Polyvinyl chloride promoted the dissemination of antibiotic resistance genes in Chinese soil: A metagenomic viewpoint

doi:10.21203/rs.3.rs-3907939/v1

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Polyvinyl chloride promoted the dissemination of antibiotic resistance genes in Chinese soil: A metagenomic viewpoint

https://doi.org/10.21203/rs.3.rs-3907939/v1

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Microplastics (MPs) are becoming progressively widespread in the surrounding and are regarded as vectors of antibiotic resistance genes (ARGs). Soils of various properties carry large amounts of microplastics and ARGs. However, a thorough research evaluating the impact of different regions of China in evolving antibiotic resistome in soil MPs is insufficient. Here, we engaged a massive investigation by putting Polyvinyl chloride microplastics (PVC) into soil in 20 provinces of China which have different physical and chemical properties. The results showed that PVC could significantly affect soil bacterial community structure and ARGs abundance. Structural equation models showed that the addition of PVC changed the characteristics of the soil, which in turn influenced the bacterial community in the soil (which included ARG-containing bacterial hosts) and, ultimately, the relative abundance of ARGs. This work improves our comprehension of the effects of microplastics on the proliferation and hosts of ARGs in various soil environments, and it serves as a crucial reference for future plastic consumption and disposal.

microplastics

Polyvinyl chloride

various regions

antibiotic resistance genes

Microplastics are plastic particles, which can be either primary or secondary, and range in size from 100 nm to 5 mm. (Khalid er ail., 2021; Huang et al., 2021). Primary MPs that are frequently sourced from the manufacturing industry include resin particles utilized as an industrial feedstock and microbeads found in personal hygiene products (Khalid et al., 2021). When larger plastic waste, such as minuscule fragments from the degradation of compost contains agricultural plastic film and MPs, is physically worn down or biodegrades, secondary microplastics are produced (Dong et al., 2021). In 2018, plastic manufacturing in the globe increased to more than 360 million metric tons at a minimum annual rate of 240 million metric tons. (Alimi et al., 2018). Over the last five years, the quantity of microplastic (MP) pollution pertaining to terrestrial ecosystems has increased 4–23 times more than in the maritime environment (Yang et al., 2022). One MP that is frequently found in soil is polyvinyl chloride (PVC), which is widely used on a daily basis (Lamichhane et al., 2022). When it comes to petroleum-based materials, it is the third most popular substance (5 million tons in 2018). (Khandare et al., 2021). PVC's chloride when burnt releases a variety of harmful chemicals, especially when heated to 200°C. Among these contaminants include polycyclic aromatic hydrocarbons and dioxins (Han et al., 2021). It appears that its negative environmental consequences could be larger than those of other synthetic polymers. As a result, it has raised a number of issues regarding PVC's impact on public health and environmental issues (Xu et al., 2022). Recent research on MP exposure and its influences in terrestrial and aquatic environments has concentrated on the population and cellular levels (Galloway and Lewis, 2016). Meanwhile, additional research on the formation of the microbial population in the plastisphere is required to completely comprehend how MPs are affecting soil ecosystems.

Soil microorganisms are recognized as biological indicators in terrestrial ecosystems that may be used to evaluate the soil's environmental quality (Zhang et al., 2022). Therefore, studying the diversity of soil microorganisms is essential. It has been discovered that microplastics have a major impact on microbial community alterations by serving as a foundation for bacterial biofilm and colonization development. Numerous investigations have demonstrated that the microbial communities on MPs differed greatly in composition and structure from those on the natural and agricultural media (Wang et al., 2020a). Microplastics can penetrate agricultural soil, change the microbial ecosystem of the soil, and impede the development of microorganisms (Zhang et al., 2019). In a pot experiment, Wang et al. (2020b) found that when the amount of mulching films remaining rose, there was a quick reduction in nitrogen, soil microbial biomass, microbial diversity, enzyme activity, and carbon. Microplastics considerably increased biological activity in soil, as well as microbiological diversity and abundance according to a research by Blaise et al. (2021). Dong et al. (2017) demonstrated how MPs may increase the variety and spread of soil microorganisms. There is currently a dearth of research on the variety of microbiological forms in soil that assimilate microplastics. Microorganisms in the soil are greatly impacted by environmental factors. It is essential to look at how microplastics affect soil microbial communities in typical areas in order to have a greater knowledge of how microplastics influence the ecology and soil environment, ecological restoration, and microplastic pollution control.

