Comparison of airborne antibiotic resistance genes in a chicken farm during winter and summer: A case study

doi:10.21203/rs.3.rs-3343843/v1

Download PDF

Research Article

Comparison of airborne antibiotic resistance genes in a chicken farm during winter and summer: A case study

https://doi.org/10.21203/rs.3.rs-3343843/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background Information regarding the abundance and dissemination of airborne antibiotic resistance genes (ARGs) in chicken farms and their correlations with airborne bacteria is limited.

Methods In this study, we investigated the bacterial communities and quantified the ARGs and mobile genetic elements (MGEs) in fecal and air samples in summer and winter.

Results The concentrations of nearly all the airborne ARG and MGE subtypes in winter is three or four orders of magnitude higher than those in summer. Findings reveal that the indoor air of a chicken farm is a reservoir of ARGs. The antibiotic target protection was the dominant resistance mechanism of ARGs in all the samples from this chicken farm. On the basis of the fecal samples, bacteria might be easily distributed in air in the intensive chicken farm during winter. Horizontal gene transfer could be a major transmission route in the atmosphere of chicken farms during winter. These results may improve the understanding of airborne ARGs transmission and its distribution characteristics and provide a reference for assessing the risks arising from chicken farms. More effort is needed to prevent and control the potential health risks induced by airborne ARGs.

Reservoir

Bioaerosol

Transmission route

Antibiotic resistance genes

Chicken farm

In recent years, antibiotic resistance has been regarded as an emerging environmental pollutant [1]. Antimicrobial resistance is gradually becoming a serious problem because massive antibiotics are used for veterinary, medical, and agricultural purposes. The ubiquitous dissemination and occurrence of antibiotic resistance genes (ARGs) in animal feces have been reported [2–5]. The concentration trend of sul genes (sulI and sulII) in different animal feces was sheep < pig < chicken [6]. Therefore, chicken farms are among the research hotspots in the antibiotic resistance area. Through agricultural fertilization, the ARGs and antibiotic resistant bacteria (ARB) in feces could enter the surrounding environments of livestock farms [7]. The dissemination and occurrence of airborne ARGs and ARB could come from animal feces through air volatilization [8]. Given that some airborne pathogens are ARB, which could directly cause infections through respiratory intake and skin exposure, their presence has undoubtedly increased the risks to human health [9, 10]. However, few studies have focused on the distribution of airborne ARGs and ARB in chicken farms.

Bacteria with different ARGs have evolved various resistance mechanisms that enable them to reduce the inhibitory action of antibiotics, impairing the ability to control bacterial infections [1]. In addition, understanding the resistance mechanism of each ARG subtype is important for the development of novel therapeutic approaches to inhibiting the growth of ARB. Considering that chicken farms among the major sites of ARGs pollution, a summary of the resistance mechanism of ARGs is urgently needed. The dissemination and acquirement of ARGs have been influenced by some factors, such as environmental factors (e.g., particles, velocity, relative humidity, and temperature) and bacterial communities [11, 12]. Han et al. [13] found that bacterial communities are a major factor of the spread of ARGs. Variations of bacteria in fecal and air samples can be used to investigate the effect of feces-borne ARB on airborne ARB in chicken farms. Few studies have been conducted on the influences of some environmental factors on the dissemination of ARGs in bioaerosols. On one hand, ARGs can be transferred through horizontal gene transfer (HGT) with mobile genetic elements (MGEs). Various MGEs (including transposes, integrons, prophages, and plasmids) could carry ARGs to different bacteria [14]. On the other hand, ARGs can be enriched by increasing the host bacteria, which is known as vertical gene transfer (VGT). To date, little is known on which transmission route is major in airborne ARB in chicken farms.

The present study tracked the distribution and diversity characteristics of MGEs and ARGs in bioaerosols, investigated the resistance mechanisms of ARGs in chicken farms, and identified the dominant transmission route of ARGs in chicken farms. In this report, we quantified three MGEs and nine ARGs and determined various bacterial communities in chicken farms. Some environmental factors, such as relative humidity (RH), wind speed, particulate matter (PM_2.5), and temperature, were monitored. The key bacteria (at the genus level) were identified using the linear discriminant analysis effect size (LEfSe) in all samples. The dominant transmission route was identified through network analysis.

Collection of samples

The samples were collected from a chicken farm (N39°54'39", E116°24'48") in Northwestern China (Fig. S1). The chicken farm adopts a completely closed architectural form. Heating equipment controlled the indoor temperature during winter. The ventilation system is controlled by using an industrial fan (FAC-60Y1, Jinling) during summer. The 20 m $\times$ 6 m $\times$ 3 m henhouse has 2500 chickens.

