1H HR-MAS NMR-Based Metabolomic Fingerprinting to Distinguish Morphological Similarities and Metabolic Profiles of Maytenus ilicifolia, a Brazilian Medicinal Plant

Understanding the maturity of coconut water through 1H NMR profiling and MPAES analyses

This study investigated the relationship between coconut maturity stages and the sugar, amino acid, and mineral profiles of coconut water (CW). Metabolite profiles were analysed using 1H NMR, covering glucose (G), fructose (F), sucrose (S), reducing sugars (RS), total sugars (TS), amino acids, and organic acids. Mineral composition was measured using Microwave Plasma Atomic Emission Spectroscopy (MPAES). The results revealed distinct metabolite and mineral profiles across different maturity stages. Immature CW had high G/F and RS/TS ratios but low S/G ratios. Conversely, mature CW showed decreased G/F and RS/TS ratios but an increase in S/G. Mineral analysis revealed potassium as the predominant mineral in CW, peaking in the youngest stage and declining with maturity. Sodium, magnesium, and calcium showed a similar pattern, with higher concentrations in early than in later stages. The study identifies the age of 9-10 months as optimal stages for selecting tender coconut water.

Recent Advancement in NMR Based Plant Metabolomics: Techniques, Tools, and Analytical Approaches

Plant metabolomics, a rapidly advancing field within plant biology, is dedicated to comprehensively exploring the intricate array of small molecules in plant systems. This entails precisely gathering comprehensive chemical data, detecting numerous metabolites, and ensuring accurate molecular identification. Nuclear magnetic resonance (NMR) spectroscopy, with its detailed chemical insights, is crucial in obtaining metabolite profiles. Its widespread application spans various research disciplines, aiding in comprehending chemical reactions, kinetics, and molecule characterization. Biotechnological advancements have further expanded NMR's utility in metabolomics, particularly in identifying disease biomarkers across diverse fields such as agriculture, medicine, and pharmacology. This review covers the stages of NMR-based metabolomics, including historical aspects and limitations, with sample preparation, data acquisition, spectral processing, analysis, and their application parts.

Application of 1H HR-MAS NMR-Based Metabolite Fingerprinting of Marine Microalgae

Natural products from the marine environment as well as microalgae, have been known for the complexity of the metabolites they produce due to their adaptability to different environmental conditions, which has been an inexhaustible source of several bioactive properties, such as antioxidant, anti-tumor, and antimicrobial. This study aims to characterize the main metabolites of three species of microalgae (Nannochloropsis oceanica, Chaetoceros muelleri, and Conticribra weissflogii), which have important applications in the biofuel and nutrition industries, by ¹H High-resolution magic angle spinning nuclear magnetic resonance (¹H HR-MAS NMR), a method which is non-destructive, is highly reproducible, and requires minimal sample preparation. Even though the three species were found in the same ecosystem and a superior production of lipid compounds was observed, important differences were identified in relation to the production of specialized metabolites. These distinct properties favor the use of these compounds as leaders in the development of new bioactive compounds, especially against environmental, human, and animal pathogens (One Health), and demonstrate their potential in the development of alternatives for aquaculture.

Plant Microbiome Engineering: Hopes or Hypes

Rhizosphere microbiome is a dynamic and complex zone of microbial communities. This complex plant-associated microbial community, usually regarded as the plant's second genome, plays a crucial role in plant health. It is unquestioned that plant microbiome collectively contributes to plant growth and fitness. It also provides a safeguard from plant pathogens, and induces tolerance in the host against abiotic stressors. The revolution in omics, gene-editing and sequencing tools have somehow led to unravel the compositions and latent interactions between plants and microbes. Similarly, besides standard practices, many biotechnological, (bio)chemical and ecological methods have also been proposed. Such platforms have been solely dedicated to engineer the complex microbiome by untangling the potential barriers, and to achieve better agriculture output. Yet, several limitations, for example, the biological obstacles, abiotic constraints and molecular tools that capably impact plant microbiome engineering and functionality, remained unaddressed problems. In this review, we provide a holistic overview of plant microbiome composition, complexities, and major challenges in plant microbiome engineering. Then, we unearthed all inevitable abiotic factors that serve as bottlenecks by discouraging plant microbiome engineering and functionality. Lastly, by exploring the inherent role of micro/macrofauna, we propose economic and eco-friendly strategies that could be harnessed sustainably and biotechnologically for resilient plant microbiome engineering.

