Sheep fecal transplantation affects growth performance in mouse models by altering gut microbiota

Abstract

Animal growth traits are important and complex traits that determine the productivity of animal husbandry. There are many factors that affect growth traits, among which diet digestion is the key factor. In the process of animal digestion and absorption, the role of gastrointestinal microbes is essential. In this study, we transplanted two groups of sheep intestinal microorganisms with different body weights into the intestines of mice of the same age to observe the effect of fecal bacteria transplantation on the growth characteristics of the mouse model. The results showed that receiving fecal microbiota transplantation (FMT) had an effect on the growth traits of recipient mice (P < 0.05). Interestingly, only mice receiving high-weight donor microorganisms showed differences. Use 16S rDNA sequencing technology to analyze the stool microorganisms of sheep and mice. The microbial analysis of mouse feces showed that receiving FMT could improve the diversity and richness of microorganisms (P < 0.05), and the microbial composition of mouse feces receiving low-weight donor microorganisms was similar to that of the control group, which was consistent with the change trend of growth traits. The feces of high-weight sheep may have higher colonization ability. The same five biomarkers were identified in the donor and recipient, all belonging to Firmicutes, and were positively correlated with the body weight of mice at each stage. These results suggest that FMT affects the growth traits of receptors by remodeling their gut microflora.

Elucidating the process by which fecal microbiota transplantation alters growth traits by altering recipient gut microbes.

Introduction

Sheep (Ovis aries) belongs to the subfamily Ovis of the bovid family, and are one of the important economic livestock species, providing human beings with a variety of products such as meat and fur. With the improvement of the quality of life, the market demand for mutton with high nutritional value is gradually increasing (Cheng et al., 2021). The key factor affecting the efficiency of mutton production is the development speed of sheep growth traits. Growth traits are complex traits composed of body weight, body length, daily gain, and other traits. It can be affected by a variety of factors, including genetics, nutrient levels, environment, and more (Liesegang et al., 2013; Pasandideh et al., 2020; Hailecherkos et al., 2021).

However, most of the current studies show that gastrointestinal microbes play an important role in the growth and development of animals (Leong et al., 2020; Pinart et al., 2021). Gastrointestinal microbes and their metabolites can alter host body weight gain and fat deposition in a variety of ways, including affecting gastrointestinal hormone secretion and nutrient absorption (Flint, 2011). Research suggests that the microbiota of obese animal models may have a higher ability to harvest energy from food (Boroni Moreira et al., 2012). Ley et al. reported that by measuring changes in the abundance of the microbiota during weight loss, they found that weight loss was associated with increased abundance of Bacteroidetes, while the abundance of Firmicutes decreased during weight loss (2006). In a study of intestinal microorganisms in fat and thin twins, obese individuals had a higher abundance of Actinomycetes (Turnbaugh et al., 2009). Currently, in the course of research on ruminants, the focus is often on the discussion of rumen microbes (Matthews et al., 2019; Mahmoudi-Abyane et al., 2020). However, in the previous study of the project, it was shown that there was a significant correlation between the differences of sheep hindgut microbes and sheep fat deposition traits, indicating that hindgut microbes also play an important role in sheep growth and development (Cheng et al., 2022).

Fecal microbiota transplantation (FMT) is a method that helps the recipient reconstitute the gut microbiota by transferring the donor’s gut microbiota to the recipient (Liu et al., 2017). FMT was first reported in 1958, and Eiseman et al. used it to treat patients with colitis, and the subjects were all significantly effective after treatment (1958). Studies have shown that FMT can effectively prevent the development of human type 1 diabetes, and microorganisms will affect the plasma metabolites of the host (de Groot et al., 2021). Zhang et al. reported that receiving FMT can significantly change the intestinal microbial composition and metabolism of the receptor (2020a). At present, the application of FMT technology in the treatment of human diseases has matured, but the research on livestock is still in the exploratory stage. The findings of Kim et al. show that FMT can affect the host’s metabolic processes by inducing changes in the gut microbiota, and thereby treat diarrheal disease in calves (2021). Qi et al. reported that the growth and intestinal function establishment of suckling pigs can be effectively promoted by inoculating feces from adult pigs to piglets via FMT (2021). Therefore, it is feasible to influence host gut microflora and development through FMT, but the specific impact of microbial changes on phenotype is not clear.

