Combining functional and structural genomics to track antibiotic resistance genes in mobile elements in clinical bacterial strains

The rise of multi-antibiotics resistant bacteria represents an emergent threat to human health. Here, we investigate antibiotic resistance mechanisms in bacteria of several species isolated from an intensive care unit in Brazil. We used whole-genome analysis to identify antibiotic resistance genes (ARGs) and plasmids in 35 strains of Gram-negative and Gram-positive bacteria, including the first genomic description of Morganella morganii and Ralstonia mannitolilytica clinical isolates from South America. We identify a high abundance of beta-lactamase genes in highly resistant organisms, including seven extended-spectrum β-lactamases shared between organisms from different species. Additionally, we identify several ARGs-carrying plasmids indicating the potential for fast transmission of resistance mechanism between bacterial strains, comprising a novel IncFII plasmid recently introduced in Brazil from Asia. Through comparative genomic analysis, we demonstrate that some pathogens identified here are very distantly related to other bacteria isolated worldwide, demonstrating the potential existence of endemic bacterial pathogens in Brazil. Also, we uncovered at least two couples of (near)-identical plasmids exhibiting multi-drug resistance, suggesting that plasmids were transmitted between bacteria of the same or different species in the hospital studied. Finally, since many highly resistant strains carry several different ARGs, we used functional genomics to investigate which of them were indeed functional. In this sense, for three bacterial strains (Escherichia coli, Klebsiella pneumoniae, and M. morganii), we identify six beta-lactamase genes out of 15 predicted in silico as the main responsible for the resistance mechanisms observed, corroborating the existence of redundant resistance mechanisms in these organisms. Importance Big data and large-scale sequencing projects have revolutionized the field, achieving a greater understanding of ARGs identification and spreading at global level. However, given that microbiota and associated ARGs may fluctuate across geographic zones, hospital-associated infections within clinical units still remain underexplored in Brazil – the largest country in South America; 210 million inhabitants – and neighboring countries. This work highlighted the identification of several ARGs shared between species co-occurring simultaneously into a Brazilian hospital, some of them associated with large plasmids, mostly endowed with transposable elements. Also, genomic features of clinically underrepresented pathogens such M. morganii and B. cepacia were revealed. Taken together, our results demonstrate how structural and functional genomics can help to identify emerging mechanisms of shared antibiotic resistance in bacteria from clinical environments. Systematic studies as the one presented here should help to prevent outbreaks of novel multidrug resistance bacteria in healthcare facilities.


Abstract
The rise of multi-antibiotics resistant bacteria represents an emergent threat to human health. Here, we investigate antibiotic resistance mechanisms in bacteria of several species isolated from an intensive care unit in Brazil. We used whole-genome analysis to identify antibiotic resistance genes (ARGs) and plasmids in 35 strains of Gram-negative and Gram-positive bacteria, including the first genomic description of Morganella morganii and Ralstonia mannitolilytica clinical isolates from South America. We identify a high abundance of beta-lactamase genes in highly resistant organisms, including seven extended-spectrum β-lactamases shared between organisms from different species. Additionally, we identify several ARGs-carrying plasmids indicating the potential for fast transmission of resistance mechanism between bacterial strains, comprising a novel IncFII plasmid recently introduced in Brazil from Asia. Through comparative genomic analysis, we demonstrate that some pathogens identified here are very distantly related to other bacteria isolated worldwide, demonstrating the potential existence of endemic bacterial pathogens in Brazil. Also, we uncovered at least two couples of (near)-identical plasmids exhibiting multi-drug resistance, suggesting that plasmids were transmitted between bacteria of the same or different species in the hospital studied. Finally, since many highly resistant strains carry several different ARGs, we used functional genomics to investigate which of them were indeed functional. In this sense, for three bacterial strains (Escherichia coli, Klebsiella pneumoniae, and M. morganii), we identify six beta-lactamase genes out of 15 predicted in silico as the main responsible for the resistance mechanisms observed, corroborating the existence of redundant resistance mechanisms in these organisms.

