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Antibiotic Treatment Drives the Diversification of the Human Gut Resistome

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Antibiotic Treatment Drives the Diversification of the Human Gut Resistome 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. 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. 1 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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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. 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 2 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. 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 3 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. 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 4 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. 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).
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