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Widespread Transfer of Mobile Antibiotic Resistance Genes Within bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.998526; this version posted March 20, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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-NC-ND 4.0 International license. 1 Widespread transfer of mobile antibiotic resistance genes within individual gut microbiomes 2 revealed through bacterial Hi-C 3 4 Alyssa Kent1*, Albert Vill1*, Qiaojuan Shi1, Michael J. Satlin2, Ilana Lauren Brito1 5 6 1 Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 7 2 Department of Internal Medicine, Weill Cornell Medicine, New York, NY 8 9 * Denotes equal contribution 10 11 Abstract 12 13 The gut microbiome harbors a ‘silent reservoir’ of antibiotic resistance (AR) genes that is thought to 14 contribute to the emergence of multidrug-resistant pathogens through the process of horizontal gene 15 transfer (HGT). To counteract the spread of AR genes, it is paramount to know which organisms harbor 16 mobile AR genes and with which organisms they engage in HGT. Despite methods to characterize the 17 bulk presence1, abundance2 and function3 of AR genes in the gut, technological limitations of short-read 18 sequencing have precluded linking bacterial taxa to specific mobile genetic elements (MGEs) and their 19 concomitant AR genes. Here, we apply and evaluate a high-throughput, culture-independent method for 20 surveilling the bacterial carriage of MGEs, based on bacterial Hi-C protocols. We compare two healthy 21 individuals with a cohort of seven neutropenic patients undergoing hematopoietic stem cell 22 transplantation, who receive multiple courses of antibiotics throughout their prolonged hospitalizations, 23 and are thus acutely vulnerable to the threat of multidrug-resistant infections4. We find that the networks 24 of HGT are surprisingly distinct between individuals, yet AR and mobile genes are more dispersed across 25 taxa within the neutropenic patients than the healthy subjects. Our data further suggest that HGT is 26 occurring throughout the course of treatment in the microbiomes of neutropenic patients and within the 27 guts of healthy individuals over a similar timeframe. Whereas most efforts to understand the spread of AR 28 genes have focused on pathogenic species, our findings shed light on the role of the human gut 29 microbiome in this process. bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.998526; this version posted March 20, 2020. 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-NC-ND 4.0 International license. 30 The acquisition of antibiotic resistance (AR) genes has rendered important pathogens, such as 31 multidrug-resistant (MDR) Enterobacteriaceae and Pseudomonas aeruginosa, nearly or fully 32 unresponsive to antibiotics. It is widely accepted that these so-called ‘superbugs’ acquire AR genes 33 through the process of horizontal gene transfer (HGT) with members of the human microbiome with 34 whom they come into contact5. The emergence of these MDR bacteria threatens our ability to perform 35 life-saving interventions, such as curative hematopoietic cell transplants for patients with hematologic 36 malignancies6. Furthermore, antibiotic use, required for vital prophylaxis in these patients, has been 37 proposed as a trigger for HGT. Although tools are available to identify AR genes within the gut 38 microbiome, and characterize their function7, abundance8,9 and their host-associations10, no studies have 39 attempted to monitor the bacterial host associations of AR genes and mobile elements during relatively 40 short periods, such as during these patients’ hospitalizations. 41 42 To determine the bacterial hosts of mobile AR genes, we utilized a high-throughput chromatin 43 conformation capture (Hi-C) method aimed at sampling long-range interactions within single bacterial 44 genomes11,12,13. Briefly, while cells are still intact, DNA within individual cells is crosslinked by 45 formaldehyde. Cells are then lysed and the DNA is cut with restriction enzymes, biotinylated, and 46 subjected to dilute ligation to promote intra-molecular linkages between crosslinked DNA. Crosslinking 47 is reversed and then ligated DNA molecules are pulled-down and made into DNA libraries for 48 sequencing. As is, this protocol has been used to improve metagenomic assemblies of bacterial genomes14 49 and has identified a handful of strong plasmid- and phage-bacterial host associations15,16,17,18, suggesting 50 that this technique could be applied to link mobile genes with specific taxa more broadly and to observe 51 the process of HGT over time. 52 53 Here, we develop a modified version of current Hi-C protocols and analytical pipelines (Extended Data 54 Figure 1) in conjunction with metagenomic shotgun sequencing to surveil the bacterial taxa harboring 55 specific mobile AR genes in the gut microbiomes