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Article

Effects of Peroral Microbiota Transplantation on the Establishment of Intestinal Microorganisms in a Newly-Hatched Chick Model

by
Kai Qiu
1,
Huiyi Cai
1,
Xin Wang
2 and
Guohua Liu
1,*
1
Key Laboratory for Feed Biotechnology of the Ministry of Agriculture, Institute of Feed Research, Chinese Academy of Agricultural Sciences, Beijing 100081, China
2
Institute of Plant Protection and Microbiology, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, China
*
Author to whom correspondence should be addressed.
Agriculture 2023, 13(5), 1001; https://doi.org/10.3390/agriculture13051001
Submission received: 21 March 2023 / Revised: 23 April 2023 / Accepted: 27 April 2023 / Published: 30 April 2023
(This article belongs to the Section Farm Animal Production)
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Abstract

:
This study was carried out to compare gut microbial community composition, diversity, and function with and without microbiota transplantation in a newly-hatched chick model. Two hundred and forty newly-hatched Arbor Acre male broilers were assigned randomly to either a microbiota transplantation group or a control group (n = 120; 6 replicates per group, and 20 broilers per replicate). Both groups were fed a basal diet that met all nutritional requirements, composed of corn, soybean meal, cottonseed meal, vitamins, and minerals. The microbiota transplantation group was inoculated with the microbiota from the ceca of healthy adult chicks on day 1 and 2, respectively, over a trial period of 42 d. For both groups, the numbers of total bacteria, Lactobacillus and Escherichia coli, operational taxonomic unit partitioning and classification, taxonomic composition, comparative microbiota, and key bacterial species were identified by a 16S rRNA sequencing analysis. The results showed that Aestuariispira, Christensenella, Fervidicella, Gracilibacter, Haloferula, Mycoplasma, Novispirillum, and Pantoea were more abundant (p < 0.05) in the microbiota transplantation group than those in the control group. This indicates that microbiota transplantation could directly influence the abundances of specific bacterial taxa in the ileum and cecum of broilers. These findings provide insight into the modulation of gut health for patients with abnormal bowel function, which should be of great interest to researchers in the area of gastroenterology, applied microbiology, and animal sciences.

1. Introduction

For many animals, the microbiome is no longer considered a passively coexisting community. The types and sources of microbes in early-life period have remarkable and lasting effects on the composition and/or function of the microbiota [1]. Microbiota transplantation, a method used to modify the composition of the intestinal microbiota against dysbiosis, has gained attention as a potential alternative to antibiotics [2,3]. Recent studies on microbiota transplants have focused on human gut microbiota transplantation in disease therapy [4], especially in the treatment of chronic gastrointestinal infections such as Clostridium difficile infection (CDI) and inflammatory bowel diseases (IBD) [5,6]. Previous studies have verified that gut microbiota is pivotal in infant immunological processes, and microbiota transplantations have become increasingly accepted as an option for the treatment and prevention of pathogenic bacterial infections [7,8,9]. Fecal microbiota transplantation is now commonly used in domestic animals to manipulate gut health [10,11,12,13], which provides a valuable model for human research.
Small differences in initial cecal community composition of broiler chickens induced by microbiota transplantation can be magnified and lead to significant differences in the diversity of mature communities [14]. Exogenous fecal microbial transplantation alters fearfulness, intestinal morphology, and gut microbiota in broilers [15]. Transplantation of cecal microbiota from vaccinated birds to new birds increases vaccine-induced antigen-specific immune globulin Y [16]. However, studies considering the effects of microbiota transplantation on metabolic activities of the microbiota in the ileum and cecum require further investigation. The role of microbiota transplantation on the composition of the gut microbial flora needs to be deeply understood in a newly-hatched chick model before efficient application in clinical treatment. In addition, the differences in the microbiological composition of the cecum and ileum after microbiota transplantation remain unknown.
In an attempt to address concerns related side effects and antimicrobial resistance, broiler chickens have been used as an animal model to analyze composition, abundance, dynamic distribution, and function of gut microbiota [17,18,19]. Therefore, the current study aims to determine whether peroral microbiota transplantation can modulate the structure of the broiler gut microbiota, potentially leading to the development of potential strategy for treatment of intestinal disorders in humans.

