Abstract

   Hybridization can substantially improve growth performance. This study
   used metagenomics and metabolome sequencing to examine whether the
   rumen microbiota and its metabolites contributed to this phenomenon. We
   selected 48 approximately 3 month-old male ♂Hu × ♀Hu (HH, n = 16),
   ♂Poll Dorset × ♀Hu (DH, n = 16), and ♂Southdown × ♀Hu (SH, n = 16)
   lambs having similar body weight. The sheep were fed individually under
   the same nutritional and management conditions for 95 days. After
   completion of the trial, seven sheep close to the average weight per
   group were slaughtered to collect rumen tissue and content samples to
   measure rumen epithelial parameters, fermentation patterns, microbiota,
   and metabolite profiles. The final body weight (FBW), average daily
   gain (ADG), and dry matter intake (DMI) values in the DH and SH groups
   were significantly higher and the feed-to-gain ratio (F/G)
   significantly lower than the value in the HH group; additionally, the
   papilla height in the DH group was higher than that in the HH group.
   Acetate, propionate, and total volatile fatty acid (VFA) concentrations
   in the DH group were higher than those in the HH group, whereas NH[3]-N
   concentration decreased in the DH and SH groups. Metagenomic analysis
   revealed that several Prevotella and Fibrobacter species were
   significantly more abundant in the DH group, contributing to an
   increased ability to degrade dietary cellulose and enrich their
   functions in enzymes involved in carbohydrate breakdown. Bacteroidaceae
   bacterium was higher in the SH group, indicating a greater ability to
   digest dietary fiber. Metabolomic analysis revealed that the
   concentrations of rumen metabolites (mainly
   lysophosphatidylethanolamines [LPEs]) were higher in the DH group, and
   microbiome-related metabolite analysis indicated that Treponema
   bryantii and Fibrobacter succinogenes were positively correlated with
   the LPEs. Moreover, we found methionine sulfoxide and
   N-methyl-4-aminobutyric acid were characteristic metabolites in the DH
   and SH groups, respectively, and are related to oxidative stress,
   indicating that the environmental adaptability of crossbred sheep needs
   to be further improved. These findings substantially deepen the general
   understanding of how hybridization promotes growth performance from the
   perspective of rumen microbiota, this is vital for the cultivation of
   new species and the formulation of precision nutrition strategies for
   sheep.

   Keywords: hybridization, rumen, metagenome, growth performance, sheep

1. Introduction

   Hybridization is an important method of breeding sheep that can produce
   heterosis, resulting in a new breed that is stronger or performs better
   than its parents ([43]1). Binary hybridization in the cultivation of
   new varieties of mutton sheep has been frequently reported in China
   ([44]2). Even though three-way crossbreeding is commonly used in pig
   production and for developing new breeds ([45]3, [46]4), relevant
   research is still in the early stages of selecting suitable species on
   mutton sheep industry ([47]5). Compared with the former, the latter
   could effectively utilize the heterosis of three hybrid individuals,
   producing a significantly stronger effect. Hu is a native sheep breed
   with perennial estrus, high fecundity, high lactation, good parenting
   traits, and strong adaptability; therefore, they are often used as
   crossbreeding ewes for mutton sheep, however, their meat production
   rate and dressing percentage are unsatisfactory ([48]6, [49]7). Poll
   Dorset is native to Australia and New Zealand, has the characteristics
   of fast growth, early maturity, and perennial estrus, and is considered
   the main parent for hybridization in the Chinese sheep breeding
   industry ([50]8). Southdown is considered the best breed for meat
   quality in the United Kingdom due to its ideal meat structure, early
   maturity, easy breeding, good carcass quality, and dressing
   percentage > 55% ([51]9, [52]10). Southdown and Poll Dorset are small
   sheep species that grow rapidly in the early stages and are suitable
   for producing high-grade fat lambs, which aligns with the current
   market demand for mutton in China. Therefore, we attempted to cultivate
   a new breed of sheep by using Poll Dorset and Southdown as sires,
   crossing them with Hu sheep, and monitoring the production traits of
   the F[1] generation.

   Growth performance is the main selection trait for breeding new sheep
   varieties. The rumen microbiota is associated with animal growth and
   health ([53]11). The rumen is colonized by bacteria, protozoa, archaea,
   fungi, and viruses, which degrade complex plant fibers and
   polysaccharides to produce volatile fatty acids (VFAs), microbial
   proteins, and vitamins, in turn providing nutrients to meet host
   requirements for maintenance and growth ([54]12, [55]13). Although diet
   plays a major role in shaping the gastrointestinal microbiota, recent
   evidence has revealed host genetics to be an important factor in
   determining the gut microbiota composition in cattle ([56]13–[57]15).
   However, research on the effects of host genetics on the rumen
   microbiota composition of hybridized sheep is limited.

   Therefore, in this study, we hypothesized that rumen microbiota
   composition and function in sheep are affected by host genetics,
   affecting growth performance in turn. We assessed the rumen microbiota
   and metabolite profiles of different hybridized sheep raised in the
   same farm environment, aiming to elucidate the underlying molecular
   mechanisms from the perspective of rumen microbiota and thus provide a
   theoretical and practical basis for improving the growth performance of
   hybridized sheep.

2. Materials and methods

2.1. Animals, experimental design, and sample collection

   This study was conducted at the Qinghuan Mutton Sheep Breeding Company
   in Huanxian County, Gansu, China from October 2022 to January 2023. The
   experimental design, procedures, and methods were approved by the
   Animal Administration and Ethics Committee of the Lanzhou Institute of
   Husbandry and Pharmaceutical Science of the Chinese Academy of
   Agricultural Science under permission number 2022–018 and followed the
   Chinese standards for the use and care of animals. Forty-eight
   approximately 3 month-old male ♂Hu × ♀Hu (HH, n = 16), ♂Poll
   Dorset × ♀Hu (DH, n = 16), and ♂Southdown × ♀Hu (SH, n = 16) lambs were
   selected and raised in a single pen under the same nutritional and
   management conditions for 95 days (including a 15 day pre-test period).
   Hu sheep were chosen from 216 approximately 3 month-old male lambs with
   an average body weight of 22.98 ± 6.37 kg, and F[1] generation sheep of
   similar body weight were selected. Poll Dorset and Southdown sires
   (aged 31 months) generated from embryo transplantation and Hu sires
   (aged 24 months) were crossed with Hu ewes (parity one or two) by
   estrus synchronization and artificial insemination. The offspring (one,
   in case of twins) were confirmed to stem from different fathers. All
   sheep were fed the same total mixed ratio composed of oat hay (6.08%
   DM), Leymus chinensis (13.04% DM), corn silage (13.53% DM), corn
   (32.78% DM), megalac (6.08% DM) and grain mixtures (28.49% DM) twice
   daily at 08:00 and 15:00 ([58]Supplementary Table S1). During the
   experimental period, all animals had access to feed and water ad
   libitum. Feed intake was recorded daily based on the feed offered and
   refusals to calculate the average dry matter intake (DMI). Body weight
   was recorded every 20 d using a digital livestock scale (TCS Electronic
   Platform Scale, Rongcheng, China) to calculate the average daily gain
   (ADG) and feed-to-gain ratio (F/G).