In aquatic environments, MPs have been suggested as horizontal gene transfer (HGT) hotspots. Because of the increased transmission of genetic resources, linking antibiotic resistance genes (ARGs) and various bacterial taxa, drug-resistant bacteria may result from this (Liu et al., 2021). The development of ambient microbial populations, which is required for the emergence and spread of ARGs, may potentially be impacted by microplastics. Because of this, MPs may have an impact on ARG transmission in addition to their impacts on environmental microbial communities (Wang et al., 2020a). For example, Shi et al. (2020) hypothesized that polystyrene microbeads may have aided in the spread of ARGs in waste leachate. According to Sun et al. (2018), soil MPs have ability to prevent ARGs from evaporating. Furthermore, Lu et al. (2020) discovered a strong relationship between MP dimensions and aging, as well as the amount of ARGs in vegetable soil within plastispheres. Zhu et al. (2021) discovered that adding manure, raising temperature of the soil, and increasing wetness levels all enhanced the amount of ARGs in plastispheres. Despite a great deal of study on the influences of MPs in soil settings, which has concentrated more on describing the reactions of ARGs than on the general modifications in the richness and composition of microbiological communities. An actual knowledge of the impacts of MPs on soil ARGs is critically required, because soils are the principal carriers of MPs and environmental antibiotic resistance in terrestrial areas.

In this work, we employed a metagenomic technique to identifying and quantifying ARGs in soil contaminated with PVC microplastics, and we compared and assessed the differences of ARGs in several soil parameters. This is due to the fact that various soil characteristics each had a unique effect the organization of the ARG profiles and the bacterial community in the plastisphere target samples (Zhu et al., 2021). The hosts of ARGs were subsequently investigated using the metagenomic assembly method. Here, we took 20 representative Chinese soils, divided them into seven geographical areas, and mixed in 1% and 2%% PVC. We postulated that (I) ARG properties vary between soils exposed to PVC, and (II) ARG bacterial hosts are altered by microplastic exposure.

2.1 Experimental microplastics

Irregularly granulated polyvinyl chloride (PVC) materials (Welab Science and Technology (Beijing) Co. Ltd), were selected to stand in for MPs because they are typical polymers used in the manufacturing of plastics. After being sieved to a size of 150–180 m (80–100 mesh), the particles were dried in a fume cupboard, immersed in methanol for seven days to eliminate solvent-soluble chemicals from their surface, and stored at 4°C until required.

2.2 Sample collection site and soil microcosm incubation

Farmland fields at the following 20 places were chosen as research sites: Jiangsu (JS), Anhui (AH), Chongqing (CQ), Ningxia (NX), Heilongjiang (HLJ), Shanxi (SXa), Shanxi (SXb), Jiangxi (JX), Shandong (SD), Beijing (BJ), Heibei (HB), Yunnan (YN), Xinjinag (XJ), Henan (HNa), Hainan (HNb), Hunan (HNc), Zhejiang (ZJ), Guangdong (GD), Sichuan (SC), Liaoning (LN), and they are divided into seven geographical regions for subsequent analysis (Fig. 1).