Using the four-point sampling method, we collected fecal samples for 7 consecutive days each in summer (sample ID: SF1–SF7) and winter (WF1–WF7). Air samples were collected in summer (SA1–SA7) and winter (WA1–WA7; Table S1) using an air sampler (Wuhan Tianhong Instruments Co., LTD; TH-150F). The air samples were sampled for 6 h. The wind speed was monitored using an anemometer (DT-8880; Zhuhai Instrument Co. LTD). The PM_2.5 of the air samples was determined through the gravimetric method with glass microfiber filters (SI, text1). The RH and temperature inside the chicken farm were detected by a humidity and temperature meter (MIC-800; Shenzhen Electronics Co. LTD).

DNA extraction

The filters were cut and ultrasonicated after air sampling [15] (SI, text2 & 3). DNA (deoxyribonucleic acid) was extracted from the filters and the feces using a FastDNA® SPIN Kit (Fig. S2). The DNA concentrations in the fecal and air samples were detected in the value of OD260 using an UV-Vis spectrophotometer (Quawell; Q5000).

Detection of MGEs and ARGs

Aminoglycoside, macrolides, quinolones, tetracyclines, and sulfonamides are the common antibiotics in this chicken farm. The ARGs were determined by real-time qPCR (CA; Bio-Rad iQ5 quantitative PCR): aminoglycoside resistance gene (strA), tetracycline resistance genes (tetO, tetM, and tetG), macrolide resistance genes (ermA and ermB), quinolone resistance genes (qnrA), and sulfonamide resistance genes (sul2 and sul1). Three MGEs (Tp614, IS613, and intl1) were also detected. The real-time qPCR conditions and primers are respectively provided in Table S2 and SI text4. The dissociation curves are in SI text5. The antibiotic mechanisms of the ARGs subtypes were confirmed using the Comprehensive Antibiotic Resistance Database.

High-throughput sequencing

To investigate the bacterial communities in all the samples, the extracted DNA of all the samples for 16S rRNA gene sequencing was used as a template. The sequences of the samples were subjected to quality control filtering using UCLUST in the QIIME software (version 1.8.0). SA1, SA3, SA5, and SA7 were the determined sequencing in the SA samples, which are similar to the SF, WA, and WF samples. The original sequencing data are available in the Sequence Read Archive (NCBI-PRJNA853872).

Assessment of variation in bacterial communities

The variation in the bacterial communities in the fecal and air samples taken in summer and in winter was assessed by LEfSe. The LEfSe analysis of the high-throughput profiles was visualized using the Galaxy/Hutlab web application (http://huttenhower.sph.harvard.edu/galaxy/) to assess the biomarkers in the different samples. The biomarker was significantly different in the key microorganisms in the different types of samples (p < 0.05). Therefore, this study assessed the biomarkers of airborne bacteria in summer and in winter.

Determination of ARGs transmission route

The ARGs transmission route in the chicken farm was investigated using the co-exclusion and co-occurrence network of airborne bacterial communities and ARGs/MGEs. On the basis of Spearman’s rank correlation, Cytoscape (v3.6.1) was used for network analysis. The numerous co-occurrence edges between the bacteria at the genus level and the ARGs indicate a high possibility of VGT. The numerous co-occurrence edges between the MGEs and the ARGs indicate a strong HGT possibility. The possibility of ARGs transfer in summer and in winter was calculated as follows:

$${\text{N}}_{\text{T}}={\text{N}}_{\text{H}}+{\text{N}}_{\text{V}}$$

$${\text{P}}_{\text{V}}=\frac{{\text{N}}_{\text{V}}}{{\text{N}}_{\text{T}}}$$

$${\text{P}}_{\text{H}}=\frac{{\text{N}}_{\text{H}}}{{\text{N}}_{\text{T}}}$$

where N_H is the number of co-occurrence edges between the MGEs and ARGs in the network, N_V is the number of co-occurrence edges between the bacterial community and the ARGs, P_v is the probability of VGT, and P_H is the probability of HGT.

Distribution and variation of MGEs and ARGs

The concentration of MGEs and ARGs in summer and in winter was quantified via qPCR (Fig. 1). In the air samples, intl1 was the main MGE and belongs to the integron. The intl1 concentration (6.9 ± 0.3 lg copies/m³) in the WA samples was the highest (Fig. S3; Table S3). In the WA samples, the high abundance of intl1 might enhance the transfer of airborne ARGs. The abundance of airborne ARG and MGE subtypes (except qnrA and intl1) in winter was obviously higher than that in summer (p < 0.01; Fig. 1a). The average concentration of the ARG subtypes (except strA and ermB) in the fecal samples taken in summer is higher than those taken in winter (Fig. 1b).

