Supplementary Figure S1 A: receiver operating characteristic (ROC) curve plotting
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1 Supplementary Figure S1 A: receiver operating characteristic (ROC) curve plotting false positive rate (FPR) and true positive rate (TPR); B: precision / recall curve plotting precision rate and recall rate. Data points are measured under different similarity cutoff values labeled aside the points (data used was retrieved from Supplementary Data 1). 1
2 Supplementary Figure S2 Comparison of the distribution of the human gut AR genes (inner cycle) and the microbiome gene set (outer cycle) at the bacterial phylum level. The ratios of genes (>1%) assigned to each phylum are shown in the pie charts. 2
3 Supplementary Figure S3 The distribution of 149 AR gene types in different populations. The dashed line indicates 50% of individuals. 3
4 Supplementary Figure S4 Comparison of the relative abundance of antibiotic resistance genes between Spanish healthy controls and IBD patients. See Figure 1 for description of the box and whisker plot. Error bars denote the lowest and highest values within 1.5 times the IQR from the first and third quartiles, respectively. Mann-Whitney test was used to test for statistical significance. Healthy: n=14; IBD: n=22. 4
5 Supplementary Figure S5 Heat map of the relative abundance of 150 COGs chosen randomly and hierarchical clustering. The figure is a representative result of 9 repeated random samplings. The sample identifiers (bars) for Chinese, Danish and Spanish individuals are colored in red, green and blue, respectively. The COGs (rows) and samples (columns) were clustered with the MultiExperiment Viewer (MeV version 4.6) using the Spearman rank correlation and complete linkage. The indicator on the top denotes the relationship between the relative abundance and color range 5
6 Supplementary Figure S6 Comparison of the hierarchical clustering tree distance between AR gene vs. COGs and COGs vs. COGs. See Figure 1 for description of the box and whisker plot. Error bars denote the lowest and highest values within 1.5 times the IQR from the first and third quartiles, respectively. Mann-Whitney test was used to test for statistical significance. See the Methods for the detailed calculation process. As shown in this figure, the tree distances of AR gene vs. COGs were significant different from that of COGs vs. COGs (Mann-Whitney test, P<0.0001), which indicates the clustering of samples generated by AR genes is different with that by other functional genes. 6
7 Supplementary Figure S7 The ratios of the abundance of gene types conferring resistance to tetracyclines (A), MLSs (B) and cephalosporins (C) in different populations. Data used were average abundances for each gene type among individuals (excluding outliers). China: n=37; Denmark: n=80; Spain: n=36. 7
8 Supplementary Figure S8 Heat map of the relative abundance of six Van operon structural genes. The sample identifiers for Chinese, Danish and Spanish individuals were colored in red, green and blue, respectively, as above. The structural genes for six Van operons, VanA (6 genes), VanB (7 genes), VanC (5 genes), VanD (6 genes), VanE (5 genes), and VanG (8 genes), are indicated on the left, and the sample identifiers are listed on the top. The indicator on the bottom denotes the relative abundance and color range. 8
9 Supplementary Table S1 Number of unique antibiotic resistance genes in different metagenomic datasets Metagenomes (MG-RAST Project No. of antibiotic No. of nr genes a ID) resistance genes Ratio ( ) Sargasso Sea Bacterioplankton Community (mgp47) Waseca County Farm Soil Metagenome (mgp8) Mediterranean Bathypelagic Metagenome (mgp3) Alvinella Pompejana Epibiont Metagenome (mgp4) Antarctica Aquatic Microbial Metagenome (mgp20) Rain Forest Soil Microbial Communities (mgp45) Lake Erie Bloom Metagenome (mgp715) Ocean Drilling Program (mgp470) Human gut b a nr, non-redundant; b A non-redundant human gut microbiome gene set gene generated from 162 European individuals and 38 Chinese individuals (this study). 9
