Introduction
Antimicrobial resistance (AMR) occurs when a bacterium is not affected by a specific compound to which other microorganisms are susceptible. This evolutionary phenomenon is exacerbated by the misuse of antimicrobials, significantly impacting public health over the last few decades (O'Neill Reference O'Neill2016). Antimicrobial resistance genes (ARGEs), usually identified in clinical settings, farms, sewage water and urban environments, arise as a consequence of a strong selective pressure associated with the use of antibiotics in human health and animal production. There is a consensus on the approach to tackle this public health issue that considers the ability of many bacteria to thrive in different environments. The One Health concept takes into account the interconnection of animal, environmental and human microbiomes to understand this complex adaptive bacterial process (Aslam et al. Reference Aslam, Khurshid, Arshad, Muzammil, Rasool and Yasmeen2021).
The Antarctic environment can be a source of novel ARGEs (Azziz et al. Reference Azziz, Giménez, Romero, Valdespino-Castillo, Falcón and Ruberto2019). Most of these naturally occurring genes have evolved for other metabolic purposes, but as a result of substrate similarity they can metabolize antimicrobial compounds with low efficiency (Martínez et al. Reference Martínez, Baquero and Andersson2007). ARGEs existed prior to the use of antimicrobials, as well as to the identification of resistance in clinical settings. This has been evidenced by the detection of clinical-type ARGEs in ancient DNA purified from isolated permafrost samples (D́Costa et al. Reference D'Costa, King, Kalan, Morar, Sung and Schwarz2011). Clinical-type ARGEs are highly effective at the metabolization of antimicrobial compounds and are frequently found to be associated with clinical settings. In addition, a broad diversity of ARGEs has been detected in natural, low-impacted environments, suggesting that different compounds with potential antimicrobial activity could play a role in those communities (Scott et al. Reference Scott, Lee and Aw2020). Several human pathogens can survive in natural environments not associated with their hosts (Struve & Krogfelt Reference Struve and Krogfelt2004, van Elsas et al. Reference Van Elsas, Semenov, Costa and Trevors2011, Crone et al. Reference Crone, Vives-Flórez, Kvich, Saunders, Malone and Nicolaisen2020). For example, sewage systems are settings where human-associated bacteria come in contact with environmental microorganisms. These sites have been described as hotspots for ARGE release and dispersion into the environment (Delgado-Blas et al. Reference Delgado-Blas, Ovejero, David, Montero, Calero-Caceres and Garcillan-Barcia2021, Vasallo et al. Reference Vassallo, Kett, Purchase and Marvasi2021).
Plasmids are genetic elements that can regulate their replication independent of the host chromosome. Additionally, plasmids may contain genes that confer the ability to conjugate with other organisms. They can also integrate other intra-genomic mobile sequences, such as insertion sequences, transposons and integrons, which enhance their ability to capture and spread genes conferring adaptive advantages, such as antibiotic resistance genes (ARGs) or metal resistance genes (MRGs; Di Cesare et al. Reference Di Cesare, Eckert, D'Urso, Bertoni, Gillan, Wattiez and Corno2016, Che et al. Reference Che, Yang, Xu, Břinda, Polz, Hanage and Zhang2021). Plasmids are recognized as common carriers of ARGEs in clinical settings (Rozwandowicz et al. Reference Rozwandowicz, Brouwer, Fischer, Wagenaar, Gonzalez-Zorn and Guerra2018). Consequently, genomic surveillance of plasmids carrying ARGEs is becoming essential to understand and monitor pathogen outbreaks in clinical settings.
The Antarctic continent has various regions with characteristic climates and associated biota. Classically, three biogeographical regions have been identified, which include the very cold and dry Continental region, the Maritime Antarctic region, with a less harsh climate, and the sub-Antarctic region, with milder weather and a characteristic biota (Convey Reference Convey2011). Antarctica is subject to strict regulations in environmental management declared in the Environmental Protocol to the Antarctic Treaty (https://www.ats.aq/e/protocol.html). Thus, human activities that take place on the continent must be associated with an environmental impact assessment, defining restricted access sites, identified as Antarctic Specially Protected Areas (ASPA). These sites have been selected considering various aspects, such as the presence of fossils, animal nests and history, amongst others.
