Research Note: The structure and diversity of antibiotic resistance genes in animal house environment
With the widespread use of antibiotics, the emergence of antibiotic-resistant bacteria ( ARB ) and the spread of antibiotic-resistant genes ( ARG ) have become a global public health problem. In 2019, there were approximately 5 million deaths worldwide related to antibiotic resistance, it is projected that by 2050, the annual number of clinical deaths caused by antibiotic resistance will reach 10 million people . Currently, researchers have detected ARGs in various environments. What is concerning is that these ARGs that exist in large quantities in the environment can also be found in the human body . ARGs can spread horizontally not only through vertical transmission among microbial generations, but also through mobile genetic elements ( MGE ) such as plasmids, bacteriophages, and transposons. Microorganisms that acquire ARGs may develop antibiotic resistance, which will make microbial infections difficult to treat. The horizontal transfer of ARGs has contributed to outbreaks of some community-acquired infections, such as the Shigella epidemic in the UK due to the transfer of plasmid-encoded ARGs . In addition, horizontal transfer also occurs between the animal environment and humans. As early as 1976, demonstrated that plasmids carrying ARGs could spread between chickens, as well as between chickens and humans. Microorganisms that acquire ARGs pose significant challenges to disease treatment. Livestock and poultry houses are one of the hotspots for antibiotic use. Indoor air in livestock houses is considered a reservoir for ARGs. The transmission of ARB and ARGs is closely related to bioaerosols. The air in livestock houses contains various types of ARGs, including tetracycline resistance genes, aminoglycoside resistance genes, macrolide resistance genes, sulfonamide resistance genes, and others. In addition, the notorious Methicillin-resistant Staphylococcus aureus ( MRSA ) has also been successfully isolated from aerosol samples collected from livestock and poultry houses. Past clinical findings have shown that these MRSA strains can cause severe skin infections, respiratory infections, and even lead to death . The presence of pathogenic microorganisms and ARGs in aerosols from livestock and poultry houses not only poses a threat to animal health but also endangers the health of farmers and nearby residents. Therefore, assessing the risk level of pathogenic microorganisms and ARGs in the indoor air of poultry houses is of great significance for protecting the health of employees and surrounding residents. This study utilized the research methods of metagenomics to analyze bacteria and ARGs in chicken house aerosols from multiple dimensions. The objectives of this study were: 1) to describe the distribution characteristics of bacteria in the air of livestock and poultry houses; 2) to describe the distribution characteristics of ARGs/MGEs in the air of livestock and poultry houses; 3) to analyze the interactive relationship between microorganisms and ARGs/MGEs in the air of livestock and poultry houses. To achieve these objectives, we collected air samples from a chicken farm for 4 consecutive wk. The collected samples were sequenced using second-generation sequencing technology, and the bacteria, ARGs/MGEs in the samples were annotated and quantified through the analysis method of metagenomics. In addition, we utilized network visualization tools to understand the interactive relationships between bacteria and ARGs/MGEs. This study systematically described the distribution and interactive relationship between bacteria and ARGs in the air of poultry houses, providing references for the production of ARBs and the prevention and control of ARGs transmission in chicken farms. 