2. Materials and Methods

2.1. Animal Care

Animal experiments were approved by the Animal Ethics Committee of the Chinese Academy of Agricultural Sciences (Approval ID: AEC-CAAS-20211009). All experimental operations complied with the guidelines for animal experiments set by the National Institute of Animal Health.

2.2. Experimental Design and Sample Collection

A total of 240 newborn Arbor Acre (AA) male broilers were purchased from a local commercial hatchery, Huadu Broiler Breeding Corporation, (Beijing, China). The birds were raised in a thermostatically controlled house with a 23-h continuous lighting schedule and allowed to access to the water and diet ad libitum. The room temperature was 33 °C for the first three days after hatching and then reduced by 3 °C/week until 24 °C. The relative humidity was kept between 45% and 55%. The experimental diets were formulated mainly with corn, soybean meal, and cottonseed meal (Table 1), which meet or exceed the nutritional requirements of the NRC (1994). All broilers were randomly divided into two groups with 6 replicates and 20 birds per replicate. Ten broilers was raised in one cage (100 × 80 × 40 cm). The broilers in the treatment group received microbiota transplantation through inoculation with microbiota from the ceca of healthy chickens.
The cecal microbiota with no salmonella and the lowest antibiotic resistance was selected as inoculant by detecting antibiotic resistance of kanamycin sulfate, ampicillin sodium, and chloromycetin. Briefly, fresh cecal content was collected aseptically from five AA male broiler chickens at 42 days of age. Then, 10 g of the contents were weighed and transferred to a sterile beaker in the biological safety equipment, and PBS buffer was added to make it a 100 mL suspension. The suspension was stirred evenly. The even suspension was transferred to two 50 mL sterile tubes, and centrifuged at 2000× g for 10 min to settle the large particles of the suspension. The suspension was then filtered by a 3-layer filter cloth (gauze, 177 μm filter cloth, 105 μm filter cloth), respectively. The homogeneous brown inoculant fluid was obtained and stored in a cooler at 4 °C. The most abundant phylum of the inoculant was identified as Bacteroidetes, Actinobacteria, Prevotella, Firmicutes, and Lactobacillus. Fifty microliters of fresh inoculation fluid were instilled into the mouth of the experimental broilers. The same procedure was conducted for control group using culture medium without microbiota. No antibiotics were used throughout the trial. The microbiota transplantation group was inoculated on day 1 and day 2, respectively, and the trial lasted for 42 days. At the end of the trial, five broilers per replicate were selected and euthanized using pentobarbital sodium (100 mg/kg BW) intravenously. The ceca and ilea from these broilers were subsequently removed for sample collection.
The ileum and cecum were cut off axially and the residual digesta and loosely attached microorganisms were removed by gently washing 3 times in saline. Attached microorganisms on the vessel walls were eluted and collected by shaking vigorously for 30 s and two times in saline containing 0.1% Tween 80. The pellets were collected by centrifuging at 2000× g and 4 °C for 20 min and then frozen in liquid nitrogen for DNA extraction.
On day 4, 6, and 12, one chicken randomly selected from each replicate was euthanized after anesthetized by pentobarbital, and the cecum samples from these broilers were collected. Total bacterial, Lactobacillus and Escherichia coli were identified and calculated with counts of viable bacteria according to our previous description [20]. In short, digesta samples were diluted and plated on specific agar plates. After being cultured in corresponding conditions for a certain period of time, they was taken out and counted immediately. Microflora concentrations were expressed as log10 colony-forming units per gram of intestinal contents.

2.3. DNA Extraction and 16S rRNA Amplicon Sequencing

Total genomic DNA samples were extracted from the microbial samples using the OMEGA DNA isolation kit (D5625-01; Omega, Buffalo, NY, USA), following the manufacturer’s instructions, and stored at −20 °C. The quantity of extracted DNA was measured with a NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis, respectively. The five samples from each replicate were mixed in equal amounts to generate one DNA sample representing the replicate. The full-length bacterial 16S rRNA was amplified by a PCR with the forward primer 27F (5’-AGAGTTTGATCMTGGCTCAG-3’) and the reverse primer 1492R (5’-ACCTTGTTACGACTT-3’). Then, PCR amplicons were purified and quantified, and Single Molecule Real Time (SMRT) sequencing analysis was carried out. The files from the PacBio platform were employed to size-trim. All sequences shorter than 2000 bp were kept. A blank control was set up in the above procedure.