Among all the resistance subtypes, tetM is the dominant ARG in the chicken farm. The tetM concentration was 7.9 ± 1.3 lg copies/m³ in air and 11.8 ± 0.7 lg copies/g in the feces (Fig. S3). In the fecal samples, the tet genes concentration in a previous study (~ 9 lg copies/g [16]) is lower than that in this study. And the abundance of airborne tet genes is much higher than the reported values (~ 2 lg copies/m³ [17]), indicating the high health risk of the chicken farm’s indoor air. In addition, this study conducted a correlation analysis between the individual ARGs and the total concentration of all the ARGs in air during winter and summer. tetM presented the strongest correlation with the total concentration of all the ARG subtypes (Fig. S4, r > 0.95, p < 0.01). Therefore, tetM could be a potential indicator in the regular monitoring of airborne ARGs in the chicken farm.

Resistance mechanisms of ARGs

The specificity of antibiotics to bacteria is crucial for its usage in clinic for curing and preventing bacterial infections. However, the wide spread of ARB is raising a serious public health concern. Four main resistance mechanisms are responsible for the antibiotics in bacteria (Fig. 2a). The enzymatic function of acquired genetic traits that could cause antibiotic inactivation is a common mechanism in various pathogenic bacteria (such as Vibrio cholera) [18]. In this study, the percentage of this mechanism in all the samples is greater than 13.0% (Figs. 2b–2e, Table S4). The reduction permeability, an important defense mechanism, can prevent the bacterial cytoplasm of several antibiotics [19]. Many antibiotics have a high specific affinity for bacterial target protein, so they can inhibit indigenous cellular functions [20]. The antibiotic targets include (i) target alteration, (ii) target replacement, and (iii) target protection (Fig. 2a). Target alteration and replacement change the composition of the target molecule [21]. Target protection only inhibits the physical association between the target protein and antibiotics, which does change the nature of target protein [20]. According to the literature, 13 distinct tetracycline ribosomal protection protein classes mediate target protection [22], of which tetM and tetO are the best characterized [23, 24]. In Figs. 2b–2e, the percentage of antibiotic target protection is the highest (> 31.0%) in resistance mechanisms in all the samples. The ARG subtypes in this mechanism include tetM, tetO, and qnrA. The active efflux of antibiotics is encoded by the genes that are commonly present in the genome of bacteria. Some efflux proteins could recognize and pump out several antibiotics, such as tetracyclines, aminoglycosides, β-lactams, nalidixic acid, and macrolides (Das et al., 2020). In this study, tetG belongs to the antibiotic efflux, and its percentage is above 12.4% in all the samples.

Distribution of bacterial community

The bacterial communities in both samples were determined. The Shannon and Chao1 indexes were respectively used to evaluate the diversity and richness of bacteria [25] (Figs. 3a & 3b). In contrast to those in feces, the bacterial communities in air could be easily mixed together from different areas due to ventilation systems [26]. Meanwhile, the bacteria richness of the SA samples is higher than that of the WA samples (p < 0.01, Figs. 3a & 3b), which was also determined using the ACE indexes (Fig. S5). This result suggests that the high temperatures in summer (24.9°C on average) are conducive to the growth of bacterial communities compared with the low temperatures in winter (13.0°C on average, Table S5).

Firmicutes, Actinobacteria, Bacteroidetes, and Proteobacteria are the predominant bacteria in all the samples (Figs. S6–S7; at the phylum level), and the same result was obtained in pig manure [27]. The relative concentration of Firmicutes increased by approximately 20.9% in the feces compared with that in the air samples (Figs. S6–S7). However, Actinobacteria decreased by approximately 30.5% in the feces compared with that in the air samples (Figs. S6–S7). At the genus level, Lactobacillus (WA: 6.6%±0.3%, WF: 1.7%±1.0%, SA: 5.7%±3.0%, and SF: 12.0%±6.0%) was found in all the samples (Fig. 3c). Acinetobacter was detected in the SA and SF samples and caused various diseases (e.g., sepsis and pneumonia) [28]. Meanwhile, Ochrobactrum (8.6%±1.3%), Rhodococcus (4.2%±3.1%), Clostridium (2.7%±1.5%), and Terrisporobacter (2.1%±1.2%) were only detected in the SA samples. The concentration of pathogens (e.g., Corynebacterium) in the SA samples is higher than that in the other samples [29], Meanwhile, some strains that belong to the Rhodococcus genus could cause broncho-pneumonia [30]. Therefore, more attention should be paid to the atmosphere of chicken farms, especially in summer.

