Metabolic imaging: Analysis of different sections of white Asparagus officinalis shoots using high-resolution mass spectrometry

Comparison of the inedible parts of white and green asparagus based on metabolomics and network pharmacology

Asparagus is a perennial herb and is widely used as food and medicine in China. In this study, untargeted metabolomics analysis was applied to compare the chemical differences between the edible and inedible parts of asparagus, as well as the inedible parts of white and green asparagus. A total of 342 compounds were identified in the asparagus extracts, and 24 steroid saponins, 31 oxylipins and 36 LysoGPLs were identified for the first time in asparagus. Metabolomics analysis showed that the inedible part of white asparagus is rich in steroidal saponins, oxylipins and alkaloids, while the inedible part of green asparagus is rich in flavonoids, phenolic acids, LysoGPLs and amino acids. The inedible part of white asparagus showed significantly higher inhibitory effects on breast cancer 4T-1 cells than that of green asparagus. Network pharmacology analysis and molecular docking showed that the biological difference is related to higher levels of steroidal saponins and oxylipins in the inedible part of white asparagus. This study is useful for the wasted resource utilization of inedible parts of asparagus.

Distinct Quality Changes of Asparagus during Growth by Widely Targeted Metabolomics Analysis

Asparagus is a popular vegetable and traditional medicine consumed worldwide due to its health benefits. The quality of asparagus, mainly attributed to small components like flavonoids and steroid, is quite differential as a result of different environments and maturities. However, the accumulation pattern and regulatory mechanism of metabolites in asparagus remain unclear so far. Herein, widely targeted metabolomics analysis was employed to study the quality and chemical composition variances of four asparagus, including three green asparagus of different maturities and one white asparagus. A total of 1045 metabolites were annotated in asparagus in which flavonoids and phenolic acids accounted for 37.51% of the total. Green asparagus was found to be rich in flavonoids, while white asparagus contained more steroids. Additionally, 461 biomarkers were screened between matured green and white asparagus, which is much more than that filtered among three green asparagus at different growth stages. These results indicated that sunlight has a stronger effect on the metabolism of asparagus compared to the general development of asparagus. Linoleic acid metabolism and alpha-linolenic acid metabolism were active during green asparagus growth, while flavone and flavonol biosynthesis and flavonoid biosynthesis resulted as two of the most important pathways when asparagus was exposed to sunlight.

Opening the Random Forest Black Box of the Metabolome by the Application of Surrogate Minimal Depth

For the untargeted analysis of the metabolome of biological samples with liquid chromatography–mass spectrometry (LC-MS), high-dimensional data sets containing many different metabolites are obtained. Since the utilization of these complex data is challenging, different machine learning approaches have been developed. Those methods are usually applied as black box classification tools, and detailed information about class differences that result from the complex interplay of the metabolites are not obtained. Here, we demonstrate that this information is accessible by the application of random forest (RF) approaches and especially by surrogate minimal depth (SMD) that is applied to metabolomics data for the first time. We show this by the selection of important features and the evaluation of their mutual impact on the multi-level classification of white asparagus regarding provenance and biological identity. SMD enables the identification of multiple features from the same metabolites and reveals meaningful biological relations, proving its high potential for the comprehensive utilization of high-dimensional metabolomics data.

Wood profiling by non-targeted liquid chromatography high-resolution mass spectrometry: Part 2, Detection of the geographical origin of spruce wood (Picea abies) by determination of metabolite pattern

A non-targeted metabolomics-based approach using liquid chromatography high-resolution mass spectrometry was used to authenticate spruce wood (Picea abies) from two geographic source areas. The two sample sites were located in Germany and only 250 km apart. In order to achieve the highest possible metabolite coverage, the spruces samples were measured with four different methods using liquid chromatography high-resolution mass spectrometry. In this way, a total of approximately 4,100 features were detected, which included non-polar, polar, and intermediate-polar metabolites. Using supervised multivariate methods, a distinction between the two sample groups could be achieved on the basis of non-polar data sets. The major metabolites contributing to differentiation were identified by MS/MS experiments and were from the following classes of compounds: ceramides, fatty acids, glycerolipids, and phytosterols. Based on the soil descriptions of the two sites, it was concluded that there is probably a close relationship between nutrient availability and the differences in concentration of the marker compounds. The results show that a metabolomics-based approach is also suitable for differentiation of origin, even if the sample sites are close to each other.

Interkingdom Comparison of Threonine Metabolism for Stem Cell Maintenance in Plants and Animals

In multicellular organisms, tissue generation, maintenance, and homeostasis depend on stem cells. Cellular metabolic status is an essential component of different differentiated states, from stem to fully differentiated cells. Threonine (Thr) metabolism has emerged as a critical factor required to maintain pluripotent/multipotent stem cells in both plants and animals. Thus, both kingdoms conserved or converged upon this fundamental feature of stem cell function. Here, we examine similarities and differences in Thr metabolism-dependent mechanisms supporting stem cell maintenance in these two kingdoms. We then consider common features of Thr metabolism in stem cell maintenance and predict and speculate that some knowledge about Thr metabolism and its role in stem cell function in one kingdom may apply to the other. Finally, we outline future research directions to explore these hypotheses.

Food Lipidomics for 155 Agricultural Plant Products

Lipids exhibit functional bioactivities based on their polar and acyl chain properties; humans obtain lipids from dietary plant product intake. Therefore, the identification of different molecular species facilitates the evaluation of biological functions and nutrition levels and new phenotype-modulating lipid structures. As a rapid screening strategy, we performed untargeted lipidomics for 155 agricultural products in 58 species from 23 plant families, wherein product-specific lipid diversities were shown using computational mass spectrometry. We characterized 716 lipid species, for which the profiles revealed the National Center for Biotechnology Information-established organismal classification and unique plant tissue metabotypes. Moreover, we annotated unreported subclasses in plant lipidology; e.g., triacylglycerol estolide (TG-EST) was detected in rice seeds (Oryza sativa) and several plant species. TG-EST is known as the precursor molecule producing the fatty acid ester of hydroxy fatty acid, which lowers ambient glycemia and improves glucose tolerance. Hence, our method can identify agricultural plant products containing valuable lipid ingredients.

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity

Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.