Sample Collection Animal feeding experiments were conducted at a company's breeding base in 2021. The experimental chicken house is located in Qingyuan city, Guangdong province, China (113.016E,23.7N). The experiment was conducted in November and lasted for 4 wk. The experiment was conducted in a ground-level free-range chicken house. The basic details of the house are as follows: it has an area of approximately 400m 2 , housing around 3,000 chickens at 90 d of age. The shed is ventilated naturally for gas exchange both inside and outside, and the chickens have free access to feed and water. This study was approved by the Animal Welfare and Ethics Committee of Foshan University (FOSU 2021-235). Ambient air particulate sampler was used for the airborne PM2.5 sampling of chicken house. Each sample was collected continuously for 24h at a flow rate of 100 L/min. We collect 3 samples per week and sample continuously for 4 wk. Over the course of the experiment, a total of 12 samples were obtained. In order to prevent sample contamination and microbial death, Samples were stored in -80℃ ultra-low temperature refrigerator immediately after sampling for later use. Metagenomics Sequencing The collected samples were entrusted to Biomarker Technologies Co, Ltd. ( http://www.biomarker.com.cn , Beijing, China) for metagenomic sequencing. The illumine NovaSeq 6,000 platform was used for metagenomic sequencing. To ensure the quality of analysis, Trimmomatic v0.33 is used to filter Raw Reads of poor quality, cutadapt 1.9.1 was used to identify and remove primer sequences to get Clean Reads. Metagenome assembly was performed using MEGAHIT (v1.1.2), contig shorter than 300bp was considered invalid. Using QUAST, evaluated the assembly results of Metagenome. Identify coding regions in the genome using the default parameters of MetaGeneMark. Nonredundant Gene Catalogue was constructed by MMseqs2, the similarity value was greater than 95%, the coverage value was greater than 90%. Data Analysis The abundance and diversity of Microorganisms were annotated Using Nr database of NCBI ( https://www.ncbi.nlm.nih.gov/ ). Using CARD ( https://card.mcmaster.ca/ ) database to predict ARGs. The INTEGRALL ( http://integrall.bio.ua.pt/ ) database was used to annotated MGEs of integrated subtypes. And transposase subtypes was annotated by CENSOR ( https://www.girinst.org/censor/index.php ) database. GraphPad Prism v9.5 was used to calculate the significance of differences between groups, the significance threshold was set to 0.05. Visualization of the data was achieved using ggplot2 package from R v4.3. Gephi64 is used to construct the correlation network graph of ARGs/MGEs and bacteria. 

Animal feeding experiments were conducted at a company's breeding base in 2021. The experimental chicken house is located in Qingyuan city, Guangdong province, China (113.016E,23.7N). The experiment was conducted in November and lasted for 4 wk. The experiment was conducted in a ground-level free-range chicken house. The basic details of the house are as follows: it has an area of approximately 400m 2 , housing around 3,000 chickens at 90 d of age. The shed is ventilated naturally for gas exchange both inside and outside, and the chickens have free access to feed and water. This study was approved by the Animal Welfare and Ethics Committee of Foshan University (FOSU 2021-235). Ambient air particulate sampler was used for the airborne PM2.5 sampling of chicken house. Each sample was collected continuously for 24h at a flow rate of 100 L/min. We collect 3 samples per week and sample continuously for 4 wk. Over the course of the experiment, a total of 12 samples were obtained. In order to prevent sample contamination and microbial death, Samples were stored in -80℃ ultra-low temperature refrigerator immediately after sampling for later use. 

The collected samples were entrusted to Biomarker Technologies Co, Ltd. ( http://www.biomarker.com.cn , Beijing, China) for metagenomic sequencing. The illumine NovaSeq 6,000 platform was used for metagenomic sequencing. To ensure the quality of analysis, Trimmomatic v0.33 is used to filter Raw Reads of poor quality, cutadapt 1.9.1 was used to identify and remove primer sequences to get Clean Reads. Metagenome assembly was performed using MEGAHIT (v1.1.2), contig shorter than 300bp was considered invalid. Using QUAST, evaluated the assembly results of Metagenome. Identify coding regions in the genome using the default parameters of MetaGeneMark. Nonredundant Gene Catalogue was constructed by MMseqs2, the similarity value was greater than 95%, the coverage value was greater than 90%. 

The abundance and diversity of Microorganisms were annotated Using Nr database of NCBI ( https://www.ncbi.nlm.nih.gov/ ). Using CARD ( https://card.mcmaster.ca/ ) database to predict ARGs. The INTEGRALL ( http://integrall.bio.ua.pt/ ) database was used to annotated MGEs of integrated subtypes. And transposase subtypes was annotated by CENSOR ( https://www.girinst.org/censor/index.php ) database. GraphPad Prism v9.5 was used to calculate the significance of differences between groups, the significance threshold was set to 0.05. Visualization of the data was achieved using ggplot2 package from R v4.3. Gephi64 is used to construct the correlation network graph of ARGs/MGEs and bacteria. 