The South Shetland Islands are located within the biogeographical region identified as Maritime Antarctica. King George Island stands out for hosting the highest number of scientific stations per area compared with the rest of the continent. Therefore, this island is considered one of the most densely populated sites of the Antarctic continent, at least during the summer, and there are reports of AMR presence associated with the human impact in this region (Hwengwere et al. Reference Hwengwere, Paramel Nair, Hughes, Peck, Clark and Walker2022). However, this island hosts nests and colonies of numerous marine mammals and birds. The fauna of this island, especially birds, have also been linked to the spread of AMR bacteria, given their migration patterns and eventual contact with human populations (Cerdà-Cuéllar et al. Reference Cerdà-Cuéllar, Moré, Ayats, Aguilera, Muñoz-González and Antilles2019).
Fildes Peninsula, the largest area of King George Island without permanent ice cover, represents a small area of the Antarctic continent containing differentially impacted sites. Taking this into consideration, we aimed to evaluate the admixture of bacteria and their genetic determinants of resistance in soil microbial communities exposed to bird and human impacts. Specifically, the objective of this work is to describe the clinical resistome of samples obtained from these sites using a metagenomic approach and to assess the potential transfer of ARGEs in these microbial communities. The analysis of metagenomic data is complemented by the identification of MRGs and their association with mobile genetic elements.
Materials and methods
Sample collection and metagenomic sequencing
Nine soil cores were collected in Fildes Peninsula during the summer campaign of 2017. Three sites were selected with the intent to collect soil exposed to different environmental conditions, especially considering influences of biotic origin (Fig. S1). The first site, named IA, is located in Ardley Island (62°12′34’′S, 58°55′44″W). Some years ago, intending to protect the birds inhabiting this area, this island was classified as ASPA 150. This site hosts a large bird colony that has been monitored for a long time, and the soil is considered to be intensively impacted by bird activity (ornithogenic soil; Guo et al. Reference Guo, Wang, Li, Rosas, Zang, Ma and Cao2018). The second sampled site, named HTP (ASPA 125d), is located at Half Three Point (62°13′38″S, 58°57′12″W). We selected this site as it is relatively far from scientific research stations and breeding sites for mammals and birds, assuming that it was less exposed to animal impact. Finally, samples next to Artigas Research Scientific Station (BCAA) were also collected, specifically from soils impacted by the wastewater treatment system, which has previously been described as not completely isolated from the Antarctic environment (Tort et al. Reference Tort, Iglesias, Bueno, Lizasoain, Salvo and Cristina2017). These samples were designated BCAA (62°11′04″S, 58°57′54″W).
Three soil cores, separated by a distance of at least 2 m from each other, were aseptically collected at each sample site and kept at 4°C until further processing at the laboratory in Uruguay. The total DNA community was extracted using a PowerSoil DNA kit from Qiagen (Hilden, Germany) following the manufacturer's instructions. Extracted DNA quantity and integrity were assessed by 0.9% (w/v) agarose gel electrophoresis in tris acetate EDTA (TAE) buffer. The extraction that presented the best quality profile (both in quantity and integrity) from each of the three replicas was used for metagenomic sequencing at Macrogen, Inc. (Seoul, South Korea). Illumina technology was used through a HiSeq2500 sequencer, which yielded more than 2 Gbp of data for each metagenome with a read length of 101 bp. Raw reads generated from these samples were deposited in at the National Center for Biotechnology Information (NCBI) in the Bioproject PRJNA1046061.
Metagenomic analysis
Metagenomic read quality was assessed using FastQC software (Andrews Reference Andrews2010). Reads were filtered and trimmed using Trimmomatic software (LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:80; Bolger et al. Reference Bolger, Lohse and Usadel2014). The remaining and corrected reads were used as input for metagenomic assembly using IDBA-UD software with default parameters. Quast software (Gurevich et al. Reference Gurevich, Saveliev, Vyahhi and Tesler2013) was used to assess the quality of the assemblies. Plasmid-derived sequences were detected using the plaSquid pipeline (Giménez et al. Reference Giménez, Ferrés and Iraola2022; https://github.com/mgimenez720/plaSquid). Plasmid replication initiator proteins were retrieved using the -ripextract option and compared against the NCBI non-redundant protein database. Abricate software, using the CARD database with 70% identity and coverage thresholds, was used to detect ARGEs in plasmidic and metagenomic contigs (https://github.com/tseemann/abricate).
