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Journal of Animal Science logoLink to Journal of Animal Science
. 2023 Sep 24;101:skad322. doi: 10.1093/jas/skad322

Relationship between hindgut microbes and feed conversion ratio in Hu sheep and microbial longitudinal development

Dan Xu 1, Jiangbo Cheng 2, Deyin Zhang 3, Kai Huang 4, Yukun Zhang 5, Xiaolong Li 6, Yuan Zhao 7, Liming Zhao 8, Jianghui Wang 9, Changchun Lin 10, Xiaobin Yang 11, Rui Zhai 12, Panpan Cui 13, Xiwen Zeng 14, Yongliang Huang 15, Zongwu Ma 16, Jia Liu 17, Kunchao Han 18, Xiaoqiang Liu 19, Fan Yang 20, Huibin Tian 21, Xiuxiu Weng 22, Xiaoxue Zhang 23,✉, Weimin Wang 24
PMCID: PMC10576521  PMID: 37742310

Abstract

Feed efficiency is an important indicator in the sheep production process, which plays an important role in improving economic benefits and strengthening energy conservation and emission reduction. Compared with the rumen, the fermentation of the hindgut microorganisms can also provide part of the energy for the host, and the composition of the hindgut microorganisms will affect the feed efficiency. Therefore, we hope to find new ways to regulate sheep feed efficiency by studying the sheep gut microbes. In this study, male Hu sheep with the same birth date were raised under the same conditions until 180 d old. The sheep were divided into high and low groups according to the feed conversion ratio (FCR) at 80 to 180 d old, and the differences in rectal microorganisms between the two groups were compared. The permutational multivariate analysis (PERMANOVA) test showed that there were differences in microorganisms between the two groups (P < 0.05). Combined with linear fitting analysis, a total of six biomarkers were identified, including Ruminobacter, Eubacterium_xylanophilum_group, Romboutsia, etc. Functional enrichment analysis showed that microorganisms may affect FCR through volatile fatty acids synthesis and inflammatory response. At the same time, we conducted a longitudinal analysis of the hindgut microbes, sampling nine-time points throughout the sheep birth to market stages. The microbiota is clearly divided into two parts: before weaning and after weaning, and after weaning microbes are less affected by before weaning microbial composition.

Keywords: feed conversion ratio, hindgut, Hu sheep, microorganism, vertical development

The relationship between rectal microbes and feed conversion ratio in sheep was explored, and the longitudinal development of microbes was elucidated.

Introduction

The world population will grow to around 8.5 billion in 2030 and to around 9.7 billion in 2050 (Nations, 2022). At the same time, the increase in gross domestic product in developing countries has led to a shift in diets toward animal protein (Henchion et al., 2017). To meet increasing dietary demands, improvements in livestock production efficiency are necessary. Sheep (Ovis aries) are one of the earliest domesticated livestock, widely distributed around the world, and are an important source of meat production. In the process of sheep production, the cost of feed can reach 70% (Zhang et al., 2017), effectively improving the feed efficiency of sheep can significantly improve commercial benefits. At present, the evaluation indicators of feed efficiency mainly include feed conversion ratio (FCR) and residual feed intake (Tortereau et al., 2020). Existing research results show that sheep with low FCR can gain weight faster and shorten the feeding cycle on the basis of maintaining less feed intake (Zhang et al., 2021a; Yaxing et al., 2022). At the same time, lower feed intake can also reduce the methane emission of sheep (Johnson et al., 2022), and on the basis of improving economic benefits, reduce energy waste, and promote the low-carbon development of animal husbandry.

We choose FCR to evaluate feed efficiency. The calculation formula of FCR is the ratio of average feed intake to average weight gain, which is affected by many factors, including diet composition, digestive tract flora, host genetics, etc. Zhao et al. reported that the AHSG gene has a significant correlation with sheep FCR (Zhao et al., 2023). Studies by Yaxing et al. have shown that the FCR traits of sheep can be effectively reduced by supplementing feed additives (Yaxing et al., 2022). De et al. reported that FCR in sheep increased under hot conditions and feed efficiency was compromised (De et al., 2017). Li et al. reported that rumen microbes can affect traits such as daily gain and feed efficiency in sheep by increasing volatile fatty acid (VFA) concentrations and upregulating metabolite biosynthetic pathways (Li et al., 2022b).