In this study, the non-redundant genes from the metagenomic samples were annotated using the NCBI Nr database, resulting in the identification of 20,320 different species-level bacteria. The top 20 dominant bacterial genera at the genus levels in different samples are shown in . At the genus level, the dominant genera included Staphylococcus, Bacillus, Corynebacterium, Lactobacillus , and Streptococcus . In our other study (from the same data source), we analyzed the pathogenic microorganisms in the samples . We referred to the "List of Human Pathogenic Microorganisms" published by the Chinese government ( www.nhc.gov.cn ). In the air samples from the chicken houses, 28 infectious pathogenic microorganisms were screened at the species level. The results showed that pathogenic microorganisms such as Staphylococcus aureus, Staphylococcus epidermidis, Prevotella, Erysipelatoclostridium, Bacteroides fragile, Campylobacter coli, Chlamydia trachomatis , and Acinetobacter baumannii had relatively high abundance in the air of the chicken house. The severe microbial aerosol pollution in animal husbandry sheds is one of the main sources of pathogenic microorganisms in the environment. These pathogens can pose harm to the health of animals or humans in multiple ways. Chlamydia trachomatis Staphylococcus aureus and Acinetobacter baumannii found in poultry sheds can cause respiratory infections in chickens, leading to symptoms such as interstitial pneumonia . On the other hand, animal hair, skin, feces, and metabolic products are the main sources of micro-organisms in aerosols from animal sheds. To some extent, the composition of microorganisms in aerosols can reflect the health status of the animals. The sequences from the metagenomic samples were annotated in the CARD database, and a total of 761 ARGs were obtained. Based on the types of antibiotic resistance genes from different genes, ARGs with relative abundances greater than 0.01% were classified as shown in . The top 3 abundant ARGs were Aminoglycoside, Macrolide-lincosamide-streptogramin B ( MLSB ), and Tetracycline resistance genes, accounting for 79.8% of the total relative abundance. This result was consistent with Yang's study . ARGs with relative abundances greater than 0.01% can be classified into 5 main types of antibiotic resistance mechanisms, as shown in . These include antibiotic inactivation (46.72%), antibiotic target alteration (18.86%), antibiotic efflux (17.96%), antibiotic target protection (10.43%), and antibiotic target replacement (6.02%). The antibiotic inactivation mechanism mainly involves aminoglycosides and MLSB classes. Among them, aminoglycoside resistance genes, such as spd (19.57%), and MLSB gene lnuA (6.27%) and ANT(4)-Ib (5.56%), are relatively abundant. All antibiotic target alteration mechanisms belong to the MLSB class, and all the ARGs in this category belong to the Erm family. The antibiotic efflux mechanism is mainly associated with fluoroquinolones and tetracyclines, with tetracycline resistance genes tet(L) (6.98%), tet(K) (3.89%), and fluoroquinolone resistance gene cmx (3.17%) being prominent. All ARGs involved in the antibiotic target protection mechanism belong to tetracycline resistance genes. We screened for ARG subtypes with typical changes in the metagenomic samples. showed that the abundance of ARGs in the aerosol gradually increased with the growth and development of the animals. There were a total of 25 types of ARGs showing an increasing trend. Among them, there were 7 types of Aminoglycoside ARGs, including APH4_Ia, APH6_Ia, APH3′_Vc, APH3′_VIIa, ANT2′'_Ia, AAC6′_Iai, aadA6/aadA10. There were 5 types of Beta-lactamases ARGs, including OXA_114a, TEM_1, EXO_1, ACT_22, CTX_M_20. There were 4 types of Multidrug ARGs, including vgaE, hmrM, efpA, clbA. The results of this study showed an increasing trend in the abundance of certain subtypes of Aminoglycoside and Beta-lactamases ARGs in the air, indicating a certain risk of enrichment of these subtypes in the aerosol of chicken coops during the development of chickens. showed that the abundance of ARGs in aerosols gradually decreased with the growth and development of animals. There were a total of 19 types of ARGs that showed a decreasing trend in abundance with animal growth and development. Among them, there were 6 types of Beta-lactamases, including mecA, AcrF, SLB_1, CfxA3, OXA_379, PER_5. There were 3 types of Tetracycline, including tetK, tet45, otrB. And 3 types of MLSB, including lsaE, Erm30, lnuB. From the types of these ARGs, it can be seen that multiple types of ARGs exhibit either an increasing or decreasing trend. Based