Importance
Big data and large-scale sequencing projects have revolutionized the field, achieving a greater understanding of ARGs identification and spreading at global level. However, given that microbiota and associated ARGs may fluctuate across geographic zones, hospital-associated infections within clinical units still remain underexplored in Brazil -the largest country in South America; 210 million inhabitants -and neighboring countries. This work highlighted the identification of several ARGs shared between species co-occurring simultaneously into a Brazilian hospital, some of them associated with large plasmids, mostly endowed with transposable elements. Also, genomic features of clinically underrepresented pathogens such M. morganii and B. cepacia were revealed. Taken together, our results demonstrate how structural and functional genomics can help to identify emerging mechanisms of shared antibiotic resistance in bacteria from clinical environments. Systematic studies as the one presented here should help to prevent outbreaks of novel multidrug resistance bacteria in healthcare facilities.

Introduction
Microbial resistance to antibiotics is a growing global concern. Established protocols in clinics to fight nosocomial infections include isolation of microorganisms from patient samples to allow its identification and to determine its antibiotic susceptibility (1). However, this process is timeconsuming (48 h or more) and prone to pathogen misidentification (2). Therefore, the advent of nextgeneration sequencing (NGS) tools has allowed the rise of novel approaches to identify microbial pathogens and to fight infection (3). Thus, in the last two decades, whole-genome sequencing (WGS) of microbial pathogens has moved from being used as a basic research tool to understand pathogen's biology and evolution (4,5), to an almost routine diagnostic tool to investigate outbreaks in hospitals and nosocomial infection pathways (6)(7)(8)(9). For diagnostics purposes, current WGS technologies could even be cost effective for slow-growing pathogens such as Mycobacterium tuberculosis, providing faster and accurate results even for antibiotic resistance determination (10). Additionally, culture independent methods based on clinical metagenomics can be used to identify several pathogens from nucleic acids

WGS analysis of clinical strains isolated from the same two weeks
We selected 35 bacterial strains isolated from different patient samples and performed WGS as represented in Fig. 1. While well-studied pathogens such as K. pneumoniae, E. coli, P. aeruginosa e S. aureus were well represented in the sampling, we were able to analyze pathogens with very few representative genomic information in March 2020. For example, two Ralstonia mannitolilytica strains were sequenced, but only nine complete or graft genomes were available at NCBI. Other underrepresented strains were Streptococcus gallolyticus (27 genomes available) and Morganella morganii (63 genomes). These numbers contrast with those from K. pneumoniae, E. coli, or S. aureus, where 8-20 thousand genome sequences are available. This evidence indicates that many clinically relevant pathogens have been underrepresented in WGS analysis efforts worldwide. For instance, phylogenetic analysis of Burkholderia cepacia 540A against all 166 available genomes in NCBI (Fig.  S2) indicates this strain is also very divergent but belongs to a branch formed by strains isolated from patients with cystic fibrosis in the UK and endophytic bacteria isolated from Australia (25). Therefore, some of the new WGSs generated here demonstrate a significant diversity of some underrepresented microorganisms and should serve as reference sequences for future studies on clinical isolates in Brazil and South America.

Identification of resistance genes in clinical strains
We next analyzed the existence of ARGs in the sequenced genomes. Analysis of ARGs using ARG-ANNOT database indicated a higher prevalence of beta-lactamase coding genes followed by amino-glycosidases ( Fig. 2A), while analysis with DeepARG tool (26) showed the multidrug category as the most abundant on the genomes analyzed (Fig. S3). We next analyzed ARGs' distribution in the two most abundant groups, E. coli and Klebsiella. In the first case, each of the six E. coli strains displays a unique set of ARGs from different categories, and five particular beta-lactamases  were found in at least one genome (Fig. 2B). Only one strain (E. coli 456A) did not present any of these five bla genes, and this strain was sensitive to all antibiotics tested by Vitek 2 (Fig. 2B/Fig.3). For the Klebsiella group, we observed a much broader set of resistance markers (Fig. 2C), which was in accordance with the largest level of antibiotic resistance of this group compared to E. coli. Next, we investigated which beta-lactamase genes were conserved into the strains analyzed. For this, we compared all ~125 bla genes identified in Fig. 2 and searched for those coding proteins with 100% identity at the amino acid sequence (Fig. 3). Using this approach, we identified seven bla genes (blaOXA blaKPC,blaTEM,blaHYDRO,and blaBLP) which have 100% aa identity between two or more strains. Notably, blaOXA-1, blaCTX-M-1, blaKPC, and blaTEM were present in 3 to 6 strains from different species. Interestingly, both blaOXA-1, and blaCTX-M-1 were found in E. coli, K. pneumoniae and M. morganii, which could reveal recent horizontal gene transfer between these strains. From these seven genes, 2 (blaOXA-1 and blaKPC) were functional in the library screening presented below. Taken together, since the analyzed strains were isolated from hospitalized patients in the same two weeks, this evidence would indicate a recent mobilization of antibiotic resistance determinants either in the environment or in the hospital settings, as was reported previously (27-29).