Hopomics: Humulus lupulus Brewing Cultivars Classification Based on LC-MS Profiling and Nested Feature Selection

Omics approaches in plant analysis find many different applications, from classification to new bioactive compounds discovery. Metabolomics seems to be one of the most informative ways of describing plants’ phenotypes, since commonly used methods such as liquid chromatography–mass spectrometry (LC-MS) and nuclear magnetic resonance spectroscopy (NMR) could provide a huge amount of information about samples. However, due to high efficiency, many disadvantages arise with the complexity of the experimental design. In the present work, we demonstrate an untargeted metabolomics pipeline with the example of a Humulus lupulus classification task. LC-MS profiling of brewing cultivars samples was carried out as a starting point. Hierarchical cluster analysis (HCA)-based classification in combination with nested feature selection was provided for sample discrimination and marker compounds discovery. Obtained metabolome-based classification showed an expected difference compared to genetic-based classification data. Nine compounds were found to have the biggest classification power during nested feature selection. Using database search and molecular network construction, five of them were identified as known hops bitter compounds.

Profiling alkaloids in Aconitum pendulum N. Busch collected from different elevations of Qinghai province using widely targeted metabolomics

Aconitum pendulum N. Busch (Ranunculaceae) is rich in alkaloids with anti-inflammatory and analgesic activities. Many studies have focused on the identification or quantification of alkaloid components using low-throughput tests. However, the metabolic differences of plants from environmentally distinct regions remain unclear. The present study profiled alkaloid chemical compounds in the rhizomes of A. pendulum from different regions. A total of 80 chemical compounds were identified using a widely targeted ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) approach. Principal component, hierarchical clustering, and orthogonal partial least squares-discriminant analyses of the chemical compounds indicated that the plants from 6 regions clearly separated into distinct groups. A total of 19 compounds contributed the most to the metabolite differences between collection areas and were identified as potential metabolic markers. The anti-inflammatory activities of the A. pendulum extracts were also evaluated and the potential environmental effects on the regulation of metabolite composition and bioactivity were explored. These results improve our understanding of the variation in chemical composition of plants from different regions and will serve as a reference for evaluating the medicinal value of A. pendulum in different environments.

NMR spectroscopy spotlighting immunogenicity induced by COVID-19 vaccination to mitigate future health concerns

In this review, the disease and immunogenicity affected by COVID-19 vaccination at the metabolic level are described considering the use of nuclear magnetic resonance (NMR) spectroscopy for the analysis of different biological samples. Consistently, we explain how different biomarkers can be examined in the saliva, blood plasma/serum, bronchoalveolar-lavage fluid (BALF), semen, feces, urine, cerebrospinal fluid (CSF) and breast milk. For example, the proposed approach for the given samples can allow one to detect molecular biomarkers that can be relevant to disease and/or vaccine interference in a system metabolome. The analysis of the given biomaterials by NMR often produces complex chemical data which can be elucidated by multivariate statistical tools, such as PCA and PLS-DA/OPLS-DA methods. Moreover, this approach may aid to improve strategies that can be helpful in disease control and treatment management in the future.

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NMR analysis of various bio-samples can explore disease course and vaccine interaction.

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Immunogenicity and reactogenicity caused by COVID-19 vaccination can be studied by NMR.

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Vaccine interaction alters metabolic pathway(s) at a certain stage, and this mechanism can be probed at the metabolic level.