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# Bacterial dynamics of the plastisphere microbiome exposed to sub-lethal antibiotic pollution

Brune Joannard<fnref n="1" /> and Concepcion Sanchez-Cid<fnref n="1" />*

**Abstract**

**Background** Antibiotics and microplastics are two major aquatic pollutants that have been associated to antibi-
otic resistance selection in the environment and are considered a risk to human health. However, little is known
about the interaction of these pollutants at environmental concentrations and the response of the microbial commu-
nities in the plastisphere to sub-lethal antibiotic pollution. Here, we describe the bacterial dynamics underlying this
response in surface water bacteria at the community, resistome and mobilome level using a combination of methods
(next-generation sequencing and qPCR), sequencing targets (16S rRNA gene, pre-clinical and clinical class 1 integron
cassettes and metagenomes), technologies (short and long read sequencing), and assembly approaches (non-assem-
bled reads, genome assembly, bacteriophage and plasmid assembly).

**Results** Our results show a shift in the microbial community response to antibiotics in the plastisphere microbiome compared to surface water communities and describe the bacterial subpopulations that respond differently to antibiotic and microplastic pollution. The plastisphere showed an increased tolerance to antibiotics and selected different antibiotic resistance bacteria (ARB) and antibiotic resistance genes (ARGs). Several metagenome assembled genomes (MAGs) derived from the antibiotic-exposed plastisphere contained ARGs, virulence factors, and genes involved in plasmid conjugation. These include *Comamonas*, *Chryseobacterium*, the opportunistic pathogen *Stenotrophomonas maltophilia*, and other MAGs belonging to genera that have been associated to human infections, such as *Achromo-bacter*. The abundance of the integron-associated ciprofloxacin resistance gene *aac(6')-lb-cr* increased under ciprofloxacin exposure in both freshwater microbial communities and in the plastisphere. Regarding the antibiotic mobilome, although no significant changes in ARG load in class 1 integrons and plasmids were observed in polluted samples, we identified three ARG-containing viral contigs that were integrated into MAGs as prophages.

**Conclusions** This study illustrates how the selective nature of the plastisphere influences bacterial response to anti-biotics at sub-lethal selective pressure. The microbial changes identified here help define the selective role of the plastisphere and its impact on the maintenance of environmental antibiotic resistance in combination with other anthropogenic pollutants. This research highlights the need to evaluate the impact of aquatic pollutants in environmental microbial communities using complex scenarios with combined stresses.

**Keywords** Plastisphere, Sub-lethal, Antibiotics, Antibiotic resistance, Metagenomics, Surface water, Microplastics

<footnote>*Correspondence:
Concepcion Sanchez-Cid
concepcion.sanchezcid-torres@ec-lyon.fr</footnote>

<footnote n="1">¹ Université de Lyon, Université Claude Bernard Lyon 1, UMR CNRS 5557, UMR INRAe 1418, VetAgro Sup, Ecologie Microbienne, 69622 Villeurbanne, France</footnote>
**Background**

Human activities have contributed to the dramatic global increase in antibiotic resistance in clinical and environmental settings [1, 2] despite the natural background levels of antibiotic resistance in the environment [3]. Urgent solutions are needed in order to preserve the efficacy of antibiotic therapy [3]. Anthropogenic activities lead to environmental pollution that might stimulate the development of antibiotic resistance in environmental reservoirs and the dissemination of this resistance to the human microbiome [4–7]. Antibiotics and their role as environmental pollutants have been widely studied, since they may impose a direct selective pressure for antibiotic resistance in environmental bacteria [8–11]. The anthropogenic uses of antibiotics and their incomplete elimination during wastewater treatment lead to a release of residual concentrations of these drugs into the environment [12, 13]. Antibiotics are often found in the environment at sub-inhibitory concentrations (i.e., too low to significantly inhibit overall community growth) [14]. However, environmental, sub-inhibitory levels of antibiotics can affect bacterial dynamics, select for antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs), and change the evolution of mobile genetic elements (MGEs) in the environment [10, 15, 16]. The presence of antibiotic residues in the environment may, therefore, increase the risk of antibiotic resistance dissemination in environmental settings that might consequently disseminate to the human microbiome.

In addition, non-antibiotic pollutants such as metals, biocides, non-antibiotic drugs, and microplastics are often found in the environment and may co-select for antibiotic resistance [17–20]. Plastics produced by humans are one of the primary sources of anthropogenic environmental pollution with 368 million tons produced in 2019 [21]. This microplastic pollution is smaller than 5 mm in size [22] and derived from the degradation of larger plastics or directly discharged into the environment by wastewater treatment plants, industries, and agricultural activities [23]. Microplastics are found ubiquitously in aquatic environments, including sea water and freshwater [24–26]. They provide a stable ecosystem for the microorganisms that are able to form biofilms on their surface. Bacterial community structure in this ecosystem, known as the “plastisphere” [27], varies substantially from that of the surrounding environment [28, 29]. Several studies have shown an enrichment of both ARB and ARGs in the plastisphere [30, 31]. Some explanations of this phenomenon are the colonization of the plastic surface by bacteria that carry antibiotic resistance genes and that proliferate in the biofilm (vertical transfer) [32] and an increased horizontal gene transfer of ARGs given the closer contact between bacteria in the biofilm and an

increased ROS generation and cell permeability [32–35]. Thus, microplastics are a reservoir of antibiotic resistance that can transport and disseminate antibiotic resistance in the environment [31], where they can persist for long periods of time [27]. In addition, microplastics may be ingested by living organisms [36, 37] that could act as dissemination routes through the food chain [38]. Given the numerous routes microplastics and their associated plastisphere can disseminate through the environment and eventually to the human microbiome, they are considered to be a threat to human health [39].

Microplastics and antibiotics often coexist in environments such as urban water. Microplastics may adsorb a wide range of chemicals such as antibiotics, heavy metals, and other xenobiotics [39]. The adsorption of antibiotics onto plastic surfaces could increase their concentrations related to the surrounding environment and the selective pressure on the bacterial communities [40]. In other words, the presence of microplastics and their associated plastisphere could increase the magnitude of the antibiotic resistance selection that antibiotics induce in environmental communities [41–43]. On the other hand, bacteria in the plastisphere could be more tolerant to antibiotics than planktonic bacteria in surface waters [44]. Also, microplastic pollution could increase the persistence of antibiotics in the environment and their associated risks for human health [45]. Therefore, there are increasing concerns that the plastisphere could be involved in environmental antibiotic resistance, so that research is needed to understand the dynamics of the bacterial communities exposed to antibiotics in the plastisphere [39, 46].

The primary goal of this study was to explore the microbial ecology of the antibiotic-exposed plastisphere to determine whether the impact of microplastics on bacterial communities imposes an additional selective pressure that changes the outcome of antibiotic-induced selection. In addition, we aimed to understand the mechanisms involved in the response to microplastics and antibiotics individually and in combination at the community level (community dynamics and selection on putative ARB, i.e., bacterial genomes encoding genes related to antibiotic resistance) and the genetic level (selection and genetic context of ARGs). We hypothesized that communities in the plastisphere would be more tolerant to antibiotics and exhibit a reduced response to antibiotic pollution than freshwater bacterial communities not exposed to microplastics. Using a combination of sequencing targets (16S rRNA gene, short and long-read metagenomic and class 1 integron cassette sequencing), plasmid, bacteriophage and genome assembly-based approaches, gene screening in non-assembled reads, and gene quantification by qPCR, we evaluated
the response to antibiotics at sub-lethal concentrations
in a river water bacterial community in the presence and
absence of microplastics.

