Fecal microbiota transplantation by spraying manner shape the gut microbiota and bacterial antibiotic resistance of chick

doi:10.21203/rs.3.rs-8233332/v1

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Fecal microbiota transplantation by spraying manner shape the gut microbiota and bacterial antibiotic resistance of chick

https://doi.org/10.21203/rs.3.rs-8233332/v1

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Background

The inappropriate use of antibiotics in poultry husbandry accelerates the development of antimicrobial resistance (AMR), which not only attenuates the efficacy of veterinary antibiotics but also threatens the public health due to the spread of resistant organisms. Exploring non-antibiotic approaches, which enhance animal performance and reduce AMR is urgently needed and critical for food safety and public health. It may be possible to reduce the use of antibiotics in poultry husbandry using chicken fecal microbiota transplantation.

Results

FMT spraying manner accelerate the development of gut microbiota of recipient chickens. One day after FMT, the gut microbiota richness of FMT recipients was higher than that of the control group. With the increase of recipient chicken day-age, the chicken gut microbiota got more matured, and the microbiota profile similarity between donor and recipient got stronger. The sensitivity of Escherichia coli and Enterococcus faecium to antibiotics could be transmitted to the recipient chicken via FMT. FMT can shape the ARGs profile in recipient chickens.

Conclusions

FMT by spraying manner achieve the colonization of donor microbiota by recipient chicken; FMT could be used to solve the problem of antimicrobial resistance.

Animal Science

General Microbiology

Applied & Industrial Microbiology

Chicken

FMT

Gut microbiota

Antimicrobial resistance

The inappropriate use of antibiotics in poultry husbandry accelerates the development of antimicrobial resistance (AMR) [1, 2], which not only attenuates the efficacy of veterinary antibiotics but also threatens the public health due to the spread of resistant organisms [3–7]. Moreover, as the antibiotics used to target pathogens are often broad-spectrum, the use of these antibiotics in animals might also disrupt the normal microbiota homeostasis, leading to an increased risk of post-treatment pathogenic infections and other chronic diseases [8–14]. To address AMR issue, many countries have begun to reduce veterinary antibiotics consumption and introduced policies to limit the use of antibiotics as growth promoting additive [15–19]. Therefore, exploring non-antibiotic approaches, which enhance animal performance and reduce AMR is urgently needed and critical for food safety and public health.

The gut microbiota plays an important role in digestion and absorption as well as colonization resistance in host animals [20–22], moreover, it is a reservoir of antibiotic resistance organism and genes [23–25]. Manipulating of gut microbiota can promote animal performance [26–30], and may also be used to address AMR issue. Manipulation of gut microbiota using fecal microbiota transplantation (FMT), which involves in the infusion of feces from healthy donors into the intestinal tract of the patient, has been effective in treating a wide array of disorders, especially antibiotic-associated recurrent Clostridium difficile infection (rCDI) [31–34]. Since FMT has been a great success in the treatment of rCDI, interests in the animal FMT study were increasing [35–38]. However, most of animal FMT studies are concentrated in mammals, and the results are not necessarily applicable to poultry, such as chickens. There were some studies on chicken FMT by oral gavage manner [39, 40], but these studies pay attention to the effect of FMT on chicken growth performance, not to addressing AMR issue. Clinical trial reported that FMT could reduce the relative abundance of ARGs carried by rCDI patients [39, 40]. We doubted whether FMT can be used to address the issue of animal source bacterial AMR. To test that, we performed chicken FMT by spraying manner and explored its impact on the gut microbiota and bacterial antibiotic resistance of recipient chicken.

In modern commercial farms, new-born chicks are raised separately from hens, which makes it difficult for newborn chicks to receive healthy maternal microbiota. The born period is also a critical period of development of gut microbiota, during which microbiota composition is unstable and colonization resistance of gut is limited [43, 44]. Accordingly, it is a good time point to perform an effective chicken FMT when chicks are just hatched.

