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

Considerations for reproducible omics in aging research

Technical advancements over the past two decades have enabled the measurement of the panoply of molecules of cells and tissues including transcriptomes, epigenomes, metabolomes and proteomes at unprecedented resolution. Unbiased profiling of these molecular landscapes in the context of aging can reveal important details about mechanisms underlying age-related functional decline and age-related diseases. However, the high-throughput nature of these experiments creates unique analytical and design demands for robustness and reproducibility. In addition, 'omic' experiments are generally onerous, making it crucial to effectively design them to eliminate as many spurious sources of variation as possible as well as account for any biological or technical parameter that may influence such measures. In this Perspective, we provide general guidelines on best practices in the design and analysis of omic experiments in aging research from experimental design to data analysis and considerations for long-term reproducibility and validation of such studies.

Unveiling the dark side of guard cell metabolism

Evidence suggests that guard cells have higher rate of phosphoenolpyruvate carboxylase (PEPc)-mediated dark CO2 assimilation than mesophyll cells. However, it is unknown which metabolic pathways are activated following dark CO2 assimilation in guard cells. Furthermore, it remains unclear how the metabolic fluxes throughout the tricarboxylic acid (TCA) cycle and associated pathways are regulated in illuminated guard cells. Here we carried out a13C-HCO3 labelling experiment in tobacco guard cells harvested under continuous dark or during the dark-to-light transition to elucidate principles of metabolic dynamics downstream of CO2 assimilation. Most metabolic changes were similar between dark-exposed and illuminated guard cells. However, illumination altered the metabolic network structure of guard cells and increased the 13C-enrichment in sugars and metabolites associated to the TCA cycle. Sucrose was labelled in the dark, but light exposure increased the 13C-labelling and leads to more drastic reductions in the content of this metabolite. Fumarate was strongly labelled under both dark and light conditions, while illumination increased the 13C-enrichment in pyruvate, succinate and glutamate. Only one 13C was incorporated into malate and citrate in either dark or light conditions. Our results indicate that several metabolic pathways are redirected following PEPc-mediated CO2 assimilation in the dark, including gluconeogenesis and the TCA cycle. We further showed that the PEPc-mediated CO2 assimilation provides carbons for gluconeogenesis, the TCA cycle and glutamate synthesis and that previously stored malate and citrate are used to underpin the specific metabolic requirements of illuminated guard cells.

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.

MPI-VGAE: protein-metabolite enzymatic reaction link learning by variational graph autoencoders

Enzymatic reactions are crucial to explore the mechanistic function of metabolites and proteins in cellular processes and to understand the etiology of diseases. The increasing number of interconnected metabolic reactions allows the development of in silico deep learning-based methods to discover new enzymatic reaction links between metabolites and proteins to further expand the landscape of existing metabolite-protein interactome. Computational approaches to predict the enzymatic reaction link by metabolite-protein interaction (MPI) prediction are still very limited. In this study, we developed a Variational Graph Autoencoders (VGAE)-based framework to predict MPI in genome-scale heterogeneous enzymatic reaction networks across ten organisms. By incorporating molecular features of metabolites and proteins as well as neighboring information in the MPI networks, our MPI-VGAE predictor achieved the best predictive performance compared to other machine learning methods. Moreover, when applying the MPI-VGAE framework to reconstruct hundreds of metabolic pathways, functional enzymatic reaction networks and a metabolite-metabolite interaction network, our method showed the most robust performance among all scenarios. To the best of our knowledge, this is the first MPI predictor by VGAE for enzymatic reaction link prediction. Furthermore, we implemented the MPI-VGAE framework to reconstruct the disease-specific MPI network based on the disrupted metabolites and proteins in Alzheimer's disease and colorectal cancer, respectively. A substantial number of novel enzymatic reaction links were identified. We further validated and explored the interactions of these enzymatic reactions using molecular docking. These results highlight the potential of the MPI-VGAE framework for the discovery of novel disease-related enzymatic reactions and facilitate the study of the disrupted metabolisms in diseases.

Plasma metabolic fingerprints for large-scale screening and personalized risk stratification of metabolic syndrome

Direct diagnosis and accurate assessment of metabolic syndrome (MetS) allow for prompt clinical interventions. However, traditional diagnostic strategies overlook the complex heterogeneity of MetS. Here, we perform metabolomic analysis in 13,554 participants from the natural cohort and identify 26 hub plasma metabolic fingerprints (PMFs) associated with MetS and its early identification (pre-MetS). By leveraging machine-learning algorithms, we develop robust diagnostic models for pre-MetS and MetS with convincing performance through independent validation. We utilize these PMFs to assess the relative contributions of the four major MetS risk factors in the general population, ranked as follows: hyperglycemia, hypertension, dyslipidemia, and obesity. Furthermore, we devise a personalized three-dimensional plasma metabolic risk (PMR) stratification, revealing three distinct risk patterns. In summary, our study offers effective screening tools for identifying pre-MetS and MetS patients in the general community, while defining the heterogeneous risk stratification of metabolic phenotypes in real-world settings.

Phenology and foraging bias contribute to sex-specific foraging patterns in the rare declining butterfly Argynnis idalia idalia

Variation in pollinator foraging behavior can influence pollination effectiveness, community diversity, and plant-pollinator network structure. Although effects of interspecific variation have been widely documented, studies of intraspecific variation in pollinator foraging are relatively rare. Sex-specific differences in resource use are a strong potential source of intraspecific variation, especially in species where the phenology of males and females differ. Differences may arise from encountering different flowering communities, sex-specific traits, nutritional requirements, or a combination of these factors. We evaluated sex-specific foraging patterns in the eastern regal fritillary butterfly (Argynnis idalia idalia), leveraging a 21-year floral visitation dataset. Because A. i. idalia is protandrous, we determined whether foraging differences were due to divergent phenology by comparing visitation patterns between the entire season with restricted periods of male-female overlap. We quantified nectar carbohydrate and amino acid contents of the most visited plant species and compared those visited more frequently by males versus females. We demonstrate significant differences in visitation patterns between male and female A. i. idalia over two decades. Females visit a greater diversity of species, while dissimilarity in foraging patterns between sexes is persistent and comparable to differences between species. While differences are diminished or absent in some years during periods of male-female overlap, remaining signatures of foraging dissimilarity during implicate mechanisms other than phenology. Nectar of plants visited more by females had greater concentrations of total carbohydrates, glucose, and fructose and individual amino acids than male-associated plants. Further work can test whether nutritional differences are a cause of visitation patterns or consequence, reflecting seasonal shifts in the nutritional landscape encountered by male and female A. i. idalia. We highlight the importance of considering sex-specific foraging patterns when studying interaction networks, and in making conservation management decisions for this at-risk butterfly and other species exhibiting strong intraspecific variation.

Leveraging glycoside-targeted metabolomics to gain insight into biological function

In plants, uridine diphosphate (UDP)-dependent glycosyltransferases (UGTs) catalyze glycosylation of secondary metabolites, but assigning physiological functions to UGTs is still a daunting task. The recent study of Wu et al. presents a useful method to resolve this problem by elegantly combining modification-specific metabolomics with isotope tracing.