   At the end of the experiment, seven sheep close to the average weight
   per group were selected and fasted for 12 h before harvesting.
   Harvesting was based on standard commercial procedures, individually
   restrained, exsanguinated, skinned, and gutted. Rumen tissue samples of
   approximately 2 cm × 2 cm were carefully separated from the left dorsal
   sac to fixed in 4% paraformaldehyde solution for hematoxylin–eosin
   (H&E) staining. Then, two portions of 5 mL rumen content were collected
   from each animal, transferred into a sterile tube, immediately frozen
   using liquid nitrogen, and stored at −80°C for DNA and metabolite
   extraction. Another 15 mL rumen content was sampled and stored in a
   sterilized container at −20°C for evaluation of fermentation
   parameters. The rumen contents of all animals were collected from the
   left dorsal sac as a mixture of liquid and solid components. All
   samples were collected within 30 min of slaughter.

2.2. Rumen epithelial parameters

   Rumen epithelial parameters, including papilla height, papilla width,
   and muscle thickness, were obtained using the H&E staining technique
   ([59]16). The fixed rumen tissues were dehydrated in alcohol, cleared
   in xylene, and embedded in paraffin. Then the cooling concretionary
   samples were sectioned at 5 μm thickness and mounted on glass slides.
   Paraffin-embedded sections were dewaxed with xylene, passed through a
   graded ethanol series to remove the xylene, rinsed with distilled
   water, and stained with H&E. Subsequently, the paraffin was sealed with
   neutral gum after dehydration and immediately examined. Finally, two
   digital slides were acquired from each animal using a Slide Viewer
   device (3D HISTECH Ltd.) at two-fold magnification; then, five papillae
   (excluding damaged ones) were chosen randomly from each digital slide
   to measure papilla height using ImageJ software (National Institutes of
   Health, Bethesda, MD, United States). Within those papillae measured
   for height, three different regions (apical, middle, and basal) were
   identified to measure papilla width. Each digital slide was divided
   into five equal parts to measure muscle thickness.

2.3. Rumen fermentation parameters

   The pH of the rumen was measured immediately after the sheep were
   slaughtered using an Ark Technology PHS-10 portable acidity meter
   (Chengdu, China). The NH[3]-N concentration was determined by
   colorimetry. Briefly, 2 g of rumen content was accurately weighed and
   mixed with deionized water at a 1:5 ratio. The mixed solution was
   shaken for 1 h at 105 r/min and centrifuged at 12,000 rpm at 4°C for
   20 min. The supernatant was then passed through a 0.45 μm polymer fiber
   filter membrane and stored for further analysis. Subsequently, the
   NH[3]-N concentration was measured using a kit (Biosino Biotechnology
   Co. Ltd., Beijing, China) according to the manufacturer’s instructions
   and a microplate reader (DR-200BS; Hiwell-Diatek, Wuxi, China). VFA
   concentrations were measured as previously described ([60]16). Briefly,
   rumen contents were centrifuged at 5,400 rpm for 10 min. We
   subsequently uniformly mixed 1 mL of the resulting supernatant and a
   0.2 mL 25% metaphosphate solution containing 2-ethylbutyric acid as an
   internal standard in a new centrifuge tube. This reaction tube was then
   immersed in an ice bath (30 min) and centrifuged at 10,000 rpm for
   another 10 min. The supernatant was passed through a 0.22 μm organic
   phase filter membrane and stored in 2 mL bottles for subsequent
   analysis. A gas chromatograph (GC 7890A; Agilent Technologies, Santa
   Clara, CA, United States) fitted with an AT-FFAP capillary column
   (50 m × 0.32 mm × 0.25 μm; Agilent Technologies) was used to determine
   VFA concentrations. The column temperature was maintained at 60°C for
   1 min, raised by 5°C/min to 115°C, and increased by 15°C/min to 180°C.
   Notably, the detector and injector temperatures were 260°C and 250°C,
   respectively.

2.4. Metagenome sequencing and bioinformatics analysis

   Total genomic DNA was extracted from rumen content sample using the
   E.Z.N.A. Soil DNA Kit (Omega Biotek, Norcross, GA, United States). The
   DNA concentration and purity were determined using TBS-380
   mini-fluorometer (Promega, Madison, WI, United States) and NanoDrop
   2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, United
   States), respectively. The DNA was fragmented to approximately 400 bp
   for paired-end library construction using a Covaris M220 (Gene Company
   Limited, Hong Kong, China). Metagenome libraries sequencing was
   performed using an Illumina NovaSeq 6,000 platform (Illumina Inc., San
   Diego, CA, United States).

   Quality control of the metagenomic sequence reads was performed using
   the Fastp software (version 0.20.0) to trim the 3′-end and 5′-end of
   reads, cut low-quality bases, and remove short reads and “N” records
   ([61]17). The quality-filtered reads were then aligned to the Ovis
   aries reference genome using BWA v 0.7.1 to filter out the host DNA
   ([62]18). The remaining reads were assembled using Multiple Megahit
   (version 1.1.2) ([63]19), and contigs with lengths ≥300 bp were
   selected as the final assembling result for the prediction of open
   reading frames (ORF) using MetaGene v 0.3.38 ([64]20). Non-redundant
   contigs were identified using CD-HIT with 95% sequence identity and 90%
   coverage ([65]21). Original sequencing reads were mapped to predicted
   genes to estimate their abundance using SOAPAligner v. 2.21 ([66]22).
   Subsequently, the non-redundant gene catalog was aligned to the NCBI
   non-redundant protein sequence database using BLASTP (version 2.2.28+)
   to obtain taxonomic annotations and species abundances ([67]23).
   Taxonomic profiles were conducted at domain, phylum, genus and species
   levels, with relative abundances calculated. The PCoA based on
   Bray–Curtis dissimilarity matrices at domain level was performed.
   Bacteria with a relative abundance >0.01% and archaea with a relative
   abundance >0.001% were analyzed. Finally, a BLAST search (version
   2.2.28+) with an optimization criterion cutoff of 1e^−5 was annotated
   against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database
   ([68]24). Carbohydrate-active enzyme (CAZyme) annotation was conducted
   using hmmscan[69]^1 against CAZy database version 5.0[70]^2 with an
   e-value cutoff of 1e^−5.

