Mass spectrometry-based untargeted metabolomics approaches for comprehensive structural annotation of bioactive metabolites from bushy cashew (Anacardium humile) fruits

Funding Information: The authors acknowledge financial support from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the institutional and financial support. Publisher Copyright: © 2023Peer reviewedPostprin

Mass spectral molecular networking to profile the metabolome of biostimulant bacillus strains

Beneficial soil microbes like plant growth-promoting rhizobacteria (PGPR) significantly contribute to plant growth and development through various mechanisms activated by plant-PGPR interactions. However, a complete understanding of the biochemistry of the PGPR and microbial intraspecific interactions within the consortia is still enigmatic. Such complexities constrain the design and use of PGPR formulations for sustainable agriculture. Therefore, we report the application of mass spectrometry (MS)-based untargeted metabolomics and molecular networking (MN) to interrogate and profile the intracellular chemical space of PGPR Bacillus strains: B. laterosporus, B. amyloliquefaciens, B. licheniformis 1001, and B. licheniformis M017 and their consortium. The results revealed differential and diverse chemistries in the four Bacillus strains when grown separately, and also differing from when grown as a consortium. MolNetEnhancer networks revealed 11 differential molecular families that are comprised of lipids and lipid-like molecules, benzenoids, nucleotide-like molecules, and organic acids and derivatives. Consortium and B. amyloliquefaciens metabolite profiles were characterized by the high abundance of surfactins, whereas B. licheniformis strains were characterized by the unique presence of lichenysins. Thus, this work, applying metabolome mining tools, maps the microbial chemical space of isolates and their consortium, thus providing valuable insights into molecular information of microbial systems. Such fundamental knowledge is essential for the innovative design and use of PGPR-based biostimulants

Seeing the forest for the trees : retrieving plant secondary biochemical pathways from metabolome networks

Over the last decade, a giant leap forward has been made in resolving the main bottleneck in metabolomics, i.e., the structural characterization of the many unknowns. This has led to the next challenge in this research field: retrieving biochemical pathway information from the various types of networks that can be constructed from metabolome data. Searching putative biochemical pathways, referred to as biotransformation paths, is complicated because several flaws occur during the construction of metabolome networks. Multiple network analysis tools have been developed to deal with these flaws, while in silico retrosynthesis is appearing as an alternative approach. In this review, the different types of metabolome networks, their flaws, and the various tools to trace these biotransformation paths are discussed

Comprehensive mass spectrometry-guided phenotyping of plant specialized metabolites reveals metabolic diversity in the cosmopolitan plant family Rhamnaceae

Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped the diversity of specialized metabolites, which then drives many other aspects of plant biodiversity. However, until recently, large-scale studies investigating the diversity of specialized metabolites in an evolutionary context have been limited by the impossibility of identifying chemical structures of hundreds to thousands of compounds in a time-feasible manner. Here we introduce a workflow for large-scale, semi-automated annotation of specialized metabolites and apply it to over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5% based on spectral library matches alone to 42.6% of total MS/MS molecular features, extending annotations from well-known plant compound classes into dark plant metabolomics. To gain insights into substructural diversity within this plant family, we also extract patterns of co-occurring fragments and neutral losses, so-called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7-O-methyltransferase activity. Our workflow provides the foundations for the automated, high-throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.</p

Assessing the Effectiveness of Chemical Marker Extraction from Amazonian Plant Cupuassu (Theobroma grandiflorum) by PSI-HRMS/MS and LC-HRMS/MS.