Every location received a sufficient number of samples. All soil target samples were delivered to the lab on ice in a low-temperature incubator, and then they were sieved through a 2 mm mesh filter. To prepare the soil samples for soil microcosm incubation, they were crushed and homogenized. This experiment contained three treatments, each with three replicates: control soil (CK), 1% w/w PVC (PVC1), and 2% w/w PVC (PVC2). For two hours, a rolling mixer was used to thoroughly mix every therapy in the jar. After that, the soil was kept at a moisture level of about 20 percent (w/w) by adding sterilized deionized water. After being exposed to 25°C for 90 days, the soil was analyzed. They were separated into two subsamples: one was kept at 4°C to study the the physicochemical characteristics of soil, and the other at -80°C to analyze antibiotics and extract DNA. Details are also given in Table S1 of the Supplementary Information, which includes site information and soil physiochemical parameters. Particular tests were conducted to determine the physicochemical qualities of soil. Using a potentiometric approach (Mettler Toledo S400-K multifunctional pH meter) and a soil-to-water ratio of 1:2.5, the pH of the soil was analyzed (Qiao et al., 2018). Using an electrode technique, the EC of the soil was found. A potassium dichromate leaching technique was used to assess the organic matter in the soil. Beijing Physical and Chemical Analysis and Testing Center was tasked with determining the TN and TP of the soil using alkali fusion-molybdenum antimony antispectrophotometry and a continuous flow analyzer (AA3, SEAL, Germany), respectively. CaCO₃ in the soil was measured using a gasometric technique. The pipette method, which combined sieving and hydrostatic settling, was employed to ascertain the soil composition. particles. To find effective Si and effective B, inductively coupled plasma emission spectrometry was employed (Yang et al., 2020). The hexaammonium hexachloride cobalt leaching-spectrophotometric technique was used to evaluate the CEC values of the soil. After being extracted utilizing an acidic ammonium oxalate solution extraction technique and a sodium hydroxide solution extraction method, respectively, amorphous iron oxide, amorphous manganese oxide, and amorphous aluminum oxide were measured spectrophotometrically.

2.3 DNA extraction and metagenomic sequencing

The OMEGA Mag-Bind Soil DNA Kit (M5635-02) (Omega Bio-Tek, Norcross, GA, USA) was utilized to obtain entire microbial samples of genomic DNA. Samples were kept cold at -20 ℃ until they were subjected to further examination., in accordance with the manufacturer's recommendations. A QubitTM 4 Fluorometer with WiFi: Q33238 (QubitTM Assay Tubes: Q32856; QubitTM 1X dsDNA HS Assay Kit: Q33231) (Invitrogen, USA) was used to assess the amount and caliber of DNAs that were extracted. Agarose gel electrophoresis was also used. The extracted microbial DNA was treated with an Illumina TruSeq Nano DNA LT Library Preparation Kit to produce 400 bp insert sized metagenome shotgun sequencing libraries. Using the PE150 method and the Illumina NovaSeq platform (Illumina, USA), each library was sequenced at Personal Biotechnology Co., Ltd. (Shanghai, China).

2.4 Quantification of ARGs

In order to get quality-filtered data for further investigation, raw sequencing reads were processed. Using Cutadapt (v1.2.1), Adapters for sequencing were first eliminated from reads sequencing (Martin 2011). Second, a sliding-window method in fastp was ultilized to reduce poor caliber readings (Chen, Zhou et al. 2018). Following the acquisition of quality-filtered reads, each sample's metagenomics sequencing reads were taxonomically classified using Kraken2 (Wood, Lu et al. 2019) in comparison to a database derived from RefSeq that contained genomes from archaea, bacteria, protozoans, viruses, viridiplantae, fungi, and metazoans. For downstream analysis, reads attributed to viridiplantae or metazoans were eliminated. alternatively [Kaiju (Menzel et al., 2016) using greedy-5 mode in opposition to a database obtained from nr, containing proteins derived from fungi, bacteria, microbial eukaryotes, viruses, and archaea. Using the meta-large presetted settings, Megahit (v1.1.2) (Liu, Chi-Man et al. 2015) was utilized to integrate for each target sample. Next, making use of mmseqs2 (Steinegger and SöDing 2017) in the "easy-linclust" zone, the produced contigs (greater than 300 bp) were combined and grouped. The threshold for sequence identity was set to 0.95, and the using the shorter contig again under cover was set to 90%. Contigs attributed to Viridiplantae or Metazoa were omitted in the next study. Using mmseqs2, the lowest common ancestor taxonomy of the non-redundant contigs was obtained by aligning them with the NCBI-nt database (Steinegger and SöDing 2017). in “taxonomy” mode. Zhu, Alexandre et al. (2010) using MetaGeneMark to forecast the genes within the contigs. All sample CDS sequences were clustered using the "easy-cluster" mode of mmseqs2 (Steinegger and SöDing 2017), with the covered residue of the shorter contig set to 90% and the identity of the protein sequence criterion set to 0.90. Salmon (Patro 2015) was used to map the outstanding reads from every sampling over the sequences of genes that are anticipated using a quasi-mapping method "—meta —minScoreFraction = 0.55" to ascertain the abundances of these genes. The CPM (copy per kilobase per million mapped reads) was then utilized in metagenomes to standardize abundance values. Utilizing the Protein Homolog Model and Perfect RGI matches (100% identity), utilizing the Resistance Gene Identifier (RGI) to allocate readings to the Comprehensive Antibiotic Resistance Database (CARD), ARGs were discovered (Alcock et al., 2020). Only fully mapped readings were taken into account. Although gene sequences linked to resistance to other antimicrobials and disinfectants drugs are available in the CARD database, we will mostly refer to ARGs because they are the major subject of our study (Zadjelovic et al., 2023).