2.5. Metabolome sequencing and bioinformatics analysis

   Rumen sample metabolite extraction was based on previously published
   procedures ([71]16). The metabolome of the rumen content was analyzed
   by ultra-performance liquid chromatography (UPLC; Shim-pack UFLC
   Shimadzu CBM30A; Shimadzu, Kyoto, Japan) and tandem mass spectrometry
   (MS/MS, QTRAP^® 6,500+, SCIEX, Framingham, MA, United States) ([72]25).
   After obtaining the liquid chromatography–mass spectrometry data of the
   samples, the extracted ion chromatographic peaks of all metabolites
   were integrated using MultiQuant software (Applied Biosystems, Foster
   City, CA, United States). The chromatographic peaks of the metabolites
   in different samples were corrected using integration ([73]26). The
   relative concentrations of rumen metabolites were screened by fold
   change (FC) (FC ≥ 2 or FC ≤ 0.5) and variable importance in projection
   (VIP) (VIP ≥ 1) to identify the different metabolites. Identified
   metabolites were annotated using the KEGG compound database ([74]27).

2.6. Statistical analysis

   All data were checked for normality and outliers using SPSS version
   26.0 (IBM Corp., Armonk, NY, United States) before any statistical
   analyses were conducted. One-way ANOVA and LSD post-hoc test was used
   to analyze the data of growth performance as well as rumen epithelial
   and fermentation parameters. Spearman’s correlation test was used for
   correlation analyses. Statistical significance was set to p < 0.05.
   GraphPad Prism version 8.5 (GraphPad Software, La Jolla, CA, United
   States) drew the statistical maps.

3. Results

3.1. Hybridization promoted growth performance in Hu sheep

   A summary of the growth performance data is presented in [75]Table 1.
   The initial body weight (IBM) did not differ significantly among the
   three groups (p = 0.156). The DH and SH groups exhibited greater ADG
   than did the HH group (p = 0.008) but did not differ from each other.
   The final body weight (FBW) was greater in the DH and SH groups than in
   the HH group (p < 0.001). DMI was also greater in the DH and SH groups,
   resulting in a lower F/G value than that in the HH group (p = 0.013).

Table 1.

   Hybridization promoted growth performance in Hu sheep.
   Measurements                     HH       DH       SH     SEM  p value
   Initial body weight (IBM, kg)   23.36    23.64    24.21  0.43   0.156
   Final body weight (FBW, kg)   44.33^b  49.31^a  49.16^a  0.81  <0.001
   Average daily gain (ADG, g/d) 223.57^b 259.29^a 258.66^a 11.36  0.008
   Dry matter intake (DMI, kg/d)  0.86^b   0.88^a   0.89^a  0.01   0.001
   Feed/gain (F/G)                7.84^a   6.86^b   6.90^b  0.33   0.013
   [76]Open in a new tab

   Different lower-case letters indicate significant differences between
   groups. HH = ♂Hu × ♀Hu; DH = ♂Poll Dorset × ♀Hu; SH = ♂Southdown × ♀Hu;
   SEM = standard error of the mean.

3.2. Hybridization increased rumen epithelial development in Hu sheep

   Three rumen epithelial development indices, including papilla height,
   papilla width, and muscle thickness, were measured using H&E staining
   ([77]Figures 1A–[78]C). Hybridization had no effect on rumen papilla
   width or muscle thickness (p > 0.128; [79]Figures 1E,[80]F). The
   largest difference was observed in papilla height, the DH group
   exhibited a greater height than the HH group (p = 0.046; [81]Figure
   1D).

Figure 1.

   [82]Figure 1
   [83]Open in a new tab

   Hybridization promoted rumen epithelial development in Hu sheep. (A)
   Rumen epithelium hematoxylin and eosin (H&E) staining in the HH group.
   (B) Rumen epithelium H&E staining in the DH group. (C) Rumen epithelium
   H&E staining in the SH group. (D) Rumen papilla height. (E) Rumen
   papilla width. (F) Muscle thickness. n = 7 individuals/group; *
   indicates p < 0.05.

3.3. Hybridization altered rumen fermentation parameters in Hu sheep

   The NH[3]-N concentration was significantly lower in the SH and DH
   groups than in the HH group (p = 0.023; [84]Figure 2). Acetate and
   propionate concentrations in the DH group were significantly higher
   than those in the HH group (p < 0.041), resulting in higher total VFAs
   (p = 0.048). The pH value as well as butyrate, valerate, isobutyrate,
   isovalerate, and acetate/propionate concentrations remained unchanged.

Figure 2.

   [85]Figure 2
   [86]Open in a new tab

   Hybridization altered rumen fermentation parameters in Hu sheep. n = 7
   individuals/group; * indicates p < 0.05.

3.4. Profiling of the rumen metagenome

   Metagenome sequencing generated a total of 1,443,800,424 raw reads,
   with an average of 68,752,401 ± 5,953,935 reads (mean ± SD) per sample.
   After quality control and removing host genes, a total of 1,284,320,034
   reads were retained, or 61,158,097 ± 5,264,516 per sample. After
   assembly, a total of 19,962,258 contigs were generated (with an average
   N50 length of 825 ± 60), with 950,584 ± 91,196 per sample
   ([87]Supplementary Table S2). The results indicated that the sequencing
   data were credible and could be used for subsequent bioinformatics
   investigations. The α-diversity analysis using Kruskal–Wallis Test
   revealed that the Shannon index was significantly increased in the DH
   group compared to that in the SH group (p = 0.016; [88]Figure 3A),
   while the Ace and Simpson indices did not differ among the three groups
   ([89]Supplementary Table S3). The rumen metagenome comprised 97.18%
   bacteria (591,631,456 sequences), 1.60% archaea (9,758,146 sequences),
   0.43% eukaryotes (2,601,458 sequences), and 0.78% viruses (4,731,066
   sequences). Principal coordinate analysis (PCoA) at the domain level
   revealed a separation between the HH and DH groups and HH and SH groups
   based on bacteria ([90]Figure 3B), as well as between the SH and HH
   groups and SH and DH groups based on archaea ([91]Figure 3C), no
   separation was observed based on eukaryotes or viruses
   ([92]Supplementary Figure S1). Therefore, the comparative analysis of
   rumen microbial taxa among the three groups focused only on bacteria
   and archaea.

