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1 Antibiotic Treatment Drives the Diversification of the

2 Human Gut Resistome 3 4 Jun Li1,#,a, Elizabeth A. Rettedal2,#,b, Eric van der Helm2,c, Mostafa Ellabaan2,d, Gianni 5 Panagiotou3,4,5*,e, Morten O.A. Sommer2,*,f

6 1Department of Infectious Diseases and Public Health, Colleague of Veterinary Medicine and Life 7 Sciences, City Univerity of Hong Kong, Hong Kong S.A.R., China 8 2Novo Nordisk Foundation Center for Biosustainability, DK-2900 Hørsholm, Denmark 9 3Systems Biology and Bioinformatics Unit, Leibniz Institute for Natural Product Research and 10 Infection Biology – Hans Knöll Institute, Jena, Germany 11 4Systems Biology & Bioinformatics Group, School of Biological Sciences, Faculty of Sciences, 12 The University of Hong Kong, Hong Kong S.A.R., China 13 5Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 14 Hong Kong S.A.R., China 15 16 # Equal contribution. * 17 Corresponding authors. 18 E-mail: [email protected] (Panagiotou G), [email protected] 19 (Sommer MOA). 20 21 Running title: Li J et al / Antibiotic Treatment Drives Resistome Diversification 22 23 aORCID: 0000-0001-7218-429X. 24 bORCID: 0000-0001-5586-0508. 25 cORCID: 0000-0002-1669-2529. 26 dORCID: 0000-0002-9736-0461. 27 eORCID: 0000-0001-9393-124X. 28 fORCID: 0000-0003-4005-5674. 29 30 Total word count: 6609. 31 Total Figure count: 4. 32 Total Table count: 0. 33 Total Supplementary Figure count: 12. 34 Total Supplementary Table count: 4.

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35 36 Abstract

37 Despite the documented antibiotic-induced disruption of the gut microbiota, the 38 impact of antibiotic intake on strain-level dynamics, evolution of resistance genes, 39 and factors influencing resistance dissemination potential remains poorly understood. 40 To address this gap we analyzed public metagenomic datasets from 24 antibiotic 41 treated subjects and controls, combined with an in-depth prospective functional study 42 with two subjects investigating the bacterial community dynamics based on 43 cultivation-dependent and independent methods. We observed that short-term 44 antibiotic treatment shifted and diversified the resistome composition, increased the 45 average copy number of antibiotic resistance genes, and altered the dominant strain 46 genotypes in an individual-specific manner. More than 30% of the resistance genes 47 underwent strong differentiation at the single nucleotide level during antibiotic 48 treatment. We found that the increased potential for horizontal gene transfer, due to 49 antibiotic administration, was ~3-fold stronger in the differentiated resistance genes 50 than the non-differentiated ones. This study highlights how antibiotic treatment has 51 individualized impacts on the resistome and strain level composition, and drives the 52 adaptive evolution of the gut microbiota.

53

54 Keywords: Antibiotics; Resistome; Gut microbiome; Strain; Evolution; Horizontal 55 gene transfer

56

57 58 59

60

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61 Introduction

62 The human intestines are densely populated by diverse microbial inhabitants, which 63 collectively constitute the gut microbiota. About 1,000 prevalent bacterial species 64 colonize the human gastrointestinal tract, playing a pivotal role in health and disease 65 of the host. Besides influencing physiology of the digestive tract, the gut microbiota 66 also affects development, immunity, and metabolism of the host [1]. External forces, 67 including antibiotic treatment or dietary intake, shape the composition of the gut 68 microbiota with the potential for rapid changes, thereby affecting the microbe-host 69 homeostasis [2,3].

70 Antibiotics have been widely used since the Second World War resulting in 71 dramatic benefits to public health [4]. However, the rapid increase of antibiotic 72 resistance (AR) has become an escalating worldwide issue [5]. Antibiotic-resistant 73 pathogens lead to treatment failure and contribute to increasing morbidity, mortality, 74 and healthcare costs. Over 70% of bacteria causing hospital-acquired infections have 75 antibiotic resistance towards at least one common antibiotic for treatment [6]. 76 Previous metagenomic studies have revealed the influence of antibiotic administration 77 on the gut microbiota in various ways, including 1) altering the global taxonomic and 78 functional composition or the diversity of the gut microbiota [7–11] 2) increasing the 79 abundance of bacteria resistant to the administered antibiotic [12] 3) expanding the 80 reservoir of resistance genes (resistome) [13] or 4) increasing the load of particular 81 antibiotic resistance genes (ARGs) [11,14]. The disruptive effects of antibiotic 82 treatment on gut microbiota can be transient or long-lasting [15]. Nevertheless, there 83 is a paucity of information about how the resistome structure shifts and how the 84 genotype of ARGs evolve and differentiate when the microbiome is challenged with 85 antibiotics. In addition, how antibiotic exposure influences strain-level variation 86 within the gut microbiome remains poorly understood.

87 Antibiotic resistance can be acquired through point mutations (de novo evolution) 88 or horizontal gene transfer (HGT) [16]. De novo resistance mutations can modify the 89 antibiotic cellular targets or alter the expression of antibiotic resistance genes and 90 therefore alter the resistance levels of the bacterial strain harboring them [17]. Unlike 91 de novo resistance mutations, horizontal gene transfer allows bacteria to adapt more 92 rapidly to an environment containing antibiotics [16]. Furthermore, the 93 gastrointestinal tract, densely populated with bacteria, enables the gut microbiota to

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94 act as a resistance reservoir which likely contributes to the spread of ARGs to 95 opportunistic pathogenic bacteria [18−20]. The exchange of ARGs has been 96 documented to occur in the human gut between strains carrying vancomycin and 97 sulfonamide resistance genes in Enterococcus faecium and Escherichia coli, 98 respectively [21,22]. Recently, the in situ HGT of ARGs in the infant gut was 99 described [23]. However, factors triggering the HGT of ARGs in the human gut 100 remains insufficiently explored. Therefore, systems level investigations are needed to 101 determine whether antibiotic administration alters the dissemination potential of 102 ARGs, and more importantly, which factors are associated with the altered 103 dissemination potential of ARGs in the human gut.

104 In this study, we first analyzed public metagenomic data from a longitudinal study 105 of 18 cefprozil treated and 6 control volunteers [12]. We surveyed the strain-level 106 dynamics, shift of the resistome structure, evolution of resistance genes at the single 107 nucleotide level, and factors associated with the variation of dissemination potential. 108 In this first stage of in silico analysis we demonstrated the diversification of the 109 resistome and in situ evolution of strains upon antibiotic intake. We then performed 110 an in-depth prospective and functional study on longitudinal samples from one 111 cefuroxime treated and one control subject using both culture-dependent and culture- 112 independent approaches.