Identification of ARGs located in plasmids
We next aimed to identify ARGs with potential mobilization through plasmids in the analyzed species. For this, we crossed the data from ARG-ANNOT with the prediction of plasmid elements generated by PlasmidFinder. Using this approach, we were able to identify nine potential plasmids from 7 species associated with at least one ARG. As shown in Fig. 4A, a ~42kb plasmid -(pKP98M3N42)harboring a blaKPC-2 and a sat-2A resistance determinant were identified in K. pneumoniae 98M3, and this plasmid also carries transposases, recombinases, and type IV secretion system genes. An identical plasmid -(pKP125M3N44; 100% nucleotide sequence identity) -was also found in K. pneumoniae 125M3 (Fig. 4A, Fig. S4A). As mentioned before, this blaKPC-2 gene was identical in these two K. pneumoniae strains and in E. coli 126M3, but it was not located in a plasmid in the latter case (Fig. 3).
In two recent studies, authors demonstrate ARG-carrying plasmids transfer in clinical settings between bacteria of the same or different species, which emphasize the significance of revealing potential plasmid-mediated outbreaks to efficiently track ARGs horizontal transmission in hospitals (30,31).
Another strain, K. pneumoniae 508B, harbors two plasmids with ~136kb (pKP508BN15) and ~62kb (pKP508BN34). Both plasmids harbor transposon elements, with the larger one harboring a sul2 resistance gene and the smaller one with two ARGs (qnr-S1 and blaLAP-2, Fig. 4B-C). In general, ARGs' coexistence with transposon elements was also observed for two plasmids identified in E. coli strains ( Fig. S4B-C) and Staphylococcus capitis 732B (Fig. S5A). Finally, two almost identical small plasmids (~2.3kb; 99.94% nucleotide identity) were identified in S. capitis 732B and S. epidermidis 452B, which harbor an aadC and ermC resistance determinants (Fig. S5B). Taken together, these data demonstrate the potential for dissemination of many ARGs genes identified here, and the existence of identical or near-identical plasmids between different species could indicate that these elements have been mobilizing among some of these species.

Experimental validation of functional beta-lactamases from highly-resistance bacteria
Once we distinguish several ARGs in the genomes analyzed, we decided to perform a functional screening to identify which of these genes could confer resistance into a heterologous host. For this, we selected three strains (E. coli 126M3, K. pneumoniae 508B, and M. morganii 538A) to construct genomic libraries into laboratory E. coli DH10B ( Table 1). The libraries were constructed into the broad host range vector pSEVA232, which contains a kanamycin resistance marker, a broad host range oriV with a medium-copy number, and a variant of the lacZα multiple cloning site with a Plac promoter (32)(33)(34). In this way, the libraries generated during this study or individual plasmids of interest can be transferred to other bacterial strains for another screening or functional evaluation (Fig. 5A). The use of a medium copy-number plasmid allows a closer assessment of ARGs' natural genetic context in contrast to other studies that use high copy plasmids (35). Accordingly, plasmids with lower copy-number and monomeric states also tend to be more stably inherited throughout bacterial populations (36). We screened ~750.000 clones of each library against each antibiotic (amoxicillin, oxacillin, and penicillin G) and obtained 44 clones with unique sequences containing ARGs ( Fig. 5B and Table 2).
Clones containing the blaKPC-2 were by far the more abundant in the screening ( Table 2 and Fig.  6). The genomic context of identified blaKPC-2 indicate that it is prone to suffer horizontal transfer, once it is flanked by transposases. Martínez et al. (37) describe a framework to prioritize the risk of ARGs, the Resistance Readiness Condition (RESCon). The RESCon algorithm considers the similarity of an ARG to known genes, functional evaluation, the clinical relevance of the antibiotic, presence of a mobile genetic element, and presence in a human pathogen. The characteristics of this ARG would categorize it as RESCon 1, an ARG with the highest possibility to thrive in a clinical setting (37). Although a framework better describing the impacts of gene transfer to prioritize risk is needed (38), the RESCon classification indicates clinical relevance of this ARG. With ARGs found in all 3 functional screenings, we were able to identify with high frequency six different bla genes using the functional approach presented here, being at least two of them present in plasmids -blaKPC-2 and blaLAP-2; see next section - (Fig. 6).
We propose that functional genomics can be combined with current approaches based on largescale sequencing in order to better understand the functional aspects of ARGs. Recent developments in machine learning (26) have provided tools to find ARGs that can be used to detect ARGs that would not be otherwise identified with sequence similarity tools. Still, the databases used to train those tools are biased for specific antibiotic resistance classes, such as beta-lactam, bacitracin, MLS, and efflux-pumps. Indeed, we could only find just a fraction of ARGs annotated through bioinformatics tools with our functional approach. This paradox could be due to the other ARGs not being functional in the experimental conditions used here or because our screening was not exhaustive enough to cover those sequences.

