Effects of microbes in pig farms on occupational exposed persons and the environment

Effects of microbes in pig farms on occupational exposed persons and the environment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of microbes in pig farms on occupational exposed persons and the environment Jinyi Han, Mengyu Li, Xin Li, Chuang Liu, Xiu-Ling Li, Kejun Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3020464/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Nov, 2023 Read the published version in AMB Express → Version 1 posted 5 You are reading this latest preprint version Abstract Pig farming has an effect on farmers and the farm environment. Pig gut microbes play an important role in this effect. However, which microbial composition is more likely to be affected remains unknown. Primarily, we collected 136 samples in pig farm A, including 70 pig fecal, 18 farmers, 4 individuals without contact with any type of farm animal (“non-exposed” persons) fecal, and 44 environmental dust samples (dust from inside and outside pig houses and the farm). Another 43 samples were collected from pig farm B, including 10 pig fecal, 24 environment samples, and 9 humans fecal. Whereafter, 16S rRNA sequencing and taxonomic composition analysis were performed. Result showed that pig farmers significantly upregulated 13 genera compared with non-exposed persons, and 76 genera were significantly upregulated inside the pig house than outside the pig house. Comparing non-exposed persons who were farther away from the pig farm, the results showed that the relative abundance of three microbes, including Turicibacter , Terrisporobacter , and Clostridium_sensu_stricto_1 , increased between the farmers and environment inside and outside the pig farm, and significant differences were observed (P < 0.05). Moreover, the abundance increased with the exposure time of farmer animals and spatial location to pigs. The greater the distance from the farm, the less effective the three microbes were. Although the distance is about 550 km, the analysis results of pig farm A and pig farm B confirm each other. This study shows that the three microbes where pig farmers co-occurring with the environment come from pig farms, which provides new ideas for blocking the transmission of microbial aerosols in pig farms and reducing pollution. pig farming 16S rRNA microbial communities occupational exposure airborne microbes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Microorganisms are also an important cause of health changes(Shang et al., 2022 ). Pig houses have many microbes(Hong et al., 2021 ), for example, aerosol-coated microbes(Moor et al., 2021 ) affect farmers working on the farm. Several studies have shown that working on a pig farm can affect farmers’ nasal microbes(Kraemer et al., 2019 ). Although gut microbes can remain stable during acute environmental changes(Sun et al., 2020 ), in many areas, farmers must live with pigs every day. Over time, breeders’ gut microbiota will also change(Moor et al., 2021 ). Therefore, exploring the effects of occupational exposure on the intestinal flora of farmers is necessary. Airborne microbes exchange inside and outside the pig house(Hong et al., 2021 ), resulting in contamination from anaerobic digestion of pig manure(Chen et al., 2021a ; Ma et al., 2021 ). Thus, the surrounding environment of pig farms has a characteristic smell. Clostridium is a main source of stink on pig farms(Zhu, 2000 ). Pig farming has been stigmatized primarily because of microorganisms. In pig gut microbe, metal contamination characteristics (especially Zn, Cu) are more severe than other animal gut microbes(Liu et al., 2020 ). Untreated pig gut microbes alter antibiotic resistance and microbial communities in soil(Gao et al., 2020a ). Therefore, the influence of pig farms on the microorganisms in the surrounding environment has always been a research hotspot. This study started with microorganisms and explored which microbial farmers and environments on pig farms are susceptible to infection. Primarily, in pig farm A, this study collected gut microbes from 18 farmers and 4 individuals without contact with any type of farm animal (“non-exposed” persons). Their gut microbiota was analyzed to find the difference in flora. Based on the analysis of 44 environmental samples from pig farm A, the path and distance of microbial diffusion were deduced. Subsequently, we conducted sampling in pig farms B to verify the above results and obtained the same trend. The result provides theory for blocking aerosol transmission of microorganisms in pig farms and reducing pollution. Furthermore, this study aims to demonstrate the sustainable development of pig farms from the perspective of microorganisms. Material and methods Sample collection Two pig farms were sampled in this study. Primarily, samples were collected from pig farm A in December 2021. A total of 136 samples were collected from 22 human fecal, 70 pig fecal, and 44 environment samples. The 22 human gut microbes were obtained from 18 farmers (13 breeders and five less exposed leaders) and four individuals without contact with any type of farm animal (“non-exposed” persons). A total of 44 environmental samples were obtained from the inside, outside, living, and cesspool of the pig house. The pig house was divided into three locations (windscreen inside pig house, floor, and handrail), and each point has four samples; In outside the farm, three points (5, 500, and 1000 meters outside the pig farm) were sampled with different distance diameters, and each point has five samples. In November of the following year, samples were collected from pig farms B. A total of 43 samples were collected from 10 pig fecal, 24 environment samples, and 9 fecal. The 9 human fecal were obtained from 7 farmers (4 breeders and 3 less exposed leaders). In addition, two of them used to be the leaders of pig farm A for microbial collection, but they had been out of work for half a year and had not been exposed to animals for three months at the time of this sampling. A total of 24 environmental samples were obtained from the inside, outside of the pig house. The pig farm was divided into 3 locations (floor and, windscreen outside pig house windscreen inside pig house); In outside the farm, 3 locations (5, 500, and 1000 meters) were sampled with different distance diameters, and each point has four samples. The specific sampling plan and number are shown in Supplementary material (Supplementary Table 1) . In addition, the animals designed in this experiment were healthy. The volunteers all took a physical within a year. Health views of pigs from both farms are supplemented in in Supplementary material (Supplementary Fig. 1) All samples were immediately transported to the laboratory in liquid nitrogen and stored at − 80°C until further analysis. DNA extraction and polymerase chain reaction (PCR) amplification DNA was extracted using the OMEGA Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) and stored at − 20°C prior to analysis. From these DNA extracts, the V3–V4 region of the 16S rRNA gene was amplified using forward (5′-ACTCCTACGGGAGGCAGCA-3′) and reverse (5′-GGACTACHVGGGTWTCTAAT-3′) primers, and Illumina linker sequences were used at 5′ end. PCR cycling conditions included initial denaturation at 95°C for 5 min and 25 cycles of denaturation at 95°C for 30 s, annealing at 59°C for 30 s and extension at 72°C for 45 s. This process was followed by a final extension step at 72°C for 5 min. PCR amplicons were purified using Vazyme VAHTS™ DNA Clean Beads (Vazyme, Nanjing, China) and quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). The subsequently constructed library was sequenced on the Illumina HiSeq 2500 platform (2×250 pairs), and sequencing was performed by Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China). 16S rRNA gene sequence assembly and clustering 16S rRNA sequencing data were processed using the Quantitative Insight Into Microbial Ecology 2 (QIIME2, 20019.4) platform(Bolyen et al., 2019). Then, the sequences were quality filtered, denoised, and merged, and chimeras were removed using the DADA2 plugin(Callahan et al., 2016 ). Non-single amplicon sequence variants (ASVs) were aligned with mafft(Katoh et al., 2002 ). Subsequently, species taxonomic annotation was performed using the SILVA database (Release132, http://www.arb-silva.de ), and the classify-sklean algorithm with default parameters was used in the analysis in QIIME2. Moreover, each naive Bayes classifier was used to obtain the composition and abundance of individual samples in the taxonomic level distribution table. The resulting ASV scale was flattened with QIIME2 using rarefaction at the 95% depth of the lowest sample sequence. Sequencing and bioinformatics were performed on QIIME2 platform of Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China) and the sequencing results were analyzed based on amplicon sequence variants (ASVs). Bioinformatics and statistical analysis Taxa abundances at the phylum and genus levels were statistically compared between groups. ASV-level alpha diversity indices, Beta diversity Venn diagram and Volcano map were analyzed at the ASVs level. And then, ASVs were aggregated at genera level, and other results were analyzed at the genus level. Alpha diversity indices, such as Observed species, and Shannon diversity index were calculated using the ASV table in QIIME2, and visualized as box plots; Beta diversity was evaluated by nonmetric multidimensional scaling (NMDS) based on the Bray–Curtis distance; The significance of differentiation of microbiota structure among groups was assessed by ANOSIM using QIIME2; Volcano map was using R package “DESeq2” to make a differential analysis of ASVs/genus; Linear discriminant analysis effect size(LEfSe), a method for biomarker discovery. LEfSe scores measure the consistency of differences in relative abundance between taxa in the groups analyzed (As farmer vs As non-exposed), with a higher score indicating higher consistency. We considered taxa with linear discriminant analysis score > 2 and P < 0.05 to be significant. Hierarchical clustering analysis consists of two parts: the left represents the hierarchical clustering tree of community samples, and the right represents the stacked histogram of species composition; UPGMA cluster analysis of Bray–Curtis distance matrix using R package “stat”, and the visualizations was performed using R package “ggtree”; Random forest analysis was applied to discriminate the samples from different groups using QIIME2 with default settings. Nested stratified k-fold cross-validation was used for automated hyperparameter optimization and sample prediction; Spiral heatmaps were plotted using heatmap tools in the genescloud platform( https://www.genescloud.cn ). The data was normalised by z-scores. The package uses popular clustering distances and methods implemented in dist and hclust functions in R. For the grouped samples (the number of samples in each group ≥ 3), the R package can be used to draw a box line diagram to visually display the differences between different sample groups, and the Kruskal-Wallis rank sum test and dunn 's test can be used as post hoc tests to verify the significance of the differences (the Kruskal-Wallis test is equivalent to the Wilcoxon test for both groups of samples). Statistical significance was defined as a P value < 0.05. Results Firmicutes were the main bacterial phyla in both farms After sequencing analysis, a total of 197,712 ASV sequences were obtained in pig farms. After classification annotation, both farms have two domains, Archaea and Bacteria. 2,081 genera were analyzed in pig farm A, 1,196 genera were analyzed in pig farms B. Taxa abundances at the phylum and genus levels were statistically compared between groups. Samples were divided into human gut microbes, pig gut microbes, and environmental microbes, and the taxonomic composition of the total samples was analyzed at the top 20 phylum and the top 20 genus levels ( Fig. 1 ) ,16 phylum, 14 genera were repeated in both farms. Among the top 20 genera, more Clostridium_sensu_stricto_1 was found in environmental samples and pig gut microbes ( Supplementary Figs. 2 and 3) . The microbial distribution of the two farms showed similar trends. The diversity index of Farmer alpha was higher Human gut microbe samples were divided into two groups, farmer and non-exposed. In both alpha diversity indices, non-exposed were slightly higher (Figs. 2 A and 2 B). The point separation between farmers and non-exposed was not significant, especially in pig farms B, but the 95% confidence circle showed that farmers’ microbial communities were closer. The Alpha diversity index showed the same trend of diversity among farmers in both farms. Farmers’ gut microbiota changes with working hours To further explore the effects of pig farming on farmers, samples of all human fecal were divided into two groups. The volcano maps showed the different ASVs and genera. 