A Comparative Study of Associated Microbiota Between Pig Farm and Pig Slaughterhouse in Guangdong, China
Jian‑Yong Wu1 · Yan‑Shan Zhu1 · Cheng Guo2 · Yao Xia1 · Zhong‑Min Guo3 · Qian‑Lin Li1 · Jia‑Hai Lu1,4,5
The goal of this study was to compare the microbiota in diferent pig-present settings in China. Bioaerosol samples from pig farms and slaughterhouses and nasal samples from pig farmers and slaughterhouse workers were collected in Guangdong, southern China. The bacterial genomic DNA was isolated and subjected to 16S sequencing. The data were analyzed using QIIME2 with the DADA2 pipeline. A total of 14,923,551 clean reads and 2785 operational taxonomic units (OTUs) were obtained, which were mostly grouped into 4 phyla (Proteobacteria, Bacteroidetes, Firmicutes, and Actinobacteria) and 220 families. The microbiota richness of nasal samples in pig-present workers was higher than that of bioaerosols collected in the vicinity of the pig enclosures. There were 31.7% (620/1954) shared OTUs between pig farm bioaerosols and pig farmers which was higher than that between pig slaughterhouses and slaughterhouse workers (23.4%, 364/1553) (p < 0.001).
Acinetobacter and Pseudomonas were the most abundant in pig-present bioaerosols, and Staphylococcus, Pseudomonas, and Corynebacterium were dominant bacterial genus in pig farmers. The bacterial patterns are also specifc to the location of sample collected. The results suggest that bioaerosol microbiota interact with human nasal microbes in the vicinity of the pig farm enclosures, providing the basis for further analysis of microbial transmission across hosts in pig-present settings.
The interaction between human and animals is critically important for transmission of zoonotic pathogens from vertebrate animals to humans. Zoonotic pathogens account
for an estimated 70% of all human-related infectious diseases [1, 2], which are still a major threat to public health as illustrated by recent COVID-19 outbreak, along with
the epidemic of swine infuenza virus, Streptococcus suis, and so on [3–5]. Pig industry comprises a big portion of animal-related work. Pig workers tend to face a greater risk
being infected by zoonotic disease than others, and would be susceptible to active infection from pig herds, which helps to facilitate transmission from pigs to pig workers [6, 7].
Further bilateral transmission may cause an amplifcation efect, leading to high levels of infection in both pigs and pig industry workers . Therefore, revealing the bacterial diversity of nasal and bioaerosol samples in pig-present settings is important for better understanding the transmission patterns of pig-borne pathogens.
Bioaerosol is a collection of airborne particles of fungi, bacteria, viruses, and their metabolic products. Bioaerosols can connect human and animal hosts, and understanding the
roles of bioaerosols can help to understand the transmission patterns of airborne pathogens [9, 10]. Corzo et al. have investigated the transmission of infuenza A virus through
air and detected viral genomes 2.1 km away from pig farms . Another study has detected Reproductive and Respiratory Syndrome virus in 26% of pigs exposed to bioaerosol
samples . Clearly, bioaerosols can facilitate pathogen survival and spread.
The recent development of Next-Generation Sequencing (NGS) methods has provided a culture-independent approach to characterize microbial communities, resulting in
signifcant enhancement of our understanding for the diverse microbial communities in specifc habitats of interest. For example, Lee, et al. have analyzed airborne microbiota of
108 samples collected from Beijing, Seoul, and Nagasaki, and determined that Beijing had the most diverse bacterial community and was infuenced by meteorological factors
. Other studies found that bacterial structure was associated with airborne particulate matter as well as spatial and seasonal variation [14, 15]. However, the microbiota in
occupational settings with animal exposure has never been investigated in China. In this study, we aimed to compare the diference of the bacterial diversity, abundance, and characteristics in pig industry samples, which would be a helpful reference for potential public health problem and pathogen surveillance in the pig industry.
Material and Methods
Ethical approval was obtained from the ethics committee of the School of Public Health, Sun Yat-Sen University (2014-18).
The surveys were conducted between May and Oct 2017. The pig-present farmers (pig farmer and slaughterhouse workers) and bioaerosols were sampled at pig farms and
slaughterhouses in seven cities of Guangdong, China. Briefy, human subjects were recruited through face-to-face interactions with study personnel during visits. The inclusion criteria were (1) had worked or lived in pig farm or the slaughterhouse for more than one year with more than 28 h a week of exposure time to pigs; and (2) at least 16 years
old. We excluded subjects who were (1) sufering from immune damage or acute respiratory tract infection; (2) pregnant female subjects; or (3) sufering immunosuppressive or immunodefciency disease (including HIV infection) or receiving immunosuppressive therapy.
