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

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-DKUS-D (56))
Program Officer
Daschner, Phillip J
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Mayo Clinic, Rochester
United States
Zip Code
Loftfield, Erikka; Vogtmann, Emily; Sampson, Joshua N et al. (2016) Comparison of Collection Methods for Fecal Samples for Discovery Metabolomics in Epidemiologic Studies. Cancer Epidemiol Biomarkers Prev 25:1483-1490
Sung, Jaeyun; Hale, Vanessa; Merkel, Annette C et al. (2016) Metabolic modeling with Big Data and the gut microbiome. Appl Transl Genom 10:10-5
Mendes-Soares, Helena; Chia, Nicholas (2016) Community metabolic modeling approaches to understanding the gut microbiome: Bridging biochemistry and ecology. Free Radic Biol Med :
Jeraldo, Patricio; Hernandez, Alvaro; Nielsen, Henrik B et al. (2016) Capturing One of the Human Gut Microbiome's Most Wanted: Reconstructing the Genome of a Novel Butyrate-Producing, Clostridial Scavenger from Metagenomic Sequence Data. Front Microbiol 7:783
Sinha, Rashmi; Chen, Jun; Amir, Amnon et al. (2016) Collecting Fecal Samples for Microbiome Analyses in Epidemiology Studies. Cancer Epidemiol Biomarkers Prev 25:407-16
Sinha, Rashmi; Vogtmann, Emily; Chen, Jun et al. (2016) Fecal Microbiome in Epidemiologic Studies-Response. Cancer Epidemiol Biomarkers Prev 25:870-1
Mendes-Soares, Helena; Mundy, Michael; Soares, Luis Mendes et al. (2016) MMinte: an application for predicting metabolic interactions among the microbial species in a community. BMC Bioinformatics 17:343
Hale, Vanessa L; Chen, Jun; Johnson, Stephen et al. (2016) Shifts in the fecal microbiota associated with adenomatous polyps. Cancer Epidemiol Biomarkers Prev :
Chen, Jun; Toyomasu, Yoshitaka; Hayashi, Yujiro et al. (2016) Altered gut microbiota in female mice with persistent low body weights following removal of post-weaning chronic dietary restriction. Genome Med 8:103
Chen, Jun; Ryu, Euijung; Hathcock, Matthew et al. (2016) Impact of demographics on human gut microbial diversity in a US Midwest population. PeerJ 4:e1514

Showing the most recent 10 out of 16 publications