6 ASVs are marked as up in farmers (Fig. 3 A), including ASV_133662 ( Streptococcus ), ASV_311074 ( Escherichia-Shigella ), ASV_111920 ( Terrisporobacter) , ASV_57963 ( Clostridium_sensu_stricto_1 ), ASV_192153 ( Faecalibacterium ), ASV_101783 ( Prevotella_9) . Then, in the volcano diagram at genera level (Fig. 3 B), 7 genera had high abundances in farmers but not in non-exposed individuals, including Terrisporobacter, Enterococcus, Turicibacter, Clostridium_sensu_stricto_1, Prevotella_7, Nitrosomonas . In addition, farmers were grouped on the basis of the length of their occupational exposure. They were divided into breeders and leaders based on frequent differences in the production area. Samples of all human gut microbes were divided into three groups: farmer, breeder and non-exposed. Random forest analysis was conducted on all human gut microbes to draw random forest analysis of the average value within the group (Fig. 3 C). In this random forest analysis, the number of k-fold cross-validations was set to 5, and the three genera significantly up-regulated by farmers increased with the increase of exposure time (Fig. 2 E). Four of the top 20 dominant genera in the Random Forest analysis were significantly up-regulated on the genus level volcano map, including Terrisporobacter, Turicibacter, Clostridium_sensu_stricto_1, Nitrosomonas . Moreover, the importance of these genera decreases with the decrease of working hours. It is worth mentioning that one of the workers who worked at the pig farms B happened to be one of the non-expose workers; The two farmers in pig farm A had been out of work for 3 months at the time of the second sampling. Therefore, we treated them as non-expose workers in the second analysis. These three people are distinguished as "as farmer" and "non-exposed" according to their working state when they are sampled. Clostridium_sensu_stricto_1 and Terrisporobacter were notably present as farmers (Fig. 3 D and 3 E). The Clostridium_sensu_stricto_1 and Terrisporobacter are always mentioned, and may coexist more easily with humans, but show significant differences three months after they leave the pigs. Dust microbes closer to pigs are more similar to pigs Beta diversity analysis was performed on environmental dust and pig fecal from the two farms. Beta diversity was evaluated by NMDS based on the Bray–Curtis distance. In the NMDS diagram, the pig house dust microbes were closest to the pig fecal (Fig. 4 A and 4 B). In pig farm A, the study found that the cesspool runs through each group (Fig. 4 A and 4 C). Then the anosim test was conducted, and the result showed that the distance between the outside group and cesspool group was not significant ( P = 0.063). Hierarchical cluster analysis of pig farm A shows clustering inside and outside pig house (Fig. 4 C). NMDS diagram and hierarchical cluster analysis of pig farms B shows more obvious clustering inside and outside pig farm (Fig. 4 B and 4 D). In other words, the microbes in the environment dust outside the farm cluster on a branch. Therefore, the closer the environmental samples were to pigs, the more different species were found. Furthermore, three genera can be considered as co-occurring microbes, including Terrisporobacter (ASV_111920), Clostridium_sensu_stricto_1 (ASV_57963), and Turicibacter (ASV_20732), which may co-occur between humans and the environment. Therefore, in this study these three genera are called the co-occurring microbes . The co-occurring microbes were used as a marker to draw environmental samples and group them into spiral heat maps. (Fig. 4 E and 4 F). As shown in the figure, the co-occurring microbes at the pig farms B were essentially zero outside the farm, which may be related to the spray disinfection of the walls and gates at the pig farms B. the co-occurring microbes at the pig farm A decreased as the distance from the farm increased. The co-occurring microbes detected by Dunn’s test were significantly different at the pig farm A and 1000 m outside the gate of the pig farm. Simple filtration can reduce cross-infection between pig houses In the spiral heat maps of the two pig farms, there were significant differences between the inside and outside windscreen of the pig farm A, while there were no significant differences between the inside and outside windscreen of the pig farm B. This was also demonstrated by the cluster analysis diagrams of the floor and inside and outside the windscreen of the pig farm ( Fig. 5 A and 5 B ) . It is understood that the pig farm A has a simple filtration system with two screens. In the pig farm B, on the other hand, has only a simple screen as a filter for exhaust air. The windscreen inside the pig house was compared with that outside the pig house to identify different microorganisms. A total of 76 genera showed high abundances on the board of the windscreen inside A pig farm house, but not in pig farm B house (Fig. 5 C and 5 D). Among the upregulation genera, the co-occurring microbes were all included. This result indicates that in A pig house neither or less of the co-occurring microbes reached the outside of the barn with air. Although microbes can be transmitted by aerosols, simple filtration can reduce cross-infection between houses. Discussion Effects of the co-occurring microbes on farmers In the analysis of humans, the study found no significant differences in Observed_species and Shannon index between the two groups, which does not deny that pig breeding will bring about differences in human gut microbes. As omnivore, human beings have complex dietary habits. Therefore, the diversity and abundance of human intestinal microbes have a certain steady state(Martel et al., 2022) . In this study, farmers were found to have lower microbial diversity than non-exposed ones, which is contrary to the results of Kates(Kates et al., 2019) but similar to the result of Vestergaard(Vestergaard et al., 2018). The biosafety of Chinese farms is becoming strict, which is reflected not only in disinfection and prevention but also in the diet control of farmers in farms. A pig farm is like a Petri dish of fixed supplies, unifying and fixing farmer’s diet and limiting microbial diversity. This finding may explain the clustering of farmer samples in the NMDS diagram. The co-occurring microbes that were prominent on farmers were considered as the three most abundant genera in the environment, which had the highest absolute abundance in pig samples of the entire sample. On the contrary, anywhere less affected by pigs, the co-occurring microbes were less abundant. Therefore, we hypothesize that the co-occurring microbes came from pigs, and they are susceptible to humans and the environment. Through classification level analysis, the co-occurring microbes belong to Firmicutes, including Turicibacter , Terrisporobacter , and Clostridium_sensu_stricto_1 . Most of the physiological contributions of gut microbes are involved in the fermentation and production of short-chain fatty acids. The abundance of Turicibacter was related to the digestion of acid detergent fiber digestibility (Niu et al., 2019) and α linolenic acid(Gao et al., 2020b). The abundance of Terrisporobacter was associated with short-chain fatty acids(Li et al., 2021) and C-reactive protein triglycerides(Lee et al., 2020). The high abundance of Clostridium_sensu_stricto_1 may be involved in β-oxidation(Usman et al., 2022), causing the degradation of most long-chain fatty acids. Therefore, the three co-occurring microbes also affect the growth traits of pigs. Xyoligosaccharides(Chen et al., 2021b) were found to reduce the relative abundance of Terrisporobacter and Clostridium_sensu_stricto_1 and improve body weight, daily gain, and feed conversion rate in weaned piglets as a substitute for antibiotics. Furthermore, Clostridium_sensu_stricto_1 was negatively associated with increased litter size and daily increase of piglets(Hu et al., 2021; Wang et al., 2021b). The three co-occurring microbes in this study are pathogenic genera, which lead to intestinal inflammation and even cancer, and such microbes are related to a variety of digestive diseases. Turicibacter affects pancreatic cancer(Jeong et al., 2020) and colon cancer(Chung et al., 2021; Wen et al., 2021), and it is associated with Parkinson’s disease(Jin et al., 2019) and autoimmune encephalomyelitis(Wang et al., 2021c). In addition, Turicibacter and Terrisporobacter affect type I diabetes(Radwan et al., 2020). Of the three co-occurring microbes, researchers are more familiar with the Clostridium_sensu_stricto_1 class. It is found to be not only associated with colitis(Yang et al., 2019), fatty liver(Yi et al., 2021), gout(Mendez-Salazar et al., 2021), etc., but also positively correlated with levels of inflammatory markers ( TNF-α , IL-1β , and IL-6 ) (Yi et al., 2021). Clostridium_sensu_stricto_1 is correlated with host inflammatory genes (including REG3G , CCL8 , and IDO1 ) (Wen et al., 2021). The surprise of our sampling was that there were three people whose identities had been switched between the two samples, making them the control group. Three months after leaving the swine farms, the farms gut microbiota partially reverted to their original microbial composition (Sun et al., 2020). Therefore, we believe that the non-expose group can be used after three months of unemployment. When this group of intestinal flora was analyzed, it was found that the co-occurring microbes showed a trend of decrease after the cessation of farming. There are significant differences between Clostridium_sensu_stricto_1 and Terrisporobacter (P<0.05). The most intuitive manifestation is that the longer one leaves the pig farm, the unique taste of the pig farm will disappear, which is related to Clostridium_sensu_stricto_1 (Zhu, 2000). Effects of the co-occurring microbes on the environment In pig house A, the three co-occurring microbes were significantly reduced on the windscreen inside the environmental samples from the pig house, probably because the windscreen is the farthest away from the pig, which is a difficult place to touch. However, dust in the air can also carry some microorganisms, which can be detected in the windscreen. In pig farm A, fewer co-occurring microbes were found on the windscreen outside the pig house. After inquiry, the ventilation of this pig farm is an easy air filtration mode. The system improved the environmental impact of the barn, although no significant difference in the co-occurring microbes was found on the inside and outside of the pig house. According to the analysis of the environmental samples of pig farm B, the environment in the pig house is a branch, and the outside environment is a branch. The co-occurring microbes were reduced outside the pig farm. But the pig farm B has no air filtration system, just a layer of gauze. Therefore, not many different bacteria were found on the windscreens inside and outside the pig house. A few co-occurring microbes also appeared in the living areas. However, no significant difference was found between the microbial community in the living area and other sites in the farm, which may be due to the dust in the farm environment or may be brought out by the farmers. The abundance of co-occurring genera in the living area is the lowest in the pig farm; thus, the environmental pollution caused by the co-occurring genera carried by farmers can be ignored. Therefore, the results indicate that the co-occurring microbes primarily affect the environment through aerosol diffusion. The co-occurring genera are transmitted through two possible forms, aerosol diffusion or carried by farmers after leaving the barn. The co-occurring genera carried by farmers are negligible; thus, their effect on the environment is primarily due to aerosol diffusion in pig houses or septic tanks. The results confirm multiple studies(Ko et al., 2008; White et al., 2019; Moor et al., 2021). The study found that two samples in cesspool contained several co-occurring microbes, and two samples in cesspool did not contain such co-occurring microbes. The difference in microbial community distributed in septic tank was the greatest. Fecal treatment channels reaching the gut microbe of septic tanks cause huge risks to the environment(Peng et al., 2021) and biological health(Ruang-Areerate et al., 2021; Salem et al., 2021), such as ARG(Van Gompel et al., 2020). Co-occurring microbes such as Clostridium_sensu_stricto_1 , Terrisporobacter , and Turicibacter are non-exposed potential hosts of antibiotic-resistant genes (ARGs) and mobile genetic elements (MGEs) (Wang et al., 2020; Wang et al., 2021a). The relative abundance of tetT gene(Zhou