2.5 Statistical analysis

R (version 3.6.3) was utilized to do the statistical analysis in this investigation. The "vegan" program was used to determine the Shannon diversity, or the diversity of ARGs (Oksanen et al., 2020). To assess differences between various treatments, Kruskal-Wallis Rank Sum Tests were used, and A p-value of less than 0.05 indicated that the result was statistically significant. Using Bray-Curtis distance as the foundation, Principal Coordinates Analysis (PCoA) was utilized to visually represent the variations in ARG profiles between the treatments. To determine if the differences were significant, the non-parametric multivariate analysis of variance (PERMANOVA) was employed once more. The congruency of sample separations was assessed using Procrustes analysis according to bacterial community, ARG signatures. Procrustes analysis aims to superimpose, align, and scale numerous graphical structures (Rohlf and Slice, 1990). The association between the ARGs and the bacterial population was also evaluated using the Mantel test, which is according to the measures for Bray-Curtis dissimilarity. We ultilized a Linear discriminant analysis of Effect Size (LEfSe) to identify ARGs that were substantially different in abundance between microplastic treatments and the control (Segata et al., 2011).

3.1 Effects of soil type on the abundance of ARGs

The quantity of ARGs in various types of soil varied significantly (Fig. 2a, 2b). Soil types contribute significantly to the distribution and concentration of arsenic radiation (Wang et al., 2018; Xu et al., 2021). HD1 and XB1 have the fewest ARGs across the soil types, whereas HZ1 has the most ARGs within the soil area. HZ1 has more ARGs in its soil (16.5%) than HD1 and XB1 (14.3%). Low OC (8.11g/kg) and TN (0.53 g/kg) of HD1 and low TN (0.57 g/kg) of XB1 may be the reason of these. Generally, antibiotic adsorption is reduced in soils with lower OC (Wang et al., 2020b). Research indicates that target ARG abundance and TN had a favorable connection. This is because nutrients, such as carbon and nitrogen, can act as a substrate to aid in the growth of bacteria, particularly bacteria resistant to antibiotics. Higher nutritional levels were also advantageous for the horizontal transmission of antibiotic ARGs (Zhang et al., 2020). The largest relative abundance of ARGs is seen in HN1 (10.2%), most likely as a result of its high clay concentration (36.4%). Previous research found a substantial positive association between ARGs and clay content, which may be explained by the fact that greater clay content soils usually have more sorption to the solid phase. Additionally, field tests confirmed that the main aspect affecting the dissemination of ARGs was soil texture (Wang et al., 2020; Wu et al., 2023). XN1 (10.0%) has a greater relative abundance of ARGs, perhaps due to its paddy soil copping type (36.44). ARG inputs, degradation, and soil movement might vary according on the type of crop being grown. Paddy soils usually show increased accumulation of ARGs because of the large microbial biomass that may support ARGs populations under anaerobic circumstances (Wang et al., 2018). The increased relative abundance of ARGs in HN1 (10.2%) is most likely caused by its higher heavy mental content. Heavy metals can cause MGEs to become more abundant or change the structure of bacterial communities, which could aid in the dissemination of ARGs (Wang et al., 2021). Furthermore, as bacteria are the primary ARG transporters, pH had a crucial influence on bacterial diversity and impacted the profiles of ARGs. No one paradigm explains the influences of pH on the prevalence of ARGs and soil antibiotics, despite a great deal of research in this area. Regarding the profiles of ARGs, the PCoA1 and PCoA2 accounted for 80.2% and 7.0% of the overall variations, respectively. The Adonis test (PERMANOVA) findings demonstrated that there were substantial differences in the bacterial profiles (R² = 0.6128, p = 0.001) between the seven zones (Fig. 2c).