Cancer metabolites: promising biomarkers for cancer liquid biopsy

Cancer exerts a multitude of effects on metabolism, including the reprogramming of cellular metabolic pathways and alterations in metabolites that facilitate inappropriate proliferation of cancer cells and adaptation to the tumor microenvironment. There is a growing body of evidence suggesting that aberrant metabolites play pivotal roles in tumorigenesis and metastasis, and have the potential to serve as biomarkers for personalized cancer therapy. Importantly, high-throughput metabolomics detection techniques and machine learning approaches offer tremendous potential for clinical oncology by enabling the identification of cancer-specific metabolites. Emerging research indicates that circulating metabolites have great promise as noninvasive biomarkers for cancer detection. Therefore, this review summarizes reported abnormal cancer-related metabolites in the last decade and highlights the application of metabolomics in liquid biopsy, including detection specimens, technologies, methods, and challenges. The review provides insights into cancer metabolites as a promising tool for clinical applications.

Insight into the relationship between metabolite dynamic changes and microorganisms of sea urchin (S. intermedius) gonads during storage

Sea urchin gonads have high nutritional value and degenerate rapidly during storage. Previous assessment of the freshness of sea urchin gonads was based on experience without valid biochemical indicators. Thus, the current study is to find biochemical indicators representing the freshness of sea urchin gonads. Results showed that the dominant genera of sea urchin gonads were changed from Psychromonas, Ralstonia, and Roseimarinus to Aliivibrio, Psychrilyobacter, and Photobacterium. The differential metabolites of sea urchin gonads were mainly produced through amino acids metabolism. Among them, GC-TOF-MS based differential metabolites had the greatest enrichment in the valine, leucine and isoleucine biosynthesis pathway, while LC-MS based differential metabolites had the greatest enrichment in the alanine, aspartate and glutamate metabolism pathway. The growth of dominant genus (Aliivibrio) had a great influence on the production of differential metabolites. These results will provide valuable information for accurately judging the freshness and shelf life of sea urchin gonads.

Unlocking the secrets of the microbiome: exploring the dynamic microbial interplay with humans through metabolomics and their manipulation for synthetic biology applications

Metabolomics is a powerful research discovery tool with the potential to measure hundreds to low thousands of metabolites. In this review, we discuss the application of GC-MS and LC-MS in discovery-based metabolomics research, we define metabolomics workflows and we highlight considerations that need to be addressed in order to generate robust and reproducible data. We stress that metabolomics is now routinely applied across the biological sciences to study microbiomes from relatively simple microbial systems to their complex interactions within consortia in the host and the environment and highlight this in a range of biological species and mammalian systems including humans. However, challenges do still exist that need to be overcome to maximise the potential for metabolomics to help us understanding biological systems. To demonstrate the potential of the approach we discuss the application of metabolomics in two broad research areas: (1) synthetic biology to increase the production of high-value fine chemicals and reduction in secondary by-products and (2) gut microbial interaction with the human host. While burgeoning in importance, the latter is still in its infancy and will benefit from the development of tools to detangle host-gut-microbial interactions and their impact on human health and diseases.

Comparative Analysis of the Physicochemical Properties and Metabolites of Farinose and Crisp Lotus Roots (Nelumbo nucifera Gaertn.) with Different Geographical Origins

Lotus roots are widely consumed vegetables because of their great taste and abundant nutrients, but their quality varies with the environments and cultivar. This study systematically compared farinose (Elian No. 5) and crisp (Elian No. 6) lotus root cultivars from three geographical origins. Pasting and texture characteristics verified that Elian No. 5 possessed lower hardness and lower ability to withstand shear stress and heating during cooking compared with Elian No. 6. Untargeted metabolite profiling was first performed using ultrahigh-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) combined with a Zeno trap. In total, 188 metabolites were identified based on the matching chemistry database. Multivariate analysis demonstrated that lotus roots from different cultivars and origins could be adequately distinguished. Sixty-one differential metabolites were identified among three Elian No. 5 samples, and 28 were identified among three Elian No. 6 samples. Isoscopoletin, scopoletin, and paprazine were the most differential metabolites between Elian No. 5 and Elian No. 6. These results can inform future research on the discrimination and utilization of lotus roots.

Ultra-fast and accurate electron ionization mass spectrum matching for compound identification with million-scale in-silico library

Spectrum matching is the most common method for compound identification in mass spectrometry (MS). However, some challenges limit its efficiency, including the coverage of spectral libraries, the accuracy, and the speed of matching. In this study, a million-scale in-silico EI-MS library is established. Furthermore, an ultra-fast and accurate spectrum matching (FastEI) method is proposed to substantially improve accuracy using Word2vec spectral embedding and boost the speed using the hierarchical navigable small-world graph (HNSW). It achieves 80.4% recall@10 accuracy (88.3% with 5 Da mass filter) with a speedup of two orders of magnitude compared with the weighted cosine similarity method (WCS). When FastEI is applied to identify the molecules beyond NIST 2017 library, it achieves 50% recall@1 accuracy. FastEI is packaged as a standalone and user-friendly software for common users with limited computational backgrounds. Overall, FastEI combined with a million-scale in-silico library facilitates compound identification as an accurate and ultra-fast tool.

Integrative analysis of the gut microbiota and faecal and serum short-chain fatty acids and tryptophan metabolites in patients with cirrhosis and hepatic encephalopathy

OBJECTIVE

The purpose of this study was to describe the changes in the gut microbiome of patients with cirrhosis and hepatic encephalopathy (HE), as well as quantify the variations in short-chain fatty acid (SCFA) and tryptophan metabolite levels in serum and faeces.

METHODS

Fresh faeces and serum were collected from 20 healthy volunteers (NC group), 30 cirrhosis patients (Cir group), and 30 HE patients (HE group). Then, 16S rRNA sequencing and metabolite measurements were performed using the faeces. Gas chromatography‒mass spectrometry and ultrahigh-performance liquid chromatography-tandem mass spectrometry were used to measure SCFA and tryptophan levels, respectively. The results were analysed by SIMCA16.0.2 software. Differences in species were identified using MetaStat and t tests. The correlations among the levels of gut microbes and metabolites and clinical parameters were determined using Spearman correlation analysis.

RESULTS

Patients with cirrhosis and HE had lower microbial species richness and diversity in faeces than healthy volunteers; these patients also had altered β-diversity. Serum valeric acid levels were significantly higher in the HE group than in the Cir group. Serum SCFA levels did not differ between the Cir and NC groups. Serum melatonin and 5-HTOL levels were significantly higher in the HE group than in the Cir group. The Cir and NC groups had significant differences in the levels of eight serum tryptophan metabolites. Furthermore, the levels of faecal SCFAs did not differ between the HE and Cir groups. Faecal IAA-Ala levels were significantly lower in the HE group than in the Cir group. There were significant differences in the levels of 6 faecal SCFAs and 7 faecal tryptophan metabolites between the Cir and NC groups. Certain gut microbes were associated with serum and faecal metabolites, and some metabolites were associated with certain clinical parameters.

CONCLUSION

Reduced microbial species richness and diversity were observed in patients with HE and cirrhosis. In both serum and faeces, the levels of different SCFAs and tryptophan metabolites showed varying patterns of change. In HE patients, the levels of some serum tryptophan metabolites, and not SCFAs, were correlated with liver function and systemic inflammation. Systemic inflammation in patients with cirrhosis was correlated with faecal acetic acid levels. In summary, this study identified metabolites important for HE and cirrhosis.