of two healthy individuals and seven patients 56 undergoing hematopoietic stem cell transplantation. These patients have prolonged hospitalizations 57 during their transplant (21 ± 4 days) and often receive multiple courses of antibiotic therapy, increasing 58 the likelihood of an MDR infection. As a result of their condition and treatment, these patients face 59 mortality rates of 40-70% when bacteremic with carbapenem-resistant Enterobacteriaceae (CRE) or 60 carbapenem-resistant Pseudomonas aeruginosa19, and therefore represent a salient population for 61 surveillance and one in which MDR pathogens may emerge and/or amplify under antibiotic selection. Gut 62 microbiome samples for patients and healthy subjects were collected over a 2-3-week period, which, for 63 the neutropenic patients started upon admission for transplant and continued during their hospitalization 64 until neutrophil engraftment (Figure 1A, Extended Data Table 1,2). 65 66 We introduced a number of improvements to current bacterial Hi-C protocols. We changed sample 67 storage and optimized restriction enzymes to improve the congruence between the composition of 68 metagenomic and Hi-C sequencing libraries (Extended Data Figure 2). We also integrated Nextera XT 69 sequencing library preparation directly into the Hi-C experimental protocol, streamlining operations and 70 decreasing sample preparation time. Importantly, within diverse bacterial communities such as the gut 71 microbiome, MGEs may be highly promiscuous and recombinogenic, complicating both assembly20 and 72 linkage analyses21. Therefore, we implemented a computational workflow to assemble genomes, 73 separating large integrated MGEs onto their own contigs, and allowing them to associate with genomes 74 via binning or Hi-C connections. In a mock community of three organisms, each harboring an identifiable 75 plasmid, we are able to confidently link each plasmid to its nascent genome (Extended Data Figure 3). 76 77 Our Hi-C experimental and computational approach results in robust linkages between contigs in human 78 microbiome samples. Hi-C read pairs linking non-mobile contigs with contradictory taxonomic 79 annotations are rarely observed (3.4% at the genus-level) and likely represent homologous sequence 80 matches, highlighting the purity of our Hi-C libraries (Figure 1B). Hi-C read pairs linking two contigs are bioRxiv preprint doi: https://doi.org/10.1101/2020.03.19.998526; this version posted March 20, 2020. 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-NC-ND 4.0 International license. 81 preferentially recruited to contigs that are longer and more abundant, but to a lesser degree than expected, 82 reducing potential bias in our dataset toward highly abundant organisms (Extended Data Figure 4). We 83 binned contigs using several tools (Maxbin22, MetaBat and Concoct), and applied a binning aggregation 84 strategy, DAS Tool23, to obtain a set of draft genomic assemblies. As misassembly can resemble HGT, we 85 removed assemblies with greater than 10% contamination, as determined by CheckM, resulting in 86 taxonomically coherent assemblies (Extended Data Figure 5), albeit a greater number of unbinned contigs 87 (24.6% of the total) (Extended Data Table 3). We then apply conservative criteria to link mobile and 88 mobile AR-containing contigs with the genomic draft assemblies, considering an MGE part of a genome 89 assembly only if it is directly linked to it by at least two uniquely-mapped Hi-C read-pairs. As MGEs are 90 known to recombine, this mitigates the potential for falsely linking contigs that merely share common 91 mobile genes. However, this also potentially reduces our ability for overall detection, especially for larger 92 MGEs, since mobile contigs are often fragmented in metagenomic assemblies24. Nevertheless, we 93 restricted our analysis to those AR-organism and MGE-organism linkages derived from high-confidence 94 read mappings. 95 96 Hi-C significantly improved our ability to detect mobile gene-bacterial host linkages beyond standard 97 metagenomic assembly alone. Hi-C confirms many of the AR gene-taxa and mobile gene-taxa 98 associations observed in the metagenomic assemblies (30.49% ± 11.49% of the AR genes; 30.1% ± 99 9.52% of the mobile genes), but importantly adds on average 31.81% ± 16.28% AR gene associations and 100 36.64% ± 11.56% mobile gene associations to those observed by metagenome assembly alone (Figure 101 1C). Furthermore, whereas metagenomic assembly methods can generally link a single mobile gene 102 cluster to one or two organisms, our Hi-C method was able to identify up to 15 bacterial hosts harboring 103 the same AR or mobile gene, requiring two or more Hi-C linkages within a single individual (mean = 104 3.53 ± 5.69 bacterial hosts per AR gene, Figure 1D; mean = 6.85 ± 10.88 bacterial hosts per gene, 105 Extended Data Figure 6).
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