With consent of written informed, a nasal swab sample was drawn from each participant. A cotton swab with sterile physiological saline was used to wipe the left and right nasal
cavities for 30 s and then placed in a collection tube containing 3 ml of 0.5% bovine serum albumin (BSA)–phosphatebufered saline (PBS) solution. At each site, samples were
collected from 5–10 subjects and pooled. Samples from each pig farm were combined into a single pool, and samples from each slaughterhouse were combined into two pools.
The bioaerosol samples were collected from pig farms in Heyuan, Yangjiang, Shaoguan, Jiangmen, and Maoming, and from slaughterhouses in Guangzhou, China (Fig. 1). The
bioaerosol sampling method was carried out as previously described . Briefy, 9600-L volumes of bioaerosols were collected for each sample during 48 h of sampling, and six
samples were collected from each site. All the collected samples were then transported and shipped to the laboratory in sterile collection tubes packed in dry ice. All samples were
stored at − 80 °C until further testing.
DNA Extraction and Amplicon Sequencing
Total genomic DNA was extracted from bioaerosols and nasal swabs using the PowerSoil DNA Isolation Kit (MoBio, USA) following the protocol’s instructions. These DNA extracts were then sent to BGI Co., Ltd (Wuhan, China) for library construction and amplicon sequencing. Sequencing analysis was performed by amplifcation of the V4 region of the 16S rRNA gene using the previously described specifc primers: forward (5′-GTGCCAGCMGCCGCGGTAA-3′) and reverse (5′-GGACTACHVGGGTWTCTAAT-3′) .
After amplifcation, sequencing libraries were prepared and submitted for sequencing on the Illumina HiSeq platform using the Next-Generation Sequencing Platform for indexing
and paired-end 2×250 bp sequencing (San Diego, USA). The obtained raw Illumina amplicon sequencing fles for the amplifed 16S rRNA V4 regions were deposited with GenBank accession number PRJNA605910 in the Sequence Read Archive (SRA) database (https://submit.ncbi.nlm.nih.gov/subs/bioproject/SUB6954900/overview).
Sequencing was performed at BGI Co., Ltd (Wuhan, China). The obtained reads were cleaned and then analyzed using the Quantitative Insights into Microbial Ecology bioinformatic pipeline, QIIME2, which was designed for analysis of microbial communities. The DADA2 package from Bioconductor was used with the default parameter setting to correct and
Fig. 1 Locations of pig farms and slaughterhouses in this study. The regions where samples were collected are marked as chocolate; black triangles indicate where pig farms are located; black dots indicate where slaughterhouses are located. GZ Guangzhou, HY Heyuan, JM Jiangmen, MM Maoming, SG Shaoguan, YJ Yangjiang
analyze the amplicon data. We then constructed a feature table, which describes the abundances of the individual OTU or sequence variants for each sample. The OTU identifed
by DADA2 exceeded the resolution of the QIIME 1 default of 97% OTU. QIIME2 includes improved quality control steps, so the OTUs identifed in this analysis were of higher
quality than what would be obtained using QIIME 1. Due to the improved quality of reads, this analysis should allow more accurate diversity and taxonomic composition deter
minations than previous analysis eforts based on QIIME 1.
To quantitatively measure sample diversity, Alpha diversities, Chao1 Diversity index, and Faith’s Phylogenetic Diversity index were calculated for each sample. The signifcance of diferences among diferent groups was evaluated by Kruskal–Wallis test, a non-parametric method to assess whether samples originate from the same distribution.
To quantitatively measure Beta diversities, the Jaccard distance and weighted Unifrac values were calculated. The pairwise distances between samples were computed using
the vegdist function in R. The metaMDS function was used to take the resulting matrices of data and generate non-metric multidimensional scaling (NMDS) plots. Dissimilarity
boxplots were also constructed. Permutational multivariate analysis of variance (PERMANOVA) was used to assess the signifcance of sample groupings using 1000 Monte Carlo simulations. Annotation was performed using the q2-feature-classifer plugin starting with a pre-trained classifer (targeting the V4 region, with endpoints defned by the 515F and 806R primers, with PE250 reads). Bar plots were constructed to visualize the community composition of each sample. The diferentially abundant OTUs (i.e., OTU with diferent abundances in diferent sample groups) were detected across all groups at the level of family, genus, and species using the q2-composition-ancom plugin.
