Plant and microbial sciences as key drivers in the development of metabolomics research

Differential Analysis of Anthocyanins in Red and Yellow Hawthorn (Crataegus pinnatifida) Peel Based on Ultra-High Performance Liquid Chromatography-Electrospray Ionization Tandem Mass Spectrometry

Anthocyanins constitute the primary pigment components in hawthorn (Crataegus pinnatifida) peel, yet their specific composition and concentration profiles remain poorly characterized. This study employed ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESI-MS/MS)-based metabolomics to systematically compare anthocyanin profiles between red-peel (CPR) and yellow-peel (CPY) hawthorn cultivars. Our analysis identified 26 anthocyanin metabolites in CPR and 24 in CPY, with cyanidin-3-O-galactoside and cyanidin-3-O-arabinoside being the predominant compounds in both. Multivariate analysis revealed seven significantly differential metabolites, including cyanidin-3-O-galactoside, cyanidin-3-O-arabinoside, pelargonidin-3-O-galactoside, pelargonidin-3-O-glucoside, pelargonidin-3-O-arabinoside, and peonidin-3-O-galactoside. Notably, all the differential metabolites exhibited reductions in CPY compared to CPR. Chromatic analysis demonstrated that CPR possessed highly significantly lower hue angle values (hab) than CPY (47.7093 ± 4.1706, 83.6427 ± 1.4604, p < 0.01), showing strong negative correlations with key anthocyanins. These findings enhance the scientific understanding of anthocyanin biosynthesis in hawthorn peel and provide a certain reference for the development and utilization of anthocyanins in hawthorn peel.

High-Resolution Tandem Mass Spectrometry for Metabolic Profiling of Ocotea diospyrifolia (Meisn.) Mez Leaves

Ocotea is an important genus of Lauraceae plant family that comprises over 400 species, many of which pose challenges in taxonomic differentiation due to their complex botanical characteristics. Chemosystematics, and more recently, chemophenetics, have emerged as valuable tools to address these challenges based on their natural products (NPs) composition. O. diospyrifolia (Meisn.) Mez is a poorly studied species with known pharmacological potential. Here, we applied ultra-high performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UHPLC-HRMS) allied to a curated in-house database with all previous isolated NPs from the Ocotea genus (OcoteaDB), gas phase fragmentations reactions, and biosynthesis. The strategy resulted in compounds annotated in confidence levels 2 (n=27), 3 (n=231), and 4 (n=21) according to the Metabolomics Standards Initiative (MSI). Additional annotations based on fragmentation proposals (n=16) were also included. The study revealed that O. diospyrifolia is a great alkaloid producer, even though different lignoids, which also comes from the shikimate pathway, were annotated. Additionally, the flavonoid profile predominantly consists of flavonol glycosides, complementing prior reports. This study provides the first comprehensive chemical profile of O. diospyrifolia leaves, which corroborates the chemotaxonomy of the species, and also contributes to further characterization studies, as the UHPLC-HRMS data is publicly available.

Advancing our understanding of root development: Technologies and insights from diverse studies

Understanding root development is critical for enhancing plant growth and health, and advanced technologies are essential for unraveling the complexities of these processes. In this review, we highlight select technological innovations in the study of root development, with a focus on the transformative impact of single-cell gene expression analysis. We provide a high-level overview of recent advancements, illustrating how single-cell RNA sequencing (scRNA-seq) has become a pivotal tool in plant biology. scRNA-seq has revolutionized root biology by enabling detailed, cell-specific analysis of gene expression. This has allowed researchers to create comprehensive root atlases, predict cell development, and map gene regulatory networks (GRNs) with unprecedented precision. Complementary technologies, such as multimodal profiling and bioinformatics, further enrich our understanding of cellular dynamics and gene interactions. Innovations in imaging and modeling, combined with genetic tools like CRISPR, continue to deepen our knowledge of root formation and function. Moreover, the integration of these technologies with advanced biosensors and microfluidic devices has advanced our ability to study plant-microbe interactions and phytohormone signaling at high resolution. These tools collectively provide a more comprehensive understanding of root system architecture and its regulation by environmental factors. As these technologies evolve, they promise to drive further breakthroughs in plant science, with substantial implications for agriculture and sustainability.