Bacterial composition of different regions was obviously different at the phylum levels, and alpha diversity of DB1 was higher than HZ1 and HB1(Fig. 2d, 2e). Compared with the seven regions, Gemmatimonadetes, Bacteroidota and Actinobacteria in HN1 was the highest, Proteobacteria and Cyanobacteria in HZ1 was the highest, Chloroflexi and Firmicutes in HD1 was the highest. This could be because that the relative abundances of Proteobacteria was negatively connected with soil pH, while the relative abundances of Chloroflexi was positively connected with soil pH (Ni et al., 2021). Numerous researchs has indicated that changes in the structure of bacterial communities are related to a variety of abiotic and biotic variables, despite the fact that pH has been found to dominate bacterial diversity, composition, and assembly processes. Bray-Curtis distances were used to perform PCoA to better show the various reactions of microorganism profiles to the various locations. (Fig. 2f). For bacteria profiles, A total of 24.1% and 29.1% of the variations were explained by the PCoA1 and PCoA2, respectively. The findings of the Adonis test (PERMANOVA) revealed that there were substantial differences in the bacterial profiles (R² = 0.7086, p = 0.001) across the seven zones (Fig. 2f). The bacteria composition of XB1 and HD1 varied more than that of HN1 and HB1, and these differences were also observed when the target samples were divided through the secondary major coordinate.

3.2 ARGs and bacterial community structures respond to the microplastic exposure

The relative abundances of soil bacterial phyla showed a recurrence. Following exposure to varying quantities of microplastics, trends in the abundance of soil bacterial phylums varied, and three typical soil locations were examined (Fig. 3a). In different soils, the bacterial phylum with higher relative abundance were Proteobacteria, Bacteroidota, Actinbacteria, Acidobacteria, Gemmatimonadetes, respectively. In the soil amended with PVC (1% and 2% w/w), the relative abundance of Proteobacteria in soil HD, XB and HN enhanced by 19.0%, 39.2%, 10.3%, 28.3%, 13.2%, 30.6%, respectively, and Bacteroidota decreased by 22.3% and 65.8% in HD soil, increased by 110.0% and 77.8% in HN soil, whereas the relative abundance of Actinobacteria, Acidobactiria, Gemmatimonadetes, Chloroflexi, Planctomycetota, Verrucomicrobia decreased by 13.4%-88.5%. Nevertheless, the addition of PVC had no discernible influence on the bacterial population in the DB and XN soil. These findings align with the conclusions of Fei et al. (2020), who found that The variety and abundance of bacterial population in an acid agricultural soil was not greatly influenced by 1% PVC MPs. The vast and diverse microbial variety present in different soils, each of which has a different ability to respond to disturbances, can be blamed for the variations in these results. Furthermore, it's not always clear how soil microorganisms react to a single artificial pressure (Lee et al., 2017; Rilling et al., 2019).

Varied soil types have different ARG responses to PVC exposure. The quantity of ARGs in HZ soil significantly decreased as a result of PVC exposure (Fig. 2b). For example, the overall abundance of ARGs to which PVC was added at 1% and 2% w/w fell by 10.0 and 11.0%, respectively. In particular, there was a decline of 8.1%, 10.2%, 6.6%, 13.7%, 15.2%, and 17.0% in the genes that provide resistance to the antibiotics tetracycline, penam and macrolide. On the other hand, PVC exposure led to a significant rise in ARG abundance in XB soil (Fig. 2b). For example, the overall abundance of ARGs rose by 0.60% and 10.0% when PVC was introduced at 1% and 2% w/w. Specifically, there was an increase of 10.3%, 16.3%, and 12.6% in the genes that confer resistance to the antibiotics tetracycline, penam, and fluoroquinolone, respectively. The quantity of soil resistance genes in the DB soil was not substantially affected by the inclusion of PVC. Comparable to the work by Song et al., PVC had a consequence on the overall amount of ARGs in various soils but not on the relative abundance of ARGs (2022). The resistance gene number trend was not entirely followed by the relative abundance of ARGs in the samples. This is caused by different changes in the soil abundance of different ARGs under the similar environmental circumstances. Antibiotics exiting in the soil select against distinct ARGs in different ways (Lu et al., 2020). PVC in the soil is difficult for microorganisms to absorb and is difficult for them to hydrolyze and fragment throughout our incubation. This might be the cause of the minor ARG changes that were seen in the PVC treatments (Song et al., 2022).