et al., 2021) was positively correlated with the three co-occurring genera ( P <0.05). The transmission of ARG can be reduced by filtering bioaerosols through the pathway(Song et al., 2021b). In addition, compost (Yang et al., 2019) reduces the risk of ARG transmission by reducing the relative abundance of Clostridium_sensu_stricto_1 and Terrisporobacter and antibiotic-resistant genes. The co-occurring flora is also used by the population to participate in some fermentation work. Terrisporobacter and Clostridium_sensu_stricto_1 participate in anaerobic digestion(Detman et al., 2021; Song et al., 2021a). Terrisporobacter is a main microorganism during Xiaoqu wine fermentation(Su et al., 2020). Clostridium_sensu_stricto_1 is abundant in glucose fermentation broth(Lu et al., 2020). In pig farm A, at 1000 m outside the pig farm, the environmental pollution in the pig house is not significant. As aerosols pass through ventilation and filtration systems, environmental pollution caused by co-occurring microbes has been mitigated. In the B pig farm, the three co-occurring microbes showed the same trend, with far fewer co-occurring microbes outside the gate of the pig farm than pig houses. In the study of pig farms, the influence distance from porcine gut microbes could be continuously reduced through disinfection. Although microbes can be transmitted by aerosols, simple filtration can reduce cross-infection between houses. Two different pig farms were sampled, this study compared the gut microbes of 25 farmers and 6 non-exposed individuals and the dust microbes inside and outside the pig house with those far away from pig farms to explore the effect of pig raising on microbial. The longer the contact time with pigs and the closer the distance to pigs, the relative abundance of three microorganisms increased, namely, Turicibacter , Terrisporobacter and Clostridium_sensu_stricto_1 , showing significant differences. This study provides theory for blocking the transmission of microbial aerosols in pig farms and reducing pollution. Declarations Ethics approval All of the experiments involving animals were carried out in accordance with the guidelines for the care and use of experimental animals established by the Ministry of Science and Technology of the People’s Republic of China (Approval Number DWLL20211193). The animal study was reviewed and approved by the Henan Agricultural University Animal Care and Use Committee. In addition, all experiments were conducted in accordance with the relevant approved guidelines and regulations during sampling, and sample conservation. Data and model availability statement The raw sequencing data in this study were deposited in the NCBI Sequence Read Archive (SRA) under accession number SUB13546643. Author ORCIDs Jinyi Han 0009-0005-9148-0832; Mengyu Li 0000-0002-9605-3777; Xuelei Han 0000-0002-7957-0297; Xin-Jian Li 0000-0003-2546-0145; Author contributions JH, ML, and X-J L conceived and designed the experiments. JH, XL, and CL analyzed the data. JH and ML wrote the manuscript. X-J L, XH, X-L L, RQ, JW, and FY provided manuscript editing. All authors statistically analyzed, discussed, critically revised the contents, and approved the final manuscript. Declaration of interest No conflict of interest to report. Acknowledgements None. Financial support statement This work was supported by National Kye Research and Development Program of China (2021YFD1301202); Agricultural Breeds Research Project of Henan Province [grant number 2022020101]. References Bolyen, E., Rideout, J., Dillon, M., Bokulich, N., Abnet, C., Al-Ghalith, G., Alexander, H., Alm, E., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J., Bittinger, K., Brejnrod, A., Brislawn, C., Brown, C., Callahan, B., Caraballo-Rodríguez, A., Chase, J., Cope, E., Da Silva, R., Diener, C., Dorrestein, P., Douglas, G., Durall, D., Duvallet, C., Edwardson, C., Ernst, M., Estaki, M., Fouquier, J., Gauglitz, J., Gibbons, S., Gibson, D., Gonzalez, A., Gorlick, K., Guo, J., Hillmann, B., Holmes, S., Holste, H., Huttenhower, C., Huttley, G., Janssen, S., Jarmusch, A., Jiang, L., Kaehler, B., Kang, K., Keefe, C., Keim, P., Kelley, S., Knights, D., Koester, I., Kosciolek, T., Kreps, J., Langille, M., Lee, J., Ley, R., Liu, Y., Loftfield, E., Lozupone, C., Maher, M., Marotz, C., Martin, B., McDonald, D., McIver, L., Melnik, A., Metcalf, J., Morgan, S., Morton, J., Naimey, A., Navas-Molina, J., Nothias, L., Orchanian, S., Pearson, T., Peoples, S., Petras, D., Preuss, M., Pruesse, E., Rasmussen, L., Rivers, A., Robeson, M., Rosenthal, P., Segata, N., Shaffer, M., Shiffer, A., Sinha, R., Song, S., Spear, J., Swafford, A., Thompson, L., Torres, P., Trinh, P., Tripathi, A., Turnbaugh, P., Ul-Hasan, S., van der Hooft, J., Vargas, F., Vázquez-Baeza, Y., Vogtmann, E., von Hippel, M., Walters, W., Wan, Y., Wang, M., Warren, J., Weber, K., Williamson, C., Willis, A., Xu, Z., Zaneveld, J., Zhang, Y., Zhu, Q., Knight, R., Caporaso, J., 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature biotechnology 37, 852-857. doi:10.1038/s41587-019-0209-9. Callahan, B., McMurdie, P., Rosen, M., Han, A., Johnson, A., Holmes, S., 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nature methods 13, 581-583. doi:10.1038/nmeth.3869. Chen, Y., Wang, F., Li, H., Aftab, S., Liu, Y., 2021a. Triple-hurdle model analysis of the factors influencing biogas digester building, use and processing by Chinese pig farmers. Sci Total Environ 761, 143259. doi:10.1016/j.scitotenv.2020.143259. Chen, Y., Xie, Y., Zhong, R., Liu, L., Lin, C., Xiao, L., Chen, L., Zhang, H., Beckers, Y., Everaert, N., 2021b. Effects of Xylo-Oligosaccharides on Growth and Gut Microbiota as Potential Replacements for Antibiotic in Weaning Piglets. Front Microbiol 12, 641172. doi:10.3389/fmicb.2021.641172. Chung, Y., Ryu, Y., An, B.C., Yoon, Y.S., Choi, O., Kim, T.Y., Yoon, J., Ahn, J.Y., Park, H.J., Kwon, S.K., Kim, J.F., Chung, M.J., 2021. A synthetic probiotic engineered for colorectal cancer therapy modulates gut microbiota. Microbiome 9, 122. doi:10.1186/s40168-021-01071-4. Detman, A., Laubitz, D., Chojnacka, A., Kiela, P.R., Salamon, A., Barberan, A., Chen, Y., Yang, F., Blaszczyk, M.K., Sikora, A., 2021. Dynamics of dark fermentation microbial communities in the light of lactate and butyrate production. Microbiome 9, 158. doi:10.1186/s40168-021-01105-x. Gao, F.Z., He, L.Y., He, L.X., Zou, H.Y., Zhang, M., Wu, D.L., Liu, Y.S., Shi, Y.J., Bai, H., Ying, G.G., 2020a. Untreated swine wastes changed antibiotic resistance and microbial community in the soils and impacted abundances of antibiotic resistance genes in the vegetables. Sci Total Environ 741, 140482. doi:10.1016/j.scitotenv.2020.140482. Gao, X., Chang, S., Liu, S., Peng, L., Xie, J., Dong, W., Tian, Y., Sheng, J., 2020b. Correlations between alpha-Linolenic Acid-Improved Multitissue Homeostasis and Gut Microbiota in Mice Fed a High-Fat Diet. mSystems 5 doi:10.1128/mSystems.00391-20. Hong, S.W., Park, J., Jeong, H., Kim, M., 2021. Evaluation of the microbiome composition in particulate matter inside and outside of pig houses. J Anim Sci Technol 63, 640-650. doi:10.5187/jast.2021.e52. Hu, C., Niu, X., Chen, S., Wen, J., Bao, M., Mohyuddin, S.G., Yong, Y., Liu, X., Wu, L., Yu, Z., Ma, X., Ju, X., 2021. A Comprehensive Analysis of the Colonic Flora Diversity, Short Chain Fatty Acid Metabolism, Transcripts, and Biochemical Indexes in Heat-Stressed Pigs. Front Immunol 12, 717723. doi:10.3389/fimmu.2021.717723. Jeong, J.Y., Kim, T.B., Kim, J., Choi, H.W., Kim, E.J., Yoo, H.J., Lee, S., Jun, H.R., Yoo, W., Kim, S., Kim, S.C., Jun, E., 2020. Diversity in the Extracellular Vesicle-Derived Microbiome of Tissues According to Tumor Progression in Pancreatic Cancer. Cancers (Basel) 12 doi:10.3390/cancers12092346. Jin, M., Li, J., Liu, F., Lyu, N., Wang, K., Wang, L., Liang, S., Tao, H., Zhu, B., Alkasir, R., 2019. Analysis of the Gut Microflora in Patients With Parkinson's Disease. Front Neurosci 13, 1184. doi:10.3389/fnins.2019.01184. Kates, A.E., Dalman, M., Torner, J.C., Smith, T.C., 2019. The nasal and oropharyngeal microbiomes of healthy livestock workers. PLoS One 14, e0212949. doi:10.1371/journal.pone.0212949. Katoh, K., Misawa, K., Kuma, K., Miyata, T., 2002. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic acids research 30, 3059-3066. doi:10.1093/nar/gkf436. Ko, G., Simmons, O.D., 3rd, Likirdopulos, C.A., Worley-Davis, L., Williams, M., Sobsey, M.D., 2008. Investigation of bioaerosols released from swine farms using conventional and alternative waste treatment and management technologies. Environ Sci Technol 42, 8849-8857. doi:10.1021/es801091t. Kraemer, J.G., Aebi, S., Oppliger, A., Hilty, M., 2019. The Indoor-Air Microbiota of Pig Farms Drives the Composition of the Pig Farmers' Nasal Microbiota in a Season-Dependent and Farm-Specific Manner. Appl Environ Microbiol 85 doi:10.1128/AEM.03038-18. Lee, S.H., You, H.S., Kang, H.G., Kang, S.S., Hyun, S.H., 2020. Association between Altered Blood Parameters and Gut Microbiota after Synbiotic Intake in Healthy, Elderly Korean Women. Nutrients 12 doi:10.3390/nu12103112. Li, H., Shang, Z., Liu, X., Qiao, Y., Wang, K., Qiao, J., 2021. Clostridium butyricum Alleviates Enterotoxigenic Escherichia coli K88-Induced Oxidative Damage Through Regulating the p62-Keap1-Nrf2 Signaling Pathway and Remodeling the Cecal Microbial Community. Front Immunol 12, 771826. doi:10.3389/fimmu.2021.771826. Liu, W.R., Zeng, D., She, L., Su, W.X., He, D.C., Wu, G.Y., Ma, X.R., Jiang, S., Jiang, C.H., Ying, G.G., 2020. Comparisons of pollution characteristics, emission situations, and mass loads for heavy metals in the manures of different livestock and poultry in China. Sci Total Environ 734, 139023. doi:10.1016/j.scitotenv.2020.139023. Lu, J.H., Chen, C., Huang, C., Lee, D.J., 2020. Glucose fermentation with biochar-amended consortium: microbial consortium shift. Bioengineered 11, 272-280. doi:10.1080/21655979.2020.1735668. Ma, S., Jiang, F., Huang, Y., Zhang, Y., Wang, S., Fan, H., Liu, B., Li, Q., Yin, L., Wang, H., Liu, H., Ren, Y., Li, S., Cheng, L., Fan, W., Deng, Y., 2021. A microbial gene catalog of anaerobic digestion from full-scale biogas plants. Gigascience 10 doi:10.1093/gigascience/giaa164. Martel, J., Chang, S.H., Ko, Y.F., Hwang, T.L., Young, J.D., Ojcius, D.M., 2022. Gut barrier disruption and chronic disease. Trends Endocrinol Metab 33, 247-265. doi:10.1016/j.tem.2022.01.002. Mendez-Salazar, E.O., Vazquez-Mellado, J., Casimiro-Soriguer, C.S., Dopazo, J., Cubuk, C., Zamudio-Cuevas, Y., Francisco-Balderas, A., Martinez-Flores, K., Fernandez-Torres, J., Lozada-Perez, C., Pineda, C., Sanchez-Gonzalez, A., Silveira, L.H., Burguete-Garcia, A.I., Orbe-Orihuela, C., Lagunas-Martinez, A., Vazquez-Gomez, A., Lopez-Reyes, A., Palacios-Gonzalez, B., Martinez-Nava, G.A., 2021. Taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism. Mol Med 27, 50. doi:10.1186/s10020-021-00311-5. Moor, J., Wuthrich, T., Aebi, S., Mostacci, N., Overesch, G., Oppliger, A., Hilty, M., 2021. Influence of pig farming on human Gut Microbiota: role of airborne microbial communities. Gut Microbes 13, 1-13. doi:10.1080/19490976.2021.1927634. Niu, Q., Li, P., Hao, S., Kim, S.W., Du, T., Hua, J., Huang, R., 2019. Characteristics of Gut Microbiota in Sows and Their Relationship with Apparent Nutrient Digestibility. Int J Mol Sci 20 doi:10.3390/ijms20040870. Peng, S., Zheng, H., Herrero-Fresno, A., Olsen, J.E., Dalsgaard, A., Ding, Z., 2021. Co-occurrence of antimicrobial and metal resistance genes in pig feces and agricultural fields fertilized with slurry. Sci Total Environ 792, 148259. doi:10.1016/j.scitotenv.2021.148259. Radwan, S., Gilfillan, D., Eklund, B., Radwan, H.M., El Menofy, N.G., Lee, J., Kapuscinski, M., Abdo, Z., 2020. A comparative study of the gut microbiome in Egyptian patients with Type I and Type II diabetes. PLoS One 15, e0238764. doi:10.1371/journal.pone.0238764. Ruang-Areerate, T., Piyaraj, P., Suwannahitatorn, P., Ruang-Areerate, P., Thita, T., Naaglor, T., Witee, U., Sakboonyarat, B., Leelayoova, S., Mungthin, M., 2021. Zoonotic Transmission of Blastocystis Subtype 1 among People in Eastern Communities of Thailand: Organic Fertilizer from Pig Feces as a Potential Source. Microbiol Spectr 9, e0036221. doi:10.1128/Spectrum.00362-21. Salem, M., Pajunen, M.I., Jun, J.W., Skurnik, M., 2021. T4-like Bacteriophages Isolated from Pig Stools Infect Yersinia pseudotuberculosis and Yersinia pestis Using LPS and OmpF as Receptors. Viruses 13 doi:10.3390/v13020296. Shang, P., Dong, S., Han, Y., Bo, S., Ye, Y., Duan, M., Chamba, Y., 2022. Environmental exposure to swine farms reshapes human gut microbiota. Chemosphere 307, 135558. doi:10.1016/j.chemosphere.2022.135558. Song, C., Li, W., Cai, F., Liu, G., Chen, C., 2021a. Anaerobic and Microaerobic Pretreatment for Improving