The digestive tract microbiota is a complex and stable system, which mainly plays the role of fermenting and decomposing the diet and participating in the host immune process (Cho and Blaser, 2012; Dang and Marsland, 2019). Microbes in the digestive tract break down complex plant fibers into smaller organic compounds. One of the most important products is VFAs, which are an important source of energy and nutrition for ruminants. However, the hindgut also has a fermentative capacity similar to that of the rumen, and the VFAs produced are absorbed and metabolized by the epithelial cells of the large intestine (Baran et al., 1979; Bergman, 1990). At present, the research on the hindgut and feed efficiency mainly focuses on pigs (McCormack et al., 2017) and chickens (Nan et al., 2022), and there is less research on the relationship between the hindgut and feed efficiency of sheep (Perea et al., 2017). Yin et al. reported that eight biomarkers were found in the rectum in different groups of average daily gain (ADG) sheep (Yin et al., 2023). This suggests that the hindgut microbiota influences sheep traits.

At the same time, the intestinal tract has been colonized by microbes since birth, and it is also in the process of dynamic changes during the development of the host (Han et al., 2018). Previous studies have shown that there are differences in the microorganisms in the rumen of sheep at different time points (Wang et al., 2019a). The current research on sheep gut microbes is mainly cross-sectional (Perea et al., 2017; Su et al., 2022), and it is impossible to explain the change process of sheep gut microbes. Therefore, it is necessary for us to conduct a comprehensive longitudinal study of sheep from birth to market.

We hypothesized that there is key differential flora in the gut of different FCR sheep, and that the differential flora interacts with the host, thereby affecting the feed efficiency of sheep. In this study, we explored the relationship between sheep hindgut microbes and FCR using 16S rDNA sequencing and identified biomarkers associated with FCR. At the same time, we selected different developmental time points of sheep, and explored the longitudinal developmental changes of hindgut microorganisms, in order to find a suitable time point to regulate the intestinal microorganisms of sheep, and then achieve the purpose of reducing FCR.

Materials and Methods

Ethics approval

The study was carried out as per animal care and experiment 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 and sampling

Animal test 1 (FCR biomarker identification)

A total of 30 experimental male 180-d-old Hu sheep with the same birth date came from a commercial sheep farm, Minqin County Defu Agricultural Technology Co., Ltd. (Gansu, China). All experimental sheep were immunized according to standard procedures, and were weaned at the age of 56 d. After weaning, each sheep had its own separate fence and trough. All experimental sheep were fed the same diet. The lambs were fed with pellet feed, and the composition of pellet feed was 27.00% barley straw, 44.00% corn, 2.20% soybean meal, 2.60% rapeseed meal, 4.20% cottonseed meal, and 20.00% concentrate. Pellet feed was purchased from Gansu Runmu Bioengineering Co., Ltd. (Gansu, China). All experimental animals had free access to water and food. When the sheep reached 80, 100, 120, 140, 160, 180 d of age, weight, and feed intake were measured at 8:00 a.m., measured using a calibrated electronic scale, and fasted for 12 h prior to measurement. During the whole experiment, the external environment and feeding method were kept consistent.

When the experimental animals reached the age of 180 d, rectal fecal samples were collected at the same depth of the rectum of the sheep using disposable PE gloves, and the samples were temporarily stored in liquid nitrogen (–196 °C), and then transferred to –80 °C ultralow temperature refrigerator for storage.

Animal test 2 (microbial longitudinal studies)

A total of four experimental male Hu sheep with the same birth date also came from Minqin County Defu Agricultural Technology Co., Ltd. (Gansu, China), and the feeding and management methods were the same as in test 1.

Since it is inconvenient to collect complete rectal feces from lambs, we use the method of collecting swabs to obtain experimental samples (Wang et al., 2019b). The experiment selected nine-time points to collect rectal swabs from each sheep, mainly including 3, 15, 30, 80, 100, 120, 140, 160, and 180 d of age (3D, 15D, 30D, 80D, 100D, 120D, 140D, 160D, and 180D). Immediately after acquisition, the samples were placed in liquid nitrogen (–196 °C) and then stored in a –80 °C ultralow temperature refrigerator.

DNA extraction and amplification

The DNA in the sample was extracted by Cetyltrimethylammonium bromide (CTAB) (Nobleryder, Beijing, China), and the concentration and purity of the extracted DNA were checked by agarose gel electrophoresis. Dilute the sample to 1 ng/µL with sterile water and store at –20 °C.