In this study, the mouse model was inoculated with ovine intestinal flora by enema, and the effect of FMT process on the receptor phenotype was observed. High-throughput sequencing technology was used to explore the effect of inoculation on the intestinal flora of mice, and to screen out the key flora colonized in the recipient’s intestine, providing new ideas for the FMT vaccination process, with a view to applying it to the treatment of sheep FMT.

Materials and Methods

Ethics approval

The study was carried out as per animal care and experimental procedures in accordance with the regulations and guidelines of the Government of Gansu People’s Congress. The program has been approved by the Animal Conservation and Ethics Committee of Gansu Agricultural University (License No. 2012-2-159).

Animals

The donor sheep came from a commercial sheep farm (Lanzhou Tianxin Breeding Co., Ltd. in Gansu Province, China) with a total of 10 male Hu sheep. Weaning was performed at 60 d of age and immunization was performed according to a standardized procedure. All lambs were kept in the same housing environment, and the lambs were fed silage after weaning for a 20-d acclimation period, during which they were allowed to eat and drink ad libitum. The lambs were weighed at birth and weaning.

A total of 15 specific pathogen free male 4-wk-old Kunming mice were purchased from Lanzhou Veterinary Research Institute, Chinese Academy of Agricultural Sciences, and were raised in the Animal Training Center of Gansu Agricultural University. The environment was kept at constant temperature (20 °C ± 3 °C) and humidity (55% ± 5%), and a 12-h light regime was used. Mice were fed with radiation sterilized pellet feed, which consisted of flour, corn, soybean meal, fish meal, and soybean oil. The nutritional composition is shown in Supplementary Table S1. They had free access to food and water during the experiment. During the experiment, the daily feed intake of each group was recorded, and they were weighed every Friday before morning feeding to measure body length, abdominal circumference, and other indicators.

Preparation of donor fecal bacteria solution

Donor Hu sheep were raised to 80 d of age and divided into two groups according to their weaning weight. Rectal feces were collected and stored in sterile 5 mL centrifuge tubes, part of which was stored in a −80 °C ultra-low temperature freezer for the detection of microorganisms. Refer to the previously reported methods to prepare the fungus liquid (Cui et al., 2015; Wang et al., 2017). Two groups of remaining fecal samples were mixed in sterile PBS solution (1 g/5 mL), respectively. After stirring evenly, filter through double-layer sterile gauze. It was centrifuged (2,000 r/min, 5 min), the supernatant was discarded, and resuspended with the same volume of sterile PBS solution to obtain a fecal bacterial solution. Add 1 mL of sterile glycerol (100%) to each 10 mL of fecal bacteria solution, and then dispense into 15 mL centrifuge tubes and store at −80 °C.

Antibiotic treatment and fecal microbial transplantation

As shown in Figure 1, after a 1-wk adaptation period, the mice were randomly divided into three groups (n = 5), of which two groups were the experimental group, which received antibiotic treatment, and the control group that did not receive antibiotic treatment. The cocktail of antibiotics is composed of 1 g/L of streptomycin sulfate, dimetridazole, and penicillin, added to drinking water for 1 wk (Guirro et al., 2019). Mice feces were collected after antibiotic treatment and stored at −80 °C for subsequent sequencing. The prepared two groups of fecal bacterial solutions were transplanted into the large intestine of the experimental group recipient mice by means of enema. The inoculation period was 3 wk, during which 200 μL fecal bacterial solutions were inoculated per mouse per day. In the control group, an equal volume of PBS solution was used for enema. Mouse feces were collected after FMT for sequencing.

Figure 1.

