NP3 MS Workflow: An Open-Source Software System to Empower Natural Product-Based Drug Discovery Using Untargeted Metabolomics.

Natural products (or specialized metabolites) are historically the main source of new drugs. However, the current drug discovery pipelines require miniaturization and speeds that are incompatible with traditional natural product research methods, especially in the early stages of the research. This article introduces the NP3 MS Workflow, a robust open-source software system for liquid chromatography-tandem mass spectrometry (LC-MS/MS) untargeted metabolomic data processing and analysis, designed to rank bioactive natural products directly from complex mixtures of compounds, such as bioactive biota samples. NP3 MS Workflow allows minimal user intervention as well as customization of each step of LC-MS/MS data processing, with diagnostic statistics to allow interpretation and optimization of LC-MS/MS data processing by the user. NP3 MS Workflow adds improved computing of the MS2 spectra in an LC-MS/MS data set and provides tools for automatic [M + H]+ ion deconvolution using fragmentation rules; chemical structural annotation against MS2 databases; and relative quantification of the precursor ions for bioactivity correlation scoring. The software will be presented with case studies and comparisons with equivalent tools currently available. NP3 MS Workflow shows a robust and useful approach to select bioactive natural products from complex mixtures, improving the set of tools available for untargeted metabolomics. It can be easily integrated into natural product-based drug-discovery pipelines and to other fields of research at the interface of chemistry and biology.

Machine learning-driven serum metabolomics: insights into the transition to early-stage heart failure with preserved ejection fraction

Introduction: Metabolomics has emerged as a powerful tool in providing readouts of early disease states before clinical manifestation. Here we used the predictive power of Unsupervised Hierarchical Clustering Analysis (UHCA) and Automated Machine Learning (AutoML) algorithms to identify serum metabolic panels in a population at risk of developing HFpEF. Methods: We studied 215 subjects staged as non-HF, pre-HFpEF and early-stage HFpEF(es-HFpEF). We evaluated clinical, laboratory, echocardiographic, and NMR-based metabolomics of blood serum data. UHCA and AutoML were used to explore metabolic fingerprints potentially related to clinical features or HFpEF. We used Metabolite Set Enrichment Analysis to explore biochemical pathways. Results: The UHCA identified three major patients (P) and two metabolites (M) clusters (Figure). The P clusters were associated with HFpEF stages, cardiac remodeling, diastolic dysfunction, and sex (Pearson Chi-square, p < 0.05) and M clusters with glycine and serine metabolism and urea cycle pathways (FDR-adjusted p-value < 0.002). Considering non-HFpEF and es-HFpEF groups, AUROC mean for feature subset combinations was 0.897 and the highest AUROC (0.995) combined metabolites, clinical, laboratory and echo features. Of the 64 models trained that included metabolites as input, serine (25), uridine (17), 2-oxoglutarate (14), citrate (14), 2-aminobutyrate (13) and taurine (13) were observed more frequently with feature importance value greater than zero. The metabolites with higher sum values of feature importance were serine (0.173), uridine (0.131), 2-aminobutyrate (0.123), choline (0.098) and dimethylamine (0.087). Conclusions: This study revealed characteristic metabolite profiles in the sera of patients at risk of developing HFpEF. These metabolite panels can add information for classificatory algorithms development and contribute to the understanding of HFpEF pathophysiology.