Antibiotic efflux accounted for 47.1–52.1% of all resistance mechanisms found in soils, with antibiotic target change (34.4–39.5%) and antibiotic inactivation (5.4–7.0%) following closely behind (Fig. 2c). The majority of antibiotics may be eliminated by ARGs using a multidrug efflux pump (MEP). These ARGs can also increase mutation and decrease intracellular antibiotic concentration to gain new resistance mechanisms. Moreover, antibiotic interaction can be impeded or limited by ARGs using the antibiotic target alteration (ATA) mechanism. They could also boost resistance and impede bactericidal and bacteriostatic actions in the process. ARGs with MEP mechanisms were consistently found in soil at higher concentrations than ARGs with ATA mechanisms, as shown in Fig., suggesting that MEP was the main resistance mechanism in different kinds of soil (Dai et al., 2022). Antibiotic efflux (6.0%, 9.1%) and antibiotic target change (7.2%, 8.6%) and antibiotic inactivation (19.4%, 18.7%) in HZ soil were also higher after PVC was applied. For the majority of soil types, PVC has no discernible impact on the quantity of resistance mechanisms. The common ARGs amongst various treatments were displayed on the heatmap. It showed that the tetracycline tetA was decreased after application of PVC in DB, HZ and XB soil, Ecol_fabG_TRC, Mtub,_mshA_INH PvrR, adeL, arnA were significantly enriched after the application of PVC in XB soil.

Six different kinds of antibiotics that are frequently prevalent in soil environments were tested in total, including penam, tetracycline, macrolide, fluoroquinolones, peptide and disinfecting agents and antiseptics. In all 20 types of soils, different degrees of correlation between environmental variables and specific kinds of ARGs were discovered. (Fig. 4). Taking tetracycline as an example, pH, clay particle, available B, amorphous Al and annual precipitation were all highly connected with tetracycline resistance genes (P < 0.05).

The SEM analysis findings (Fig. 5) demonstrated that the SEM explained 81% of the overall variation of ARGs in the various soils. The picture illustrates the many features of soil, such as pH, CEC, sand, clay, and silt value; nutrients, such as TN, TP, and OC value; and climate, which includes yearly precipitation and average temperature. PVC exhibited negative impacts (λ = -0.23, P < 0.01) on the characteristics of the soil, negative effects (λ = -0.08, P > 0.05) on the bacteria, and positive effects (λ = 0.13, P > 0.05) on ARGs. The bacterial community was negatively impacted by soil characteristics (λ = -0.40, P < 0.01), but positively by ARG composition (λ = 0.43, P < 0.001), and positively by the bacterial community on the ARGs (λ = 0.32, P < 0.05). Both the bacterial community (λ = 0.05, P > 0.05) and the ARG composition (λ = 0.15, P < 0.01) showed favorable impacts from nutrients. The ARGs were significantly impacted by climate conditions (λ = 0.39, P < 0.05), whereas the ARGs were negatively impacted (λ = -0.189, P < 0.01). The properties of the soil were altered by the addition of PVC, which eventually impacted the relative abundance of ARGs in the soil as well as the bacterial community (which included bacterial hosts that contained ARGs) (Lu et al., 2022). The main cause of the observed results was that the MPs altered the structure of the soil, especially its porosity, a physical route that influences the ARG host bacteria (Lu et al., 2022). In a similar vein, Sun et al. (2018) found that MPs stopped ARGs from dispersing in the soil; the results also suggest that the bacteria most likely put self-defense ahead of taking on a "strong opponent" when under external pressure. Larger quantities of ARGs with ribosome protection (tetM + tetO) and efflux pump (tetC + tetE + tetG) mechanisms were thus favored under these circumstances. This is in line with the study's findings on the elevated abundance of tetA (efflux pump mechanism) genes. Lu et al. (2020) found that weathering and Microplastics size had a significant effect on the ARG abundance in plastispheres in agricultural soil. Furthermore, when Dai et al. (2022) examined MPs' impact on ARGs, they discovered that MPs changed the component of ARGs and significantly reduced the majority of their abundance.