Methane Production From Paper Waste in Anaerobic Digestion. Front Microbiol 12, 688290. doi:10.3389/fmicb.2021.688290. Song, L., Wang, C., Jiang, G., Ma, J., Li, Y., Chen, H., Guo, J., 2021b. Bioaerosol is an important transmission route of antibiotic resistance genes in pig farms. Environ Int 154, 106559. doi:10.1016/j.envint.2021.106559. Su, C., Zhang, K.Z., Cao, X.Z., Yang, J.G., 2020. Effects of Saccharomycopsis fibuligera and Saccharomyces cerevisiae inoculation on small fermentation starters in Sichuan-style Xiaoqu liquor. Food Res Int 137, 109425. doi:10.1016/j.foodres.2020.109425. Sun, J., Liao, X.P., D'Souza, A.W., Boolchandani, M., Li, S.H., Cheng, K., Luis Martinez, J., Li, L., Feng, Y.J., Fang, L.X., Huang, T., Xia, J., Yu, Y., Zhou, Y.F., Sun, Y.X., Deng, X.B., Zeng, Z.L., Jiang, H.X., Fang, B.H., Tang, Y.Z., Lian, X.L., Zhang, R.M., Fang, Z.W., Yan, Q.L., Dantas, G., Liu, Y.H., 2020. Environmental remodeling of human gut microbiota and antibiotic resistome in livestock farms. Nat Commun 11, 1427. doi:10.1038/s41467-020-15222-y. Usman, M., Zhao, S., Jeon, B.H., Salama, E.S., Li, X., 2022. Microbial beta-oxidation of synthetic long-chain fatty acids to improve lipid biomethanation. Water Res 213, 118164. doi:10.1016/j.watres.2022.118164. Van Gompel, L., Luiken, R.E.C., Hansen, R.B., Munk, P., Bouwknegt, M., Heres, L., Greve, G.D., Scherpenisse, P., Jongerius-Gortemaker, B.G.M., Tersteeg-Zijderveld, M.H.G., Garcia-Cobos, S., Dohmen, W., Dorado-Garcia, A., Wagenaar, J.A., Urlings, B.A.P., Aarestrup, F.M., Mevius, D.J., Heederik, D.J.J., Schmitt, H., Bossers, A., Smit, L.A.M., 2020. Description and determinants of the faecal resistome and microbiome of farmers and slaughterhouse workers: A metagenome-wide cross-sectional study. Environ Int 143, 105939. doi:10.1016/j.envint.2020.105939. Vestergaard, D.V., Holst, G.J., Basinas, I., Elholm, G., Schlunssen, V., Linneberg, A., Santl-Temkiv, T., Finster, K., Sigsgaard, T., Marshall, I.P.G., 2018. Pig Farmers' Homes Harbor More Diverse Airborne Bacterial Communities Than Pig Stables or Suburban Homes. Front Microbiol 9, 870. doi:10.3389/fmicb.2018.00870. Wang, J., Gu, J., Wang, X., Song, Z., Dai, X., Guo, H., Yu, J., Zhao, W., Lei, L., 2021a. Enhanced removal of antibiotic resistance genes and mobile genetic elements during swine manure composting inoculated with mature compost. J Hazard Mater 411, 125135. doi:10.1016/j.jhazmat.2021.125135. Wang, Q., Gu, J., Wang, X., Ma, J., Hu, T., Peng, H., Bao, J., Zhang, R., 2020. Effects of nano-zerovalent iron on antibiotic resistance genes and mobile genetic elements during swine manure composting. Environ Pollut 258, 113654. doi:10.1016/j.envpol.2019.113654. Wang, R., Liu, N., Yang, Y., Lei, Y., Lyu, J., Dai, Z., Kim, I.H., Li, J., Wu, Z., Li, D., 2021b. Flavor supplementation during late gestation and lactation periods increases the reproductive performance and alters fecal microbiota of the sows. Anim Nutr 7, 679-687. doi:10.1016/j.aninu.2021.01.007. Wang, S., Chen, H., Wen, X., Mu, J., Sun, M., Song, X., Liu, B., Chen, J., Fan, X., 2021c. The Efficacy of Fecal Microbiota Transplantation in Experimental Autoimmune Encephalomyelitis: Transcriptome and Gut Microbiota Profiling. J Immunol Res 2021, 4400428. doi:10.1155/2021/4400428. Wen, X., Wang, H.G., Zhang, M.N., Zhang, M.H., Wang, H., Yang, X.Z., 2021. Fecal microbiota transplantation ameliorates experimental colitis via gut microbiota and T-cell modulation. World J Gastroenterol 27, 2834-2849. doi:10.3748/wjg.v27.i21.2834. White, J.K., Nielsen, J.L., Madsen, A.M., 2019. Microbial species and biodiversity in settling dust within and between pig farms. Environ Res 171, 558-567. doi:10.1016/j.envres.2019.01.008. Yang, W.Y., Lee, Y., Lu, H., Chou, C.H., Wang, C., 2019. Analysis of gut microbiota and the effect of lauric acid against necrotic enteritis in Clostridium perfringens and Eimeria side-by-side challenge model. PLoS One 14, e0205784. doi:10.1371/journal.pone.0205784. Yi, Z., Liu, X., Liang, L., Wang, G., Xiong, Z., Zhang, H., Song, X., Ai, L., Xia, Y., 2021. Antrodin A from Antrodia camphorata modulates the gut microbiome and liver metabolome in mice exposed to acute alcohol intake. Food Funct 12, 2925-2937. doi:10.1039/d0fo03345f. Zhou, Q., Zhou, T., Feng, F., Huang, S., Sun, Y., 2021. The response of copper resistance genes, antibiotic resistance genes, and intl1/2 to copper addition during anaerobic digestion in laboratory. Ecotoxicol Environ Saf 210, 111822. doi:10.1016/j.ecoenv.2020.111822. Zhu, J., 2000. A review of microbiology in swine manure odor control. Agriculture, Ecosystems & Environment 78, 93-106. doi:10.1016/s0167-8809(99)00116-4. Supplementary Files Supplementary1.docx Cite Share Download PDF Status: Published Journal Publication published 30 Nov, 2023 Read the published version in AMB Express → Version 1 posted Reviewers agreed at journal 10 Aug, 2023 Reviewers invited by journal 04 Jul, 2023 Editor assigned by journal 21 Jun, 2023 First submitted to journal 19 Jun, 2023 Editorial decision: Major Revision 14 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3020464","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":215760797,"identity":"34e90be5-97ad-4353-bfff-89f8f26bfe22","order_by":0,"name":"Jinyi Han","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIie3PIQvCUBDA8Xs8mEWY8Vb0K5wMpsK+ieWJsGY1DRwIW9Pq9xCWJwfTMFg1GJbMs4hJdIJ5Lwq+X7oH94d7AIbxgwiyzgPfgy3lodJNAJvESaw5aSYA+BnKrtfTSkaiIBqHl77L4AGE/rQ1mUQFKcyvrscQVJAHi6j1sKygDC2epQxHEhFrJcMIn7zar0WMuokLTsyKpLR0k3wJzoaHO7YkKa2/nDmVeOeBvS1vVR367QngiSR+H6p1vWEnlai1Ng3DMP7WC2P1QwJXbmFZAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0005-9148-0832","institution":"Henan Agricultural University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jinyi","middleName":"","lastName":"Han","suffix":""},{"id":215760798,"identity":"a533ab85-f949-492d-8b8b-550f46400595","order_by":1,"name":"Mengyu Li","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mengyu","middleName":"","lastName":"Li","suffix":""},{"id":215760799,"identity":"b9cfc40b-371c-4442-a16b-46be18176a43","order_by":2,"name":"Xin Li","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Li","suffix":""},{"id":215760800,"identity":"58b2cab4-a4bf-40d8-8e0c-25e8a0239c46","order_by":3,"name":"Chuang Liu","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chuang","middleName":"","lastName":"Liu","suffix":""},{"id":215760801,"identity":"ae414f9a-9260-44e2-a6a8-ce82eee35859","order_by":4,"name":"Xiu-Ling Li","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiu-Ling","middleName":"","lastName":"Li","suffix":""},{"id":215760802,"identity":"87afbc98-c4ed-49ab-b56c-9e9b0fa82cb7","order_by":5,"name":"Kejun Wang","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kejun","middleName":"","lastName":"Wang","suffix":""},{"id":215760803,"identity":"ad1fa40b-4488-4c46-8ab4-5aff1ae68945","order_by":6,"name":"Ruimin Qiao","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ruimin","middleName":"","lastName":"Qiao","suffix":""},{"id":215760804,"identity":"7091e319-121d-4dd9-9ca9-480f00cb44f1","order_by":7,"name":"Feng Yang","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Yang","suffix":""},{"id":215760805,"identity":"c104a818-7911-433a-bc46-f4e0c851b691","order_by":8,"name":"Xuelei Han","email":"","orcid":"","institution":"Henan Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xuelei","middleName":"","lastName":"Han","suffix":""},{"id":215760806,"identity":"406c3854-196b-426e-91b9-9fd3c7dc093a","order_by":9,"name":"Xin-Jian Li","email":"","orcid":"https://orcid.org/0000-0003-2546-0145","institution":"Henan Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin-Jian","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2023-06-04 11:20:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3020464/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3020464/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13568-023-01631-x","type":"published","date":"2023-11-30T15:01:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":39655913,"identity":"023dad13-9209-4c3c-b575-dd7465a9125b","added_by":"auto","created_at":"2023-07-06 18:52:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":305174,"visible":true,"origin":"","legend":"\u003cp\u003eRelative abundance of the sample classification level. (A, B) The phylum level of the top 20 phyla in the total sample. (C, D) The genus level of the top 20 genera in the total sample.\u003c/p\u003e","description":"","filename":"Onlinefigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3020464/v1/ffbdcbe4c37b7aacab385bd6.png"},{"id":39655912,"identity":"6cc4dd58-53ad-473f-ac25-1f5a25040b8f","added_by":"auto","created_at":"2023-07-06 18:52:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":238925,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of human fecal samples from different pig farms: (A, B) alpha diversity index of farmers and non-exposed individuals; (C, D) Bray–Curtis distance matrix for NMDS analysis;\u003c/p\u003e","description":"","filename":"Onlinefigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3020464/v1/62fb71c1c43546c3fbc8c9bf.png"},{"id":39655914,"identity":"e979d5b4-49f8-4436-ab17-c2fbc398b76a","added_by":"auto","created_at":"2023-07-06 18:52:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":305038,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of fecal samples from all humans: (A, B) ASVs level and genera level farmer vs non-exposed volcano diagram. log2FoldChange as abscissa, −log10 (P-value) as ordinate, horizontal dotted line Y = −log10 (0.05), vertical dotted line x = ±1. The red dots are marked as up when log2FoldChange ≥1 and P-value \u0026lt; 0.05, indicating the upregulated ASVs in farmers. The blue dots are marked as down when log2FoldChange ≤ −1 and P-value \u0026lt; 0.05, indicating downregulated ASVs in farmers and upregulated ASVs in non-exposed individuals. No difference is found in gray spots; (C) Random forest analysis of the intra-group mean in human samples. (D, E) LEfSe was combined with LDA score to differentiate bacterial biomarker groups biologically and statistically. Bacterial with P \u0026lt; .05 and LDA score \u0026gt; 2 were considered significant.\u003c/p\u003e","description":"","filename":"Onlinefigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3020464/v1/2d0ac8dd43fcc108fc71dd37.png"},{"id":39655911,"identity":"a7b309cb-5222-4e19-893e-adac45b98592","added_by":"auto","created_at":"2023-07-06 18:52:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":474567,"visible":true,"origin":"","legend":"\u003cp\u003eOutdoor environment analysis: (A, B) NMDS of environmental samples and pig gut microbes; (C, E) hierarchical clustering analysis of pig farm A environmental dust; (E, F) spiral heat map of environmental dust co-occurring genera abundance.\u003c/p\u003e","description":"","filename":"Onlinefigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3020464/v1/445ebc3c20a8076392323adf.png"},{"id":39655915,"identity":"5ab8d6d6-4828-4fe6-8a89-875fb5e5ad97","added_by":"auto","created_at":"2023-07-06 18:52:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":243204,"visible":true,"origin":"","legend":"\u003cp\u003e(A, B) Hierarchical clustering analysis of environmental microbes; (C, D) indoor and outdoor volcano map of windscreen.\u003c/p\u003e","description":"","filename":"Onlinefigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3020464/v1/a095f42315979d3c4c3ec780.png"},{"id":47561464,"identity":"a50314cf-450e-4047-8fe1-54403e215b42","added_by":"auto","created_at":"2023-12-04 15:11:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1892202,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3020464/v1/78a2a945-3fa2-4189-8762-94a945fe0ef4.pdf"},{"id":39655916,"identity":"9d47f05b-461a-48b1-8e36-1b754e3404de","added_by":"auto","created_at":"2023-07-06 18:52:42","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":8099509,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3020464/v1/eb6502135ab4392b86a312c3.docx"}],"financialInterests":"","formattedTitle":"Effects of microbes in pig farms on occupational exposed persons and the environment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMicroorganisms are also an important cause of health changes(Shang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Pig houses have many microbes(Hong et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), for example, aerosol-coated microbes(Moor et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) affect farmers working on the farm. Several studies have shown that working on a pig farm can affect farmers\u0026rsquo; nasal microbes(Kraemer et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although gut microbes can remain stable during acute environmental changes(Sun et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), in many areas, farmers must live with pigs every day. Over time, breeders\u0026rsquo; gut microbiota will also change(Moor et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, exploring the effects of occupational exposure on the intestinal flora of farmers is necessary.