2.4. 16S rRNA Amplicon Pyro-Sequencing

All PCR amplicons were purified and quantified in turn by Agencourt AMPure Beads (Beckman Coulter, Indianapolis, IN, USA) and a PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). Then, the same amount of amplicons were mixed together and submitted to the PacBio Sequel platform at Shanghai Personal Biotechnology Co., Ltd (Shanghai, China) for single-molecule real-time (SMRT) sequencing. Raw sequences were firstly treated using the PacBio SMRT Link portal (version 5.0.1.9585) and then filtered with full pass ≥ 3 and predicted accuracy ≥ 99. The CCS with predicted accuracy < 99 is eliminated as noise, and all sequences longer than 2000 bp were removed.

2.5. Sequence Analysis

According to a previous report [21], the sequencing data were handled using QIIME (v1.8.0). In short, raw sequencing reads with exact barcode matches were located to their samples and determined as valid sequences. The left sequences were clustered into operational taxonomic units (OTUs) at 97% sequence identity based on chimera detection. One typical sequence was chosen from each OTU and identified using BLAST (NCBI, 16S ribosomal RNA Database). The abundance and taxonomic identification of the OTU in each sample was recorded in an OTU table. Only OTUs representing more than 99.999% of all sequences across whole samples were kept. In order to minimize difference in sequencing depth between samples, an averaged OTU table was created by averaging 100 evenly resampled OTU subsets to guarantee sequencing depth over 90% for subsequent analysis.

2.6. Bioinformatics and Statistical Analysis

Sequence data were primarily analyzed with QIIME and R packages (v3.2.0). OTU-level alpha diversity indices were calculated using the OTU table in QIIME. Beta diversity analyses were performed using UniFrac distance metrics [22]. Statistical significant differences in the pairwise UniFrac distances among groups were measured using Student’s t test and the Monte Carlo permutation test with 1000 permutations. Differences were displayed with box-whiskers plots. Principal component analysis (PCA) was carried out based on the genus-level compositional profiles [23]. Significant differences in community structure among groups were evaluated with per-mutational multivariate analyses of variance [24] and with analyses of similarities [24,25], using R package “vegan”. Taxonomic composition and abundance were visualized using MEGAN and GraPhlAn [26]. Venn diagrams were generated to visualize the shared and unique OTUs among samples and groups using the R package “Venn Diagram” based on the occurrence of OTUs across samples/groups regardless of relative abundance [27]. Taxa abundance at the levels of phylum, class, order, family, genus, and species was statistically compared among samples/groups using Meta-stats [28], and visualized as violin plots. Linear discriminant analysis effect size (LEfSe) with default parameters was performed to detect differentially abundant taxa among groups [29]. Partial least squares discriminant analysis (PLS-DA) was introduced as a supervised model to identify variations in the microbiota among groups using the “PLSDA” function in R package “mixOmics” [30]. Random forest analysis was applied to discriminate samples from different groups using the R package “Random Forest”, with 1000 trees and default parameters [31,32]. The generalization error estimation was conducted using 10-fold cross-validation. The expected “baseline” error was also included; this error was obtained by a classifier that simply predicts the most common category label. Co-occurrence analysis was performed by calculating Spearman’s rank correlations between pairs of predominant taxa. Correlations with p < 0.01 and |RHO| > 0.6 were visualized as co-occurrence network using Cytoscape [33]. Based on the Kyoto Encyclopedia of Genes and Genomes (KEGG), microbial functions were predicted with phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) using high-quality sequences [34].

3. Results

3.1. Numbers of Total Bacteria, Lactobacillus and Escherichia coli

As shown in Table 2, on day 4, 6 and 12, compared with control group, the number of Escherichia coli decreased (p < 0.05), while the number of Lactobacillus increased in the microbiota transplantation group (p < 0.05). No significant difference was observed for the number of total bacteria (p > 0.05).