Characterization of Sampling Population
39 samples (including 15 pig farm bioaerosols, 12 slaughterhouse bioaerosols, 6 slaughterhouse workers, and 6 pig farmers) were collected from nine pig-present settings
(including 6 pig farms and 3 pig slaughterhouses) located in Jiangmen, Heyuan, Maoming, Shaoguan, Yangjiang, and Guangzhou cities of Guangdong, Southern China, between
May 2017 and Oct 2017 (Table 1).
A total of 14,923,551 clean reads were obtained, with an average of clean reads of 382,655±84,547, which accounted for 97.7% of the raw data. Of these reads, 7,329,892 clean
reads were from pig farm bioaerosols, with an average of 407,216±77,324 clean reads, accounting for 98.5% of the raw data. For slaughterhouse bioaerosols, 3,432,275 clean
reads were obtained, with an average of 381,364±89,214 clean reads, accounting for 98.5% of the raw data. From nasal samples of pig farmers, 1,834,688 clean reads were
obtained, with an average of 307,615±99,936 clean reads, accounting for 95.7% of the raw data. From slaughterhouse workers, 2,315,696 clean reads were obtained, with an average of 385,949±48,293 clean reads, accounting for 95.6% of the raw data (Table 2).
Taxonomy Assignment at Phylum Level
The reads were grouped into a total of 2,785 OTUs (with ≥ 97% mean relative abundance for all sample groups), including 1222, 1043, 1352, and 874 OTUs from pig farm bioaerosols, slaughterhouse bioaerosols, pig farmer nasal samples, and slaughterhouse worker nasal samples, respectively. All 2,785 OTUs were grouped into 4 phyla and 220 families. The composition of bacterial community was highly diverse, and was dominated at the phylum level by Proteobacteria (45.8±32.1%), Bacteroidetes (23.1±29.3%), Firmicutes (22.2±28.6%), and Actinobacteria (9.6±14.4%). Other phyla were rarely represented, including Tenericutes, Cyanobacteria, Fusobacteria, Chlorofexi, and Euryarchaeota (Table S1 and S2).
Diferences and Similarities of the Microbiota
Phyla with sequences of more than 10,000 reads, including Proteobacteria, Bacteroidetes, and Firmicutes, and Actinobacteria, were used to construct a phylogenetic tree (Fig. S1). Proteobacteria was the most diversified including Acinetobacter, Moraxella, Psychrobacter, Escherichia, Pseudomonas, and Stenotrophomonas.This was followed by Firmicutes, including Staphylococcus, Exiguobacterium, Alloiococcus, Lactobacillus, Streptococcus, Anaerococcus, Finegoldia, and SMB53. Next was Bacteroides, including Wautersiella, Chryseobacterium, and Myroides. Finally, Actinobacteria was the least diverse, including Micrococcus, Arthrobacter, and Corynebacterium. Furthermore, the most abundant genera in the pig-present bioaerosols were Acinetobacter and Pseudomonas, and the pig worker nasal samples were dominated by Staphylococcus, Pseudomonas, and
Corynebacterium (Fig. 2).
Diferentially Abundant Taxa at the Genus Level
At the genus level, the bacterial abundances in farm samples differed, with farm and slaughterhouse specificity. For different locations and regions, Actinobacteria, Pseu
domonas, and Wautersiella were significantly abundant in pig-present bioaerosols. Staphylococcus, Corynebacterium, Pseudomonas, and Lactobacillus were most abundant in pig workers. The genus Psychrobacter was more abundant in slaughterhouse workers than that in pig farmers, and was also detected in pig-present bioaerosols. In addition, the genera Alloiococcus, Peptoniphilus, and Anaerococcus, were significantly more abundant in pig workers than in pig-present bioaerosols. To better understand the different abundances of bacterial genera in different setting and regions, a hierarchical clustering heat map of bacterial genera was constructed, as shown in Fig. 3.
