RESUMEN
Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.
RESUMEN
Actinobacteria have been a rich source of novel, structurally complex natural products for many decades. Although the largest genus is Streptomyces, from which the majority of antibiotics in current and past clinical use were originally isolated, other less common genera also have the potential to produce a wealth of novel secondary metabolites. One example is the Kutzneria genus, which currently contains only five reported species. One of these species is Kutzneria albida DSM 43870T, which has 46 predicted biosynthetic gene clusters and is known to produce the macrolide antibiotic aculeximycin. Here, we report the isolation and structural characterization of two novel 30-membered glycosylated macrolides, epemicins A and B, that are structurally related to aculeximycin, from a rare Kutzneria sp. The absolute configuration for all chiral centers in the two compounds is proposed based on extensive 1D and 2D NMR studies and bioinformatics analysis of the gene cluster. Through heterologous expression and genetic inactivation, we have confirmed the link between the biosynthetic gene cluster and the new molecules. These findings show the potential of rare Actinobacteria to produce new, structurally diverse metabolites. Furthermore, the gene inactivation represents the first published report to genetically manipulate a representative of the Kutzneria genus.
Asunto(s)
Actinobacteria/química , Antibacterianos/farmacología , Macrólidos/farmacología , Actinobacteria/genética , Actinobacteria/metabolismo , Antibacterianos/biosíntesis , Antibacterianos/química , Antibacterianos/aislamiento & purificación , Proteínas Bacterianas/química , Proteínas Bacterianas/genética , Proteínas Bacterianas/metabolismo , Descubrimiento de Drogas , Macrólidos/química , Macrólidos/aislamiento & purificación , Macrólidos/metabolismo , Staphylococcus aureus Resistente a Meticilina/efectos de los fármacos , Pruebas de Sensibilidad Microbiana , Familia de Multigenes , Sintasas Poliquetidas/química , Sintasas Poliquetidas/genética , Sintasas Poliquetidas/metabolismo , Dominios Proteicos , EstereoisomerismoRESUMEN
To accelerate the shift to bio-based production and overcome complicated functional implementation of natural and artificial biosynthetic pathways to industry relevant organisms, development of new, versatile, bio-based production platforms is required. Here we present a novel yeast-based platform for biosynthesis of bacterial aromatic polyketides. The platform is based on a synthetic polyketide synthase system enabling a first demonstration of bacterial aromatic polyketide biosynthesis in a eukaryotic host.