Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Apr 8;27(4):585-600.e4.
doi: 10.1016/j.chom.2020.03.005. Epub 2020 Apr 1.

Structure of the Mucosal and Stool Microbiome in Lynch Syndrome

Yan Yan  1 , David A Drew  2 , Arnold Markowitz  3 , Jason Lloyd-Price  1 , Galeb Abu-Ali  1 , Long H Nguyen  4 , Christina Tran  3 , Daniel C Chung  5 , Katherine K Gilpin  2 , Dana Meixell  5 , Melanie Parziale  5 , Madeline Schuck  5 , Zalak Patel  3 , James M Richter  5 , Peter B Kelsey  5 , Wendy S Garrett  6 , Andrew T Chan  7 , Zsofia K Stadler  8 , Curtis Huttenhower  9
Affiliations

Structure of the Mucosal and Stool Microbiome in Lynch Syndrome

Yan Yan et al. Cell Host Microbe. .

Abstract

The gut microbiota has been associated with colorectal cancer (CRC), but causal alterations preceding CRC have not been elucidated. To prospectively assess microbiome changes prior to colorectal neoplasia, we investigated samples from 100 Lynch syndrome patients using 16S rRNA gene sequencing of colon biopsies, coupled with metagenomic and metatranscriptomic sequencing of feces. Colectomy and CRC history represented the largest effects on microbiome profiles. A subset of Clostridiaceae were depleted in stool corresponding with baseline adenomas, while Desulfovibrio was enriched both in stool and in mucosal biopsies. A classifier leveraging stool metatranscriptomes resulted in modest power to predict interval development of preneoplastic colonic adenoma. Predictive transcripts corresponded with a shift in flagellin contributors and oxidative metabolic microenvironment, potentially factors in local CRC pathogenesis. This suggests that the effectiveness of prospective microbiome monitoring for adenomas may be limited but supports the potential causality of these consistent, early microbial changes in colonic neoplasia.

Keywords: Lynch syndrome; colorectal cancer; human microbiome; metagenomics; metatranscriptomics.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests C.H. is a member of the Seres Therapeutics scientific advisory board. A.T.C is a Stuart and Suzanne Steele MGH Research Scholar.