Alpha and Beta Diversity Analysis
The alpha diversity analysis was performed using Chao1 and Faith’s phylogenetic diversity index (Faith’s PDI). Nasal samples from pig farmers showed the highest Chao1, and
Faith’s PDI values, whereas slaughterhouse bioaerosols displayed the lowest values. Because multiple samples were collected at sampling sites, we also performed a linear mixed
regression with the location as a random efect to compare any diferences between groups. The bacterial richness in nasal samples from pig farm and slaughterhouse workers
was signifcantly higher than that of pig-present bioaerosols (Chao1, p < 0.05; Faith’s PDI, p < 0.05). The abundance of pig farmer’s nasal samples was higher than that in slaughter nasal samples, and pig farm bioaerosol samples exhibited higher abundances than slaughter bioaerosol samples, as shown by Chao1 and Faith’s phylogenetic diversity (Faith’s PD) indices (p<0.05 or p<0.01) (Fig. 4a, b; Table S3).
Ordination method-based non-metric multidimensional scaling (NMDS) plots with weighted Unifrac and Jaccard values (Fig. S2) showed a distinct clustering of farm bioaerosols, pig farmers, slaughterhouse workers, and slaughterhouse bioaerosol samples, which was confrmed by permutational multivariate analysis of variance (PERMANOVA). Profles of nasal samples from pig farmers, nasal samples from slaughterhouse workers, pig farm bioaerosol samples, and slaughterhouse bioaerosol samples were similar, indicating an efect of pig-present setting factors on the human nasal microbiota. Interestingly, microbial communities isolated from pig industry workers seemed to exhibit signifcantly lower beta diversity values than microbial communities isolated from pig-present bioaerosols (weighted distances from the centroid; p < 0.01 or 0.001; Fig. 4c, d), indicating a more homogeneous microbial community structure in pig industry workers (Table 3).
Analysis of Shared OTUs
The ratio of shared OTUs between microbial communities isolated from pig-present bioaerosols and microbial communities isolated from farmers in the pig-present settings
was 29.3% (816/2785). The sequencing of microbial communities from bioaerosols at pig farm and ones isolated from pig farmers shared 31.7% (620/1954) of OTUs. The
ratio of shared OTUs between the microbial communities of the slaughterhouse and the slaughterhouse workers was 23.4% (364/1553), with statistically signifcant diference (p<0.001). Only 8.7% (242/2785) OTUs were shared between pig farmers, slaughterhouse workers, pig farm bioaerosols, and slaughterhouse bioaerosols. The number of shared OTUs in pig farms and pig-present farmers was higher than that between slaughterhouses and slaughterhouse workers, suggesting that pig farm bioaerosols have a greater impact on the microbial colonization in noses of farmers (Fig. 5; Fig. S3; Table S4).
The animal-exposed occupational population potentially has a high risk of susceptivity in zoonotic infection, and assessing the transmission risk and achieving early prevention
is critical [18–20]. The high density of animals feeding in large-scale farms could create the conditions required for pathogen survival and spread [21, 22], those who work for
signifcant amounts of time in such environments might have latent infections, and could act as a carrier to introduce zoonotic pathogens into the general population and lead to
a serious public health crisis [23–25]. Here, this study was conducted to reveal the microbiota associated with diference pig-present settings and it is also benefcial for future
prevention and control of zoonotic pathogens.
Our study collected about 9600 L of bioaerosol for each animal-exposed bioaerosol samples, which was similar to the volume of air that humans inhale each day about 10,000
L . The 16S rRNA analyses results revealed Proteobacteria as the dominant phylum, followed by Bacteroidetes and Firmicutes in pig farm and slaughterhouse bioaerosols.
These phyla were consistent with those identifed in inhalable microbial communities in Beijing, China . However, Dueker et al.  analyzed waterfront sites air samples
collected from New York, USA, and identifed Verrucomicrobia, Proteobacteria, and Firmicutes as the dominant bacteria. The proportions of Proteobacteria in this study varied
between individuals, and this variation may be associated with multiple factors, such as temperature, humidity, surroundings, or biases caused by 16S rRNA primer selection [29, 30].