\u003c/p\u003e \u003cp\u003eAirborne microbes exchange inside and outside the pig house(Hong et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), resulting in contamination from anaerobic digestion of pig manure(Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e; Ma et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, the surrounding environment of pig farms has a characteristic smell. \u003cem\u003eClostridium\u003c/em\u003e is a main source of stink on pig farms(Zhu, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Pig farming has been stigmatized primarily because of microorganisms. In pig gut microbe, metal contamination characteristics (especially Zn, Cu) are more severe than other animal gut microbes(Liu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Untreated pig gut microbes alter antibiotic resistance and microbial communities in soil(Gao et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Therefore, the influence of pig farms on the microorganisms in the surrounding environment has always been a research hotspot.\u003c/p\u003e \u003cp\u003eThis study started with microorganisms and explored which microbial farmers and environments on pig farms are susceptible to infection. Primarily, in pig farm A, this study collected gut microbes from 18 farmers and 4 individuals without contact with any type of farm animal (\u0026ldquo;non-exposed\u0026rdquo; persons). Their gut microbiota was analyzed to find the difference in flora. Based on the analysis of 44 environmental samples from pig farm A, the path and distance of microbial diffusion were deduced. Subsequently, we conducted sampling in pig farms B to verify the above results and obtained the same trend. The result provides theory for blocking aerosol transmission of microorganisms in pig farms and reducing pollution. Furthermore, this study aims to demonstrate the sustainable development of pig farms from the perspective of microorganisms.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eTwo pig farms were sampled in this study. Primarily, samples were collected from pig farm A in December 2021. A total of 136 samples were collected from 22 human fecal, 70 pig fecal, and 44 environment samples. The 22 human gut microbes were obtained from 18 farmers (13 breeders and five less exposed leaders) and four individuals without contact with any type of farm animal (\u0026ldquo;non-exposed\u0026rdquo; persons). A total of 44 environmental samples were obtained from the inside, outside, living, and cesspool of the pig house. The pig house was divided into three locations (windscreen inside pig house, floor, and handrail), and each point has four samples; In outside the farm, three points (5, 500, and 1000 meters outside the pig farm) were sampled with different distance diameters, and each point has five samples.\u003c/p\u003e \u003cp\u003eIn November of the following year, samples were collected from pig farms B. A total of 43 samples were collected from 10 pig fecal, 24 environment samples, and 9 fecal. The 9 human fecal were obtained from 7 farmers (4 breeders and 3 less exposed leaders). In addition, two of them used to be the leaders of pig farm A for microbial collection, but they had been out of work for half a year and had not been exposed to animals for three months at the time of this sampling. A total of 24 environmental samples were obtained from the inside, outside of the pig house. The pig farm was divided into 3 locations (floor and, windscreen outside pig house windscreen inside pig house); In outside the farm, 3 locations (5, 500, and 1000 meters) were sampled with different distance diameters, and each point has four samples. The specific sampling plan and number are shown in Supplementary material \u003cb\u003e(Supplementary Table\u0026nbsp;1)\u003c/b\u003e. In addition, the animals designed in this experiment were healthy. The volunteers all took a physical within a year. Health views of pigs from both farms are supplemented in in Supplementary material \u003cb\u003e(Supplementary Fig.\u0026nbsp;1)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAll samples were immediately transported to the laboratory in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and polymerase chain reaction (PCR) amplification\u003c/h2\u003e \u003cp\u003eDNA was extracted using the OMEGA Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA) and stored at \u0026minus;\u0026thinsp;20\u0026deg;C prior to analysis. From these DNA extracts, the V3\u0026ndash;V4 region of the 16S rRNA gene was amplified using forward (5\u0026prime;-ACTCCTACGGGAGGCAGCA-3\u0026prime;) and reverse (5\u0026prime;-GGACTACHVGGGTWTCTAAT-3\u0026prime;) primers, and Illumina linker sequences were used at 5\u0026prime; end. PCR cycling conditions included initial denaturation at 95\u0026deg;C for 5 min and 25 cycles of denaturation at 95\u0026deg;C for 30 s, annealing at 59\u0026deg;C for 30 s and extension at 72\u0026deg;C for 45 s. This process was followed by a final extension step at 72\u0026deg;C for 5 min. PCR amplicons were purified using Vazyme VAHTS\u0026trade; DNA Clean Beads (Vazyme, Nanjing, China) and quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). The subsequently constructed library was sequenced on the Illumina HiSeq 2500 platform (2\u0026times;250 pairs), and sequencing was performed by Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e16S rRNA gene sequence assembly and clustering\u003c/h2\u003e \u003cp\u003e16S rRNA sequencing data were processed using the Quantitative Insight Into Microbial Ecology 2 (QIIME2, 20019.4) platform(Bolyen et al., 2019). Then, the sequences were quality filtered, denoised, and merged, and chimeras were removed using the DADA2 plugin(Callahan et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Non-single amplicon sequence variants (ASVs) were aligned with mafft(Katoh et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Subsequently, species taxonomic annotation was performed using the SILVA database (Release132, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.arb-silva.de\u003c/span\u003e\u003cspan address=\"http://www.arb-silva.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the classify-sklean algorithm with default parameters was used in the analysis in QIIME2. Moreover, each naive Bayes classifier was used to obtain the composition and abundance of individual samples in the taxonomic level distribution table. The resulting ASV scale was flattened with QIIME2 using rarefaction at the 95% depth of the lowest sample sequence. Sequencing and bioinformatics were performed on QIIME2 platform of Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China) and the sequencing results were analyzed based on amplicon sequence variants (ASVs).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics and statistical analysis\u003c/h2\u003e \u003cp\u003eTaxa abundances at the phylum and genus levels were statistically compared between groups. ASV-level alpha diversity indices, Beta diversity Venn diagram and Volcano map were analyzed at the ASVs level. And then, ASVs were aggregated at genera level, and other results were analyzed at the genus level. Alpha diversity indices, such as Observed species, and Shannon diversity index were calculated using the ASV table in QIIME2, and visualized as box plots; Beta diversity was evaluated by nonmetric multidimensional scaling (NMDS) based on the Bray\u0026ndash;Curtis distance; The significance of differentiation of microbiota structure among groups was assessed by ANOSIM using QIIME2; Volcano map was using R package \u0026ldquo;DESeq2\u0026rdquo; to make a differential analysis of ASVs/genus; Linear discriminant analysis effect size(LEfSe), a method for biomarker discovery. LEfSe scores measure the consistency of differences in relative abundance between taxa in the groups analyzed (As farmer vs As non-exposed), with a higher score indicating higher consistency. We considered taxa with linear discriminant analysis score\u0026thinsp;\u0026gt;\u0026thinsp;2 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to be significant. Hierarchical clustering analysis consists of two parts: the left represents the hierarchical clustering tree of community samples, and the right represents the stacked histogram of species composition; UPGMA cluster analysis of Bray\u0026ndash;Curtis distance matrix using R package \u0026ldquo;stat\u0026rdquo;, and the visualizations was performed using R package \u0026ldquo;ggtree\u0026rdquo;; Random forest analysis was applied to discriminate the samples from different groups using QIIME2 with default settings. Nested stratified k-fold cross-validation was used for automated hyperparameter optimization and sample prediction; Spiral heatmaps were plotted using heatmap tools in the genescloud platform(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genescloud.cn\u003c/span\u003e\u003cspan address=\"https://www.genescloud.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The data was normalised by z-scores. The package uses popular clustering distances and methods implemented in dist and hclust functions in R.\u003c/p\u003e \u003cp\u003eFor the grouped samples (the number of samples in each group\u0026thinsp;\u0026ge;\u0026thinsp;3), the R package can be used to draw a box line diagram to visually display the differences between different sample groups, and the Kruskal-Wallis rank sum test and dunn 's test can be used as post hoc tests to verify the significance of the differences (the Kruskal-Wallis test is equivalent to the Wilcoxon test for both groups of samples). Statistical significance was defined as a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFirmicutes \u003cem\u003ewere the main bacterial phyla in both farms\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eAfter sequencing analysis, a total of 197,712 ASV sequences were obtained in pig farms. After classification annotation, both farms have two domains, Archaea and Bacteria. 2,081 genera were analyzed in pig farm A, 1,196 genera were analyzed in pig farms B. Taxa abundances at the phylum and genus levels were statistically compared between groups. Samples were divided into human gut microbes, pig gut microbes, and environmental microbes, and the taxonomic composition of the total samples was analyzed at the top 20 phylum and the top 20 genus levels \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e,16 phylum, 14 genera were repeated in both farms. Among the top 20 genera, \u003cem\u003emore Clostridium_sensu_stricto_1\u003c/em\u003e was found in environmental samples and pig gut microbes (\u003cb\u003eSupplementary Figs.\u0026nbsp;2 and 3)\u003c/b\u003e. The microbial distribution of the two farms showed similar trends.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eThe diversity index of Farmer alpha was higher\u003c/h2\u003e \u003cp\u003eHuman gut microbe samples were divided into two groups, farmer and non-exposed. In both alpha diversity indices, non-exposed were slightly higher (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The point separation between farmers and non-exposed was not significant, especially in pig farms B, but the 95% confidence circle showed that farmers\u0026rsquo; microbial communities were closer. The Alpha diversity index showed the same trend of diversity among farmers in both farms.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFarmers\u0026rsquo; gut microbiota changes with working hours\u003c/h2\u003e \u003cp\u003eTo further explore the effects of pig farming on farmers, samples of all human fecal were divided into two groups. The volcano maps showed the different ASVs and genera. 6 ASVs are marked as up in farmers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), including ASV_133662 (\u003cem\u003eStreptococcus\u003c/em\u003e), ASV_311074 (\u003cem\u003eEscherichia-Shigella\u003c/em\u003e), ASV_111920 (\u003cem\u003eTerrisporobacter)\u003c/em\u003e, ASV_57963 (\u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e), ASV_192153 (\u003cem\u003eFaecalibacterium\u003c/em\u003e), ASV_101783 (\u003cem\u003ePrevotella_9)\u003c/em\u003e. Then, in the volcano diagram at genera level (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), 7 genera had high abundances in farmers but not in non-exposed individuals, including \u003cem\u003eTerrisporobacter, Enterococcus, Turicibacter, Clostridium_sensu_stricto_1, Prevotella_7, Nitrosomonas\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, farmers were grouped on the basis of the length of their occupational exposure. They were divided into breeders and leaders based on frequent differences in the production area. Samples of all human gut microbes were divided into three groups: farmer, breeder and non-exposed. Random forest analysis was conducted on all human gut microbes to draw random forest analysis of the average value within the group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). In this random forest analysis, the number of k-fold cross-validations was set to 5, and the three genera significantly up-regulated by farmers increased with the increase of exposure time (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Four of the top 20 dominant genera in the Random Forest analysis were significantly up-regulated on the genus level volcano map, including \u003cem\u003eTerrisporobacter, Turicibacter, Clostridium_sensu_stricto_1, Nitrosomonas\u003c/em\u003e. Moreover, the importance of these genera decreases with the decrease of working hours.