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P742 Genetic risk for Crohn’s disease has little effect on intestinal microbiota composition

J. Sabino*1, S. Vieira-Silva2, 3, I. Cleynen4, K. Nys1, K. Machiels1, M. Joossens2, 3, 5, G. Falony2, 3, J. Wang2, 3, V. Ballet1, M. Ferrante1, G. Van Assche1, S. Van Der Merwe6, J. Raes2, 3, S. Vermeire1

1KU Leuven, Department of Clinical and Experimental Medicine, Translational Research Centre for Gastrointestinal Disorders (TARGID), Leuven, Belgium, 2REGA institute, KU Leuven, Department of Microbiology and Immunology, Leuven, Belgium, 3Vlaams Instituut voor Biotechnologie (VIB), Centre for the Biology of Disease, Leuven, Belgium, 4KU Leuven, Human Genetics, Leuven, Belgium, 5VUB, Department of Microbiology, Brussels, Belgium, 6University Hospitals of Leuven, Department of Hepatology, Leuven, Belgium

Background

Crohn’s disease (CD) has a multifactorial pathogenesis with input from genetics, immunologic factors, and environmental triggers, including intestinal microbiota. To date, 174 risk loci have been identified for CD, with a remarkable predominance in pathways associated with host-microbiota interactions. Dysbiosis with reduced richness and diversity is a replicated finding in patients with CD. Whether this is related to the genetic imprinting of a patient or linked to inflammation is unknown. We investigated if these genetic risk variants associate to intestinal microbiota composition differences.

Methods

Faecal and blood samples were collected from 30 CD patients from whom Immunochip data were available. A genetic risk score (GRS) was calculated for each patient, taking into account the risk allele frequency and odds ratio of each single nucleotide polymorphism (SNP). The GRS associated with CD was calculated with 197 SNPs available on the Immunochip. Autophagy, ER stress, and NOD2 associated GRS were also generated. Patients were divided into 4 groups according to quartiles of the general GRS. Further, 16S rDNA paired-end sequencing targeting the V4 hypervariable region was performed using the Illumina MiSeq sequencer. Sequencing depth was downsized to 10 000 reads/sample. The Ribosomal Database Project classifier was used for taxonomic assignment. Statistical analyses were performed with R package phyloseq, using parametric and non-parametric tests, with multiple testing correction (FDR). Correlation between genera abundances and genetic risk scores was performed with Spearman correlation.

Results

The microbiota richness (alpha diversity) and overall microbiota composition were not significantly different amongst patients belonging to Q1 (n = 8) or Q4 (n = 8) of their GRS. At genus level, no differences were observed. When looking specifically to particular pathways, we observed that microbiota richness (ANOVA p-value 0.045) and community composition (Bray-Curtis dissimilarity adonis p-value 0.03) differed according to autophagy GRS. However, no significant taxon abundance differences were observed at phylum, genus, or operational taxonomic unit (OTU) level. Noteworthy, some differences at genus level (eg, Anaerostipes, Roseburia, and Megamonas) were observed between the different groups of autophagy GRS before multiple testing correction.

Conclusion

Host-genetics seem to influence the intestinal microbiota composition through pathways associated with host-microbiome interactions, particularly autophagy. However, this influence is small. Using a larger sample size differences at the genus level might be detected. Environmental factors seem to have a larger effect on gut microbiota than host-genetics do.