Mapping of plasmids in the Brazilian territory
To evaluate the biogeographic distribution of functional ARGSs from K. pneumoniae, we performed genomic comparative analysis including 50 plasmids deposited in NCBI data bank and two of the plasmids identified in this study (pKP98M3N42, pKP508BN34). Distance-tree analysis showed the presence of two distinguishable groups. Plasmid pKP98M3N42 (identical to plasmid pKP125M3N44, also identified in this work) is located in the first group -comprising seven strains all reported in Brazilian cities from 2009 to 2015, but 1 in USA -, which should indicate that pKP98M3N42 is sharing structural features with the other plasmids positioned in this group (Fig. 7A). Additionally, pKP98M3N42 shares 100-99.9% sequence identity with IncX3 plasmids of these seven strains that have been previously related to play an important function in mediating horizontal transmission of blaKPC-2 genes among hospital-associated members of the Enterobacteriaceae family (39,40). Moreover, IncX3 plasmids carrying blaKPC-2 genes were also reported in countries very distant geographically, such as United States (41), Brazil (42,43), Australia (44), Italy (45), France (46), South Korea (47, 48), China (49), Israel and Greece (39), to cite some. Interestingly, although IncX3 self-transmissible plasmids are widespread globally, the evident diversification of plasmids in the first separated branch of the tree (Fig.  7A) could indicate that plasmids from strains isolated in clinically relevant bacteria in Brazilian ground undergo their own structural reorganization.
On the other hand, distance tree representation of the 50 close plasmids' sequences related to pKP508BN34 did not show the presence of evident different groups. BLAST analysis showed that plasmid pKP508BN34 is widely distributed in K. pneumoniae strains, with 100-99,9% sequence identity and with query coverage ranging from 84% to 100% to the 18 closest related plasmids, some of them associated to hypervirulent strains -such as strain KP58, bearing plasmid pKP58-3 - (Fig. 7B). Mortality due to infection of K. pneumoniae was very rare in the past. However, this pathogen's fast evolution due to the gaining of hypervirulence plasmids allowed this bacterium to cause severe community-transmitted infections in relatively young and healthy hosts since the late 1980s (54, 55). pKP508BN34, with 63.067 bp in size and harboring various mobile elements that contain antimicrobial resistance genes, including qnrS1 and blaLAP-2, belongs to the IncFII plasmid group. As seen in the branch highlighted in Fig. 7B, most of the strains carrying the closest related plasmids to pKP508BN34 were described in different cities of China -but one in Germany, one in Japan, and three in Thailand -, and were associated with diverse host diseases, such as pneumoniae, pulmonary infection, urinary tract infection, intestinal infection, and diarrhea, according to the data deposited in the bioprojects of NCBI. In some cases, related strains were reported in asymptomatic patients (strains TH164 carrying plasmid pTH164-3 and strain TH114 bearing plasmid pTH114-3, both from Thailand), something reasonable since K. pneumoniae is also a member of the gut microbiota (50). To the best of our knowledge, this is the first time that this plasmid is reported in Brazil.
As shown in Fig. 8A-B, plasmids pKP98M3N42 and pKP508BN34 are highly structurally conserved between K. pneumoniae strains available in the databank. However, plasmid pKP508BN15 (which is present in K. pneumoniae 508B) presented a strong structural diversification in the region close to the antibiotic markers, and these changes seem to be related to the activity of the ISSpu21 transposon element located in this region (Fig. 8C). Interestingly, most related sequences available in the database are from strains worldwide including some isolated from Asia, Africa, North America, and Europe, but no example of sequences from South America.