113

114 Results

115 Antibiotic treatment diversifies the resistome composition and increases the copy 116 number of ARGs at the intraspecies level

117 We analyzed public metagenomic data from a longitudinal study of 18 cefprozil 118 treated and 6 healthy control volunteers [12] (each subject was sampled at day 0, 7, 119 and 90) that aimed to investigate whether the initial taxonomic composition of the gut 120 microbiota is associated with the reshaped post-antibiotic microbiota. Using this data 121 we investigated the dynamics and diversification of the resistome, strain-level 122 selection, variation of the dissemination potential of antibiotic resistance, and the 123 single-nucleotide level differentiation under antibiotic treatment. 124 To evaluate to what extent the composition of the resistome was altered in

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125 response to antibiotic treatment, we quantified the compositional distance between 126 samples at different time points. An NMDS plot based on the Jaccard distance of 127 ARGs presence/absence profile of each sample revealed that the resistome 128 composition was more drastically altered in the antibiotic treated group than the 129 control group during treatment (Figure 1A). The compositional distances between 130 baseline and treatment samples within the same treated individual were significantly 131 larger than those in the control group (0.14 vs. 0.06 on average, P value < 0.01 with 132 Wilcoxon rank-sum test, Figure 1A). Additionally, we observed that the 133 compositional differences between post-treatment (90 days) and the baseline samples 134 within the same individual were also significantly larger in the treated group than in 135 the control (0.135 vs. 0.075 on average, P value = 0.04, Wilcoxon rank-sum test, 136 Figure 1A), revealing the persistent diversified ARG composition after antibiotic 137 perturbation. 138 We next studied whether antibiotic treatment could select similar sets of antibiotic 139 resistance genes and converge the resistome composition across individuals. If the gut 140 resistome has a more common response, instead of an individualized response, to an 141 antibiotic treatment, we would expect the resistome compositions to become more 142 similar after the treatment across individuals. We found that the dissimilarity of the 143 ARGs composition measured by the Jaccard distance between individuals increased 144 significantly over time in the antibiotic treated group (baseline: 0.44 vs. treatment: 145 0.52, on average, P value < 0.01 with Wilcoxon rank-sum test, Figure 1B), indicating 146 that the overall composition of the resistome diversified under antibiotic treatment 147 across individuals. No significant increase of resistome divergence was observed in 148 the control group (P value > 0.05 with Wilcoxon rank-sum test, Figure 1B). When 149 considering the ARGs abundance rather than the presence/absence, we observed a 150 similar pattern. The Bray-Curtis distance based on the ARG abundance profile 151 between individuals increased significantly during treatment (0.615 vs. 0.685, P value 152 < 0.01 with Wilcoxon rank-sum test, Figure S1). The diversified resistome suggests 153 that antibiotic exposure drives an individualized selection of antibiotic resistance 154 genes. Additional statistical analyses revealed that both the baseline species-level 155 taxonomic composition and the baseline resistome composition are significantly 156 correlated with the resistome composition after treatment (P values < 0.001, Mantel 157 test with permutation = 5000). 158 The significantly diversified resistome composition during antibiotic treatment

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159 encouraged us to investigate whether the copy number of the ARGs (the ratio of gene 160 depth to the relative genomic abundance of the species harboring this gene) could be 161 altered during treatment. Incorporating the reference genomes of the 28 most 162 prevalent species (at least 1% of relative abundance in at least half of the samples 163 from 24 individuals, Table S1), we quantified the average copy number of each ARG. 164 We found that genes annotated to confer resistance towards most classes of antibiotics, 165 including aminoglycosides, beta-lactams, tetracyclines, and glycopeptides, increased 166 the copy number significantly by 22% on average during the treatment (P value < 167 0.001, Wilcoxon rank-sum test, Figure 1C). The copy number of efflux pumps 168 annotated as ARGs also increased significantly by 8% (P value < 0.001, Wilcoxon 169 rank-sum test, Figure 1C). In contrast, ARGs in the control group had no significant 170 increase in copy number (P value = 0.76, Wilcoxon rank-sum test, Figure 1C) during 171 the same period. The significant increase in copy number of antibiotic resistance 172 genes in response to antibiotic treatment could be explained by either the selection of 173 strains with existing ARGs (one or multiple copies) or the horizontal transfer of 174 ARGs. 175

176 Antibiotic treatment drives single nucleotide level differentiation at non- 177 synoymous sites

178 Next, we investigated how the genotype of ARGs varied at the single nucleotide level 179 within each species’ population during antibiotic treatment. The analysis of single 180 nucleotide variants (SNV) based on the shotgun metagenomic data of 72 samples 181 from 24 individuals revealed that there is a significantly higher proportion of 182 differentiated sites (see Methods for definition) in the treated subjects than the control 183 group (3.0% vs. 1.1%, P value <0.001, Wilcoxon signed-rank test, Figure 1D). There 184 is also a significantly higher proportion (31% vs. 13%, P value < 0.001 with 185 Wilcoxon signed-rank test, Figure S2A) of genes containing differentiated sites in the 186 treated subjects than the controls, consistent with antibiotic exposure driving 187 compositional changes in ARG allele frequency and spectrum. By examining sub- 188 categories of ARGs, we observed that most genes, including those annotated as efflux 189 pumps or conferring resistance towards beta-lactams, tetracyclines, and 190 glycopeptides, have a significantly higher proportion of differentiated sites in the

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191 treated group compared to the control (P value < 0.001, Wilcoxon signed-rank test, 192 Figure 1D), indicating antibiotic-driven differentiation of the human gut resistome.

193 To evaluate the potential functional influence of the differentiated sites in the 194 treated subjects, we investigated the frequency of differentiation at either non- 195 synonymous sites (0-fold degenerate sites) or synonymous sites (4-fold degenerate 196 sites). Compared with all the sites in the coding region, single nucleotide 197 differentiation is drastically enriched at non-synonymous sites (91.1% vs. 64.9% for 198 differentiated sites and all sites, respectively), suggesting that intraspecies level 199 selection tended to influence the functional potential of the ARGs within the 200 population of each bacterial species.

201 We further investigated whether there are some commonly differentiated sites in 202 the ARGs among antibiotic treated individuals. We found that 11 genes habored at 203 least one recurrent differentiated site, and these genes were observed in at least 25% 204 of the treated individuals. These commonly differentiated genes, which belong to the 205 MexE or RND efflux family, originate mainly from the species Bacteroides uniformis 206 and Bacteroides vulgatus, or other Bacteroides species (Table S2).

207

208 Increased HGT potential of the resistome is associated with SNV differention

209 We further investigated whether the dissemination potential of ARGs was altered 210 during antibiotic treatment. The chance of HGT transfer (HGT potential) for a 211 particular gene is associated with two factors, the intrinsic HGT tendency of the gene 212 and its prevalence in the population. Therefore, we estimated the HGT potential for 213 the ARG families by combining the relative abundance and the HGT rate [24] (HGT 214 potential = HGT rate abundance, see Methods). According to the above definition, 215 the gene-level variation of HGT potential is noticeably correlated with the abundance 216 variation. We found that the HGT potential increased in both differentiated and non- 217 differentiated ARG families during antibiotic treatment (P value < 0.05 with 218 Wilcoxon signed-rank test), while the HGT potential in differentiated ARGs increased 219 more drastically and significantly than in the non-differentiated ones (19% vs. 5% on 220 average, P value < 0.01, Wilcoxon signed-rank test, Figure 1E). This suggested that 221 ARGs with differentiated sites could play more important roles in dissemination of 222 antibiotic resistance upon antibiotic intake. The pattern of the differentiation-

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223 associated increase of HGT potential was not observed in the control subject at the 224 same time period (Figure S2B).

225 We discovered a significantly positive correlation between community-level fold- 226 change of the HGT potential and the average rate of differentiated sites in the ARGs 227 (Pearson correlation coefficient 0.50, P value = 0.038, Figure S3B), suggesting that 228 ARGs with a higher proportion of differentiated sites tend to increase their HGT 229 potential more drastically. One explanation for this differentiation or SNV-associated 230 increase of the HGT potential is that ARGs with multiple genotypes co-exist within 231 the population for particular species before the antibiotic treatment and these 232 genotypes confer distinct selective advantages or link with other beneficial alleles of 233 ARGs under antibiotic pressure. Therefore, antibiotic treatment tends to select ARGs 234 with selectively advantageous mutations, increasing their abundance and HGT 235 potential. In constrast, non-differentiated ARGs genes harboring homogeneous alleles 236 or multiple alleles with similar selective advantage under antibiotic pressure would 237 randomly select the strains harboring them and maintain their allele frequency.