FMT and sample collection timeline. HFMT, mice inoculated with high weaning weight sheep feces; LFMT, mice inoculated with low weaning weight sheep feces; PBS, control mice inoculated with PBS.

DNA extraction and amplification

Microbial DNA was extracted from fecal samples using CTAB (Nobleryder, Beijing, China). The quality and concentration of extracted DNA were detected by agarose gel electrophoresis. Some samples were diluted to 1 ng/μL with sterile water (TransGen Biotech, Beijing, China). The remaining DNA samples were stored at −20 °C. PCR amplification was performed using specific primers 341F (CCTAYGGGRBGCASCAG) and 806R (GGACTACNNGGGTATCTAAT) with barcodes, and the extracted DNA was used as a template (Sun et al., 2013). The PCR used a 30 μL system: 15 μL Phusion High-Fidelity PCR Master Mix (New England Biolabs, USA), 0.2 μm upstream and downstream primers, 10 ng template DNA, and 2 μL sterile water. Cycling conditions were as follows: 1 min at 98 °C; 10 s at 98 °C, 30 s at 50 °C, and 30 s at 72 °C for 30 cycles; lastly, 5 min at 72 °C. PCR products of the same volume were mixed with 1 × loading buffer (contained SYB green) (New England Biolabs, USA) and detected by 2% agarose gel electrophoresis. The samples with a length of 400 to 450 bp and bright stripes were selected for further experiments. PCR products were mixed in equidensity ratios. Then, mixture of PCR products was purified with GeneJET Gel Extraction Kit (Thermo Scientific, MA, USA).

Library construction and data processing

The library was constructed using the NEB Next Ultra DNA Library Prep Kit (Illumina, CA, USA), and the library quality was evaluated using the Qubit @ 2.0 Fluorometer (Thermo Scientific, MA, USA) and Agilent Bioanalyzer 2100 system (Agilent, CA, USA). Illumina NovaSeq 6000 (Illumina, CA, USA) was used for sequencing, and the paired-end reads with a length of 250 bp were finally generated. Raw data obtained by 16S rDNA sequencing has been uploaded to the NCBI sequence read archive (Submission ID: SUB11455111 and BioProject ID: PRJNA836368).

The sequenced DNA fragments were paired-end reads using FLASH (Version 1.2.7; Magoč and Salzberg, 2011). Paired-end reads were assigned to each sample according to the unique barcodes to obtain Raw Tags, and get Clean Tags through strict quality control and filtration (Bokulich et al., 2013). The Clean Tags of all samples are clustered by Uparse algorithm (Version 7.0.1001; Edgar, 2013), and the sequence is clustered into operational taxonomic units (OTUs) with 97% consistency. The OTUs were annotated, and the OTUs were compared and annotated with SILVA138 database by Mothur method (Wang et al., 2007). After the taxonomic information is obtained, the community composition of the sample at each classification level is counted: kingdom, phylum, class, order, family, genus, and species. MUSCLE (Version 3.8.31) software was used for fast multi-sequence alignment to obtain the phylogenetic relationship of OTUs (Edgar, 2004). Finally, the data of each sample is homogenized, and the one with the least amount of data in the sample is used as the standard. Based on the homogenized data, QIIME (Quantitative Insights Into Microbial Ecology) software is used for data analysis, and its internal Perl script is used to analyze the Alpha and Beta diversity of samples. Calculate the UniFrac distance between samples, and then use principal coordinate analysis (PCoA) sorting for visualization. Linear discriminant analysis (LDA) effect quantity (LEfSe) analysis was used to identify different groups of potential microbial biomarkers, in which LDA score = 4. Use IBM SPSS statistics 25 to analyze the differences between different groups. The Spearman correlation coefficient is calculated by R software (Version 2.15.3), and the correlation heat map is drawn.

