Supplementary material Gut Page 55 of 338 Gut 1 2 3 498 4 Supplementary Figure 1| Overview of the workflow for the multi-omics strategy 5 499 used in this study. Host phenome, serum and faecal metabolomes, and gut 6 500 microbiome of 235 ESRD patients and 81 healthy controls were characterised and 7 501 integrated into a multi-omics dataset. (A) Data types; (B) Differential filtering refers 8 9 502 to statistical significance computation based on differential abundance of features in 10 503 the patient and control groups; (C) Correlation and effect size analyses of multi-omics 11 504 dataset; (D) Predictive model building and clustering of metagenomics species with 12 Confidential: For Review Only 505 13 metabolites into co-abundance based networks; (E) Animal experiments testing the 14 506 effect of ESRD microbiota (left panel), ESRD-associated species (centre panel) and 15 507 probiotics (left panel). 16 508 17 18 509 Supplementary Figure 2| Differences in levels of serum metabolites between 19 510 ESRD patients and healthy controls. (A) Boxplot shows the serum metabolites that 20 511 differ significantly between ESRD patients and healthy controls. Top panel, ESRD 21 22 512 patient-enriched metabolites. Bottom panel, healthy control-enriched metabolites. The 23 513 uremic toxins that are enriched in ESRD patients are highlighted in red. Boxes 24 514 represent the interquartile range between the first and third quartiles and median 25 515 26 (internal line). Whiskers denote the lowest and highest values within 1.5 times the 27 516 range of the first and third quartiles, respectively; dots represent outliers beyond the 28 517 whiskers. (B) Changes in the composition of bile acids in two groups. Average 29 518 relative abundance (normalized from their raw abundance) of 16 bile acids of ESRD 30 31 519 patients and healthy controls are shown. Bottom panel: the ratios of unconjugated vs. 32 520 conjugated bile acids, unconjugated PBA/SBA and conjugated PBA are calculated for 33 521 each individual and compared between patients and controls. Abbreviations of the bile 34 35 522 acids are listed at Online Methods. (C) Correlation analysis of the quantified 36 523 concentrations of five targeted serum uremic toxins and non-targeted abundance in 60 37 524 randomly chosen serum samples (ESRD patients, n=40; and healthy controls, n=20). 38 525 39 40 526 Supplementary Figure 3| Schematic representation of the metabolic pathways of 41 527 the serum uremic toxins and bile acids associated with gut microbiota. (A) 42 528 Production of serum uremic toxins by gut microbiota via degrading diet-originated 43 44 529 aromatic amino acids, polyphenols and choline. (B) Metabolism of bile acids. The key 45 530 enzymes encoded by gut microbes are highlighted by orange boxes, and details are 46 531 shown in online supplementary table 14. 47 48 532 49 533 Supplementary Figure 4| Concentrations of serum uremic toxins and bile acids 50 534 are highly correlated with ESRD-associated clinical parameters. The heatmap 51 535 52 panels show the Spearman correlation coefficient between serum uremic toxins or bile 53 536 acids and ESRD-associated clinical parameters, for which significance levels in 54 537 correlation tests are denoted: +, q < 0.05; *, q < 0.01; **, q < 0.001. Top: all cohort; 55 538 bottom left and right: patients and controls, respectively. Information on metabolites 56 57 539 included in clusters S10, S19, S20, S24, S37, and S38 are provided in online 58 540 supplementary table 5. 59 541 60 https://mc.manuscriptcentral.com/gut Wang X, et al. Gut 2020;0:1–12. doi: 10.1136/gutjnl-2019-319766 Supplementary material Gut Gut Page 56 of 338 1 2 3 542 4 Supplementary Figure 5| Differences in faecal metabolite levels between ESRD 5 543 patients and healthy controls. (A) Boxplot shows the faecal metabolites that differ 6 544 significantly between ESRD patients and healthy controls. The uremic toxins that are 7 545 enriched in ESRD patients and the SCFAs that are prominently enriched in healthy 8 9 546 controls are highlighted in red. (B) Targeted metabolomic profiling of 16 faecal bile 10 547 acids of ESRD patients and healthy controls. Bottom panel: the abundance of total 11 548 PBAs, total SBAs, and the ratio of PBA/SBA are calculated for each individual and 12 Confidential: For Review Only 549 q q 13 compared between patients and controls. Significance levels: ‘*’, < 0.05, ‘+’, < 14 550 0.10. (C) Effect size of faecal metabolites that drive the variance of faecal 15 551 metabolome. 16 552 17 18 553 Supplementary Figure 6| Correlation analysis between serum uremic toxins and 19 554 their faecal precursors. Fitted lines and 95% confidence intervals are shown. 