238

239 Antibiotic pressure shifts the intraspecies population structure

240 The diversified resistome composition and single nucleotide differentiation revealed 241 the influence of antibiotic treatment on the human gut resistome. Therefore, we 242 further investigated how antibiotic pressure drives changes in the intraspecies-level 243 population structure of intestinal species regarding the dominant strain genotypes. We 244 reconstructed the phylogenomic tree for the concatenated genomes of the dominant 245 strains from 28 prevalent species (see Methods). Our results reveal that the 28 246 dominant strains of the treated subjects present significantly closer phylogenetic 247 relationships between samples from the same individual than the samples across 248 individuals (P value < 0.01, permutation test, Figure 1F), indicating an individualized 249 antibiotic selection for the dominant strains within each subject. This individual- 250 specific strain selection during treatment was also observed for each individual 251 species, e.g., Ruminococcus. bromii (Figure S4A). Furthermore, we noticed a 252 significantly higher divergence (phylogenetic distance) of the dominant strains 253 between the baseline and treated samples in the treated group than the control (0.052 254 vs. 0.015 on average, P value < 0.01, Wilcoxon signed-rank test, Figure 1F−G),

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255 suggesting that antibiotic pressure drove a significant shift of the dominant strain 256 genotype, therefore influencing the intraspecies population structure. This is 257 consistent with the aforementioned strong differentiation trend for the ARGs. 258 Interestingly, when we examined the post-treatment (day 90) recovery patterns by 259 comparing the phylogenetic distances of the dominant strains between baseline 260 treated and post-treatment samples in the treated group, we found that the dominant 261 strains in only 39% (7 out of 18) of the individuals had a closer phylogenetic 262 relationship with the corresponding baseline samples than the treatment samples 263 (Figure 1F). This suggests that the shift in intraspecies population structure upon 264 antibiotic treatment could be long-lasting. 265 If antibiotic treatment tends to select dominant strains or ARGs with more similar 266 genotypes, we should observe smaller phylogenetic distances between treatment 267 samples across individuals, as compared with the distances between the baseline 268 samples across individuals. The results show that there are no significantly different 269 phylogenetic distances between the cross-individual treatment samples and cross- 270 individual baseline samples of the dominant strains or domain ARG genotype (P 271 value >0.05, Wilcoxon signed-rank test, Figure 1F and S4B), suggesting that 272 antibiotic treatment tends to select dominant strains and ARGs with individual- 273 specific genotypic signatures. 274 Furthermore, we found that the differences in genome-wide nucleotide diversity in 275 28 prevalent species are not significant between the treated and control groups 276 (average fold-change: 1.12 vs. 1.05, P value = 0.73, Wilcoxon signed-rank test, Figure 277 1H), indicating no large-scale selective sweep or strain domination in most of the 278 species. Interestingly, we did find in particular individuals and species (e.g., 279 Bacteroides uniformis in individuals 10, 13, 17, and 19) a drastically decreased (less 280 than half of the baseline) genome-wide nucleotide diversity (Table S3), suggesting 281 that incomplete selective sweep occured due to antibiotic exposure. Combined with 282 the strong divergence of the dominant strains in the treated group, our analysis 283 indicates that cefprozil intake reshapes the intraspecies population structure by 284 selecting individual-specific strains and ARGs. 285

286 Cefuroxime treatment increases antibiotic resistance levels of the human gut 287 microbiota

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288 The analyses based on public data revealed the general tendency for differentiation 289 and diversification of the resistome upon antibiotic treatment. Subsequently, we 290 prospectively and functionally assessed the impact of antibiotic treatment on the 291 resistance levels of the gut microbiota to a panel of antibiotics in one treated and 292 control subject using both cultivation-based multiplex phenotyping and cultivation- 293 independent functional and shotgun metagenomics (Figure 2A and Methods). We 294 defined the level of phenotypically resistant gut microbiota as the ratio of colony 295 counts on the antibiotic-containing plates to the antibiotic-free plates. The overall 296 level of phenotypically resistant gut microbiota increased significantly by 140% (P 297 value < 0.05, Wilcoxon rank-sum test) on the β-lactam plates during cefuroxime 298 treatment (day 5) (Figure 2B−C, Table S4). Even though the phenotypic resistance 299 levels to various β-lactams partially recovered after the treatment, resistance never 300 returned to their initial levels, even 3 months after treatment. In addition, the 301 resistance levels of non-β-lactam plates showed a moderate (49% on average) but 302 statistically significant increase (P value < 0.05, Wilcoxon rank-sum test) during 303 treatment in the cefuroxime-treated subject (Figure 2B−C), suggesting that certain 304 bacteria, possibly harboring multiple types of antibiotic resistance genes or multiple- 305 antibiotic resistance genes, were selected during cefuroxime treatment. In contrast, no 306 statistically significant increase in resistance was observed in either β-lactam or non- 307 β-lactam antibiotic plates in the control subject (Figure 2C).

308 To evaluate the variation in taxonomic composition of cultured resistant bacteria 309 in both cefuroxime-treated and control subjects at different time points, we performed 310 16S rRNA sequencing of the colonies grown on different antibiotic containing plates 311 (Table S3). We observed that the community dissimilarity (beta-diversity), measured 312 by the Morisita-Horn index, between antibiotic plates from the treated subject 313 decreased after the cefuroxime treatment. This converging pattern of resistant 314 communities among different antibiotic plates was not observed in the control subject 315 (Figure 2D and Figure S5). In addition, the pattern of converged taxonomic 316 composition of the resistant bacteria is significantly stronger (P value < 0.01, 317 Wilcoxon rank-sum test) on β-lactam plates than on the non-β-lactam plates (Figure 318 2D and Figure S6). An NMDS plot based on the 16S rRNA taxonomic profiles of 319 each cultured antibiotic plate revealed that the taxonomy profiles on β-lactam plates 320 were significantly altered (P value < 0.01, permutational MANOVA test) over time

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321 (Figure 2E). Such strong differentiation was not observed on the non-β-lactam plates 322 (P value > 0.05, permutational MANOVA test).

323 By comparing the relative resistance of each species (16S based taxonomy) on β- 324 lactam plates between baseline (day 0) and treatment (day 5) samples, we identified 325 12 species with significantly increased phenotypic resistance during treatment (BH 326 adjusted P value < 0.05 using permutation test) from all β-lactam plates in the 327 cefuroxime-treated subject (Figure 2F). Most of these species, mainly from the genus 328 Bacteroides, sustained enhanced resistance for one to three months (Figure 2F). We 329 further implemented the same statistical tests on non-β-lactam plates from the 330 cefuroxime-treated subject, as well as on both β-lactam and non-β-lactam plates from 331 the control subject. No species with significantly increased resistance were identified, 332 suggesting that the cefuroxime treatment might select for species with higher 333 resistance to multiple β-lactams.

334

335 Antibiotic treatment reduces genome-wide nucleotide diversity in Escherichia 336 coli and Enterococcus faecium

337 We next investigated the temporal variation of genome-wide nucleotide diversity 338 (clonal diversity) in different species with metagenomic analysis of the cefuroxime 339 treated and control subjects (Figure 2A and Methods). We found that Escherichia coli 340 and Enterococcus faecium showed exceptional variation patterns of whole genome 341 diversity compared with all other species during cefuroxime treatment (Figure 3A). 342 We observed strong evidence for the occurrence of genome-wide selective sweeps in 343 these two species with a sharp decrease of genome level nucleotide diversity during 344 treatment (Figure 3A−B), indicating that particular strains with low or intermediate 345 initial frequency were strongly selected in the face of antibiotic pressure. These 346 strains dominated the population with clonal expansion during antibiotic treatment 347 and reduced the heterogeneity of the population. In addition, the temporal variation 348 patterns between the E. coli and E. faecium were quite different in terms of their post- 349 treatment recovery mode. The genome-wide nucleotide diversity of E. coli recovered 350 gradually but never returned to its initial level after the cefuroxime treatment (Figure 351 3A), suggesting the persistence of antibiotic selected strains of E. coli. This 352 incomplete recovery of the nucleotide diversity indicates that the fitness of the strain

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353 surviving antibiotic treatment was still competitive within the population in the 354 antibiotic-free environment. In contrast, the diversity of E. faecium recovered rapidly 355 after antibiotic treatment, suggesting that the antibiotic selected strain had a fitness 356 cost in absence of antibiotic treatment, as indicated in previous studies [25,26]. No 357 genome-wide reduction of nucleotide diversity was observed in other species during 358 treatment in the treated subject (Figure 3A). We observed random temporal variations 359 of the genome-wide diversity in the control subject, which remained relatively stable 360 over time (Figure S7), indicating that the overall strain level dynamics in the gut 361 microbiome are more pronounced during antibiotic treatment, consistent with the 362 observation using the public data.