**Materials and methods**

Experimental setup

One liter of urban river water from the Rhône river in
Lyon was sampled on January 4, 2023 (45°45′08.3″N
4°50′11.3″E), in a polypropylene container and pro-
cessed 30 min after collection. In order to reduce the
amount of antibiotic residues and other xenobiotics
potentially present in the samples, 500 ml of river water
were autoclaved. One milliliter of river water contain-
ing bacteria was incubated in parallel in 9 ml of 1:10 TSB
overnight at 25 °C and 185 rpm. Then, 0.5 ml of overnight
culture was inoculated into 4.5 ml of sterile Rhône river
water. Three conditions were prepared in triplicate: bac-
teria exposed to microplastics, bacteria exposed to anti-
biotics, and bacteria exposed to both microplastics and
antibiotics. In addition, triplicate controls that were not
polluted in the laboratory were included in the study to
compare their evolution over time to that of polluted
samples. Polystyrene was selected as a model of micro-
plastics given its low biodegradability and frequent detec-
tion in environmental settings [21]. Approximately 100
spherical polystyrene particles with a diameter of 430 µm
(Sigma-Aldrich) were added per sample. Ciprofloxa-
cin and gentamicin were selected as antibiotic models
given their incomplete removal during wastewater treat-
ment [47] and their reported effects on antibiotic resist-
ance selection at sub-inhibitory concentrations [15, 16,
48]. Gentamicin (Duchefa Biochemie) and ciprofloxacin
(Sigma-Aldrich) were added together at 100 ng/ml each.
The choice of this antibiotic concentration was based on
a sub-inhibitory effect of both antibiotics at 100 ng/ml on
river water bacteria in vitro (Figure S1 in Supplementary
Information). Samples were incubated at 25 °C for 3 days
and shaken to emulate river currents (185 rpm) before
extracting DNA from bacteria in the plastisphere and in
river water. In order to reduce the influence of external
physiochemical factors, temperature was kept constant
through the incubation and samples were protected from
UV exposure.

DNA extraction and estimation of bacterial community
abundance by qPCR

Samples without microplastics were centrifuged at
2500 rpm for 10 min and the pellet was resuspended in
500 µl of the extraction buffer from the NucleoSpin Tis-
sue Kit (Macherey-Nagel). Polystyrene beads were left to
decant and, after completely removing all the surround-
ing water, they were resuspended in 500 µl of extraction
buffer. Samples were transferred to a Lysis Matrix D tube

(MP Biomedicals), heated for 5 min at 95 °C, and under-
went beat-beating at 5.5 m/s for 30 s (twice for river water
bacterial pellets and three times for bacteria in the plas-
tisphere). Total genomic DNA was then extracted using
the NucleoSpin Tissue Kit (Macherey-Nagel) according
to manufacturer's instructions.

In order to determine whether the added antibiotics
had an inhibitory effect on environmental bacteria at
the community level (i.e., significant inhibition of over-
all bacterial growth), the size of the bacterial communi-
ties in river water and the plastisphere with and without
antibiotic exposure was estimated by quantifying the 16S
rRNA gene by qPCR using the 341F (5'-CCTACGGGA
GGCAGCAG- 3') and 534R (5'-ATTACCGCGGCT
GCTGGCA-3') primers [49]. qPCR amplification was
carried out using the CFX Duet Real-Time PCR System
(Bio-Rad) in a 20 µl reaction volume containing Quanti-
Nova SYBR Green PCR Master Mix (Qiagen), 0.75 µM
of each primer, and 2 µl of DNA. Two non-template con-
trols were included in the assay. Standard curves for all
the assays were obtained using tenfold serial dilutions
of a linearized plasmid pGEM-T Easy Vector (10⁸–10³
copies) containing the 16S rRNA gene of *Pseudomonas
aeruginosa* PAO1. Cycling conditions for qPCR amplifi-
cation were 95 °C for 2 min followed by 35 cycles of 95 °C
for 5 s and 60 °C for 30 s. Melting curves were generat-
ing by increasing temperature from 60 °C to 95 °C after
amplification. The reaction had an efficiency of 100% and
a linearity R² coefficient of 0.98.

16S rRNA gene sequencing and bioinformatic analysis
of bacterial community composition

The V4 hypervariable region of the 16S rRNA gene was
amplified using forward 515F (5'-TCGTCGGCAGCG
TCAGATGTGTATAAGAGACAGGTGYCAGCMGCC
GCGGTAA-3') and reverse 806Rb ('5-GTCTCGTGG
GCTCGGAGATGTGTATAAGAGACAGGGACTACN-
VGGGTWTCTAAT-3') primers with Illumina overhangs
[50]. This amplicon (~250 bp) was chosen to optimize
read overlap for the sequence merging step. DNA was
amplified by PCR using the Platinum Taq DNA Polymer-
ase (Invitrogen) and the following conditions: 94 °C for
2 min, 30 cycles of 94 °C for 30 s, 55 °C for 30 s and 72 °C
for 30 s, and a final extension for 5 min at 72 °C. DNA
libraries were prepared from amplified products using
the Platinum Taq DNA Polymerase (Invitrogen) and the
Nextera XT Index Kit V2 (Illumina) according to the Illum-
mina's protocol for amplicon sequencing library prepa-
ration. Paired-end sequencing (2×251 bp) of barcoded
amplicons was performed using the MiSeq System and
the MiSeq Reagent Kit v2 (Illumina) in the laboratory on
our sequencer.
Sequences were treated using the DADA2 pipeline (version 1.16.0, R version 4.1.3) [51] to remove primers, trim the last 10 bases of the forward reads and the last 40 bases of the reverse reads based on read quality scores (Figures S2a and S2b in Supplementary Information), merge forward and reverse reads, remove chimeric reads, and obtain amplicon sequence variants (ASVs). The number of reads that went through each step of the pipeline are shown in Table S1 (Supplementary Information). ASVs were annotated taxonomically to the genus level using the Ribosomal Database Project (RDP trainset 18 downloaded from the DADA2 repository) [52]. Then, ASVs that had less than ten copies in total were removed. Sequencing depths obtained after sequence treatment and ASV removal are shown in Table S2 in Supplementary Material. The final ASV table with ASV abundance and taxonomic annotation, as well as a file with ASV sequences, have been uploaded as Additional Files 1 and 2, respectively. Statistical differences in ASV abundance between conditions were determined using the DESeq2 package in R (version 1.34.0) [53]. Log2FoldChange values were adjusted using the Approximate Posterior Estimation for generalized linear model in the apeglm package in R (version 1.16.0). Differences were defined based on a *p*-value < 0.05 and a log2FoldChange ≥ 2.

## Metagenomic sequencing and bioinformatic analyses

Metagenomics libraries were prepared from ≤1 ng of DNA using the Nextera XT Library Prep Kit and Indexes (Illumina) as detailed in Illumina's "Nextera XT DNA Library Prep Kit" reference guide, using 12 amplification cycles for the indexing PCR. DNA sequencing was performed using the MiSeq System and the MiSeq Reagent Kit v2 (Illumina) in the laboratory on our sequencer. The same samples were re-sequenced in the laboratory on our sequencer using the MiniON, R10.4.1 flow cells, and the Native Barcoding Kit 24 V14 (Oxford Nanopore Technologies) to obtain longer reads. Six samples were barcoded per run, since DNA input was too low to run them separately. Long-read metagenomics libraries were prepared as detailed in Oxford Nanopore's "Ligation Sequencing gDNA-Native Barcoding Kit 24 V14" protocol.

Short reads sequenced using the MiSeq System were quality-filtered according to the criteria described by Minoche et al. [54]. Sequencing depths of short and long reads are shown in Table S2 in Supplementary Material. Forward and reverse short reads were concatenated and blasted against the CARD amino acid database (version 3.2.8) [55] using Diamond [56], which translates nucleotide queries prior to blasting, in order to determine the abundance of total ARGs in the metagenomic reads. The obtained results were filtered at a minimum amino acid identity of 60%, a minimum length of 33 amino acids, and

a maximum *e*-value of 10<sup>-5</sup>. The best hit was used, singletons were removed, and gene abundance was normalized by sequencing depth.