Conclusions
Here, we sampled clinical bacterial strains to investigate the existence of ARGs and their association with mobile genetic elements. We could identify several ARGs shared between strains from different species, and some of these ARGs are associated with large plasmids, mostly endowed with transposable elements. Instead of focusing on strains from the same species or genus, our approach considered strains co-occurring simultaneously into a hospital setup, aiming to identify shared resistance mechanisms that could have been mobilizing in this environment. While our analysis does not provide unequivocal evidence that these resistance mechanisms are being mobilized among the strains analyzed, we found strongly conserved ARGs located in plasmids and associated with transposon elements that could represent potential mechanisms for the dissemination of antibiotic resistance among clinical strains.
Additionally, by using functional genomics, it was possible to investigate which of the ARGs candidates identified in silico could be the main factors associated with the resistance to beta-lactam antibiotics. Furthermore, for many of the bacterial species analyzed here, we could find a low number of available complete genome sequences at the NCBI database. Therefore, while thousand genome sequences are available for classical pathogens (E. coli, K. pneumoniae, P. aeruginosa, etc.), other clinically relevant pathogens such M. morganii and B. cepacia are underrepresented, which makes challenging to track genomic events associated with the acquisition of pathogenicity elements or resistance mechanisms in hospital-associated infections. Finally, analysis of plasmids from Klebsiella strains allowed the identification of both well-known circulating variants in Brazil as new variants that seem to be recently acquired from Asia. Thus, we argue that more systematic efforts should be made to monitor the introduction and propagation of mobile genetic elements harboring ARGs, especially in South America, in order to avoid outbreaks of novel multidrug resistance bacteria.

Sample collection
Samples were taken at the Ribeirão Preto Clinics Hospital (HCRP, Ribeirão Preto, Brazil), a tertiary reference hospital in Latin America with 920 beds and 35,000 hospitalizations per year. Samples were isolated from different patients hospitalized at the weeks 44 and 48 of 2018. Strains were obtained from different samples, as indicated in Fig. 1A. Thirty-five strains were randomly selected from a total of 105 available and represent different species, which have a major prevalence for Klebsiella genus, E. coli, Pseudomonas aeruginosa, and the genus Staphylococcus. After strain characterization by Vitek 2, samples were inactivated and used for genomic DNA extraction and sequencing, as indicated below.

DNA extraction and genome sequencing
Total genomic DNA was extracted using Wizard Genomic DNA Purification Kit (Promega, Madison, WI, USA) following the manufacturer's instructions. Fig. 1B represents schematically the overall strategy used for WGS analysis. The DNA concentrations were measured fluorometrically (Qubit® 3.0, kit Qubit® dsDNA Broad Range Assay Kit, Life Technologies, Carlsbad, CA, USA). Purified DNA from 35 isolates was prepared for sequencing using Nextera XT DNA Library Prep Kit (Illumina). Libraries were quality assessed using 2100 Bioanalyzer (Agilent Genomics, Santa Clara, CA, USA) and subsequently sequenced using HiSeq 2x150 bp cycle kits (Illumina). On average, 5.5 million reads were generated per library. Adapters were trimmed using Trimmomatic v0.36. Samples were filtered of possible human contamination by aligning the trimmed reads against reference databases using Bowtie2 v2-2.2.3 with the following parameters (-D 20 -R 3 -N 1 -L 20 -very-sensitivelocal). Overlapped reads were merged using Flash version 1.2.11. Merged and unmerged reads were assembled using Spades v3.12.0 with the following parameters (-k 21, 33,55,77,99,127 --merge). Genome quality (completeness and contamination) was evaluated using CheckM v1.0.7 and QUAST. Genome annotations were performed using Prokka v1.11 with default parameters. Amino acid sequences of all genes identified using Prokka were aligned to the NDARO (National Database of Antibiotic Resistant Organisms) database obtained from NCBI (March, 2020). The alignment was performed using Diamond v0.8.24 with the following parameters (blastx -k 5 -f 6 -E value 0.001). Alignments with >= 60 similarity were selected for further analysis. Quality assessment of sequenced genomes is provided in Fig. S1. All genomes are available at NCBI under the BioProject number PRJNA641571.