363

364 Functionally selected ARGs experience strong selection at the single nucleotide 365 level

366 We next adopted functional metagenomics to investigate the influence of the 367 cefuroxime intake on the prevalence of different types of functionally validated ARGs 368 over time. The results from all functional selections before treatment identified 369 various types of ARGs, while β-lactamases increased drastically and dominated (87% 370 of relative abundance) the recovered genes (Figure 4A−B) during the cefuroxime 371 treatment. The β-lactamases decreased post-treatment but remained prevalent (37%) 372 in the functional selections (Figure 4A). When quantifying the ARGs abundance from 373 β-lactam and non-β-lactam plates separately, we noticed a very similar dominance of 374 β-lactamases on the β-lactam plates during the treatment (Figure S8A). Additionally, 375 we detected an increase of efflux genes on the non-β-lactam plates during the 376 cefuroxime treatment period (Figure S8B), suggesting co-selection of other resistance 377 genes. This consistent with our results from the cultivation plates, where resistance 378 levels were also enhanced on some non-β-lactam antibiotic plates. To validate the 379 increased abundance of β-lactamases in the gut microbiota of the treated subject, we 380 mapped the shotgun metagenomic data to the functionally validated β-lactamases. 381 The results showed that β-lactamases increased drastically during treatment in the 382 cefuroxime-treated subject while there was no such trend in the control subject 383 (Figure S9).

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384 Since the genome-wide selective sweep in E. coli and E. faecium revealed a strong 385 selective force imposed by antibiotic treatment at the intraspecies level, we 386 subsequently investigated further how the functional ARGs diversified within the 387 population of each species during the cefuroxime treatment. The analysis of SNV, 388 combining functional and shotgun metagenomics data, revealed that the 114 389 functionally selected ARGs contain a significantly higher proportion of differentiated 390 sites in the cefuroxime-treated subject than in the control (0.037 vs. 0.004, P value < 391 0.01 with Wilcoxon signed-rank test) (Figure 4C). At the same time, the proportion of 392 functional ARGs with SNV signals of selection (genes with at least one differentiated 393 site) is significantly higher in the treated subject than in the control (56% vs. 22%, P 394 value < 0.01 with Fisher’s exact test) during or after treatment (Figure 4C), consistent 395 with our findings based on the computationally annotated metagenomics datasets 396 (Figure 1D). Further analysis revealed that the nucleotide diversity (π) of ARGs 397 decreased significantly (P value < 0.01 with Wilcoxon signed-rank test) during 398 treatment in the cefuroxime-treated subject (Figure S10), consistent with the strong 399 diversification of the allele frequency at the single nuecleotide level. 400

401 Differentiated functional ARGs tend to be recently horizontally transferred

402 To evaluate the taxonomic origin and potential of recent HGT in functionally selected 403 ARGs, we identified the last common ancestor (LCA) of highly homologous hits 404 (identity >95% at amino acid level) of the functional ARGs against the NR database 405 using blast (see Methods). We explicitly defined recent HGT-related ARGs by 406 considering the confidence level of LCA and the taxonomic information from NR hits 407 (see Methods). This analysis indicates that there is a higher proportion of 408 differentiated functionally selected ARGs involved in recent HGT events than in the 409 non-differentiated ones (57% vs. 42%) (Figure 4D). To validate this trend of recent 410 HGT in differentiated ARGs, we incorporated the public data described above [12] 411 and observed a very similar pattern— the differentiated ARGs tend to be more 412 recently transferred than the non-differentiated ones (Figure S11), suggesting a 413 stronger HGT trend in the differentiated ARGs.

414 The higher rate of recent HGTs in differentiated ARGs motivated us to investigate 415 further whether these ARGs were associated with plasmid-mediated bacterial 416 conjugation. We searched for all highly homologous hits (> 95% identity) in the

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417 NCBI plasmid RefSeq database using the ARGs as queries. We found that about 33% 418 of differentiated and 11% of non-differentiated ARGs have highly similar (> 95% 419 identity at amino acid level) plasmid hits (Figure 4D), supporting the role of plasmids 420 in harboring and circulating these ARGs. Since only plasmid hits with high identity (> 421 95%) were considered, we can be quite confident that a substantial proportion (33%) 422 of differentiated ARGs are actively transferred among species via plasmids in the 423 recent evolutionary history. In our prospective study, we also observed that the HGT 424 potential in differentiated ARGs increased more drastically than in the non- 425 differentiated ones (304% vs. 142%, P value < 0.01 with Wilcoxon signed-rank test, 426 Figure S12) as we proposed above using the public dataset.

427

428 Discussion

429 In this study, we observed antibiotic-induced strain-level dynamics, resistome 430 diversification, and increased resistance dissemination potential within the human gut. 431 The integrative approach deployed in this study enabled the elucidation of complex 432 dynamics of the resistome and gut microbial strains during antibiotic treatment. We 433 used computational analyses of existing metagenomic datasets to find evidence for 434 antibiotic induced diversification of the resistome, as well as substantial treatment 435 induced differentiation of antibiotic resistance genes. We further elucidated 436 widespread strain level dynamics exacerbated by antibiotic treatment. Combining 437 these results, we showed that the increased dissemination potential of antibiotic 438 resistance gene is strongly associated with the frequency of the differentiated sites 439 during antibiotic treatment. We then performed a functional survey in a prospective 440 intervention study, enabling detailed culture-based and culture-independent 441 characterization of the human gut resistome and resistance phenotypes. The consistent 442 findings from two experimental designs reflect the generalization of our conclusions.

443 Previous studies have provided evidence that certain factors could influence the 444 HGT potential in the human intestine. For instance, it has been shown that human 445 intestinal epithelial cells produce proteinaceous compounds that modulate antibiotic 446 resistance transfer via plasmid conjugation in E. coli [27]. Another study using a 447 mouse colitis model demonstrated that pathogen–driven inflammation of the gut 448 promoted conjugative gene transfer between Enterobacteriaceae species due to the

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449 transient bloom of the pathogenic Enterobacteriaceae [28]. Our results revealed a 450 similar but more comprehensive scenario about the increased HGT potential under 451 antibiotic treatment, supporting the hypothesis that antibiotic pressure could drive the 452 dissemination of the resistome [16]. According to the definition of HGT potential, the 453 overall increase of ARGs abundance could be the major reason for the overall 454 increased HGT potential, although the increased ARG abundance may neither 455 guarantee an increased HGT potential (see Methods), nor is it proportionate to overall 456 increased abundance [24]. More importantly, we observed that the increased HGT 457 potential of ARGs was compounded by antibiotic selection of these genes at the 458 single nucleotide level, highlighting the association between evolutionary plasticity 459 and the horizontal transfer of ARGs [29]. The single nucleotide level differentiation 460 could be explained by the existence of a huge multiplicity of bacterial clones within 461 single species in the gut before the antibiotic perturbation. Antibiotic treatment is the 462 driving force selecting the alleles conferring higher survival advantage, leading to 463 genotype differention and abundance increase.