Figure 3.

   [93]Figure 3
   [94]Open in a new tab

   Alpha diversity index and microbial compositional profiles of rumen
   sample principal coordinate analysis (PCoA). (A) Alpha diversity as
   presented by the Shannon index. (B) PCoA based on bacterial domain. (C)
   PCoA based on archaeal domain. n = 7 individuals/group; * indicates
   p < 0.05.

3.5. Compositional profiles of the rumen microbiome and taxonomic differences

   The dominant bacterial phyla in the rumen were Bacteroidetes (HH:
   44.39%; DH: 44.87%; SH: 45.53%) and Firmicutes (HH: 42.72%; DH: 40.33%;
   SH: 43.24%), followed by Spirochaetes (HH: 2.81%; DH: 4.64%; SH:
   2.55%); however, no significant differences were found among the three
   groups (p > 0.05; [95]Supplementary Figure S2A). A total of 5,413
   genera were identified. Among them, unclassified o Bacteroidales (HH:
   19.53%; DH: 14.50%; SH: 20.21%), Prevotella (HH: 14.31%; DH: 19.93%;
   SH: 14.87%), and unclassified c Clostridia (HH: 8.33%; DH: 7.03%; SH:
   8.83%) were the predominant bacteria in the rumen samples
   ([96]Supplementary Figure S2B). A total of 20,943 species were found,
   among which the dominant bacteria were the Bacteroidales bacterium (HH:
   19.35%; DH: 14.42%; SH: 20.00%), Prevotella sp. (HH: 12.37%; DH:
   16.29%; SH: 12.71%) and Clostridia bacterium (HH: 8.33%; DH: 7.03%; SH:
   8.83%) ([97]Supplementary Figure S2C).

   Differential analysis revealed a total of 28 significantly different
   bacterial species between the DH and HH groups. Among them, 18
   exhibited significantly higher abundances in the DH group. The relative
   abundances of Treponema bryantii, Fibrobacter sp., Prevotella sp.
   tc2-28, Spirochaetia bacterium, and Alloprevotella sp. were the top
   five most significantly increased. Additionally, the relative
   abundances of Prevotella copri, Prevotella sp. BP1-148, Prevotella sp.
   E13-3, Fibrobacter succinogenes, Fibrobacter sp. UWB4, Fibrobacter sp.
   UWB2, Fibrobacter sp. UWR2, Fibrobacter sp. UWOV1, and Fibrobacter sp.
   UWB1 was also significantly increased in the DH group. Ten bacteria
   were significantly enriched in the HH group, mainly Bacteroidales
   bacterium, Anaerolineaceae bacterium, Sarcina sp., Sarcina sp. DSM
   11001, and Flexilinea sp. ([98]Figure 4A). Compared to levels in the HH
   group, only one bacterium (Bacteroidaceae bacterium) was significantly
   enriched in the SH group, whereas three (Succiniclasticum ruminis,
   Sarcina sp. DSM 11001, and Ruminococcus sp. M6(2020)) were
   significantly more abundant in the HH group ([99]Figure 4B). When
   compared to levels in the DH group, 19 bacterial abundances were
   significantly different, including six (Bacteroidales bacterium,
   Pyramidobacter sp., Clostridiaceae bacterium, Bacteroidales bacterium
   WCE2004, Bacteroidetes bacterium, and unclassified p Firmicutes) with
   higher abundances in the SH group and 13 (Treponema sp.,
   Acidaminococcaceae bacterium, Fibrobacter sp., Treponema bryantii,
   Schwartzia sp., Prevotella sp.tc2-28, Spirochaetia bacterium,
   Schwartzia succinivorans, Fibrobacter succinogenes, Fibrobacter sp.
   UWB2, Fibrobacter sp. UWB3, Eubacterium ruminantium, and Fibrobacter
   sp. UWB12) with higher abundances in the DH group ([100]Supplementary
   Figure S3A).

Figure 4.

   [101]Figure 4
   [102]Open in a new tab

   Differential rumen bacterial and archaeal species. (A) Significantly
   different bacterial species between the HH and DH groups. (B)
   Significantly different bacterial species between the HH and SH groups.
   (C) Significantly different archaeal species between the HH and DH
   groups. (D) Significantly different archaeal species between the HH and
   SH groups. n = 7 individuals/group.

   At the species level, the abundance of two archaeal species
   (Nitrosopumilales archaeon and Candidatus Methanomethylophilus alvus)
   was significantly enriched and one (Methanophagales archaeon)
   significantly decreased in the DH group compared to levels in the HH
   group ([103]Figure 4C). The abundance of three archaea
   (Methanomicrobium sp., Methanolobus bombayensis, and Methanococcus
   voltae) was significantly enriched and three (Candidatus Helarchaeota
   archaeon, Thermoplasmata archaeon M8B2D, and Theionarchaea archaeon)
   decreased in the SH group compared to levels in the HH group
   ([104]Figure 4D). When compared with levels in the DH group, the
   abundances of Methanocorpusculum sp. and Candidatus Altiarchaeales
   archaeon WOR SM1 86-2 were significantly higher, whereas the abundances
   of Candidatus Methanomethylophilaceae archaeon, Candidatus
   Methanomethylophilus sp., Methanomicrobium sp., Methanobacterium sp.
   ERen5, unclassified g Methanobacterium, and Methanolobus bombayensis
   were significantly lower than those in the SH group ([105]Supplementary
   Figure S3B).

