabolome of a subset of mussels from the 2018 sampling period. Mussels at the mortality sites were diagnosed in the field as affected (case) or unaffected (control) based on behavioral and physical signs. Hemolymph was collected in the field by non-lethal methods from the anterior adductor muscle for analysis. We used ultra-high-performance liquid chromatography with quadrupole time-of-flight mass spectroscopy to detect targeted and untargeted metabolites in hemolymph and compared metabolomic profiles by field assessment of clinical status. Targeted biomarker analysis found 13 metabolites associated with field assessments of clinical status. Of these, increased gamma-linolenic acid and N-methyl-l-alanine were most indicative of case mussels, while adenine and inosine were the best indicators of control mussels. Five pathways in the targeted analysis differed by clinical status; two of these, purine metabolism and glycerophospholipid metabolism, were also indicated in the untargeted analysis. In the untargeted nalysis, 22 metabolic pathways were associated with clinical status. Many of the impacted pathways in the case group were catabolic processes, such as degradation of amino acids and fatty acids. Hierarchical clustering analysis matched clinical status in 72% (18 of 25) of mussels, with control mussels more frequently (5 of 16) not matching clinical status. Our study demonstrated that metabolomic analysis of hemolymph is suitable for assessing mussel condition and complements field-based indicators of health.

Untargeted plasma metabolome identifies biomarkers in patients with extracranial arteriovenous malformations

Objective: This study aimed to investigate the plasma metabolic profile of patients with extracranial arteriovenous malformations (AVM). Method: Plasma samples were collected from 32 AVM patients and 30 healthy controls (HC). Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was employed to analyze the metabolic profiles of both groups. Metabolic pathway enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes (KEGG) database and MetaboAnalyst. Additionally, machine learning algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO) and random forest (RF) were conducted to screen characteristic metabolites. The effectiveness of the serum biomarkers for AVM was evaluated using a receiver-operating characteristics (ROC) curve. Result: In total, 184 differential metabolites were screened in this study, with 110 metabolites in positive ion mode and 74 metabolites in negative mode. Lipids and lipid-like molecules were the predominant metabolites detected in both positive and negative ion modes. Several significant metabolic pathways were enriched in AVMs, including lipid metabolism, amino acid metabolism, carbohydrate metabolism, and protein translation. Through machine learning algorithms, nine metabolites were identify as characteristic metabolites, including hydroxy-proline, L-2-Amino-4-methylenepentanedioic acid, piperettine, 20-hydroxy-PGF2a, 2,2,4,4-tetramethyl-6-(1-oxobutyl)-1,3,5-cyclohexanetrione, DL-tryptophan, 9-oxoODE, alpha-Linolenic acid, and dihydrojasmonic acid. Conclusion: Patients with extracranial AVMs exhibited significantly altered metabolic patterns compared to healthy controls, which could be identified using plasma metabolomics. These findings suggest that metabolomic profiling can aid in the understanding of AVM pathophysiology and potentially inform clinical diagnosis and treatment.

Exploring the Microbiome in Human Reproductive Tract: High-Throughput Methods for the Taxonomic Characterization of Microorganisms

Microorganisms are important due to their widespread presence and multifaceted roles across various domains of life, ecology, and industries. In humans, they underlie the proper functioning of multiple systems crucial to well-being, including immunological and metabolic functions. Emerging research addressing the presence and roles of microorganisms within human reproduction is increasingly relevant. Studies implementing new methodologies (e.g., to investigate vaginal, uterine, and semen microenvironments) can now provide relevant insights into fertility, reproductive health, or pregnancy outcomes. In that sense, cutting-edge sequencing techniques, as well as others such as meta-metabolomics, culturomics, and meta-proteomics, are becoming more popular and accessible worldwide, allowing the characterization of microbiomes at unprecedented resolution. However, they frequently involve rather complex laboratory protocols and bioinformatics analyses, for which researchers may lack the required expertise. A suitable pipeline would successfully enable both taxonomic classification and functional profiling of the microbiome, providing easy-to-understand biological interpretations. However, the selection of an appropriate methodology would be crucial, as it directly impacts the reproducibility, accuracy, and quality of the results and observations. This review focuses on the different current microbiome-related techniques in the context of human reproduction, encompassing niches like vagina, endometrium, and seminal fluid. The most standard and reliable methods are 16S rRNA gene sequencing, metagenomics, and meta-transcriptomics, together with complementary approaches including meta-proteomics, meta-metabolomics, and culturomics. Finally, we also offer case examples and general recommendations about the most appropriate methods and workflows and discuss strengths and shortcomings for each technique.

Omics approaches for the assessment of biological responses to nanoparticles

Nanotechnology has enabled the development of innovative therapeutics, diagnostics, and drug delivery systems. Nanoparticles (NPs) can influence gene expression, protein synthesis, cell cycle, metabolism, and other subcellular processes. While conventional methods have limitations in characterizing responses to NPs, omics approaches can analyze complete sets of molecular entities that change upon exposure to NPs. This review discusses key omics approaches, namely transcriptomics, proteomics, metabolomics, lipidomics and multi-omics, applied to the assessment of biological responses to NPs. Fundamental concepts and analytical methods used for each approach are presented, as well as good practices for omics experiments. Bioinformatics tools are essential to analyze, interpret and visualize large omics data, and to correlate observations in different molecular layers. The authors envision that conducting interdisciplinary multi-omics analyses in future nanomedicine studies will reveal integrated cell responses to NPs at different omics levels, and the incorporation of omics into the evaluation of targeted delivery, efficacy, and safety will improve the development of nanomedicine therapies.

Plasma metabolomic profiling in two rabbit lines divergently selected for intramuscular fat content

This study provides a thorough comparison of the plasma metabolome of two rabbit lines divergently selected for intramuscular fat content (IMF). The divergent selection led to a correlated response in the overall adiposity, turning these lines into a valuable animal material to study also the genetics of obesity. Over 900 metabolites were detected, and the adjustment of multivariate models, both discriminant and linear, allowed to identify 322 with differential abundances between lines, which also adjusted linearly to the IMF content. The most affected pathways were those of lipids and amino acids, with differences between lines ranging from 0.23 to 6.04 standard deviations, revealing a limited capacity of the low-IMF line to obtain energy from lipids, and a greater branched-chain amino acids catabolism in the high-IMF line related to its increased IMF content. Additionally, changes in metabolites derived from microbial activity supported its relevant role in the lipid deposition. Future research will focus on the analysis of the metabolomic profile of the cecum content, and on the integration of the several -omics datasets available for these lines, to help disentangle the host and microbiome biological mechanisms involved in the IMF deposition.