20 555 21 22 556 Supplementary Figure 7| Validation of the ESRD-associated serum (A) and 23 557 faecal metabolites (B) in an independent cohort. Boxplot shows the comparison of 24 558 serum uremic toxins (left panel) and faecal uremic toxin precursors and SCFAs (right 25 559 26 panel). Red boxes, ESRD patients (n = 12); blue boxes, healthy controls (n = 12). 27 560 Significance levels: ‘***’, q < 0.001, ‘**’, q < 0.01, ‘*’, q < 0.05, ‘+’, q < 0.10. 28 561 29 562 30 Supplementary Figure 8| Alteration of microbial taxonomic composition and 31 563 functions in ESRD patients. (A) Shannon’s diversity index of ESRD patients and 32 564 healthy controls. (B-C) Distance-based redundancy analysis (dbRDA) reveals a 33 565 significant difference of gut microbial taxonomic composition (B) and functional 34 35 566 profiles (C) in ESRD patients (n = 223) and healthy controls (n = 69). KEGG 36 567 orthologue (KO)-based and MGS-based Bray-Curtis dissimilarity was used to assess 37 568 the functional and taxonomic difference, respectively. The display is based on sample 38 569 39 scores on the primary constrained axis (CAP1) and primary multidimensional scaling 40 570 (MDS1), see Online Methods for details. 41 571 42 572 43 Supplementary Figure 9| Differences in bacterial species between ESRD patients 44 573 and healthy controls. (A) List of species that were significantly enriched in ESRD 45 574 patients or healthy controls. (B-C) Stratification of patients and healthy controls. The 46 575 20 most discriminant species enriched in ESRD patients (B) or healthy controls (C). 47 48 576 The receiver operating characteristic (ROC) curves for classification of ESRD 49 577 patients and healthy controls are shown; values of the area under the ROC curve 50 578 (AUC) are listed in the right panels. (D-F) Distribution of MGS encoding the key 51 579 52 acetate (D), propionate (E) and butyrate (F) synthesis genes in ESRD patients and 53 580 healthy controls. MGS were grouped in genera. Red colour denotes statistically 54 581 significant abundance difference (q < 0.05). 55 582 56 57 583 Supplementary Figure 10| Differences in microbial functions between ESRD 58 584 patients and healthy controls (A-C) Alterations in microbial pathways and 59 60 https://mc.manuscriptcentral.com/gut Wang X, et al. Gut 2020;0:1–12. doi: 10.1136/gutjnl-2019-319766 Supplementary material Gut Page 57 of 338 Gut 1 2 3 585 functional modules. ( Alterations in gut functional modules involved in amino acid 4 D) 5 586 degradation and bile acid metabolism. Red, ESRD-enriched; cyan, control-enriched. 6 587 7 588 Supplementary Figure 11| The correlation networks in ESRD patients and 8 9 589 healthy controls. (A-C) ESRD-specific (A), control-specific (B) and shared (C) 10 590 correlation networks of the gut MGS, serum and faecal metabolomes. Vertices 11 591 indicate variables and lines indicate significant Spearman’s correlations (|ρ| > 0.35 12 Confidential: For Review Only 592 q 13 and < 0.01). The serum and faecal variables were clustered to simplify the networks. 14 593 (D-E) Number of correlations between serum metabolite clusters and the gut 15 594 microbiota (D) or faecal metabolite clusters (E). The serum metabolite clusters of 16 595 uremic toxins and bile acids are highlighted in red. ( ) The clusters of serum uremic 17 F 18 596 toxins and bile acids correlated to MGSs (listed on the right; unclassified MGS are 19 597 omitted) and faecal metabolites. (G) Conservation of MGS-uremic toxins/SBAs 20 598 correlations in the ESRD and control networks. Correlations that were significant 21 22 599 (|ρ| > 0.35 and q < 0.01) in at least one network (ESRD or control) are shown. ‘**’, P 23 600 < 0.01; ‘***’, P < 0.001. 24 601 25 602 26 Supplementary Figure 12| The effect size of gut microbiota on the host serum 27 603 metabolome in different studies. The approach described in the Methods section 28 604 was used for all studies. 29 605 30 31 606 Supplementary Figure 13| Number of medications used for ESRD patients. 32 607 33 608 Supplementary Figure 14| Distribution of the key synthetases involved in the 34 35 609 biosynthesis of uremic toxins and SBAs. (A) Relative abundance of key synthetase- 36 610 encoding genes in ESRD patients and healthy controls (left panels) and in MGS- 37 611 assigned genes (right panels). The number of synthetase-encoding genes in each MGS 38 612 39 is shown in brackets. Boxes represent the interquartile range between the first and 40 613 third quartiles and median (internal line). Whiskers denote the lowest and highest 41 614 values within 1.5 times the range of the first and third quartiles, respectively, and 42 615 circles represent outliers beyond the whiskers. ( ) Correlations between the 43 B 44 616 abundance of key synthetase-encoding genes and faecal concentrations of 45 617 corresponding metabolites. Best-fit lines and 95% confidence intervals (CI) are 46 618 indicated.