Untargeted high-resolution paired mass distance data mining for retrieving general chemical relationships

Yu, Mengxiao; Petrick, Lauren

doi:10.1038/s42004-020-00403-z

Cited by 32 publications

(39 citation statements)

References 26 publications

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“…However, the gatekeeper discovery framework can be extended in future work to multivariate linear regression to consider covariates, or other machine learning algorithms such as random forest or support vector machine. Additionally, the correlation threshold among metabolites can be reduced by the user to reveal additional biological pathways or gatekeepers, or correlation can be replaced by other relationships such as reactomics or paired mass distances 7 . As a general data analysis framework, gatekeeper discovery is flexible for direct adoption to different environmental health studies and even different omics.…”

Section: Discussionmentioning

confidence: 99%

“…In particular, metabolomics approaches enable unbiased measurement of thousands of metabolites to identify changes in the metabolome profile as a result of exposures or disease processes 5 . Metabolites are also connected by biological pathways 6 and biochemical reactions 7 that themselves can be associated with specific health conditions or diseases 8 . Further, exogenous exposures influence health outcomes via interaction with endogenous metabolites 9 .…”

Section: Introductionmentioning

confidence: 99%

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Molecular Gatekeeper Discovery: Workflow for Linking Multiple Environmental Biomarkers to Metabolomics

Teitelbaum²,

Dolios³

et al. 2021

Preprint

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The exposome reflects the many exposures to various factors across the life-course that can affect health. Sensitive techniques like metabolomics can reveal the underlying molecular basis linking exposures to disease and generate hypotheses for future quantitative toxicological studies. Current applications of metabolomics are primarily to identify metabolic changes linking a single exposure and a health outcome(s); there is no general framework for multiple exposures. Here, we explore the concept of ‘molecular gatekeepers’—key metabolites that link single or multiple exposure biomarkers with correlated clusters of endogenous metabolites—to inform health-relevant biological targets. We performed untargeted metabolomics on plasma from 152 adolescent girls participating in the Growing Up Healthy Study in New York City, using liquid chromatography-high resolution mass spectrometry (LC-HRMS). We then performed network analysis to link metabolites to environmental biomarkers including five trace elements (Cd, Mn, Pb, Se, and Hg) and five perfluorinated chemicals (PFCs; n-PFOS, Sm-PFOS, n-PFOA, PFHxS, PFNA) previously measured in the same samples. We defined any metabolite associated with at least one environmental biomarker and correlated with at least one other metabolite (Spearman rho > 0.9) as a ‘molecular gatekeeper’. Associations of gatekeepers with health outcomes (e.g., body mass index, age at menarche) were tested with linear models. After removing redundant peaks, 964 (positive mode) and 1784 (negative mode) metabolite features were used for network analysis. Of 95 and 138 metabolites, respectively, associated with at least one exposure, 28 and 43 were molecular gatekeepers. Further, lysophosphatidylcholine(16:0) and taurodeoxycholate were correlated with both n-PFOA and n-PFOS, suggesting a shared dysregulation from multiple xenobiotic exposures. One annotated gatekeeper, sphingomyelin(d18:2/14:0), was significantly associated with age at menarche; yet, no direct association was detected between any exposure biomarkers and age at menarche. Thus, molecular gatekeepers may provide a general data analysis framework to discover molecular linkages between exposure biomarkers and health outcomes that may otherwise be obscured by complex interactions in direct measurements. This framework may aid in identifying vulnerable biological pathways for future exposome research.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Molecular Gatekeeper Discovery: Workflow for Linking Multiple Environmental Biomarkers to Metabolomics

Teitelbaum²,

Dolios³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Network analysis, capitalizing on peak-peak relationships to increase annotation scope and accuracy, has been broadly used in metabolomics data annotation. Workflows employing the concept of molecular connectivity have been used to build networks (e.g., GNPS 28–30 , MetDNA 31 , CliqueMS 32 and others 33–35 ). Ions connected by either biochemistry or mass spectrometry phenomena often share MS2 fragmentation pattern similarity.…”