Multi-Omics Pipeline and Omics-Integration Approach to Decipher Plant's Abiotic Stress Tolerance Responses

The present day's ongoing global warming and climate change adversely affect plants through imposing environmental (abiotic) stresses and disease pressure. The major abiotic factors such as drought, heat, cold, salinity, etc., hamper a plant's innate growth and development, resulting in reduced yield and quality, with the possibility of undesired traits. In the 21st century, the advent of high-throughput sequencing tools, state-of-the-art biotechnological techniques and bioinformatic analyzing pipelines led to the easy characterization of plant traits for abiotic stress response and tolerance mechanisms by applying the 'omics' toolbox. Panomics pipeline including genomics, transcriptomics, proteomics, metabolomics, epigenomics, proteogenomics, interactomics, ionomics, phenomics, etc., have become very handy nowadays. This is important to produce climate-smart future crops with a proper understanding of the molecular mechanisms of abiotic stress responses by the plant's genes, transcripts, proteins, epigenome, cellular metabolic circuits and resultant phenotype. Instead of mono-omics, two or more (hence 'multi-omics') integrated-omics approaches can decipher the plant's abiotic stress tolerance response very well. Multi-omics-characterized plants can be used as potent genetic resources to incorporate into the future breeding program. For the practical utility of crop improvement, multi-omics approaches for particular abiotic stress tolerance can be combined with genome-assisted breeding (GAB) by being pyramided with improved crop yield, food quality and associated agronomic traits and can open a new era of omics-assisted breeding. Thus, multi-omics pipelines together are able to decipher molecular processes, biomarkers, targets for genetic engineering, regulatory networks and precision agriculture solutions for a crop's variable abiotic stress tolerance to ensure food security under changing environmental circumstances.

Metabolic Adaption of Flexor Carpi Radialis to Amplexus Behavior in Asiatic Toads (Bufo gargarizans)

Amplexus is a type of mating behavior among toads that is essential for successful external fertilization. Most studies have primarily focused on the behavioral diversity of amplexus, and less is known regarding the metabolic changes occurring in amplectant males. The aim of this study was to compare the metabolic profiles of amplectant Asiatic toad (Bufo gargarizans) males in the breeding period (BP group) and the resting males in the non-breeding period (NP group). A metabolomic analysis was conducted on the flexor carpi radialis (FCR), an essential forelimb muscle responsible for clasping during courtship. A total of 66 differential metabolites were identified between the BP and NP groups, including 18 amino acids, 12 carbohydrates, and 8 lipids, and they were classified into 9 categories. Among these differential metabolites, 13 amino acids, 11 carbohydrates, and 7 lipids were significantly upregulated in the BP group compared to the NP group. In addition, a KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis identified 17 significant metabolic pathways, including ABC transporters, aminoacyl-tRNA biosynthesis, arginine biosynthesis, pantothenate and CoA biosynthesis, and fructose and mannose metabolism. These results suggest that amplectant male toads are metabolically more active than those during the non-breeding period, and this metabolic adaptation increases the likelihood of reproductive success.

Jujube Fruit Metabolomic Profiles Reveal Cultivar Differences and Function as Cultivar Fingerprints

Jujube is a nutritious fruit, and is high in vitamin C, fiber, phenolics, flavonoids, nucleotides, and organic acids. It is both an important food and a source of traditional medicine. Metabolomics can reveal metabolic differences between Ziziphus jujuba fruits from different jujube cultivars and growth sites. In the fall of 2022, mature fresh fruit of eleven cultivars from replicated trials at three sites in New Mexico-Leyendecker, Los Lunas, and Alcalde-were sampled from September to October for an untargeted metabolomics study. The 11 cultivars were Alcalde 1, Dongzao, Jinsi (JS), Jinkuiwang (JKW), Jixin, Kongfucui (KFC), Lang, Li, Maya, Shanxi Li, and Zaocuiwang (ZCW). Based on the LC-MS/MS analysis, there were 1315 compounds detected with amino acids and derivatives (20.15%) and flavonoids (15.44%) as dominant categories. The results reveal that the cultivar was the dominant factor in metabolite profiles, while the location was secondary. A pairwise comparison of cultivar metabolomes revealed that two pairs had fewer differential metabolites (i.e., Li/Shanxi Li and JS/JKW) than all the other pairs, highlighting that pairwise metabolic comparison can be applied for cultivar fingerprinting. Differential metabolite analysis also showed that half of drying cultivars have up-regulated lipid metabolites compared to fresh or multi-purpose fruit cultivars and that specialized metabolites vary significantly between cultivars from 35.3% (Dongzao/ZCW) to 56.7% (Jixin/KFC). An exemplary analyte matching sedative cyclopeptide alkaloid sanjoinine A was only detected in the Jinsi and Jinkuiwang cultivars. Overall, our metabolic analysis of the jujube cultivar's mature fruits provides the largest resource of jujube fruit metabolomes to date and will inform cultivar selection for nutritional and medicinal research and for fruit metabolic breeding.

Pretreatment with Bifidobacterium longum BAA2573 ameliorates dextran sulfate sodium (DSS)-induced colitis by modulating gut microbiota

Objectives

Inflammatory bowel disease (IBD) is a chronic lifelong inflammatory disease. Probiotics such as Bifidobacterium longum are considered to be beneficial to the recovery of intestinal inflammation by interaction with gut microbiota. Our goals were to define the effect of the exclusive use of BAA2573 on dextran sulfate sodium (DSS)-induced colitis, including improvement of symptoms, alleviation of histopathological damage, and modulation of gut microbiota.

Methods

In the present study, we pretreated C57BL/6J mice with Bifidobacterium longum BAA2573, one of the main components in an over-the-counter (OTC) probiotic mixture BIFOTO capsule, before modeling with DSS. 16S rDNA sequencing and liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based non-targeted metabolomic profiling were performed with the collected feces.

Results

We found that pretreatment of Bifidobacterium longum BAA2573 given by gavage significantly improved symptoms and histopathological damage in DSS-induced colitis mice. After the BAA2573 intervention, 57 genera and 39 metabolites were significantly altered. Pathway enrichment analysis demonstrated that starch and sucrose metabolism, vitamin B6 metabolism, and sphingolipid metabolism may contribute to ameliorating colitis. Moreover, we revealed that the gut microbiome and metabolites were interrelated in the BAA2573 intervention group, while Alistipes was the core genus.

Conclusion

Our study demonstrates the impact of BAA2573 on the gut microbiota and reveals a possible novel adjuvant therapy for IBD patients.

A 3D-printed SERS bionic taster for dynamic tumor metabolites detection

The variation of tumor-associated metabolites in extracellular microenvironment timely reflects the development, the progression and the treatment of cancers. Conventional methods for metabolite detection lack the efficiency to grasp the dynamic metabolic alterations. Herein, we developed a SERS bionic taster which enabled real-time analysis of extracellular metabolites. The instant information of cell metabolism was provided by the responsive Raman reporters, which experienced SERS spectral changes upon metabolite activation. Such a SERS sensor was integrated into a 3D-printed fixture which fits the commercial-standard cell culture dishes, allowing in-situ acquisition of the vibrational spectrum. The SERS taster can not only accomplish simultaneous and quantitative analysis of multiple tumor-associated metabolites, but also fulfill the dynamic monitoring of cellular metabolic reprogramming, which is expected to become a promising tool for investigating cancer biology and therapeutics.

Untargeted metabolomics analysis in drug-naïve patients with severe obsessive-compulsive disorder

Introduction

Obsessive-compulsive disorder (OCD), characterized by the presence of obsessions and/or compulsions, is often difficult to diagnose and treat in routine clinical practice. The candidate circulating biomarkers and primary metabolic pathway alteration of plasma in OCD remain poorly understood.

