Integrated LC-MS and GC-MS-Based Metabolomics Reveal the Effects of Plant Competition on the Rye Metabolome

MetMiner: A user-friendly pipeline for large-scale plant metabolomics data analysis

The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user-friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large-scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user-friendly, full-functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large-scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant-specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co-expression network analysis strategy for efficient biomarker metabolite screening in large-scale metabolomics data mining. In two case studies, we validated MetMiner's efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease.

Novel Strategy for Human Deep Vein Thrombosis Diagnosis Based on Metabolomics and Stacking Machine Learning

Deep vein thrombosis (DVT) is a serious health issue that often leads to considerable morbidity and mortality. Diagnosis of DVT in a clinical setting, however, presents considerable challenges. The fusion of metabolomics techniques and machine learning methods has led to high diagnostic and prognostic accuracy for various pathological conditions. This study explored the synergistic potential of dual-platform metabolomics (specifically, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS)) to expand the detection of metabolites and improve the precision of DVT diagnosis. Sixty-one differential metabolites were identified in serum from DVT patients: 22 from GC-MS and 39 from LC-MS. Among these, five key metabolites were highlighted by SHapley Additive exPlanations (SHAP)-guided feature engineering and then used to develop a stacking diagnostic model. Additionally, a user-friendly interface application system was developed to streamline and automate the application of the diagnostic model, enhancing its practicality and accessibility for clinical use. This work showed that the integration of dual-platform metabolomics with a stacking machine learning model enables faster and more accurate diagnosis of DVT in clinical environments.

Intercropping of Narrow-Leafed Lupin (Lupinus angustifolius L.) and Barley (Hordeum vulgare L.) Affects the Flavonoid Composition of Both Crops

Barley (Hordeum vulgare L.) is a common cereal crop in agricultural production and is often included in legume-cereal intercropping. Flavonoids, a major class of secondary metabolites found in barley, are involved in plant defense and protection. However, the effect of intercropping on barley flavonoids remains unknown. Herein, an intercropping system involving barley and lupin (Lupinus angustifolius L.) was studied. Intercropping increased the level of luteolin in lupin roots. Lupin-barley intercropping considerably increased genistein, rutin, and apigenin in barley shoots. Genistein and apigenin were also detected in intercropped barley roots and rhizosphere soil. The three flavonoids have been reported as defense compounds, suggesting that lupin triggers a defense response in barley to strengthen its survival ability.

Allelopathy and allelobiosis: efficient and economical alternatives in agroecosystems

Chemical interactions in plants often involve plant allelopathy and allelobiosis. Allelopathy is an ecological phenomenon leading to interference among organisms, while allelobiosis is the transmission of information among organisms. Crop failures and low yields caused by inappropriate management can be related to both allelopathy and allelobiosis. Therefore, research on these two phenomena and the role of chemical substances in both processes will help us to understand and upgrade agroecosystems. In this review, substances involved in allelopathy and allelobiosis in plants are summarized. The influence of environmental factors on the generation and spread of these substances is discussed, and relationships between allelopathy and allelobiosis in interspecific, intraspecific, plant-micro-organism, plant-insect, and mechanisms, are summarized. Furthermore, recent results on allelopathy and allelobiosis in agroecosystem are summarized and will provide a reference for the future application of allelopathy and allelobiosis in agroecosystem.

Improving quality of analysis by suppression of unwanted signals through band-selective excitation in NMR spectroscopy for metabolomics studies

INTRODUCTION

Nuclear Magnetic Resonance (NMR) spectroscopy stands as a preeminent analytical tool in the field of metabolomics. Nevertheless, when it comes to identifying metabolites present in scant amounts within various types of complex mixtures such as plants, honey, milk, and biological fluids and tissues, NMR-based metabolomics presents a formidable challenge. This predicament arises primarily from the fact that the signals emanating from metabolites existing in low concentrations tend to be overshadowed by the signals of highly concentrated metabolites within NMR spectra.

OBJECTIVES

The aim of this study is to tackle the issue of intense sugar signals overshadowing the desired metabolite signals, an optimal pulse sequence with band-selective excitation has been proposed for the suppression of sugar's moiety signals (SSMS). This sequence serves the crucial purpose of suppressing unwanted signals, with a particular emphasis on mitigating the interference caused by sugar moieties' signals.

METHODS

We have implemented this comprehensive approach to various NMR techniques, including 1D 1H presaturation (presat), 2D J-resolved (RES), 2D 1H-1H Total Correlation Spectroscopy (TOCSY), and 2D 1H-13C Heteronuclear Single Quantum Coherence (HSQC) for the samples of dates-flesh, honey, a standard stock solution of glucose, and nine amino acids, and commercial fetal bovine serum (FBS).