Therefore, in this study, we selected two types of chicken as FMT donors and performed FMT with spraying manner to newborn chicks, then explored the colonization and development of gut microbiota in recipient chickens with 16S rRNA gene high-throughput sequencing technology. The bacterial antibiotic resistance phenotype and the antimicrobial resistance genes (ARGs) in chicken gut were also measured by AST (antimicrobial susceptibility test) and High-throughput qPCR (HT-qPCR) respectively. Our data here demonstrate FMT could shape the gut microbiota and bacterial antibiotic resistance of recipient chicken. Accordingly, it is feasible to transfer selected or designed microbiota from donor to recipient by FMT with spraying manner in chicken. Moreover, FMT could be used to address AMR issue since bacterial antibiotic resistance could be transmitted from donor to recipient.

Gut microbiota and bacterial antibiotic resistance of FMT donor chicken

Commercial chicken and SPF chicken were selected as FMT donor due to their different rearing environment and antibiotic usage. The gut micrbiota profile of donor chicken was analyzed with 16S rRNA gene high throughput sequencing technology. Within-sample diversity (α-diversity) revealed that the gut microbiota of commercial chicken (D_COM) had higher diversity than those of SPF chicken (D_SPF) (Fig. S1a). Principal coordinate analysis (PCoA) of Unweighted UniFrac distance showed that the gut microbiota of commercial chicken and SPF chicken formed two distinct clusters and separated from each other (Adonis test, R² = 0.76, P = 0.003; Fig. S1b), indicating the differences in the gut microbiota profile between SPF chicken and commercial chicken were significant. At the phylum level classification, both of the two groups consisted mainly Firmicutes, Bacteroidetes, Actinobacteria and Proteobacteria. However, Actinobacteria and Proteobacteria were present at higher relative abundance in commercial chicken compared with SPF chicken, whereas the relative abundance of Firmicutes was higher in SPF chicken (P < 0.05, Mann Whitney test; Fig. S1c). At genus level classification, Bacteroides was the dominant genus in both two chicken groups. [Ruminococcus]_torques_group, Olsenella and unclassified_o__Bacteroidales were present at higher relative abundance in commercial chicken compared with SPF chicken, whereas the relative abundance of Lactobacillus and unclassified_f__Lachnospiraceae were higher in SPF chicken. The other genera present, and their relative levels are indicated in Fig. S1d

To measure bacterial antibiotic resistance of donor chicken, firstly, AST was performed to determine the MIC of selected antibiotics to Escherichia coli and Enterococcus faecium isolated from donor chicken cecum. The AST results showed that the MIC of Ampicillin, Florfenicol, Neomycin, Ceftiofur, Meropenem and Doxycycline to Escherichia coli isolated from donor SPF chicken, was significantly lower than that of Commercial chicken (Mann Whitney test, P < 0.05; Fig. S2a, b). Similar to Escherichia. Coli AST results, the MIC of Florfenicol, Erythromycin, Tilmicosin, Ampicillin and Vancomycin to Enterococcus. faecium isolated from donor SPF chicken, was significantly lower than that of Commercial chicken, while Ciprofloxacin was higher in SPF chicken (Mann Whitney test, P < 0.05; Fig. S2c, d). To globally reveal bacterial antibiotic resistance of donor chicken. HT-qPCR was performed to investigate the abundance and diversity of ARGs in chicken. The results showed that the relative copy number of ARGs was different. ARGs conferring resistance to Tetracycline and Sulfonamide were present at higher relative abundance in commercial chicken compared with SPF chicken, whereas MLSB, Aminoglycoside, β-Lactamase and Chloramphenicol were higher in SPF chicken (Mann Whitney test, P < 0.05; Fig. S2e, f).