Quality-filtered short reads were co-assembled using MEGAHIT [57] and reads were mapped onto the contigs using Bowtie2 [58]. Profiles were created for each individual sample and merged using the anvi'o [59] metagenomic workflow (anvi'o version 7.1). The assembled contigs were blasted against the CARD antibiotic gene database to identify ARGs. Results were filtered at an amino acid identity percentage of 60%, 100 amino acid length, and an *e*-value of 10<sup>-5</sup>. The best hit was used. Finally, contigs were binned based on their differential coverage across samples using anvi'o, and the bins were refined based on differential coverage and sequence composition. Bins with <50% completion and ≥10% redundancy were discarded, since they were considered low-quality metagenome assembled genome (MAG) drafts according to Bowers et al. [60]. Obtained bins had a completion from 78.9 to 100% and a redundancy from 2.8 to 7% (Table S3 in Supplementary Information). In parallel, two hybrid genome assembly methods were used on long and short reads. Quality-filtered short reads were co-assembled to long reads obtained from the Oxford Nanopore sequencing using Unicycler [61] or OPERA-MS without reference-guided binning [62] and MAGs were obtained using the anvi'o metagenomic workflow as described above. A single MAG of 100% completion and 0% redundancy was obtained from the Unicycler assembly, whereas 9 MAGs with a completion from 54.9 to 100% and a redundancy of 0 to 7% were obtained from the OPERA-MS hybrid assembly (Figure S3 in Supplementary Information).

In order to identify mechanisms related to a potential risk for human health, the MAGs were annotated using COG 2020, pfam v32 and SEED to identify type IV secretion system genes and virulence genes were identified in the MAGs using the BV-BRC database https://www.bv-brc.org/ [63]. Plastic degradation genes were identified using the online plastic degradation database PlasticDB https://plasticdb.org/ [64]. To determine the genetic context of ARGs, the PlasX machine-learning approach [65] was used to distinguish between chromosomal and plasmid contigs.

## Identification of antibiotic resistance genes in plasmids and bacteriophages

Plasmid reads were assembled from long and short reads using metaplasmidSPAdes (SPAdes version 3.15.5) [66] to identify changes in the plasmidome under antibiotic and/or microplastic pollution. Reads were co-assembled both altogether and per condition and ARGs were identified in plasmid contigs using CARD as described above (amino
acid identity > 60% and alignment length > 150 amino acids).

Viral contigs were obtained from long and short reads using metaviralSPAdes (same SPAdes version) [67]. Reads from each sample were mapped onto the viral contigs using bowtie2 and ARGs were identified using CARD as described above (amino acid identity > 60% and alignment length > 150 amino acids). The taxonomy and bacterial hosts of ARG-containing viral contigs was determined using PhageScope [68]. In order to determine whether viral contigs were integrated in the MAGs, all obtained MAGs (regardless of the assembly approach) were blasted against the contigs (nucleotide blast). Blast results were filtered at > 99% nucleotide identity and > 1000 nucleotide alignment length.

## Evaluation of class 1 integron abundance and cassette array composition

Class 1 integron abundance was estimated by a qPCR amplification of the integrase *intI1* gene using HS463a (5'-CTGGATTTCGATCACGGCACG-3') and HS464 (5'-ACATGCGTGTAAAT-CATCGTCG-3') primers following the protocol and the amplification conditions described above (annealing temperature = 60 °C). Standards were obtained from freshwater DNA and cloned and transformed using the TOPO TA cloning Kit (Thermo Fisher Scientific). The reaction had an efficiency of 90.5% and a linearity *R*² coefficient of 0.998.

Class 1 integron cassettes are often missed using metagenomic sequencing approaches. Therefore, in order to increase sensitivity and determine the composition of class 1 integron cassette arrays, they were amplified by PCR and sequenced. Both clinical and preclinical cassettes were evaluated in this study. Clinical class 1 integron cassettes were amplified using HS458 (5'-GTTTGATGTTATGGAGCAGCAACG-3') and HS459 (5'-GCAAAAAGGCAGCAATTATGAGCC-3') primers as described by Holmes et al. [69]. Preclinical class 1 integron cassettes were amplified using MRG284 (5'-GTTACGCCGTGGGTCGATG-3') and MRG285 (5'-CCAGAGCAGCCGTAGAGC-3') primers as described by Gillings et al. [70]. Then, amplicons were cleaned-up using AMPure XP beads (Beckman Coulter) and sequenced using the MiSeq System in the laboratory on our sequencer as described for metagenomic reads. Reads were trimmed using the Fastq Quality Trimmer tool of the FASTX-Toolkit. Nucleotides that did not meet a minimum quality score of Q20 were trimmed from the sequences and sequences shorter than 100 nucleotides after trimming were removed. Sequencing depths obtained after sequence treatment are shown in Table S2 in Supplementary Material. Then, reads were concatenated and blasted against the CARD database as

described for short read metagenomic reads to determine the overall ARG content of pre-clinical and clinical class 1 integron cassettes, identify ciprofloxacin and gentamicin resistance genes to target by qPCR, and determine whether the ARGs present in the MAGs are potentially associated to class 1 integrons.

## Gentamicin and ciprofloxacin resistance gene abundance

The abundance of a ciprofloxacin resistance gene (*aac(6')-Ib-cr*) and a gentamicin resistance gene (*aac(6')-IIc*) identified in class 1 integrons as well as that of the plasmid-borne ciprofloxacin resistance gene *qnrB* was determined in the freshwater and the plastisphere microbiomes by qPCR. Standards were obtained from freshwater DNA and cloned and transformed using the TOPO TA cloning Kit (Thermo Fisher Scientific). DNA was amplified as detailed above. The *aac(6')-Ib-cr* gene was amplified using forward 5'-TTGCGATGCTCTATG AGTGGCTA-3' and reverse 5'-CTCGAATGCCTGGCG TGTTT-3' primers [71] and an annealing temperature of 57 °C. The reaction had an efficiency of 85.5% and a linearity *R*² coefficient of 0.998. The *qnrB* gene was amplified using forward 5'-CTTCACACATTGCGATCTGAC'-3' and reverse 5'- CAACGATGCCTGGTAGTTGT-3' primers [72] and an annealing temperature of 60 °C. The reaction had an efficiency of 98.4% and a linearity *R*² coefficient of 0.998. The *aac(6')-IIc* gene was amplified using forward 5'-CGACCCGACTCCGAACAA-3' and reverse 5'-GCACGAATCCTGCCTTCTCA-3' primers [73] and an annealing temperature of 60 °C. The reaction had an efficiency of 93.6% and a linearity *R*² coefficient of 1. The number of copies of each gene were normalized by the copies of the 16S rRNA gene per sample.

## Statistical analyses

All statistical analyses on metagenomic and qPCR data were carried out using GraphPad Prism 9. Normal distribution was tested using the Shapiro-Wilk test. Normally-distributed data were analyzed using one-way ANOVA and Tukey's multiple comparison tests. Data that did not follow a normal distribution were analyzed using the Kruskal-Wallis and Dunn's multiple comparison tests. A *p*-value lower than 0.05 was regarded as significant. Regarding multiple comparison tests, only significant differences are shown.