Acknowledgments The authors acknowledge the institutional and financial support from the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES) and the Brazilian Research Council (CNPq). Funding This research received no external funding.Peer reviewedPublisher PD

Metabolomics Data Processing and Data Analysis—Current Best Practices

Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows

Bayesian methods for small molecule identification

Confident identification of small molecules remains a major challenge in untargeted metabolomics, natural product research and related fields. Liquid chromatography-tandem mass spectrometry is a predominant technique for the high-throughput analysis of small molecules and can detect thousands of different compounds in a biological sample. The automated interpretation of the resulting tandem mass spectra is highly non-trivial and many studies are limited to re-discovering known compounds by searching mass spectra in spectral reference libraries. But these libraries are vastly incomplete and a large portion of measured compounds remains unidentified. This constitutes a major bottleneck in the comprehensive, high-throughput analysis of metabolomics data. In this thesis, we present two computational methods that address different steps in the identification process of small molecules from tandem mass spectra. ZODIAC is a novel method for de novo that is, database-independent molecular formula annotation in complete datasets. It exploits similarities of compounds co-occurring in a sample to find the most likely molecular formula for each individual compound. ZODIAC improves on the currently best-performing method SIRIUS; on one dataset by 16.5 fold. We show that de novo molecular formula annotation is not just a theoretical advantage: We discover multiple novel molecular formulas absent from PubChem, one of the biggest structure databases. Furthermore, we introduce a novel scoring for CSI:FingerID, a state-of-the-art method for searching tandem mass spectra in a structure database. This scoring models dependencies between different molecular properties in a predicted molecular fingerprint via Bayesian networks. This problem has the unusual property, that the marginal probabilities differ for each predicted query fingerprint. Thus, we need to apply Bayesian networks in a novel, non-standard fashion. Modeling dependencies improves on the currently best scoring

The role of plant secondary metabolites in shaping regional and local plant community assembly

The outstanding diversity of Amazonian forests is predicted to be the result of several processes. While tree lineages have dispersed repeatedly across the Amazon, interactions between plants and insects may be the principal mechanism structuring the communities at local scales. Using metabolomic and phylogenetic approaches, we investigated the patterns of historical assembly of plant communities across the Amazon based on the Neotropical genus of trees Inga (Leguminosae) at four, widely separated sites. Our results show a low degree of phylogenetic structure and a mixing of chemotypes across the whole Amazon basin, suggesting that although biogeography may play a role, the metacommunity for any local community in the Amazon is the entire basin. Yet, local communities are assembled by ecological processes, with the suite of Inga at a given site more divergent in chemical defences than expected by chance Synthesis. To our knowledge, this is the first study to present metabolomic data for nearly 100 species in a diverse Neotropical plant clade across the whole Amazonia. Our results demonstrate a role for plant–herbivore interactions in shaping the clade's community assembly at a local scale, and suggest that the high alpha diversity in Amazonian tree communities must be due in part to the interactions of diverse tree lineages with their natural enemies providing a high number of niche dimension

Genomic and Metabolomic Analysis of the Potato Common Scab Pathogen Streptomyces scabiei

Streptomyces scabiei is a key causative agent of common scab disease, which causes significant economic losses to potato growers worldwide. This organism produces several phytotoxins that are known or suspected to contribute to host–pathogen interactions and disease development; however, the full metabolic potential of S. scabiei has not been previously investigated. In this study, we used a combined metabolomic and genomic approach to investigate the metabolites that are produced by S. scabiei. The genome sequence was analyzed using antiSMASH and DeepBGC to identify specialized metabolite biosynthetic gene clusters. Using untargeted liquid chromatography-coupled tandem mass spectrometry (LC-MS2), the metabolic profile of S. scabiei was compared after cultivation on three different growth media. MS2 data were analyzed using Feature-Based Molecular Networking and hierarchical clustering in BioDendro. Metabolites were annotated by performing a Global Natural Products Social Molecular Networking (GNPS) spectral library search or using Network Annotation Propagation, SIRIUS, MetWork, or Competitive Fragmentation Modeling for Metabolite Identification. Using this approach, we were able to putatively identify new analogues of known metabolites as well as molecules that were not previously known to be produced by S. scabiei. To our knowledge, this study represents the first global analysis of specialized metabolites that are produced by this important plant pathogen