10 Supplementary Table S2 Detailed information of sequencing depth and antibiotic resistance gene number in 162 samples from three different populations China Sample ID # of reads Data (Gb) # of resistance gene # of resistance gene type # of resistance gene type/gb NLF001 29,930, NLF002 26,116, NLF005 32,905, NLF006 26,877, NLF007 31,756, NLF009 28,356, NLF010 29,674, NLF011 30,069, NLF014 31,854, NLM001 25,784, NLM003 26,799, NLM005 28,415, NLM008 30,013, NLM009 29,723, NLM015 28,572, NLM016 29,429, NLM017 61,914, NLM023 61,573, NLM025 31,671, NOF001 25,022, NOF002 33,856, NOF004 24,785, NOF006 26,273, NOF007 33,654, NOF009 33,066, NOF011 27,191, NOF013 29,626, NOF014 40,524, NOM004 32,019, NOM007 31,091, NOM008 39,347, NOM012 30,035, NOM013 30,309, NOM018 44,379, NOM022 41,858,
11 NOM023 29,584, NOM026 35,779, NOM029 34,491, MEAN±SD 32,745,765±8,258, ± 0.62 Denmark Sample ID # of reads Data (Gb) ± 40.2 # of resistance gene 67.7±9.9 29±6 # of resistance gene type # of resistance gene type/gb MH ,239, MH ,077, MH ,136, MH ,829, MH ,704, MH ,437, MH ,719, MH ,411, MH ,011, MH ,218, MH ,208, MH ,338, MH ,257, MH ,468, MH ,671, MH ,178, MH ,557, MH ,081, MH ,229, MH ,964, MH ,454, MH ,415, MH ,598, MH ,097, MH ,948, MH ,812, MH ,190, MH ,652, MH ,148, MH ,460, MH ,822, MH ,543, MH ,636, MH ,306,
12 MH ,764, MH ,247, MH ,210, MH ,466, MH ,661, MH ,246, MH ,568, MH ,479, MH ,124, MH ,520, MH ,539, MH ,355, MH ,923, MH ,444, MH ,725, MH ,911, MH ,538, MH ,788, MH ,086, MH ,444, MH ,969, MH ,021, MH ,286, MH ,574, MH ,156, MH ,261, MH ,128, MH ,215, MH ,747, MH ,945, MH ,234, MH ,699, MH ,160, MH ,568, MH ,670, MH ,684, MH ,101, MH ,179, MH ,143, MH ,195, MH ,697, MH ,381,
13 MH ,038, MH ,011, MH ,220, MH ,988, MH ,130, MH ,074, MH ,047, MH ,083, MH ,495, MEAN±SD ±25,334, ± Spain Sample ID # of reads Data (Gb) 189.6± 44.0 # of resistance gene 54± ±6 # of resistance gene type # of resistance gene type/gb O2.UC-1 61,879, O2.UC-11 61,253, O2.UC-12 58,927, O2.UC-13 68,563, O2.UC-14 43,343, O2.UC-16 64,811, O2.UC-17 63,583, O2.UC-18 67,094, O2.UC-19 54,537, O2.UC-20 53,637, O2.UC-21 57,856, O2.UC-22 63,220, O2.UC-23 64,898, O2.UC-24 64,629, O2.UC-4 68,735, V1.CD-1 69,669, V1.CD-11 75,344, V1.CD-12 53,519, V1.CD-13 58,145, V1.CD-14 63,574, V1.CD-15 53,938, V1.CD-2 70,150, V1.CD-3 67,524, V1.CD-4 70,111, V1.CD-6 69,437, V1.CD-8 68,642, V1.CD-9 64,146, V1.UC-10 63,137,
14 V1.UC-13 58,381, V1.UC-14 68,566, V1.UC-15 68,192, V1.UC-17 72,222, V1.UC-18 67,064, V1.UC-19 68,295, V1.UC-21 65,652, V1.UC-6 59,270, V1.UC-7 51,911, V1.UC-8 61,781, V1.UC-9 62,758, MEAN±SD 63,292,492±6,537, ± ± ± ±2 14
15 Supplementary Table S3 Antibiotic resistance gene types presented in each sample of China, Demark and Spain Country China (n=38) Denmark (n=85) Spain (n=39) All samples (n=162) Gene types aac6ie, acrb, ant6ia, arna, baca, bl2e_cepa, ermb, ermf, macb, tet32, tet37, tet40, teto, tetpb, tetq, tetw, vanra, vanrg ant6ia, baca, tet32, tet40, teto, tetq, tetw, vanra, vanrg ant6ia, aph3iiia, baca, bl2e_cepa, bl2e_cfxa, ermb, mefa, tet32, tet40, teto, tetq, tetw, vanra, vanrg ant6ia, baca, tet32, tet40, teto, tetq, tetw, vanra, vanrg 15
16 Supplementary Table S4 Resistant clones from combined fosmid libraries of three healthy Chinese individuals (200,000 clones) Antibiotic Class MIC (μg/ml)# No. of resistant clones Tetracycline Tetracyclines 4 74 Gentamicin Aminoglycosides Amoxicillin β-lactams 8 12 Cefalexin β-lactams 16 - Meropenem β-lactams Amikacin Aminoglycosides 64 - Polymyxin B Polypeptides Colistin Polypeptides Levofloxacin Quinolones Fosfomycin Others # MIC, minimal inhibitory concentration. These values are the lowest concentrations that can prevent the growth of E. coli EPI300 (EPICENTRE). 16
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