## Results

### Dynamics of bacterial community response and putative ARB selection

Bacterial biomass in the plastisphere non-exposed to antibiotics—estimated by a qPCR of the 16S rRNA gene—was lower (*p*-value < 0.05) than that of the antibiotic-exposed plastisphere and of freshwater bacterial
communities non-exposed to microplastics, both in the presence and absence of antibiotics (Fig. 1A). On the other hand, the abundance of bacterial communities in the antibiotic-exposed plastisphere was similar to that of freshwater bacterial communities (Fig. 1A). In order to determine whether the total bacterial biomass in the samples was comparable, the biomass from samples in the plastisphere was added to that of the surrounding water of those samples. No significant differences in terms of total biomass were detected between samples exposed and not exposed to microplastics (Fig. 1B). Antibiotic levels were, therefore, considered as sub-lethal both in freshwater and in the plastisphere.

Overall bacterial community composition evaluated by the sequencing of the 16S rRNA gene was influenced by the exposure to antibiotics, microplastics, and both together (Figure S3 in Supplementary Information). A shift in community composition was observed between the antibiotic-exposed freshwater and plastisphere microbiomes. Antibiotic exposure at sub-lethal

levels alone had the highest impact on freshwater bacterial community composition. A significant shift of bacterial community composition at the ASV level was found in freshwater bacteria exposed to antibiotics and had 16 ASVs that were higher (*p*-value < 0.05) and 7 ASVs that were significantly lower than in non-polluted controls (Fig. 2A). The ASVs that had a higher abundance in the antibiotic-exposed freshwater microbiome belonged to several genera including *Acinetobacter*, *Brucella*, *Chryseobacterium*, *Klebsiella*, *Pseudomonas*, *Sphingobacterium*, and *Stenotrophomonas*. The ASVs with a decreased abundance upon antibiotic exposure belonged to *Achromobacter*, *Caulobacter*, *Chryseobacterium*, *Empedobacter*, *Herbaspirillum*, and *Raoultella*. On the other hand, only one ASV from *Stenotrophomonas* had a lower relative abundance in the plastisphere than in non-polluted freshwater controls, whereas one ASV (not annotated to the genus level) belonging to the *Enterobacteriaceae* family was more abundant in the plastisphere. This was the case for both the antibiotic-exposed and

<figure><img src="image_2.png" /><figcaption>**Fig. 1** Dynamics in bacterial biomass in freshwater bacteria exposed to antibiotics, microplastics or both. A Comparison between freshwater bacteria non-exposed to microplastics and bacteria in the plastisphere (ANOVA *p*-value = 0.004). B Comparison between freshwater bacteria non-exposed to microplastic and total biomass (freshwater and plastisphere bacteria) in samples exposed to microplastics (ANOVA *p*-value = 0.2). Only significant pairwise comparisons (*p*-value < 0.05) are shown. Bacterial biomass was estimated by a qPCR of the 16S rRNA gene. MP-/AB- non-polluted freshwater controls, MP-/AB+ antibiotic-exposed freshwater, MP+/AB- non-antibiotic-exposed plastisphere, MP+/AB+ antibiotic-exposed plastisphere. *n* = 3</figcaption></figure>
non-antibiotic-exposed plastisphere (Fig. 2B, C). In addition, no significant differences were found between bacterial communities in the plastisphere exposed and not exposed to antibiotics.

Regarding the differences in community composition between antibiotic-exposed freshwater and non-antibiotic-exposed plastisphere, six ASVs belonging to several genera including *Chryseobacterium*, *Citrobacter*, *Pseudomonas*, and *Sphingobacterium* were found at a higher abundance in antibiotic-exposed freshwater communities than in the non-antibiotic-exposed plastisphere (Fig. 2D). On the other hand, four ASVs belonging to *Acinetobacter* and *Chryseobacterium* were more abundant in the plastisphere. Furthermore, a shift in the response to antibiotics was observed in the plastisphere with nine ASVs belonging to *Acinetobacter*, *Brenneria*, *Chryseobacterium*, *Comamonas*, *Empedobacter*, *Janthinobacterium*, and *Stenotrophomonas* showing a higher abundance in the antibiotic-exposed plastisphere and six ASVs related to *Chryseobacterium*, *Citrobacter*, *Klebsiella*, *Enterobacteriaceae*, *Pseudomonas*, and *Stenotrophomonas* showing a higher abundance in freshwater communities (Fig. 2E). Finally, two ASVs belonging to the *Acinetobacter* genus were consistently more abundant in the plastisphere than in antibiotic-exposed freshwater communities (Fig. 2D,E), whereas an ASV related to *Citrobacter* consistently showed a lower abundance in the plastisphere. However, different ASVs belonging to the same genera (*Acinetobacter*, *Chryseobacterium* and *Stenotrophomonas*) had different changes in abundance upon exposure to anthropogenic pollution depending on the nature of the pollutant (antibiotics, microplastics, or both) (Fig. 2A-E). These results indicate the presence of bacterial subpopulations that respond differently to these pollutants.

An assembly-based metagenomic approach was used to obtain a better understanding of the bacterial dynamics underlying the exposure to pollutants in the freshwater and the plastisphere communities. The hybrid assembly from long and short reads using the OPERA-MS assembler provided the higher number of MAGs (9) (Table S3 in Supplementary Information). The five MAGs obtained from the assembly of short reads were assigned to

*Acinetobacter*, *Achromobacter* sp. 002902905, *Aeromonas*, *Herbaspirillum*, and *Stenotrophomonas maltophilia* and had a completion ranging from 78.9 to 100% and a redundancy of 2–7% (Table S3 in Supplementary Material). All these MAGs, except *Acinetobacter*, as well as five more associated to *Klebsiella*, *Comamonas*, *Chryseobacterium*, and two bacteria from the *Enterobacteriaceae* family were obtained using the OPERA-MS hybrid assembler. These MAGs had a completion from 54.9 to 100% and a redundancy of 0 to 7% (Table S3 in Supplementary Material). Finally, one single MAG associated to *Stenotrophomonas maltophilia* was found in the reads assembled using the Unicycler hybrid assembler. This MAG had a completion of 100%—similarly to the ones obtained using the other two approaches—and a redundancy of 0%, whereas the redundancy of the MAG obtained from short reads was of 4.2% and that of the MAG obtained using OPERA-MS was of 7% (Table S3 in Supplementary Material). Although OPERA-MS showed an overall better performance than the short-read assembly or the Unicycler hybrid assembler, it missed the *Acinetobacter* bin obtained using short reads and a complete, single-strain (0% redundancy) *Stenotrophomonas maltophilia* genome was obtained using Unicycler. In addition, when the same MAG was obtained using different assembly approaches, MAG abundance trends were similar regardless of the method used (Fig. 3 shows the abundance of the highest quality MAGs and Figures S4 and S5 show the abundance of the same MAGs obtained using other assembly approaches).

Different dynamics were observed during antibiotic and microplastic exposure (Fig. 3). All ARG-containing contigs identified in these MAGs were associated to chromosomes according to PlasX. The *Achromobacter* (Fig. 3A) and *Herbaspirillum* (Fig. 3B) MAGs were significantly more abundant in antibiotic-exposed samples. For *Achromobacter*, the increase in abundance was observed both in freshwater and in the plastisphere, whereas the abundance of *Herbaspirillum* increased more in the antibiotic-exposed freshwater communities. These MAGs contained chromosomally-encoded genes from efflux pumps operons involved in the resistance to ciprofloxacin and gentamicin (Table 1); some of these genes were

(See figure on next page.)