3.2. Taxonomic Information of Inoculant

Microbiota of inoculant were analyzed by Krona v2.6 software (Figure S1A). The phylum of the inoculant was identified as Bacteroidetes, Firmicutes, Actinobacteria, and Proteobacteria. The most abundant class of the inoculant was Bacteroidia, Gammaproteobacteria, Bacilli, Erysipelotrichia, and Coriobacteriia. The most abundant order of the inoculant was Bacteroidales, Lactobacillales, Erysipelotrichales, Coriobacteriales, and Clostridiales. The most abundant family of the inoculant was Prevotellaceae, Enterobacteriaceae, Lactobacillaceae, Enterococcaceae, and Erysipelotrichaceae. The most abundant genus of the inoculant was Genus, Prevotella, Citrobacter, Lactobacillus, Flavonifractor, and Solobacterium. The most abundant species of the inoculant was Prevotella_paludivivens, Citrobacter_Frreudii, Lactobacillus_paracasei, Enterococcus_avium, and Solobacterium_moorei.

3.3. OTU Partition and Classification

Based on quality control, 139,629 high quality sequences were chosen. On average, 5706 and 5930 sequences per sample were obtained in the control group and microbiota transplantation group, respectively, with 19,057 OTUs identified across all samples (Figure S1B). Based on 97% species similarities, an average of 207 phyla, 206 classes, 201 orders, 177 families, 185 genera, and 210 species were identified in the control group, and an average of 68 phyla, 68 classes, 64 orders, 67 families, 68 genera, and 68 species were found in the microbiota transplantation group. A total of 417 common OTUs existed in the control group and the microbiota transplantation group (Figure S1C). Meanwhile, 610 unique OTUs in the control group and 608 unique OTUs in the microbiota transplantation group were identified, respectively.

3.4. Composition of Microbiota at Phylum, Class, Order, Family and Genus

The taxonomic composition and distribution of microbiota at the phylum level (Figure S2A) indicated that microbiota transplantation changed the microbiota composition of the digesta of the ileum and the cecum. The microbiota transplantation increased the diversity of Actinobacteria and Firmicutes, while decreasing the abundance of some pathogenic bacteria, opportunistic pathogens (such as some members of Proteobacteria), and other commensals. This alteration in microbiota diversity still existed at the class level (Figure S2B). Corynebacterium abundance increased after microbiota transplantation. Compared with the cecum, Bacilli abundance in the ileum was increased, while the abundance of Bacteroidia, Clostridia, Negativicutes, Betaproteobacteria, Synergistia, Coriobacteriia, Erysipelotrichia, Erysipelotrichia, Deltaproteobacteria, and Mollicutes were decreased. Microbiota transplantation also affected diversity on the order level as well. The relative amounts of Helicobacteraceae and Rikenellaceae increased as those of Acidaminococcaceae and Enterobacteriaceae decreased (Figure S2C). In addition, relative to the cecum, the contents of Lactobacillaceae, Paeudomonadaceae and Enterobacteriaceae increased in the ileum, but those of Bacteroidanceae, Rikenellaceae, Prevotellaceae, Ruminococcaceae, Acidaminococcaceae, and Lachnospiraceae were reduced.
As shown in Figure S2D, the taxonomic profile and allocation of the microbiota at the family level indicated that microbiota transplantation increased the amount of Clostridiales, Pseudomonadales, and Coriobacteriales and decreased Enterobacteriales. In comparison to the cecum, the abundances of Lactobacillales and Enterobacteriales increased in the ileum, whereas the abundances of Bacteroidales and Coriobacteriales decreased. After microbiota transplantation, the relative abundance of the genera Pseudomonas, Alistipes, Parabacteroides, and Helicobacter grew, but the abundances of Shigella and Faecalibacterium decreased (Figure S2E). Relative to the cecum, the contents of Bacteroides, Phascolarctobacterium, Alistipes, and Parabacteroides decreased in the ileum, whereas those of Lactobacillus, Shigella, Helicobacter, Candidatus, Arthromitus, and Pseudomonas increased.

3.5. Diversity Analysis of Microbiota

As shown in Table 3, the indices including Chao1, ACE and Shannon in the control and microbiota transplantation group were no different for microbiota in xecum and Ileum. The Simpson of microbiota in the microbiota transplantation group was significantly decreased relative to the control group (p < 0.05). The microbiota transplantation group showed lower Simpson and Shannon for ileal microbiota than the control group (p < 0.05).
The composition of the ileo-cecal microbiota of microbiota transplantation group differed relative to the control group (Figure 1A). Treatments were easily parted by PCA analysis. The microbiota diversity between cecum and ileum was also compared. In the PLS discriminant model, there was distinct separation between the control and microbiota-transplantation samples in terms of projected variable importance (Figure 1B).