Chromatography-Based Metabolomics as a Tool in Bioorganic Research of Honey

This review presents the latest research on chromatography-based metabolomics for bioorganic research of honey, considering targeted, suspect, and untargeted metabolomics involving metabolite profiling and metabolite fingerprinting. These approaches give an insight into the metabolic diversity of different honey varieties and reveal different classes of organic compounds in the metabolic profiles, among which, key metabolites such as biomarkers and bioactive compounds can be highlighted. Chromatography-based metabolomics strategies have significantly impacted different aspects of bioorganic research, including primary areas such as botanical origins, honey origin traceability, entomological origins, and honey maturity. Through the use of different tools for complex data analysis, these strategies contribute to the detection, assessment, and/or correlation of different honey parameters and attributes. Bioorganic research is mainly focused on phytochemicals and their transformation, but the chemical changes that can occur during the different stages of honey formation remain a challenge. Furthermore, the latest user- and environmentally friendly sample preparation methods and technologies as well as future perspectives and the role of chromatography-based metabolomic strategies in honey characterization are discussed. The objective of this review is to summarize the latest metabolomics strategies contributing to bioorganic research onf honey, with emphasis on the (i) metabolite analysis by gas and liquid chromatography techniques; (ii) key metabolites in the obtained metabolic profiles; (iii) formation and accumulation of biogenic volatile and non-volatile markers; (iv) sample preparation procedures; (v) data analysis, including software and databases; and (vi) conclusions and future perspectives. For the present review, the literature search strategy was based on the PRISMA guidelines and focused on studies published between 2019 and 2024. This review outlines the importance of metabolomics strategies for potential innovations in characterizing honey and unlocking its full bioorganic potential.

Multiomics and biotechnologies for understanding and influencing cadmium accumulation and stress response in plants

Cadmium (Cd) is one of the most toxic heavy metals faced by plants and, additionally, via the food chain, threatens human health. It is principally dispersed through agro-ecosystems via anthropogenic activities and geogenic sources. Given its high mobility and persistence, Cd, although not required, can be readily assimilated by plants thereby posing a threat to plant growth and productivity as well as animal and human health. Thus, breeding crop plants in which the edible parts contain low to zero Cd as safe food stuffs and harvesting shoots of high Cd-containing plants as a route for decontaminating soils are vital strategies to cope with this problem. Recently, multiomics approaches have been employed to considerably enhance our understanding of the mechanisms underlying (i) Cd toxicity, (ii) Cd accumulation, (iii) Cd detoxification and (iv) Cd acquisition tolerance in plants. This information can be deployed in the development of the biotechnological tools for developing plants with modulated Cd tolerance and detoxification to safeguard cellular and genetic integrity as well as to minimize food chain contamination. The aim of this review is to provide a current update about the mechanisms involved in Cd uptake by plants and the recent developments in the area of multiomics approach in terms of Cd stress responses, as well as in the development of Cd tolerant and low Cd accumulating crops.

Application of multi-omics in the study of traditional Chinese medicine

Traditional Chinese medicine (TCM) is playing an increasingly important role in disease treatment due to the advantages of multi-target, multi-pathway mechanisms, low adverse reactions and cost-effectiveness. However, the complexity of TCM system poses challenges for research. In recent years, there has been a surge in the application of multi-omics integrated research to explore the active components and treatment mechanisms of TCM from various perspectives, which aids in advancing TCM's integration into clinical practice and holds immense importance in promoting modernization. In this review, we discuss the application of proteomics, metabolomics, and mass spectrometry imaging in the study of composition, quality evaluation, target identification, and mechanism of action of TCM based on existing literature. We focus on the workflows and applications of multi-omics based on mass spectrometry in the research of TCM. Additionally, potential research ideas for future exploration in TCM are outlined. Overall, we emphasize the advantages and prospects of multi-omics based on mass spectrometry in the study of the substance basis and mechanism of action of TCM. This synthesis of methodologies holds promise for enhancing our understanding of TCM and driving its further integration into contemporary medical practices.

Advances in mass spectrometry imaging for plant metabolomics-Expanding the analytical toolbox

Mass spectrometry imaging (MSI) has become increasingly popular in plant science due to its ability to characterize complex chemical, spatial, and temporal aspects of plant metabolism. Over the past decade, as the emerging and unique features of various MSI techniques have continued to support new discoveries in studies of plant metabolism closely associated with various aspects of plant function and physiology, spatial metabolomics based on MSI techniques has positioned it at the forefront of plant metabolic studies, providing the opportunity for far higher resolution than was previously available. Despite these efforts, profound challenges at the levels of spatial resolution, sensitivity, quantitative ability, chemical confidence, isomer discrimination, and spatial multi-omics integration, undoubtedly remain. In this Perspective, we provide a contemporary overview of the emergent MSI techniques widely used in the plant sciences, with particular emphasis on recent advances in methodological breakthroughs. Having established the detailed context of MSI, we outline both the golden opportunities and key challenges currently facing plant metabolomics, presenting our vision as to how the enormous potential of MSI technologies will contribute to progress in plant science in the coming years.