\u003c/p\u003e \u003cp\u003eIt is worth mentioning that one of the workers who worked at the pig farms B happened to be one of the non-expose workers; The two farmers in pig farm A had been out of work for 3 months at the time of the second sampling. Therefore, we treated them as non-expose workers in the second analysis. These three people are distinguished as \"as farmer\" and \"non-exposed\" according to their working state when they are sampled. \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e and \u003cem\u003eTerrisporobacter\u003c/em\u003e were notably present as farmers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e and \u003cem\u003eTerrisporobacter\u003c/em\u003e are always mentioned, and may coexist more easily with humans, but show significant differences three months after they leave the pigs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDust microbes closer to pigs are more similar to pigs\u003c/h2\u003e \u003cp\u003eBeta diversity analysis was performed on environmental dust and pig fecal from the two farms. Beta diversity was evaluated by NMDS based on the Bray\u0026ndash;Curtis distance. In the NMDS diagram, the pig house dust microbes were closest to the pig fecal (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In pig farm A, the study found that the cesspool runs through each group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Then the anosim test was conducted, and the result showed that the distance between the outside group and cesspool group was not significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.063). Hierarchical cluster analysis of pig farm A shows clustering inside and outside pig house (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). NMDS diagram and hierarchical cluster analysis of pig farms B shows more obvious clustering inside and outside pig farm (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). In other words, the microbes in the environment dust outside the farm cluster on a branch.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTherefore, the closer the environmental samples were to pigs, the more different species were found. Furthermore, three genera can be considered as co-occurring microbes, including \u003cem\u003eTerrisporobacter\u003c/em\u003e (ASV_111920), \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e (ASV_57963), and \u003cem\u003eTuricibacter\u003c/em\u003e (ASV_20732), which may co-occur between humans and the environment. Therefore, in this study these three genera are called \u003cb\u003ethe co-occurring microbes\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe co-occurring microbes were used as a marker to draw environmental samples and group them into spiral heat maps. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). As shown in the figure, the co-occurring microbes at the pig farms B were essentially zero outside the farm, which may be related to the spray disinfection of the walls and gates at the pig farms B. the co-occurring microbes at the pig farm A decreased as the distance from the farm increased. The co-occurring microbes detected by Dunn\u0026rsquo;s test were significantly different at the pig farm A and 1000 m outside the gate of the pig farm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSimple filtration can reduce cross-infection between pig houses\u003c/h2\u003e \u003cp\u003eIn the spiral heat maps of the two pig farms, there were significant differences between the inside and outside windscreen of the pig farm A, while there were no significant differences between the inside and outside windscreen of the pig farm B. This was also demonstrated by the cluster analysis diagrams of the floor and inside and outside the windscreen of the pig farm \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. It is understood that the pig farm A has a simple filtration system with two screens. In the pig farm B, on the other hand, has only a simple screen as a filter for exhaust air.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe windscreen inside the pig house was compared with that outside the pig house to identify different microorganisms. A total of 76 genera showed high abundances on the board of the windscreen inside A pig farm house, but not in pig farm B house (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Among the upregulation genera, the co-occurring microbes were all included. This result indicates that in A pig house neither or less of the co-occurring microbes reached the outside of the barn with air. Although microbes can be transmitted by aerosols, simple filtration can reduce cross-infection between houses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEffects of the co-occurring microbes on farmers\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the analysis of humans,\u0026nbsp;the study found no significant differences in Observed_species and Shannon index between the two groups, which does not deny that pig breeding will bring about differences in human gut microbes. As omnivore, human beings have complex dietary habits. Therefore, the diversity and abundance of human intestinal microbes have a certain steady state(Martel et al., 2022)\u0026nbsp;.\u003c/p\u003e\n\u003cp\u003eIn this study, farmers were found to have lower microbial diversity than non-exposed ones, which is contrary to the results of Kates(Kates et al., 2019)\u0026nbsp;but similar to the result of Vestergaard(Vestergaard et al., 2018). The biosafety of Chinese farms is becoming strict, which is reflected not only in disinfection and prevention but also in the diet control of farmers in farms. A pig farm is like a Petri dish of fixed supplies, unifying and fixing farmer\u0026rsquo;s diet and limiting microbial diversity. This finding may explain the clustering of farmer samples in the NMDS diagram.\u003c/p\u003e\n\u003cp\u003eThe co-occurring microbes that were prominent on farmers were considered as the three most abundant genera in the environment, which had the highest absolute abundance in pig samples of the entire sample. On the contrary, anywhere less affected by pigs, the co-occurring microbes were less abundant. Therefore, we hypothesize that the co-occurring microbes came from pigs, and they are susceptible to humans and the environment. Through classification level analysis, the co-occurring microbes belong to Firmicutes, including \u003cem\u003eTuricibacter\u003c/em\u003e, \u003cem\u003eTerrisporobacter\u003c/em\u003e, and \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost of the physiological contributions of gut microbes are involved in the fermentation and production of short-chain fatty acids. The abundance of \u003cem\u003eTuricibacter\u003c/em\u003e was related to the digestion of acid detergent fiber digestibility\u0026nbsp;(Niu et al., 2019)\u0026nbsp;and \u0026alpha; linolenic acid(Gao et al., 2020b). The abundance of \u003cem\u003eTerrisporobacter\u003c/em\u003e was associated with short-chain fatty acids(Li et al., 2021)\u0026nbsp;and C-reactive protein triglycerides(Lee et al., 2020). The high abundance of \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e may be involved in \u0026beta;-oxidation(Usman et al., 2022), causing the degradation of most long-chain fatty acids. Therefore, the three co-occurring microbes also affect the growth traits of pigs. Xyoligosaccharides(Chen et al., 2021b)\u0026nbsp;were found to reduce the relative abundance of \u003cem\u003eTerrisporobacter\u0026nbsp;\u003c/em\u003eand \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e and improve body weight, daily gain, and feed conversion rate in weaned piglets as a substitute for antibiotics. Furthermore, \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e was negatively associated with increased litter size and daily increase of piglets(Hu et al., 2021; Wang et al., 2021b).\u003c/p\u003e\n\u003cp\u003eThe three co-occurring microbes in this study are pathogenic genera, which lead to intestinal inflammation and even cancer, and such microbes are related to a variety of digestive diseases. \u003cem\u003eTuricibacter\u003c/em\u003e affects pancreatic cancer(Jeong et al., 2020)\u0026nbsp;and colon cancer(Chung et al., 2021; Wen et al., 2021), and it is associated with Parkinson\u0026rsquo;s disease(Jin et al., 2019)\u0026nbsp;and autoimmune encephalomyelitis(Wang et al., 2021c). In addition, \u003cem\u003eTuricibacter\u0026nbsp;\u003c/em\u003eand \u003cem\u003eTerrisporobacter\u003c/em\u003e affect type I diabetes(Radwan et al., 2020). Of the three co-occurring microbes, researchers are more familiar with the \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e class. It is found to be not only associated with colitis(Yang et al., 2019), fatty liver(Yi et al., 2021), gout(Mendez-Salazar et al., 2021), etc., but also positively correlated with levels of inflammatory markers (\u003cem\u003eTNF-\u0026alpha;\u003c/em\u003e, \u003cem\u003eIL-1\u0026beta;\u003c/em\u003e, and \u003cem\u003eIL-6\u003c/em\u003e)\u0026nbsp;(Yi et al., 2021). \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e is correlated with host inflammatory genes (including \u003cem\u003eREG3G\u003c/em\u003e, \u003cem\u003eCCL8\u003c/em\u003e, and \u003cem\u003eIDO1\u003c/em\u003e)\u0026nbsp;(Wen et al., 2021).\u003c/p\u003e\n\u003cp\u003eThe surprise of our sampling was that there were three people whose identities had been switched between the two samples, making them the control group. Three months after leaving the swine farms, the farms gut microbiota partially reverted to their original microbial composition\u0026nbsp;(Sun et al., 2020). Therefore, we believe that the non-expose group can be used after three months of unemployment. When this group of intestinal flora was analyzed, it was found that the co-occurring microbes showed a trend of decrease after the cessation of farming. There are significant differences between \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e and \u003cem\u003eTerrisporobacter\u003c/em\u003e (P\u0026lt;0.05). The most intuitive manifestation is that the longer one leaves the pig farm, the unique taste of the pig farm will disappear, which is related to\u003cem\u003e\u0026nbsp;Clostridium_sensu_stricto_1\u003c/em\u003e (Zhu, 2000).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEffects of the co-occurring microbes on the environment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn pig house A, the three co-occurring microbes\u0026nbsp;were significantly reduced on the windscreen inside the environmental samples from the pig house, probably because the windscreen is the farthest away from the pig, which is a difficult place to touch. However, dust in the air can also carry some microorganisms, which can be detected in the windscreen. In pig farm A, fewer co-occurring microbes were found on the windscreen outside the pig house. After inquiry, the ventilation of this pig farm is an easy air filtration mode. The system improved the environmental impact of the barn, although no significant difference in the co-occurring microbes was found on the inside and outside of the pig house.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the analysis of the environmental samples of pig farm B, the environment in the pig house is a branch, and the outside environment is a branch. The co-occurring microbes were reduced outside the pig farm. But the pig farm B has no air filtration system, just a layer of gauze. Therefore, not many different bacteria were found on the windscreens inside and outside the pig house.