Results

Differences of fecal microorganisms in different weaning weight sheep

Weaning weight is an important growth trait of sheep. We compared the difference of weaning weight between the two groups, and the results showed that there was a significant difference between the two groups (P < 0.05; Supplementary Figure S1). The differences of microbial composition in sheep feces between the two groups were compared. Results as shown in Figure 2A, the microbial communities between the two groups were separated, and the clustering of individuals with high weaning weight was more obvious, indicating that the microbial communities in the group of individuals with high weaning weight were more similar. The results of UPGMA cluster tree also showed that the group cohesion between the two groups was obvious. At the phylum level, the dominant phyla of the two groups were Bacteroidea and Firmicutes (Figure 2C). The heatmap of cluster analysis revealed a higher similarity of all genera among different samples within-group (Figure 2B). Simper analysis was used to explore the key flora causing the difference between the two groups of microorganisms (Figure 2D). The results showed that Bacteroidota contributed the most to the difference, followed by Firmicutes and Proteobacteria.

Figure 2.

Analysis of microorganisms in feces of sheep with different weaning weights. Principal coordinate analysis based on UniFrac distance (A). Genus-level species abundance cluster heatmap (B). UPGMA clustering tree and phylum-level species relative abundance histograms (C). Simper analysis, the area of the bubbles represents the abundance of species in the sample (D).

Effect of FMT on growth traits of mice

FMT was used to further study the effect of microorganisms on host growth performance. A total of 15 mice were included in this cross-sectional study. The control group was named PBS, the mice transplanted with low birth weight sheep fecal fluid were named LFMT, and the mice transplanted with high birth weight sheep fecal fluid were named HFMT. Supplementary Table S2 is the description and comparative analysis of the growth traits of mice in different treatment groups. Figure 3A records the changes in body weight of mice in different stages. The results show that the mice grow faster in the early stage, and the body weight of mice in each group gradually shows significant differences with the extension of transplantation time. At the age of 8 wk, the weight of mice in the HFMT group was significantly higher than that in the PBS group (P < 0.05). At the age of 9 wk, the weight of mice in the HFMT group was significantly higher than that in the PBS group and LFMT group (P < 0.05). However, there was no significant difference between PBS and LFMT (P > 0.05). The daily weight gain of each stage was analyzed, and the results were similar to that of body weight. The daily gain of 8 to 9 wk in the HFMT group was significantly higher than that in the other two groups (P < 0.05) (Figure 3B). Abdominal circumference is one of the indicators of abdominal fat deposition in mice. The results showed that the HFMT group was significantly higher than the other two groups (P < 0.05; Figure 3D). Therefore, the BMI of the three groups of mice was compared, and the results were consistent with the trend of abdominal circumference (Figure 3E), and there was no significant difference in body length among the three groups (P > 0.05; Figure 3C), suggesting that FMT may alter the efficiency of fat deposition.

Figure 3.

Analysis of differences in growth traits in mice. Stage weight changes (A), different letters represent significant differences between groups (P < 0.05). Stage daily gain (B), *P < 0.05. Body length (C). Abdominal circumference (D). Body mass index (BMI) (E).

Effects of FMT on the intestinal flora of mice

FMT mainly affects the traits of mice by affecting the intestinal microbiota of mice. By comparing the gut microbes between groups and before and after FMT, the effect of FMT on gut microbiota can be effectively judged. PBS, LFMT, and HFMT were named EPBS, ELFMT, and EHFMT after transplantation. A total of 3,621 OTUs were obtained by clustering the filtered sequences using the Uparse algorithm. Supplementary Figure S2 shows that with the increase of sequencing depth and sample size, the dilution curve and species accumulation curve tend to be flat, which indicates that the test conditions meet the analysis requirements. Analysis of the OTUS of the receptor and the donor, Figure 4A shows that ELFMT, EHFMT, LBS, and HBS shared 472 OTUS. Before and after FMT processing, LFMT, HFMT, ELFMT, and EHFMT shared six OTUS. This shows that FMT has a great impact on the composition of mice’s intestinal OTUS.

Figure 4.