3.6. Taxonomic Composition and Key Metabolic Pathways Comparison of Microbiota

The 20 most differentially abundant taxa were further studied (Figure 2). It was seen that Aestuariispira, Christensenella, Fervidicella, Gracilibacter, Haloferula, Mycoplasma, Novispirillum, and Pantoea were more abundant (p < 0.05) in the microbiota transplantation group than those in the control group, while Acidaminococcus, Alkalimonas, Anaerovorax, Brassicibacter, Caldlcoprobacter, Celluloslbacter, Cltrobacter, Cyanoblum, Desulfonispora, Desulfotomaculum, Desulfotomaculum, Fusicatenibacter, and Paraprevotella were less abundant in the transplantation group than in the control group (p < 0.05). The hierarchical GraPhlAn tree showed that Fimicutes, Bacilli, Bacteroidetes, Clostridia, Clostridiales, Lactobacillales, and Lactobacillus were the most affected by microbiota transplantation (Figure 3A). These might be useful targets for future studies of microbiota transplantation. It was indicated by heat-map results that microbiota transplantation substantially modified the gut microbiota composition, by increasing the relative abundances of Olsenella, Coprobacter, Sutterella, Parabacteroides, Sporobacter, Vampirovibrio, Lutispora, and Pelomonas, and decreasing those of Faeceliibacterium, Campylobacter, Acetivibrio, Paraprevotella, Odoribacter, Eisenbergiella, and Ruminiclostridium (Figure 3B). This suggested that there was a close correlation between the ileo-cecal microbiota composition and microbiota transplantation.
The relationship between KEGG metabolic pathways and microbiota transplantation was evaluated using metabolic alterations analysis (Figure 4). Several pathways differed to some extent, including metabolism, environmental information processing, genetic information processing, organismal systems, cellular processes, and disease information. The majority of metabolic pathways in the microbiota transplantation group were more prevalent when compared with the control group, particularly carbohydrate metabolism.