464 Due to the fact that the disturbed microbiota could lead to adverse health 465 outcomes [30], secondary infections [31], or increased risk of colorectal cancer [32], 466 personalized medicine for a bacterial infection should in the future incorporate 467 information of the patient’s gut microbiota. The knowledge of the personalized gut 468 microbiota sets the basis for predicting the stability or dynamics at the whole 469 community level or individual bacterium upon perturbation [33]. Although we cannot 470 predict the clinical impact or the gut microbe dynamics due to insufficient sample size 471 and lack of clinical records in our study, our data suggests that antibiotic therapy leads 472 to personalized resistome diversification and individual-specific, strain-level selection 473 in the gut microbiota. The selective sweep observed in E. coli and E. faecium 474 highlights the influence of antibiotic treatment on the intraspecies level dynamics. In 475 line with this, future antibiotic treatment should be more personalized regarding the 476 dosage, duration, and combination of drugs based on the unique strains and resistome 477 composition in each patient to minimize the unintended disruption of the gut 478 microbiota. Unfortunately, how the initial gut microbiota composition relates to 479 antibiotic treatment efficacy, side effects, long-term susceptibility to different 480 pathogens, or diseases is poorly explored thus far. Therefore, more studies with 481 longitudinal sampling and sequencing of the gut microbiota, evaluation of antibiotic

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482 efficacy, and survilliance of susceptibility for infections or other diseases are needed. 483 Such data could uncover the microbiota-dependent antibiotic efficacy and side 484 effects, the interaction networks between antibiotic and gut microbes, and long-term 485 microbial dynamics, paving the way for future microbiome-based diagnosis and 486 treatment.

487 Although our strain-level analyses offer novel insights into the dynamics of the 488 resistome compostion, copy number variation, and single nucleotide level 489 differentiation of ARGs upon antibiotic treatment, the scarcity of reference strain 490 genomes for many species and the incompeleteness of the ARG database as well as 491 the non-robust annotation methods could potentially bias these quantifications to 492 certain extent and limit the generalization of our conclusions. Another caveat is that 493 although metadata, including gender, age, weight, etc., for each individual were 494 provided in the original study of the public data, the sample size was not sufficient for 495 further in-depth analyses regarding these potentially confounding factors. Future 496 studies with more individuals, increased sampling density, and the development of 497 more comprehensive ARGs databases and accurate annotation methodologies would 498 be of great value.

499

500

501 Materials and methods

502 Retrieval of public data 503 A total of 72 samples from 18 cefprozil treated subjects and 6 control subjects used in 504 the study of Raymond et al. [34] were downloaded from NCBI SRA PRJEB8094. 505 Each subject provided three longitudinal samples—baseline (day 0), treatment (day 7), 506 and post treatment (day 90).

507 Experimental Design 508 To functionally validate the findings of our computational analysis based on public 509 metagenomic datasets, two healthy adult human female subjects (age 25−29, diet not 510 controlled) who had not taken any antibiotics for at least one year were selected for 511 this study. One subject underwent a standard 5-day treatment with cefuroxime (500 512 mg, 3 times a day) while the other subject had no treatment, serving as a control.

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513 Eight fecal samples were collected longitudinally over a period of three months 514 corresponding to pre-treatment (Day 0), two time points during treatment (Day 2 and 515 5), one week post-treatment (Day 12), two weeks post-treatment (Day 19), three 516 weeks post-treatment (Day 26), one month post-treatment (Day 33), and three months 517 post-treatment (Day 97). All participants consented to these experiments and sample 518 collections and downstream experiments and data processing followed ethical 519 guidelines (Hvidovre Hospital) throughout the study. Samples were transported to an 520 anaerobic chamber within an hour of collection. Five grams were separated out for 521 culturing (only on Day 0, Day 5, Day 12, Day 19, Day 33, and Day 97) and 2.5 grams 522 were used for DNA extractions. The remaining stool samples were stored at -80°C.

523

524 Cultivation, DNA extraction, and 16S gene sequencing 525 The stool samples were cultivated with or without the presence of 16 antibiotics, 526 followed by DNA extraction and sequencing of 16S rRNA as described previously 527 [35]. Briefly, five grams of fecal sample was resuspended in 50 mL of prereduced 528 (resazurin, 0.1 mg/mL) 1X PBS and 10-fold serial dilutions were plated on Gifu 529 Anaerobic Media (GAM) agar with or without antibiotics in duplicate. The plates 530 were incubated anaerobically at 37°C for 5 days. Bacterial colonies were then 531 manually scraped off the surface of the agar and collected in 10 mL tubes. DNA was 532 extracted from the collected samples using the MoBio UltraClean Microbial DNA 533 Isolation kit. 534 535 Identification of the species with enhanced resistance from cultured plates 536 The relative resistance level for each OTU is defined as the ratio of the relative 537 abundance of the OTU from each antibiotic plate to the relative abundance of the 538 same OTU in the control plate at each time point. To test whether certain OTUs had 539 overall enhanced resistance over time, the resistance levels (e.g., all β-lactam plates) 540 for each OTU were compared using a Wilcoxon signed-rank test [37] between the test 541 time points (during or post treatment) and the baseline.

542

543 DNA extraction, library construction, and sequencing for culture-independent 544 methods

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545 The fecal samples for culture-independent methodologies were extracted using 2.5 546 grams of sample with the MoBio PowerMax Mega Soil DNA Isolation kit following 547 the standard protocol. DNA from the treated subject’s samples from Day 0, Day 5, 548 Day 19, Day 26, and Day 33 was used to construct functional metagenomic libraries 549 as modified from Sommer et. al. [19]. 550 All DNA fragments from 1056 functional clones were sequenced using Sanger 551 sequencing, imported into CLC Main Workbench where the cloning vector sequence 552 was removed and reads of poor quality were discarded. Assembly of reads from all 553 samples was attempted simultaneously in order to simplify the final output and 554 identify sequences sharing the same sequence. All assembled contigs were hand 555 checked for errors and correct alignment. A total of 197 contigs consisting of 2 or 556 more sequences and 104 unassembled single sequences were retrieved. Open reading 557 frames (ORFs) were annotated using ORFinder 558 (http://www.ncbi.nlm.nih.gov/gorf/gorf.html). ORFs were annotated by comparing 559 the protein (pFAM database, blastX, cdd database) or nucleotide (blastn, CARD 560 database, ARDB database) sequences to known sequences in several databases with 561 80% identity and coverage. Sequences reads were removed from the annotation list if 562 no match to known or suspected antibiotic resistance genes could be found. Forward 563 and reverse reads from the same sequence that overlapped along the same resistance 564 gene(s) were merged into a single annotation.

565

566 Deduction of HGTs and calculation of HGT rate 567 The protein sequences of the functional ARGs were mapped to the NCBI NR 568 database using blastp (-e 1E–5) first. The latest common ancestor (LCA) at species 569 level based on highly homologous (identity > 95%) hits for each ARG was deduced 570 using MEGAN [38]. By definition, 100% confidence of the LCA at species level 571 reflects an explicit origin of species and 0% confidence reveals a, theoretically, 572 infinite gene flow among species. An ARG was deduced as recent HGT related when 573 all following criteria were satisfied, 1) more than 2 highly homologous hits (identity > 574 95%); 2) confidence of LCA at species level less than 50%; and 3) the highly 575 homologous hits were observed in at least two species, excluding the hits with 576 incomplete taxonomic information at species level. To estimate the plasmid-mediated

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577 recent HGTs, we mapped all the ARGs against the NCBI plasmid RefSeq database 578 [39] and only hits with > 95% identity remained for further analysis.