Using the extracted DNA as a template, the 16S V3 to V4 region was amplified using specific primers (314F: CCTAYGGGRBGCASCAG, 806R: GGACTACNNGGGTATCTAAT) with Barcode (Sun et al., 2013). PCR used a 30 μL volume containing 15 μL Phusion® High-Fidelity PCR Master Mix (New England Biolabs, Ipswich, MA, United States), 0.2 μm upstream and downstream primers, 10 ng template DNA, and 2 μL sterile water. The cycling conditions were 1 min at 98 °C; 30 cycles of 10 s at 98 °C, 30 s at 50 °C, and 30 s at 72 °C; and finally, 5 min at 72 °C. The amplified products were detected by 2% agarose gel electrophoresis.

Library construction and sequencing

Library construction by the TruSeq DNA PCR-Free Library Preparation Kit (Illumina, San Diego, CA, United States; Jones et al., 2015). Use the Qubit @ 2.0 Fluorometer (Thermo Fisher Scientific, Waltham, MA, United States) and a Bioanalyzer 2100 system (Agilent, Santa Clara, CA, United States) to assess the quality of the library. Sequence the library through the Illumina NovaSeq 6000 (Illumina, CA, United States) platform, and generate a pair-end sequence with a length of 250 bp after off-machine (Kwak et al., 2022).

Bioinformatics analysis

Sequences are assigned to samples based on each sample’s unique barcode, and the sample’s barcode and primer sequences are cut. The sequenced pairs were merged by FLASH (Version 1.2.7) to generate raw tags (Magoč and Salzberg, 2011), and high-quality clean tags were generated after QIIME (Version 1.9.1) filtering (Bokulich et al., 2013). Use the UCHIME algorithm to detect chimera sequences and delete them, and finally get Effective Tags for subsequent analysis (Edgar et al., 2011; Haas et al., 2011).

Use QIIME2 (Version 2023.2.0) for subsequent analysis, and denoise the Effective Tags based on the DADA2 (Callahan et al., 2016) algorithm to generate amplicon sequence variants (ASVs) with a similarity of 100%. The α-diversity and β-diversity of the samples were calculated using QIIME2 (Version 2023.2.0). The BLAST algorithm was used to compare and annotate the sample sequence with the reference sequence (SILVA138 database) to determine the community composition at each species level (kingdom, phylum, class, order, family, genus, and species).

R software (Version 4.2.2) was used for data analysis. Both principal coordinate analysis (PCoA) and principal component analysis (PCA) are done through the vegan package. Analysis of group differences was performed using Wilcox_test. Linear fit analysis and Source Tracker analysis were performed using R (Version 4.2.2). Use the ggplot2 package for image plotting. Using PICRUSt2 (Version 2.3.0; Douglas et al., 2020) to predict the function of sample microorganisms based on the characteristic sequence and ASVs abundance, this study mainly used the results of Kyoto encyclopedia of genes and genomes (KEGG) and metabolic pathways from all domains of life (MetaCyc) databases.

Results

Phenotypic characterization and grouping of experimental sheep

A total of 30 Hu sheep with the same date of birth were used in this cross-sectional study. All the experimental sheep have accurate records of average daily feed intake and average daily gain. At 80 to 180 d old, this is an important period for sheep fattening. Descriptive statistical analyzes of growth traits and FCR of experimental populations at this stage are shown in Supplementary Table S1. The FCR traits had a high coefficient of variation, and showed an increasing trend with age, which indicated that when the Hu sheep grew to a certain stage, the energy intake began to be mainly used to maintain daily metabolism rather than growth and development.

Taking FCR traits as grouping conditions, the sheep population was divided into a high FCR group (H-FCR) and a low FCR group (L-FCR) on average, as shown in Figure 1A, there was a significant difference in FCR between the two groups (P < 0.05), so the grouping was considered reasonable. The phenotypic characters of sheep in different groups were analyzed (Supplementary Figure S1). The results showed that there was no significant difference in body weight between the two groups. However, the average daily feed intake of L-FCR was significantly lower than that of H-FCR (P < 0.05). On the contrary, the ADG of L-FCR was significantly higher than that of H-FCR (P < 0.05).

Figure 1.

Figure 1.