Polychlorinated biphenyls modify Arabidopsis root exudation pattern to accommodate degrading bacteria, showing strain and functional trait specificity

Introduction

The importance of plant rhizodeposition to sustain microbial growth and induce xenobiotic degradation in polluted environments is increasingly recognized.

Methods

Here the "cry-for-help" hypothesis, consisting in root chemistry remodeling upon stress, was investigated in the presence of polychlorinated biphenyls (PCBs), highly recalcitrant and phytotoxic compounds, highlighting its role in reshaping the nutritional and signaling features of the root niche to accommodate PCB-degrading microorganisms.

Results

Arabidopsis exposure to 70 µM PCB-18 triggered plant-detrimental effects, stress-related traits, and PCB-responsive gene expression, reproducing PCB phytotoxicity. The root exudates of plantlets exposed for 2 days to the pollutant were collected and characterized through untargeted metabolomics analysis by liquid chromatography-mass spectrometry. Principal component analysis disclosed a different root exudation fingerprint in PCB-18-exposed plants, potentially contributing to the "cry-for-help" event. To investigate this aspect, the five compounds identified in the exudate metabolomic analysis (i.e., scopoletin, N-hydroxyethyl-β-alanine, hypoxanthine, L-arginyl-L-valine, and L-seryl-L-phenylalanine) were assayed for their influence on the physiology and functionality of the PCB-degrading strains Pseudomonas alcaliphila JAB1, Paraburkholderia xenovorans LB400, and Acinetobacter calcoaceticus P320. Scopoletin, whose relative abundance decreased in PCB-18-stressed plant exudates, hampered the growth and proliferation of strains JAB1 and P320, presumably due to its antimicrobial activity, and reduced the beneficial effect of Acinetobacter P320, which showed a higher degree of growth promotion in the scopoletin-depleted mutant f6'h1 compared to Arabidopsis WT plants exposed to PCB. Nevertheless, scopoletin induced the expression of the bph catabolic operon in strains JAB1 and LB400. The primary metabolites hypoxanthine, L-arginyl-L-valine, and L-seryl-L-phenylalanine, which increased in relative abundance upon PCB-18 stress, were preferentially used as nutrients and growth-stimulating factors by the three degrading strains and showed a variable ability to affect rhizocompetence traits like motility and biofilm formation.

Discussion

These findings expand the knowledge on PCB-triggered "cry-for-help" and its role in steering the PCB-degrading microbiome to boost the holobiont fitness in polluted environments.

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

Critical analysis of polycyclic tetramate macrolactam biosynthetic gene cluster phylogeny and functional diversity

Polycyclic tetramate macrolactams (PTMs) are bioactive natural products commonly associated with certain actinobacterial and proteobacterial lineages. These molecules have been the subject of numerous structure-activity investigations since the 1970s. New members continue to be pursued in wild and engineered bacterial strains, and advances in PTM biosynthesis suggest their outwardly simplistic biosynthetic gene clusters (BGCs) belie unexpected product complexity. To address the origins of this complexity and understand its influence on PTM discovery, we engaged in a combination of bioinformatics to systematically classify PTM BGCs and PTM-targeted metabolomics to compare the products of select BGC types. By comparing groups of producers and BGC mutants, we exposed knowledge gaps that complicate bioinformatics-driven product predictions. In sum, we provide new insights into the evolution of PTM BGCs while systematically accounting for the PTMs discovered thus far. The combined computational and metabologenomic findings presented here should prove useful for guiding future discovery.IMPORTANCEPolycyclic tetramate macrolactam (PTM) pathways are frequently found within the genomes of biotechnologically important bacteria, including Streptomyces and Lysobacter spp. Their molecular products are typically bioactive, having substantial agricultural and therapeutic interest. Leveraging bacterial genomics for the discovery of new related molecules is thus desirable, but drawing accurate structural predictions from bioinformatics alone remains challenging. This difficulty stems from a combination of previously underappreciated biosynthetic complexity and remaining knowledge gaps, compounded by a stream of yet-uncharacterized PTM biosynthetic loci gleaned from recently sequenced bacterial genomes. We engaged in the following study to create a useful framework for cataloging historic PTM clusters, identifying new cluster variations, and tracing evolutionary paths for these molecules. Our data suggest new PTM chemistry remains discoverable in nature. However, our metabolomic and mutational analyses emphasize the practical limitations of genomics-based discovery by exposing hidden complexity.