579 A total of 154,805 gene families were retrieved from the HGTree database [40]. 580 Protein sequences of the functional ARGs were mapped to HGTree families with 581 blastp e-value 1E–5. The HGTree family with the highest number of hits, which 582 satisfied the blast evalue < 1E–5 and coverage >50% in the short sequence of query 583 and subject, was defined as the gene family of that ARG. Phylogenetic reconciliation 584 analysis was carried out using RANGER-DTL [41] based on the species tree and gene 585 tree deduced by the HGTtree. Only the interspecies level HGTs remained for 586 downstream analysis. The number of HGTs was divided by the total length of the 587 phylogenetic tree in this family to deduce the family-level HGT rate.

588

589 Shotgun metagenome library construction and sequencing 590 Culture-independent fecal extracts from treated subject samples Day 0, Day 2, Day 5, 591 Day 12, Day 19, Day 26, Day 33, and Day 97 and control subject samples Day 0, Day 592 2, Day 19, and Day 97 were used to build shotgun metagenomic libraries using the 593 Nextera XT kit with the standard protocol. The HiSeq 1500 was used for 100 bp PE 594 sequencing in the CGS of The University of Hong Kong and the average throughput 595 for each sample was 10.5 Gbp. The raw sequences can be found in BGID 596 (CRA000815).

597

598 Quality control for the raw sequences of shotgun metagenomic data 599 To retrieve the high quality reads for downstream analyses, we used a series of quality 600 control steps to remove the low quality reads/bases as described previously [42]. In 601 the first step, all the Illumina primer/adaptor/linker sequences were removed from 602 each read. Subsequently, we mapped all the reads to the human genome with BWA 603 version 0.7.4-r385 [43], and reads with > 95% identity and 90% coverage to the 604 human genome were removed as human DNA contamination. We further filtered the 605 low quality regions, reads, and PCR duplicates using a previously described 606 procedure [44].

607

608 Reference mapping, gene copy number calculation, variant calling, dominant

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609 strain identification, and annotation of antibiotic resistance genes 610 The Metagenomic Intra-Species Diversity Analysis System (MIDAS) [45] was 611 adopted to calculate the gene copy number and call single nucleotide variants within 612 each species using the default setting. To filter out low quality SNVs, the read error 613 was controlled by a base quality score 30 and mapping quality were controlled by 614 MAPQ 20 from MIDAS. The relative abundances of genes in each sample were 615 further estimated using a RPKM measurement (number of reads per kbp length of 616 gene per million mappable reads) using in-house scripts. The 28 prevalent species in 617 the public shotgun metagenomic data were defined as the species with at least 1% of 618 relative abundance in at least half of the samples from 24 individuals (Table S1). To 619 annotate the antibiotic resistance genes, a hidden Markov Model based profile 620 searching was carried out using Resfams [46] with default parameters on the 621 pangenome genes from MIDAS. To identify the genotypes of the dominant strain of 622 each prevalent species at either genome level or gene level, we used the script of 623 “call_consensus.py” from MIDAS package. The phylogenetic tree was reconstructed 624 using FastTree [47] with maximum likelihood method with default parameters.

625

626 Definition of HGT potential

627 For a single gene i, the HGT potential (HGTPi) is defined as,

628 HGTPi = HiAi i)

629 where Hi is the aforementioned HGT rate in this gene family i and Ai is the relative 630 abundance of this gene in the sample.

631 For a set of n genes, the overall HGT potential is defined as,

) 632 ∑$Ͱͥ $$ ii)

633 According to formula i), the increase of the HGTP for a gene is proportional to the 634 increase of the abundance. However, for a set of genes, the overall increased 635 abundance may not necessarily lead to an increase of overall HGTP according to 636 formula ii). For example, a set of genes A, B, and C have HGT rates 0.5, 1, and 2, 637 respectively. The initial and final relative abundance for these three genes are (0.1, 638 0.3), (0.2, 0.3), (0.3, 0.1), respectively. The overall fold-change of the abundance and 639 HGTP is 16.7% and -23.5%, respectively. This example illustrates that the variation

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640 of the HGTP for a gene set is not only correlated to the overall abundance variation 641 (an increased overall abundance may lead to a decreased overall HGT potential), but 642 also the dependency between the abundance change and the HGT rate of each gene.

643

644 Detecting differentiated sites 645 To identify the potential adaptively evolved variant sites in the genes, we calculated 646 the difference of the allele frequency spectrum, which was extracted from the variants 647 calling using MIDAS mentioned above, using Fisher’s exact test between treatment 648 and baseline samples at each single nucleotide variant site. For example, the A:T ratio 649 for a particular variant site in one ARG is 1:9 initially but was altered to 7:3 during 650 the treatment, indicating potentially adaptive evolution where the allele frequency 651 distribution has been differentiated. To correct for the influence of sequence depth on 652 the statistical power, the maximum depth for each site was down-sampled a maximum 653 of 10 before Fisher’s exact test was carried out. The raw P values were adjusted to 654 false discovery rate (FDR) using Benjamin’s method [48].

655

656 Statistical Analysis

657 The enhanced overall resistance level in the culture-dependent plates and the species 658 with enriched resistance across multiple plates were analyzed using the Wilcoxon 659 signed-rank test [37]. Detection of differentiated sites according to the allele 660 frequency spectrum and the higher proportion of differentiated sites in the ARGs in 661 the treated subjects were carried out based on Fisher’s exact test and Wilcoxon 662 signed-rank test, respectively. The difference between resistant bacterial communities 663 when comparing baseline and treatment plates was carried out using the permutational 664 MANOVA test using VEGAN [49]. The NMDS analysis and the stress value 665 calculation were performed using VEGAN. The statistical differences between groups 666 regarding compositional distances (Bray–Curtis dissimilarity) of gut microbiota or 667 resistome, the Jaccard distance of resistome, the copy number of ARG families, HGT 668 potential, phylogenetic distances, genome-wide nucleotide diversity, or nucleotide 669 diversity of ARGs, were tested using Wilcoxon signed-rank test. The statistical 670 correlation of the taxonomic composition and resistome profile was performed by 671 Mentel test.

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672

673 Authors' contributions

674 MOAS, EAR, and GP designed this study. EAR performed the experiments. JL, EAR,

675 ME, and EVDH analyzed the data. JL and EAR drafted the manuscript. All authors

676 commented on and revised the manuscript.

677

678 Competing interests 679 All authors declare no conflict of interest.

680

681 Acknowledgements 682 This project was supported by the Lundbeck Foundatation and EU FP7-Health 683 Program Evotar (282004). The study was approved (REG-026-2014) by the regional 684 ethics committee and Danish national medicine agency. JL and GP would like to 685 thank the Centre for Genomic Sciences (CGS) of The University of Hong Kong 686 (HKU) for their support. GP would like to thank Deutsche Forschungsgemeinschaft 687 (DFG) CRC/Transregio 124 ‘Pathogenic fungi and their human host: Networks of 688 interaction’, subproject B5. We would especially like to thank Dr Agnes Chan (CGS) 689 for fruitful discussions. We would like to thank Dr. Wendy Kwok for her language 690 editing. 691 We thank David Westergaard for his involvement in the initial stage of the project 692 providing the ARG annotation pipeline of the shotgun metagenomics analysis. The 693 raw sequences were deposited in BGID (CRA000815). 694