\u003c/p\u003e\n\u003cp\u003eA few co-occurring microbes also appeared in the living areas. However, no significant difference was found between the microbial community in the living area and other sites in the farm, which may be due to the dust in the farm environment or may be brought out by the farmers. The abundance of co-occurring genera in the living area is the lowest in the pig farm; thus, the environmental pollution caused by the co-occurring genera carried by farmers can be ignored. Therefore, the results indicate that the co-occurring microbes primarily affect the environment through aerosol diffusion. The co-occurring genera are transmitted through two possible forms, aerosol diffusion or carried by farmers after leaving the barn. The co-occurring genera carried by farmers are negligible; thus, their effect on the environment is primarily due to aerosol diffusion in pig houses or septic tanks. The results confirm multiple studies(Ko et al., 2008; White et al., 2019; Moor et al., 2021).\u003c/p\u003e\n\u003cp\u003eThe study found that two samples in cesspool contained several co-occurring microbes, and two samples in cesspool did not contain such co-occurring microbes. The difference in microbial community distributed in septic tank was the greatest. Fecal treatment channels reaching the gut microbe of septic tanks cause huge risks to the environment(Peng et al., 2021)\u0026nbsp;and biological health(Ruang-Areerate et al., 2021; Salem et al., 2021), such as ARG(Van Gompel et al., 2020).\u003c/p\u003e\n\u003cp\u003eCo-occurring microbes such as \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e, \u003cem\u003eTerrisporobacter\u003c/em\u003e, and \u003cem\u003eTuricibacter\u003c/em\u003e are non-exposed potential hosts of antibiotic-resistant genes (ARGs) and mobile genetic elements (MGEs)\u0026nbsp;(Wang et al., 2020; Wang et al., 2021a). The relative abundance of \u003cem\u003etetT\u003c/em\u003e gene(Zhou et al., 2021)\u0026nbsp;was positively correlated with the three co-occurring genera (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003eThe transmission of ARG can be reduced by filtering bioaerosols through the pathway(Song et al., 2021b). In addition, compost\u0026nbsp;(Yang et al., 2019)\u0026nbsp;reduces the risk of ARG transmission by reducing the relative abundance of \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e and \u003cem\u003eTerrisporobacter\u0026nbsp;\u003c/em\u003eand antibiotic-resistant genes. The co-occurring flora is also used by the population to participate in some fermentation work. \u003cem\u003eTerrisporobacter\u0026nbsp;\u003c/em\u003eand \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e participate in anaerobic digestion(Detman et al., 2021; Song et al., 2021a). \u003cem\u003eTerrisporobacter\u003c/em\u003e is a main microorganism during Xiaoqu wine fermentation(Su et al., 2020). \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e is abundant in glucose fermentation broth(Lu et al., 2020).\u003c/p\u003e\n\u003cp\u003eIn pig farm A, at 1000 m outside the pig farm, the environmental pollution in the pig house is not significant. As aerosols pass through ventilation and filtration systems, environmental pollution caused by co-occurring microbes has been mitigated. In the B pig farm, the three co-occurring microbes showed the same trend, with far fewer co-occurring microbes outside the gate of the pig farm than pig houses. In the study of pig farms, the influence distance from porcine gut microbes could be continuously reduced through disinfection. \u0026nbsp;Although microbes can be transmitted by aerosols, simple filtration can reduce cross-infection between houses.\u003c/p\u003e\n\u003cp\u003eTwo different pig farms were sampled, this study compared the gut microbes of 25 farmers and 6 non-exposed individuals and the dust microbes inside and outside the pig house with those far away from pig farms to explore the effect of pig raising on microbial. \u0026nbsp;The longer the contact time with pigs and the closer the distance to pigs, the relative abundance of three microorganisms increased, namely, \u003cem\u003eTuricibacter\u003c/em\u003e, \u003cem\u003eTerrisporobacter\u003c/em\u003e and \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e, showing significant differences. This study provides theory for blocking the transmission of microbial aerosols in pig farms and reducing pollution.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics\u0026nbsp;approval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll of the experiments involving animals were carried out in accordance with the guidelines for the care and use of experimental animals established by the Ministry of Science and Technology of the People\u0026rsquo;s Republic of China (Approval Number DWLL20211193). The animal study was reviewed and approved by the Henan Agricultural University Animal Care and Use Committee. In addition, all experiments were conducted in accordance with the relevant approved guidelines and regulations during sampling, and sample conservation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Data and model availability statement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data in this study were deposited in the NCBI Sequence Read Archive (SRA) under accession number SUB13546643.\u003c/p\u003e\n\u003cp\u003eAuthor ORCIDs\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJinyi Han 0009-0005-9148-0832; Mengyu Li 0000-0002-9605-3777;\u003c/p\u003e\n\u003cp\u003eXuelei Han 0000-0002-7957-0297; \u0026nbsp;Xin-Jian Li 0000-0003-2546-0145;\u003c/p\u003e\n\u003cp\u003eAuthor contributions \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJH, ML, and X-J L conceived and designed the experiments. JH, XL, and CL analyzed the data. JH and ML wrote the manuscript. X-J L, XH, X-L L, RQ, JW, and FY provided manuscript editing. All authors statistically analyzed, discussed, critically revised the contents, and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eDeclaration of interest\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo conflict of interest to report.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinancial support statement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Kye Research and Development Program of China (2021YFD1301202); Agricultural Breeds Research Project of Henan Province [grant number 2022020101].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBolyen, E., Rideout, J., Dillon, M., Bokulich, N., Abnet, C., Al-Ghalith, G., Alexander, H., Alm, E., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J., Bittinger, K., Brejnrod, A., Brislawn, C., Brown, C., Callahan, B., Caraballo-Rodr\u0026iacute;guez, A., Chase, J., Cope, E., Da Silva, R., Diener, C., Dorrestein, P., Douglas, G., Durall, D., Duvallet, C., Edwardson, C., Ernst, M., Estaki, M., Fouquier, J., Gauglitz, J., Gibbons, S., Gibson, D., Gonzalez, A., Gorlick, K., Guo, J., Hillmann, B., Holmes, S., Holste, H., Huttenhower, C., Huttley, G., Janssen, S., Jarmusch, A., Jiang, L., Kaehler, B., Kang, K., Keefe, C., Keim, P., Kelley, S., Knights, D., Koester, I., Kosciolek, T., Kreps, J., Langille, M., Lee, J., Ley, R., Liu, Y., Loftfield, E., Lozupone, C., Maher, M., Marotz, C., Martin, B., McDonald, D., McIver, L., Melnik, A., Metcalf, J., Morgan, S., Morton, J., Naimey, A., Navas-Molina, J., Nothias, L., Orchanian, S., Pearson, T., Peoples, S., Petras, D., Preuss, M., Pruesse, E., Rasmussen, L., Rivers, A., Robeson, M., Rosenthal, P., Segata, N., Shaffer, M., Shiffer, A., Sinha, R., Song, S., Spear, J., Swafford, A., Thompson, L., Torres, P., Trinh, P., Tripathi, A., Turnbaugh, P., Ul-Hasan, S., van der Hooft, J., Vargas, F., V\u0026aacute;zquez-Baeza, Y., Vogtmann, E., von Hippel, M., Walters, W., Wan, Y., Wang, M., Warren, J., Weber, K., Williamson, C., Willis, A., Xu, Z., Zaneveld, J., Zhang, Y., Zhu, Q., Knight, R., Caporaso, J., 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature biotechnology 37, 852-857. doi:10.1038/s41587-019-0209-9.\u003c/li\u003e\n\u003cli\u003eCallahan, B., McMurdie, P., Rosen, M., Han, A., Johnson, A., Holmes, S., 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nature methods 13, 581-583. doi:10.1038/nmeth.3869.\u003c/li\u003e\n\u003cli\u003eChen, Y., Wang, F., Li, H., Aftab, S., Liu, Y., 2021a. Triple-hurdle model analysis of the factors influencing biogas digester building, use and processing by Chinese pig farmers. Sci Total Environ 761, 143259. doi:10.1016/j.scitotenv.2020.143259.\u003c/li\u003e\n\u003cli\u003eChen, Y., Xie, Y., Zhong, R., Liu, L., Lin, C., Xiao, L., Chen, L., Zhang, H., Beckers, Y., Everaert, N., 2021b. Effects of Xylo-Oligosaccharides on Growth and Gut Microbiota as Potential Replacements for Antibiotic in Weaning Piglets. Front Microbiol 12, 641172. doi:10.3389/fmicb.2021.641172.\u003c/li\u003e\n\u003cli\u003eChung, Y., Ryu, Y., An, B.C., Yoon, Y.S., Choi, O., Kim, T.Y., Yoon, J., Ahn, J.Y., Park, H.J., Kwon, S.K., Kim, J.F., Chung, M.J., 2021. A synthetic probiotic engineered for colorectal cancer therapy modulates gut microbiota. Microbiome 9, 122. doi:10.1186/s40168-021-01071-4.\u003c/li\u003e\n\u003cli\u003eDetman, A., Laubitz, D., Chojnacka, A., Kiela, P.R., Salamon, A., Barberan, A., Chen, Y., Yang, F., Blaszczyk, M.K., Sikora, A., 2021. Dynamics of dark fermentation microbial communities in the light of lactate and butyrate production. Microbiome 9, 158. doi:10.1186/s40168-021-01105-x.\u003c/li\u003e\n\u003cli\u003eGao, F.Z., He, L.Y., He, L.X., Zou, H.Y., Zhang, M., Wu, D.L., Liu, Y.S., Shi, Y.J., Bai, H., Ying, G.G., 2020a. Untreated swine wastes changed antibiotic resistance and microbial community in the soils and impacted abundances of antibiotic resistance genes in the vegetables. Sci Total Environ 741, 140482. doi:10.1016/j.scitotenv.2020.140482.\u003c/li\u003e\n\u003cli\u003eGao, X., Chang, S., Liu, S., Peng, L., Xie, J., Dong, W., Tian, Y., Sheng, J., 2020b. Correlations between alpha-Linolenic Acid-Improved Multitissue Homeostasis and Gut Microbiota in Mice Fed a High-Fat Diet. mSystems 5 doi:10.1128/mSystems.00391-20.\u003c/li\u003e\n\u003cli\u003eHong, S.W., Park, J., Jeong, H., Kim, M., 2021. Evaluation of the microbiome composition in particulate matter inside and outside of pig houses. J Anim Sci Technol 63, 640-650. doi:10.5187/jast.2021.e52.\u003c/li\u003e\n\u003cli\u003eHu, C., Niu, X., Chen, S., Wen, J., Bao, M., Mohyuddin, S.G., Yong, Y., Liu, X., Wu, L., Yu, Z., Ma, X., Ju, X., 2021. A Comprehensive Analysis of the Colonic Flora Diversity, Short Chain Fatty Acid Metabolism, Transcripts, and Biochemical Indexes in Heat-Stressed Pigs. Front Immunol 12, 717723. doi:10.3389/fimmu.2021.717723.\u003c/li\u003e\n\u003cli\u003eJeong, J.Y., Kim, T.B., Kim, J., Choi, H.W., Kim, E.J., Yoo, H.J., Lee, S., Jun, H.R., Yoo, W., Kim, S., Kim, S.C., Jun, E., 2020. Diversity in the Extracellular Vesicle-Derived Microbiome of Tissues According to Tumor Progression in Pancreatic Cancer. Cancers (Basel) 12 doi:10.3390/cancers12092346.\u003c/li\u003e\n\u003cli\u003eJin, M., Li, J., Liu, F., Lyu, N., Wang, K., Wang, L., Liang, S., Tao, H., Zhu, B., Alkasir, R., 2019. Analysis of the Gut Microflora in Patients With Parkinson\u0026apos;s Disease. Front Neurosci 13, 1184. doi:10.3389/fnins.2019.01184.\u003c/li\u003e\n\u003cli\u003eKates, A.E., Dalman, M., Torner, J.C., Smith, T.C., 2019. The nasal and oropharyngeal microbiomes of healthy livestock workers. PLoS One 14, e0212949. doi:10.1371/journal.pone.0212949.\u003c/li\u003e\n\u003cli\u003eKatoh, K., Misawa, K., Kuma, K., Miyata, T., 2002. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic acids research 30, 3059-3066. doi:10.1093/nar/gkf436.\u003c/li\u003e\n\u003cli\u003eKo, G., Simmons, O.D., 3rd, Likirdopulos, C.A., Worley-Davis, L., Williams, M., Sobsey, M.D., 2008. Investigation of bioaerosols released from swine farms using conventional and alternative waste treatment and management technologies. Environ Sci Technol 42, 8849-8857. doi:10.1021/es801091t.\u003c/li\u003e\n\u003cli\u003eKraemer, J.G., Aebi, S., Oppliger, A., Hilty, M., 2019. The Indoor-Air Microbiota of Pig Farms Drives the Composition of the Pig Farmers\u0026apos; Nasal Microbiota in a Season-Dependent and Farm-Specific Manner. Appl Environ Microbiol 85 doi:10.1128/AEM.03038-18.\u003c/li\u003e\n\u003cli\u003eLee, S.H., You, H.S., Kang, H.G., Kang, S.S., Hyun, S.H., 2020. Association between Altered Blood Parameters and Gut Microbiota after Synbiotic Intake in Healthy, Elderly Korean Women. Nutrients 12 doi:10.3390/nu12103112.\u003c/li\u003e\n\u003cli\u003eLi, H., Shang, Z., Liu, X., Qiao, Y., Wang, K., Qiao, J., 2021. Clostridium butyricum Alleviates Enterotoxigenic Escherichia coli K88-Induced Oxidative Damage Through Regulating the p62-Keap1-Nrf2 Signaling Pathway and Remodeling the Cecal Microbial Community. Front Immunol 12, 771826. doi:10.3389/fimmu.2021.771826.\u003c/li\u003e\n\u003cli\u003eLiu, W.R., Zeng, D., She, L., Su, W.X., He, D.C., Wu, G.Y., Ma, X.R., Jiang, S., Jiang, C.H., Ying, G.G., 2020. Comparisons of pollution characteristics, emission situations, and mass loads for heavy metals in the manures of different livestock and poultry in China. Sci Total Environ 734, 139023. doi:10.1016/j.scitotenv.2020.139023.