Methods

We recruited 32 drug-naïve patients with severe OCD and 32 compared healthy controls and applied the untargeted metabolomics approach by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) to assess their circulating metabolic profiles. Both univariate and multivariate analyses were then utilized to filtrate differential metabolites between patients and healthy controls, and weighted Correlation Network Analysis (WGCNA) was utilized to screen out hub metabolites.

Results

A total of 929 metabolites were identified, including 34 differential metabolites and 51 hub metabolites, with an overlap of 13 metabolites. Notably, the following enrichment analyses underlined the importance of unsaturated fatty acids and tryptophan metabolism alterations in OCD. Metabolites of these pathways in plasma appeared to be promising biomarkers, such as Docosapentaenoic acid and 5-Hydroxytryptophan, which may be biomarkers for OCD identification and prediction of sertraline treatment outcome, respectively.

Conclusion

Our findings revealed alterations in the circulating metabolome and the potential utility of plasma metabolites as promising biomarkers in OCD.

Expanding our coverage: Strategies to detect a greater range of metabolites

Whilst the study of metabolites can arguably be traced back several hundred years it began in earnest in the 20th century with studies based on single metabolites or simple metabolic pathways. The advent of metabolomics and in particular the adoption of high-resolution mass spectrometry now means we can faithfully annotate and quantify in excess of 1000 plant metabolites. Whilst this is an impressive leap it falls well short of the estimated number of metabolites in the plant kingdom. This, whilst considerable and important insights have been achieved using commonly utilized approaches, there is a need to improve the coverage of the metabolome. Here, we review three largely complementary strategies (i) methods based on using chemical libraries (ii) methods based on molecular networking and (iii) approaches that link metabolomics and genetic variance. It is our contention that using all three approaches in tandem represents the best approach to tackle this challenge.

Hello darkness, my old friend: 3-KETOACYL-COENZYME A SYNTHASE4 is a branch point in the regulation of triacylglycerol synthesis in Arabidopsis thaliana

Plant lipids are important as alternative sources of carbon and energy when sugars or starch are limited. Here, we applied combined heat and darkness or extended darkness to a panel of ∼300 Arabidopsis (Arabidopsis thaliana) accessions to study lipid remodeling under carbon starvation. Natural allelic variation at 3-KETOACYL-COENZYME A SYNTHASE4 (KCS4), a gene encoding an enzyme involved in very long chain fatty acid (VLCFA) synthesis, underlies the differential accumulation of polyunsaturated triacylglycerols (puTAGs) under stress. Ectopic expression of KCS4 in yeast and plants proved that KCS4 is a functional enzyme localized in the endoplasmic reticulum with specificity for C22 and C24 saturated acyl-CoA. Allelic mutants and transient overexpression in planta revealed the differential role of KCS4 alleles in VLCFA synthesis and leaf wax coverage, puTAG accumulation, and biomass. Moreover, the region harboring KCS4 is under high selective pressure and allelic variation at KCS4 correlates with environmental parameters from the locales of Arabidopsis accessions. Our results provide evidence that KCS4 plays a decisive role in the subsequent fate of fatty acids released from chloroplast membrane lipids under carbon starvation. This work sheds light on both plant response mechanisms and the evolutionary events shaping the lipidome under carbon starvation.

Diagnosis of Parkinson's Disease via the Metabolic Fingerprint in Saliva by Deep Learning

Parkinson's disease (PD) is the second cause of the neurodegenerative disorder, affecting over 6 million people worldwide. The World Health Organization estimated that population aging will cause global PD prevalence to double in the coming 30 years. Optimal management of PD shall start at diagnosis and requires both a timely and accurate method. Conventional PD diagnosis needs observations and clinical signs assessment, which are time-consuming and low-throughput. A lack of body fluid diagnostic biomarkers for PD has been a significant challenge, although substantial progress has been made in genetic and imaging marker development. Herein, a platform that noninvasively collects saliva metabolic fingerprinting (SMF) by nanoparticle-enhanced laser desorption-ionization mass spectrometry with high-reproducibility and high-throughput, using ultra-small sample volume (down to 10 nL), is developed. Further, excellent diagnostic performance is achieved with an area-under-the-curve of 0.8496 (95% CI: 0.7393-0.8625) by constructing deep learning model from 312 participants. In conclusion, an alternative solution is provided for the molecular diagnostics of PD with SMF and metabolic biomarker screening for therapeutic intervention.

Uncovering Phytotoxic Compounds Produced by Colletotrichum spp. Involved in Legume Diseases Using an OSMAC-Metabolomics Approach

Different fungal species belonging to the Colletotrichum genus cause anthracnose disease in a range of major crops, resulting in huge economic losses worldwide. Typical symptoms include dark, sunken lesions on leaves, stems, or fruits. Colletotrichum spp. have synthesized, in vitro, a number of biologically active and structurally unusual metabolites that are involved in their host's infection process. In this study, we applied a one strain many compounds (OSMAC) approach, integrated with targeted and non-targeted metabolomics profiling, to shed light on the secondary phytotoxic metabolite panels produced by pathogenic isolates of Colletotrichum truncatum and Colletotrichum trifolii. The phytotoxicity of the fungal crude extracts was also assessed on their primary hosts and related legumes, and the results correlated with the metabolite profile that arose from the different cultural conditions. To the best of our knowledge, this is the first time that the OSMAC strategy integrated with metabolomics approaches has been applied to Colletotrichum species involved in legume diseases.

OsTST1, a key tonoplast sugar transporter from source to sink, plays essential roles in affecting yields and height of rice (Oryza sativa L.)

MAIN CONCLUSION

OsTST1 affects yield and development and mediates sugar transportation of plants from source to sink in rice, which influences the accumulation of intermediate metabolites from tricarboxylic acid cycle indirectly. Tonoplast sugar transporters (TSTs) are essential for vacuolar sugar accumulation in plants. Carbohydrate transport across tonoplasts maintains the metabolic balance in plant cells, and carbohydrate distribution is crucial to plant growth and productivity. Large plant vacuoles store high concentrations of sugars to meet plant requirements for energy and other biological processes. The abundance of sugar transporter affects crop biomass and reproductive growth. However, it remains unclear whether the rice (Oryza sativa L.) sugar transport protein OsTST1 affects yield and development. In this study, we found that OsTST1 knockout mutants generated via CRISPR/Cas9 exhibited slower development, smaller seeds, and lower yield than wild type (WT) rice plants. Notably, plants overexpressing OsTST1 showed the opposite effects. Changes in rice leaves at 14 days after germination (DAG) and at 10 days after flowering (DAF) suggested that OsTST1 affected the accumulation of intermediate metabolites from the glycolytic pathway and the tricarboxylic acid (TCA) cycle. The modification of the sugar transport between cytosol and vacuole mediated by OsTST1 induces deregulation of several genes including transcription factors (TFs). In summary, no matter the location of sucrose and sink is, these preliminary results revealed that OsTST1 was important for sugar transport from source to sink tissues, thus affecting plant growth and development.

