Gut Microbiome Function Predicts Response to Anti-integrin Biologic Therapy in Inflammatory Bowel Diseases

Abstract

The gut microbiome plays a central role in inflammatory bowel diseases (IBDs) pathogenesis and propagation. To determine whether the gut microbiome may predict responses to IBD therapy, we conducted a prospective study with Crohn's disease (CD) or ulcerative colitis (UC) patients initiating anti-integrin therapy (vedolizumab). Disease activity and stool metagenomes at baseline, and weeks 14, 30, and 54 after therapy initiation were assessed. Community α-diversity was significantly higher, and Roseburia inulinivorans and a Burkholderiales species were more abundant at baseline among CD patients achieving week 14 remission. Several significant associations were identified with microbial function; 13 pathways including branched chain amino acid synthesis were significantly enriched in baseline samples from CD patients achieving remission. A neural network algorithm, vedoNet, incorporating microbiome and clinical data, provided highest classifying power for clinical remission. We hypothesize that the trajectory of early microbiome changes may be a marker of response to IBD treatment.

Keywords: Microbiome; butyrate; remission; roseburia; treatment response; vedolizumab.

Figure 1. The differences of baseline stool samples between remission group and non-remission group

(a) The alpha-diversity measured in Fisher’s alpha in remission and non-remission groups, segregated by diagnosis; (b) the beta-diversity measured by Bray-Curtis dissimilarity in intra- and inter-group fashion in remission and non-remission groups among CD and UC patients; (c–d) PCoA plots of baseline samples for CD and UC patients; and (e–f) the top 15 most abundant species in baseline samples for CD and UC patients. (box marks the interquartile range (IQR), the whiskers mark the range between lower quartile-1.5 IQR and higher quartile+1.5 IQR, and dots mark the outliers; *, q<0.1; **, q<0.01; ***, q<0.001; ns, not significant).

Figure 2. The significantly differentiated taxa and pathways between remission and non-remission groups in baseline samples

(a) Two taxa, Burkholderiales and Roseburia inulinivorans, were significantly more abundant in CD remission baseline samples; and (b) pathways that were significantly differentiated between remission and non-remission groups in baseline samples for CD (left) and UC (right) patients (q<0.1). (Pathway codes: A, super-pathway of arginine and polyamine biosynthesis; B, super-pathway of branched amino acid biosynthesis; C, Calvin-Benson-Bassham cycle; D, L-citrulline biosynthesis; E, dTDP-L-rhamnose biosynthesis I; F, super-pathway of N-acetyleglucosamine, N-acetylmannosamin and N-acetylneuraminate degradation; G, super-pathway of β-D-glucuronide and D-glucuronate degradation; H, super-pathway of hexitol degradation; I, L-isoleucine biosynthesis I; J, super-pathway of polyamine biosynthesis I; K, L-histidine degradation III; L, GDP-mannose biosynthesis; M, acetyl-CoA fermentation to butanoate II; N, colonic acid building blocks biosynthesis; O, lipid IVA biosysnthesis; P, N10-formyl-tetrahydrofolate biosysnthesis; Q, pentose phosphate pathway; R, pyruvate fermentation to acetate and lactate II.).

Figure 3. Longitudinal changes in taxa and pathways between remission and non-remission groups

(a–b) Log2 fold change (log2FC) in CD (a) and UC (b) patients’ microbiome pathways that represented significant change at week 14 follow-up in comparison with baseline samples, divided into remission and non-remission groups (FDR<0.1); (c) log2FC of species that represented significant change at week 14 follow-up in comparison with baseline sample (left panel, CD; right panel, UC), divided into remission and non-remission groups (FDR<0.1); and (d) the persistency index, P, for subjects with a later follow-up (wk30 or wk54) available. Horizontal bars indicate the t-test performed on respect group pair and the significance level (p<0.05; **, p <0.01; ***, p <0.001; ns, not significant). (Pathway codes: A, super-pathway of arginine and polyamine biosynthesis; B, super-pathway of branched amino acid biosynthesis; C, Calvin-Benson-Bassham cycle; D, L-citrulline biosynthesis; E, dTDP-L-rhamnose biosynthesis I; F, super-pathway of N-acetyleglucosamine, N-acetylmannosamin and N-acetylneuraminate degradation; G, super-pathway of β-D-glucuronide and D-glucuronate degradation; H, super-pathway of hexitol degradation; I, L-isoleucine biosynthesis I; J, super-pathway of polyamine biosynthesis I; K, L-histidine degradation III; L, GDP-mannose biosynthesis; M, acetyl-CoA fermentation to butanoate II; N, colonic acid building blocks biosynthesis; O, lipid IVA biosysnthesis; P, N10-formyl-tetrahydrofolate biosysnthesis; Q, pentose phosphate pathway; R, pyruvate fermentation to acetate and lactate II.).

Figure 4. The architecture, training, and performance of vedoNet

(a) The vedoNet and associated other model variates (vedoNet.tx, vedoNet.hybrid, etc) are based on a neural network structure with an input layer, a few hidden layers with softmax dropout and rectified linear unit, and a binary output layer to classify if input data will support treatment outcome as remission or non-remission. The input data is a vector with two parts: the clinical metadata, and the microbiome profile which varies for different models (pathways, taxa, or a combination of both). The training deployed a 5-fold cross validation scheme, which resampled the subjects without replacement for test set and train set.