Mice fecal microbial diversity. Fecal OTUs analysis of donor sheep and recipient mice (A). Comparison of Shannon index before and after FMT (B). Comparison of Chao1 index before and after FMT (C). Principal coordinate analysis before FMT (D). Principal coordinate analysis after FMT (E).

It can be seen from Figure 4C that the Chao1 index in the LFMT and HFMT group after antibiotics is significantly lower than in the PBS group with nonantibiotic treatment (P < 0.05). Based on the analysis of the weighted Unifrac distance from the main coordinate, the separation between the control group and the receptor group is obvious, while there is no separation in the receptor group (Figure 4D). The above results prove that the pseudo-germ-free mice model is successfully established, and antibiotic treatment effectively reduces the richness of intestinal microorganisms. When the mouse accepted the FMT, the receptor group Shannon index and the Chao1 index were significantly higher than that of the control group (P < 0.05; Figure 4B and C). Therefore, it can be considered that FMT can improve the diversity and richness of receptor intestinal microorganisms. The PCOA chart shows that the ELFMT group is separated from EHFMT (Figure 4E). The ELFMT group and the EPBS group partially overlap. Anosim and MRPP methods were used to verify the significance of the differences between the two groups (Table 1). The results showed that there was no significant difference between the two receptor groups before receiving FMT, but there was a significant difference with the control group (P < 0.05). After receiving FMT, there was also a significant difference between the two receptor groups (P < 0.05), and the difference values R-value and A-value between the EHFMT group and EPBS group were higher than the ELFMT group and EPBS group. Accepting HBS group FMT has the greatest impact on the structure of mice’s intestinal microbial region.

Table 1.

Analysis of differences in community structure among groups

Method		PBS-LFMT	PBS-HFMT	LFMT-HFMT	EPBS-ELFMT	EPBS-EHFMT	ELFMT-EHFMT
ANOSIM	R-value	0.94	1.00	−0.11	0.49	0.54	0.15
	P-value	0.01	0.01	0.90	0.01	0.01	0.04
MRPP	A	0.21	0.21	−0.01	0.07	0.08	0.03
	P-value	0.01	0.01	0.64	0.01	0.01	0.05

R > 0, the difference between groups is greater than the difference within the group, R < 0, the difference within the group is greater than the difference between the groups, the greater the absolute value of R indicates the greater the relative difference, A and R have the same function. P < 0.05 indicated that the difference was statistically significant.

FMT alters gut microbial composition in mice

The gut microbiota of different groups of mice before and after FMT treatment were annotated based on the SLIVA database. Among them, the number of OTUs that can be annotated into the database is 3,565 (98.45%), the proportion of phylum level is 87.90%, the proportion of class level is 87.13%, the proportion of order level is 83.46%, and the proportion of family level is 70.70%. The proportion of genus level was 42.23%. The clustering tree showed that ELFMT had a higher clustering relationship with the microbial composition of the EPBS group, which was consistent with the results of PCoA (Figure 5A). At the phylum level, the abundance of Bacteroidetes was lower in LFMT and HFMT, while the control group and mice receiving FMT had a similar species composition, with the predominant phyla Bacteroidetes. At the genus level, the dominant genus of LFMT and HFMT was Acinetobacter, and the dominant genus of PBS, EPBS, and ELFMT was Bacteroides (9.53% to 26.81%). Interestingly, the predominant genus of EHFMT was Prevotellaceae_UCG-001 (12.29%), which suggested that Prevotellaceae_UCG-001 might be the key genus responsible for the differences in the traits of mice (Figure 5B). The phylum and genus-level microorganisms were clustered according to species abundance, and LFMT was similar to HFMT before FMT treatment, while ELFMT was similar to the control group after treatment (Figure 5C). The ELFMT group was highly clustered up-regulated in Prevotellaceae_UCG-001 and Lachnospiraceae_NK4A136_group.

Figure 5.

Microbial composition analysis in mice. UPGMA clustering tree and phylum-level species relative abundance histograms (A). Genus-level species composition histogram and relative abundance table (B). Heatmap of phylum-level (top) and genus-level (bottom) species abundance clusters (C).