FMT increased gut microbiota richness of recipient chicken

As the two FMT donor groups showed a significant difference in gut microbiota (Fig. S1), we next investigated the effect of FMT on the gut microbiota of recipient chickens. After FMT, we detected the gut microbiota of recipient chickens on 1, 5, 10, 20 and 30 days post FMT. Comparing with the control group (R_CON), FMT increased the richness of gut microbiota of FMT recipient chickens, which could even be observed 1 day post FMT (1 dpt) (Fig. 1a). While at the time point of 20 and 30 dpt, the microbiota richness of the two FMT recipient groups were similar to that of two donor groups, the group receiving SPF chicken microbiota (R_SPF) had a lower microbiota richness than the group receiving SPF chicken microbiota (R_COM) (Fig. S1a; Fig. 1a). Although, the microbiota richness of three recipient groups were different, the degree of variation in OTU richness within each recipient group was decreasing with chicken age, indicating that the microbiota richness within a group became more stable with chicken age (Fig. 1b). The quantitative statistics of shared OTU between groups showed that there were more shared OUT between the two groups receiving FMT and their corresponding donors (320–613), while the control group shared less OTU with the two groups receiving FMT or donor groups (267–320 ) (Fig. 1c). In addition, OTU correlation analysis showed that there was a significant correlation between the two groups receiving FMT and the corresponding donors at 5 time points, and the correlation became higher with the increase of chicken day-age, while the correlation between the control group and the two donor group was weak (Fig. 1d), suggesting that the microbiota colonization rate of FMT groups was increasing continuously with chicken day-age.

FMT shaped the distinct gut microbiota of recipient chicken

Principal coordinate analysis (PCoA) of Unweighted UniFrac distance revealed that the gut microbiota of three recipient groups formed three distinct clusters in 1, 5, 10, 20, 30 days post FMT (Fig. 2a), suggesting that the gut microbiota similarity between individuals within a test group was significantly higher than among inter-groups. In addition, PCoA using total samples or samples in each recipient group showed that the chicken day-age affected the dynamics of gut microbiota (Fig. S3). However, PCoA results showed that samples of chicken in 1 and 5 days post FMT could be divided into two clusters, while 5 and 10 days could not (Fig. 2a, c), which suggested that chicken day-age could be as a factor to distinguish different microbiota in 1–5 days post FMT, but can not in 5–10 days post FMT. And the Fig. 2b, d showed that the effect of chicken day-age on the dynamics of gut microbiota is less than that of FMT, moreover, the effect of FMT is increasing with the chicken day-age (Fig. 2).

The analysis of similarities of weighted UniFrac distances further indicated that FMT made the gut microbiota more convergent in 1 day post FMT (Fig. 3a). Moreover, with the increase of chicken day-age, the similarity of chicken gut mcirobiota among individuals within a group became higher and the microbiota structure tended to be stable at 10 days post FMT (Fig. 3b). FMT caused shifts from recipient to their corresponding donor in chicken gut microbiota (Fig. 3c). The gut microbiota similarity between FMT recipient chicken and the corresponding donor reached the maximum at 30 days post FMT (Fig. 3d). These results suggested that initial exposure to microbiota had a determining role in shaping the gut microbiota profiles at a later stage of life.