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818

819 Figures legends 820 Figure 1 Divergence of the resistome and dominant strains in the prevalent 821 species upon antibiotic intake 822 A. NMDS based on the Jaccard distance of antibiotic resistance gene profiles 823 (presence/absence) (stress value < 0.05). E7, E90, C7, C90 represent the samples from 824 Day 7 treatment, Day 90 post-treatment, Day 7 control, Day 90 control samples, 825 respectively. The red and blue ovals surround samples from the same antibiotic 826 treated and control subject, respectively. The radii of the ovals reflect the 827 compositional distances between samples from the same individual. A larger radius 828 suggests a more drastically diverged resistome profile. B. Distribution of the Jaccard 829 distance of ARGs profile between individuals at the same time point. C. Fold-change 830 of the ARG copy number of major categories of ARGs. The boxes describe the mean 831 and the standard deviation. D. Distribution of the percentage of differentiated site in 832 various ARG categories. E. Variation of the HGT potential over time for 833 differentiated and non-differentiated ARGs, in treated subjects at baseline, treatment, 834 and post-treatment timepoints. F. Phylogenomic tree based on 835 the concatenated dominant strains sequences of 28 prevalent species. Green, red, and 836 blue colors denote the baseline of the treated, other time points of the treated, and

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837 control samples, respectively. The samples were named using the rules of 838 “P+individual ID+E/C (treated or control group)+time point (day 0, 7, and 90), e.g., 839 “P3E7” refers to the sample from day 7 (under antibiotic treatment) from individual 3. 840 The green color denotes the baseline of treated subject. G. Density distribution of the 841 phylogenetic distance between baseline and treated samples in Figure 1F. A larger 842 phylogenetic distance reveals a faster evolution under antibiotic pressure. H. 843 Distribution of the fold-change of the genome-wide nucleotide diversity in 28 844 prevalent species during the treatment. *P < 0.05; **P < 0.01; ***P < 0.001 845 846 Figure 2 Cefuroxime treatment enhanced the resistance to multiple antibiotics 847 and converged the cultured resistant bacterial communities 848 A. Experimental design: Two healthy adult human donors were selected for this 849 study. One subject underwent a standard 5-day treatment with cefuroxime (500 mg, 3 850 times a day) while the other subject was not treated, serving as a control. Fecal 851 samples from multiple time points were extracted for downstream experiments, 852 including cultivation-based multiplex phenotyping, DNA sequencing of 16S rRNA, 853 and functional and shotgun metagenomics (see Methods for more details). B. 854 Summary of the average percentage resistance levels before, during, and after 855 treatment from both β-lactam and non-β-lactam plates in the treated subject. C. 856 Variation of the relative antibiotic resistance over time in the presence of different 857 antibiotics. D. Heatmap of the pairwise community dissimilarity, as measured by the 858 Morisita-Horn index, across cultivated plates at different time points in the treated 859 and control subjects. E. NMDS based on Morisita-Horn dissimilarity of communities 860 cultured with different β-lactam antibiotics in treated subjects (stress value < 0.05). 861 The brown ovals surround the samples from sample time points. Cefo: cefotaxime, 862 Ceph: cephalexin, Cefu: cefuroxime, Pip: pipercillin, Cefe: cefepime, Amp: 863 ampicillin, Diclo: Dicloxacillin. F. Heatmap of antibiotic resistance from β-lactam 864 plates for the species with significantly boosted resistance during treatment. + 865 denotes the resistance is significantly higher (BH adjusted P value < 0.01 with 866 permutation test) than that in the baseline sample in the treated subject.

867 868 Figure 3 The variation of the genome-wide nucleotide diversity

27 bioRxiv preprint doi: https://doi.org/10.1101/537670; this version posted February 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

869 A. PCA based on the genome-wide nucleotide diversity at different time points in the 870 treated subject. Each dot represents one species. Two species, Escherichia coli 871 and Enterococcus faecium show exceptional genome-wide selective sweeps. Three 872 sub-figures describing the pattern of the temporal variation at the genome-wide 873 diversity were shown juxtaposed with the species. B. The nucleotide diversity before 874 and during treatment across the genome coordinates in Escherichia coli 875 and Enterococcus faecium in the treated subject. 876 877 Figure 4 Variation of the gene abundance, differentiated sites, and HGT trend 878 in the functional metagenomic ARGs 879 A. Temporal variation of the gene abundance for functionally selected β-lactamases. 880 B. Temporal variation of the gene abundance for functionally selected non-β- 881 lactamase antibiotic resistance genes. C. Cumulative percentage of functional ARGs 882 with different proportions of differentiated sites. The proportions of genes with 883 differentiated sites before and during the treatment in both subjects are shown in the 884 subfigure. D. Proportion of genes with recent HGT signatures and genes with plasmid 885 mediated HGT in both differentiated and non-differentiated ARGs. 886 887 Supplementary material 888 889 Table S1. Information of 28 prevalent species used in the strain-level analysis 890 891 Table S2. Information of the genes with common differentiated sites across treated 892 individuals (frequency of the recurrent differentiated site >25%) in 28 prevalent 893 species. 894 895 Table S3. The fold-change (<0.5) of genome-wide diversity during treatment for 896 particular species. 897 898 Table S4. Average colonies grown on different cultivated antibiotic plates from 899 different time points. 900 901 Figure S1. The average Bray-Curtis distance based on the ARGs abundance profile 902 between individuals. 903 E0, E7 and E90 refer to day 0, 7 and 90 in the treated group, respectively. C0, C7 and 904 C90 refer to day 0, 7 and 90 in the control group, respectively. 905

28 bioRxiv preprint doi: https://doi.org/10.1101/537670; this version posted February 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

906 Figure S2. Variation of the HGT potential over time for two types of ARGs in the 907 control subjects. 908 909 Figure S3. Correlation between the variation of HGT potentials and HGT or SNP rate. 910 A. Pearson correlation (correlation coefficient 0.35, P value<0.001) between the gene- 911 level change of HGT potential and the gene HGT rate. B. Pearson correlation 912 (correlation coefficient 0.50 with P value=0.038) between the gene-level change of 913 HGT potential and the gene SNP rate (proportions of differentiated sites). The bar 914 height is the mean value and the error bar reflect the standard deviation. The ribbons 915 reflect the standard error of the regression. C. The variation of the HGT potential in 916 28 prevalent species across all 18 treated subjects. 917 918 Figure S4. The phylogenomic tree of A. dominant strain in species Ruminococcus 919 bromii and B. all dominant genotypes of antibiotic resistance genes. 920 921 Figure S5. The average Morisita-Horn dissimilarity of the resistant and controlled 922 communities cultured. 923 924 Figure S6. The pairwise Morisita-Horn dissimilarity (beta-diversity) between 925 cultured communities with antibiotics from all time points. 926 Green squares surround all pairwise community dissimilarity between all plates at 927 each time point. Red squares surround all pairwise community dissimilarity between 928 beta-lactam plates at each time point. Cefo: cefotaxime, Ceph: cephalexin, Cefu: 929 cefuroxime, Pip: pipercillin, Cefe: cefepime, Amp: ampicillin, Diclo: Dicloxacillin, 930 Cipro: Ciprofloxacin, Sulfa: Sulfamethoxazole, Tet: Tetracycline, Clinda: 931 Clindamycin, Ery: Erythromycin, Metro: Metronidazole, Vanc: Vancomycin 932 933 Figure S7. Variation of the genome-wide diversity over time in the control subject.

934 Figure S8. Abundance variation for various types of antibiotic resistant genes in A. 935 beta-lactam plates and B. non-beta-lactam plates.

936 Figure S9. Abundance variation for β-lactamases.

937 Figure S10. Temporal variation of the nucleotide diversity in the predicted ARGs in 938 both treated and control subject.

939 Figure S11. The percentage of genes with recent HGT signatures based on public 940 database.

941 Figure S12. Variation of the relative abundance and HGT potential of the 942 functionally selected ARGs. Left panel: relative abundance of the functional ARGs 943 over time in both subjects. Right panel: variation of the HGT potential over time for 944 two types of ARGs in both treated and control subjects.