To understand the dynamics of resistance via a genome-centred approach, we also ran MetaWRAP software (Uritskiy et al. Reference Uritskiy, DiRuggiero and Taylor2018). This wrapper software groups metagenomic contigs in bins composed of sequences derived from the same species genomes, called metagenome-assembled genomes (MAGs). CheckM software was also run to filter out the assembled MAGs by completeness (< 50%) and contamination (> 10%; Parks et al. Reference Parks, Imelfort, Skennerton, Hugenholtz and Tyson2015). MRGs were searched by using the experimentally confirmed resistance genes of the BacMet database (v. 2.0; Pal et al. Reference Pal, Bengtsson-Palme, Rensing, Kristiansson and Larsson2014), the blastp algorithm from BLAST+ software (v. 2.12.0) and filtered to 75% of identity and coverage using custom scripts (Altschul et al. Reference Altschul, Gish, Miller, Myers and Lipman1990).
To assess human impact in the sequenced microbial communities, the crAssphage genome was used as an indicator of human impact. The crAssphage genome was used as a reference for mapping with bowtie2 using default parameters (Langmead & Salzberg Reference Langmead and Salzberg2012).
Construction of a strain for exogenous plasmid isolation
A recombinant strain derived from Escherichia coli DH5α expressing kanamycin resistance (Kmr) and GFP protein (gfp gene) was constructed. For this, a mini-transposon mTn5-gusA-nptII-pgfp12 (Xi et al. Reference Xi, Lambrecht, Vanderleyden and Michiels1999) was used to insert the aforementioned genes into the chromosome of E. coli DH5α. This was done by biparental conjugation using E. coli S17.1-λpir with the plasmid pUT::mTn5-gusA-nptII-pgfp12 as a donor. This plasmid can only replicate in λpir strains able to express π protein. Resistant (Nalr, Kmr) transconjugants were selected, intending to work with recombinant DH5α clones in which just the mini-transposon mTn5-gusA-nptII-pgfp12 was inserted into the chromosome. Three clones were selected and further replicated in Luria-Bertani (LB) solid medium, and the stability of the fluorescent phenotype was checked.
Exogenous isolation assay
The recombinant fluorescent strain derived from E. coli DH5α carrying the gfp gene in the chromosome was used as an acceptor for an in vitro exogenous isolation assays. We grew 5 mL of cell cultures for 24 h in LB broth at 37°C and stirred them at 200 rpm. The entire culture was centrifuged at 3900 g for 5 min and resuspended in 1 ml LB broth. The microbial communities contained in nine soil samples were used as conjugation donors, and three samples obtained from each site were evaluated. For this, 1 g of soil was resuspended in 9 ml of 1/10 diluted sterile tryptic soy broth (TSB) with five autoclaved glass spheres of 5 mm diameter and stirred for 90 min at 200 rpm and 37°C to disaggregate soil particles. A volume of 4 ml of soil suspension was thoroughly mixed with 1 ml of resuspended recipient cells and centrifuged at 3900 g for 5 min. The mixture was resuspended in 100 μl of LB. These conjugation mixtures were thoroughly pipetted over a 45 μm pore-size filter, placed on LB agar and incubated for 48 h at 25°C (Kopmann et al. Reference Kopmann, Jechalke, Rosendahl, Groeneweg, Krögerrecklenfort and Zimmerling2013). This procedure was repeated for all nine soil samples, with three samples from each site used as donor communities.
Then, filters were washed with 5 ml of 0.85% (w/v) NaCl. Three serial dilutions were prepared from this suspension, and volumes of 100 μl were plated on LB agar supplemented with kanamycin (50 μg/ml; Km), nalidixic acid (25 μg/ml; Nal), cycloheximide (10 μg/ml; Chm) and one of the following antibiotics: tetracycline (10 μg/ml), ampicillin (50 μg/ml), trimethoprim (25 μg/ml) or gentamycin (10 μg/ml). Plates were incubated at 37°C for 24 h and fluorescent colonies were identified and selected under ultraviolet light.
