13C Labeling of Nematode Worms to Improve Metabolome Coverage by Heteronuclear Nuclear Magnetic Resonance Experiments

Application of 15N-Edited 1H-13C Correlation NMR Spectroscopy─Toward Fragment-Based Metabolite Identification and Screening via HCN Constructs

Many key building blocks of life contain nitrogen moieties. Despite the prevalence of nitrogen-containing metabolites in nature, 15N nuclei are seldom used in NMR-based metabolite assignment due to their low natural abundance and lack of comprehensive chemical shift databases. However, with advancements in isotope labeling strategies, 13C and 15N enriched metabolites are becoming more common in metabolomic studies. Simple multidimensional nuclear magnetic resonance (NMR) experiments that correlate 1H and 15N via single bond 1JNH or multiple bond 2-3JNH couplings using heteronuclear single quantum coherence (HSQC) or heteronuclear multiple bond coherence are well established and routinely applied for structure elucidation. However, a 1H-15N correlation spectrum of a metabolite mixture can be difficult to deconvolute, due to the lack of a 15N specific database. In order to bridge this gap, we present here a broadband 15N-edited 1H-13C HSQC NMR experiment that targets metabolites containing 15N moieties. Through this approach, nitrogen-containing metabolites, such as amino acids, nucleotide bases, and nucleosides, are identified based on their 13C, 1H, and 15N chemical shift information. This approach was tested and validated using a [15N, 13C] enriched Daphnia magna (water flea) metabolite extract, where the number of clearly resolved 15N-containing peaks increased from only 11 in a standard HSQC to 51 in the 15N-edited HSQC, and the number of obscured peaks decreased from 59 to just 7. The approach complements the current repertoire of NMR techniques for mixture deconvolution and holds considerable potential for targeted metabolite NMR in 15N, 13C enriched systems.

Targeted Compound Selection with Increased Sensitivity in 13C-Enriched Biological and Environmental Samples Using 13C-DREAMTIME in Both High-Field and Low-Field NMR

Chemical characterization of complex mixtures by Nuclear Magnetic Resonance (NMR) spectroscopy is challenging due to a high degree of spectral overlap and inherently low sensitivity. Therefore, NMR experiments that reduce overlap and increase signal intensity hold immense potential for the analysis of mixtures such as biological and environmental media. Here, we introduce a ¹³C version of DREAMTIME (Designed Refocused Excitation And Mixing for Targets In Vivo and Mixture Elucidation) NMR, which, when analyzing ¹³C-enriched materials, allows the user to selectively detect only the compound(s) of interest and remove all other peaks in a ¹³C spectrum. Selected peaks can additionally be "focused" into sharp "spikes" to increase sensitivity. ¹³C-DREAMTIME is first demonstrated at high field strength (500 MHz) with simultaneous selection of eight amino acids in a ¹³C-enriched cell free amino acid mixture and of six metabolites in an extract of ¹³C-enriched green algae and demonstrated at low field strength (80 MHz) with a standard solution of ¹³C-d-glucose and ¹³C-l-phenylalanine. ¹³C-DREAMTIME is then applied at high-field to analyze metabolic changes in ¹³C-enrichedDaphnia magna after exposure to polystyrene "microplastics," as well as at low-field to track fermentation of ¹³C-d-glucose using wine yeast. Ultimately, ¹³C-DREAMTIME reduces spectral overlap as only selected compounds are recorded, resulting in the detection of analyte peaks that may otherwise not have been discernable. In combination with focusing, up to a 6-fold increase in signal intensity can be obtained for a given peak. ¹³C-DREAMTIME is a promising experiment type for future reaction monitoring and for tracking metabolic processes with ¹³C-enriched compounds.

Innovative Application of Metabolomics on Bioactive Ingredients of Foods

Metabolomics, as a new omics technology, has been widely accepted by researchers and has shown great potential in the field of nutrition and health in recent years. This review briefly introduces the process of metabolomics analysis, including sample preparation and extraction, derivatization, separation and detection, and data processing. This paper focuses on the application of metabolomics in food-derived bioactive ingredients. For example, metabolomics techniques are used to analyze metabolites in food to find bioactive substances or new metabolites in food materials. Moreover, bioactive substances have been tested in vitro and in vivo, as well as in humans, to investigate the changes of metabolites and the underlying metabolic pathways, among which metabolomics is used to find potential biomarkers and targets. Metabolomics provides a new approach for the prevention and regulation of chronic diseases and the study of the underlying mechanisms. It also provides strong support for the development of functional food or drugs. Although metabolomics has some limitations such as low sensitivity, poor repeatability, and limited detection range, it is developing rapidly in general, and also in the field of nutrition and health. At the end of this paper, we put forward our own insights on the development prospects of metabolomics in the application of bioactive ingredients in food.

Quo Vadis Caenorhabditis elegans Metabolomics-A Review of Current Methods and Applications to Explore Metabolism in the Nematode

Metabolomics and lipidomics recently gained interest in the model organism Caenorhabditis elegans (C. elegans). The fast development, easy cultivation and existing forward and reverse genetic tools make the small nematode an ideal organism for metabolic investigations in development, aging, different disease models, infection, or toxicology research. The conducted type of analysis is strongly depending on the biological question and requires different analytical approaches. Metabolomic analyses in C. elegans have been performed using nuclear magnetic resonance (NMR) spectroscopy, direct infusion mass spectrometry (DI-MS), gas-chromatography mass spectrometry (GC-MS) and liquid chromatography mass spectrometry (LC-MS) or combinations of them. In this review we provide general information on the employed techniques and their advantages and disadvantages in regard to C. elegans metabolomics. Additionally, we reviewed different fields of application, e.g., longevity, starvation, aging, development or metabolism of secondary metabolites such as ascarosides or maradolipids. We also summarised applied bioinformatic tools that recently have been used for the evaluation of metabolomics or lipidomics data from C. elegans. Lastly, we curated metabolites and lipids from the reviewed literature, enabling a prototypic collection which serves as basis for a future C. elegans specific metabolome database.