4. Discussion

The term “microbiome” could refer to the collective genomes of microorganisms or the collective microorganisms of a certain environment, typically in relation to the gut microbiome. The microbiome can also be referred to instead as microbiota, which refers directly to the organisms present. More than 10 trillion microbial cells exist in a broiler chicken’s body surface and in vivo, in which the largest and most diverse population are located in the gut [35]. Owing to the instability of microbiota compositions and the susceptibility to the surrounding environment, it is a crucial period for colonization of gut microbial flora occurs in infancy [36,37].
Qualitative comparisons were performed by building a Venn diagram between the otherwise identical broilers in the microbiota transplantation group and control group. The present study showed that the majority of the OTUs was same between two groups. It implied that the exclusive OTUs of each group had less abundant values, which agrees with a related literature report [38]. The bacterial abundance and diversity were lower in the microbiota transplantation group relative to the control group. It may be that microbiota transplantation decreased bacterial diversity and abundance because bacitracin was secreted to suppress pathogens and benefit the emerging gut microbes. In contrast, a recent study about piglets found similar OTU numbers in the microbiota transplantation group compared with the control group [36,39]. The discrepancy might be due to the differences of host species, antibiotic use, or environmental conditions.
In order to compare mean species richness among two or more sets of samples, alpha diversity analysis is typically carried out using analysis of variance [40]. Here, the species richness, the Shannon diversity index, and the Simpson diversity index were calculated to show the alpha diversity of microbiota. The Chao1 estimator indicted that our samples were sufficient in finding all of the common species in the gut. ACE values calculated by random additions of samples indicated that a reduction in the whole kind of strains was less likely in the control group. However, ACE was stable, suggesting that the diversity of 16S sequences in the samples is well detected. The lower values of ACE and Chao1 in the microbiota transplantation group indicated that microbial taxa were less abundant than control group. Two additional indices used for community analysis are the Simpson index and the Shannon index. Both the Shannon and the Simpson indices were decreased by the microbiota transplantation, which indicates that the microbiota group has less diversity. This is the first report of microbiota transplantation in broiler chickens. Combined with our PCA results, the reduced Chao1, ACE, Shannon, and Simpson’s indices may be because of decreases in the abundances of pathogenic or opportunistically pathogenic bacteria.
Our results clearly demonstrated, for the first time, that microbiota transplantation affects the ileo-cecal microbial communities in broilers. The abundance of Actinobacteria is higher in healthy individuals throughout the host’s lifetime, and these individuals show increased longevity [41]. Several beneficial traits have also been attributed to some Actinobacteria strains at local and global levels, such as suppressing pathogen or enhancing immune capacity. The findings of the current study were in accordance with a previous report that microbiota transplantation repopulates bacteria rapidly, recovering the superiority of Actinobacteria, Firmicutes, and Bactericides in the distal gut [36].
However, in the microbiota transplantation group, the content of phylum Bacteroidetes increased more in the cecum compared to the ileum. It is conflicting with the previous report that microbiota transplantation groups have more abundant Bacteroidetes than control counterparts [42]. One explanation for the inconsistency might be that Bacteroidetes species somehow adjust to the microbiota transplantation. An increasing proportion of Firmicutes and decreasing population of Bacteroides were witnessed in the microbiota of obese subjects [43]. Altered diversities were also observed in the ileo-cecal microbiota at the class, order, family, and genus levels. PCA is reliable for comparing the microbial community structures of different microorganisms [44]. Therefore, we can conclude that the microbiota from different samples in the present study were remixed after microbiota transplantation.
PPLS-DA is a relatively simple statistical technique to predict and classify microarray expression data [45]. The result of our PLS-DA revealed a distinct separation of microbiota transplantation samples from the control group. The KEGG pathway database containing metabolic and regulatory processes provides valuable data for researchers in the field of life science [46]. The metabolic alterations identified in the current study were submitted to predict the correlation between KEGG metabolic pathways and microbiota transplantation. Most metabolic pathways were more enriched in the microbiota transplantation group than the control group, especially with respect to carbohydrate metabolism. These data might help us understand the biological processes, higher-order biological system functions, and molecular mechanisms related to microbiota transplantation.
The microbiota transplantation technique is mainly used to cure CDI in humans; especially in infants, it has been widely used for CDI therapy [7]. However, in newly born broilers, it is often underestimated. It is critical for the microbiota colonization of poultry at birth [47]. Recent studies conducted on layers have demonstrated microbiota transplantation could safely decrease the intestinal bacterial resistance as well as resist invasion of high-resistant pathogenic bacteria in poultry breeding environments [38,48]. In this study, it was clearly demonstrated that microbiota transplantation modifies the microbiota composition of the broiler chickens. The abundant phyla in inoculated and transplanted broilers were Actinobacteria and Firmicutes, which suggests that phylum Actinobacteria and Firmicutes colonized successfully. However, further studies are necessary to illuminate the effects of microbiota transplantation on intestinal bacterial resistance.
In the current study, Aestuariispira, Christensenella, Fervidicella, Gracilibacter, Haloferula, Mycoplasma, Novispirillum, and Pantoea were more abundant in the microbiota transplantation group than those in the control group. Among them, Christensenella has shown to be a key factor for improving type 2 diabetes [49]. Gracilibacter has been significantly correlated with motor function of Parkinson’s disease mice [50]. Haloferula is widely accepted to be a beneficial bacterium in aquaculture [51]. Pantoea, a highly versatile and diverse genus within the Enterobacteriaceae, is beneficial to gut commensals [52]. Therefore, the results indicating that oral microbiota transplantation with mature bacteria solution could enhance the colonization and proliferation of intestinal probiotics could also be significant for the development of probiotic products.

5. Conclusions

The effect of microbiota transplantation on the microbial composition, abundance, dynamic distribution, and function in the gut of broilers was clearly investigated in the present study using 16S rRNA sequencing. Our results indicated that microbiota transplantation could directly modify the abundances of specific bacterial taxa in the ileum and cecum of broilers for the first time, providing insights also for modulating the gut health of animals.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture13051001/s1. Figure S1. Operational Taxonomic Unit (OTU) detected in the samples; Figure S2. Relative abundance at the phylum (A), class (B), order (C), family (D) and genus (E) levels.

Author Contributions

Conceptualization, project administration, and funding acquisition: H.C. and G.L.; methodology and supervision: K.Q. and G.L.; data curation and writing: K.Q., X.W. and G.L. All authors have read and agreed to the published version of the manuscript.

Funding

The study received the financial supports from National Key Research and Development Program of China (2021YFD1300203-05), Modern Agroindustry Technology Research System (CARS-41), and Agricultural Science and Technology Innovation Program (ASTIP) of the Chinese Academy of Agricultural Sciences.