on the number of different subtypes of ARGs, Aminoglycoside and Beta-lactamases were the main types of ARGs showing an increasing trend. In the decreasing trend, Beta-lactamases were the main type of ARGs. The possibility of microorganisms acquiring resistance may be related to the abundance of ARGs, and low abundance of ARGs has lower transmission risk compared to high abundance. However, the abundance of ARGs is influenced by multiple factors. Pei's report suggested that the levels of tetW, tetO, sul1, and sul2 were significantly lower under 20℃ conditions compared to 4℃ conditions . Our study obtained similar results, showing a negative correlation between the abundance of tetracycline and sulfonamide resistance genes and temperature, as shown in . This indicates that temperature is an important factor affecting the abundance of ARGs. However, some subtypes of Beta-lactamases, Tetracycline, MLSB, and other ARGs showed a decreasing trend. The reasons for the decrease or decreasing trend of these ARGs during chicken development are not clear, and the causes of this phenomenon may be multifaceted. Past studies have shown that factors such as light exposure, heavy metals, and anaerobic composting can lead to a decrease in the abundance of ARGs . In this study, we screened 4 types of MGEs from the metagenomic samples. Based on the Spearman correlation analysis, the interactive relationships among ARGs, MGEs, and bacterial genera were obtained, as shown in Figure 5 ( r > 0.7, p < 0.05). The network diagram of the association between ARGs and MGEs is shown in . The ARGs nodes that were significantly correlated with IS4 were the most abundant, with a total of 13. Next were Intl1, IS481, and IS1595, which were significantly correlated with 10, 9, and 4 ARGs. IS4 was positively correlated with aadA, mecR1, and mecl. IS1595 was positively correlated with tet.K and lnuB. IS481 was positively correlated with dfrG, sul1, FosB3, aadA5, and tetL. Intl1 was positively correlated with dfrG, sul1, FosB3, aadA5, cmx, and tetL. Based on the significant co-occurrence relationship between ARGs and MGEs, we inferred that these MGEs which showed strong positive correlation with ARGs may serve as the mobile carriers of these ARGs. These MGEs play a driving role in the horizontal transfer and spread of ARGs. Meanwhile, we constructed a co-occurrence network between MGEs and bacterial genera at the horizontal level, as shown in , to explore the potential hosts of MGEs. IS481 and intl1 share potential hosts of Jeotgalicoccus and Corynebacterium , while the potential hosts for IS1595 are Prevotella and Bifidobacterium . IS481 and intl1 shared potential hosts of Jeotgalicoccus and Corynebacterium , while the potential hosts for IS1595 were Prevotella and Bifidobacterium . Based on this, we inferred that dfrG, sul1, FosB3, aadA5, and tetL may potentially undergo horizontal transfer to Jeotgalicoccus and Corynebacterium , while tet.K and lnuB may potentially undergo horizontal transfer to Prevotella and Bifidobacterium . In addition, we associated MGEs with the infectious pathogens in the samples, and the results are shown in . Intl1 and IS481 showed strong positive correlations with Chlamydia psittaci . IS4 exhibited strong positive correlations with Campylobacter fetus and Erysipelatoclostridium sp . IS1595 showed strong positive correlations with Clostridium perfringens . Meanwhile, we found from the results that Intl1 and IS481 exhibited a high degree of similarity in carrying different subtypes of ARGs, as well as in their selection of potential hosts. This suggests that Intl1 and IS481 may have similar mechanisms of horizontal transmission of ARGs. This study preliminarily investigated the distribution of ARGs in open chicken houses. As one of the hotspots for ARGs, chicken sheds were found to have ARG abundance at a hazardous level in this study, potentially posing a threat to public health security through the horizontal transfer of microorganisms via MGEs. Despite past studies having observed horizontal transmission of ARGs among microorganisms in the laboratory, exhibiting corresponding antibiotic resistance . However, in practical production, the horizontal transfer of ARGs between microorganisms is a rather complex process. The process of ARGs horizontal transfer has limitations when relying solely on correlation prediction. In addition, this study found that various ARGs accumulated or decreased over a certain period of time. Elucidating the mechanisms underlying this phenomenon may provide valuable insights into controlling ARGs pollution. 

The authors declare no conflicts of interest. 