Figures

Figure 1:
Figure 1:. Metagenomics and metatranscriptomics of the stool and colonic biopsy microbiomes in Lynch syndrome.
(A) 100 participants with Lynch syndrome provided 87 stool specimens and 187 matched colon biopsies at baseline, coupled with one-year clinical follow-up. During colonoscopy, study biopsies were taken from the right (ascending), and the left (sigmoid) colon, except where not possible due to prior colectomy. The former samples were metagenomically and metatranscriptomically shotgun sequenced to yield taxonomic and functional profiles; the latter were taxonomically profiled using 16S rRNA gene sequencing. (B) Features of the microbiome were associated with current and interval clinical outcomes using whole-community omnibus tests and feature-wise linear modeling, in addition to discriminative modeling using random forests. Metatranscriptomically targeted analyses were used to differentiate progression-linked microbiome functional potential from expressed molecular activities.
Figure 2:
Figure 2:. related to Table S4, Within- and between-subject structure and function of the Lynch syndrome gut microbiome.
Principal coordinate analysis (PCoA) of (A) biopsy genera taxonomic profiles, (B) stool metagenomic species, t-SNE embeddings based on Bray-Curtis dissimilarity matrices from clade-specific genes of (C) stool metagenomic functional profiles (KOs), and (D) stool metatranscriptome functions (all Bray-Curtis dissimilarity). Combined adenomas included baseline adenomas and 1–2 year interval adenomas. Color indicates distribution of alpha-diversity (Gini-Simpson index) for the species-specific metagenomic and metatranscriptomic contributions to each KOs in (C) and (D). Functional features in t-SNE were determined by a sparse selection of functional features with greatest variation across individuals that were also distinct from other labeled features (specifically a minimum Euclidean distance between z-scored coordinates of labeled features of 80). In all cases, major clinical covariates including history of colectomy surgery and current or interval (i.e. combined) adenomas incidence covary with, but are not the major drivers of, microbiome diversity (see Fig. 4). (E) Bray-Curtis beta-diversity scores within- and between- subject (Mann-Whitney tests, p<0.001). As expected, subjects’ microbiomes are self-stable over time, with biogeographical and technical differences between 16S-based and metagenome-based mucosal and stool taxonomic profiles.
Figure 3:
Figure 3:. related to Table S2, Taxonomic and functional features of the gut microbiome with Lynch syndrome clinical indicators.
(A) Clinical phenotype annotations for all Lynch cohort samples, with (B) the most abundant (average) 10 genera in biopsies and (C) most abundant 10 species in stool. (D) Similarly, the 10 most abundant and orthogonal (see STAR Methods) metagenomic pathways, and correspondingly (E) the metatranscriptomic pathways meeting the same abundance criteria and at least 80% prevalence, as normalized by metagenomic copy number (see STAR Methods) among stool samples. The subjects provided biopsy but not stool samples, indicated in grey. Hierarchical clustering is based on Euclidean distance among biopsy taxonomic profiles.
Figure 4:
Figure 4:. related to Table S5, Associations of gut microbiome taxonomic and functional features with Lynch clinical indicators and across CRC cohorts.
(A) Variance of each microbiome measurement type (16S, metagenomic, and metatranscriptomic taxonomic and functional profiles) associated with individual Lynch clinical variables by 9999-iteration PERMANOVA based on Bray-Curtis dissimilarities. To reduce the impact of repeated measures from multiple biopsies from the same subject, we included only one biopsy from each subject, with the following priority: left colon, right colon, and others. Sample location applies only to biopsy samples; surgery type variances are calculated only within subjects with subtotal or segmental colectomies. Combined adenomas included baseline adenomas and 1–2 year interval adenomas. Stars indicate statistical significance (* q<0.05). (B) Correlation matrix of linear discriminant analysis (LDA) loadings based on genus-level abundances common between this study’s Lynch population and recent published CRC microbiome studies including adenoma (i.e. early) patients (Hale et al., 2018; Thomas et al., 2019) (Methods). LDA was applied to the resulting taxonomic profiles using a ternary outcome per study (control, adenoma, and CRC when available). Hierarchical clustering is based on Pearson correlation among LDA loading within the Lynch population or across other CRC studies. Stars indicate statistical significance (*p<0.05; **p<0.01; ***p<0.001). (C) Significant associations between individual taxonomic features and clinical covariates in biopsy and (D) stool profiles by Kruskal-Wallis tests; all associations meeting FDR corrected q<0.25 are shown. Taxonomic results were visualized using GraPhlAn (Asnicar et al., 2015). Analysis for biopsy samples was run on residuals after regressing the effects of collection site.
Figure 5:
Figure 5:. related to Table S6, Associations of microbiome functional potential and metatranscriptomic activity with Lynch clinical indicators.
Significant associations between individual functional features (gene families summarized as KEGG Orthogroups (Kanehisa et al., 2014)) as profiled from (A) metagenomes and (B) metatranscriptomes. All significant metagenomic associations are shown; selected metatranscriptomic associations are shown comprising cell motility, drug resistance, metabolism of terpenoids and polyketides, and xenobiotics biodegradation (see Table S6 for complete results). P values are based on Spearman correlation for age and Bristol stool scale, and Mann-Whitney tests for surgery. Stars indicate FDR q<0.25.
Figure 6:
Figure 6:. Individual microbial metabolic functions and overall diversity of microbial functional contributions are depleted after colectomy.
(A) 165 total microbial gene families were metatranscriptomically significantly depleted or, less often, enriched in surgery patients (Kruskal-Wallis, FDR corrected q<0.25, Table S6); the abundances for the subset selected in Fig. 5B are shown here. (B) Contributional alpha-diversity of surgery-linked microbiome transcripts is generally depleted after surgery (Kruskal-Wallis tests, stars indicate significant level at FDR q<0.05). This holds true even among gene families more highly expressed with surgery. (C) The composition of contributing species represented in metagenomes and metatranscriptomes of flagellin protein fliC for subjects without and (D) with colectomy surgery. There is no significant change in metagenomic contribution between surgical treatment. The relative transcriptomic contribution of R. intestinalis and R. hominis were different in subjects with/without surgery (Kruskal-Wallis tests, FDR corrected, q<0.01).
Figure 7:
Figure 7:. related to Table S8, Gut microbial transcriptional activity is a weak predictor of one-year adenomas development at baseline.
Of random forest (RF) classifiers evaluated to predict current or 1–2 year interval adenomas from taxonomic or functional features of the microbiome (Fig. S6 and Fig. S7), interval adenomas were specifically best-predicted using metatranscriptional expression profiles (A), in contrast with (B) baseline adenomas predicted no better than chance (and with other prediction feature types; Fig. S6 and Fig. S7). While prediction accuracy is nowhere near high enough for direct clinical utility, it suggests (C-E) further microbial transcriptional mechanisms that may drive adenomas and eventually tumorigenesis. The 20 transcript families given the highest importance scores by the RF are shown, sorted by differential abundance that were signed by Gini index, along with their abundances and contributional alpha-diversities in subjects with and without interval adenomas development.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Abu-Ali GS, Mehta RS, Lloyd-Price J, Mallick H, Branck T, Ivey KL, Drew DA, DuLong C, Rimm E, Izard J, et al. (2018). Metatranscriptome of human faecal microbial communities in a cohort of adult men. Nature microbiology. 3(3), 356–366. Published online 2018/01/18 DOI: 10.1038/s41564-017-0084-4. - DOI - PMC - PubMed
    1. Abubucker S, Segata N, Goll J, Schubert AM, Izard J, Cantarel BL, Rodriguez-Mueller B, Zucker J, Thiagarajan M, Henrissat B, et al. (2012). Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS computational biology. 8(6), e1002358 Published online 2012/06/22 DOI: 10.1371/journal.pcbi.1002358. - DOI - PMC - PubMed
    1. Albenberg L, Esipova TV, Judge CP, Bittinger K, Chen J, Laughlin A, Grunberg S, Baldassano RN, Lewis JD, Li H, et al. (2014). Correlation between intraluminal oxygen gradient and radial partitioning of intestinal microbiota. Gastroenterology. 147(5), 1055–1063.e1058. Published online 2014/07/22 DOI: 10.1053/j.gastro.2014.07.020. - DOI - PMC - PubMed
    1. Andersen-Nissen E, Smith KD, Strobe KL, Barrett SL, Cookson BT, Logan SM, and Aderem A (2005). Evasion of Toll-like receptor 5 by flagellated bacteria. Proceedings of the National Academy of Sciences of the United States of America. 102(26), 9247–9252. Published online 2005/06/16 DOI: 10.1073/pnas.0502040102. - DOI - PMC - PubMed
    1. Antonic V, Stojadinovic A, Kester KE, Weina PJ, Brucher BL, Protic M, Avital I, and Izadjoo M (2013). Significance of infectious agents in colorectal cancer development. Journal of Cancer. 4(3), 227–240. Published online 2013/03/06 DOI: 10.7150/jca.5835. - DOI - PMC - PubMed

Publication types

MeSH terms

Substances

LinkOut - more resources