SUPPLEMENTARY FIGURE LEGENDS Figure S1 Study design. Figure was adapted from Paramsothy et al. 5 Blood and stool samples were collected for biochemical and molecular microbiological analyses at baseline, week 4 and week 8 of therapy (blinded and open label), as well as 8 weeks following conclusion of therapy. Mucosal samples were collected at baseline and at week8 conclusion of therapy (blinded and open label). Figure S2 Microbial signatures associated with therapy primary outcome. A: Cluster analysis of taxonomic profiles arising from shotgun sequencing. Raw data was analysed using MetaPhlan2, relative abundances square-root transformed and a Bray-Curtis resemblance matrix was generated. B: Box plot of number of OTUs arising from 16S rrna gene profiling of mucosal biopsy DNA extracts. Blinded and open label patients are combined but stratified according to the primary endpoint. Comparisons across groups did not reach significance. Tx0: Baseline sample (week 0) of FMT patients; Tx8: Week 8 sample of FMT patients; P8: Week 8 sample of open label patients on placebo; PTx8: Week 8 sample of open label patients on FMT; _Y: Patients positive outcomes according to the primary endpoint; _N: Patients with negative outcomes according to the primary endpoint. Figure S3 Microbial signatures associated with therapy primary outcome. A: Microbial taxa that discriminated positive and negative therapy outcomes in blinded patients on FMT. B: Microbial taxa that discriminated positive and negative therapy outcomes in open label patients on FMT. Results are from 16S rrna gene profiling of faecal samples. Differences 1
were calculated using LEfSE with outcome as a predictor. Only taxa that showed a Linear Discriminant Analysis (LDA) score >2 and P<0.05 were plotted. Tx8R: Week 8 FMT in blinded patients with positive outcome; Tx8: Week 8 FMT in blinded patients with negative outcome; PTx8R: Week 8 FMT in open label patients with positive outcome; PTx8: Week 8 FMT in open label patients with negative outcome. Figure S4 Microbial signatures associated with therapy primary outcome in the 16S amplicon sequencing data sets. A: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of faecal samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. B: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of faecal samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. C: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of mucosal biopsy samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. D: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of mucosal biopsy samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. All negative binomial models for taxa abundances were created using DESeq2. Black dots indicate significant results with unadjusted P-values. Tx0: Baseline sample (week 0) of FMT patients; Tx8: Week 8 sample of FMT patients; P8: Week 8 sample of open label patients on placebo; PTx8: Week 8 sample of open label patients on FMT; _Y: Patients positive outcomes according to the primary endpoint; _N: Patients with negative outcomes according to the primary endpoint. 2
Figure S5 Microbial signatures associated with endoscopic remission. A: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of faecal samples. B: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of faecal samples. C: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of mucosal biopsy samples. D: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of mucosal biopsy samples. E: Taxa that discriminated positive and negative therapy outcomes in shotgun metagenomic profiling of faecal samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. Blinded and open label were combined across all analysis. All negative binomial models for taxa abundances were created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P-values. Figure S6 Microbial signatures associated with endoscopic response. A: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of faecal samples. B: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of faecal samples. C: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of mucosal biopsy samples. D: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of mucosal biopsy samples. E: Taxa that discriminated positive and negative therapy outcomes in shotgun metagenomic profiling of faecal samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. Blinded and open label were combined across all analysis. All negative binomial models for taxa abundances were 3
