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General reactomics data analysis framework for retrieving chemical relationship from untargeted mass-spectrometry data

Miao Yu (Presenter) and Lauren Petrick

Pittcon 2021

2021-03-08

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Typical workflow for HRMS metabolomics/NTA

  • Collect samples

  • Acquire data (peaks) using mass spectrometry

  • Annotate peaks for identification with compound name

  • Build links between compounds using pathway/network analysis

Sample -> Peaks -> Compounds -> Relationship among compounds

  • Problems

    • Time consuming - too many peaks ~20k
    • Standards coverage - unknown unknown
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Skip the annotation

Sample -> Peaks -> Compounds -> Relationship among compounds

  • Mass spectrum could directly measure relationship (reactions)

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Why Reactions?

  • Unit: Gene (5) < Protein (20+2) < Metabolite (100K) < Compound (100M)

  • Combination: Gene (20,000-25,000) < Protein (20,000-25,000) < Compound(???)

  • Small molecule combination is a chemical reaction or paired mass distance

  • Paired Mass Distances (PMD) is unique for reactions

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Sources of PMDs in real data

Where is PMD?

  • Isotopologues

    • [M]+ [M+1]+
    • 1.006 Da

  • in source reaction

    • [M+H]+ [M+Na]+
    • 21.982 Da

  • Homologous series

    • Lipid −[CH2]−
    • 14.016 Da

  • Xenobiotic metabolism

    • Phase I hydrolation
    • 15.995 Da
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Reactomics

Retrieving general chemical relationship

Yu, M., & Petrick, L. (2020). Untargeted high-resolution paired mass distance data mining for retrieving general chemical relationships. Communications Chemistry, 3(1). doi:10.1038/s42004-020-00403-z

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General reactomics data analysis framework

Step 1: Remove redundant peaks

  • regular metabolomics / NTA workflow

Step 2: Extract high frequency PMDs

  • reaction level evaluation

Step 3: Relationship network

  • network based evaluation
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General reactomics data analysis framework

Demo data: ST000560

  • Immortalized immunoglobulin-producing cell lines

  • 4 replicates from 6 cell lines

  • 3 patients with IgA nephropathy and 3 healthy controls

  • 1918 features or peaks

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Reactomics Workflow

STEP1: Remove redundant peaks

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Gap between features and compounds

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GlobalStd Algorithm

Yu, M., Olkowicz, M., & Pawliszyn, J. (2019). Structure/reaction directed analysis for LC-MS based untargeted analysis. Analytica Chimica Acta, 1050, 16–24. doi:10.1016/j.aca.2018.10.062

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GlobalStd Algorithm: before (1918) and after (196)

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For annotation - PMDDA

Yu, M., Dolios, G., & Petrick, L. (2021). Reproducible Untargeted Metabolomics Data Analysis Workflow for Exhaustive MS/MS Annotation. doi:10.26434/chemrxiv.13565159.v1

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Reactomics Workflow

STEP2: Extract high frequency PMDs

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High frequency PMDs for retrieving chemical relationship

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Reaction level changes

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Reactomics Workflow

STEP3: Relationship network

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PMD Network

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Software

pmd package

  • GlobalStd algorithm
  • pmd-reaction database based on KEGG/HMDB
  • quantitative pmd analysis
  • pmd network analysis
  • pmd MS/MS annotation algorithm

rmwf package

  • NIST 1950 data
  • Rmarkdown template for reactomics
  • From raw data to report
  • part of xcmsrocker docker image

enet package (under development)

  • network analysis for relationships among small molecules
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Acknowledgement

  • Institute for Exposomic Research, ISMMS

  • Department of Environmental Medicine and Public Health, ISMMS

  • Dr. Petrick's research group

  • Q&A: miao.yu@mssm.edu

  • Search 'reactomics'

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Typical workflow for HRMS metabolomics/NTA

  • Collect samples

  • Acquire data (peaks) using mass spectrometry

  • Annotate peaks for identification with compound name

  • Build links between compounds using pathway/network analysis

Sample -> Peaks -> Compounds -> Relationship among compounds

  • Problems

    • Time consuming - too many peaks ~20k
    • Standards coverage - unknown unknown
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