29 A C

2.4 0.1 2.2 2

1.8

0.1 1.6 1.4

1.2

[ 1 NMDS2 0.25 **

-0.3 0.20 0.8 0.15

0.10 0.6 Gene copy number

0.05 0.4 0.00 ance from baseline

-0.5 Dist E7 E90 C7 C90 0.2 -0.6 -0.4 -0.2 0.0 0.2 0 NMDS1 Aminoglycoside Beta-lactam Chloramphenicol Glycopeptide Tetracycline Efflux Inactivation Treated Antibiotic types Mechanism Control

B D 5 E [ *** [ * C90 4 1000 [

[ * Differentiated *** C7 800 Non-differentiated > 0.1 3 [ P *** C0 [ 600 treated

2 [ am ples E90 *** S 400 [ E7 *** [ * 1 ** sites per geneDifferentiated po tentia l 200 E0 [

0 HGT 0

0 0.2 0.4 0.6 0.8 No. of

All

e ptid Jaccard distance of the ARGs Efflux

bioRxiv preprint doi: https://doi.org/10.1101/537670; this version posted February 1, 2019. The copyright holder for this preprint (which was not Treated

certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under Treated Baseline

aCC-BY 4.0 International license. Baseline

Beta-lactam

Tetracycline Tetracycline

e op yc Gl

Posttreatment Posttreatment

Aminoglycoside Aminoglycoside Chloramphenicol Chloramphenicol F G P18E90 P18E0 P11E7 P11E90 P18E7 30 P11E0 Treated P13E90 P5E7 P5E90 P13E7 P5E0 Control P20E90 P13E0 20 P20E0 P12E7 P2E7 P20E7 10 P12E90 Frequency P25C90 P6C90 P25C7 P12E0 P6C0 P25C0 P2E0 P6C7 0 P2E90 0 0.05 0.10 0.15 0.20 P9E90 P3E7 Phylogenetic distance between baseline and treatment P9E7 P8C7 H Subdoligranulum sp P9E0 P22E90 P8C0 Ruminococcus bromii P22E0 Parabacteroides merdae P19E7 P8C90 Parabacteroides distasonis P22E7 Oscillospiraceae bacterium P19E0 P3E0 Oscillibacter sp P21E7 Odoribacter splanchnicus P19E90 P4E7 P21E0 P3E90 Lachnospiraceae bacterium P15E90 Faecalibacterium prausnitzii P21E90 Faecalibacterium cf Eubacterium rectale P15E0 Eubacterium eligens P4E90 Clostridiales bacterium P4E0 Burkholderiales bacterium P15E7 P38C0 Species P14E90 Bilophila wadsworthia P38C7 Barnesiella intestinihominis P17E90 P14E0 Bacteroides xylanisolvens P14E7 Bacteroides vulgatus P17E0 P23C0 Bacteroides uniformis P10E0 P23C7 Bacteroides thetaiotaomicron P17E7 P10E90 P23C90 Bacteroides ovatus P7C7 Bacteroides caccae P7C0 Bacteroidales bacterium P7C90 P38C90 0.05 Alistipes shahii P1E0 Alistipes putredinis P1E7 P1E90 Alistipes onderdonkii P10E7 Alistipes finegoldii 2 1 0 1 2 Fold change of the diversity A C Ciprofloxacin Tetracycline Erythromycin Metronidazole Vancomycin Chloramphenicol Clindamycin Cephalexin Controlled subject 100 Treated Cefuroxime Treated subject 80 Control Pre-treatment Treatment Post-treatment 60 0 2 5 12 19 26 33 97 Time point of sampling (days) 40

Collecting samples Relative resistance 20

0 t 100 Cefuroxime Cefotaxime Cefepime Dicloxacillin Ampicillin Gentamycin Sulfamethoxazole Pipercillin

80

Plate cultivation Funtional Shotgun 60 and counting 16S sequencing metagenomics metagenomics 40 B 20 Relative resistance Baseline During treamtment 3 months after treatment 0 1 5 1 5 1 5 1 5 -lactam 31.4 74.1 65.0 1 5 1 5 1 5 1 5 12 19 33 97 12 19 33 97 12 19 33 97 Day 12 19 33 97 Non- -lactam 14.8 22.1 14.3 12 19 33 97 12 19 33 97 12 19 33 97 12 19 33 97

D Antibiotics Day 0 Day 5 Day 12 Day 33 Day 97 Cefepime - 1 Cefotaxime Cefuroxime Cephalexin - 0.8 Pipercillin No antibiotic Treated subject Treated Gentamycin - 0.6

bioRxiv preprint doi: https://doi.org/10.1101/537670Vancomycin; this version posted February 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under CefepimeaCC-BY 4.0 International license. Cefotaxime - 0.4 Cefuroxime Cephalexin Pipercillin - 0.2 No antibiotic

Control subject Gentamycin Vancomycin - 0

E F

D5 Amp + + + + Bacteroides OTU00003 D12 Cefo + + + Bacteroides OTU00032 D5 Ceph D5 Cefu D12 Cefu D5 Diclo D5 Cefo D97 fCeo D5 Cefe + + OTU00033 D0 Cefo D12 Cefu D97 Cefu D12 Cefo D12 Ceph D0 Cefu D33 Cefu D12 Cefe + + Coprococcus OTU00035 D12 Ceph D12 Cefe D33 Cefo D5 Pip D33 Ceph + + + + + Bacteroides OTU00056 D33D97 fCee fCeD12e Pip D12D33 D97PipD12 Pip D12DiclDiclooxx Amp D33 D97AmpD33 Amp D97Diclox Ceph + + Parabacteroides OTU00070 D0 Ceph D12D97 Amp Pip 0.2 0.0 0.2 0.4

NMDS2 + + + Bacteroides OTU00081 1.0 NMDS distance + + + 0.8 Bacteroides OTU00085 0.4 D0 Pip D0 Cefe 0.6 + + + + Lachnospiraceae OTU00086 0.4 D0 Amp + + + Bacteroides OTU00112

0.6 0.2 0.0 + + Bacteroides OTU00159 Day 0 Day 5 D0 Diclo + OTU00199 1.0 0.5 0.0 0.5 15 1 0.07 - - - Day 1 NMDS1 Day 5 Day 12 Day 19 Day 33 Day 97 bioRxiv preprint doi: https://doi.org/10.1101/537670; this version posted February 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

A 1E-03 B Before treatment During treatment 1E-8 1E-04 2 Pi diversity Pi 1E-05 Before During Post treatment Escherichia coli 1E-4 Π Escherichia coli 1E 6 0 1E-03 - 1E-04 PCA2 0 1 2 3 4 5 Pi diversity Pi 1E-05 Before During Post treatment All other species 1E-2

2 Nucleotide diversity, 1E-3 1E-03 1E-04

1E-4 Enterococcus faecium Pi diversity Pi 1E-05 Before During Post treatment 4 Enterococcus faecium 0 1 2 0 5 10 Genome coordinates, Mbp

PCA1 bioRxiv preprint doi: https://doi.org/10.1101/537670; this version posted February 1, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license.

A B Beta Lactamases Other Resistance Genes 100 100 Other Other Gene Mod 80 80 VEB-PER ABC Trans TEM 60 60 APH CLassD AAC 40 CepA 40 Emr CblA CAT 20 20 Tet Percentage of recovered genes Percentage of recovered genes 0 0 5 26 33 97 0 0 5 26 33 97 Timepoint (days) Timepoint (days) C D

Control 100 Treated Differentiated

75 75 Non-differentiated

75 50 50 50

25 25 25

0 Percentage of genes During treatment All time points 0 Proprotion of genes with differentiated site Cumulative percantage of genes 0 0.000 0.025 0.050 0.075 Frequency of differentiated sites Potential recent HGT Plasmid related