Microbiota and metabolite-based prediction tool for colonic polyposis with and without a known genetic driver

• Published in: Gut microbes Vol. 17; no. 1; p. 2474141
• Main Authors: Katona, Bryson W., Shukla, Ashutosh, Hu, Weiming, Nyul, Thomas, Dudzik, Christina, Arvanitis, Alex, Clay, Daniel, Dungan, Michaela, Weber, Marina, Tu, Vincent, Hao, Fuhua, Gan, Shuheng, Chau, Lillian, Buchner, Anna M., Falk, Gary W., Jaffe, David L., Ginsberg, Gregory, Palmer, Suzette N., Zhan, Xiaowei, Patterson, Andrew D., Bittinger, Kyle, Ni, Josephine
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 01.12.2025; Taylor & Francis Group
• Subjects: Adult; Aged; alanine; biomarker; Colonic Polyps - genetics; Colonic Polyps - metabolism; Colonic Polyps - microbiology; Colorectal Neoplasms - genetics; Colorectal Neoplasms - metabolism; Colorectal Neoplasms - microbiology; Feces - microbiology; Female; Gastrointestinal Microbiome; Humans; Intestinal Mucosa - microbiology; leucine; Male; Metabolome; microbiome; Middle Aged; Polyposis; Prospective Studies; Research Paper; RNA, Ribosomal, 16S - genetics; leucine; serrated polyposis syndrome; colonic adenomatous polyposis of unknown etiology; alanine; tyrosine; biomarker; Polyposis; microbiome; metabolome
• ISSN: 1949-0976; 1949-0984; 1949-0984
• DOI: 10.1080/19490976.2025.2474141

Despite extensive investigations into the microbiome and metabolome changes associated with colon polyps and colorectal cancer (CRC), the microbiome and metabolome profiles of individuals with colonic polyposis, including those with (Gene-pos) and without (Gene-neg) a known genetic driver, remain comparatively unexplored. Using colon biopsies, polyps, and stool from patients with Gene-pos adenomatous polyposis (  = 9), Gene-neg adenomatous polyposis (  = 18), and serrated polyposis syndrome (SPS,  = 11), we demonstrated through 16S rRNA sequencing that the mucosa-associated microbiota in individuals with colonic polyposis is representative of the microbiota associated with small polyps, and that both Gene-pos and SPS cohorts exhibit differential microbiota populations relative to Gene-neg polyposis cohorts. Furthermore, we used these differential microbiota taxa to perform linear discriminant analysis to differentiate Gene-neg subjects from Gene-pos and from SPS subjects with an accuracy of 89% and 93% respectively. Stool metabolites were quantified via H NMR, revealing an increase in alanine in SPS subjects relative to non-polyposis subjects, and Partial Least Squares Discriminant Analysis (PLS-DA) analysis indicated that the proportion of leucine to tyrosine in fecal samples may be predictive of SPS. Use of these microbial and metabolomic signatures may allow for better diagnostric and risk-stratification tools for colonic polyposis patients and their families as well as promote development of microbiome-targeted approaches for polyp prevention.