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

# 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?

- in source reaction

- `\([M+H]^+\)`  `\([M+Na]^+\)`
  - 21.982 Da
]

- Lipid `\(-[CH_2]-\)`
  - 14.016 Da

- Xenobiotic metabolism

- Phase I hydrolation
  - 15.995 Da
]

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# Reactomics

> Retrieving general chemical relationship

.half[
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](https://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=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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class: inverse, center, middle

# Reactomics Workflow

## STEP1: Remove redundant peaks

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## Gap between features and compounds

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## GlobalStd Algorithm

.half[
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

.half[
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](http://yufree.github.io/pmd/)
  
  - GlobalStd algorithm
  - pmd-reaction database based on KEGG/HMDB
  - quantitative pmd analysis
  - pmd network analysis
  - pmd MS/MS annotation algorithm

### [rmwf package](https://github.com/yufree/rmwf)

- 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

- 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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<img src="../figure/mssmlabmember.jpg" width="300px" style="display: block; margin: auto;" />
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