Genomic sequencing analysis of the receptor strain
Fluorescent colonies grown on LB agar Nal Km Chm plus one of the previously mentioned antibiotics were transferred to fresh media to confirm their phenotypic stability. Selected colonies were grown in 5 ml LB broth with the corresponding antibiotics. Genomic DNA was extracted using a Quick-DNA Fungal/Bacterial Miniprep kit (Zymo Research, USA) following the manufacturer's instructions, and DNA integrity and quantity were checked via 0.95% agarose gel electrophoresis. Genomic DNA was sequenced at the facilities of Genoma Mayor, Universidad Mayor (Santiago, Chile) using Illumina technology. Raw reads were trimmed using the same software and parameters as previously described for metagenomic data. Filtered reads were mapped against the E. coli DH5α (GCA_000755445.1) reference assembly and miniTn5 assembly (HQ328084.1) and filtered out. The remaining reads were assembled using SPAdes software (Bankevich et al. Reference Bankevich, Nurk, Antipov, Gurevich, Dvorkin and Kulikov2012). Additionally, plaSquid software was used to detect and classify plasmidic contigs, which were further analysed by looking for ARGEs and MRGs as previously described.
To detect plasmidic contigs retrieved through exogenous isolation in the metagenome sequenced from the donor sample, metagenomic reads were mapped against plasmidic contigs using BWA software (Li & Durbin Reference Li and Durbin2010). Additionally, the SAMtools toolkit was used to compute the coverage of plasmidic contigs with quality-filtered metagenomic reads (Li et al. Reference Li, Handsaker, Wysoker, Fennell, Ruan and Homer2009).
Results
Genome-centric resistome analysis
We assembled a different number of contigs for each sequenced metagenome. N50 values ranged from 1280 bp in the HTP metagenome to 3506 bp in the BCAA metagenome. The total length of assembled metagenomes also varied between 33 and 103 Mbp. The presence of human faecal contamination was analysed by mapping these metagenomes against the crAssphage genome as an indicator of human impact (data not shown). This analysis showed that the BCAA sample was the only one containing reads that could be assigned to this phage. Additionally, through taxonomic profiling of the communities, we detected the presence of Faecalibacterium, Prevotella, Acinetobacter and other genera associated with the human gut microbiome in BCAA (Fig. S2).
In order to analyse the genomic resistance traits of the most abundant genomes in the samples, we assembled MAGs from shotgun metagenomic data (Fig. 1). We could identify a total of 20 medium- and high-quality MAGs; 19 of them (95%) included MRGs, while seven encoded ARGEs (35%). Most of the MAGs detected were classified at the family or genus level, corresponding to environmental bacteria. The BCAA metagenome had more MAGs identified. Five of them were classified within the Comamonadaceae family and another two were within the Flavobacterium genus (Fig. 1). Additionally, we detected two genomes of Pseudomonadaceae and Pseudomonas in the BCAA sample. The Psychrobacter genus was also present in two of the three metagenomes analysed. Additionally, an Aeromonadaceae MAG, found in the IA sample, had the most resistance traits identified, with 30 different resistance genes, mainly MRGs. The only MAG detected without known resistance traits corresponded to Helicobacter genus and was found in the IA sample.
Phylogenetic tree of metagenome-assembled genomes (MAGs) detected by MetaWRAP software using 43 marker genes detected with CheckM software. Concatenated genes at the protein level were used for phylogenetic tree construction with the Neighbour-Joining algorithm in the ape R package. Tip colours represent the taxonomic classification assigned by MetaWRAP software and tip shapes indicate the sample from which the MAG was assembled. Metal and antibiotic resistance genes (ARGs) for each MAG are represented as light blue and red bars, respectively. BCAA = Artigas Research Scientific Station.
Gene-centric resistome analysis
Figure 2 shows all ARGEs (38 genes encoding resistance against nine different antibiotic classes) identified in the metagenomes of the three samples analysed. BCAA contains a higher number of diverse ARGEs compared to the other two samples. This was the only metagenome in which we could detect ARGEs encoded in plasmidic contigs. In fact, nearly 20% of ARGEs found, encoding resistance to seven different antibiotic types, were assigned to plasmidic contigs. Within this metagenome, genes for resistance to trimethoprim and tetracycline were found exclusively in plasmidic contigs. Macrolide resistance genes were also evenly distributed in chromosomal and plasmidic contigs.
Tile plot of antimicrobial resistance genes (ARGEs) in the three metagenomes analysed. ARGEs encoded in plasmidic contigs, detected by plaSquid analysis, are indicated in navy blue. Colours in the top bar indicate the antibiotic class for each detected ARGE. BCAA = Artigas Research Scientific Station; MDR = multi-drug resistance.
