HMDB 5.0: the Human Metabolome Database for 2022

Nucleic Acids Research, Journal Year: 2021, Volume and Issue: 50(D1), P. D622 - D631

Published: Oct. 21, 2021

Abstract The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological chemical properties since 2007. Over the past 15 years, grown evolved significantly to meet needs of metabolomics community respond continuing changes in internet computing technology. This year's update, 5.0, brings a number important improvements upgrades database. These should make more useful appealing larger cross-section users. In particular, these include: (i) significant increase metabolite entries (from 114 100 217 920 compounds); (ii) enhancements quality depth descriptions; (iii) addition new structure, spectral pathway visualization tools; (iv) inclusion many much accurately predicted data sets, including NMR spectra, MS retention indices collision cross section (v) HMDB’s search functions facilitate better compound identification. Many other minor updates content, interface, general performance website have also made. Overall, we believe greatly enhance ease use its potential applications not only but exposomics, lipidomics, nutritional science, biochemistry clinical chemistry.

Language: Английский

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The exposome and health: Where chemistry meets biology

Science, Journal Year: 2020, Volume and Issue: 367(6476), P. 392 - 396

Published: Jan. 24, 2020

Despite extensive evidence showing that exposure to specific chemicals can lead disease, current research approaches and regulatory policies fail address the chemical complexity of our world. To safeguard future generations from increasing number polluting environment, a systematic agnostic approach is needed. The “exposome” concept strives capture diversity range exposures synthetic chemicals, dietary constituents, psychosocial stressors, physical factors, as well their corresponding biological responses. Technological advances such high-resolution mass spectrometry network science have allowed us take first steps toward comprehensive assessment exposome. Given increased recognition dominant role nongenetic factors play in an effort characterize exposome at scale comparable human genome warranted.

Language: Английский

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Mass spectrometry-based metabolomics in microbiome investigations

Nature Reviews Microbiology, Journal Year: 2021, Volume and Issue: 20(3), P. 143 - 160

Published: Sept. 22, 2021

Language: Английский

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Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: Aug. 28, 2020

Abstract The metabolome includes not just known but also unknown metabolites; however, metabolite annotation remains the bottleneck in untargeted metabolomics. Ion mobility – mass spectrometry (IM-MS) has emerged as a promising technology by providing multi-dimensional characterizations of metabolites. Here, we curate an ion CCS atlas, namely AllCCS, and develop integrated strategy for using or chemical structures. AllCCS atlas covers vast structures with >5000 experimental records ~12 million calculated values >1.6 small molecules. We demonstrate high accuracy wide applicability medium relative errors 0.5–2% broad spectrum combined silico MS/MS spectra facilitates match substantially improves coverage both from biological samples. Together, is versatile resource that enables confident annotation, revealing comprehensive metabolic insights towards processes.

Language: Английский

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CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification

Metabolites, Journal Year: 2019, Volume and Issue: 9(4), P. 72 - 72

Published: April 13, 2019

Metabolite identification for untargeted metabolomics is often hampered by the lack of experimentally collected reference spectra from tandem mass spectrometry (MS/MS). To circumvent this problem, Competitive Fragmentation Modeling-ID (CFM-ID) was developed to accurately predict electrospray ionization-MS/MS (ESI-MS/MS) chemical structures and aid in compound via MS/MS spectral matching. While earlier versions CFM-ID performed very well, CFM-ID’s performance predicting certain classes compounds, including many lipids, quite poor. Furthermore, capabilities were limited because it did not use available nor exploit metadata its matching algorithm. Here, we describe significant improvements speed. These include (1) implementation a rule-based fragmentation approach lipid prediction, which greatly improves speed accuracy CFM-ID; (2) inclusion experimental other enhance abilities; (3) development new scoring functions that 21.1%; (4) classification algorithm correctly classifies unknown chemicals (based on their spectra) >80% cases. This improved version called 3.0 freely as web server. Its source code also accessible online.