Up to now, the Human Microbiome Project has achieved a lot of results, which promoted the study of microbiome and host health from correlation to causality [45–47]. However, the animal microbiome project is relatively scarce, and poultry microbiome large-scale research is minimal. The microbiome of animals is a rich resource that can be excavated and utilized, in which we can find microbiota or microbes related to promoting growth and pathogen resistance of animal hosts [48]. FMT could transplants phenotype-associated microbiota to recipients to transfer donor phenotypes [43]. At present, animal FMT has been preliminarily studied, but most of studies focus on mammal FMT and poultry FMT is very few. There is a lack of research to solve the problem of bacterial resistance using FMT. Natural antibiotics are chemicals produced by microbes that kill competitors, and the development of bacterial resistance is a natural process [4, 49–51], which should be returned to ecology to find ways and solved by natural methods. The application of FMT to solve the problem of bacterial resistance is from the point of view of microecology.
Pathogen infection is a intractable problem in poultry farming. Antibiotics will be used to remove pathogenic microbes in modern farm. In this study, we found that the antimicrobial sensitivity of Escherichia coli and Enterococcus faecium in SPF chickens was higher than that in commercial chickens, but its gut microbiota richness was lower than the latter, which may be due to the fact that SPF chickens could not be exposed to more microbes in a closed environment [52, 53]. Although the gut microbiota richness of SPF chicken is low, it will not have much infaust effect on the physiology of chicken since the function of gut microbiota of SPF chicken can reach the threshold [54].
In modern poultry farms, the improper use of antibiotics destroys the microbiome of poultry, and the residual microbiota carried by poultry or microbes in the environment become disordered, which makes it impossible to get healthy microbiota in the intestinal tract of poultry and reduces the colonization resistance of poultry intestines to pathogens, which may be one of the reasons why large-scale poultry are more likely to infected by pathogens. After FMT to newborn chickens, we found that FMT could accelerate the development of gut microbiota of recipient chickens. One day after FMT, the gut microbiota richness of FMT recipients was higher than that of the control group, which was of great significance to the colonization resistance of early chicken gut to pathogens [20]. With the increase of recipient chicken day-age, the gut microbiota got more matured, and the microbiota similarity between donor and recipient got stronger, which indicated that FMT by spraying manner could achieve the colonization of donor microbiota by recipient chicken. Although it is not clear how the microbes migrate into the intestinal tract of the recipient chicken by spraying manner FMT, it is preferred to perform a chicken FMT by spraying manner, because the activity of a large amount of bacteria will be lost under the corrosion of chicken gastric acid by oral gavage manner [55], and in addition, the time and labor can be saved with spraying manner.
Due to the long-term use of antibiotics in poultry farm, the last batch of poultry residual antimicrobial resistant bacteria are left behind in the breeding environment. When the new batch of poultry is checked in, it is still exposed to the environment containing a large number of antimicrobial resistant bacteria, so that the resistant bacteria colonize in poultry, which leads to the use of antibiotics must be increased in the treatment of poultry infection, which leads to a vicious circle. Two kinds of chickens carrying different levels of bacterial resistance were selected as donors for FMT. It was found that the sensitivity of Escherichia coli and Enterococcus faecium to antibiotics could be transmitted to the recipient chicken via FMT. Similarly, the results of antimicrobial resistance genes showed that FMT can shape the ARGs profile in recipient chickens, indicating that FMT can regulate the resistance of bacteria in chickens, and may be used to solve the problem of antimicrobial resistance.
In this study,the chicken FMT is not a comprehensive and systematic research, and more studies require follow-up. It is important to select a optimal chicken day-age to perform FMT to reduce the possible stress response of chicks. This study showed that the gut microbiota of the chicken becomes stable at about 10 day-age of chicken, at which performing FMT may be a little late. In this study, the inocula dose used in FMT is also the first attempt, and it is unknown whether doses would change the effect of FMT. Perhaps when the chicken FMT is more mature, we can transplants microbiota with high performance, antibiotics-sensitive, strong resistance to pathogens to recipient chicken to improve the production performance of poultry and solve the problem of bacterial resistance.

FMT by spraying manner accelerate the development of recipient chicken gut microbiota and make donor chicken microbiota colonize in recipient chicken. FMT by spraying manner transfer the antimicrobial resistance levels of Escherichia coli and Enterococcus faecium to the recipient chicken, and regulate the ARGs profile in the gut of the recipient chicken.These findings support FMT by spraying manner could be as an effective way to regulate the gut microbiota and bacterial resistance of chicken and may be used to solve the problem of bacterial resistance in poultry husbandry.

