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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; Greene, 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; Larson, David; Nelson, Heidi; Chia, Nicholas.
Afiliação
  • Hale VL; Department of Veterinary Preventative Medicine, The Ohio State University College of Veterinary Medicine, Columbus, OH, USA; Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA; Department of Surgery, Mayo Clinic, Rochester, MN, USA.
  • Jeraldo P; Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA; Department of Surgery, Mayo Clinic, Rochester, MN, USA.
  • Mundy M; Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
  • Yao J; Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA.
  • Keeney G; Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
  • Scott N; Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
  • Cheek EH; Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
  • Davidson J; Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
  • Greene M; Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
  • Martinez C; Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
  • Lehman J; Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
  • Pettry C; Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
  • Reed E; Division of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA.
  • Lyke K; Department of Surgery, Mayo Clinic, Rochester, MN, USA.
  • White BA; Carl R. Woese Institute for Genomic Biology, Department of Animal Sciences, Division of Nutritional Sciences, University of Illinois, Champaign-Urbana, IL, USA.
  • Diener C; Human Systems Biology Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico.
  • Resendis-Antonio O; Human Systems Biology Laboratory, National Institute of Genomic Medicine, Mexico City, Mexico; Coordinación de la Investigación Científica, Red de Apoyo a la Investigación, UNAM, Mexico City, Mexico.
  • Gransee J; Mayo Clinic Metabolomics Core Laboratory, Mayo Clinic, Rochester, MN, USA.
  • Dutta T; Mayo Clinic Metabolomics Core Laboratory, Mayo Clinic, Rochester, MN, USA.
  • Petterson XM; Mayo Clinic Metabolomics Core Laboratory, Mayo Clinic, Rochester, MN, USA.
  • Boardman L; Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA.
  • Larson D; Department of Surgery, Mayo Clinic, Rochester, MN, USA.
  • Nelson H; Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA; Department of Surgery, Mayo Clinic, Rochester, MN, USA.
  • Chia N; Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Rochester, MN, USA; Department of Surgery, Mayo Clinic, Rochester, MN, USA. Electronic address: chia.nicholas@mayo.edu.
Methods ; 149: 59-68, 2018 10 01.
Article em En | MEDLINE | ID: mdl-29704665
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Metabolômica / Microbiota / Sulfeto de Hidrogênio / Mucosa Intestinal / Modelos Biológicos Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Colorretais / Metabolômica / Microbiota / Sulfeto de Hidrogênio / Mucosa Intestinal / Modelos Biológicos Idioma: En Ano de publicação: 2018 Tipo de documento: Article