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Associations between host gene expression, the mucosal microbiome, and clinical outcome in the pelvic pouch of patients with inflammatory bowel disease.

Morgan XC, Kabakchiev B, Waldron L, Tyler AD, Tickle TL, Milgrom R, Stempak JM, Gevers D, Xavier RJ, Silverberg MS, Huttenhower C - Genome Biol. (2015)

Bottom Line: To achieve power for a genome-wide microbiome-transcriptome association study, we use principal component analysis for transcript and clade reduction, and identify significant co-variation between clades and transcripts.Transcript-microbe associations are surprisingly modest, but the most strongly microbially-associated host transcript pattern is enriched for complement cascade genes and for the interleukin-12 pathway.Understanding these effects is essential for basic biological insights as well as for well-designed and adequately-powered studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Harvard T. H. Chan School of Public Health, 655 Huntington Ave, Boston, MA, 02115, USA. xmorgan@hsph.harvard.edu.

ABSTRACT

Background: Pouchitis is common after ileal pouch-anal anastomosis (IPAA) surgery for ulcerative colitis (UC). Similar to inflammatory bowel disease (IBD), both host genetics and the microbiota are implicated in its pathogenesis. We use the IPAA model of IBD to associate mucosal host gene expression with mucosal microbiomes and clinical outcomes. We analyze host transcriptomic data and 16S rRNA gene sequencing data from paired biopsies from IPAA patients with UC and familial adenomatous polyposis. To achieve power for a genome-wide microbiome-transcriptome association study, we use principal component analysis for transcript and clade reduction, and identify significant co-variation between clades and transcripts.

Results: Host transcripts co-vary primarily with biopsy location and inflammation, while microbes co-vary primarily with antibiotic use. Transcript-microbe associations are surprisingly modest, but the most strongly microbially-associated host transcript pattern is enriched for complement cascade genes and for the interleukin-12 pathway. Activation of these host processes is inversely correlated with Sutterella, Akkermansia, Bifidobacteria, and Roseburia abundance, and positively correlated with Escherichia abundance.

Conclusions: This study quantifies the effects of inflammation, antibiotic use, and biopsy location upon the microbiome and host transcriptome during pouchitis. Understanding these effects is essential for basic biological insights as well as for well-designed and adequately-powered studies. Additionally, our study provides a method for profiling host-microbe interactions with appropriate statistical power using high-throughput sequencing, and suggests that cross-sectional changes in gut epithelial transcription are not a major component of the host-microbiome regulatory interface during pouchitis.

No MeSH data available.


Related in: MedlinePlus

Linear discriminant analysis for clinical outcome. Linear discriminant analysis was used to determine which genes and clades were most discriminant between clinical outcomes after controlling for antibiotic use. All samples with antibiotic use were removed prior to analysis, and an LDA fitting model with leave-one-out cross-validation was used. (A, B) The separation of clinical outcomes by LD1 and LD2. See also (Additional file 6).
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Fig5: Linear discriminant analysis for clinical outcome. Linear discriminant analysis was used to determine which genes and clades were most discriminant between clinical outcomes after controlling for antibiotic use. All samples with antibiotic use were removed prior to analysis, and an LDA fitting model with leave-one-out cross-validation was used. (A, B) The separation of clinical outcomes by LD1 and LD2. See also (Additional file 6).

Mentions: CDL and CP were best discriminated by this model, particularly with respect to FAP (Figure 5). However, accuracy was low upon cross-validation (mean AUC 0.57 across all outcomes and models, Additional file 6A), primarily due to the model’s lower discrimination of AP and NP outcomes. These represent the extremes of outcome phenotypes in several respects, particularly with respect to inflammation. While this is also true for antibiotic usage (highly prevalent in CDL and rare in FAP), this analysis specifically excluded all samples from antibiotic-treated patients, as these proved to be very well-discriminated using microbial profiles alone. Indeed, when antibiotic-treated samples were included, discrimination accuracy for the CDL (AUC 0.67), CP (AUC 0.88), and FAP (AUC 0.71) outcomes was much higher based solely on models of microbiome profiles (Additional file 6B). When we examined the separation ability of the LDs (Figure 5, Additional file 6C), they were most discriminant between FAP and CDL.Figure 5


Associations between host gene expression, the mucosal microbiome, and clinical outcome in the pelvic pouch of patients with inflammatory bowel disease.