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Analysis of group traits and microbial diversity. (A) Feed efficiency traits. (B) Shannon index. (C) Chao1 index. (D) Division of ASVs. (E) Based on the Bray–Curtis distance, principal coordinate analysis between different groups was performed. And PERMANOVA analysis. P < 0.05, there is a significant difference between groups. Analysis of species composition and differences between groups.

Experimental sheep diversity analysis

We amplified and sequenced 16S rDNA sequences from two groups of sheep rectal fecal samples. A total of 2,030,932 original sequences were obtained after slicing off the machine, and a total of 1,525,418 Effective Tags were obtained after quality control for subsequent analysis (Supplementary Table S2). The Effective Tags were denoized by DADA2, and a total of 7,518 ASVs were obtained. As shown in Figure 1D, 2,421 ASVs were shared between the two groups, L-FCR had 2,938 unique ASVs, and H-FCR had 2,159. The results of the diversity analysis showed that there was no significant difference in α diversity between the two groups (Figure 1B and C). As the sequencing depth increases, the dilution curve tends to be flat, which shows that the sequencing depth meets the analysis requirements (Supplementary Figure S2). According to the counting statistics of ASVs the Bray–Curtis distance is calculated, and the PCoA is performed based on this (Figure 1E). As a result, as shown in Figure 1E, there was a separation between L-FCR and H-FCR, which indicated that there was a difference in the structure of the flora between the two groups. In order to verify the significance of the difference, we used the permutational multivariate analysis (PERMANOVA) of variance method to analyze the differences in the flora structure, and the results showed that there were significant differences in the flora structure between the two groups (P < 0.05).

Analysis of species composition and differences between groups

Annotated ASVs based on the silva database, Figure 2A shows the microbial composition at the phylum level in the sheep rectal swabs. About 20% of ASVs are classified into Firmicutes and Bacteroidetes but their relative abundance exceeds 80%. In addition, the dominant phyla also included Spirochaetota, Proteobacteria, and Desulfobacterota. At the genus level, the dominant genera were Rikenellaceae_RC9_gut_group, UCG-005 (Oscillospiraceae) and Bacteroides (Figure 2B). The top 10 species at the class, order, and family levels are shown in Supplementary Figure 3.

Figure 2.

Figure 2.

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Microbial composition of sheep rectum and its relationship with FCR. (A) TOP10 phylum-level microorganisms. (B) TOP10 genus-level microorganisms. (C) Comparison of differences between different FCR groupings for phylum-level species with an average relative abundance greater than 0.1%. (D) Difference comparison at the genus level for different FCR groupings. (E) Linear fitting analysis between genus-level difference microorganisms and FCR, the abscissa is FCR, R is coefficient of determination, and the larger R is, the higher the fitting level is. When P < 0.05, it means that there is a significant fitting relationship.

Microbial differences between the two groups were analyzed using the Wilcox test for species with a relative abundance of more than 0.1%, and only Campilobacterota was significantly different between the two groups at the phylum level, H-FCR has a higher relative abundance (P < 0.05; Figure 2C). A total of 11 genera with significant differences were obtained at the genus level, among which Eubacterium_xylanophilum_group, Prevotellaceae_UCG-003, UCG-010 (Oscillospirales), Barnesiellaceae, Ruminobacter, Romboutsia, Ruminococcaceae, and Turicibacter of H-FCR were significantly higher than those of L-FCR (P < 0.05). In L-FCR, only Family_XIII_AD3011_group, CHKCI001, and Anaerostipes were significantly higher than H-FCR (P < 0.05).

The relationship between differential microorganisms and FCR was explored using a linear fitting model. As shown in Figure 2E, among the 11 differential microorganisms, Eubacterium_xylanophilum_group, Ruminobacter, and Romboutsia had a significant linear correlation with FCR, and were positively correlated with FCR (P < 0.05). However, the linear correlation between Prevotellaceae_UCG −003, UCG-010 (Oscillospirales), Turicibacter, and FCR shows a significant trend (P < 0.10), which may be due to the small sample size. Therefore, it is considered that the above six genera can be used as biomarkers related to FCR.

Differences in functional enrichment between groups

Using PICRUSt2 to predict the microbial functional pathways of rectal feces, it can be seen from Figure 3A that there are 21 level 2 pathways with a relative abundance greater than 1%, of which 12 belong to metabolic level 1 pathway. The ones with the highest relative abundance are amino acid metabolism, metabolism of cofactors and vitamins and carbohydrate metabolism.

Figure 3.