An automated workflow for multi-omics screening of microbial model organisms

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

Transcriptomic and metabolomic analysis reveals that symbiotic nitrogen fixation enhances drought resistance in common bean

Common bean (Phaseolus vulgaris L.), one of the most important legume crops, uses atmospheric nitrogen through symbiosis with soil rhizobia, reducing the need for nitrogen fertilization. However, this legume is particularly sensitive to drought conditions, prevalent in arid regions where this crop is cultured. Therefore, studying the response to drought is important to sustain crop productivity. We have used integrated transcriptomic and metabolomic analysis to understand the molecular responses to water deficit in a marker-class common bean accession cultivated under N2 fixation or fertilized with nitrate (NO3-). RNA-seq revealed more transcriptional changes in the plants fertilized with NO3- than in the N2-fixing plants. However, changes in N2-fixing plants were more associated with drought tolerance than in those fertilized with NO3-. N2-fixing plants accumulated more ureides in response to drought, and GC/MS and LC/MS analysis of primary and secondary metabolite profiles revealed that N2-fixing plants also had higher levels of abscisic acid, proline, raffinose, amino acids, sphingolipids, and triacylglycerols than those fertilized with NO3-. Moreover, plants grown under nitrogen fixation recovered from drought better than plants fertilized with NO3-. Altogether we show that common bean plants grown under symbiotic nitrogen fixation were more protected against drought than the plants fertilized with nitrate.

A compounds annotation strategy using targeted molecular networking for offline two-dimensional liquid chromatography-mass spectrometry analysis: Yupingfeng as a case study

Component overlapping and long-time consumption hinder the data processing of offline two-dimensional liquid chromatography mass spectrometry (offline 2D-LC MS) system. Although molecular networking has been commonly employed in data processing of liquid chromatography mass spectrometry (LC-MS), its application in offline 2D-LC MS is challenged by voluminous and redundant data. In light of this, for the first time, a data deduplication and visualization strategy combining hand-in-hand alignment with targeted molecular networking (TMN) for compounds annotation of offline 2D-LC MS data was developed and applied to the chemical profile of Yupingfeng (YPF), a classical traditional Chinese medicine (TCM) prescription, as a case study. Firstly, an offline 2D-LC MS system was constructed for the separation and data acquisition of YPF extract. Then the data of 12 fractions derived from YPF were deconvoluted and aligned as a whole data file by hand-in-hand alignment, resulting in a 49.2% reduction in component overlapping (from 17951 to 9112 ions) and an improvement in the MS² spectrum quality of precursor ions. Subsequently, the MS²-similarity adjacency matrix of focused parent ions was computed by a self-building Python script, which realized the construction of an innovative TMN. Interestingly, the TMN was found to be able to efficiently distinguish and visualize the co-elution, in-source fragmentations and multi-type adduct ions in a clustering network. Consequently, a total of 497 compounds were successfully identified depending on only seven TMN analysis guided by product ions filtering (PIF) and neutral loss filtering (NLF) for the targeted compounds in YPF. This integrated strategy improved the efficiency of targeted compound discovery in offline 2D-LC MS data, also shown a huge scalability in accurate compound annotation of complex samples. In conclusion, our study developed available concepts and tools while providing a research paradigm for efficient and rapid compound annotation in complex samples such as TCM prescriptions, with YPF as an example.

Widely targeted metabolomic profiling combined with transcriptome analysis sheds light on flavonoid biosynthesis in sweet orange 'Newhall' (C. sinensis) under magnesium stress

Sweet orange 'Newhall' peels (SOPs) are abundant in flavonoids, making them increasingly popular in the realms of nutrition, food, and medicine. However, there is still much unknown about flavonoid components in SOPs and the molecular mechanism of flavonoid biosynthesis when subjected to magnesium stress. The previous experiment conducted by the research group found that the total flavonoid content of Magnesium deficiency (MD) was higher than Magnesium sufficiency (MS) in SOPs. In order to study the metabolic pathway of flavonoids under magnesium stress, an integrative analysis of the metabolome and transcriptome was performed in SOPs at different developmental stages, comparing MS and MD. A comprehensive analysis revealed the identification of 1,533 secondary metabolites in SOPs. Among them, 740 flavonoids were classified into eight categories, with flavones identified as the dominant flavonoid component. The influence of magnesium stress on flavonoid composition was evaluated using a combination of heat map and volcanic map, which indicated significant variations between MS and MD varieties at different growth stages. The transcriptome detected 17,897 differential genes that were significantly enriched in flavonoid pathways. Further analysis was performed using Weighted gene correlation network analysis (WGCNA) in conjunction with flavonoid metabolism profiling and transcriptome analysis to identify six hub structural genes and ten hub transcription factor genes that play a crucial role in regulating flavonoid biosynthesis from yellow and blue modules. The correlation heatmap and Canonical Correspondence Analysis (CCA) results showed that CitCHS had a significant impact on the synthesis of flavones and other flavonoids in SOPs, as it was the backbone gene in the flavonoid biosynthesis pathway. The qPCR results further validated the accuracy of transcriptome data and the reliability of candidate genes. Overall, these results shed light on the composition of flavonoid compounds in SOPs and highlight the changes in flavonoid metabolism that occur under magnesium stress. This research provides valuable insights for improving the cultivation of high-flavonoid plants and enhancing our understanding of the molecular mechanisms underlying flavonoid biosynthesis.

Simultaneous Quantitation and Discovery (SQUAD) Analysis: Combining the Best of Targeted and Untargeted Mass Spectrometry-Based Metabolomics

Untargeted and targeted approaches are the traditional metabolomics workflows acquired for a wider understanding of the metabolome under focus. Both approaches have their strengths and weaknesses. The untargeted, for example, is maximizing the detection and accurate identification of thousands of metabolites, while the targeted is maximizing the linear dynamic range and quantification sensitivity. These workflows, however, are acquired separately, so researchers compromise either a low-accuracy overview of total molecular changes (i.e., untargeted analysis) or a detailed yet blinkered snapshot of a selected group of metabolites (i.e., targeted analysis) by selecting one of the workflows over the other. In this review, we present a novel single injection simultaneous quantitation and discovery (SQUAD) metabolomics that combines targeted and untargeted workflows. It is used to identify and accurately quantify a targeted set of metabolites. It also allows data retro-mining to look for global metabolic changes that were not part of the original focus. This offers a way to strike the balance between targeted and untargeted approaches in one single experiment and address the two approaches' limitations. This simultaneous acquisition of hypothesis-led and discovery-led datasets allows scientists to gain more knowledge about biological systems in a single experiment.

Designed Concave Octahedron Heterostructures Decode Distinct Metabolic Patterns of Epithelial Ovarian Tumors

Epithelial ovarian cancer (EOC) is a polyfactorial process associated with alterations in metabolic pathways. A high-performance screening tool for EOC is in high demand to improve prognostic outcome but is still missing. Here, a concave octahedron Mn₂ O₃ /(Co,Mn)(Co,Mn)₂ O₄ (MO/CMO) composite with a heterojunction, rough surface, hollow interior, and sharp corners is developed to record metabolic patterns of ovarian tumors by laser desorption/ionization mass spectrometry (LDI-MS). The MO/CMO composites with multiple physical effects induce enhanced light absorption, preferred charge transfer, increased photothermal conversion, and selective trapping of small molecules. The MO/CMO shows ≈2-5-fold signal enhancement compared to mono- or dual-enhancement counterparts, and ≈10-48-fold compared to the commercialized products. Subsequently, serum metabolic fingerprints of ovarian tumors are revealed by MO/CMO-assisted LDI-MS, achieving high reproducibility of direct serum detection without treatment. Furthermore, machine learning of the metabolic fingerprints distinguishes malignant ovarian tumors from benign controls with the area under the curve value of 0.987. Finally, seven metabolites associated with the progression of ovarian tumors are screened as potential biomarkers. The approach guides the future depiction of the state-of-the-art matrix for intensive MS detection and accelerates the growth of nanomaterials-based platforms toward precision diagnosis scenarios.