Language: Английский

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High-confidence structural annotation of metabolites absent from spectral libraries

Nature Biotechnology, Journal Year: 2021, Volume and Issue: 40(3), P. 411 - 421

Published: Oct. 14, 2021

Abstract Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines database generation annotation with confidence score consisting kernel density P value estimation support vector machine enforced directionality features. On diverse datasets, annotates substantial number hits at low false discovery rates outperforms library search. To demonstrate annotate structures never reported before, annotated 12 natural bile acids. The nine was confirmed by manual evaluation two using synthetic standards. In human samples, manually validated 315 molecular currently absent from Human Metabolome Database. Application to data 17,400 led 1,715 high-confidence structural annotations were libraries.

Language: Английский

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Use cases, best practice and reporting standards for metabolomics in regulatory toxicology

Nature Communications, Journal Year: 2019, Volume and Issue: 10(1)

Published: July 10, 2019

Metabolomics is a widely used technology in academic research, yet its application to regulatory science has been limited. The most commonly cited barrier translation lack of performance and reporting standards. MEtabolomics standaRds Initiative Toxicology (MERIT) project brings together international experts from multiple sectors address this need. Here, we identify the relevant applications for metabolomics toxicology develop best practice guidelines, standards acquiring analysing untargeted targeted metabolite data. We recommend that these guidelines are evaluated implemented several use cases.

Language: Английский

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Soil Organic Matter Characterization by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR MS): A Critical Review of Sample Preparation, Analysis, and Data Interpretation

Environmental Science & Technology, Journal Year: 2021, Volume and Issue: 55(14), P. 9637 - 9656

Published: July 7, 2021

The biogeochemical cycling of soil organic matter (SOM) plays a central role in regulating health, water quality, carbon storage, and greenhouse gas emissions. Thus, many studies have been conducted to reveal how anthropogenic climate variables affect sequestration nutrient cycling. Among the analytical techniques used better understand speciation transformation SOM, Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) is only technique that has sufficient resolving power separate accurately assign elemental compositions individual SOM molecules. global increase application FTICR MS address complexity highlighted challenges opportunities associated with sample preparation, analysis, spectral interpretation. Here, we provide critical review recent strategies for characterization by emphasis on collection, data Data processing visualization methods are presented suggested workflows detail considerations needed molecular information derived from MS. Finally, highlight current research gaps, biases, future directions improve our understanding chemistry within terrestrial ecosystems.

Language: Английский

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patRoon: open source software platform for environmental mass spectrometry based non-target screening

Journal of Cheminformatics, Journal Year: 2021, Volume and Issue: 13(1)

Published: Jan. 6, 2021

Abstract Mass spectrometry based non-target analysis is increasingly adopted in environmental sciences to screen and identify numerous chemicals simultaneously highly complex samples. However, current data processing software either lack functionality for sciences, solve only part of the workflow, are not openly available and/or restricted input formats. In this paper we present patRoon , a new R open-source platform, which provides comprehensive, fully tailored straightforward workflows. This platform makes use, evaluation mixing well-tested algorithms seamless by harmonizing various common (primarily open) tools under consistent interface. addition, offers strategies simplify perform automated (environmental) effectively. implements several effective optimization significantly reduce computational times. The ability time-efficient annotation samples demonstrated with simple reproducible workflow using open-access spiked from drinking water treatment plant study. easily combine evaluate different was three commonly used feature finding algorithms. article, combined already published works, demonstrate that helps make comprehensive readily accessible wider community researchers.

Language: Английский

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Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Nov. 4, 2022

Abstract Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, metabolite annotation is a major challenge metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), enable global from knowns unknowns The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, MS/MS similarity peak correlation network. To demonstrate principle, apply vitro enzymatic system different biological samples, with ~100–300 putative annotated each data set. Among them, >80% are corroborated silico tools. Finally, validate 5 that absent common libraries through repository mining synthesis of chemical standards. Together, enables efficient annotations, substantially advances discovery recurrent for samples model organisms, towards deciphering dark matter

Language: Английский

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