Section: Introductionmentioning

confidence: 99%

Metabolite discovery through global annotation of untargeted metabolomics data

Chen

Wang

et al. 2021

Preprint

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A primary goal of metabolomics is to identify all biologically important metabolites. One powerful approach is liquid chromatography-high resolution mass spectrometry (LC-MS), yet most LC-MS peaks remain unidentified. Here, we present a global network optimization approach, NetID, to annotate untargeted LC-MS metabolomics data. We consider all experimentally observed ion peaks together, and assign annotations to all of them simultaneously so as to maximize a score that considers properties of peaks (known masses, retention times, MS/MS fragmentation patterns) as well network constraints that arise based on mass difference between peaks. Global optimization results in accurate peak assignment and trackable peak-peak relationships. Applying this approach to yeast and mouse data, we identify a half-dozen novel metabolites, including thiamine and taurine derivatives. Isotope tracer studies indicate active flux through these metabolites. Thus, NetID applies existing metabolomic knowledge and global optimization to annotate untargeted metabolomics data, revealing novel metabolites.

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“…While metabolomics aims at revealing changes in levels of all possible metabolites in biological samples 1 , non-targeted analysis (NTA) aims at comprehensive profiling of compounds in environmental samples 2 . To achieve these goals, both approaches use high-resolution mass spectrometry (HRMS) to perform unbiased measurement of small molecules followed by identification of unknowns 3 . In most HRMS-based workflows, small molecule profiles will first be extracted across samples as peaks or features 4 .…”

Section: Introductionmentioning

confidence: 99%

“…However, adducts or in-source reactions might be quite different among different sample matrices or instrument parameters 21 , even for peaks from the same compound 22 . Therefore, a frequency-based paired-mass distances algorithm, such as the GlobalStd algorithm, could be an alternative solution to determine pseudo-spectra for exhaustive and local MS/MS analysis as it is designed to extract independent peaks without predefined redundant peaks information 3,16 . With such high complexity and no gold standard for metabolomics data pre-processing, reproducibility is important.…”

Section: Introductionmentioning

confidence: 99%

Reproducible Untargeted Metabolomics Data Analysis Workflow for Exhaustive MS/MS Annotation

Dolios²,

Petrick³

2021

Preprint

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<p>Unknown features in untargeted metabolomics and non-targeted analysis (NTA) are identified using fragment ions from MS/MS spectra to predict the structures of the unknown compounds. The precursor ion selected for fragmentation is commonly performed using data dependent acquisition (DDA) strategies or following statistical analysis using targeted MS/MS approaches. However, the selected precursor ions from DDA only cover a biased subset of the peaks or features found in full scan data. In addition, different statistical analysis can select different precursor ions for MS/MS analysis, which make the <i>post-hoc</i> validation of ions selected by new statistical methods impossible for precursor ions selected by the original statistical method. Here we propose an automated, exhaustive, statistical model-free workflow: paired mass distance-dependent analysis (PMDDA), for untargeted mass spectrometry identification of unknown compounds. By removing redundant peaks and performing pseudo-targeted MS/MS analysis on independent peaks, we can comprehensively cover unknown compounds found in full scan analysis using a “one peak for one compound” workflow without a priori redundant peak information. We show that compared to DDA, PMDDA is more comprehensive and robust against samples' matrix effects. Further, more compounds were identified by database annotation using PMDDA compared with CAMERA and RAMClustR. Finally, compounds with signals in both positive and negative modes can be identified by the PMDDA workflow, to further reduce redundancies. The whole workflow is fully reproducible as a docker image xcmsrocker with both the original data and the data processing template. </p>

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Untargeted high-resolution paired mass distance data mining for retrieving general chemical relationships

Cited by 32 publications

References 26 publications

Molecular Gatekeeper Discovery: Workflow for Linking Multiple Environmental Biomarkers to Metabolomics

Molecular Gatekeeper Discovery: Workflow for Linking Multiple Environmental Biomarkers to Metabolomics

Metabolite discovery through global annotation of untargeted metabolomics data

Reproducible Untargeted Metabolomics Data Analysis Workflow for Exhaustive MS/MS Annotation

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