According to the experimental results of mouse phenotype and intestinal microbiota, when mice received FMT of LBS sheep feces, there was no significant difference in growth traits between the mice and the control group. The growth traits and microbial structure of mice receiving HBS sheep fecal FMT were significantly different from those in the control group (P < 0.05). Therefore, when we deeply studied the effect of FMT on host intestinal flora, we chose the HFMT group with more significant response to FMT.

Identification of the flora colonized by FMT and its relationship to the host

To determine whether specific flora successfully colonized the gut through the FMT process, we used LEfSe analysis for validation. As shown in Figure 6A, compared with the control group EPBS, a total of 23 biomarkers were identified in the donor group HBS. In Figure 6B, EPBS in the control group was compared with EHFMT in the receptor group, and a total of seven biomarkers were identified in the receptor group. Interestingly, five biomarkers in EHFMT were also identified in its donor HBS, which indicates that these five florae may be successfully colonized in the receptor through the FMT process. The phylogenetic tree showed that the five biomarkers identified belonged to Firmicutes (Figure 6C and D). Comparative analysis of their abundance showed that HBS group had the highest abundance, followed by the EHFMT group and the lowest abundance in the EPBS group, indicating that a high abundance of related bacteria in the donor can improve the abundance of bacteria in the receptor (Supplementary Figure S3; Supplementary Table S3). The correlation analysis between the colonized microorganisms and the growth traits of mice showed that the five microbiota were positively correlated with the body weight of mice at 5 to 9 wk and the abdominal circumference at the 9th wk. Lachnospirales, Lachnospiraceae, and Clostridia were significantly positively correlated with the abdominal circumference of mice at week 9 (P < 0.05; Figure 6E). This validates the inference that FMT alters host traits by altering the host gut microbiome.

Figure 6.

Identification of key biomarkers and their relationship to growth traits in mice. LEfSe analysis (EPBS vs. HBS) (A). LEfSe analysis (EHFMT vs. HBS) (B). Species evolution tree (EPBS vs. HBS) (C). Species evolution tree (EHFMT vs. HBS) (D). Correlation analysis of key biomarkers with growth traits in mice (E), *P < 0.05.

Discussion

There is a symbiotic relationship between microbes and hosts for a long time, and the composition of gastrointestinal microbes is often different between individuals with different traits (Canfora et al., 2019). Studies have shown that gut microbes are an important “organ” that affects animal growth and development (Cox et al., 2014). Our observations revealed differences in the structure and composition of the microbiota in sheep feces of different body weights, and the phyla Firmicutes and Bacteroidetes that contributed the most to the differences. Results from previous studies showing that low body weight is associated with members of the Bacteroidetes, whereas Firmicutes are associated with obesity and high body weight, are similar to our results (Ridaura et al., 2013; Magne et al., 2020).

At present, FMT has been widely used in humans, and is often used to treat intestinal-related diseases, and has achieved good results (El-Salhy et al., 2020). The results of Zhang et al. showed that FMT effectively reduced the level of disease activity index in experimental animals and significantly increased the body weight of recipient animals (Wang et al., 2020). Considering that in the process of animal husbandry production, animal growth traits are closely related to production efficiency, we used a mouse model for FMT treatment and compared the differences in growth traits between fecal microbial transplantation from different sources and the control group. The results showed that there were significant differences between different treatment groups, and the growth traits of mice receiving high body weight sheep feces were significantly higher than those of the other two groups, indicating that the microbes in high body weight sheep feces can effectively improve the growth performance of the host, while the microbes in low body weight sheep feces have little effect on the host.