Value Addition Employing Waste Bio-Materials in Environmental Remedies and Food Sector

Overall, combating food waste necessitates a multifaceted approach that includes education, infrastructure, and policy change. By working together to implement these strategies, we can help reduce the negative impacts of food waste and create a more sustainable and equitable food system. The sustained supply of nutrient-rich agrifood commodities is seriously threatened by inefficiencies caused by agricultural losses, which must be addressed. As per the statistical data given by the Food and Agriculture Organisation (FAO) of the United Nations, nearly 33.33% of the food that is produced for utilization is wasted and frittered away on a global level, which can be estimated as a loss of 1.3 billion metric tons per annum, which includes 30% cereals, 20% dairy products 35% seafood and fish, 45% fruits and vegetables, and 20% of meat. This review summarizes the various types of waste originating from various segments of the food industry, such as fruits and vegetables, dairy, marine, and brewery, also focusing on their potential for developing commercially available value-added products such as bioplastics, bio-fertilizers, food additives, antioxidants, antibiotics, biochar, organic acids, and enzymes. The paramount highlights include food waste valorization, which is a sustainable yet profitable alternative to waste management, and harnessing Machine Learning and Artificial Intelligence technology to minimize food waste. Detail of sustainability and feasibility of food waste-derived metabolic chemical compounds, along with the market outlook and recycling of food wastes, have been elucidated in this review.

Dysregulated Gln-Glu-α-ketoglutarate axis impairs maternal decidualization and increases the risk of recurrent spontaneous miscarriage

Recurrent spontaneous miscarriage (RSM) affects 1%-2% of fertile women worldwide and poses a risk of future pregnancy complications. Increasing evidence has indicated that defective endometrial stromal decidualization is a potential cause of RSM. Here, we perform liquid chromatography with mass spectrometry (LC-MS)-based metabolite profiling in human endometrial stromal cells (ESCs) and differentiated ESCs (DESCs) and find that accumulated α-ketoglutarate (αKG) derived from activated glutaminolysis contributes to maternal decidualization. Contrarily, ESCs obtained from patients with RSM show glutaminolysis blockade and aberrant decidualization. We further find that enhanced Gln-Glu-αKG flux decreases histone methylation and supports ATP production during decidualization. In vivo, feeding mice a Glu-free diet leads to a reduction of αKG, impaired decidualization, and an increase of fetal loss rate. Isotopic tracing approaches demonstrate Gln-dependent oxidative metabolism as a prevalent direction during decidualization. Our results demonstrate an essential prerequisite of Gln-Glu-αKG flux to regulate maternal decidualization, suggesting αKG supplementation as a putative strategy to rectify deficient decidualization in patients with RSM.

Analytical methods for amatoxins: A comprehensive review

Amatoxins are toxic bicyclic octapeptides found in certain wild mushroom species, particularly Amanita phalloides. These mushrooms contain predominantly α- and β-amanitin, which can lead to severe health risks for humans and animals if ingested. Rapid and accurate identification of these toxins in mushroom and biological samples is crucial for diagnosing and treating mushroom poisoning. Analytical methods for the determination of amatoxins are critical to ensure food safety and prompt medical treatment. This review provides a comprehensive overview of the research literature on the determination of amatoxins in clinical specimens, biological and mushroom samples. We discuss the physicochemical properties of toxins, highlighting their influence on the choice of the analytical method and the importance of sample preparation, particularly solid-phase extraction with cartridges. Chromatographic methods are emphasised with a focus on liquid chromatography coupled to mass spectrometry as one of the most relevant analytical method for the determination of amatoxins in complex matrices. Furthermore, current trends and future perspectives in amatoxin detection are also suggested.

Gut microbiome and serum metabolome analyses identify biomarkers associated with sexual maturity in quails

Increasing evidence indicates that the gut microbiome plays an important role in host aging and sexual maturity. However, the gut microbial taxa associated with sexual maturity in quails are unknown. This study used shotgun metagenomic sequencing to identify bacterial taxa associated with sexual maturity in d 20 and d 70 quails. We found that 17 bacterial species and 67 metagenome-assembled genomes (e.g., Bacteroides spp. and Enterococcus spp.) significantly differed between the d 20 and d 70 groups, including 5 bacterial species (e.g., Enterococcus faecalis) enriched in the d 20 group and 12 bacterial species (e.g., Christensenella massiliensis, Clostridium sp. CAG:217, and Bacteroides neonati) which had high abundances in the d 70 group. The bacterial species enriched in d 20 or d 70 were key biomarkers distinguishing sexual maturity and significantly correlated with the shifts in the functional capacities of the gut microbiome. Untargeted serum metabolome analysis revealed that 5 metabolites (e.g., nicotinamide riboside) were enriched in the d 20 group, and 6 metabolites (e.g., D-ribose, stevioside, and barbituric acid) were enriched in the d 70 group. Furthermore, metabolites with high abundances in the d 20 group were significantly enriched for the KEGG pathways of arginine biosynthesis, nicotinate and nicotinamide metabolism, and lysine degradation. However, glutathione metabolism and valine, leucine and isoleucine biosynthesis were enriched in high-abundance metabolites from the d 70 group. These results provide important insights into the effects of gut microbiome and host metabolism on quail sexual maturity.

Short-Term Stability of Serum and Liver Extracts for Untargeted Metabolomics and Lipidomics

Thermal reactions can significantly alter the metabolomic and lipidomic content of biofluids and tissues during storage. In this study, we investigated the stability of polar metabolites and complex lipids in dry human serum and mouse liver extracts over a three-day period under various temperature conditions. Specifically, we tested temperatures of -80 °C (freezer), -24 °C (freezer), -0.5 °C (polystyrene box with gel-based ice packs), +5 °C (refrigerator), +23 °C (laboratory, room temperature), and +30 °C (thermostat) to simulate the time between sample extraction and analysis, shipping dry extracts to different labs as an alternative to dry ice, and document the impact of higher temperatures on sample integrity. The extracts were analyzed using five fast liquid chromatography-mass spectrometry (LC-MS) methods to screen polar metabolites and complex lipids, and over 600 metabolites were annotated in serum and liver extracts. We found that storing dry extracts at -24 °C and partially at -0.5 °C provided comparable results to -80 °C (reference condition). However, increasing the storage temperatures led to significant changes in oxidized triacylglycerols, phospholipids, and fatty acids within three days. Polar metabolites were mainly affected at storage temperatures of +23 °C and +30 °C.

Identification of biomarkers for risk assessment of arsenicosis based on untargeted metabolomics and machine learning algorithms

BACKGROUND

Long-term exposure to inorganic arsenic may lead to arsenicosis. There are, however, currently no validated metabolic biomarkers used for the identification of arsenicosis risk. This study aims to identify metabolites associated with arsenicosis and establish prediction models for risk assessment based on untargeted metabolomics and machine learning algorithms.