Institutional Review Board Statement

The experiment was approved (ID: AECCAAS-20210102) by the Animal Care and Use Committee of the Institute of Feed Research of the Chinese Academy of Agricultural Sciences.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data in the current study are available unconditionally from the corresponding author or the public database. Available online: http://www.ncbi.nlm.nih.gov/bioproject/960812 (accessed on 17 April 2023).

Conflicts of Interest

The authors declare no conflict of interest.

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Agriculture 13 01001 g001 550
Figure 1. Analysis of the operational taxonomic units (OTUs). (A) Principal component analysis (PCA) of the OTUs. Beta-diversity is shown by PCA, based on UniFrac method. The plot displays two main clusters: PC1, principal coordinate 1; PC2, principal coordinate 2. (B) The partial least squares discriminant analysis of the OTUs. Each dot represents a sample. Different colors of dots belong to different groups, the closer the distance between two dots, the similar microbial composition between two samples is. Blue indicates cecum (M1–M6) and ileum (H1–H6) samples in the control group. Red indicates cecum (M13−M18) and ileum (H13–H18) samples with microbiota transplantation.
Figure 1. Analysis of the operational taxonomic units (OTUs). (A) Principal component analysis (PCA) of the OTUs. Beta-diversity is shown by PCA, based on UniFrac method. The plot displays two main clusters: PC1, principal coordinate 1; PC2, principal coordinate 2. (B) The partial least squares discriminant analysis of the OTUs. Each dot represents a sample. Different colors of dots belong to different groups, the closer the distance between two dots, the similar microbial composition between two samples is. Blue indicates cecum (M1–M6) and ileum (H1–H6) samples in the control group. Red indicates cecum (M13−M18) and ileum (H13–H18) samples with microbiota transplantation.
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Figure 2. The abundance distribution of the top 20 most significantly different taxa. The red color (a) represents taxa abundance of samples in the control group, whereas the blue color (b) represents taxa abundance of samples in the microbiota transplantation group. The Aestuariispira, Christensenella, Fervidicella, Gracilibacter, Haloferula, Mycoplasma, Novispirillum and Pantoea were significantly more abundant (p < 0.05) in the microbiota transplantation group than in the control group.
Figure 2. The abundance distribution of the top 20 most significantly different taxa. The red color (a) represents taxa abundance of samples in the control group, whereas the blue color (b) represents taxa abundance of samples in the microbiota transplantation group. The Aestuariispira, Christensenella, Fervidicella, Gracilibacter, Haloferula, Mycoplasma, Novispirillum and Pantoea were significantly more abundant (p < 0.05) in the microbiota transplantation group than in the control group.
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Figure 3. The classification of species in samples. (A) The sample species classification tree. Classification tree shows all taxa in the samples, from the phylum to species (from the inner circle to the outer circle). The node size corresponds to the average relative abundance of the taxa. The classification of the relative abundance of the top 20 units was also in the picture with the letters (from the phylum to species, from outer to inner circle). (B) Heat-map of the hierarchy cluster results for the statistical significant microbial operational taxonomic units (OTUs) of the two groups at the species level. Red regions represent the high abundance species after cluster analysis, whereas green regions represent the low abundance species after cluster analysis. M1–M6 (cecum), H1–H6 (ileum): samples in the control group. M13–M18 (cecum), H13–H18 (ileum): samples in the microbiota transplantation group.
Figure 3. The classification of species in samples. (A) The sample species classification tree. Classification tree shows all taxa in the samples, from the phylum to species (from the inner circle to the outer circle). The node size corresponds to the average relative abundance of the taxa. The classification of the relative abundance of the top 20 units was also in the picture with the letters (from the phylum to species, from outer to inner circle). (B) Heat-map of the hierarchy cluster results for the statistical significant microbial operational taxonomic units (OTUs) of the two groups at the species level. Red regions represent the high abundance species after cluster analysis, whereas green regions represent the low abundance species after cluster analysis. M1–M6 (cecum), H1–H6 (ileum): samples in the control group. M13–M18 (cecum), H13–H18 (ileum): samples in the microbiota transplantation group.