Computational methods for processing and interpreting mass spectrometry-based metabolomics

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

The role of metabolomics in informing strategies for improving photosynthesis

Photosynthesis plays a vital role in acclimating to and mitigating climate change, providing food and energy security for a population that is constantly growing, and achieving an economy with zero carbon emissions. A thorough comprehension of the dynamics of photosynthesis, including its molecular regulatory network and limitations, is essential for utilizing it as a tool to boost plant growth, enhance crop yields, and support the production of plant biomass for carbon storage. Photorespiration constrains photosynthetic efficiency and contributes significantly to carbon loss. Therefore, modulating or circumventing photorespiration presents opportunities to enhance photosynthetic efficiency. Over the past eight decades, substantial progress has been made in elucidating the molecular basis of photosynthesis, photorespiration, and the key regulatory mechanisms involved, beginning with the discovery of the canonical Calvin-Benson-Bassham cycle. Advanced chromatographic and mass spectrometric technologies have allowed a comprehensive analysis of the metabolite patterns associated with photosynthesis, contributing to a deeper understanding of its regulation. In this review, we summarize the results of metabolomics studies that shed light on the molecular intricacies of photosynthetic metabolism. We also discuss the methodological requirements essential for effective analysis of photosynthetic metabolism, highlighting the value of this technology in supporting strategies aimed at enhancing photosynthesis.

Imaging plant metabolism in situ

Mass spectrometry imaging (MSI) has emerged as an invaluable analytical technique for investigating the spatial distribution of molecules within biological systems. In the realm of plant science, MSI is increasingly employed to explore metabolic processes across a wide array of plant tissues, including those in leaves, fruits, stems, roots, and seeds, spanning various plant systems such as model species, staple and energy crops, and medicinal plants. By generating spatial maps of metabolites, MSI has elucidated the distribution patterns of diverse metabolites and phytochemicals, encompassing lipids, carbohydrates, amino acids, organic acids, phenolics, terpenes, alkaloids, vitamins, pigments, and others, thereby providing insights into their metabolic pathways and functional roles. In this review, we present recent MSI studies that demonstrate the advances made in visualizing the plant spatial metabolome. Moreover, we emphasize the technical progress that enhances the identification and interpretation of spatial metabolite maps. Within a mere decade since the inception of plant MSI studies, this robust technology is poised to continue as a vital tool for tackling complex challenges in plant metabolism.

Unraveling plant-microbe interactions: can integrated omics approaches offer concrete answers?

Advances in high throughput omics techniques provide avenues to decipher plant microbiomes. However, there is limited information on how integrated informatics can help provide deeper insights into plant-microbe interactions in a concerted way. Integrating multi-omics datasets can transform our understanding of the plant microbiome from unspecified genetic influences on interacting species to specific gene-by-gene interactions. Here, we highlight recent progress and emerging strategies in crop microbiome omics research and review key aspects of how the integration of host and microbial omics-based datasets can be used to provide a comprehensive outline of complex crop-microbe interactions. We describe how these technological advances have helped unravel crucial plant and microbial genes and pathways that control beneficial, pathogenic, and commensal plant-microbe interactions. We identify crucial knowledge gaps and synthesize current limitations in our understanding of crop microbiome omics approaches. We highlight recent studies in which multi-omics-based approaches have led to improved models of crop microbial community structure and function. Finally, we recommend holistic approaches in integrating host and microbial omics datasets to achieve precision and efficiency in data analysis, which is crucial for biotic and abiotic stress control and in understanding the contribution of the microbiota in shaping plant fitness.

Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

Progress and challenges in exploring aquatic microbial communities using non-targeted metabolomics

Advances in bioanalytical technologies are constantly expanding our insights into complex ecosystems. Here, we highlight strategies and applications that make use of non-targeted metabolomics methods in aquatic chemical ecology research and discuss opportunities and remaining challenges of mass spectrometry-based methods to broaden our understanding of environmental systems.

Synthesis of Metabolites and Metabolite-like Compounds Using Biocatalytic Systems

Methodologies for the synthesis and purification of metabolites, which have been developed following their discovery, analysis, and structural identification, have been involved in numerous life science milestones. The renewed focus on the small molecule domain of biological cells has also created an increasing awareness of the rising gap between the metabolites identified and the metabolites which have been prepared as pure compounds. The design and engineering of resource-efficient and straightforward synthetic methodologies for the production of the diverse and numerous metabolites and metabolite-like compounds have attracted much interest. The variety of metabolic pathways in biological cells provides a wonderful blueprint for designing simplified and resource-efficient synthetic routes to desired metabolites. Therefore, biocatalytic systems have become key enabling tools for the synthesis of an increasing number of metabolites, which can then be utilized as standards, enzyme substrates, inhibitors, or other products, or for the discovery of novel biological functions.

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.