METHODS

In total, 105 coal-borne arsenicosis patients, with 35 subjects in each of the mild, moderate, and severe subgroups according to their symptom severity, and 60 healthy residents were enrolled from Guizhou, China. Ultra-high performance liquid chromatography-tandem mass spectrometer (UHPLC-MS/MS) was utilized to acquire the plasma metabolic profiles of the studied subjects. Statistical analysis was used to identify disease-associated metabolites. Machine learning algorithms and the identified metabolic biomarkers were resorted to assess the arsenicosis risk.

RESULTS

A total of 143 metabolic biomarkers, with organic acids being the majority, were identified to be closely associated with arsenicosis, and the most involved pathway was glycine, serine, and threonine metabolism. Comparative analysis of metabolites in arsenicosis patients with different symptom severity revealed 422 altered molecules, where disrupted metabolism of beta-alanine and arginine demonstrated the most significance. For risk assessment, the model established by a single biomarker (L-carnosine) could undoubtedly discriminate arsenicosis patients from the healthy. For classifying arsenicosis patients with different severity, the model established using 52 metabolites and linear discriminate analysis (LDA) algorithm yielded an accuracy of 0.970-0.979 on calibration set (n = 132) and 0.818-0.848 on validation set (n = 33).

CONCLUSION

Altered metabolites and disrupted pathways are prevalent in arsenicosis patients; The disrupted metabolism of one carbon and dysfunction of antioxidant defense system may partially be causes of the systematic multi-organ damage and carcinogenesis in arsenicosis patients; Metabolic biomarkers, combined with machine learning algorithms, could be efficient for risk assessment and early identification of arsenicosis.

Applications of multi-omics techniques to unravel the fermentation process and the flavor formation mechanism in fermented foods

Fermented foods are important components of the human diet. There is increasing awareness of abundant nutritional and functional properties present in fermented foods that arise from the transformation of substrates by microbial communities. Thus, it is significant to unravel the microbial communities and mechanisms of characteristic flavor formation occurring during fermentation. There has been rapid development of high-throughput and other omics technologies, such as metaproteomics and metabolomics, and as a result, there is growing recognition of the importance of integrating these approaches. The successful applications of multi-omics approaches and bioinformatics analyses have provided a solid foundation for exploring the fermentation process. Compared with single-omics, multi-omics analyses more accurately delineate microbial and molecular features, thus they are more apt to reveal the mechanisms of fermentation. This review introduces fermented foods and an overview of single-omics technologies - including metagenomics, metatranscriptomics, metaproteomics, and metabolomics. We also discuss integrated multi-omics and bioinformatic analyses and their role in recent research progress related to fermented foods, as well as summarize the main potential pathways involved in certain fermented foods. In the future, multilayered analyses of multi-omics data should be conducted to enable better understanding of flavor formation mechanisms in fermented foods.

Qualitative Analysis of Polyphenols in Glycerol Plant Extracts Using Untargeted Metabolomics

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

Extraction of phytochemicals from the pomegranate (Punica granatum L., Punicaceae) by reverse iontophoresis

Plant metabolic profiling can provide a wealth of information regarding the biochemical status of the organism, but sample acquisition typically requires an invasive and/or destructive extraction process. Reverse iontophoresis (RI) imposes a small electric field across a biological membrane to substantially enhance the transport of charged and polar compounds and has been employed, in particular, to extract biomarkers of interest across human skin. The objective of this work was to examine the capability of RI to sample phytochemicals in a minimally invasive fashion in fructo (i.e., from the intact fruit). RI was principally used to extract a model, bioactive compound - specifically, ellagic acid - from the fruit peel of Punica granatum L. The RI sampling protocol was refined using isolated peel, and a number of experimental factors were examined and optimised, including preparation of the peel samples, the current intensity applied and the pH of the medium into which samples were collected. The most favourable conditions (3 mA current for a period of 1 hour, into a buffer at pH 7.4) were then applied to the successful RI extraction of ellagic acid from intact pomegranates. Multiple additional phytochemicals were also extracted and identified by liquid chromatography with tandem mass spectrometry (LC-MS/MS). A successful proof-of-concept has been achieved, demonstrating the capability to non-destructively extract phytochemicals of interest from intact fruit.

Identifying Hair Biomarker Candidates for Alzheimer's Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies

High-resolution mass spectrometry (HRMS)-based untargeted metabolomics strategies have emerged as an effective tool for discovering biomarkers of Alzheimer's disease (AD). There are various HRMS-based untargeted metabolomics strategies for biomarker discovery, including the data-dependent acquisition (DDA) method, the combination of full scan and target MS/MS, and the all ion fragmentation (AIF) method. Hair has emerged as a potential biospecimen for biomarker discovery in clinical research since it might reflect the circulating metabolic profiles over several months, while the analytical performances of the different data acquisition methods for hair biomarker discovery have been rarely investigated. Here, the analytical performances of three data acquisition methods in HRMS-based untargeted metabolomics for hair biomarker discovery were evaluated. The human hair samples from AD patients (N = 23) and cognitively normal individuals (N = 23) were used as an example. The most significant number of discriminatory features was acquired using the full scan (407), which is approximately 10-fold higher than that using the DDA strategy (41) and 11% higher than that using the AIF strategy (366). Only 66% of discriminatory chemicals discovered in the DDA strategy were discriminatory features in the full scan dataset. Moreover, compared to the deconvoluted MS/MS spectra with coeluted and background ions from the AIF method, the MS/MS spectrum obtained from the targeted MS/MS approach is cleaner and purer. Therefore, an untargeted metabolomics strategy combining the full scan with the targeted MS/MS method could obtain most discriminatory features along with a high quality MS/MS spectrum for discovering the AD biomarkers.

Natural variation of respiration-related traits in plants

Plant respiration is one of the greatest global metabolic fluxes, but rates of respiration vary massively both within different cell types as well as between different individuals and different species. Whilst this is well known, few studies have detailed population-level variation of respiration until recently. The last 20 years have seen a renaissance in studies of natural variance. In this review, we describe how experimental breeding populations and collections of large populations of accessions can be used to determine the genetic architecture of plant traits. We further detail how these approaches have been used to study the rate of respiration per se as well as traits that are intimately associated with respiration. The review highlights specific breakthroughs in these areas but also concludes that the approach should be more widely adopted in the study of respiration per se as opposed to the more frequently studied respiration-related traits.

Widely targeted metabolomic, transcriptomic, and metagenomic profiling reveal microbe-plant-metabolic reprogramming patterns mediated by Streptomyces pactum Act12 enhance the fruit quality of Capsicum annuum L

Plant growth-promoting rhizobacteria, such as Streptomyces pactum Act12, promote crop growth and stress resistance, but their contribution to fruit quality is still poorly understood. Herein we conducted a field experiment to ascertain the effects of S. pactum Act12-mediated metabolic reprogramming and underlying mechanisms in pepper (Capsicum annuum L.) fruit based on widely targeted metabolomic and transcriptomic profiling. We additionally performed metagenomic analysis to elucidate the potential relationship between S. pactum Act12-mediated reshaping of rhizosphere microbial communities and pepper fruit quality. Soil inoculation with S. pactum Act12 considerably increased the accumulation of capsaicinoids, carbohydrates, organic acids, flavonoids, anthraquinones, unsaturated fatty acids, vitamins, and phenolic acids in pepper fruit samples. Consequently, fruit flavor, taste, and color were modified, accompanied by elevated contents of nutrients and bioactive compounds. Increased microbial diversity and recruitment of potentially beneficial taxa were observed in inoculated soil samples, with crosstalk between microbial gene functions and pepper fruit metabolism. The reformed structure and function of rhizosphere microbial communities were closely associated with pepper fruit quality. Our findings indicate that S. pactum Act12-mediated interactions between rhizosphere microbial communities and pepper plants are responsible for intricate fruit metabolic reprogramming patterns, which enhance not only overall fruit quality but also consumer acceptability.