RESULTS

The outcomes of this approach were significant. The suppression of the high-intensity sugar signals has considerably enhanced the visibility and sensitivity of the signals emanating from the desired metabolites.

CONCLUSION

This, in turn, enables the identification of a greater number of metabolites. Additionally, it streamlines the experimental process, reducing the time required for the comparative quantification of metabolites in statistical studies in the field of metabolomics.

Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with the progression of gastroesophageal cancer

BACKGROUND

Previous metabolic studies in upper digestive cancer have mostly been limited to cross-sectional study designs, which hinders the ability to effectively predict outcomes in the early stage of cancer. This study aims to identify key metabolites and metabolic pathways associated with the multistage progression of epithelial cancer and to explore their predictive value for gastroesophageal cancer (GEC) formation and for the early screening of esophageal squamous cell carcinoma (ESCC).

METHODS

A case-cohort study within the 7-year prospective Esophageal Cancer Screening Cohort of Shandong Province included 77 GEC cases and 77 sub-cohort individuals. Untargeted metabolic analysis was performed in serum samples. Metabolites, with FDR q value < 0.05 and variable importance in projection (VIP) > 1, were selected as differential metabolites to predict GEC formation using Random Forest (RF) models. Subsequently, we evaluated the predictive performance of these differential metabolites for the early screening of ESCC.

RESULTS

We found a distinct metabolic profile alteration in GEC cases compared to the sub-cohort, and identified eight differential metabolites. Pathway analyses showed dysregulation in D-glutamine and D-glutamate metabolism, nitrogen metabolism, primary bile acid biosynthesis, and steroid hormone biosynthesis in GEC patients. A panel of eight differential metabolites showed good predictive performance for GEC formation, with an area under the receiver operating characteristic curve (AUC) of 0.893 (95% CI = 0.816-0.951). Furthermore, four of the GEC pathological progression-related metabolites were validated in the early screening of ESCC, with an AUC of 0.761 (95% CI = 0.716-0.805).

CONCLUSIONS

These findings indicated a panel of metabolites might be an alternative approach to predict GEC formation, and therefore have the potential to mitigate the risk of cancer progression at the early stage of GEC.

Molecular mechanism overview of metabolite biosynthesis in medicinal plants

Medicinal plants are essential and rich resources for plant-based medicines and new drugs. Increasing attentions are paid to the secondary metabolites of medicinal plants due to their unique biological activity, pharmacological action, and high utilization value. However, the development of medicinal plants is constrained by limited natural resources and an unclear understanding of the mechanisms underlying active medicinal ingredients, thereby rendering the utilization and exploration of secondary metabolites more challenging. Besides, with the advancement of research on biosynthesis and molecular metabolism of natural products from medicinal plants, the methods for studying the biological activity and pharmacological effects of these products are constantly evolving. In recent years, significant progress has been made in the biosynthetic pathways and related regulatory genes of secondary metabolites in medicinal plants, which has greatly advanced both basic research and the development of clinical applications for medicinal plants. In this review, we discuss the past two decades of international research on the development of medicinal plant resources, mainly focusing on the biosynthetic pathway of secondary metabolites, intracellular signal transduction processes, multi-omics applications, and the application of gene editing technology in related research progress. We also discuss future development trends to promote the deep mining and development of natural products from medicinal plants, providing a useful reference.

Exploring the material basis and mechanism of action of clinacanthus nutans in treating renal cell carcinoma based on metabolomics and network pharmacology

BACKGROUND

Clinacanthus nutans (for abbreviation thereafter) is often used as medicine in the form of fresh juice in the folk to treat many kinds of cancers, including renal cell carcinoma (RCC). It is speculated that its active ingredient may have heat sensitivity, but there are currently no reports on this aspect. Therefore, based on the folk application for fresh juice of C nutans, this study used metabonomics and network pharmacology to explore the material basis and mechanism of action of C nutans against RCC.

METHODS

Firstly, untargeted metabolomics profiling was performed by Liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry to screen the metabolites down-regulated by heat in the extract of C nutans. Secondly, we collected the targets of metabolites in the Swiss Target Prediction platform. In addition, the targets of RCC were obtained in the GeneCards database. The "component-target-disease" network was established by Cytoscape3.9.0 software. Then we constructed a protein-protein interaction network in the STRING network platform to screen core targets. The gene ontology and kyoto encyclopedia of genes and genomes enrichment analysis of core targets were carried out to predict the relevant pathway of C nutans in the treatment of RCC. Finally, the molecular docking verification of the core targets were carried out.

RESULTS

In this study, 35 potential active ingredients and 125 potential targets were obtained. And the core targets were Cellular tumor antigen p53, Signal transducer and activator of transcription 3, and so on. Then, 48 biological processes, 30 cell components, and 36 molecular functions were obtained by gene ontology enrichment analysis. Besides, 44 pathways were obtained by Kyoto encyclopedia of genes and genomes enrichment analysis, including Pathway in cancer, PI3K-Akt signal pathway, P53 signal pathway, and so on. The docking model between the core target and its corresponding components was stable.

