Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data.

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.

Bursts in biosynthetic gene cluster transcription are accompanied by surges of natural compound production in the myxobacterium Sorangium sp.

A better understanding of the genetic regulation of the biosynthesis of microbial compounds could accelerate the discovery of new biologically active molecules and facilitate their production. To this end, we have investigated the time course of genome-wide transcription in the myxobacterium Sorangium sp. So ce836 in relation to its production of natural compounds. Time-resolved RNA sequencing revealed that core biosynthesis genes from 48 biosynthetic gene clusters (BGCs; 92% of all BGCs encoded in the genome) were actively transcribed at specific time points in a batch culture. The majority (80%) of polyketide synthase and non-ribosomal peptide synthetase genes displayed distinct peaks of transcription during exponential bacterial growth. Strikingly, these bursts in BGC transcriptional activity were associated with surges in the net production rates of known natural compounds, indicating that their biosynthesis was critically regulated at the transcriptional level. In contrast, BGC read counts from single time points had limited predictive value about biosynthetic activity, since transcription levels varied >100-fold among BGCs with detected natural products. Taken together, our time-course data provide unique insights into the dynamics of natural compound biosynthesis and its regulation in a wild-type myxobacterium, challenging the commonly cited notion of preferential BGC expression under nutrient-limited conditions. The close association observed between BGC transcription and compound production warrants additional efforts to develop genetic engineering tools for boosting compound yields from myxobacterial producer strains.