The proposed project will explore the hypothesis that chronic exposure to microbially-produced DNA damaging toxins such as hydrogen sulfide lead to an increased risk of colorectal cancer. According to this hypothesis, sulfate-reducing bacteria (SRBs) in colon can therefore lead to colon cancer unless their ability to generate hydrogen sulfide is attenuated by a competing metabolism such as methanogenesis. To test this, we will combine metabolic, regulatory, and evolutionary modeling with high throughput genomic technologies to explore the relationship between SRBs, methanogens, and the gastrointestinal microbial community in colorectal cancer and normal colonoscopy patients. We propose to conduct metagenome sequencing and assembly to study the possible interactions between the microbiome and hydrogen sulfide production based on the metabolic and regulatory networks of both microbes and tumors. If successful, we will have generated models that are capable of predicting the levels of various metabolite byproducts including toxic DNA damaging agents that impact the incidence of CRC and quantified the relationship between DNA damage and multiple subtypes of cancer.

Public Health Relevance

Colorectal cancer is common (142,570 new cancers annually) and lethal (51,370 cancer deaths each year), and has plausible connections to several microbial agents. If our hypotheses are proven correct, we can identify microbiomes that cause cancer and find ways to quantify that risk by using a combination of modern computational and sequencing techniques and attenuate that risk my manipulating the gut microbiome using antibiotics, probiotics, or prebiotics. This would radically change the way colon cancer is treated

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA179243-02
Application #
8862435
Study Section
Special Emphasis Panel (ZRG1-DKUS-D (56))
Program Officer
Daschner, Phillip J
Project Start
2014-06-05
Project End
2019-05-31
Budget Start
2015-06-01
Budget End
2016-05-31
Support Year
2
Fiscal Year
2015
Total Cost
$391,338
Indirect Cost
$106,718
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905
Druliner, Brooke R; Wang, Panwen; Bae, Taejeong et al. (2018) Molecular characterization of colorectal adenomas with and without malignancy reveals distinguishing genome, transcriptome and methylome alterations. Sci Rep 8:3161
Hale, Vanessa L; Jeraldo, Patricio; Mundy, Michael et al. (2018) Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer. Methods 149:59-68
Multinu, Francesco; Harrington, Sean C; Chen, Jun et al. (2018) Systematic Bias Introduced by Genomic DNA Template Dilution in 16S rRNA Gene-Targeted Microbiota Profiling in Human Stool Homogenates. mSphere 3:
Kim, Minsoo; Druliner, Brooke R; Vasmatzis, Nikolaos et al. (2018) Inferring modes of evolution from colorectal cancer with residual polyp of origin. Oncotarget 9:6780-6792
Chen, Xianfeng; Johnson, Stephen; Jeraldo, Patricio et al. (2018) Hybrid-denovo: a de novo OTU-picking pipeline integrating single-end and paired-end 16S sequence tags. Gigascience 7:1-7
Sinha, Rashmi; Abu-Ali, Galeb; Vogtmann, Emily et al. (2017) Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nat Biotechnol 35:1077-1086
Mundy, Michael; Mendes-Soares, Helena; Chia, Nicholas (2017) Mackinac: a bridge between ModelSEED and COBRApy to generate and analyze genome-scale metabolic models. Bioinformatics 33:2416-2418
Wolf, P G; Parthasarathy, G; Chen, J et al. (2017) Assessing the colonic microbiome, hydrogenogenic and hydrogenotrophic genes, transit and breath methane in constipation. Neurogastroenterol Motil 29:1-9
Jennings, Matthew E; Chia, Nicholas; Boardman, Lisa A et al. (2017) Draft Genome Sequence of Methanobrevibacter smithii Isolate WWM1085, Obtained from a Human Stool Sample. Genome Announc 5:
Vogtmann, Emily; Chen, Jun; Kibriya, Muhammad G et al. (2017) Comparison of Fecal Collection Methods for Microbiota Studies in Bangladesh. Appl Environ Microbiol 83:

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