Mass spectrometry searches using MASST

Wang, Mingxun; Jarmusch, Alan K.; Vargas, Fernando; Aksenov, Alexander A.; Gauglitz, Julia M.; Weldon, Kelly C.; Petras, Daniel; Silva, Ricardo R. da; Quinn, Robert A.; Melnik, Alexey V.; Hooft, Justin J. J. van der; Caraballo‐Rodríguez, Andrés Mauricio; Nothias, Louis Félix; Aceves, Christine M.; Panitchpakdi, Morgan; Brown, Élizabeth; Ottavio, Francesca Di; Sikora, Nicole; Elijah, Emmanuel O.; Labarta-Bajo, Lara; Gentry, Emily C.; Shalapour, Shabnam; Kyle, Kathleen E.; Puckett, Sara P.; Watrous, Jeramie D.; Carpenter, Carolina S.; Bouslimani, Amina; Ernst, Madeleine; Swafford, Austin D.; Zúñiga, Elina I.; Balunas, Marcy J.; Klassen, Jonathan L.; Loomba, Rohit; Knight, Rob; Bandeira, Nuno; Dorrestein, Pieter C.

doi:10.1038/s41587-019-0375-9

Cited by 181 publications

(191 citation statements)

References 27 publications

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“…The extension to metabolomics continues to suffer from a lack of public libraries, but also appears to be a powerful approach. A new tool has recently been developed to match MS and MS/MS spectra searching for identical or analogous features in public repositories of metabolomics data [ 104 ].…”

Section: Translational and Clinical Proteomicsmentioning

confidence: 99%

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Ristori

Mortera

Marzano

et al. 2020

IJMS

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Autism spectrum disorders (ASDs) are neurodevelopmental disorders characterized by behavioral alterations and currently affect about 1% of children. Significant genetic factors and mechanisms underline the causation of ASD. Indeed, many affected individuals are diagnosed with chromosomal abnormalities, submicroscopic deletions or duplications, single-gene disorders or variants. However, a range of metabolic abnormalities has been highlighted in many patients, by identifying biofluid metabolome and proteome profiles potentially usable as ASD biomarkers. Indeed, next-generation sequencing and other omics platforms, including proteomics and metabolomics, have uncovered early age disease biomarkers which may lead to novel diagnostic tools and treatment targets that may vary from patient to patient depending on the specific genomic and other omics findings. The progressive identification of new proteins and metabolites acting as biomarker candidates, combined with patient genetic and clinical data and environmental factors, including microbiota, would bring us towards advanced clinical decision support systems (CDSSs) assisted by machine learning models for advanced ASD-personalized medicine. Herein, we will discuss novel computational solutions to evaluate new proteome and metabolome ASD biomarker candidates, in terms of their recurrence in the reviewed literature and laboratory medicine feasibility. Moreover, the way to exploit CDSS, performed by artificial intelligence, is presented as an effective tool to integrate omics data to electronic health/medical records (EHR/EMR), hopefully acting as added value in the near future for the clinical management of ASD.

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Section: Translational and Clinical Proteomicsmentioning

confidence: 99%

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Ristori

Mortera

Marzano

et al. 2020

IJMS

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“…On the other hand, we sought to sort out the uncharacterized metabolites that particularly existed in the predatory stage and were detected in over half of the strains of each NTF species; their putative chemical identities are provided in the supporting information (Tables S5-S8 and Figures S8 and S9, Appendix A, and Appendix B). The chemical information of these uncharacterized but important metabolites, including MS/MS spectral lists, molecular formula predicted by SIRIUS 4.0 [35], and common biological sample sources linking to public metabolomics datasets by MASST [36], may serve as the future reference for NTF research.…”

Section: Discussionmentioning

confidence: 99%

Nematode-Trapping Fungi Produce Diverse Metabolites during Predator–Prey Interaction

et al. 2020

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Nematode-trapping fungi are natural antagonists of nematodes. These predatory fungi are capable of switching their lifestyle from a saprophytic to predatory stage in the presence of nematodes by developing specialized trapping devices to capture and consume nematodes. The biochemical mechanisms of such predator–prey interaction have become increasingly studied given the potential application of nematode-trapping fungi as biocontrol agents, but the involved fungal metabolites remain underexplored. Here, we report a comprehensive liquid–chromatography mass spectrometry (LC–MS) metabolomics study on one hundred wild isolates of nematode-trapping fungi in three different species, Arthrobotrys oligospora, Arthrobotrys thaumasia, and Arthrobotrys musiformis. Molecular networking analysis revealed that the fungi were capable of producing thousands of metabolites, and such chemical diversity of metabolites was notably increased as the fungi switched lifestyle to the predatory stage. Structural annotations by tandem mass spectrometry revealed that those fungal metabolites belonged to various structural families, such as peptide, siderophore, fatty alcohol, and fatty acid amide, and their production exhibited species specificity. Several small peptides (<1.5 kDa) produced by A. musiformis in the predatory stage were found, with their partial amino acid sequences resolved by the tandem mass spectra. Four fungal metabolites (desferriferrichrome, linoleyl alcohol, nonadecanamide, and citicoline) that were significantly enriched in the predatory stage were identified and validated by chemical standards, and their bioactivities against nematode prey were assessed. The availability of the metabolomics datasets will facilitate comparative studies on the metabolites of nematode-trapping fungi in the future.

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“…Imitating the model of WebBLAST server for public sequencing data, a web-based MS/MS spectra data search engine called MASST has been introduced. MASST will allow users to search against all shared data on three central repositories (Metabolomics Workbench, MetaboLights, and GNPS) by converting data to a uniform open format and providing all library hits including the origin of the matched MS/MS spectral data, all dataset matches, and sample information or other metadata associated with that dataset [92]. This unique approach, called meta-annotation, will significantly improve the precision of metabolite annotation.…”

Section: The Advancement and Limitations Of Metabolite Annotation/idementioning

confidence: 99%

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

et al. 2020

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Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional “pre-pre-” and “post-post-” analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

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Mass spectrometry searches using MASST

Cited by 181 publications

References 27 publications

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Proteomics and Metabolomics Approaches towards a Functional Insight onto AUTISM Spectrum Disorders: Phenotype Stratification and Biomarker Discovery

Nematode-Trapping Fungi Produce Diverse Metabolites during Predator–Prey Interaction

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

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