Figure 3.

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KEGG functional pathway prediction. (A) Functional pathways with relative abundance greater than 1%. (B) Difference analysis of functional pathways among different FCR groups.

We compared differences in functional pathways between groups. The results of PCA showed that there was no significant separation between the two groups, which indicated that the main functions of the microorganisms in the two groups were similar (Supplementary Figure S4A). As shown in Figure 3B, among the groups of different FCRs, there were five functional pathways with significant differences, and H-FCR was significantly higher than L-FCR (P < 0.05). Functional pathways that differed between groups included energy metabolism, immune system, signaling molecules and interaction, and drug resistance: antimicrobial (Supplementary Table S3).

At the same time, the MetaCyc database is also used to predict and analyze the functional pathways of microbial metabolism. Principal coordinates analysis showed similar results to the KEGG database (Supplementary Figure S4B). Difference analysis showed that L-FCR has five pathways significantly higher than H-FCR (P < 0.05) (Supplementary Figure S4C), which is opposite to KEGG, including fatty acid biosynthesis initiation and palmitoleate biosynthesis, etc. (Supplementary Table S3).

Longitudinal development of rectal fecal microbial diversity

Since rectal fecal microbes are correlated with sheep FCR, we investigated the longitudinal development of rectal swabs microbiota. We collected rectal swabs at nine-time points in sheep development for microbial studies. The 16S rDNA sequence overview is shown in Supplementary Table S4, and dilution curves are shown in Supplementary Figure S5. A total of 8,995 ASVs were obtained in the rectal feces of nine developmental time points of sheep, among which 100 to 140D had the largest number of ASVs, and a total of 79 ASVs were shared in all stages (Figure 4A). It is considered that they may be the core ASVs in the gut, and they have been annotated into nine phylum-level species, with a relative abundance of more than 30%. The annotation results are shown in Supplementary Table S5. The diversity analysis results showed that with the growth and development of sheep, the Shannon index and Chao1 index of microorganisms in rectal feces both showed an increasing trend at the early stage and tended to be stable after reaching a peak at 120D but there was a decreasing trend, and the Chao1 index at 160D was significantly lower than that at 120D (P < 0.05) (Figure 4B and C). This indicates that the early intestinal microflora of sheep is in a state of gradual establishment, and the microbial diversity and richness both show an increasing trend. PCoA results based on the Bray–Curtis distance showed that the microbiota was clearly divided into two parts during the 3 to 180D period, the before-weaning period and the after-weaning period (Figure 4D). PERMANOVA analysis revealed that the microflora structure was significantly different between groups (P < 0.05). In order to explore the effect of sheep’s early intestinal flora on the flora at 180D, Source Tracker was used for prediction (Figure 4E). As shown in Figure 3E, preweaning intestinal flora can only explain 1.55% of the microbial sources in 180D, while postweaning microflora at each stage can explain 59.27% of the microbial sources in 180D, indicating that weaning has a greater impact on the microflora.

Figure 4.

Figure 4.

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Longitudinal development of fecal microbial diversity in the gut. (A) Composition analysis of ASVs, the ASVs on the petals are all ASVs at a time point, and the center of the petals are ASVs shared by all stages. (B) Shannon index. (C) Chao1 index. (D) Principal coordinates analysis based on Bray–Curtis distance, circle points are before weaning and square points are after weaning. PERMANOVA analysis was the same as above. (E) Source tracker analysis, each sector represents the contribution of microorganisms at the corresponding stage to the composition of intestinal microorganisms at 180D.

Changes in microbial composition and abundance of biomarkers

The clustering tree based on Bray–Curtis distance obtained the same result as PCoA, and the clustering tree was divided into two clusters: before weaning and after weaning branches (Figure 5A). The annotation results showed that at the phylum level, Firmicutes showed a relatively stable relative abundance throughout the whole stage, while the relative abundance of Bacteroidetes was low before weaning and then increased in abundance. Interestingly, Proteobacteria had higher abundance before weaning and decreased after weaning (Figure 5A). Cluster analysis was performed on the microorganisms at the genus level. Among the TOP30 genera, 13 belonged to Firmicutes and seven belonged to Bacteroidetes. Bacteroides were highly clustered at all stages, and most genera were highly clustered after weaning. Only Escherichia–Shigella and Butyricicoccus are highly clustered before weaning, while Lachnoclostridium is highly clustered in 3D (Figure 5B).

