Covering: 1995 to 2023
Advances in bioanalytical methods, particularly mass spectrometry, have provided valuable molecular insights into the mechanisms of life. Non-targeted metabolomics aims to ...detect and (relatively) quantify all observable small molecules present in a biological system. By comparing small molecule abundances between different conditions or timepoints in a biological system, researchers can generate new hypotheses and begin to understand causes of observed phenotypes. Functional metabolomics aims to investigate the functional roles of metabolites at the scale of the metabolome. However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics. In contrast, the field of natural products has a history of identifying the structures and bioactivities of primary and specialized metabolites. Here, we propose to expand and reframe functional metabolomics by integrating concepts from the fields of natural products and chemical biology. We highlight emerging functional metabolomics approaches that shift the focus from correlation to physical interactions, and we discuss how this allows researchers to uncover causal relationships between molecules and phenotypes.
In this review we discuss emerging functional metabolomics strategies and their potential use to reveal mechanistic insights in large-scale natural product discovery studies.
spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various ...applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications to spectrum plotting from online data repositories and assisting data analysis tasks for dozens of other projects. Here, we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.
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43.
Mass spectrometry searches using MASST Wang, Mingxun; Jarmusch, Alan K; Vargas, Fernando ...
Nature biotechnology,
01/2020, Volume:
38, Issue:
1
Journal Article
Peer reviewed
Open access
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FZAB, GEOZS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Spectrum alignment of tandem mass spectrometry (MS/MS) data using the modified cosine similarity and subsequent visualization as molecular networks have been demonstrated to be a useful strategy to ...discover analogs of molecules from untargeted MS/MS-based metabolomics experiments. Recently, a neutral loss matching approach has been introduced as an alternative to MS/MS-based molecular networking with an implied performance advantage in finding analogs that cannot be discovered using existing MS/MS spectrum alignment strategies. To comprehensively evaluate the scoring properties of neutral loss matching, the cosine similarity, and the modified cosine similarity, similarity measures of 955 228 peptide MS/MS spectrum pairs and 10 million small molecule MS/MS spectrum pairs were compared. This comparative analysis revealed that the modified cosine similarity outperformed neutral loss matching and the cosine similarity in all cases. The data further indicated that the performance of MS/MS spectrum alignment depends on the location and type of the modification, as well as the chemical compound class of fragmented molecules.
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The marine sponge-derived fungus Stachylidium bicolor 293 K04 is a prolific producer of specialized metabolites, including certain cyclic tetrapeptides called endolides, which are characterized by ...the presence of the unusual amino acid N-methyl-3-(3-furyl)-alanine. This rare feature can be used as bait to detect new endolide-like analogs through customized fragment pattern searches of tandem mass spectrometry data using the Mass Spec Query Language (MassQL). Here, we integrate endolide-specific MassQL queries with molecular networking to obtain substructural information guiding the targeted isolation and structure elucidation of the new proline-containing endolides E (1) and F (2). We showed that endolide F (but not E) is a moderate antagonist of the arginine vasopressin V1A receptor, a member of the G protein-coupled receptor superfamily.
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Molecular networking (MN) has become a popular data analysis method for untargeted mass spectrometry (MS)/MS-based metabolomics. Recently, MN has been suggested as a powerful tool for drug metabolite ...identification, but its effectiveness for drug metabolism studies has not yet been benchmarked against existing strategies. In this study, we compared the performance of MN, mass defect filtering, Agilent MassHunter Metabolite ID, and Agilent Mass Profiler Professional workflows to annotate metabolites of sildenafil generated in an in vitro liver microsome-based metabolism study. Totally, 28 previously known metabolites with 15 additional unknown isomers and 25 unknown metabolites were found in this study. The comparison demonstrated that MN exhibited performances comparable or superior to those of the existing tools in terms of the number of detected metabolites (27 known metabolites and 22 unknown metabolites), ratio of false positives, and the amount of time and effort required for human labor-based postprocessing, which provided evidence of the efficiency of MN as a drug metabolite identification tool.
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Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and ...(meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
Glycoproteomics has rapidly become an independent analytical platform bridging the fields of glycomics and proteomics to address site-specific protein glycosylation and its impact in biology. Current ...glycopeptide characterization relies on time-consuming manual interpretations and demands high levels of personal expertise. Efficient data interpretation constitutes one of the major challenges to be overcome before true high-throughput glycopeptide analysis can be achieved. The development of new glyco-related bioinformatics tools is thus of crucial importance to fulfill this goal. Here we present SweetNET: a data-oriented bioinformatics workflow for efficient analysis of hundreds of thousands of glycopeptide MS/MS-spectra. We have analyzed MS data sets from two separate glycopeptide enrichment protocols targeting sialylated glycopeptides and chondroitin sulfate linkage region glycopeptides, respectively. Molecular networking was performed to organize the glycopeptide MS/MS data based on spectral similarities. The combination of spectral clustering, oxonium ion intensity profiles, and precursor ion m/z shift distributions provided typical signatures for the initial assignment of different N-, O- and CS-glycopeptide classes and their respective glycoforms. These signatures were further used to guide database searches leading to the identification and validation of a large number of glycopeptide variants including novel deoxyhexose (fucose) modifications in the linkage region of chondroitin sulfate proteoglycans.
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We aimed to construct machine learning (ML) radiomics models to predict response to lenvatinib monotherapy for unresectable hepatocellular carcinoma (HCC).
Patients with HCC receiving lenvatinib ...monotherapy at three institutions were retrospectively identified and assigned to training and external validation cohorts. Tumor response after initiation of lenvatinib was evaluated. Radiomics features were extracted from contrast-enhanced CT images. The K-means clustering algorithm was used to distinguish radiomics-based subtypes. Ten ML radiomics models were constructed and internally validated by 10-fold cross-validation. These models were subsequently verified in an external validation cohort.
A total of 109 patients were identified for analysis, namely, 74 in the training cohort and 35 in the external validation cohort. Thirty-two patients showed partial response, 33 showed stable disease, and 44 showed progressive disease. The overall response rate (ORR) was 29.4%, and the disease control rate was 59.6%. A total of 224 radiomics features were extracted, and 25 significant features were identified for further analysis. Two distant radiomics-based subtypes were identified by K-means clustering, and subtype 1 was associated with a higher ORR and longer progression-free survival (PFS). Among the 10 ML algorithms, AutoGluon displayed the highest predictive performance (AUC = 0.97), which was relatively stable in the validation cohort (AUC = 0.93). Kaplan-Meier analysis showed that responders had a better overall survival HR = 0.21; 95% confidence interval (CI): 0.12-0.36; P < 0.001 and PFS (HR = 0.14; 95% CI: 0.09-0.22; P < 0.001) than nonresponders.
Valuable ML radiomics models were constructed, with favorable performance in predicting the response to lenvatinib monotherapy for unresectable HCC.
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