Identification of antibiotic resistance genes, plasmids and phylogenomic analysis
The identification of antibiotic resistance genes was performed using the ABRicate pipeline by searching annotated genes using reference databases (ARG-ANNOT, NCBI AMRFinderPlus, CARD and ResFidner), as well as DeepARG (26). The identification of plasmid was performed using Plasmidfinder and contigs harboring ARGs and plasmid related genes were further analyzed in detail. For genomic comparison, reference and assembly genomic data were downloaded from NCBI databank. Phylogenomic analyses were performed using Parsnp and Gingr (51) and phylogenetic trees visualized using iTOL (52).

Genomic libraries construction
For the cloning of the genomic DNAs in the pSEVA232 (32)(33)(34) vector, 2µg of genomic DNA from each strain were digested with Sau3AI. While, pSEVA232 digestion using BamHI and further dephosphorylation were performed. Genomic fragments from 1.5 to 6 kb and the linearized pSEVA232 vector were selected and incubated with the T4 DNA Ligase enzyme in a 2:1 insert/vector ratio. Then, ligations were transformed in the electrocompetent E. coli DH10B (53) with a MicroPulser electroporator (Bio-Rad -Hercules, USA). The resulting libraries were analyzed for the percentage of plasmids carrying genomic DNA and the average size of the insert they contained.

Determination of Minimum Inhibitory Concentrations (MICs)
The MICs were determined in the same culture medium (solid LB) and conditions of the screenings, employing serial dilution of the test antibiotics (Amoxicillin, Oxacillin, or Penicillin G). Solid medium plates supplemented with kanamycin (50 µg mL -1 ), IPTG (100 μM) and dilutions of each beta-lactam antibiotic were inoculated with approximately 2.5 × 10 6 CFUs of E. coli DH10B harboring the pSEVA232 vector. We performed dilutions of antibiotics and culture media according to the CLSI M100 supplement (54).

Screening and phenotype confirmation
Three pools of clones (2.5 × 10 6 clones per plate) were plated from each library on solid LB supplemented with kanamycin (50 µg µL -1 ), IPTG (100 µM) and inhibitory concentrations of each betalactam antibiotic (8 µg mL -1 for amoxicillin, 32 µg mL -1 for penicillin G or 256 µg mL -1 for oxacillin). Positive clones were cultured in liquid LB medium supplemented with kanamycin (50 µg mL -1 ) for extraction of plasmid DNA with the Wizard Plus SV Minipreps DNA Purification System Kit (Promega -Madison, USA). Plasmids with unique EcoRI/HindIII restriction patterns were subjected to retransformation and phenotypic confirmation by streaking the clones in solid LB medium supplemented with kanamycin (50 µg mL -1 ), IPTG (100 µM) and the test antibiotics.

Extraction of insert sequence from assembled genomes
Plasmid DNA was sequenced on both strands by primer walking using the ABI PRISMDye Terminator Cycle Sequencing Ready Reaction kit (PerkinElmer) and an ABI PRISM 377 sequencer (Perkin-Elmer) according to the manufacturer's instructions. A python script was developed to extract and annotate inserts from Sanger reads, which is available on GitHub. First, the algorithm converts .ab1 files to the fasta format and searches for the read sequence in the assembled genome using BLAST+ (55). We then extract the inter-read region to a fasta file, which is then annotated by PROKKA (56).    Figure 3. Shared beta-lactamases with 100% identity at the protein level. Seven beta-lactamase coding genes were found as shared among gram-negative strains analyzed. Connected circles indicate that the genes are presented in those strains. On the right, the antibiotic resistance profile of the analyzed strains. Genes for blaOXA-1 and blaKPC are highlighted (red triangles) since these genes were identified in the functional screening carried out in this study. On the right, antibiotic resistance levels are indicated, with numbers indicating the resistance levels in µg/mL. Red indicates resistance to the antibiotic while blue denotes sensibility. The final column indicated if the strain is ESBL positive or negative.