Figure 5.

Figure 5.

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Dynamic changes in microbial composition. (A) Bray–Curtis distance-based phylogenetic tree and stacked plot of TOP10 microorganisms at the phylum level. (B) Clustering heatmap of TOP30 microorganisms at the genus level. (C) Abundance changes of FCR-related biomarkers at different stages.

We also explored the longitudinal development of the identified FCR-related biomarkers (Figure 5C). The results showed that the relative abundance of UCG-010 (Oscillospirales) gradually increased in the early stage, reached a peak at 120D, and then decreased in abundance. The abundance of Turicibacter increases at 140 to 180D. Romboutsia, Prevotellaceae_UCG-003, and Eubacterium_xylanophilum_group were the most abundant at 15D, 100D, and 160D, respectively.

Developmental changes in microbial function

Based on the KEGG database, the microbial functional pathways at each stage were predicted. The PCA obtained the same results as the species analysis, and the predicted functional pathways were clearly divided into two parts: before weaning and after weaning (Supplementary Figure S6). In pathway level 1, the functional pathways of microorganisms in each stage are mainly metabolism. We carried out an enrichment analysis on the TOP30 functional pathways in pathway level 2, and we standardized the data (Figure 6). The enrichment results showed that there were differences in the enrichment of microbial functional pathways between preweaning and postweaning microorganisms. Among them, xenobiotics biodegradation and metabolism, carbohydrate metabolism, membrane transport, cellular community—prokaryotes and metabolism of other amino acids were only highly enriched before weaning, while immune system, translation and cell growth and death were only highly enriched after weaning.

Figure 6.

Figure 6.

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The enrichment heat map of TOP30 functional pathways based on the KEGG database.

Discussion

It can be seen from Supplementary Figure S1 that sheep with low FCR have less feed intake but grow faster, indicating that sheep with low FCR can digest the nutrients in the feed more thoroughly so that sheep can obtain more energy by eating less feed. There are many factors affecting feed efficiency, among which alimentary tract microorganisms of ruminants can improve the VFAs supplied to the host through higher fermentation efficiency, thus affecting feed efficiency. Previous studies have shown that rumen microorganisms have an impact on the feed efficiency traits of ruminants (Li and Guan, 2017; Zhang et al., 2021b) but research on hindgut microbes is relatively scarce. Quan et al. reported that feed efficiency traits in pigs were influenced by the microbial composition and function of rectal feces (Quan et al., 2019). We therefore investigated the relationship between the gut microbiota and feed efficiency in sheep.

The results of the diversity analysis showed that there was no significant difference in microbial diversity and richness of sheep among different FCR groups, which was consistent with the results of the study on pigs (Si et al., 2020; Jiang et al., 2021). Studies on the rumen of sheep showed different results, Zhang et al. showed high α-diversity in the high feed efficiency group, while McLoughlin et al. showed high α-diversity in the low feed efficiency group (McLoughlin et al., 2020; Zhang et al., 2021b). Previous reports showed significant differences based on principal coordinate analysis of rumen-weighted UniFrac distances in sheep with different feed efficiencies, and we observed a similar phenomenon in rectal swabs (McLoughlin et al., 2020). Consistent with previous reports, Firmicutes was the most abundant phylum in rectal feces (Cheng et al., 2022a). Interestingly, Ma et al. believed that the dominant bacterial genera in sheep rectum were Christensenellaceae_R7_group and Ruminococcaceae_UCG-005 (Ma et al., 2022), Zhang et al. believed that the dominant bacterial genera were Bacteroides and Ruminococcus (Zhang et al., 2018), while our study showed that the dominant bacterial genera were Rikenellaceae_RC9_gut_group and UCG-005 (Oscillospiraceae). This may be due to gut microbes being influenced by species (Cheng et al., 2022b), environment (Zhang et al., 2023), and diet composition (Seddik et al., 2018). Mainz et al. reported that overeating increased the relative abundance of Campilobacterota in the gut of mice(Mainz et al., 2022), suggesting that the high abundance of Campilobacterota in H-FCR may be related to its high feed intake. We identified 11 significantly different bacterial genera in the rectum of different FCR sheep, and we used linear fitting analysis to verify the results, and finally got six biomarkers related to FCR. Reducing the abundance of Eubacterium_xylanophilum_group alleviates colitis in a microbiota-dependent manner and has also been shown to significantly reduce body weight loss (Li et al., 2022a; Peng et al., 2022). The same is that Romboutsia is also considered to be a microorganism associated with inflammation (Zhuge et al., 2022). Therefore, we believe that they may affect the FCR by affecting the intestinal inflammatory response to interfere with the absorption of nutrients, sheep with low FCR may have healthier guts. The study by Shi et al. showed that the abundance of Ruminobacter was significantly decreased in goat subgroups with high daily gain and low FCR subgroups, which is similar to our results (Shi et al., 2020). Previous studies have shown that Prevotellaceae_UCG-003 is negatively correlated with isovaleric acid, and that acetic acid, isobutyric acid, and isovaleric acid were all significantly higher in the group with the lowest abundance of UCG-010 (Oscillospirales). These results suggest that they may affect FCR through a volatile fatty acid pathway (Pang et al., 2022; Li et al., 2023b). Hernandez-Patlan’s report showed that broilers in the experimental group had a numerically more efficient FCR compared with the control group, and the abundance of Turicibacter in the experimental group was significantly reduced, which is consistent with our results in sheep (Hernandez-Patlan et al., 2019).