The alpha diversity values of pig-present bioaerosols were lower than those of pig-present workers, and the abundance of bacteria in bioaerosols was relatively low, indicating
that not all microorganisms were continuously discharged and suspended in the bioaerosols. These alpha diversity diferences between pig farmers and slaughterhouse workers suggest that pig farming leads to an increased bacterial diversity in the noses of workers compared to those of individuals working in slaughterhouses. The high concentration
of diverse air-borne bacteria present in pig farms may lead to a modifcation and an enrichment of the farmer’s nasal microbiota . We also found signifcant variation in samples from farmers working on the same farm, with Chao1 diversity index values ranging from 64 to 1024 in Heyuan. Additionally, there was signifcant variation in samples from slaughterhouses in Baiyun district, with Faith’s phylogenetic diversity index values ranging from 3.22 to 55.44, likely due to the infuence on bioaerosols on pig-present settings by
dust, bacteria, and fungi . Factors such as temperature, humidity, and surroundings can also obviously impact the alpha diversity.
Our results revealed a high number of shared OTUs between communities isolated from pig-present bioaerosols and pig workers, indicating the frequent exchange of microorganisms. The result suggests an important role of bioaerosols in the transmission of animal-associated bacteria to pig workers . However, there were more shared OTUs between pig farm bioaerosol samples and pig farmer nasal samples than between slaughterhouse bioaerosol samples and nasal samples from slaughterhouse workers, and the ratio of shared OTUs between slaughterhouse bioaerosol samples and nasal samples from slaughterhouse workers was similar to that in cow-exposed or non-exposed control as described by Kraemer et al. , and indicated that exposed factor of bioaerosol in slaughterhouse had poor impact on microbial diversity of slaughterhouse worker nasal cavity. This might be explained by a more extensive exposure time to pigs of the pig farmers compared to that of slaughterhouse workers, with long-lasting exposure to relatively stable pig populations as pig farmers in China mostly live and work at the pig farms, with almost fulltime exposure to pigs. In contrast, the slaughterhouse workers generally have a relatively fxed working time with more time without pig exposure compared to pig farmers. To counter this great exposure of pig farmers, it is necessary to improve daily protective measures for the farmers, including to put on mask while working, to avoid unnecessary direct exposure to pigs, and to keep pigsty with safe distance from human living areas.
Although we only examined microbiota from pig-present bioaerosols and pig worker nasal samples, however, common microorganisms, such as Staphylococcus and Escherichia, can be carriers of drug resistance genes. These bacteria easily colonize the nasal cavity of animal-exposed workers, and further work should classify resistance genes. Increasing awareness of the potential interactions between humans, animals, and the environment underscores the need for better surveillance of zoonotic pathogens at human–animal–environment interfaces [18, 19].
This study also had some limitations worth noting as follows: (1) this study only used amplicon sequencing, and did not isolate or verify microorganisms, preventing collection of
more detailed data from bacterial classifcation or identifcation of antimicrobial resistance genes; and (2) sampling was performed during the summer of Guangdong, China, so there was no information collected about samples in winter, when there might be a higher incidence of infectious diseases. Thus, the infuence of seasonal factors on microbial diversity was not evaluated.
In conclusion, respiratory bacterial colonization in the pig farmers was strongly associated with airborne bacteria. There was a signifcantly higher proportion of shared OTUs and
bacterial diversity between pig farms and pig farmers than that between slaughterhouses and slaughterhouse workers. Strengthening the prevention activity in pig farms is important to reduce pathogen dissemination and accumulation and longterm surveillance should be performed to allow monitoring of zoonotic pathogen circulation.
Acknowledgements This work was funded by The National Key Research and Development Program of China (Grant No.2018YFD0500500), The Guangdong Provincial Science and Technology Project (Grant No. 2018B020241002), The National Science and Technology Major Project (Grant No. 2018ZX10101002), The National Natural Science Fund of China (Grant No. 31872499), and The Guangdong Provincial Science and Technology Project (Grant No.2018B020207013). We would like to thank profs. Changxu Song and
Leyi Zhang from South China Agricultural University for their kind help of contacting with the pig farms and collecting samples.
Author Contributions JHL designed the experiments; JYW and YSZ conducted most of the experiments; YX and JYW analyzed data; JYW, CG, ZMG, and QLL drafted the paper. All authors read, commented, and approved the fnal version of the manuscript.
Data Availability The obtained raw amplicon sequencing fles for the amplifed 16S rRNA V4 regions were deposited with GenBank accession number PRJNA605910 in the Sequence Read Archive (SRA) database.
Compliance with Ethical Standards
Conflict of interest The authors declare that the research was conducted in the absence of any commercial or fnancial relationships that could be construed as a potential confict of interest.
Ethical Approval Ethical statement was obtained from the ethics committee of the School of Public Health, Sun Yat-Sen University (2014-18).
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