Emodin Exerts its Therapeutic Effects Through Metabolic Remodeling in Severe Acute Pancreatitis-Related Intestinal Injury

Background

Intestinal injury caused by severe acute pancreatitis (SAP) can induce peripancreatic and systemic infection, and aggravate systemic inflammation. Emodin has demonstrated efficacy in mitigating SAP-associated intestinal injury. Although metabolites in tissues cause histopathophysiological changes, data on the mechanisms of emodin on metabolic processes remain scant.

Methods

The SAP-related intestinal injury rat model was induced by injection of 3.5% sodium taurocholate solution through the biliopancreatic duct. The protective effect of emodin on intestinal injury was evaluated by histologic analyses. On the other hand, we assessed the effect of emodin on metabolic remodeling in intestinal tissues using untargeted metabolomics.

Results

Out of the analyzed 1187 metabolites, untargeted metabolomics identified 99 differential metabolites in the intestinal tissues. Emodin significantly alleviated the inflammatory injury in the pancreas and intestines. Emodin treatment led to significant changes in bile acid metabolism, amino acid metabolism, intestinal microbiota related metabolism, and glycerol phospholipid metabolism in the intestinal tissues. In addition, using the weighted gene co-expression network analysis, we constructed emodin related metabolite–metabolite interaction network and showed that intestinal microbiota related metabolites and glycerol phospholipid metabolism were associated with emodin treatment. Glycine, LPC (0:0/22:6), Spermidine, 11β-hydroxyprogesterone, and N1-methyl-2-Pyridone-5-carboxamide may be efficient molecules after emodin treatment.

Conclusion

Taken together, our data demonstrated that intestinal injury caused by SAP induces an obvious metabolic disorder. Emodin exerts its therapeutic effects through metabolic remodeling.

Multi-omics assisted breeding for biotic stress resistance in soybean

Biotic stress is a critical factor limiting soybean growth and development. Soybean responses to biotic stresses such as insects, nematodes, fungal, bacterial, and viral pathogens are governed by complex regulatory and defense mechanisms. Next-generation sequencing has availed research techniques and strategies in genomics and post-genomics. This review summarizes the available information on marker resources, quantitative trait loci, and marker-trait associations involved in regulating biotic stress responses in soybean. We discuss the differential expression of related genes and proteins reported in different transcriptomics and proteomics studies and the role of signaling pathways and metabolites reported in metabolomic studies. Recent advances in omics technologies offer opportunities to reshape and improve biotic stress resistance in soybean by altering gene regulation and/or other regulatory networks. We suggest using 'integrated omics' to precisely understand how soybean responds to different biotic stresses. We also discuss the potential challenges of integrating multi-omics for the functional analysis of genes and their regulatory networks and the development of biotic stress-resistant cultivars. This review will help direct soybean breeding programs to develop resistance against different biotic stresses.

Comprehensive Two-Dimensional Gas Chromatography as a Bioanalytical Platform for Drug Discovery and Analysis

Over the last decades, comprehensive two-dimensional gas chromatography (GC×GC) has emerged as a significant separation tool for high-resolution analysis of disease-associated metabolites and pharmaceutically relevant molecules. This review highlights recent advances of GC×GC with different detection modalities for drug discovery and analysis, which ideally improve the screening and identification of disease biomarkers, as well as monitoring of therapeutic responses to treatment in complex biological matrixes. Selected recent GC×GC applications that focus on such biomarkers and metabolite profiling of the effects of drug administration are covered. In particular, the technical overview of recent GC×GC implementation with hyphenation to the key mass spectrometry (MS) technologies that provide the benefit of enhanced separation dimension analysis with MS domain differentiation is discussed. We conclude by highlighting the challenges in GC×GC for drug discovery and development with perspectives on future trends.

Integrated Transcriptomic and Metabolomic Analyses Reveal the Molecular and Metabolic Basis of Flavonoids in Areca catechu L

Areca catechu L., of the Arecaceae family, is widely distributed in tropical Asia. In A. catechu, the extracts and compounds, including flavonoids, have various pharmacological activities. Although there are many studies of flavonoids, the molecular mechanism of their biosynthesis and regulation remains unclear in A. catechu. In this study, 331 metabolites were identified from the root, stem, and leaf of A. catechu using untargeted metabolomics, including 107 flavonoids, 71 lipids, 44 amino acids and derivatives, and 33 alkaloids. The transcriptome analysis identified 6119 differentially expressed genes, and some were enriched in the flavonoid pathway. To analyze the biosynthetic mechanism of the metabolic differences in A. catechu tissues, 36 genes were identified through combined transcriptomic and metabolomic analysis, in which glycosyltransferase genes Acat_15g017010 and Acat_16g013670 were annotated as being involved in the glycosylation of kaempferol and chrysin by their expression and in vitro activities. Flavonoid biosynthesis could be regulated by the transcription factors, AcMYB5 and AcMYB194. This study laid a foundation for further research on the flavonoid biosynthetic pathway of A. catechu.

No skin off your back: the sampling and extraction of sebum for metabolomics

INTRODUCTION

Sebum-based metabolomics (a subset of "sebomics") is a developing field that involves the sampling, identification, and quantification of metabolites found in human sebum. Sebum is a lipid-rich oily substance secreted by the sebaceous glands onto the skin surface for skin homeostasis, lubrication, thermoregulation, and environmental protection. Interest in sebomics has grown over the last decade due to its potential for rapid analysis following non-invasive sampling for a range of clinical and environmental applications.

OBJECTIVES

To provide an overview of various sebum sampling techniques with their associated challenges. To evaluate applications of sebum for clinical research, drug monitoring, and human biomonitoring. To provide a commentary of the opportunities of using sebum as a diagnostic biofluid in the future.

METHODS

Bibliometric analyses of selected keywords regarding skin surface analysis using the Scopus search engine from 1960 to 2022 was performed on 12th January 2023. The published literature was compartmentalised based on what the work contributed to in the following areas: the understanding about sebum, its composition, the analytical technologies used, or the purpose of use of sebum. The findings were summarised in this review.

RESULTS

Historically, about 15 methods of sampling have been used for sebum collection. The sample preparation approaches vary depending on the analytes of interest and are summarised. The use of sebum is not limited to just skin diseases or drug monitoring but also demonstrated for other systemic disease. Most of the work carried out for untargeted analysis of metabolites associated with sebum has been in the recent two decades.

CONCLUSION

Sebum has a huge potential beyond skin research and understanding how one's physiological state affects or reflects on the skin metabolome via the sebaceous glands itself or by interactions with sebaceous secretion, will open doors for simpler biomonitoring. Sebum acts as a sink to environmental metabolites and has applications awaiting to be explored, such as biosecurity, cross-border migration, localised exposure to harmful substances, and high-throughput population screening. These applications will be possible with rapid advances in volatile headspace and lipidomics method development as well as the ability of the metabolomics community to annotate unknown species better. A key issue with skin surface analysis that remains unsolved is attributing the source of the metabolites found on the skin surface before meaningful biological interpretation.

Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the 'correct' pipeline isn't straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.