Diverse kinds of ARGEs were found in the three metagenomes, encoding resistance to different antibiotics. Most of them are components of non-specific resistance mechanisms, which could confer resistance to multiple drugs. Various aminoglycoside resistance genes were found, some of them evenly distributed across samples, such as the aadA gene. However, the gene aph(3′)-IIa was found in metagenomes IA and HTP but not in BCAA. In turn, in BCAA we could find a gene encoding a variant of the enzyme APH(3′)-Ia in a plasmidic contig highly similar to the Acinetobacter baumanii pAC30b plasmid (CP007579.1). Beta-lactam resistance was also an important trait found in these Antarctic metagenomes. TEM-4 was only found in metagenome IA, while OXA and VEB-1 variants were found in BCAA. The OXA-15 gene was found in a highly fragmented plasmidic contig. Macrolide resistance was another trait found in the BCAA metagenome. Genes mphD and msrE were located in the same plasmidic contig, with high sequence identity to a region of megaplasmid pXBB1-9 hosted in Acinetobacter johnsonii XB1. Additionally, sulphonamide resistance genes were detected in chromosomal contigs, and a tetracycline resistance gene was detected in a plasmidic contig, both in the BCAA metagenome.
In order to compare the presence of resistance genes between samples, we normalized the count of ARGEs and MRGs relative to the 16S rRNA count. This analysis demonstrated the significant presence of MRGs in the metagenomes of the three samples analysed, with a mean value of 1.5 genes per 16S rRNA gene (Fig. S3). When considering only plasmidic contigs, the normalized MRG count were decreased by more than 17-fold for the BCAA metagenome. The main traits found confer resistance to multiple metals such as copper, zinc, cadmium, arsenic and lead. In the case of plasmidic ARGEs, these were only detected in BCAA-derived plasmid sequences (Fig. 2).
Exogenous plasmid isolation assay
The samples collected were used for an in vitro exogenous plasmid isolation assay to determine whether the ARGEs contained in these soil samples could be potentially transferred to human-associated bacteria. Nine soil microbial communities were used as donors, and E. coli DH5α::mTn5-gusA-nptII-pgfp12 was used as a receptor strain. We could detect the acquisition of resistance to tetracycline antibiotics in one of the nine transconjugation assays; three colonies were seen to grow with a fluorescent phenotype and resistance to tetracycline (Fig. S4). In this case, one transconjugant strain was sequenced along with cargo plasmids. In order to facilitate plasmid detection and assembly, reads mapping against the E. coli chromosome were discarded. Plasmidic contigs were detected with plaSquid software, and their classifications and traits are shown in Table I. Multiple plasmidic replication origins were found in the transconjugant strain. Three of them could be classified in replicon types IncP1, IncQ and ColE1. These three replicons have different mobility characteristics. MOBP1 and MOBQ relaxases were found in different contigs, which demonstrates the mobilization capacity of some of these replicons.
Plasmidic contigs sequenced from transconjugant strain Escherichia coli DH5ɑGfp+.
MOB = mobilization; ND = not detected; Rep = replicon; RIP = replication initiator protein.
The largest contig encodes an IncP1 replicon that is part of a self-mobilizable plasmid, given the presence of the type IV secretion system (TIVSS) genes (Fig. 3a). This was the only self-conjugative replicon found in the transconjugant strain. However, we could not find any ARGE in this 43 kb contig. Notably, we could find a tetracycline resistance gene tetC in a 1345 bp contig (Contig-31; Table I). This was the only selected resistance phenotype included in the transconjugation assay, and it could not be linked to any particular replicon.
Diagram of annotated plasmids that could be assembled from sequencing of transconjugant clones obtained by exogenous plasmid isolation assays. The IncP plasmid contains the modules needed for self-conjugation, while the IncQ-like plasmid contains a relaxase gene that makes it mobilizable.
