P787 The microbiota and it's role in anti-TNF therapy non-response

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Abstract

Background: The human microbiome has an important role in the pathogensis of inflammatory bowel disease. Dysbiosis is not static and requires evaluation with a longitudinal approach.

Methods: CD patients commencing anti-TNF therapy had 3-monthly visits for 12 months with collection of biofluids (urine, faeces and serum) and disease assessment with biochemistry and faecal calprotectin (FC) or mucosal healing. A response index combining biochemistry (fall in FC or a decrease in CRP) and mucosal healing was used to define therapeutic response in the presence adequate drug level.

Collection of 168 faecal samples from 68 anti-TNF naive CD patients (luminal phenotype undergoing anti-TNF therapy without surgical resections) and 20 healthy controls (HC). Liquid-Chromotography Mass Spectroscopy (LC-MS) with lipid, bile acid and HILIC profiling of faecal metabolites was undertaken. 16SrRNA extraction using powerlyzerkit®, sequencing with MiseQ illumina® and processing using Mothur was performed. 16S changes were then compared to response index to anti-TNF therapy.

Results: There were 18 non-responders and 9 responders to anti-TNF therapy according to biochemical and mucosal healing parameters (response index). Comparison between HC and CD demonstrated that there were 21 OTU (operational taxonomic units) which were different including eschericia, streptococcus and bacteroides. Faecalibacterium was lower in the CD cohort while bacteroides was increased prior to anti-TNF therapy.

With regard to response to anti-TNF therapy, there was no significant change in species richness or beta diversity over time.

Comparing responders and non-responders to anti-TNF therapy, clostridiales was lower while lactobacilles was higher in responders to anti-TNF therapy after bonferroni correction (Fig. 1).

Bifidobacterium is elevated in non-responders while faecalibacterium and ruminococcus increased over time responders.

Faecalibacterium and bacteroides were lower in responders while Enterobacteriales and Lactobacilliales are higher in responders compared to HC.

Faecal metabonomic analysis demonstrated models which could separate responders from non responders with HILIC (R2X 0.42, Q2Y 0.32, p=0.001), lipid (R2X 0.28, Q2Y 0.70, p=6.78×10–4) and BA (R2X 0.27, Q2Y 0.26, p=0.024). Bile acids observed are known to be products of gut bacterial oxidation.

Conclusions: Analysis of microbiome and metabonome reveals important pathways of anti-TNF response and species which might be responsible for response to anti-therapy with a relative stability of the microbiome despite treatment response.

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