The KEGG functional pathway of hindgut microorganisms is still mainly based on metabolism, and hindgut microorganisms ferment undigested nutrients through their own metabolism (Arieli and Sklan, 1985). These mainly include carbohydrates in cereal-based ingredients and amino acids of proteins not absorbed by the small intestine, which is consistent with the results of KEGG level 2 (Abdallah et al., 2020; Sanz-Fernandez et al., 2020). The main functions of different FCR sheep are similar. In the KEGG database, the oxidative phosphorylation abundance of H-FCR is higher, and the growth rate of the flora is directly proportional to the oxidative phosphorylation (Jones, 1978), which may intensify the growth of harmful bacteria. The metabolic pathways were also predicted. The MetaCyc database showed that L-FCR has a high abundance in the biosynthetic pathways of fatty acids and palm oleate. Microbial fermentation will produce short-chain fatty acids, which can provide 70% of the energy for sheep (Bergman, 1990), while palm oil salt has an inhibitory effect on inflammation (Wu et al., 2012). The results of differential microbial and functional pathway analysis indicated that intestinal flora may affect FCR through the synthesis of VFAs and the regulation of inflammatory responses.

We also investigated the longitudinal development of the hindgut microbiota after our study showed that the hindgut microbiota was associated with FCR. In the life cycle of sheep, with the increase of sheep growth stage. A diversity showed an increasing trend in the early stage, which was consistent with the results of pigs (Wang et al., 2019b), and the same conclusion was also obtained in the rumen of sheep (Li et al., 2023a). Interestingly, we observed a decline in diversity after peaking, and Han had similar results (Han et al., 2018). The results of diversity showed that the gut of lambs would gradually establish microflora from a relatively sterile state at birth through breast milk, environment, and other ways, so the diversity would gradually increase. However, in the later stage of development, when the feed remains unchanged, the food composition might selectively promote the growth of some microorganisms while inhibiting others, resulting in a decline in microbial diversity. However, further experiments are needed to verify this. From a nutritional point of view, young ruminants such as lambs have a different digestion pattern than mature ruminants (Drackley, 2008). The principal coordinate analysis also reflected this point, and the nine-time nodes were divided into before weaning and after weaning. De Rodas et al. believe that the introduction of solid feed has a greater impact on intestinal flora than age, environment and other factors (De Rodas et al., 2018). Source Tracker analysis revealed that the postweaning microbiota contributed most to the composition of the microbiota at 180D. The results of Holman et al. showed that there was a significant change in the fecal microbiome between 3 and 7 d after weaning, and the source of this change may be mainly due to the transition from breast milk to plant-based diets (Frese et al., 2015; Holman et al., 2021).