Another interesting contig identified was an IncQ-like plasmid, whose entire replicon could be assembled and had a coverage of more than 7X in the metagenome of BCAA site (Contig-4; Table I). This mobilizable plasmid encodes for a relaxase but lacks a TIVSS. The only TIVSS detected was the one associated with the IncP1 replicon. In addition, two aminoglycoside resistance genes could be found in this replicon. One of them, rmtG, encodes a ribosomal methylase that gives high levels of aminoglycoside resistance (Fig. 3b). This phenotype was verified by the growth of the transconjugant strain in LB medium supplemented with 250 μg/ml of kanamycin compared to the lack of growth of the receptor strain, which includes the nptll gene. Another aminoglycoside resistance gene, aadA6, and a multidrug efflux pump, qacH, were detected in a 2187 bp contig (Contig-12; Table I). Plasmidic ARGEs were not located in the same contigs as the replicon sequence determinants, given the high fragmentation of plasmidic sequences.
Discussion
The Antarctic continent features a variety of regions with distinct climates and associated biota. Traditionally, some islands surrounding the continent and the West coast of the Antarctic Peninsula have been grouped into a biogeographical region known as Maritime Antarctica. Unlike the colder and drier Continental region, Maritime Antarctica has a milder climate, which supports a unique biota that has been increasingly impacted by global climate change (Convey Reference Convey2011) and local activities from scientific stations in the area. According to previous studies (Tin et al. Reference Tin, Fleming, Hughes, Ainley, Convey, Moreno and Snape2009), the presence of numerous research stations in certain sites is associated with a significant environmental impact. Global warming also affects microbial communities in Antarctic soils, leading to observable trends. All of these factors influence the evolution and composition of microbial communities in this region.
Sewage treatment and disposal policies in the Antarctic continent are under discussion today. It has been demonstrated that non-treated sewage deposition into sea water may have a significant impact on marine wildlife (Stark et al. Reference Stark, Smith, King, Lindsay, Stark and Palmer2015). Additionally, there have been previous reports of human microbiome-associated bacteria being delivered into the environment from the sewage waters of several Antarctic research stations (Power et al. Reference Power, Samuel, Smith, Stark, Gillings and Gordon2016, Hernández et al. Reference Hernández, Calısto-Ulloa, Gómez-Fuentes, Gómez, Ferrer, González-Rocha and Montory2019). In this study, we found a greater number of various clinical-type ARGEs in the BCAA sample compared to at the other two sites. This sample was collected from soil near a septic chamber, where there had been previously reported leaking events during high-occupancy periods, which directly dispersed human microbiome components into the environment (Tort et al. Reference Tort, Iglesias, Bueno, Lizasoain, Salvo and Cristina2017). Additionally, samples were taken during the summer when scientific stations have high occupancies of scientific and maintenance staff. Environmental monitoring protocols should be established to quantify this kind of impact. In addition, the installation of a wastewater treatment plant is being evaluated as a future operation at BCAA.
Some of the MAGs recovered belong to taxonomic groups that have been reported and studied in the Antarctic environment. For instance, Psychrobacter and Flavobacterium are commonly found in the Antarctic environment. Several bacteria belonging to these genera are adapted to extreme conditions, and on occasions some isolates were studied for biotechnological applications (Herrera et al. Reference Herrera, Braña, Fraguas and Castro-Sowinski2019, Acevedo-Barrios et al. Reference Acevedo-Barrios, Rubiano-Labrador, Navarro-Narvaez, Escobar-Galarza, González, Mira and Miranda-Castro2022, Paun et al. Reference Paun, Banciu, Lavin, Vasilescu and Fanjul-Bolado2022). The impact of penguins on Ardley Island soil samples could be confirmed by the presence of a Fusobacterium MAG in the assembled metagenome. This genus has been reported as dominant in the gut microbiome of Adélie and gentoo penguins established on this island (Zeng et al. Reference Zeng, Li, Han and Luo2021). In addition, metal resistance traits seem to be widely distributed in bacteria that are dominant in the three samples analysed. For example, we could detect several traits of metal resistance in environmental bacteria such as Burkholderia. Interestingly, the highest load of MRGs was detected in a MAG belonging to the Aeromonadaceae family, present in the sample collected from the ornithogenic soil of Ardley Island. Penguin colonies have been demonstrated to accumulate high concentrations of metals in faeces and feathers; thus, MRGs could be an adaptive trait for thriving in ornithogenic soils (Romaniuk et al. Reference Romaniuk, Ciok, Decewicz, Uhrynowski, Budzik and Nieckarz2018, Castro et al. Reference Castro, Neves, Francelino, Schaefer and Oliveira2021).
