Microbiota and metabolite-based prediction tool for colonic polyposis with and without a known genetic driver Article Swipe
Bryson W. Katona
,
Ashutosh Shukla
,
Weiming Hu
,
Thomas Nyul
,
Christina Dudzik
,
Alex Arvanitis
,
Daniel C. Clay
,
Michaela Dungan
,
Marina Weber
,
Vincent Tu
,
Fuhua Hao
,
Shuheng Gan
,
Lillian Chau
,
Anna M. Buchner
,
Gary W. Falk
,
David L. Jaffe
,
Gregory G. Ginsberg
,
Suzette N. Palmer
,
Xiaowei Zhan
,
Andrew D. Patterson
,
Kyle Bittinger
,
Josephine Ni
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1080/19490976.2025.2474141
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/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 (N = 9), Gene-neg adenomatous polyposis (N = 18), and serrated polyposis syndrome (SPS, N = 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 1H 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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/19490976.2025.2474141
- https://www.tandfonline.com/doi/pdf/10.1080/19490976.2025.2474141?needAccess=true
- OA Status
- gold
- Cited By
- 3
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408386101
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408386101Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1080/19490976.2025.2474141Digital Object Identifier
- Title
-
Microbiota and metabolite-based prediction tool for colonic polyposis with and without a known genetic driverWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-03-11Full publication date if available
- Authors
-
Bryson W. Katona, Ashutosh Shukla, Weiming Hu, Thomas Nyul, Christina Dudzik, Alex Arvanitis, Daniel C. Clay, Michaela Dungan, Marina Weber, Vincent Tu, Fuhua Hao, Shuheng Gan, Lillian Chau, Anna M. Buchner, Gary W. Falk, David L. Jaffe, Gregory G. Ginsberg, Suzette N. Palmer, Xiaowei Zhan, Andrew D. Patterson, Kyle Bittinger, Josephine NiList of authors in order
- Landing page
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https://doi.org/10.1080/19490976.2025.2474141Publisher landing page
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https://www.tandfonline.com/doi/pdf/10.1080/19490976.2025.2474141?needAccess=trueDirect link to full text PDF
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
- OA URL
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https://www.tandfonline.com/doi/pdf/10.1080/19490976.2025.2474141?needAccess=trueDirect OA link when available
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Biology, Metabolite, Computational biology, Bioinformatics, Genetics, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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42Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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