Combining Feature-Based Molecular Networking and Contextual Mass Spectral Libraries to Decipher Nutrimetabolomics Profiles

Reproducible mass spectrometry data processing and compound annotation in MZmine 3

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

The Impact of the Culture Regime on the Metabolome and Anti-Phytopathogenic Activity of Marine Fungal Co-Cultures

Co-cultivation, coupled with the OSMAC approach, is considered an efficient method for expanding microbial chemical diversity through the activation of cryptic biosynthetic gene clusters (BGCs). As part of our project aiming to discover new fungal metabolites for crop protection, we previously reported five polyketides, the macrolides dendrodolides E (1) and N (2), the azaphilones spiciferinone (3) and 8α-hydroxy-spiciferinone (4), and the bis-naphtho-γ-pyrone cephalochromin (5) from the solid Potato Dextrose Agar (PDA) co-culture of two marine sediment-derived fungi, Plenodomus influorescens and Pyrenochaeta nobilis. However, some of the purified metabolites could not be tested due to their minute quantities. Here we cultivated these fungi (both axenic and co-cultures) in liquid regime using three different media, Potato Dextrose Broth (PDB), Sabouraud Dextrose Broth (SDB), and Czapek-Dox Broth (CDB), with or without shaking. The aim was to determine the most ideal co-cultivation conditions to enhance the titers of the previously isolated compounds and to produce extracts with stronger anti-phytopathogenic activity as a basis for future upscaled fermentation. Comparative metabolomics by UPLC-MS/MS-based molecular networking and manual dereplication was employed for chemical profiling and compound annotations. Liquid co-cultivation in PDB under shaking led to the strongest activity against the phytopathogen Phytophthora infestans. Except for compound 1, all target compounds were detected in the co-culture in PDB. Compounds 2 and 5 were produced in lower titers, whereas the azaphilones (3 and 4) were overexpressed in PDB compared to PDA. Notably, liquid PDB co-cultures contained meroterpenoids and depside clusters that were absent in the solid PDA co-cultures. This study demonstrates the importance of culture regime in BGC regulation and chemical diversity of fungal strains in co-culture studies.

Feature-Based Molecular Networking Facilitates the Comprehensive Identification of Differential Metabolites in Diabetic Cognitive Dysfunction Rats

Cognitive dysfunction is a frequent complication of type 2 diabetes mellitus (T2DM), usually accompanied by metabolic disorders. However, the metabolic changes in diabetic cognitive dysfunction (DCD) patients, especially compared to T2DM groups, are not fully understood. Due to the subtle differences in metabolic alterations between DCD groups and T2DM groups, the comprehensive detection of the untargeted metabolic profiles of hippocampus and urine samples of rats was conducted by LC-MS, considering the different ionization modes and polarities of the examined compounds, and feature-based molecular networking (FBMN) was performed to help identify differential metabolites from a comprehensive perspective in this study. In addition, an association analysis of the differential metabolites in hippocampus and urine was conducted by the O2PLS model. Finally, a total of 71 hippocampal tissue differential metabolites and 179 urine differential metabolites were identified. The pathway enrichment results showed that glutamine and glutamate metabolism, alanine, aspartate, and glutamate metabolism, glycerol phospholipid metabolism, TCA cycle, and arginine biosynthesis in the hippocampus of DCD animals were changed. Seven metabolites (AUC > 0.9) in urine appeared as key differential metabolites that might reflect metabolic changes in the target tissue of DCD rats. This study showed that FBMN facilitated the comprehensive identification of differential metabolites in DCD rats. The differential metabolites may suggest an underlying DCD and be considered as potential biomarkers for DCD. Large samples and clinical experiments are needed for the subsequent elucidation of the possible mechanisms leading to these alterations and the verification of potential biomarkers.

Advances in Metabolic Profiling of Biological Samples

Metabolomics constitutes a promising approach to clinical diagnostics, but its practical implementation in clinical settings is hindered by the requirement for rapid and efficient analytical methods [...].