**Fig. 2** Significant differences (*p*-value < 0.05) in ASV abundance between experimental conditions. **A** Non-polluted water (MP-/AB-) versus antibiotic-polluted water (MP-/AB+). **B** Non-polluted water (MP-/AB-) versus plastisphere in the absence of antibiotics (MP+/AB-). **C** Non-polluted water (MP-/AB-) versus antibiotic-polluted plastisphere (MP+/AB+). **D** Antibiotic-polluted water (MP-/AB+) versus plastisphere in the absence of antibiotics (MP+/AB-). **E** Antibiotic-polluted water (MP-/AB+) versus antibiotic-polluted plastisphere (MP+/AB+). Statistical analyses on ASV abundances obtained from the sequencing of the 16S rRNA gene were performed using the DESeq2 package in R. Colors represent taxonomic affiliation of each ASV at the genus level. Labels represent the unique number associated to each ASV to help identify ASV variants from the same genus. No significant differences in ASV abundance were found between the antibiotic-exposed and the non-antibiotic-exposed plastisphere. *n* = 3
<figure><img src="image_3.png" /><figcaption>**Fig. 2** (See legend on previous page.)</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3** Relative abundance of the MAGs obtained from the short-read co-assembly and from the hybrid co-assembly of short and long reads. **A** *Achromobacter* sp. 002902905 (MEGAHIT short-read assembly). **B** *Herbaspirillum* (OPERA-MS hybrid assembly). **C** *Stenotrophomonas maltophilia* (Unicycler hybrid assembly). **D** *Aeromonas* (OPERA-MS hybrid assembly). **E** *Acinetobacter* (MEGAHIT short-read assembly). **F** *Comamonas* (OPERA-MS hybrid assembly). **G** *Chryseobacterium* (OPERA-MS hybrid assembly). **H** *Enterobacteriaceae* (OPERA-MS hybrid assembly). The percent of recruitment represents the percentage of reads from a sample that maps onto a MAG and is thus normalized by sequencing depth. ANOVA *p*-values: 0.0008 (**A**), 0.0002 (**B**), 0.0001 (**C**), 0.0001 (**D**), 0.0009 (**E**), < 0.0001 (**F**), 0.016 (**G**), < 0.0001 (**H**). Only pairwise comparisons with *p*-value < 0.05 are shown. *n* = 3</figcaption></figure>

found in class 1 integron cassette array sequences. Other
ARGs mainly involved in antibiotic efflux (Table S4 in
Supplementary Material) and genes related to virulence
and conjugation (Table S5 in Supplementary Material)
were also found in these MAGs. In addition, the *Stenotrophomonas maltophilia* MAG was predominant in all
the samples (Fig. 3C), and it was the only MAG obtained
from the hybrid assembly of short and long reads using
Unicycler (Table S3 in Supplementary Information),
with a completion of 100% and a redundancy of 0%.
The abundance of the *Stenotrophomonas maltophilia* MAG was significantly reduced in the plastisphere, and
it reached its highest upon antibiotic exposure in freshwater.
In addition to the integron-associated *smeD* and *oqxB* efflux mechanisms involved in resistance to ciprofloxacin (Table 1), this MAG contained a *qnrB71* ciprofloxacin resistance gene and a *aph(3')-IIc* gentamicin
resistance gene that was found in the sequences from the

class 1 integron cassettes. Although *qnrB71* and *oqxB*
are known plasmid-borne ciprofloxacin resistance deter-
minants, the contigs that contained these genes were
predicted to be chromosomal and they were not found
in plasmid contigs (Table S6 in Supplementary Infor-
mation). Other genes related to antibiotic efflux and a
beta-lactamase were also found in the MAG from *Steno-
trophomonas maltophilia* in addition to genes related to
conjugation and virulence.

On the other hand, the *Aeromonas* (Fig. 3D) and *Acinetobacter* (Fig. 3E) MAGs were significantly more abundant in the plastisphere not exposed to antibiotics than in any other samples. These MAGs contained chromosomally encoded genes related to ciprofloxacin resistance but not to gentamicin resistance. The genes in *Acinetobacter* were not found in the sequences from the class 1 integron cassettes, whereas the *acrB* gene in the *Aeromonas* MAG was. The other ARGs found in these MAGs were
<table><caption>**Table 1** Characterization and genetic context of the genes related to ciprofloxacin and gentamicin resistance found in the MAGs</caption><thead><tr><th>MAG</th><th>ARGs</th><th>CPX resistance</th><th>GM resistance</th><th>Full operon</th><th>Resistance mechanism</th><th>Found in integron reads</th></tr></thead><tbody><tr><td rowspan="2">Acinetobacter</td><td>abeM</td><td>Yes</td><td>No</td><td>Yes</td><td>Antibiotic efflux</td><td>No</td></tr><tr><td>adeFGH</td><td>Yes</td><td>No</td><td>Yes</td><td>Antibiotic efflux</td><td>No</td></tr><tr><td rowspan="3">Achromobacter sp002902905</td><td>adeF</td><td>Yes</td><td>No</td><td>No</td><td>Antibiotic efflux</td><td>Yes</td></tr><tr><td>axyXY-OprZ</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Antibiotic efflux</td><td>Yes</td></tr><tr><td>mexB</td><td>Yes</td><td>No</td><td>No</td><td>Antibiotic efflux</td><td>No</td></tr><tr><td rowspan="2">Aeromonas</td><td>acrB</td><td>Yes</td><td>No</td><td>No</td><td>Antibiotic efflux</td><td>Yes</td></tr><tr><td>axyXY-OprZ</td><td>Yes</td><td>Yes</td><td>No</td><td>Antibiotic efflux</td><td>Yes</td></tr><tr><td rowspan="5">Herbaspirillum</td><td>adeF</td><td>Yes</td><td>No</td><td>No</td><td>Antibiotic efflux</td><td>Yes</td></tr><tr><td>mexD</td><td>Yes</td><td>Yes</td><td>No</td><td>Antibiotic efflux</td><td>Yes</td></tr><tr><td>axyY</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Antibiotic efflux</td><td>No</td></tr><tr><td>smeE</td><td>Yes</td><td>No</td><td>No</td><td>Antibiotic efflux</td><td>Yes</td></tr><tr><td>acrD</td><td>No</td><td>Yes</td><td>Yes</td><td>Antibiotic efflux</td><td>No</td></tr><tr><td rowspan="4">Stenotrophomonas maltophilia</td><td>smeDEF</td><td>Yes</td><td>No</td><td>Yes</td><td>Antibiotic efflux</td><td>Yes</td></tr><tr><td>aph(3')-Ilc</td><td>No</td><td>Yes</td><td>-</td><td>Antibiotic inactivation</td><td>Yes</td></tr><tr><td>qnrB71</td><td>Yes</td><td>No</td><td>-</td><td>Antibiotic target protection</td><td>No</td></tr><tr><td>oqxB</td><td>Yes</td><td>No</td><td>Yes</td><td>Antibiotic efflux</td><td>Yes</td></tr><tr><td rowspan="2">Comamonas</td><td>oqxB</td><td>Yes</td><td>No</td><td>Yes</td><td>Antibiotic efflux</td><td>Yes</td></tr><tr><td>acrB</td><td>Yes</td><td>No</td><td>No</td><td>Antibiotic efflux</td><td>Yes</td></tr><tr><td>Chryseobacterium</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="4">Enterobacteriaceae</td><td>acrD</td><td>No</td><td>Yes</td><td>Yes</td><td>Antibiotic efflux</td><td>No</td></tr><tr><td>qnrB57</td><td>Yes</td><td>No</td><td>-</td><td>Antibiotic target protection</td><td>No</td></tr><tr><td>acrAB</td><td>Yes</td><td>No</td><td>Yes</td><td>Antibiotic efflux</td><td>Yes</td></tr><tr><td>cpxA</td><td>No</td><td>Yes</td><td>Yes</td><td>Antibiotic efflux</td><td>Yes</td></tr></tbody></table>

Results obtained from blasting MAG sequences against the CARD antibiotic resistance gene database.