\u003c/li\u003e\n\u003cli\u003eLu, J.H., Chen, C., Huang, C., Lee, D.J., 2020. Glucose fermentation with biochar-amended consortium: microbial consortium shift. Bioengineered 11, 272-280. doi:10.1080/21655979.2020.1735668.\u003c/li\u003e\n\u003cli\u003eMa, S., Jiang, F., Huang, Y., Zhang, Y., Wang, S., Fan, H., Liu, B., Li, Q., Yin, L., Wang, H., Liu, H., Ren, Y., Li, S., Cheng, L., Fan, W., Deng, Y., 2021. A microbial gene catalog of anaerobic digestion from full-scale biogas plants. Gigascience 10 doi:10.1093/gigascience/giaa164.\u003c/li\u003e\n\u003cli\u003eMartel, J., Chang, S.H., Ko, Y.F., Hwang, T.L., Young, J.D., Ojcius, D.M., 2022. Gut barrier disruption and chronic disease. Trends Endocrinol Metab 33, 247-265. doi:10.1016/j.tem.2022.01.002.\u003c/li\u003e\n\u003cli\u003eMendez-Salazar, E.O., Vazquez-Mellado, J., Casimiro-Soriguer, C.S., Dopazo, J., Cubuk, C., Zamudio-Cuevas, Y., Francisco-Balderas, A., Martinez-Flores, K., Fernandez-Torres, J., Lozada-Perez, C., Pineda, C., Sanchez-Gonzalez, A., Silveira, L.H., Burguete-Garcia, A.I., Orbe-Orihuela, C., Lagunas-Martinez, A., Vazquez-Gomez, A., Lopez-Reyes, A., Palacios-Gonzalez, B., Martinez-Nava, G.A., 2021. Taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism. Mol Med 27, 50. doi:10.1186/s10020-021-00311-5.\u003c/li\u003e\n\u003cli\u003eMoor, J., Wuthrich, T., Aebi, S., Mostacci, N., Overesch, G., Oppliger, A., Hilty, M., 2021. Influence of pig farming on human Gut Microbiota: role of airborne microbial communities. Gut Microbes 13, 1-13. doi:10.1080/19490976.2021.1927634.\u003c/li\u003e\n\u003cli\u003eNiu, Q., Li, P., Hao, S., Kim, S.W., Du, T., Hua, J., Huang, R., 2019. Characteristics of Gut Microbiota in Sows and Their Relationship with Apparent Nutrient Digestibility. Int J Mol Sci 20 doi:10.3390/ijms20040870.\u003c/li\u003e\n\u003cli\u003ePeng, S., Zheng, H., Herrero-Fresno, A., Olsen, J.E., Dalsgaard, A., Ding, Z., 2021. Co-occurrence of antimicrobial and metal resistance genes in pig feces and agricultural fields fertilized with slurry. Sci Total Environ 792, 148259. doi:10.1016/j.scitotenv.2021.148259.\u003c/li\u003e\n\u003cli\u003eRadwan, S., Gilfillan, D., Eklund, B., Radwan, H.M., El Menofy, N.G., Lee, J., Kapuscinski, M., Abdo, Z., 2020. A comparative study of the gut microbiome in Egyptian patients with Type I and Type II diabetes. PLoS One 15, e0238764. doi:10.1371/journal.pone.0238764.\u003c/li\u003e\n\u003cli\u003eRuang-Areerate, T., Piyaraj, P., Suwannahitatorn, P., Ruang-Areerate, P., Thita, T., Naaglor, T., Witee, U., Sakboonyarat, B., Leelayoova, S., Mungthin, M., 2021. Zoonotic Transmission of Blastocystis Subtype 1 among People in Eastern Communities of Thailand: Organic Fertilizer from Pig Feces as a Potential Source. Microbiol Spectr 9, e0036221. doi:10.1128/Spectrum.00362-21.\u003c/li\u003e\n\u003cli\u003eSalem, M., Pajunen, M.I., Jun, J.W., Skurnik, M., 2021. T4-like Bacteriophages Isolated from Pig Stools Infect Yersinia pseudotuberculosis and Yersinia pestis Using LPS and OmpF as Receptors. Viruses 13 doi:10.3390/v13020296.\u003c/li\u003e\n\u003cli\u003eShang, P., Dong, S., Han, Y., Bo, S., Ye, Y., Duan, M., Chamba, Y., 2022. Environmental exposure to swine farms reshapes human gut microbiota. Chemosphere 307, 135558. doi:10.1016/j.chemosphere.2022.135558.\u003c/li\u003e\n\u003cli\u003eSong, C., Li, W., Cai, F., Liu, G., Chen, C., 2021a. Anaerobic and Microaerobic Pretreatment for Improving Methane Production From Paper Waste in Anaerobic Digestion. Front Microbiol 12, 688290. doi:10.3389/fmicb.2021.688290.\u003c/li\u003e\n\u003cli\u003eSong, L., Wang, C., Jiang, G., Ma, J., Li, Y., Chen, H., Guo, J., 2021b. Bioaerosol is an important transmission route of antibiotic resistance genes in pig farms. Environ Int 154, 106559. doi:10.1016/j.envint.2021.106559.\u003c/li\u003e\n\u003cli\u003eSu, C., Zhang, K.Z., Cao, X.Z., Yang, J.G., 2020. Effects of Saccharomycopsis fibuligera and Saccharomyces cerevisiae inoculation on small fermentation starters in Sichuan-style Xiaoqu liquor. Food Res Int 137, 109425. doi:10.1016/j.foodres.2020.109425.\u003c/li\u003e\n\u003cli\u003eSun, J., Liao, X.P., D\u0026apos;Souza, A.W., Boolchandani, M., Li, S.H., Cheng, K., Luis Martinez, J., Li, L., Feng, Y.J., Fang, L.X., Huang, T., Xia, J., Yu, Y., Zhou, Y.F., Sun, Y.X., Deng, X.B., Zeng, Z.L., Jiang, H.X., Fang, B.H., Tang, Y.Z., Lian, X.L., Zhang, R.M., Fang, Z.W., Yan, Q.L., Dantas, G., Liu, Y.H., 2020. Environmental remodeling of human gut microbiota and antibiotic resistome in livestock farms. Nat Commun 11, 1427. doi:10.1038/s41467-020-15222-y.\u003c/li\u003e\n\u003cli\u003eUsman, M., Zhao, S., Jeon, B.H., Salama, E.S., Li, X., 2022. Microbial beta-oxidation of synthetic long-chain fatty acids to improve lipid biomethanation. Water Res 213, 118164. doi:10.1016/j.watres.2022.118164.\u003c/li\u003e\n\u003cli\u003eVan Gompel, L., Luiken, R.E.C., Hansen, R.B., Munk, P., Bouwknegt, M., Heres, L., Greve, G.D., Scherpenisse, P., Jongerius-Gortemaker, B.G.M., Tersteeg-Zijderveld, M.H.G., Garcia-Cobos, S., Dohmen, W., Dorado-Garcia, A., Wagenaar, J.A., Urlings, B.A.P., Aarestrup, F.M., Mevius, D.J., Heederik, D.J.J., Schmitt, H., Bossers, A., Smit, L.A.M., 2020. Description and determinants of the faecal resistome and microbiome of farmers and slaughterhouse workers: A metagenome-wide cross-sectional study. Environ Int 143, 105939. doi:10.1016/j.envint.2020.105939.\u003c/li\u003e\n\u003cli\u003eVestergaard, D.V., Holst, G.J., Basinas, I., Elholm, G., Schlunssen, V., Linneberg, A., Santl-Temkiv, T., Finster, K., Sigsgaard, T., Marshall, I.P.G., 2018. Pig Farmers\u0026apos; Homes Harbor More Diverse Airborne Bacterial Communities Than Pig Stables or Suburban Homes. Front Microbiol 9, 870. doi:10.3389/fmicb.2018.00870.\u003c/li\u003e\n\u003cli\u003eWang, J., Gu, J., Wang, X., Song, Z., Dai, X., Guo, H., Yu, J., Zhao, W., Lei, L., 2021a. Enhanced removal of antibiotic resistance genes and mobile genetic elements during swine manure composting inoculated with mature compost. J Hazard Mater 411, 125135. doi:10.1016/j.jhazmat.2021.125135.\u003c/li\u003e\n\u003cli\u003eWang, Q., Gu, J., Wang, X., Ma, J., Hu, T., Peng, H., Bao, J., Zhang, R., 2020. Effects of nano-zerovalent iron on antibiotic resistance genes and mobile genetic elements during swine manure composting. Environ Pollut 258, 113654. doi:10.1016/j.envpol.2019.113654.\u003c/li\u003e\n\u003cli\u003eWang, R., Liu, N., Yang, Y., Lei, Y., Lyu, J., Dai, Z., Kim, I.H., Li, J., Wu, Z., Li, D., 2021b. Flavor supplementation during late gestation and lactation periods increases the reproductive performance and alters fecal microbiota of the sows. Anim Nutr 7, 679-687. doi:10.1016/j.aninu.2021.01.007.\u003c/li\u003e\n\u003cli\u003eWang, S., Chen, H., Wen, X., Mu, J., Sun, M., Song, X., Liu, B., Chen, J., Fan, X., 2021c. The Efficacy of Fecal Microbiota Transplantation in Experimental Autoimmune Encephalomyelitis: Transcriptome and Gut Microbiota Profiling. J Immunol Res 2021, 4400428. doi:10.1155/2021/4400428.\u003c/li\u003e\n\u003cli\u003eWen, X., Wang, H.G., Zhang, M.N., Zhang, M.H., Wang, H., Yang, X.Z., 2021. Fecal microbiota transplantation ameliorates experimental colitis via gut microbiota and T-cell modulation. World J Gastroenterol 27, 2834-2849. doi:10.3748/wjg.v27.i21.2834.\u003c/li\u003e\n\u003cli\u003eWhite, J.K., Nielsen, J.L., Madsen, A.M., 2019. Microbial species and biodiversity in settling dust within and between pig farms. Environ Res 171, 558-567. doi:10.1016/j.envres.2019.01.008.\u003c/li\u003e\n\u003cli\u003eYang, W.Y., Lee, Y., Lu, H., Chou, C.H., Wang, C., 2019. Analysis of gut microbiota and the effect of lauric acid against necrotic enteritis in Clostridium perfringens and Eimeria side-by-side challenge model. PLoS One 14, e0205784. doi:10.1371/journal.pone.0205784.\u003c/li\u003e\n\u003cli\u003eYi, Z., Liu, X., Liang, L., Wang, G., Xiong, Z., Zhang, H., Song, X., Ai, L., Xia, Y., 2021. Antrodin A from Antrodia camphorata modulates the gut microbiome and liver metabolome in mice exposed to acute alcohol intake. Food Funct 12, 2925-2937. doi:10.1039/d0fo03345f.\u003c/li\u003e\n\u003cli\u003eZhou, Q., Zhou, T., Feng, F., Huang, S., Sun, Y., 2021. The response of copper resistance genes, antibiotic resistance genes, and intl1/2 to copper addition during anaerobic digestion in laboratory. Ecotoxicol Environ Saf 210, 111822. doi:10.1016/j.ecoenv.2020.111822.\u003c/li\u003e\n\u003cli\u003eZhu, J., 2000. A review of microbiology in swine manure odor control. Agriculture, Ecosystems \u0026amp; Environment 78, 93-106. doi:10.1016/s0167-8809(99)00116-4.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"amb-express","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ambe","sideBox":"Learn more about [AMB Express](http://amb-express.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/AMBE/default.aspx","title":"AMB Express","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"pig farming, 16S rRNA, microbial communities, occupational exposure, airborne microbes ","lastPublishedDoi":"10.21203/rs.3.rs-3020464/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3020464/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePig farming has an effect on farmers and the farm environment. Pig gut microbes play an important role in this effect. However, which microbial composition is more likely to be affected remains unknown. Primarily, we collected 136 samples in pig farm A, including 70 pig fecal, 18 farmers, 4 individuals without contact with any type of farm animal (\u0026ldquo;non-exposed\u0026rdquo; persons) fecal, and 44 environmental dust samples (dust from inside and outside pig houses and the farm). Another 43 samples were collected from pig farm B, including 10 pig fecal, 24 environment samples, and 9 humans fecal. Whereafter, 16S rRNA sequencing and taxonomic composition analysis were performed. Result showed that pig farmers significantly upregulated 13 genera compared with non-exposed persons, and 76 genera were significantly upregulated inside the pig house than outside the pig house. Comparing non-exposed persons who were farther away from the pig farm, the results showed that the relative abundance of three microbes, including \u003cem\u003eTuricibacter\u003c/em\u003e, \u003cem\u003eTerrisporobacter\u003c/em\u003e, and \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e, increased between the farmers and environment inside and outside the pig farm, and significant differences were observed (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, the abundance increased with the exposure time of farmer animals and spatial location to pigs. The greater the distance from the farm, the less effective the three microbes were. Although the distance is about 550 km, the analysis results of pig farm A and pig farm B confirm each other. This study shows that the three microbes where pig farmers co-occurring with the environment come from pig farms, which provides new ideas for blocking the transmission of microbial aerosols in pig farms and reducing pollution.\u003c/p\u003e","manuscriptTitle":"Effects of microbes in pig farms on occupational exposed persons and the environment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-07-06 18:52:37","doi":"10.21203/rs.3.rs-3020464/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2023-08-10T04:19:34+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-07-04T20:19:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-06-21T05:27:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"AMB Express","date":"2023-06-19T23:05:40+00:00","index":"","fulltext":""},{"type":"decision","content":"Major Revision","date":"2023-06-14T09:02:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"amb-express","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ambe","sideBox":"Learn more about [AMB Express](http://amb-express.springeropen.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/AMBE/default.aspx","title":"AMB Express","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0fd7245d-3f76-442d-a1c4-d317a9b8d886","owner":[],"postedDate":"July 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-12-04T15:09:18+00:00","versionOfRecord":{"articleIdentity":"rs-3020464","link":"https://doi.org/10.1186/s13568-023-01631-x","journal":{"identity":"amb-express","isVorOnly":false,"title":"AMB Express"},"publishedOn":"2023-11-30 15:01:22","publishedOnDateReadable":"November 30th, 2023"},"versionCreatedAt":"2023-07-06 18:52:37","video":"","vorDoi":"10.1186/s13568-023-01631-x","vorDoiUrl":"https://doi.org/10.1186/s13568-023-01631-x","workflowStages":[]},"version":"v1","identity":"rs-3020464","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3020464","identity":"rs-3020464","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}