Variation in bacterial community in both air and feces

Figure 4 shows the biomarkers among the microbes in the fecal and air samples taken in summer and winter revealed by the LEfSe algorithm (p < 0.05, LDA scores < 3.5). The bacteria with no significant difference are in yellow, and the significantly different biomarkers are colored with the group (Fig. 4). Taking the fecal and air samples obtained in winter as bacterial examples (Fig. 4a), the number of airborne biomarkers (53) is lower than that of bacteria with no significant difference (249; Table S6). Figure 4b shows that the airborne bacteria contained 272 species of bacteria (at the genus level), which were significantly different from those in the fecal samples taken in summer (Fig. 4b, Table S6). Airborne bacteria may migrate from outside the chicken farm during summer. The fecal biomarkers in winter included Pseudomonadaceae and Erysipelothrix, which could be reproduced and grown at 4°C and 15°C, respectively (Fig. S8). The numbers of fecal biomarkers in summer and in winter were 10 and 27, respectively (Fig. 4c, Table S6). No obvious difference in the bacterial composition of the fecal samples taken in summer and in winter was observed. Consistent with the highest diversity of bacterial communities in the air samples indicated by the Shannon index, the number of airborne biomarkers was significantly higher in summer (271) than in winter (0; Fig. 4d, Table S6).

Influence of bacteria, environmental factors, and MGEs on composition and abundance of ARGs

In the environment, the abundance and dissemination of ARGs are associated with the bacteria [31]. Various environmental factors (for example wind speed, RH, and temperature) play significant roles in the spread and occurrence of bioaerosols [32, 33]. We checked for significant (p < 0.05) and strong (R > 0.5) positive correlations among the bacterial taxa (at the genus level, Top 20), the environmental factors, and the ARGs/MGEs (Fig. 5). Such correlations can imply the hosts of airborne ARGs [34]. The possible host of airborne ARGs/MGEs is Acinetobater (Fig. 5). In addition, the possible hosts of ARGs/MGEs is fewer in WA than in the SA samples. Eight of the top 20 genera (such as Corynebacterium and Ruminoccaceae) and the ARGs/MGEs had a significant correlation in summer (p < 0.05), indicating that the profiles of airborne ARGs might be significantly shaped by the contribution of these bacteria (Fig. 5b). However, Rothia and Kocuria were negatively correlated with the ARGs, indicating that the appearance of these bacteria would negatively affect the dissemination and occurrence of ARGs in bioaerosols (Figs. 5a & S9).

The chicken farm had no ventilation in winter, whereas the mechanical ventilation system was operated in summer. The wind speed in the chicken farm was 0.00 ± 0.0 m/s in winter (Table S5). Thus, the PM_2.5 concentration during winter (472.6 µg/m³) was higher than that during summer (93.1 µg/m³; Table S5). PM_2.5 and Jeotgalicoccus had a positive correlation in winter (Fig. 5a), indicating that particles might contribute to the growth of bacteria. At high RH (> 80%), the concentration of the total airborne aerobic bacteria (such as Corynebacterium) can decrease [35, 36]. The RH had a negative influence on most of the ARGs/MGEs subtypes, which may be attributed to the decrease of the possible host bacteria (Corynebacterium) (Fig. 5b). Temperature had a positive effect on some ARGs/MGEs. The increase in temperature (Table S5) facilitated the proliferation and spread of the ARGs.

ARGs transmission route in the atmospheric environment

ARGs could transfer in various bacteria through either HGT or VGT. The co-exclusion and co-occurrence relationships in MGEs, bacterial communities, and ARGs in the chicken farm were through the network to study the transmission route of ARGs in bioaerosols [37] (Fig. 5). On the basis of the number of co-occurrence edges in MGEs, bacterial communities, and ARGs, the transmission route of ARGs in bioaerosols was quantified (Table S7). In winter, most of the subtypes of ARGs and MGEs had more co-occurrence edges than the bacterial communities in the WA samples (Fig. 5a). Both Tp614 and intl1 (Plasmid and Integron, respectively) showed obvious correlations with tetG, tetO, and tetM among the visualized linkages (p > 0.05). The airborne ARGs in winter exhibited a higher possibility of HGT (P_H = 71.4%) than VGT (28.6%; Eqs. 1 & 2), indicating that HGT might play a key role in airborne ARGs transfer during winter. By comparison, the subtypes of ARGs with bacterial communities had more co-occurrence edges than the MGEs in the SA samples (Fig. 5b). Therefore, the airborne ARGs in summer had a higher possibility of VGT (P_v = 57.1%) than HGT (42.9%; Eqs. 1 & 3). This finding suggests that the VGT of ARGs may play an important role in the spread of airborne ARGs in summer.