mainly related to antibiotic inactivation (beta-lactamases were present in both MAGs and an aminoglycoside nucleotidyltransferase was detected in the *Acinetobacter* MAG). Both MAGs contained genes related to conjugation, although genes related to virulence were only detected in *Aeromonas*. Moreover, none of these two MAGs (or any other MAG analyzed in this study) contained polystyrene degradation genes, although a low-density polyethylene degradation gene was found in the *Acinetobacter* MAG. Three MAGs showed an increased relative abundance in the antibiotic-exposed plastisphere: *Comamonas* (Fig. 3F), *Chryseobacterium* (Fig. 3G), and a MAG of unknown genus belonging to the *Enterobacteriaceae* family (3H). This increase was significant compared to any other condition in the case of *Comamonas* and *Enterobacter*. Ciprofloxacin resistance genes found in class 1 integrons (*oqxB* and *acrB*) were found in the *Comamonas* MAG, whereas the *Enterobacter* MAG contained resistance mechanisms against ciprofloxacin (*qnrB57*, *acrAB*) and gentamicin (*cpxA*, *acrD*). On the other hand, a higher variability is observed in the relative abundance of the *Chryseobacterium* MAG in polluted samples, and no antibiotic resistance genes were found

in this MAG, suggesting its increased relative abundance under pollution could be due to other mechanisms. All these MAGs contained virulence and type IV secretion genes and lacked plastic degradation genes. Finally, two more MAGs associated to the *Enterobacteriaceae* family and *Klebsiella* were identified, and both of them showed a decreased abundance under both antibiotic and microplastic pollution (Figure S6 in Supplementary Information).

## Dynamics of ARG selection and mobilization potential

A decrease of total ARG load in the non-assembled metagenomic reads was observed in the plastisphere compared to freshwater, both in the presence and absence of antibiotics (Fig. 4A). A slight increase in the proportion of ARGs found in clinical (Fig. 4B) and pre-clinical (Fig. 4C) class 1 integron sequences was found in freshwater communities exposed to antibiotics. None of the pollutants had a significant impact on overall ARG detection in class 1 integrons. In addition, the abundance of class 1 integrons determined by qPCR increased very slightly in the presence of antibiotics, microplastics and both combined (Fig. 5A). Despite the variable increase
<figure><img src="image_5.png" /><figcaption>**Fig. 4** Changes in ARG load in the metagenomes (A) and in clinical (B) and pre-clinical (C) integron cassette arrays from freshwater bacteria exposed to antibiotics, microplastics, and both combined. ARG load was determined by blasting non-assembled metagenomic sequences, clinical and pre-clinical integron sequences against the CARD antibiotic resistance database. ARG abundance is normalized by sequencing depth. ANOVA *p*-values: 0.0015 (A), 0.16 (B), 0.08 (C). Only pairwise comparisons with *p*-value < 0.05 are shown. *n* = 3</figcaption></figure>

of the abundance of the integron-associated gentamicin
resistance gene *aac(6')-II* and the ciprofloxacin resistance
gene *qnrB* in the non-antibiotic exposed plastisphere, no
effects (at *P*<0.05) were observed in the presence of pol-
lution. Only the abundance of the *aac(6')-Ib-cr*, detected
in clinical class 1 integron sequences and related to cip-
rofloxacin resistance increased under antibiotic expo-
sure, both in freshwater and in the plastisphere. None of
the genes involved in gentamicin and ciprofloxacin resist-
ance detected in the metagenomic analyses (including
those quantified by qPCR) were found in plasmid contigs
(Table S6 in Supplementary Information).

More ARG-containing viral contigs were obtained
from the short reads than from the hybrid assembly of
short and long reads (Table S7 in Supplementary Infor-
mation). Thus, further analyses were done on the co-
assembly of all short reads together. Three viral contigs
belonging to the *Caudoviricetes* bacteriophage class and
containing ARGs were obtained from the co-assembly
of all short reads together (Fig. 6). Viral contigs 1 and 2
(Fig. 6A,B) showed an increased relative abundance in
antibiotic-exposed freshwater and a reduced abundance
in the plastisphere. VC1 contained a L1 β-lactamase and
its putative host was *Pseudomonas aeruginosa* (Table 2).
VC2 contained a *aph(3')-IIc* gentamicin resistance gene
and its putative host was *Stenotrophomonas maltophilia*
(Table 2). Both antibiotic resistance genes were found in
the *Stenotrophomonas maltophilia* complete MAG, and
when this MAG was aligned to the viral contigs, both
were found within the *Stenotrophomonas maltophilia*
genome (Table S8 in Supplementary Information).

The MAG aligned to VC1 had a 100% identity over the
whole viral contig length and to VC2 at a 99.995% iden-
tity over 115,208 of the 115,306 nucleotides of the viral
contig. This suggests the presence of two prophages in the
*Stenotrophomonas maltophilia* genome. Finally, viral
contig 3 increased its relative abundance under antibi-
otic pollution, particularly in the plastisphere (Fig. 6C).
It contained an *axyXY* gene related to ciprofloxacin and
gentamicin resistance, its putative host was *Sinorhizo-
bium meliloti* (Table 2) and it was found in the MAG
associated to *Achromobacter* (99.8% identity over 24,835
of the 38,066 nucleotides of the viral contig).

**Discussion**

The ubiquitous presence of microplastics in surface
waters may induce changes in antibiotic selection behav-
ior and in the structure and functioning of aquatic bac-
terial communities due to the selective nature of the
plastisphere (microorganisms attached to the plastic).
The selective impact of microplastics on the microbial
communities present in aquatic ecosystems could lead
to changes in their antibiotic resistome [29] and could
have consequences in clinics. The main goal of this study
was to explore the microbial ecology of the antibiotic-
exposed plastisphere microbiome and determine how the
response of the plastisphere communities to antibiotics
at sub-lethal levels compares to that of freshwater ecosys-
tems. Our research shows how the plastisphere selective
environment influences bacterial dynamics and induces a
shift in the response to antibiotics.
<figure><img src="image_6.png" /><figcaption>**Fig. 5** Relative abundance of class 1 integrons (A), the *aac(6')-II* gentamicin resistance gene (B), and the *qnrB* (C) and *aac(6')-Ib-cr* (D) ciprofloxacin resistance genes in freshwater bacteria exposed to antibiotics, microplastics and both combined. Gene abundance was determined by qPCR and normalized by 16S rRNA gene abundance. ANOVA *p*-values: 0.09 (A), 0.15 (C), Kruskal–Wallis *p*-values: 0.07 (B), and 0.002 (D). *n* = 3</figcaption></figure>

Antibiotics at sub-lethal doses arguably have the strongest impact on freshwater bacteria. Bacterial communities exposed to ciprofloxacin and gentamicin at sub-lethal levels had the most distinct composition. Some of the ASVs that showed an increased abundance in the 16S rRNA gene sequences upon antibiotic exposure (Fig. 2A) are associated to genera (*Herbaspirillum*, *Stenotrophomonas*) that contained both ciprofloxacin and gentamicin resistance genes in the MAGs obtained in this study. Besides, these MAGs were also enriched in the antibiotic-exposed freshwater. While ciprofloxacin resistance genes were found in 100% of the complete reference genomes of *Herbaspirillum* and *Stenotrophomonas* available at NCBI (Table S9 in Supplementary Information), suggesting a possible intrinsic resistance to this antibiotic, no gentamicin resistance genes were found in these reference genomes. Our results show that

a gentamicin resistance gene present in the genome of *Stenotrophomonas maltophilia* is encoded by a prophage integrated into its genome, suggesting that this putative ARB could have acquired this resistance through transduction (Table 2). *Stenotrophomonas maltophilia* is an opportunistic pathogen with a wide range of resistance mechanisms and is considered a serious threat to human health [74]. Moreover, another putative ARB related to the genus *Achromobacter* that increased its abundance under antibiotic sub-lethal pressure in freshwater was identified in this study using assembly-based metagenomic approaches. This MAG also contained genetic mechanisms to resist ciprofloxacin and gentamicin pressure as did the associated reference genomes found in the NCBI database. All these putative ARB contained virulence genes and genes related to conjugation. In addition, antibiotic exposure induced the selection of a
<figure><img src="image_7.png" /><figcaption>**Fig. 6** Relative abundance of the ARG-containing viral contigs obtained from the viral assembly of short metagenomic reads. **A** Viral contig 1. **B** Viral contig 2. **C** Viral contig 3. The percent of recruitment represents the percentage of reads from a sample that map onto each contig and is thus normalized by sequencing depth. ANOVA *p*-values: < 0.0001 (A, B); 0.0014 (C). Only pairwise comparisons with *p*-value < 0.05 are shown. *n* = 3</figcaption></figure>