3.3 Correlations between bacterial abundance and ARG abundance in the soil

A network diagram derived from the Pearson association between ARGs and bacterial phyla was created in order to better quantify the connection between the bacterial community and ARG profiles in the current investigation. The research demonstrated a substantial association (p < 0.05) between the ARG profiles and the makeup of the bacterial population, as seen in the Fig. 6a. PvrR, berA, adeL were significantly positively correlated with Proteobacteria. MexS, adeL, Ecol_fabG_TRC became were negatively correlated with Acidobateria. The strong correlations could suggest that microplastics altered the bacterial hosts or the microbial populations in the soil, which in turn altered the soil ARGs. The influences of MPs on ARGs in soil settings have not been extensively studied to date, and there are also not many research examining the relationship between ARGs and the composition of microbial communities. Few studies have looked into the ARG properties in the biofilms connected to MPs. The ARG profiles of non-biodegradable polyethylene terephthalate (PET) and biodegradable polyhydroxy alkanoate (PHA) differed greatly, as reported by Sun et al. (2021). This variation may have been caused by the different microbial community topologies.

This study showed that A favorable association was noted between the proportional abundance of Proteobacteria and ARGs (Fig. 6b, 6c). This demonstrated that Proteobacteria was the host bacteria for ARGs. Metronidazole resistance genes mtrA became significantly positively correlated with Proteobacteria with the addition of PVC, and its relative abundance increased with the addition of PVC. This might be the outcome of the fact that various microorganisms respond differently to environmental stimuli in terms of resistance and adaptability. The functional genes that provide bacteria resilience to adversity are activated in response to various stimuli (Crits-Christoph et al., 2018). The triclosan resistance genes Ecol fabG TRC and the multidrug resistance genes PvrR and MexS had a strong positive correlation with Chloroflexi, Planctomycetota, Verrucomicrobia, Gemmatimonadetes and Acidobacteria with the addition of PVC. This implies that both the bacterial hosts of these resistance genes and the constitution of the population of bacteria were altered by the introduction of microplastics to the soil. While both bacterial diversity and abundance in the soil were completely impacted, certain ARG-rich bacteria developed and created a new pattern in which MPs increased the relative abundance of the bacterial hosts of ARGs (Lu et al., 2022).

We carried out an comprehensive study of alterations in ARGs to provide a complete picture of the soil system following MPs amendment. The PVC MPs change the abundance and diversity of bacteria and ARGs in the soil. The response of soil bacterial communities and ARGs to the addition of PVC varied greatly in various regions of China, the influence on the HZ and HB regions is more significant. The bacterial and ARGs network co-occurrence analysis revealed a noteworthy association between the composition of the bacterial communities and ARG profiles. The crucial associations may indicate that microplastics modified the microbial communities in the soil or the bacterial hosts, which by turn changed the soil ARGs. Our research can aid in the better management of MPs and antibiotic resistance genes in the environment and advances our knowledge of ARG venture in Chinese ecosystems in relation to the effects of changing soil qualities and climate change.

Author contributions

Shuwen Zhao: Resource, Investigation, Writing-Original draft, Data curation; Qianru Zhang: Editing, Supervision, Project administration, Conceptualization; Huang Qilan: Data curation, Formal analysis; Chuchen Zhang: Visualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

The datasets used or analyzed during the study are available from the corresponding author on reasonable request.

Acknowledgements

This work was financially supported by the International Science & Technology Innovation Program of Chinese Academy of Agriculture Science (CAAS-CFSGLCA-IEDA-202302, CAAS-ZDRW202110), the National Natural Science Foundation of China (31670516), and Young Scientist Exchange Program between the People’s Republic of China and the Republic of Korea.

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Polyvinyl chloride promoted the dissemination of antibiotic resistance genes in Chinese soil: A metagenomic viewpoint

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Abstract

Figures

1. Introduction

2. Materials and methods

2.1 Experimental microplastics

2.2 Sample collection site and soil microcosm incubation

2.3 DNA extraction and metagenomic sequencing

2.4 Quantification of ARGs

2.5 Statistical analysis

3. Results and discussion

3.1 Effects of soil type on the abundance of ARGs

3.2 ARGs and bacterial community structures respond to the microplastic exposure

3.3 Correlations between bacterial abundance and ARG abundance in the soil

4. Conclusion

Declarations

References

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