Morgan XC, Kabakchiev B, Waldron L, Tyler AD, Tickle TL, Milgrom R, Stempak JM, Gevers D, Xavier RJ, Silverberg MS, Huttenhower C - Genome Biol. (2015)

Linear discriminant analysis for clinical outcome. Linear discriminant analysis was used to determine which genes and clades were most discriminant between clinical outcomes after controlling for antibiotic use. All samples with antibiotic use were removed prior to analysis, and an LDA fitting model with leave-one-out cross-validation was used. (A, B) The separation of clinical outcomes by LD1 and LD2. See also (Additional file 6).
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4414286&req=5

Fig5: Linear discriminant analysis for clinical outcome. Linear discriminant analysis was used to determine which genes and clades were most discriminant between clinical outcomes after controlling for antibiotic use. All samples with antibiotic use were removed prior to analysis, and an LDA fitting model with leave-one-out cross-validation was used. (A, B) The separation of clinical outcomes by LD1 and LD2. See also (Additional file 6).
Mentions: CDL and CP were best discriminated by this model, particularly with respect to FAP (Figure 5). However, accuracy was low upon cross-validation (mean AUC 0.57 across all outcomes and models, Additional file 6A), primarily due to the model’s lower discrimination of AP and NP outcomes. These represent the extremes of outcome phenotypes in several respects, particularly with respect to inflammation. While this is also true for antibiotic usage (highly prevalent in CDL and rare in FAP), this analysis specifically excluded all samples from antibiotic-treated patients, as these proved to be very well-discriminated using microbial profiles alone. Indeed, when antibiotic-treated samples were included, discrimination accuracy for the CDL (AUC 0.67), CP (AUC 0.88), and FAP (AUC 0.71) outcomes was much higher based solely on models of microbiome profiles (Additional file 6B). When we examined the separation ability of the LDs (Figure 5, Additional file 6C), they were most discriminant between FAP and CDL.Figure 5

Bottom Line: To achieve power for a genome-wide microbiome-transcriptome association study, we use principal component analysis for transcript and clade reduction, and identify significant co-variation between clades and transcripts.Transcript-microbe associations are surprisingly modest, but the most strongly microbially-associated host transcript pattern is enriched for complement cascade genes and for the interleukin-12 pathway.Understanding these effects is essential for basic biological insights as well as for well-designed and adequately-powered studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Harvard T. H. Chan School of Public Health, 655 Huntington Ave, Boston, MA, 02115, USA. xmorgan@hsph.harvard.edu.

ABSTRACT

Background: Pouchitis is common after ileal pouch-anal anastomosis (IPAA) surgery for ulcerative colitis (UC). Similar to inflammatory bowel disease (IBD), both host genetics and the microbiota are implicated in its pathogenesis. We use the IPAA model of IBD to associate mucosal host gene expression with mucosal microbiomes and clinical outcomes. We analyze host transcriptomic data and 16S rRNA gene sequencing data from paired biopsies from IPAA patients with UC and familial adenomatous polyposis. To achieve power for a genome-wide microbiome-transcriptome association study, we use principal component analysis for transcript and clade reduction, and identify significant co-variation between clades and transcripts.

Results: Host transcripts co-vary primarily with biopsy location and inflammation, while microbes co-vary primarily with antibiotic use. Transcript-microbe associations are surprisingly modest, but the most strongly microbially-associated host transcript pattern is enriched for complement cascade genes and for the interleukin-12 pathway. Activation of these host processes is inversely correlated with Sutterella, Akkermansia, Bifidobacteria, and Roseburia abundance, and positively correlated with Escherichia abundance.

Conclusions: This study quantifies the effects of inflammation, antibiotic use, and biopsy location upon the microbiome and host transcriptome during pouchitis. Understanding these effects is essential for basic biological insights as well as for well-designed and adequately-powered studies. Additionally, our study provides a method for profiling host-microbe interactions with appropriate statistical power using high-throughput sequencing, and suggests that cross-sectional changes in gut epithelial transcription are not a major component of the host-microbiome regulatory interface during pouchitis.

No MeSH data available.


Related in: MedlinePlus