created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P-values. Figure S7 Microbial signatures associated with clinical remission. A: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of faecal samples. B: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of faecal samples. C: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of mucosal biopsy samples. D: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of mucosal biopsy samples. E: Taxa that discriminated positive and negative therapy outcomes in shotgun metagenomic profiling of faecal samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. Blinded and open label were combined across all analysis. All negative binomial models for taxa abundances were created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P-values. Figure S8 Pathway counts of carbohydrate digestion and absorption for PICRUSt predictions from 16S rrna gene profiling of mucosal microbiome. Figure S9 Microbial functional changes associated with other therapeutic outcomes. A: KEGG pathways that discriminated positive and negative outcomes for endoscopic remission. B: MetaCyc pathways that discriminated positive and negative outcomes for endoscopic 4
remission. C: KEGG pathways that discriminated positive and negative outcomes for endoscopic response. D: MetaCyc pathways that discriminated positive and negative outcomes for endoscopic response. E: KEGG pathways that discriminated positive and negative outcomes for clinical remission. F: MetaCyc pathways that discriminated positive and negative outcomes for clinical remission. Pathway abundances from shotgun metagenomic profiling of faecal samples were calculated using HUMANn2 and modelled using negative binomial GLMs with outcome as a predictor. Blinded and open label were combined across all analysis. All negative binomial models for pathway abundances were created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P-values. Figure S10 Microbial signatures associated with therapy primary outcome when blinded and open label were stratified. A: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of faecal samples. B: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of faecal samples. C: OTUs that discriminated positive and negative therapy outcomes in 16S rrna gene profiling of mucosal biopsy samples. D: OTUs that discriminated positive and negative therapy outcomes in 16S rrna transcript profiling of mucosal biopsy samples. Relative abundances of OTUs were modelled using negative binomial GLMs with outcome as a predictor. All negative binomial models for taxa abundances were created using DESeq2. Black dots indicate significant results with unadjusted P-values. Tx8_Y: Week 8 FMT in blinded patients with positive outcome; Tx8_N: Week 8 FMT in blinded patients with negative outcome; PTx8_Y: Week 8 FMT in open label patients with positive outcome; PTx8_N: Week 8 FMT in open label patients with negative outcome. 5
Figure S11 Relative abundance of Prevotella copri in shotgun metagenomic profiling of faecal samples following stratification of patients according to primary outcome. Figure S12 Metabolites altered by FMT therapy. Metabolites found to be increased post-fmt as compared to baseline, with consistent fold-change observed across mean-pair and group-mean analyses. These five metabolites were selected as they were not altered by placebo (i.e. no change between baseline and post-placebo) and they were found to be different between post- FMT and post-placebo. For a complete list of metabolites altered by FMT, see Table S2. Metabolites with a p<0.05 and q<0.2 across the comparisons involving post-fmt were reported. Statistical test was performed using a paired t-test. q-value was calculated after FDR adjustment of p-value. Figure S13 Donor profiles associated with primary outcome. A: Shannon s diversity index of donor batches according to shotgun metagenomic sequencing and MetaPhlan2. Red: >50% positive outcomes; black: >50% negative outcomes. B: Non-metric multidimensional scaling plot of Bray-Curtis matrix of square-root transformed species relative abundances in donor batches. Data is from shotgun metagenomic sequencing and MetaPhlan2. Red: >50% positive outcomes; black: >50% negative outcomes. C: OTUs that discriminated donor batches with positive and negative outcomes in 16S rrna gene sequencing. Relative abundances of taxa were modelled using negative binomial GLMs with outcome as a predictor. D: OTUs that discriminated donor batches with positive and negative outcomes in 16S rrna transcript 6
sequencing. Relative abundances of taxa were modelled using negative binomial GLMs with outcome as a predictor. All negative binomial models for taxa abundances were created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P- values. E: Non-metric multidimensional scaling plot of Bray-Curtis matrix of transformed KEGG pathway abundances in donor batches. Data is from shotgun metagenomic sequencing and HUMANn2. Red: >50% positive outcomes; black: >50% negative outcomes. F: Nonmetric multidimensional scaling plot of Bray-Curtis matrix of transformed MetaCyc pathway abundances in donor batches. Data is from shotgun metagenomic sequencing and HUMANn2. Red: >50% positive outcomes; black: >50% negative outcomes. Figure S14 Donor profiles associated with endoscopic remission. A: Mosaic plot of donor batches classified according to number of positive endoscopic