<table><caption>**Table 2** Taxonomy, content, putative hosts, and presence in MAGs of the ARG-containing viral contigs identified in this study</caption><thead><tr><th>Viral contig ID</th><th>Contig length (bp)</th><th>ARGs</th><th>Taxonomy</th><th>Genes</th><th>Completeness</th><th>Host</th><th>MAGs containing the contig</th></tr></thead><tbody><tr><td>VC1</td><td>232,259</td><td>L1* (β-lactams)</td><td>Caudoviricetes</td><td>203</td><td>ND</td><td>P. aeruginosa</td><td>S. maltophilia</td></tr><tr><td>VC2</td><td>115,306</td><td>aph(3')-llc* (gentamicin)</td><td>Caudoviricetes</td><td>111</td><td>Medium quality</td><td>S. maltophilia</td><td>S. maltophilia</td></tr><tr><td>VC3</td><td>38,066</td><td>axyXY** (CPX + GM)</td><td>Caudoviricetes</td><td>31</td><td>ND</td><td>Sinorhizobium meliloti</td><td>Achromobacter</td></tr></tbody></table>

VC viral contig.

*L1 and aph(3')-llc were also found in the Stenotrophomonas maltophilia MAG.

**AxyXY was also found in the *Achromobacter* MAG. Viral contig taxonomy, genetic content, completeness, and putative hosts were determined by PhageScope. To determine if the viral contigs were contained in MAG sequences, a nucleotide blast analysis at > 99% similarity and > 1000 alignment length was performed.

ciprofloxacin resistance gene, *aac(6')-Ib-cr*, an increase in class 1 integron abundance and in the proportion of ARGs carried by these integrons. This latter effect relates to changes in the integron-associated resistome, and it could be a consequence of the selection of integrons and/ or integron-carrying bacteria or of gene mobilization into class 1 integrons. These findings are consistent with previous studies from our group showing the impact of antibiotic sub-inhibitory pressure on bacterial community structure, ARB and ARG selection, and mobilization in class 1 integrons [15, 16].

Nevertheless, the presence of microplastics also had a selective effect on freshwater bacteria. We observed a selection of specific members of the community, such as *Acinetobacter*. ASV related to this genus showed an increased abundance in the plastisphere (Fig. 2) and the same trend was observed in the ARG-containing MAG associated to this genus (Fig. 3C). *Acinetobacter* is a well-known biofilm member [75] commonly found in the plastisphere [30, 35, 40]. *Aeromonas* was

also present containing virulence genes and ARGs conferring resistance to a wide range of antibiotics and is a genus associated with pathogenicity in humans [76]. All of the available complete reference genomes of *Acinetobacter* and *Aeromonas* contained ciprofloxacin efflux mechanisms (Figure S9 in Supplementary Information), similarly to the results observed in our MAGs, suggesting an intrinsic resistance to sub-lethal levels of ciprofloxacin in these genomes. None of these MAGs contained plastic degradation genes, which suggests their success in the plastisphere could be related to their ability to form biofilms on the plastic surface rather than to plastic degradation mechanisms. The selective nature of the plastisphere favoring bacteria that can colonize onto the plastic surface has been extensively reported in the past [77] and is supported by the lower biomass observed in the non-antibiotic-exposed plastisphere and the lower overall ARG load detected in the plastisphere. On the other hand, a significant increase of bacterial biomass was observed in the plastisphere exposed to antibiotics
(Fig. 1) without a concomitant shift in bacterial commu-
nity composition. Antibiotic exposure might favor the
development of the biofilm in the plastisphere. A study
where *E. coli* was exposed to a combination of polysty-
rene, the microplastic type used in this study, and nor-
floxacin, a fluoroquinolone, showed a stimulatory effect
on growth induced by the mixture of these two pollutants
[78]. In addition, both ciprofloxacin and gentamicin at
sub-inhibitory concentrations have been associated to a
stimulation of biofilm formation [79, 80]. These mecha-
nisms could underly the increase in bacterial biomass in
the antibiotic-exposed plastisphere and further research
should explore this matter.

Finally, sub-lethal concentrations of antibiotics had a
different effect on bacterial communities in freshwater
and in the plastisphere. Bacteria that are able to colo-
nize the plastisphere showed a more stable community
composition than river water communities under antibi-
otic exposure (Fig. 2, Figure S3) and an overall reduced
response to antibiotic pressure at the genetic level with
the sole exception of the increased abundance of the cip-
rofloxacin resistance gene *aac(6')-Ib-cr* (Fig. 5D). This
stability in the community structure in the plastisphere
is consistent to the extensively-described increased tol-
erance of biofilms to antibiotics [81, 82]. In addition,
the presence of different ASVs of *Acinetobacter*, *Chry-
seobacterium*, and *Stenotrophomonas* that changed in
abundance under antibiotic, microplastic, and combined
pressure indicates that bacterial subpopulations may
respond differently to anthropogenic selective pressures
and thus affect the outcome of environmental antibiotic
resistance. The assembly-based analyses of metagen-
omic reads showed an enrichment of *Achromobacter* in
the antibiotic-exposed plastisphere (Fig. 3A), a genus
associated to respiratory tract infections and intrinsi-
cally resistant to several antibiotics [83]. Although the
relative abundance of other community members, such
as *Herbaspirillum* (Fig. 3B), a genus associated to rare
infections in humans [84], and *Stenotrophomonas malt-
ophilia* (Fig. 3C), a well-known opportunistic patho-
gen, were significantly reduced in the plastisphere, they
increased under antibiotic exposure in the plastisphere.
Furthermore, three MAGs associated to *Comamonas*,
*Chryseobacterium*, and an unknown *Enterobacteriaceae*
were significantly enriched in the antibiotic-exposed
plastisphere (Fig. 3F–H). The presence of ciprofloxacin
resistance mechanisms in the *Comamonas* MAG and
of ciprofloxacin and gentamicin resistance mechanisms
in the MAG associated to an *Enterobacteriaceae* could
partially explain their success in the antibiotic-exposed
plastisphere, although there is some variability in the
presence of resistance mechanisms to both antibiotics
in the reference genomes of *Comamonas* (Table S9 in

Supplementary Information). Nevertheless, the absence
of resistance mechanisms to any of these antibiotics in
both the *Chryseobacterium* MAG (Table 1) and com-
plete reference genomes (Table S9 in Supplementary
Information) raises questions about its enrichment in the
antibiotic-exposed plastisphere. Besides, *Chryseobacte-
rium* ASVs showed different behavior under antibiotic,
microplastic, or combined pressures (Fig. 2). Our results
suggest that other mechanisms could favor the survival
of some *Chryseobacterium* populations in the antibiotic-
exposed plastisphere even though they do not contain
any antibiotic resistance mechanisms. Some potential
mechanisms underlying this phenomenon could involve
biofilm formation, persistence, unspecific stress responses,
or the presence of antibiotic resistance genes in plasmids
that were overlooked in this study. Although the evaluation
of these mechanisms was out of the scope of this study,
further research should shed some light into the enrich-
ment of (allegedly) non-antibiotic-resistant members in the
antibiotic-exposed plastisphere.

