1. Volume 21, Issue 5, Pages 603-610.e3 (May 2017)
Gut Microbiome Function Predicts Response to Anti-integrin Biologic Therapy in Inflammatory Bowel Diseases 
Ashwin N. Ananthakrishnan, Chengwei Luo, Vijay Yajnik, Hamed Khalili, John J. Garber, Betsy W. Stevens, Thomas Cleland, Ramnik J. Xavier 
Cell Host & Microbe 
Volume 21, Issue 5, Pages e3 (May 2017)
DOI: /j.chom
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2. Cell Host & Microbe 2017 21, 603-610. e3DOI: (10. 1016/j. chom. 2017
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3. 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 and D) PCoA plots of baseline samples for CD (C) and UC (D) patients.
(E and F) The top 15 most abundant species in baseline samples for CD (E) and UC (F) 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).
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4. 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.
(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.
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5. Figure 3
Longitudinal Changes in Taxa and Pathways between Remission and Non-remission Groups
(A and 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).
(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.
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6. 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.
(B) Performance of the different models in classifying remission at week 14.
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