Animal and study design

The SPF (specific pathogen free) chicken and commercial chicken were selected as FMT donor. SPF chicken was 200-day-old White leghorn and it was reared without antibiotics in an environment free of specific pathogens from a poultry farm in Jinan, Shandong Province, China. Commercial chicken was 300-day-old Hy-line brown and it was reared in an open environment from a poultry farm in Dezhou, Shandong Province, China, moreover, several antibiotics were administered to the commercial chicken from birth to 90 days old, including terramycin, streptomycin, erythromycin, ampicillin and kanamycin. The recipient chickens are newly hatched, and the breed is White leghorn. 120 chicks were randomly divided into 3 groups with 40 chicks in each group. Within two hours of hatching, two FMT groups were sprayed with 100 µL donor microbiota solution, while the control group was sprayed with the same dose of PBS buffer. After FMT, the chicks were fed and drinking water freely in SPF farm, and raised until 30 days after FMT. The composition of feed for recipient chickens was formulated based on the nutritional requirements as set out by the national standards.

FMT microbiota solution preparation

The fresh cecum contents were mixed with PBS buffer in the proportion of 1:10 (g/mL), stirred to uniform, then centrifuged at 1800 r/min for 10 min to precipitate the large particles in the mixture, then the suspension was filtered by sterilized two-layer filter cloth (80 mesh filter cloth, 150 mesh filter cloth), finally, the FMT microbiota solution was obtained and stored at 4 ℃ for a short period of time.

Sample collection

The cecum contents of 6 SPF chickens and commercial chickens were used for analysis of microbiome, isolation of Escherichia coli and Enterococcus faecium and analysis of ARGs. The feces at 1, 5 days post FMT (cecum content was too little to collect, so feces were collected) and cecum contents at 10, 20, 30 days post FMT of recipient chickens were used for microbiome analysis. The number of samples per day in each group was 4. During the experimental cycle, the feces of recipient chickens were collected for the isolation of Escherichia coli and Enterococcus faecium and the detail information for this two strains show in the Table S1. The samples used for ARGs analysis were the same as those used for microbiome analysis at 10, 20 and 30 days post FMT.

DNA extraction and PCR amplification

Microbial DNA was extracted from chicken fecal or cecal samples using the E.Z.N.A.® soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) according to manufacturer’s instructions. The final DNA concentration and purity values were determined using a NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, USA), and DNA quality was checked by 1% agarose gel electrophoresis. The V3-V4 hypervariable regions of the bacterial 16S rRNA gene were amplified using primers 338F (5’- ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’) as previously described [56], using a thermocycler PCR system. The PCR reactions were conducted using the following reaction conditions: 3 min of denaturation at 95°C, 27 cycles of 95°C for 30 s, annealing at 55°C for 30 s, elongation at 72°C for 45s, and a final extension step at 72°C for 10 min. PCR reactions were performed in triplicate in a 20-µL final reaction mixture. Each reaction mixture contained 4 µL of 5 × FastPfu Buffer, 2 µL of 2.5 mM dNTPs, 0.8 µL of each primer (5 µM), 0.4 µL of FastPfu Polymerase and 10 ng of template DNA. The resultant PCR products were extracted from a 2% agarose gel and further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using QuantiFluor™-ST (Promega, USA) according to the manufacturer’s instructions.

Illumina MiSeq sequencing

Purified amplicons were pooled at equimolar ratios and paired-end sequenced (2 × 300) on an Illumina MiSeq platform (Illumina, San Diego, USA) according to the standard protocols issued by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China).

Processing of sequencing data

Raw FASTQ files were demultiplexed, quality-filtered by Trimmomatic [57] and merged by FLASH [58] using the following criteria: (i) The reads were truncated at any site receiving an average quality score < 20 over a 50-bp sliding window. (ii) Primers were exactly matched allowing for 2 nucleotide mismatches, and reads containing ambiguous bases were removed. (iii) Sequences with overlaps that were longer than 10 bp were merged according to their overlapping sequence [59]. Operational taxonomic units (OTUs) were clustered with 97% similarity cutoff using UPARSE (version 7.1 http://drive5.com/uparse/) [60] and chimeric sequences were identified and removed using UCHIME [61]. The taxonomy of each 16S rRNA gene sequence was analyzed by RDP Classifier algorithm (http://rdp.cme.msu.edu/) against the Silva (SSU123) 16S rRNA database using a confidence threshold of 70%.