3.6. Functional profiles and differences of the rumen microbiome

   The functions of the rumen microbiome were determined using KEGG
   profiles and genes encoding CAZymes. For KEGG profiles, a total of six
   pathways were annotated at the first level among the three groups,
   including “metabolism” (HH: 49.78%; DH: 50.16%; SH: 50.85%), “genetic
   information processing” (HH: 16.78%; DH: 16.71%; SH: 17.05%),
   “environmental information processing” (HH: 12.92%; DH: 12.98%; SH:
   12.77%), “cellular processes” (HH: 9.71%; DH: 9.68%; SH: 9.42%), “human
   diseases” (HH: 6.67%; DH: 6.47%; SH: 6.22%) and “organismal systems”
   (HH: 4.15%; DH: 4.00%; SH: 3.70%). At the second level, we obtained
   only one profile, “metabolism of cofactors and vitamins,” that was
   significantly enriched in the DH group compared to level in the HH
   groups ([106]Supplementary Figure S4A). Nine KEGG profiles were
   significantly enriched in the SH group compared to levels in the HH
   group, among them, “replication and repair,” “nucleotide metabolism,”
   and “biosynthesis of other secondary metabolites” were the most
   dominant ([107]Supplementary Figure S4B). Seven KEGG profiles were
   significantly different between the DH and SH groups. “glycan
   biosynthesis and metabolism” and “nucleotide metabolism” were the most
   enriched in the SH group, while “signal transduction” was enriched in
   the DH group ([108]Supplementary Figure S4C). When comparing the
   identified KEGG pathways at the third level, “biofilm
   formation—pseudomonas aeruginosa,” “bacterial chemotaxis,” and
   “riboflavin metabolism” were significantly enriched in the DH group,
   while the “lysosome” and “biosynthesis of various plant secondary
   metabolites” pathways were significantly decreased compared to levels
   in the HH group ([109]Figure 5A). A total of 21 differential pathways
   were identified in the HH and SH groups. Among them, 13 were
   significantly enriched in the SH group, mainly including “biosynthesis
   of amino acids,” “homologous recombination,” “peptidoglycan
   biosynthesis,” “nucleotide metabolism,” “pyrimidine metabolism,”
   “mismatch repair,” “DNA replication,” “alanine, aspartate and glutamate
   metabolism,” “carbon fixation pathways in prokaryotes,” “beta-lactam
   resistance,” “lysine biosynthesis,” “glycine, serine and threonine
   metabolism,” and “one carbon pool by folate”; whereas eight were
   significantly enriched in the HH group ([110]Figure 5B). Twenty
   differential pathways were identified in the DH and SH groups. Among
   them, five were significantly enriched in the DH group and 15 in the SH
   group. “Carbon metabolism,” “aminoacyl-tRNA biosynthesis,” “nucleotide
   metabolism,” “pyrimidine metabolism,” and “other glycan degradation”
   were the top five pathways the most significantly enriched in the SH
   group ([111]Supplementary Figure S5A).

Figure 5.

   [112]Figure 5
   [113]Open in a new tab

   Differential rumen microbiome functions. (A) Significantly differential
   KEGG functions at the third level between the HH and DH groups. (B)
   Significantly differential KEGG functions at the third level between
   the HH and SH groups. (C) Significantly differential CAZy functions at
   the family level between the HH and DH groups. (D) Significantly
   differential CAZy functions at the family level between the HH and SH
   groups. Significant differences were tested by linear discriminant
   analysis effect size (LEfSe) analysis, with linear discriminant
   analysis (LDA) score > 2 and p < 0.05. n = 7 individuals/group.

   For the CAZyme profiles, 55 differentially expressed genes encoding
   CAZymes were identified between the HH and DH groups at the family
   level ([114]Figure 5C). Among them, 23 involved in the breakdown of
   carbohydrates (including cellulose, hemicellulose, starch, protein, and
   lignin) were enriched in the DH group (2 carbohydrate esterases [CEs],
   18 glycoside hydrolases [GHs], and 3 polysaccharide lyases [PLs]), and
   18 in the HH group (2 CEs, 15 GHs, and 1 PL). Five glycosyltransferases
   (GTs) involved in carbohydrate synthesis were enriched in the DH group,
   while three were enriched in the HH group. Several non-catalytic
   CAZymes were involved in the degradation of complex carbohydrates as
   parts of carbohydrate-binding modules (CBMs), including two enriched in
   the DH group and four in the HH group. A total of six differentially
   expressed genes encoding CAZymes were observed between the HH and SH
   groups ([115]Figure 5D). Among these, 2 GHs and 1 GT were enriched in
   the SH group, whereas 1 GH and 2 CBMs were enriched in the HH group. We
   obtained 41 differential genes encoding CAZymes between the DH and SH
   groups ([116]Supplementary Figure S5B). Among those involved in
   carbohydrate breakdown, 21 were enriched in the SH group (2 CEs, 15
   GHs, 2 GLs, and 2 PLs), whereas 12 were enriched in the DH group (2
   CEs, 8 GHs, and 2 PLs). Additionally, 3 GTs were enriched in the DH
   group.

3.7. Correlation analysis between phenotype and microbiome

   Spearman’s correlation coefficient analysis identified correlations
   between growth performance, rumen epithelial parameters, fermentation
   parameters, and significantly different microbiomes (|r| > 0.5;
   p < 0.05). When we compared the HH and DH groups, the relative
   abundance of Alloprevotella sp. was positively correlated with FBW, as
   well as the relative abundance of Fibrobacter sp. UWR2 and Fibrobacter
   sp. were positively correlated with DMI, and the relative abundance of
   Alistipes sp. was negatively correlated with FBW, ADG, and DMI
   ([117]Figure 6A). When we compared the HH and SH groups, the relative
   abundance of Bacteroidaceae bacterium was positively correlated with
   FBW and DMI; however, the relative abundances of Ruminococcus sp.
   M6(2020) and Theionarchaea archaeon were negatively correlated with
   NH[3]-N concentration ([118]Figure 6B). When we compared the DH and SH
   groups, we found that Pyramidobacter sp. was negatively correlated to
   acetate, propionate, and butyrate concentrations, the relative
   abundances of Candidatus Methanomethylophilus sp. and Candidatus
   Methanomethylophilaceae archaeon were negatively related to acetate,
   propionate, and total VFA concentrations ([119]Supplementary Figure
   S6).

Figure 6.

   [120]Figure 6
   [121]Open in a new tab

   Correlation analysis between growth performance, rumen epithelial
   parameters, fermentation parameters, and significantly different
   microbiomes. (|r| > 0.5). (A) Between the HH and DH groups. (B) Between
   the HH and SH groups. n = 7 individuals/group. *p < 0.05, **p < 0.01,
   and ***p < 0.001.

