Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer

Vanessa L. Hale, Patricio Jeraldo, Michael Mundy, Janet Yao, Gary Keeney, Nancy Scott, E. Heidi Cheek, Jennifer Davidson, Megan Green, Christine Martinez, John Lehman, Chandra Pettry, Erica Reed, Kelly Lyke, Bryan A. White, Christian Diener, Osbaldo Resendis-Antonio, Jaime Gransee, Tumpa Dutta, Xuan Mai Petterson & 4 others Lisa Allyn Boardman, David Larson, Heidi Nelson, Nicholas D Chia

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Multi-omic data and genome-scale microbial metabolic models have allowed us to examine microbial communities, community function, and interactions in ways that were not available to us historically. Now, one of our biggest challenges is determining how to integrate data and maximize data potential. Our study demonstrates one way in which to test a hypothesis by combining multi-omic data and community metabolic models. Specifically, we assess hydrogen sulfide production in colorectal cancer based on stool, mucosa, and tissue samples collected on and off the tumor site within the same individuals. 16S rRNA microbial community and abundance data were used to select and inform the metabolic models. We then used MICOM, an open source platform, to track the metabolic flux of hydrogen sulfide through a defined microbial community that either represented on-tumor or off-tumor sample communities. We also performed targeted and untargeted metabolomics, and used the former to quantitatively evaluate our model predictions. A deeper look at the models identified several unexpected but feasible reactions, microbes, and microbial interactions involved in hydrogen sulfide production for which our 16S and metabolomic data could not account. These results will guide future in vitro, in vivo, and in silico tests to establish why hydrogen sulfide production is increased in tumor tissue.

Original languageEnglish (US)
JournalMethods
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Hydrogen Sulfide
Colonic Neoplasms
Tumors
Metabolomics
Neoplasms
Microbial Genome
Microbial Interactions
Tissue
Computer Simulation
Colorectal Neoplasms
Mucous Membrane
Genes
Fluxes

ASJC Scopus subject areas

  • Molecular Biology
  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer. / Hale, Vanessa L.; Jeraldo, Patricio; Mundy, Michael; Yao, Janet; Keeney, Gary; Scott, Nancy; Cheek, E. Heidi; Davidson, Jennifer; Green, Megan; Martinez, Christine; Lehman, John; Pettry, Chandra; Reed, Erica; Lyke, Kelly; White, Bryan A.; Diener, Christian; Resendis-Antonio, Osbaldo; Gransee, Jaime; Dutta, Tumpa; Petterson, Xuan Mai; Boardman, Lisa Allyn; Larson, David; Nelson, Heidi; Chia, Nicholas D.

In: Methods, 01.01.2018.

Research output: Contribution to journalArticle

Hale, VL, Jeraldo, P, Mundy, M, Yao, J, Keeney, G, Scott, N, Cheek, EH, Davidson, J, Green, M, Martinez, C, Lehman, J, Pettry, C, Reed, E, Lyke, K, White, BA, Diener, C, Resendis-Antonio, O, Gransee, J, Dutta, T, Petterson, XM, Boardman, LA, Larson, D, Nelson, H & Chia, ND 2018, 'Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer', Methods. https://doi.org/10.1016/j.ymeth.2018.04.024
Hale, Vanessa L. ; Jeraldo, Patricio ; Mundy, Michael ; Yao, Janet ; Keeney, Gary ; Scott, Nancy ; Cheek, E. Heidi ; Davidson, Jennifer ; Green, Megan ; Martinez, Christine ; Lehman, John ; Pettry, Chandra ; Reed, Erica ; Lyke, Kelly ; White, Bryan A. ; Diener, Christian ; Resendis-Antonio, Osbaldo ; Gransee, Jaime ; Dutta, Tumpa ; Petterson, Xuan Mai ; Boardman, Lisa Allyn ; Larson, David ; Nelson, Heidi ; Chia, Nicholas D. / Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer. In: Methods. 2018.
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