Throughout the developmental stages, Firmicutes maintained a high abundance, while Bacteroides had a low abundance before weaning, and Proteobacteria had a high abundance before weaning but a low abundance after weaning. This has also been observed in Tibetan sheep (Wang et al., 2019a). More studies have shown that Proteobacteria can respond to unstable intestinal microbial community structure (Carvalho et al., 2012; Smoliński et al., 2021), which reflects the instability of intestinal flora structure in sheep. Bacteroides are also enriched at all stages, mainly providing nutrients and vitamins to the host by metabolizing polysaccharides and oligosaccharides (Zafar and Saier, 2021) and are the core genus of intestinal bacteria. Escherichia–Shigella is only highly enriched before weaning, and similar results are found in the intestines of human infants and piglets (Marrs et al., 2021; Choudhury and Kleerebezem, 2022). Studies have shown that the core microbiota in goat milk includes Escherichia–Shigella, so we believe that Escherichia–Shigella in the intestines of young animals may come from breast milk (Esteban-Blanco et al., 2020). Meanwhile, Escherichia–Shigella may be one of the reasons for the susceptibility to diarrhea in young animals (Gomez et al., 2022). Qi et al. reported that anaerobic bacteria such as Lachnoclostridium are passed to the infant’s gut through breastfeeding, so our results show its enrichment in 3D (Qi et al., 2022). The sources of intestinal microorganisms in newborn animals mainly include breast milk, vagina, teat skin and the environment but obviously, the addition of solid feed has a greater impact on the intestinal tract (Mach et al., 2015; Vo et al., 2017). Most of the FCR-related biomarkers we identified increased in abundance after weaning. Therefore, we believe that postweaning is an appropriate period for microbial regulation.

The results of principal component analysis of KEGG functional pathways were consistent with those of species analysis, and the functional pathways before weaning and after weaning were quite different. Carbohydrate metabolic pathways enriched before weaning may be related to the fermentation of lactose, which transports metabolites to the host via membrane transport (He et al., 2008). After weaning, the abundance of harmful bacteria decreased, the intestinal microflora tended to be stable, and the anti-interference ability was enhanced, so the immune system pathways were enriched (Shi et al., 2017). Meanwhile, lipid metabolism and glycan biosynthesis and metabolic pathways were highly enriched at 160D, suggesting that this may be a critical stage of fat deposition.

Conclusion

Taken together, our results reveal a hindgut microbiota associated with sheep FCR and probe the longitudinal development of the sheep hindgut microbiota. We found that there were differences in microbial flora in different FCR groups, and identified six biomarkers with a significant linear relationship with FCR, which may affect FCR through VFAs and inflammatory responses. The longitudinal development of hindgut microorganisms tends to be in a stable state. Cluster analysis and functional prediction divide the microorganisms at different stages into two parts: before weaning and after weaning. Weaning will have a huge impact on the microflora. We suggest that modulation of FCR by modulation of the microbiota is appropriate after weaning.

Supplementary Material

skad322_suppl_Supplementary_Figures_S1
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skad322_suppl_Supplementary_Figures_S2
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skad322_suppl_Supplementary_Figures_S3
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skad322_suppl_Supplementary_Figures_S4
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skad322_suppl_Supplementary_Figures_S5
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skad322_suppl_Supplementary_Figures_S6
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skad322_suppl_Supplementary_Tables_S1-S5
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Acknowledgments

This work was supported by the National Key Research and Development Program (2022YFD1302000), the National Natural Science Foundation of China (32260818), the Major Science and Technology Projects in Gansu Province (22ZD6NC069), the West Light Foundation of The Chinese Academy of Sciences (20JR10RA506).

Glossary

Abbreviations:

ADG

average daily gain

ASVs

amplicon sequence variants

FCR

feed conversion ratio

KEGG

Kyoto encyclopedia of genes and genomes

PCA

principal component analysis

PCoA

principal coordinate analysis

RFI

residual feed intake

VFAs

volatile fatty acids

Contributor Information

Dan Xu, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Jiangbo Cheng, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Deyin Zhang, The State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu 730020, China.

Kai Huang, The State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu 730020, China.

Yukun Zhang, The State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu 730020, China.

Xiaolong Li, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Yuan Zhao, The State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu 730020, China.

Liming Zhao, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Jianghui Wang, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Changchun Lin, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Xiaobin Yang, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Rui Zhai, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Panpan Cui, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Xiwen Zeng, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Yongliang Huang, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Zongwu Ma, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Jia Liu, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Kunchao Han, The State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu 730020, China.

Xiaoqiang Liu, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Fan Yang, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Huibin Tian, The State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu 730020, China.

Xiuxiu Weng, The State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu 730020, China.

Xiaoxue Zhang, College of Animal Science and Technology, Gansu Agricultural University, Lanzhou, Gansu 730070, China.

Weimin Wang, The State Key Laboratory of Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu 730020, China.

Conflict of Interest statement

None.

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