CONCLUSION

This research is based on the folk application of C nutans, showed its potential active ingredients by metabonomics, and predicted the potential mechanism of C nutans in the treatment of RCC by network pharmacology. It provides new references for follow-up research and new drug development.

Detection, Diagnosis, and Preventive Management of the Bacterial Plant Pathogen Pseudomonas syringae

Plant diseases caused by the pathogen Pseudomonas syringae are serious problems for various plant species worldwide. Accurate detection and diagnosis of P. syringae infections are critical for the effective management of these plant diseases. In this review, we summarize the current methods for the detection and diagnosis of P. syringae, including traditional techniques such as culture isolation and microscopy, and relatively newer techniques such as PCR and ELISA. It should be noted that each method has its advantages and disadvantages, and the choice of each method depends on the specific requirements, resources of each laboratory, and field settings. We also discuss the future trends in this field, such as the need for more sensitive and specific methods to detect the pathogens at low concentrations and the methods that can be used to diagnose P. syringae infections that are co-existing with other pathogens. Modern technologies such as genomics and proteomics could lead to the development of new methods of highly accurate detection and diagnosis based on the analysis of genetic and protein markers of the pathogens. Furthermore, using machine learning algorithms to analyze large data sets could yield new insights into the biology of P. syringae and novel diagnostic strategies. This review could enhance our understanding of P. syringae and help foster the development of more effective management techniques of the diseases caused by related pathogens.

Pseudotargeted Metabolomics Approach Enabling the Classification-Induced Ginsenoside Characterization and Differentiation of Ginseng and Its Compound Formulation Products

The use of diversified ginseng extracts in health-promoting foods is difficult to differentiate, as they share bioactive ginsenosides among different Panax species (e.g., P. ginseng, P. quinquefolius, P. notoginseng, and P. japonicus) and different parts (e.g., root, leaf, and flower). This work was designed to develop a pseudo-targeted metabolomics approach to discover ginsenoside markers facilitating the precise authentication of ginseng and its use in compound formulation products (CFPs). Versatile mass spectrometry experiments on the QTrap mass spectrometer achieved classified characterization of the neutral, malonyl, and oleanolic acid-type ginsenosides, with 567 components characterized. A pseudo-targeted metabolomics approach by multiple reaction monitoring (MRM) of 262 ion pairs could assist to establish key identification points for 12 ginseng species. The simultaneous detection of 14 markers enabled the identification of ginseng from 15 ginseng-containing CFPs. The pseudo-targeted metabolomics strategy enabled better performance in differentiating among multiple ginseng, compared with the full-scan high-resolution mass spectrometry approach.

First genome-scale insights into the virulence of the snow mold causal fungus Microdochium nivale

Pink snow mold, caused by a phytopathogenic and psychrotolerant fungus, Microdochium nivale, is a severe disease of winter cereals and grasses that predominantly occurs under snow cover or shortly after its melt. Snow mold has significantly progressed during the past decade, often reaching epiphytotic levels in northern countries and resulting in dramatic yield losses. In addition, M. nivale gradually adapts to a warmer climate, spreading to less snowy territories and causing different types of plant diseases throughout the growing period. Despite its great economic importance, M. nivale is poorly investigated; its genome has not been sequenced and its crucial virulence determinants have not been identified or even predicted. In our study, we applied a hybrid assembly based on Oxford Nanopore and Illumina reads to obtain the first genome sequence of M. nivale. 11,973 genes (including 11,789 protein-encoding genes) have been revealed in the genome assembly. To better understand the genetic potential of M. nivale and to obtain a convenient reference for transcriptomic studies on this species, the identified genes were annotated and split into hierarchical three-level functional categories. A file with functionally classified M. nivale genes is presented in our study for general use. M. nivale gene products that best meet the criteria for virulence factors have been identified. The genetic potential to synthesize human-dangerous mycotoxins (fumonisin, ochratoxin B, aflatoxin, and gliotoxin) has been revealed for M. nivale. The transcriptome analysis combined with the assays for extracellular enzymatic activities (conventional virulence factors of many phytopathogens) was carried out to assess the effect of host plant (rye) metabolites on the M. nivale phenotype. In addition to disclosing plant-metabolite-upregulated M. nivale functional gene groups (including those related to host plant protein destruction and amino acid metabolism, xenobiotic detoxication (including phytoalexins benzoxazinoids), cellulose destruction (cellulose monooxygenases), iron transport, etc.), the performed analysis pointed to a crucial role of host plant lipid destruction and fungal lipid metabolism modulation in plant-M. nivale interactions.