Correlations of ARGs and MGEs among all samples

The succession of ARG compositions in all the samples was determined by principal component analysis (PCA; Fig. 6). PCA illustrates that the profiles of the ARGs in the fecal samples in summer and in winter had high similarity. Nevertheless, the ARG profiles in the summer fecal samples significantly separated along PC1, indicating that the SF samples had different resistome profiles compared with the WF samples (Fig. 6). For example, the concentration of ermB in the SF samples is significantly lower than that in the WF sample (p < 0.01; Fig. 1b). The profiles of the airborne ARGs collected around the feces (from the SA samples) and feces-borne ARGs (from the SF samples) in summer show distinct distribution patterns, which are similar to the conclusion on the bacterial community (Fig. 4). Furthermore, among the atmospheric environmental samples taken in summer and in winter (SA and WA samples), the ARGs showed an obvious distinction (Fig. 6). The concentration of ARGs in the SA samples was nearly three or four orders of magnitude lower than that in WA samples (Fig. 1a). The samples were divided into three distinct clusters on the basis of the sample types: the cluster constituted by SA samples, the cluster constituted by WA samples, and the cluster constituted by the WF and SF samples.

Indoor air of the chicken farm as a reservoir for ARGs

Antibiotics are mostly synthesized using fungal or bacterial species as chemical weaponries to kill (bactericidal) or inhibit other bacteria (bacteriostatic) [38]. Antibiotics could not cure bacterial infections [39]. However, the spread of ARGs can result in highly resistant bacteria. Previous reports discovered that the feces of livestock are a reservoir of ARGs, especially in chicken farms [6]. When feces are used as fertilizers, the ARGs in animal feces got to the soil [40]. Then, the ARGs in feces could be transferred to the vegetables [41]. This phenomenon indicates that ARGs in chicken farms would lead to serious risks to human health. Additionally, ARGs can also be transferred by the air in the chicken farm. However, airborne MGEs and ARGs have been overlooked. By identifying the MGEs and ARGs in the intensive chicken farm, the indoor air of chicken farms was determined as a reservoir of ARGs (Fig. 1). The airborne tetM concentration (7.9 ± 1.3 lg copies/m³) in this chicken farm is higher than that of a hospital (~ 3 lg copies/m³ [42]). This finding shows that bioaerosols are an important transmission route of ARGs in the chicken farm.

Resistance mechanisms analysis of ARGs

In available bacterial genome sequences, at least 20,000 potential ARGs of nearly 400 various types have been listed in the database [43]. Meanwhile, extensive knowledge regarding the characters of different mechanisms of resistance in each ARGs subtype. Most resistance mechanisms serve either to reduce the concentration of antibiotics around the molecular target (such as antibiotic inactivation and antibiotic efflux) or change the fundamental nature of the target (for instance target alteration and target replacement) to reduce the antibiotic’s inhibitory effect. Antibiotics are degraded by the action of antibiotic inactivation. With this mechanism, ARB may be enriched in a given environment [44]. For example, the related bacteria as degrader can be used to enhance the removal of sulfonamides from negative to 100% in activated sludge [45]. However, the related bacteria should be controlled to improve the effectiveness of antibiotic drugs and avoid the enrichment of ARGs in livestock farm. Target protection is the main cause of clinically significant resistance in pathogens [20]. The dominating mechanism is also antibiotic target protection in this chicken farm (Figs. 2b–2e). A comprehensive understanding of the resistance mechanism will help raise our fundamental knowledge and devise approaches for overcoming antibiotic resistance, which may restore the efficacy of antibacterial drugs.

Influences of various factors on airborne ARGs

The variation in airborne ARGs in the environment is an extremely multifactorial and complex problem. In this study, the ARGs in the bioaerosols showed complicated co-exclusion and co-occurrence relationships, which were strongly influenced by the interaction of environmental factors and the bacterial community. Meanwhile, the biomarkers of the bacterial community in the air were season dependent. The bacterial communities in the WA samples are slightly different from those in the WF samples (Fig. 4a). In the fecal samples, the bacteria during winter might be easily distributed to the air. A previous research also shows that a main source of airborne bacteria is the feces in livestock farms [46]. Additionally, the bacteria from other areas (for example outdoor) could be mixed by the mechanical ventilation during summer, which may increase the biomarkers of bacteria in the SA samples (Fig. 4b). The airborne ARGs concentration during summer may be further influenced by different bacteria [31]. Mechanical ventilation is reduced to keep the chicken farms warm in winter, so the concentration of indoor airborne ARGs is enriched and much higher than that in summer. For cooling in chicken farms, mechanical ventilation and water curtain are used in summer. The wind speed was 0.01 ± 0.01 m/s in the chicken farm (Table S5), promoting the dissemination of airborne ARGs in summer. Given that air disinfection is rarely conducted in common chicken farms, the high dynamic nature of the air can result in the fast transmissibility of airborne ARGs, hence farmers pose major risks or the surrounding air is spread ARGs [47]. From this perspective, the airborne ARGs in chicken farms and their underlying influences on environmental health and human must be controlled.