This study represents the first effort to assess the mobilization capacity of clinical ARGEs detected in Antarctic soils exposed to various types of environmental impact. We identified 36 classes of ARGEs in a soil sample collected near the septic chamber of BCAA, a higher number compared to the ARGEs identified in the other two metagenomes. Additionally, we determined that some of these resistance genes are associated with mobile genetic elements and are potentially transferable to exogenous bacterial acceptors.
We obtained a transconjugant clone of E. coli using the microbial community contained in a soil sample from BCAA as a donor. Although optimal conditions of temperature and incubation time were not adjusted to the Antarctic environmental conditions, we found that some plasmids could be mobilized to another recipient bacteria. In these community-wide conjugation experiments, we mobilized together different types of plasmids, ranging from totally self-conjugative, such as the IncP1 replicon, to other plasmids containing just an OriT as a mobilization element, such as the ColEI replicon. These multiple-replicon transfer events catalysed by the presence of IncP1 conjugative plasmids have already been reported in other environments (Schlüter et al. Reference Schlüter, Szczepanowski, Pühler and Top2007, Brown et al. Reference Brown, Sen, Yano, Bauer, Rogers, Van der Auwera and Top2013). Even more interesting is the fact that IncP1 backbones with high similarity to the pKJK5 plasmid, such as the one reported in this work, seem to be highly effective at thriving in soil microbiomes and at mobilizing ARGEs (Heuer et al. Reference Heuer, Binh, Jechalke, Kopmann, Zimmerling and Krögerrecklenfort2012).
Another remarkable feature of IncP1- and IncQ-like replicons is their broad host range, which enables ARGE transfer between phylogenetically distant bacterial species. This may be a key step for ARGE dispersal among different environmental compartments (Smalla et al. Reference Smalla, Heuer, Götz, Niemeyer, Krögerrecklenfort and Tietze2000, Klümper et al. Reference Klümper, Riber, Dechesne, Sannazzarro, Hansen, Sørensen and Smets2015). Soil microbial communities impacted by sewage or manure are important hotspots for ARGE transfer events, given the ecological connectivity of microbial communities that have evolved and adapted to the different environments (Cohen et al. Reference Cohen, Gophna and Pupko2011, Martínez et al. Reference Martínez, Coque and Baquero2015). Additionally, there is evidence for the presence and release of antibiotics in Antarctic research station sewage, which could further act as a selective pressure for newly resistant clones (Hernández et al. Reference Hernández, Calısto-Ulloa, Gómez-Fuentes, Gómez, Ferrer, González-Rocha and Montory2019). Policy frameworks should take into consideration the microbiological risk for the selection of ARGEs within the Antarctic microbiosphere (Hernando-Amado et al. Reference Hernando-Amado, Coque, Baquero and Martínez2019).
Our results suggest that the high diversity of clinical-type ARGEs and the presence of human gut bacteria indicate significant microbiological impacts from inadequate sewage infrastructure at Antarctic research stations. Additionally, anthropogenic impacts on soil microbiomes include the potential transfer of mobile genetic elements, which could drive the evolution of indigenous microbial communities and threaten the genetic diversity of Antarctic soil microbiomes. Future research should include a quantitative evaluation of ARGEs and specific bacterial genes as indicators, using soil and water samples collected at predefined sites and throughout the year (Berendonk et al. Reference Berendonk, Manaia, Merlin, Fatta-Kassinos, Cytryn, Walsh and Martinez2015). With the planned installation of a new effluent treatment system at BCAA, such analyses could be integrated into routine monitoring procedures.
Author contributions
MG conceived the project, collected the samples, conducted the analyses and wrote the first draft of the manuscript. GA contributed to the analysis of the molecular dataset and to the editing of the final manuscript. SB conceived the project, obtained funding resources and contributed to the editing of the manuscript.
Acknowledgements
The authors thank the crew from the Instituto Antártico Uruguayo, who worked at the Artigas Scientific Antarctic Base (BCAA) during the summer campaigns.
Financial support
This work was supported by PEDECIBA Biología (Programa de Ciencias Básicas-Área Biología), ANII (Agencia Nacional de Investigación e Innovación) and IAU (Instituto Antártico Uruguayo).
Competing interests
The authors declare none.
Supplemental material
Four supplemental figures will be found at https://doi.org/10.1017/S0954102024000300.