remissions. If a high number of the patients receiving a particular donor batch showed positive outcome, the donor batch was allocated to the DonorRemission = Yes group (Red), while all other donors were allocated to the DonorRemission = No group (Black). B: Shannon s diversity index of donor batches according to 16S rrna gene profiling. Red: >50% positive outcomes; black: >50% negative outcomes. C: Shannon s diversity index of donor batches according to 16S rrna transcript profiling. Red: >50% positive outcomes; black: >50% negative outcomes. D: Non-metric multidimensional scaling plot of Bray-Curtis matrix of square-root transformed OTU relative abundances in donor batches. Circle corresponds to 16S rrna gene profiling; Triangle corresponds to 16S rrna transcript profiling. Red: >50% positive outcomes; black: >50% negative outcomes. E: OTUs that discriminated donor batches with positive and negative outcomes in 16S rrna gene sequencing. F: OTUs that discriminated donor batches with positive and negative outcomes in 16S rrna transcript sequencing. G: Taxa that 7
discriminated donor batches with positive and negative outcomes in shotgun metagenomic profiling. Relative abundances of taxa were modelled using negative binomial GLMs with outcome as a predictor. H: KEGG pathways that discriminated donor batches with positive and negative outcomes in shotgun metagenomic profiling. I: MetaCyc pathways that discriminated donor batches with positive and negative outcomes in shotgun metagenomic profiling. Pathway abundances were modelled using negative binomial GLMs with outcome as a predictor. All negative binomial models for taxa and pathway abundances were created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P-values. Figure S15 Donor profiles associated with endoscopic response and clinical remission. A: Mosaic plot of donor batches classified according to number of positive endoscopic response outcomes. B: Mosaic plot of donor batches classified according to number of positive clinical remission outcomes. If more than 50% of the patients receiving a particular donor batch showed positive outcome, the donor batch was allocated to the DonorRemission = Yes group (Red), while all other donors were allocated to the DonorRemission = No group (Black). C: Shannon s diversity index of donor batches according to shotgun metagenomic sequencing and MetaPhlan2. Red: >50% positive endoscopic response outcomes; black: >50% negative endoscopic response outcomes. D: Shannon s diversity index of donor batches according to shotgun metagenomic sequencing and MetaPhlan2. Red: >50% positive clinical remission outcomes; black: >50% negative clinical remission outcomes. E: Taxa that discriminated donor batches with positive and negative clinical remission outcomes in shotgun metagenomic profiling. Relative abundances of taxa were modelled using negative binomial GLMs with outcome as a predictor. F: KEGG pathways that discriminated donor batches 8
with positive and negative clinical remission outcomes in shotgun metagenomic profiling. G: MetaCyc pathways that discriminated donor batches with positive and negative clinical remission outcomes in shotgun metagenomic profiling. Pathway abundances were modelled using negative binomial GLMs with outcome as a predictor. All negative binomial models for taxa and pathway abundances were created using DESeq2. Black dots in log fold-change graphs indicate significant results with unadjusted P-values. Figure S16 Changes in microbial diversity and composition with faecal microbiota transplantation. A: Diagnostic plot of mixed model for number of OTUs. B: Diagnostic plot of mixed model for Shannon s diversity index. Models: Diversity ~ Time * TreatmentGroup + random(patient.id). X-axis shows fitted values (expected values from model) while Y-axis shows the residuals (difference between observed and expected values). C: Changes in number of OTUs in open label patients across sample groups following data fitting into the model. D: Changes in Shannon s diversity index in open label patients across sample groups following data fitting into the model. E: Non-metric multidimensional scaling plot of Bray- Curtis matrix of square-root transformed OTU relative abundances. Blinded and open-label time points were combined. Samples were stratified according to type of therapy (P: placebo; Tx: FMT). F: Changes in z-score abundances of OTUs across type of treatment. OTUs correspond to those shown in heatmap in Figure 6E. Tx0: Baseline sample (week 0) of blinded FMT patients; Tx4: Week 4 sample of blinded FMT patients; Tx8: Week 8 sample of blinded FMT patients; PTx0: Baseline sample (week 0) of open label patients; P4: Week 4 sample of open label patients on placebo; P8: Week 8 sample of open label patients on placebo; PTx4: Week 4 sample of open label patients on 9
FMT; PTx8: Week 8 sample of open label patients on FMT; PTxF: Follow-up sample 8 weeks post-fmt of open label patients. 10