All of the MAGs maintained in the antibiotic-exposed
plastisphere except *Chryseobacterium* contained some
ARGs associated to class 1 integrons and genes related
to horizontal gene transfer. In addition to the potential
of dissemination of these ARGs to other bacterial hosts
in the biofilm, the presence of virulence genes in these
MAGs raises concerns about their pathogenicity to
humans. For example, the presence in the plastisphere
of potentially mobilizable ARGs and virulence genes
in *Stenotrophomonas maltophilia* could be considered
a high risk for human health [85]. Thus, microplastics
could also contribute to the maintenance of the selected
communities through biofilm formation and increased
tolerance to antibiotic exposure and other environmental
perturbations.

In addition, although no ciprofloxacin nor gentamicin
resistance genes were found in plasmid contigs, the
assembly of viral contigs enabled the identification of
three ARG-containing contigs (Table 2). One of these
contained a gentamicin resistance gene, *aph(3')-IIc*, that
was identified in the complete MAG of *Stenotropho-
monas maltophilia*. Besides, the putative host of this
viral contig was *Stenotrophomonas maltophilia*, and the
bacteriophage sequence was found within its genome,
suggesting the integration of the bacteriophage genetic
content in a prophage form. Another potential transduc-
tion event involving both ciprofloxacin and gentamicin
resistance was identified in the MAG of *Achromobacter*
with a *axyXY*-containing bacteriophage integrated in its
genome. Our results identify two potential transduction
events involving gentamicin and ciprofloxacin resist-
ance genes that could have led to the acquisition of new
resistance mechanisms by two opportunistic pathogens,
*Stenotrophomonas maltophilia* and *Achromobacter*. This research points to a possible underestimation of the role of transduction in environmental settings and underlines the need to include bacteriophages in future environmental resistome and mobilome studies. Furthermore, the absence of antibiotic resistance genes in plasmid contigs could imply an overestimation of the role that conjugation plays in environmental settings, where the contact between bacteria is reduced compared to in vitro scenarios and plasmids (or the ARGs they carry) might be lost in the absence of strong selective pressures. However, the lack of detection of ARGs in plasmid contigs could also reflect a methodological flaw in the obtention, sequencing and/or assembly of these mobile genetic elements. Further research should explore the potential biases associated to the study of the plasmidome to address these questions.

Finally, although it was not the main purpose of
this study, our results reflect the biases associated to
metagenomic approaches. For instance, genome assem-
bly approaches performed differently, with OPERA-MS
overall being superior to the assembly of short reads
using MEGAHIT and the hybrid assembly of short and
long reads using Unicycler. This was not surprising, since
hybrid assemblies optimized for metagenomic datasets
such as OPERA-MS [86] usually outperform short-read
approaches, and Unicycler is less adapted to complex
datasets and shows an optimal performance with patho-
gen genomes [87]. However, some information was miss-
ing from the OPERA-MS hybrid assembly. *Acinetobacter*
contigs could not be binned into a MAG of sufficient
quality, whereas a 78% completion, 2% redundancy *Aci-
netobacter* MAG was obtained from short reads, and
the *Stenotrophomonas maltophilia* MAG obtained using
OPERA-MS showed the highest redundancy (7%) of the
three approaches, whereas a complete MAG with 0%
redundancy was observed using Unicycler. These dif-
ferences support previous conclusions about potential
misassembly between similar sequences using hybrid
assembly approaches [88]. Another example of methodo-
logical biases is the underestimation for class 1 integron
sequences in the metagenomic reads. No integrons were
found in non-assembled reads and in the hybrid assem-
bles, whereas only five integrons were identified in non-
binned contigs from the short read assembly and none
in the MAGs (results not shown). However, class 1 inte-
grons were detected by qPCR and sequenced, and they
contained ARGs that were relevant to this study. Finally,
as mentioned above, the used of total (chromosomal and
hopefully plasmid) DNA might reduce the sensitivity of
plasmid DNA detection in sequencing approaches and

result in misleading conclusions about the environmen-
tal mobilome. This could lead to an underestimation of
the role of the plasmidome and potential transforma-
tion, conjugation or vesiculation events taking place
in the environment. These aspects should be carefully
considered in the future, and research efforts should
include a variety of DNA extracts (bacterial, viral, plas-
mid DNA), sequencing targets (metagenomes, plasmids,
bacteriophages, integrons, specific genes), long and
short read platforms and several analysis approaches
(non-assembled read screening and multiple assembly
approaches) to obtain a full picture of the environmen-
tal resistome and the potential risks it poses to human
health.

Considering the low degradability of microplastics [21] and their potential role as a vector capable of protecting bacteria from environmental stressors, transporting them over a long transport range and disseminating them to surrounding environments through detachment from the plastisphere [30], the risk associated to their presence goes beyond simply an enrichment of ARB and ARGs in the plastisphere. Many antibiotics are easily degraded in the environment and the resistance mechanisms they select for could be loss from the communities in the absence of selective pressure. Yet, the maintenance of these resistance mechanisms in the plastisphere alone implies a potential risk for human health. In order to quantify the risk of antibiotic resistance dissemination associated to surface water microplastic pollution, future efforts should focus on long-term studies evaluating the persistence of ARB and ARGs in the plastisphere, the evolution of the microplastic structure and its behavior in a complex scenario with interactions with other pollutants [46, 89], and other environmental stresses such as temperature [90] or UV exposure [91].

Conclusions

This research shows how the selective nature of the plas-
tisphere changes the response of bacterial communities
to antibiotic pressure at sub-lethal doses. Here we iden-
tify microbial responses that improve our understanding
on the selective role of the plastisphere and its impact on
the maintenance of environmental antibiotic resistance
in combination with other anthropogenic pollutants.
Our research identified bacteriophages as potential key
players on the dissemination of antibiotic resistance in
environmental settings. This work provides new insights
into the microbial ecology of the antibiotic-exposed plas-
tisphere and highlights the need to evaluate the impact of
aquatic pollutants in environmental communities using
complex scenarios with combined stresses.
**Supplementary Information**

The online version contains supplementary material available at https://doi.org/10.1186/s40168-024-01803-2.

**Additional File 1.** Table with ASV abundance and taxonomic annotation.

**Additional File 2.** ASV sequences.

**Additional File 3.**

**Acknowledgements**

The authors would like to acknowledge the Agence Regionale de la Santé Île-de-France and the Agence Nationale de la Recherche for funding this work.

**Authors' contributions**

BJ: experimental work, short-read sequencing, qPCR analysis, bioinformatic analysis of bacterial communities, class 1 integron cassettes and non-assembled metagenomic reads, and manuscript writing. CS: conception and experimental design, supervision, long-read sequencing, assembly-based analyses, manuscript writing, and editing.

**Funding**

BJ received funding from the Agence Regionale de la Santé Île-de-France (Research Year Contract in Medicine, Pharmacy or Odontology number 2022–52). CS received funding from the Agence Nationale de la Recherche through the JPI AMR Aquatic Pollutants projects SARA and CONTACT.

**Availability of data and materials**

The datasets generated and analyzed during the current study are available at the DDBJ repository, BioProject PRJDB17794, DRA accession DRA018264. The code used to analyze all the sequences included in this study is available at: https://github.com/conscid/Joannard-and-Sanchez-Cid-2024

**Declarations**

**Ethics approval and consent to participate**

Not applicable.

**Competing interests**

The authors declare no competing interests.

Received: 4 December 2023 Accepted: 27 March 2024

Published online: 24 May 2024

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