3.8. Rumen metabolome analysis

   In total, 947 metabolites were identified in the 21 rumen samples. The
   orthogonal projections to latent structures-discriminant analysis
   (OPLS-DA) score plots showed good separation of metabolites between the
   HH and DH (R^2X = 0.419, R^2Y = 0.994, Q^2 = 0.345), HH and SH
   (R^2X = 0.495, R^2Y = 0.992, Q^2 = 0.580), and DH and SH (R^2X = 0.451,
   R^2Y = 0.991, Q^2 = 0.315) groups ([122]Figure 7A). After screening the
   relative concentrations of rumen metabolites by FC (FC ≥ 2 and
   FC ≤ 0.5) and VIP (VIP ≥ 1), we obtained 14 metabolites significantly
   differently enriched in the HH and DH groups, including nine
   upregulated and five downregulated metabolites ([123]Figure 7B). Levels
   of lysophosphatidylethanolamine (LPE) (P-18:1), (P-17:0), (O-17:1),
   (P-17:1), and methionine sulfoxide were significantly increased in the
   DH group, while thr-tyr, cyclo (his-pro) and heparin was significantly
   decreased. Interestingly, methionine sulfoxide was only present in the
   DH group. A total of 35 metabolites were significantly differently
   enriched between the HH and SH groups, with19 upregulated and 16
   downregulated ([124]Figure 7C). Levels of 6 from organic acid and its
   derivatives, 3 from fatty acyls (FA), and 2 from amino acid and its
   derivatives were significantly increased in the SH group, while 6 from
   nucleotides and their metabolites, 2 from heterocyclic compounds, 3
   from amino acids and its derivatives were significantly decreased.
   N-methyl-4-aminobutyric acid was identified only in the SH group.
   Between the DH and SH groups, 21 metabolites with significant
   differences were observed, including 3 upregulated and 18
   downregulated. Levels of 3-hydroxy-3-methyl butyric acid,
   7-ketocholesterol, and lysophosphatidylcholine (LPC) (14:0/0:0) were
   significantly increased, while 4 from nucleotides and their
   metabolites, 10 from glycerophospholipids (GP), 2 from alcohol and
   amines metabolites were significantly decreased ([125]Supplementary
   Figure S7).

Figure 7.

   [126]Figure 7
   [127]Open in a new tab

   Change in rumen content metabolite levels. (A) OPLS-DA analysis. (B)
   Significantly differential metabolites between the HH and DH groups.
   (C) Significantly differential metabolites between the HH and SH
   groups. n = 7 individuals/group.

   KEGG pathway analysis revealed that of these differential metabolites,
   six were annotated to 16 pathways between the HH and DH groups
   ([128]Figure 8A). “Cysteine and methionine metabolism,” “phenylalanine
   metabolism,” “glycine, serine, and threonine metabolism,” “biosynthesis
   of amino acids,” “glycolysis/gluconeogenesis,” and “pentose phosphate
   pathway” were significantly enriched in the DH group, however, “Fc
   epsilon RI signaling pathway” was enriched in the HH group. Twelve
   metabolites were annotated in 48 pathways between the HH and SH groups
   ([129]Figure 8B). Among them, relating to amino acid metabolism, bile
   acid metabolism and tricarboxylic acid cycle pathways were mainly
   enriched in the SH group, for example, “phenylalanine metabolism,”
   “histidine metabolism,” “glycine, serine and threonine metabolism,”
   “taurine and hypo taurine metabolism,” “bile secretion,” and “citrate
   cycle (TCA cycle).” The HH group enriched nucleotide metabolism and ABC
   transporters related to purine metabolism. Seven metabolites were
   annotated in 12 pathways between the DH and SH groups
   ([130]Supplementary Figure S8). “Fat digestion and absorption,”
   “glycerolipid metabolism,” and “vitamin digestion and absorption” were
   mainly enriched in the DH group, while “valine, leucine and isoleucine
   degradation” and “choline metabolism in cancer” were mainly enriched in
   the SH group.

Figure 8.

   [131]Figure 8
   [132]Open in a new tab

   Metabolic pathway enrichment analysis. (A) Between the HH and DH
   groups. (B) Between the HH and SH groups. n = 7 individuals/group.

3.9. Microbiome-related metabolite analysis

   Spearman’s correlation coefficient analysis identified correlations
   between differential microbiomes and metabolites (|r| > 0.8; p < 0.05).
   Treponema bryantii, Fibrobacter succinogenes, Fibrobacter sp. UWB4 and
   Fibrobacter sp. levels were positively correlated with LPE (P-17:0);
   Alloprevotella sp.was positively correlated with 4-hydroxyquinoline;
   and Alistipes sp. was negatively correlated with hydroxypiperazic acid
   when we compared the HH and DH groups ([133]Figure 9A). Bacteroidales
   bacterium and Ruminococcus sp. M6 (2020) were positively correlated
   with 3-hydroxy-3-methyl butyric acid and dopaquinone, respectively;
   whereas Theionarchaea archaeon and Thermoplasmata archaeon M8B2D were
   negatively correlated with tetradecanedioic acid in the HH and SH
   groups ([134]Figure 9B). Candidatus Altiarchaeales archaeon WOR SM1
   86-2 was positively correlated with LPE (16:0/0:0), LPE (0:0/16:0), LPE
   (O-18:2), phosphatidylcholine (PC) (O-1:0/O-16:0),
   lysophosphatidylserine (LPS) (20:0), lysophosphatidic acid (LPA)
   (22:6); 7-ketocholesterol was positively correlated with Candidatus
   Methanomethylophilaceae archaeon, Candidatus Methanomethylophilus sp.,
   and Bacteroidales bacterium between the DH and SH groups
   ([135]Supplementary Figure S8).

Figure 9.

   [136]Figure 9
   [137]Open in a new tab

   Correlation analysis between microbiome and metabolite profiles in
   rumen samples. (|r| > 0.8). (A) Between the HH and DH groups. (B)
   Between the HH and SH groups. n = 7 individuals/group. *p < 0.05,
   **p < 0.01, and ***p < 0.001.

4. Discussion

   From the perspective of genetics and breeding, introducing a favorable
   exogenous gene may be a better choice for improving the low to moderate
   heritability of traits ([138]28). In this study, we screened Poll
   Dorset and Southdown sheep as sires to improve the growth performance
   of Hu sheep, focusing on how hybridization affects growth performance
   and investigating the regulation mechanism of the rumen microbiome and
   its metabolites.

   Hybridization results in heterosis and improves animal growth. As
   expected, our data showed that hybridization increased DMI and ADG,
   resulting in higher FBW and lower F/G. Our previous study indicated
   that F[1] hybrid offspring grew faster and became larger than did Hu
   sheep, particularly during the later periods ([139]29). Another study
   on a three-way cross system between Angus, Qaidam, and yak indicated
   that the F[2] offsprings were all significantly better than those of
   the yak and 1/2 yak ([140]28). These results suggest the importance of
   hybridization for improving growth performance of animals.