Major transmission route of airborne ARGs

Once bacterial resistance occurs, the ARGs are easily replicated and transferred in the bacterial community in a given environment. However, the VGT and HGT possibilities of various ARGs in airborne bacteria have been rarely reported. Wu et al. reported that statistical correlation can indirectly identify the major transmission route [48]. The ARGs increase during summer might be by the proliferation of ARB in bioaerosols. At the phylum level, Fusobacteria are the potential ARG host bacteria with tetM in the SA and WA samples (p < 0.05; Fig. S10). Fusobacteria is among the top 10 bacteria in the SA and WA samples (3.1%±1.2% and 0.9%±0.3%; Figs. S6 & S7), implying that the ARGs with bacterial communities in the air could be increased. The positive correlation is between airborne Fusobacteria and tetM (r: 0.99) in summer, which is stronger than that in winter (r: 0.89; Fig. S11). The changes in the tetM in the air samples taken in summer (slope: 0.004) with the changes in Fusobacteria were faster than those taken in winter (slope: 0.001; Fig. S11). Therefore, the VGT possibility of the airborne ARGs in summer was much higher than that in winter. HGT might play a major role in the airborne ARGs transfer in winter and had a low possibility in summer (Fig. 5). The concentrations of ARGs were high in the air samples collected in winter (Fig. 1). HGT might have been the dominant transmission route of airborne ARGs during winter.

In the present research, the concentration of airborne ARGs in the chicken farm is even higher than that in a hospital, and the abundance of nearly all the airborne MGEs and ARGs subtypes in winter is much higher than those in summer. This research demonstrates that indoor air is a reservoir of ARG in chicken farms. Understanding the resistance mechanisms of each ARG subtype is crucial for the rational development of novel therapeutic approaches to inhibiting resistance. The dominant resistance mechanism of ARGs in this chicken farm is antibiotic target protection. This study investigated the variation of bacteria in the fecal and air samples in summer and in winter by LEfSe. In the intensive chicken farm, the bacteria in the winter fecal samples might easily spread to the air. Most of the airborne ARG subtypes exhibited higher co-occurrence with MGEs rather than bacteria in winter. HGT might be a major transmission route of airborne ARGs in the chicken farm during winter. These results could provide a better understanding of the airborne ARGs transmission route and improve the management strategy in chicken farms.

Ethical Approval

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

LS contributed to the methodology, formal analysis, visualization, data curation, writing-review and editing. JM, GJ and YZ contributed to the investigation and sample collection. CW contributed to conceptualization, project administration, and funding acquisition. HC contributed to the methodology.

Funding

This study was supported by the National Natural Science Foundation of China (Grant No. 51678402).

Availability of data and materials

Not applicable.