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Figure 4. The second grade distribution of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The abscissa represents the KEGG functional category, whereas the ordinate represents relative abundance of the functional category in each group. Bigger bars means more abundant in the functional category.
Figure 4. The second grade distribution of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The abscissa represents the KEGG functional category, whereas the ordinate represents relative abundance of the functional category in each group. Bigger bars means more abundant in the functional category.
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Table
Table 1. Composition of the basal diet, as-fed basis.
Table 1. Composition of the basal diet, as-fed basis.
ItemDay 1~21Day 22~42
Ingredients (%)
Corn58.6461.62
Soybean meal30.9323.76
Cottonseed meal3.005.00
Soybean oil2.555.25
Dicalcium phosphate1.801.40
Limestone1.401.41
Salt0.400.40
L-Lysine0.380.33
L-Methionine0.200.16
L-Threonine-0.03
Choline0.200.13
Premix a0.500.50
Total100.00100.00
Nutrient levels (%, unless otherwise indicated)
Metabolism energy (kcal/kg) b29503150
Crude protein20.0018.00
Crude fiber1.931.87
Calcium1.000.90
Total phosphorus0.650.55
Available phosphorus0.450.40
Lysine1.251.08
Methionine0.520.45
Methionine + Cysteine0.890.89
Tryptophan0.270.23
a Providing the following quantities of vitamins and microminerals per kilogram of complete diet: 12,000 IU vitamin A as retinyl acetate, 2000 IU vitamin D3 as cholecalciferol, 20 IU vitamin E as dl-alpha tocopheryl acetate, 2.15 mg vitamin K as menadione nicotinamide bisulfte, 2.00 mg thiamin as thiamine mononitrate, 8.00 mg riboflavin, 4.5 mg pyridoxine as pyridoxine hydrochloride, 0.02 mg vitamin B12, 26 mg d-pantothenic acid as d-calcium pantothenate, 68 mg niacin as nicotinamide and nicotinic acid, 1.00 mg folic acid, 0.20 mg biotin, 8 mg Cu as copper sulfate, 110 mg Fe as iron sulfate, 0.34 mg I as potassium iodate, 105 mg Mn as manganese sulfate, 0.15 mg Se as sodium selenite, and 78 mg Zn as zinc oxide. b Calculated value.
Table
Table 2. Effects of microbiota transplantation on numbers of total bacteria, Lactobacillus and Escherichia coli.
Table 2. Effects of microbiota transplantation on numbers of total bacteria, Lactobacillus and Escherichia coli.
ControlMicrobiota TransplantationSEMp-Value
Total bacteria (lg/g)
Day 49.69.30.330.149
Day 68.18.00.290.648
Day 128.38.20.270.723
Lactobacillus (lg/g)
Day 47.37.60.280.048
Day 66.37.40.360.025
Day 126.77.60.420.031
Escherichia coli (lg/g)
Day 48.97.80.430.042
Day 67.86.60.320.023
Day 127.56.40.350.037
SEM: standard error of means, a pooled value of the two groups (n = 6). lg/g: Take the number of microorganisms per gram of sample as a logarithmic value based on 10.
Table
Table 3. Microbial diversity indices.
Table 3. Microbial diversity indices.
ControlMicrobiota TransplantationSEMp-Value
Cecum
Chao1295.64274.3664.720.642
ACE324.01288.8070.150.487
Simpson0.950.920.030.048
Shannon5.705.420.600.249
Ileum
Chao183.5679.4531.770.816
ACE93.0984.5437.810.753
Simpson0.890.770.040.015
Shannon3.853.020.460.027
SEM: standard error of means, a pooled value of the two groups (n = 6).
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Qiu, K.; Cai, H.; Wang, X.; Liu, G. Effects of Peroral Microbiota Transplantation on the Establishment of Intestinal Microorganisms in a Newly-Hatched Chick Model. Agriculture 2023, 13, 1001. https://doi.org/10.3390/agriculture13051001

AMA Style

Qiu K, Cai H, Wang X, Liu G. Effects of Peroral Microbiota Transplantation on the Establishment of Intestinal Microorganisms in a Newly-Hatched Chick Model. Agriculture. 2023; 13(5):1001. https://doi.org/10.3390/agriculture13051001

Chicago/Turabian Style

Qiu, Kai, Huiyi Cai, Xin Wang, and Guohua Liu. 2023. "Effects of Peroral Microbiota Transplantation on the Establishment of Intestinal Microorganisms in a Newly-Hatched Chick Model" Agriculture 13, no. 5: 1001. https://doi.org/10.3390/agriculture13051001

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