Escherichia coli, Enterococcus faecium isolation and antimicrobial susceptibility testing

Fecal or cecal sample was seeded on MacConkey agar plate (Merck Millipore, Germany) and CHROMagar™Orientation agar plate (chromagar,France) for Escherichia coli and Enterococcus faecium respectively, then the plates were incubated at 37 ºC for 18–24 h. Colonies with a morphology and size similar to that of Escherichia coli and Enterococcus faecium were selected and identified using VITEK® MS (BioMerieux, France). 26 strains of Escherichia coli and 24 strains of Enterococcus faecium were identified from donor commercial chickens, while 30 strains of Escherichia coli and 26 strains of Enterococcus faecium were were identified from donor SPF chickens. In the recipient chicken group, 54 strains of Escherichia coli and 35 strains of Enterococcus faecium, 54 strains of Escherichia coli and 30 strains of Enterococcus faecium, 54 strains of Escherichia coli and 19 strains of Enterococcus faecium were identified from chicken exposure to commercial chicken gut microbiota group, chicken exposure to commercial chicken gut microbiota group and the control group. The strains information could see in the Table S1.The antibiotics used in Escherichia coli antimicrobial susceptibility testing were Florfenicol, Gentamycin, Ciprofloxacin, Ampicillin, Ceftiofur, Colistin, Neomycin, Meropenem and Doxycycline. The antibiotics used in Enterococcus faecium antimicrobial susceptibility testing were Doxycycline, Ceftiofur, Ciprofloxacin, Tilmicosin, Erythromycin, Florfenicol, Ampicillin and Vancomycin. The minimal inhibitory concentration (MIC) of antibiotic to Escherichia coli was determined using the agar dilution method and the MIC of antibiotic to Enterococcus faecium was determined using the Micro-broth dilution method according to CLSL protocols [62].

HT-qPCR

DNA was diluted to 20 ng µl using sterile water and stored at -20°C until further analysis. A total of 286 primer sets (Supplementary Data 2) were used to interrogate the sediment DNA. These primer sets targeted resistance genes for all major classes of antibiotics (285 primer sets) and the 16S rRNA gene. HT-qPCR was performed using the Wafergen SmartChip Real-time PCR system. For each primer set, a non-template negative control was included. The thermal cycle consisted of 10 min at 95°C, followed by 40 cycles of denaturation at 95°C for 30 s and annealing at 60°C for 30 s. Melting curve analyses were automatically generated by Wafergen software. All quantitative PCRs were performed in technical triplicates. Wells with efficiencies beyond the range 1.7–2.3 or an r² under 0.99 were discarded. Only data for samples with three technical replicates and at least three repeated sampling replicates that generated amplification products were regarded as positive and used in further data analysis. Relative copy number was calculated following methods described previously [63]: relative gene copy number = 10^{(31 – CT)(10/3)}, where CT refers to quantitative PCR results and 31 refers to the detection limit.

Data Analysis

Bar charts, Scatter diagrams and Heatmaps were generated by GraphPad Prism 7. Spearman correlations coefficients and Mann Whitney test were determined by GraphPad Prism 7 [64]. PCoA (Unweighted UniFrac and Bray–Curtis distance based) were conducted using R 3.2.3 with the vegan package.

Additional files

Additional file1 : Fig. S1 Gut microbiota of donor chicken.
Additional file2 :Fig. S2 Antimicrobial resistance of donor chicken gut microbes.