   Rumen microecology is closely associated with animal growth. In this
   study, hybridization significantly changed the rumen fermentation
   pattern in sheep, especially the NH[3]-N concentration. NH[3]-N is
   mainly derived from microbe-mediated amino acid deamination and
   non-protein nitrogen hydrolysis in the rumen ([141]30). The rumen
   microbiome utilizes NH[3]-N to provide primary protein synthesis
   precursors for the host. NH[3]-N also has a feedback effect on rumen
   microbiota structure and epithelial function, affecting rumen
   fermentation and host health ([142]31). The NH[3]-N concentration was
   significantly lower in the SH and DH groups than in the HH group,
   indicating that the hybrid offspring had a higher utilization
   efficiency of nitrogen sources to synthesize bacterial proteins. VFAs
   are the main energy source for ruminants, which ferment plant materials
   into VFAs that regulate a variety of physiological functions in the
   rumen ([143]32). Acetate, propionate, and butyrate are the three major
   VFAs and provide approximately 70% of energy requirements. Acetate
   concentration is mainly affected by the microbial degradation of
   fibrous substances. Reports have shown that rumen acetate concentration
   increases when ruminants are fed a high-fiber diet ([144]33) or when
   the rumen microbiota is dominated by Prevotella ([145]34). Of the
   acetate in the rumen, 67% is used for oxidative energy, while the rest
   is used for body fat synthesis. Propionate contributes to energy
   supplementation via the gluconeogenic pathway to promote glucose
   synthesis ([146]35). In this study, acetate and propionate levels in
   the DH group significantly increased, resulting in higher total VFAs,
   indicating a higher energy supply to the host and improved growth
   performance.

   The rumen epithelium is an important indicator of rumen development and
   is responsible for the absorption and metabolism of nutrients and
   microbial byproducts. Evidence has shown that VFAs are absorbed through
   the rumen epithelium and that their absorption capacity is mainly
   related to the surface area and expression of VFA transporter vectors
   in the epithelium ([147]36). The rumen epithelium is abundant in
   mitochondria, powering epithelial metabolism; a thicker epithelium
   indicates more efficient feed utilization in cattle ([148]37).
   Additionally, butyrate stimulates epithelial cellular proliferation
   ([149]38). Our results indicate that hybridization increased papilla
   height, while no change was observed in papilla width or muscle
   thickness, which suggests an increased nutrient contact surface area to
   completely crush the feed to promote digestion and feed efficiency
   ([150]39). Moreover, these variations in rumen epithelial physical
   structures are considered to potentially influence the rumen
   microbiota.

   Using PCoA, the present study found significant differences in the
   rumen microbiome between the F[1] hybrid offspring and Hu sheep.
   Hybridization can produce new gene flow and different gene expression
   patterns in offspring. Sika and elk deer have different rumen
   microbiota than do their hybrid offspring; pure and hybrid mice have
   different microbial clustering characteristics suggesting a significant
   effect of host genetics on the rumen microbiome that may result from
   vertical transmission ([151]40–[152]42). Moreover, we found that
   Bacteroidetes and Firmicutes were the dominant bacteria at the phylum
   level, however, hybridization had no considerable effect on their
   relative abundances. Evidence has shown that Bacteroidetes have low
   heritability estimates, while Firmicutes have moderate heritability
   estimates, suggesting that they are largely affected by environmental
   factors, such as diet ([153]15, [154]43). Concurrently, the different
   heritability estimates indicate that host effects are not equal for
   different rumen microbial phylotypes. At the species level, we observed
   that bacteria related to fiber degradation were more abundant in the DH
   group, Treponema bryantii, Prevotella copri, and Fibrobacter
   succinogenes were particularly abundant. Treponema bryantii, when
   present with cellulolytic species such as Bacteroides succinogenes, can
   promote the digestion of cellulosic materials to increase feed
   efficiency ([155]44). A higher abundance of Treponema bryantii and
   Bacteroides sp. were found in the cecum of pigs with high feed
   efficiency ([156]45). Prevotella are considered the most important
   bacteria for polysaccharide degradation and fermentation based on their
   genomic glycoside hydrolase profiles, gene expression, and abundance in
   the rumen ([157]46). Prevotella copri might be the keystone bacterial
   species associated with host feed intake and fat metabolism, and
   evidence shows that its abundance increases in pigs with high average
   daily feed intake ([158]47) and promotes fat accumulation in pigs fed
   formula diets ([159]48). Studies in humans indicate that Prevotella
   copri largely increases the microbial potential for branched-chain
   amino acid biosynthesis ([160]49), which is beneficial for milk protein
   yield in dairy cattle and animal growth ([161]50, [162]51). Moreover,
   Prevotella copri is heritable (h^2 > 0.15) in the cecum and feces of
   pigs ([163]52). Fibrobacter succinogenes is regarded as the most highly
   cellulolytic bacterium in ruminal microbiomes and specializes in the
   production of succinate, acetate, and formate ([164]53). An increase in
   the Fibrobacter succinogenes population in the rumen is attributed to
   an increase in dry matter degradability ([165]54). In the present
   study, the relative abundances of Fibrobacter sp. UWR2 and Fibrobacter
   sp. were positively linked to DMI, suggesting their essential roles in
   DMI and dry matter degradability. In the SH group, the abundance of the
   Bacteroidaceae bacterium was higher than that of the HH group.
   Bacteroidaceae bacterium is also a rumen cellulolytic bacterium that
   can convert succinate to propionate as the sole energy source
   ([166]55). Therefore, the higher abundance of polysaccharide-degrading
   bacteria in the rumen of hybrid sheep enhances the degradation of
   dietary carbohydrates, thus promoting digestive ability and growth
   performance. This may be attributed to host genetics because we
   excluded the effects of environmental factors. However, this
   speculation needs to be verified by monitoring the rumen microbiota of
   Poll Dorset and Southdown sheep. The DH group lacked archaea related to
   nutrient digestion and absorption, however, the abundance of
   methanogenic bacteria (Methanomicrobium sp., Methanolobus bombayensis,
   and Methanococcus voltae) increased in the SH group compared to levels
   in the HH group, and methane production is an energy-intensive process,
   resulting in low production performance. However, in this study, the
   abundance of these methanogenic bacteria was quite low. We also found
   that DMI and feed protein utilization efficiency in the SH group were
   higher than those in the HH group, which suggests that the
   contributions of DMI and feed protein utilization efficiency surpassed
   the methane production rate, which may be the reason for the FBW gain
   in the SH group. Moreover, Xue et al. found that some methanogenic
   bacteria with low abundance were enriched in the rumens of dairy cows
   with high milk protein yields ([167]50); however, further studies are
   needed to validate our hypotheses.