ECDC/EMEA. European Centre for Disease Control & European Medicines Agency. The Bacterial Challenge: Time to React. London, UK. 2009.
Ji X, Shen Q, Liu F, Ma J, Xu G, Wang Y, et al. Antibiotic Resistance Gene Abundances Associated with Antibiotics and Heavy Metals in Animal Manures and Agricultural Soils Adjacent to Feedlots in Shanghai, China. J Hazard Mater. 2012;235–236,178–185.
Just NA, Létourneau V, Kirychuk SP, Singh B, Duchaine C. Potentially Pathogenic Bacteria and Antimicrobial Resistance in Bioaerosols from Cage-Housed and Floor-Housed Poultry Operations. Ann Occup Hyg. 2012;56(4):440–449.
He LY, Liu YS, Su HC, Zhao JL, Liu SS, Chen J, et al. Dissemination of Antibiotic Resistance Genes in Representative Broiler Feedlots Environments: Identification of Indicator ARGs and Correlations with Environmental Variables. Environ Sci Technol. 2014;48(22):13120–13129.
Wu B, Duan H, Qi Q, Cai Y, Zhong Z, Chai T. Identifying Virulence Factor Genes in E. Coli in Animal Houses and Their Transmission to Outside Environments. J Aerosol Sci. 2018;117:189–199.
Cheng W, Chen H, Su C, Yan S. Abundance and Persistence of Antibiotic Resistance Genes in Livestock Farms: A Comprehensive Investigation in Eastern China. Environ Int. 2013,61:1–7.
Zhao J, Chen Z, Chen S, Deng Y, Liu Y, Tian W, et al. Prevalence and Dissemination of OqxAB in Escherichia Coli Isolates from Animals, Farmworkers, and the Environment. Antimicrob. Agents Chemother. 2010;54(10):4219–4224.
McEachran AD, Blackwell BR, Hanson JD, Wooten KJ, Mayer GD, Cox SB, et al. Antibiotics, Bacteria, and Antibiotic Resistance Genes: Aerial Transport from Cattle Feed Yards via Particulate Matter. Environ Health Perspect. 2015;123(4):337–343.
Dijkshoorn L, Nemec A, Seifert H, An Increasing Threat in Hospitals: Multidrug-Resistant Acinetobacter Baumannii. Nat Rev Microbiol. 2007;5(12):939–951.
Wang C, Song L, Zhang ZW, Wang YZ, Xie X. Microwave-Induced Release and Degradation of Airborne Antibiotic Resistance Genes (ARGs) from Escherichia Coli Bioaerosol Based on Microwave Absorbing Material. J Hazard Mater. 2020;394:122535.
Zhang J, Yang M, Zhong H, Liu M, Sui Q, Zheng L, et al. Deciphering the Factors Influencing the Discrepant Fate of Antibiotic Resistance Genes in Sludge and Water Phases during Municipal Wastewater Treatment. Bioresour Technol. 2018;265:310–319.
Ansari MI, Grohmann E, Malik A. Conjugative Plasmids in Multi-resistant Bacterial Isolates from Indian Soil. J Appl Microbiol. 2008;104:1774−1781.
Han XM, Hu HW, Chen QL, Yang LY, Li HL, Zhu YG, et al. Antibiotic Resistance Genes and Associated Bacterial Communities in Agricultural Soils Amended with Different Sources of Animal Manures. Soil Biol. Biochem. 2018;126:91–102.
D'Costa VM, Griffiths E, Wright GD. Expanding the Soil Antibiotic Resistome: Exploring Environmental Diversity. Curr Opin Microbiol. 2007;10(5):481–489.
Song L, Wang C, Wang YZ. Optimized Determination of Airborne Tetracycline Resistance Genes in Laboratory Atmosphere. Front Environ Sci Eng. 2020;14(6):95−106.
Mu Q, Li J, Sun Y, Mao D, Wang Q, Luo Y. Occurrence of Sulfonamide-, Tetracycline-, Plasmid-Mediated Quinolone- and Macrolide-Resistance Genes in Livestock Feedlots in Northern China. Environ Sci Pollut Res. 2015;22(9):6932–6940.
Ling AL, Pace NR, Hernandez MT, Lapara TM. Tetracycline Resistance and Class 1 Integron Genes Associated with Indoor and Outdoor Aerosols. Environ Sci Technol. 2013;47(9):4046–4052.
Das B, Verma J, Kumar P, Ghosh A, Ramamurthy T. Antibiotic Resistance in Vibrio Cholerae: Understanding the Ecology of Resistance Genes and Mechanisms. Vaccine. 2020;38:A83–A92.
Darley E, Weeks J, Jones L, Daniels V, Wootton M, MacGowan A, et al. NDM-1 Polymicrobial Infections Including Vibrio Cholerae. Lancet. 2012;380(9850):1358.
Wilson DN, Hauryliuk V, Atkinson GC, O’Neill AJ. Target Protection as a Key Antibiotic Resistance Mechanism. Nat Rev Microbiol. 2020;18(11):637–648.
Andersson DI, Hughes D. Antibiotic Resistance and Its Cost: Is It Possible to Reverse Resistance? Nat Rev Microbiol. 2010;8:260–71.
Roberts MC. Tet mechanisms of resistance https://faculty.washington.edu/marilynr/tetweb1.pdf (2020). 2020.
Nguyen F, Starosta AL, Arenz S, Sohmen D, Dönhöfer A, Wilson DN. Tetracycline Antibiotics and Resistance Mechanisms. Biol Chem. 2014;395:559–575.
Connell SR, Tracz DM, Nierhaus KH, Taylor DE. Ribosomal Protection Proteins and Their Mechanism of Tetracycline Resistance. Antimicrob Agents Chemother. 2003;47(12):3675–3681.
Xu P, Wang J, Hong F, Wang S, Jin X, Xue T, et al. Melatonin Prevents Obesity through Modulation of Gut Microbiota in Mice. J Pineal Res. 2017;62(4):1–13.
Spengler JD, Chen Q, Dilwali KM. Indoor Air Quality Factors in Designing a Healthy Buildings. In Indoor Air Quality Handbook. New York: McGraw-Hill 2001;pp.5.1−5.29.