Intervention of gut microbiota by FMT can improve the growth performance of animals, inoculation increases host feed intake and body weight, and receptors receiving fecal microorganisms from obese individuals have a significant effect, which is consistent with the results of this study (Siegerstetter et al., 2018; Zhang et al., 2020b). Gut microbial communities changed before and after the mice were treated with FMT. Antibiotic treatment was performed before receiving treatment to reduce the influence of preexisting gut microbes on the FMT process (Reikvam et al., 2011; Hintze et al., 2014). The FMT process significantly increased the diversity and richness of microorganisms in the feces of mice, and the microbial colony structure was also changed. FMT affects the host by colonizing donor microbes, Ohara reported (2019). Foreign microbes significantly increase the diversity of receptors (Khoruts and Sadowsky, 2016).

The process of FMT can effectively alter the host’s gut microbial composition (Wrzosek et al., 2018). However, in our study, only the HFMT group had altered microbial composition, whereas the fecal microbial composition of the LFMT group was more similar to that of the control group. Combined with the mouse phenotypic results, we believe that the microbes from the feces of high-weight sheep are more likely to colonize the mouse gut, and that there are key flora in colonized microbes that have a positive effect on host growth traits. At the genus level, Prevotellaceae_UCG-001 and Lachnospiraceae_NK4A136_group are highly enriched in the HFMT group. Tang et al. showed that Prevotellaceae_UCG-001 was highly positively correlated with host intramuscular fat (2020). Xu et al. also showed that individuals with high daily gain and body weight had a higher abundance of Prevotellaceae_UCG-001 in the gastrointestinal tract (2022). Studies have shown that when the host is treated with drugs with anti-obesity effects, the abundance of Lachnospiraceae_NK4A136_group in the gut is significantly reduced (Yang et al., 2020). It can be speculated that the above two genera may affect daily gain and body weight by affecting the host’s fat deposition. BMI is often used to evaluate the degree of obesity in the body (Flegal et al., 2009). In this study, the BMI of the HFMT group was significantly higher than that of the other two groups, which verified our speculation.

The results of the LEfSe analysis showed that there were more biomarkers in the donor feces compared with the control group, mainly due to interspecies differences. However, comparing the recipient group to the control group, five of the seven biomarkers identified in the recipient group were seen in the donor’s biomarkers, all belonging to the Firmicutes—Clostridia. Most studies have shown that a high abundance of Firmicutes can lead to overweight and obesity, which has been verified in the gut microbes of humans, sheep, mice, and other animals (Horie et al., 2017; Indiani et al., 2018; Yang et al., 2020). Collado reported that body weight in overweight individuals was positively correlated with BMI and Clostridia performance, which was similar to our results (Collado et al., 2008). Studies have shown that the abundance of Lachnospiraceae in the feces of normal animals is higher than that of individuals with weight loss, and there will be differences in VFA, especially butyric acid (Berger et al., 2021; Richards-Rios et al., 2021). Therefore, we believe that FMT successfully colonizes part of the microorganisms in the host in the host’s gut, and thus affects the growth traits of the host. We made a correlation analysis between the identified colonization flora and the growth traits of mice, and the results showed that they were all positively correlated with the later growth traits of mice, which was in line with our point of view.

Conclusions

In summary, this study selected a mouse model as the FMT object, verified the relationship between microbial changes and host trait changes, and revealed the impact of microorganisms on the host. Gut microbes play an important role in the development of animals, and there are differences in the microbes in the host gut with different traits. FMT can effectively colonize part of the donor’s flora in the recipient’s gut, and significantly improve the recipient’s growth traits. The effect of FMT on mouse models provides a new direction for growth trait regulation. In future work, we aim to apply the FMT method to the actual production process to improve the growth traits of animals and improve production efficiency through FMT.

Glossary

Abbreviations

BMI

body mass index

CTAB

cetyltrimethylammonium bromide

FMT

fecal microbial transplantation

LDA

linear discriminant analysis

LEfSe

linear discriminant analysis effect size

OTU

operational taxonomic unit

PCoA

principal coordinate analysis

QIIME

quantitative insights into microbial ecology

SPF

specific pathogen free

Conflict of Interest Statement

The authors declare no real or perceived conflicts of interest.