Additional file3 : Fig. S3 PCoA of Unweighted UniFrac distance using total samples or samples in each recipient group

Additional file4 : Fig. S4 The antimicrobial resistance levels of Enterococcus faecium from the three recipient chicken.
Additional file5 : Table S1 The detail information for Escherichia coli and Enterococcus faecium isolated from recipient chicken feces.
Additional file6 : Table S2 286 sets of ARGs primers used in this study

Acknowledge

Not applicable

Acknowledge

Not applicable

Funding

This project was supported in part by the National Natural Science Foundation of China (grant no. 31672609, 31501928, 31660713), the Natural Science Foundation of Shandong Province (grant no. ZR2013CQ037, ZR2014CP021, ZR2015CM007), Agricultural Scientific and Technological Innovation Project of Shandong Academy of Agricultural Sciences (CXGC2016C07, CXGC2016B14, CXGC2017A01) and Special Fund for Local Scientific and Technological Development Guided by the Central Government (research on monitoring and control technology of co-pathogenic bacteria resistance in poultry and its herdsmen).

Available of data and material

The 16S rRNA gene sequences for this study has been deposited in the National Center for Biotechnology Information (NCBI) under the BioProject PRJNAXXX

Authors’ contributions

Xx designed the experiments . XX, XX performed the experiments . JZ, QZ, YZ, YL, MH, XW and MD analyzed the data. JZ and JQ wrote the paper. All authors analyzed the data and approved the final version of the manuscript.

Ethics approval

The animal experiments were approved by the ethical committee of the Institutional Animal Experiment Commission (Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences), Jinan, China.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests

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Supplementary tables S1 and S2 are not available with this version.

The authors declare no competing interests.

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Fig. S1 Gut microbiota of donor chicken. (a) Observed OTUs of the donor commercial chicken (D_COM) and SPF chicken (D_SPF) gut microbiota. (b) PCoA (principal coordinates analysis) with Unweighted UniFrac distance showing that the gut microbiota of SPF chicken separate from those of commercial chicken. Relative abundance in the gut microbiota of donor chicken at phylum (c) and genus (d) level. Data bars represent means. Error bars represent s.d. Significance test were performed with Mann Whitney test. * P < 0.05, ** P < 0.01, ns means no significant difference between two groups.
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Fig. S2 Antimicrobial resistance of donor chicken gut microbes. MIC distribution of antibiotics to Escherichia Coli (a) and Enterococcus Faecium (c). The MIC of antibiotics to Escherichia Coli (b) and Enterococcus Faecium (d). (e) Heatmap of ARGs detected in the gut of donor chicken. Resistance genes were classified based on the antibiotics to which they conferred resistance: Tetracycline, Aminoglycosides, macrolide-lincosamide-streptogramin B (MLSB), β-lactams, Sulfonamides, Chloramphenicol, Vancomycin, Multidrug or others. (f) Abundance of ARGs per 16S rRNA gene copy.
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Fig. S3 PCoA of Unweighted UniFrac distance using total samples or samples in each recipient group
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Fig. S4 The antimicrobial resistance levels of Enterococcus faecium from the three recipient chicken. The heatmap (a) and histogram (b) showing the MIC distribution. (c) The MIC of antibiotics to Enterococcus faecium. * P < 0.05, ** P < 0.01, *** P < 0.001.

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Fecal microbiota transplantation by spraying manner shape the gut microbiota and bacterial antibiotic resistance of chick

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Abstract

Background

Results

Conclusions

Figures

Background

Results

Gut microbiota and bacterial antibiotic resistance of FMT donor chicken

FMT increased gut microbiota richness of recipient chicken

FMT shaped the distinct gut microbiota of recipient chicken

Discussion

Conclusions

Methods

Animal and study design

FMT microbiota solution preparation

Sample collection

DNA extraction and PCR amplification

Illumina MiSeq sequencing

Processing of sequencing data

HT-qPCR

Data Analysis

Additional files

Acknowledge

Not applicable

Declarations

References

Supplementary Tables

Additional Declarations

Supplementary Files

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