Li H, Cheng W, Li B, Xu Y, Zheng X. The Fate of Antibiotic Resistance Genes during Co-Composting of Swine Manure with Cauliflower and Corn Straw. Bioresour Technol. 2020;300:122669.
Lu R, Li Y, Li W, Xie Z, Fan C, Liu P, et al. Bacterial Community Structure in Atmospheric Particulate Matters of Different Sizes during the Haze Days in Xi’an, China. Sci Total Environ. 2018;637–638,244–252.
Hennart M, Panunzi LG, Rodrigues C, Gaday Q, Baines SL, Barros-Pinkelnig M, et al. Population Genomics and Antimicrobial Resistance in Corynebacterium Diphtheriae. Genome Med. 2020;12(1):1–18.
Muscatello G, Gilkerson JR, Browning GF. Detection of Virulent Rhodococcus Equi in Exhaled Air Samples from Naturally Infected Foals. J Clin Microbiol. 2009;47(3):734–737.
Forsberg KJ, Sanket P, Gibson MK, Lauber CL, Rob K, Noah F, et al. Bacterial Phylogeny Structures Soil Resistomes Across Habitats. Nat. 2014;509:612−616.
Ouyang W, Xu Y, Cao J, Gao X, Gao B, Hao Z, et al. Rainwater Characteristics and Interaction with Atmospheric Particle Matter Transportation Analyzed by Remote Sensing around Beijing. Sci Total Environ. 2019;651:532–540.
Smets W, Moretti S, Denys S, Lebeer S. Airborne Bacteria in the Atmosphere: Presence, Purpose, and Potential. Atmos Environ. 2016;139:214–221.
Li B, Yang Y, Ma L, Ju F, Guo F, Tiedje JM, et al. Metagenomic and Network Analysis Reveal Wide Distribution and Co-Occurrence of Environmental Antibiotic Resistance Genes. ISME J. 2015;9(11):2490–2502.
Rahman MS, Labuza TP. Water Activity and Food Preservation. In Handbook of Food Preservation. New York: Marcel Dekker 1999;pp.339−382.
Islam MA, Ikeguchi A, Naide T. Concentrations of Aerosol Numbers and Airborne Bacteria, and Temperature and Relative Humidity, and Their Interrelationships in a Tie-stall Dairy Barn. Animals. 2019:9(12):10−23.
Deng Y, Jiang YH, Yang Y, He Z, Luo F, Zhou J. Molecular Ecological Network Analyses. BMC Bioinformatics. 2012;13(1).
Martínez JL. Antibiotics and Antibiotic Resistance Genes in Natural Environments. Sci. 2008;321:365–7.
Wright GD. Antibiotic Resistance in the Environment: A Link to the Clinic? Curr Opin Microbiol. 2010;13(5):589–594.
Marti E, Jofre J, Balcazar JL. Prevalence of Antibiotic Resistance Genes and Bacterial Community Composition in a River Influenced by a Wastewater Treatment Plant. PLoS One. 2013;8(10):1–8.
Jechalke S, Heuer H, Siemens J, Amelung W, Smalla K. Fate and Effects of Veterinary Antibiotics in Soil. Trends Microbiol. 2014;22(9):536–545.
Wang YZ, Wang C, Song L. Distribution of Antibiotic Resistance Genes and Bacteria from Six Atmospheric Environments: Exposure Risk to Human. Sci Total Environ. 2019;694:133750.
Liu B, Pop M. ARDB - Antibiotic Resistance Genes Database. Nucleic Acids Res. 2009;37:443–447.
Nunes OC, Manaia CM, Kolvenbach BA, Corvini PFX. Living with Sulfonamides: A Diverse Range of Mechanisms Observed in Bacteria. Appl Microbiol Biotechnol. 2020;104(24):10389–10408.
Chen J, Xie S. Overview of Sulfonamide Biodegradation and the Relevant Pathways and Microorganisms. Sci Total Environ. 2018;640–641(3):1465–1477.
Andersen CI, Von Essen SG, Smith LM, Spencer J, Jolie R, Donham KJ. Respiratory Symptoms and Airway Obstruction in Swine Veterinarians: a Persistent Problem. Am J Ind Med. 2004;46:386−392.
Xie J, Jin L, Luo X, Zhao Z, Li X. Seasonal Disparities in Airborne Bacteria and Associated Antibiotic Resistance Genes in PM2.5 between Urban and Rural Sites. Environ Sci Technol Lett. 2018;5(2):74–79.
Wu D, Huang XH, Sun JZ, Graham DW, Xie B. Antibiotic Resistance Genes and Associated Microbial Community Conditions in Aging Landfill Systems. Environ Sci Technol. 2017;51(21):12859–12867.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Comparison of airborne antibiotic resistance genes in a chicken farm during winter and summer: A case study

Status:

Version 1

Abstract

Figures

Introduction

Materials and methods

Collection of samples

DNA extraction

Detection of MGEs and ARGs

High-throughput sequencing

Assessment of variation in bacterial communities

Determination of ARGs transmission route

Results

Distribution and variation of MGEs and ARGs

Resistance mechanisms of ARGs

Distribution of bacterial community

Variation in bacterial community in both air and feces

Influence of bacteria, environmental factors, and MGEs on composition and abundance of ARGs

ARGs transmission route in the atmospheric environment

Correlations of ARGs and MGEs among all samples

Discussion

Indoor air of the chicken farm as a reservoir for ARGs

Resistance mechanisms analysis of ARGs

Influences of various factors on airborne ARGs

Major transmission route of airborne ARGs

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1