   KEGG functional taxonomic analysis revealed that amino acid
   biosynthesis and metabolism were enriched in the SH group. Metabolic
   products such as lysine, aspartate, glycine, alanine, glutamate,
   serine, and threonine are important contributors to rumen microbial
   protein synthesis, constituting 90% of the microbial proteins that
   arrive in the small intestine and provide a protein source for the host
   ([168]56). Our previous study identified that the muscle protein
   content was higher in the SH group than in the HH group ([169]29),
   which is in line with the results of the rumen microbiota. Genes
   encoding CAZymes involved in deconstructing carbohydrates (GHs, CEs,
   PLs, and CBMs) were enriched in the DH group, indicating that these
   sheep were more capable of degrading complex substrates to promote
   microbial fermentation, which is consistent with our VFA results.

   In addition to affecting the composition and function of the rumen
   microbiome, hybridization alters rumen metabolites and metabolic
   pathways. First, LPEs are cell membrane components and are the second
   most abundant in vivo after LPCs ([170]57, [171]58). LPEs reportedly
   induce the activation of mitogen-activated protein kinase signaling and
   exhibit anti-apoptotic effects in pheochromocytoma cells ([172]59). LPE
   (P-18:1) has oleic acid as its acyl chain, stimulating neurite
   outgrowth and neuroprotective effects against glutamate-induced
   excitotoxicity ([173]60). Additionally, LPEs are involved in lipid
   droplet formation by suppressing lipolysis and fatty acid biosynthesis,
   thereby promoting the absorption of lipid substances ([174]61). This
   evidence indicates that LPEs enriched in the DH group benefited from
   fat deposition, resulting in FBW gain. Interestingly, methionine
   sulfoxide was only present in the DH group. The exposure of proteins to
   reactive oxygen species and hydrogen peroxide may lead to the oxidation
   of free methionine and methionine residues, forming methionine
   sulfoxide ([175]62). Methionine sulfoxide levels are elevated during
   oxidative stress, aging, and inflammation. The results of this study
   imply that the sheep in the DH group had poor environmental
   adaptability. However, methionine sulfoxide can be diastereoselectively
   repaired by methionine sulfoxide reductase to produce methionine
   ([176]63). Methionine augments the endogenous antioxidant capacity of
   rice proteins by stimulating methionine sulfoxide reductase expression
   and enhancing glutathione synthesis via the Nrf2-ARE pathway ([177]64).
   The beneficial effects of methionine supplementation on the antioxidant
   activity of heat-stressed birds have been previously demonstrated
   ([178]65). The present study shows that although sheep in the DH group
   were in a state of oxidative stress, this phenomenon can be improved by
   regulating the methionine sulfoxide reductase system, which will be our
   next research direction. N-methyl-4-aminobutyric acid was only present
   in the SH group. The metabolic product aminobutyric acid acts as a key
   inhibitory neurotransmitter of the central nervous system and functions
   via sedation, relieving fever and decreasing heat production ([179]66).
   Aminobutyric acid reduces stress caused by various environmental
   conditions and improves productivity ([180]67). For example,
   aminobutyric acid improves growth performance, meat quality, and feed
   intake by promoting the activation of characteristic enzymes and
   improving the absorption and immune function of the intestinal mucosa
   under heat-stress conditions ([181]68, [182]69). The characteristic
   metabolites in both the DH and SH groups were related to oxidative
   stress, indicating that the environmental adaptability of the F[1]
   generation sheep needs improvement, which we expect to occur in the
   F[2]generation. α-Ketoglutaric acid is an intermediate metabolite of
   the tricarboxylic acid cycle and is generated from glucose and
   glutamine, providing substrates for the synthesis of carbohydrates,
   amino acids, fats, and other biomolecules ([183]70). In this study,
   α-ketoglutarate was significantly increased in the SH group compared to
   levels in the HH group, further enhancing amino acid metabolism
   ability. Our results were consistent with a previous report by Kong
   ([184]71), who confirmed the tricarboxylic acid cycle pathways were
   enhanced in the muscle of Southdown × Hu F[1] generation sheep.

   Four metabolites and six microbiomes showed regulatory relationships in
   growth performance between the DH and HH groups. Alloprevotella sp. can
   ferment carbohydrates to produce acetate and succinate as
   end-metabolites and is positively correlated with 4-hydroxyquinoline,
   which has antibacterial and anti-inflammatory effects that maintain
   rumen health ([185]72). Treponema bryantii, Fibrobacter succinogenes,
   Fibrobacter sp., and Fibrobacter sp.UWB4 are associated with fiber
   degradation and production of propionate, which are positively
   correlated with LPE (P-17:0). Propionate can promote glucose synthesis
   via the gluconeogenic pathway in the rumen, and glucose is a precursor
   for the form of glycerol and fatty acids, which further help format
   LPEs. LPEs originate in part from the lipids in the diet, but also from
   gut microbial synthesis. Phospholipid levels in the host cells of
   germ-free mice differ from those in the host cells of conventional
   mice, suggesting a relationship between gut microbes and
   glycerophospholipids ([186]73). The relative abundance of
   Faecalibacterium and Prevotella is related to circulating lipids,
   particularly LEPs and phosphatidylglycerol ([187]74); however, we found
   in the present study that Fibrobacter was also related to the levels of
   LPE (P-17:0). Bacteroidales bacterium was positively correlated with
   3-hydroxy-3-methyl butyric acid between the SH and HH groups.
   3-Hydroxy-3-methyl butyric acid is a leucine metabolite that stimulates
   the growth hormone/insulin-like growth factor-1 axis ([188]75). These
   results indicate that the rumen microbiota of sheep in the DH group
   were mainly involved in body fat deposition by increasing DMI and dry
   matter degradability, whereas that in the SH group mainly participated
   in amino acid metabolism to provide energy. However, the causal
   relationship between the microbiota and metabolites requires further
   research. In addition, the rumen microbiomes and metabolites data of
   Poll Dorset and Southdown sheep should be considered and further
   elucidated in future studies.

5. Conclusion

   The results of this study support the feasibility of using
   crossbreeding programs to promote growth and increase feed efficiency
   by altering the rumen microbiota and metabolites in sheep. In the DH
   group, Treponema bryantii, Prevotella copri, and Fibrobacter
   succinogenes levels increased, and their functions were mainly enriched
   at the CAZyme level, contributing to higher acetate, propionate, total
   VFA, and LPE levels to further promote growth. Bacteroidaceae bacterium
   was enriched in the SH group and is involved in amino acid metabolism,
   fulfilling the demand for ruminal microbial proteins that are utilized
   by hosts to promote growth. Additionally, methionine sulfoxide and
   N-methyl-4-aminobutyric acid were characteristic metabolites in the DH
   and SH groups, respectively, indicating that the F[1] generation
   crossbred sheep had poor environmental adaptability. Taken together,
   these findings provide new ideas and nutritional strategies for the
   cultivation of new sheep breeds.

Acknowledgments