Microorganism Strain AMP APS PIT CFX CFX-A CTX CAZ CPM CFO ERT IMP MER CIP GEN ESBL
Acinetobacter baumannii 112M3 -  Figure 4. Schematic representation of genes of three K. pneumoniae plasmids. A) Plasmid pKP98M3N42 (43 kb) from K. pneumoniae 98M3. This plasmid carries two ARGs (blaKPC-2 and sat-2A), elements of a type IV secretion system, two resolvases, and a Tn3 transposase. This plasmid is very similar to pKP125M3N44 from K. pneumoniae 125M3 (Fig. S4A). Whole-plasmid visualization was performed using a python module for prokaryotic genome analyses -DnaFeaturesView -and matplotlib module combined, as well as ARG-ANNOT and Prokka's results (57).B) Plasmid pKP508BN15 (136.8 kb) from K. pneumoniae 508B transports a sul2 and an rRNA adenine n-6-methyltransferase (ermC) resistance markers. A transposase, a xerC recombinase, and a type IV secretion system virB8 protein are also found in this plasmid. C) Plasmid pKP508BN34 (63 kb) is also from K. pneumoniae 508B and carries several transposases and two resistance determinants, qnr-S1 and blaLAP-2. Legends represent the colors code for the identified genes.  ORFs were colored according to its function: red -ORFs directly related to antibiotic resistance; orange -ORFs related to virulence; yellow -ORFs related to horizontal transfer of the ARG; blue -ORFs with no identified relation to pathogenesis. The cloned region represents contig regions that were identified in our screenings. Overlapping cloned regions are darker, while dim regions have fewer overlapping identified clones. Figure 7. Distance relationship analysis for two plasmids from K. pneumoniae. A) Distance relationship of the 7 closest plasmids' nucleotide sequences to pKP98M3N42 showing an E-value of less than 0.0 and a minimal sequence cover of 70% in BLAST analysis. The tree was produced using pairwise alignments by means of the Fast-minimum evolution method. Year denotes the collection data and the asterisk indicates the data reported in the public database. B) Distance relationship of the 18 closest plasmids' nucleotide sequences to pKP508BN34 showing an E-value of less than 0.0 and a minimal sequence cover of 50% in BLAST analysis. The tree was produced using pairwise alignments by means of the Fast-minimum evolution method. Year states the collection data and the asterisk indicates the data reported in the public database, informed when collection data was not available. iTOL (https://itol.embl.de) was used for tree visualization.

Figure 8.
Structural comparison of plasmids from K. pneumoniae strains. Nearly identified plasmids were analyzed using blast, and the best hits were used for comparison using BLAST Ring Image Generator (BRIG) (58). For simplification, only divergent regions between the plasmids are shown. A) pKP98M3N42 (black) and pKP1253N44 (magenta) are most similar to plasmids found in K. pneumoniae and E. coli, which have been frequently reported in Brazil (as in Fig. 7A). B) pKP508BN34 (black) was most similar to plasmids only isolated from K. pneumonia, mostly identified in Asia (shown in Fig. 7B) with a high degree of conservation. C) pKP508BN15 (black) was most similar to plasmids found in several different bacterial species (K. pneumoniae, P. damselae subsp. piscicida, Vibrio alginolyticus, and V. cholerae) reported in Asia, Africa, and North America. Both sul2 and rRNA adenine n-6-methyltransferase (ermC) resistance markers are located in a highly divergent region of the plasmid and are not present in most related plasmids. A Figure S1. General features of genomes sequenced in this study.   Figure S4. Schematic representation of genes in identified plasmids exposing the coexistence of ARGs with transposon elements. A) Plasmid pKP125M3N44 (43 kb) from K. pneumoniae 125M3. This plasmid is identical to plasmid pKP98M3N42 from K. pneumoniae 98M3 (Fig. 4A). B) Plasmid pEC126M3N22 (73,9 kb) from E. coli 126M3. C) Plasmid pEC537BN37 (43,3 kb) from E. coli 537B. Legends represents the colors code for the identified genes.