Response of rumen fermentation and microbiota to high-energy diet in fattening sheep

doi:10.21203/rs.3.rs-2131318/v1

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Response of rumen fermentation and microbiota to high-energy diet in fattening sheep

https://doi.org/10.21203/rs.3.rs-2131318/v1

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Background

In mutton sheep production, higher dietary energy is often used to achieve better animal performance. Whereas, changing the diet formula will affect rumen fermentation and microbiota of ruminants. In this study, we investigated the effect of dietary energy on rumen fermentation and ruminal microbiota in fattening sheep. Fifteen 2-month-old white-headed Suffolk sheep (♂) × Hu sheep (♀) crossbred lambs were randomly divided into 3 treatments: 8.67 MJ/kg (LE, n = 5), 10.38MJ/kg (CON, n = 5), and 12.31 MJ/kg (HE, n = 5) based on the dietary energy of feeds fed.

Results

After 70 days of feeding, sheep were slaughtered, and the ruminal fluids were collected and analyzed to determine fermentation parameters, microbiota was determined using metagenomics sequencing. We found that there was no effect on the microbial cell protein (MCP) and butyric acid concentration was significantly high in the HE group. Metagenomic sequencing revealed that ACE and Chao indexes of the HE group were significantly decreased. Among the major classified taxa, 4 genera across all the kingdoms differed in relative abundance among the three dietary energy levels. Correlation analysis revealed that the relative abundances of Prevotella_brevis, Succiniclasticum_ruminis, Prevotellaceae_bacterium, and Lachnospiraceae_bacterium were significantly correlated with rumen fermentation. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis further showed high-energy diet increased lipid metabolism. The expression of the Carbohydrate-Active enzymes (CAZy) gene indicated which participates in energy metabolism increased, but those which regulate plant cell wall degradation decreased in the HE group.

Conclusions

These results suggest that a high-energy diet had limited influence on the rumen fermentation pattern. Whereas, a high-energy diet changes rumen microbiota composition which leading microbial lipid metabolism enhanced and crude fiber's metabolism capacities weakened. Our results provide scientific evidence for fattening sheep production.

fattening sheep

dietary energy

ruminal fermentation

ruminal microbiota

Rumen harbors numerous microorganisms, which form a highly interconnected complex ecosystem [1]. The rumen microbial community comprises bacteria, fungi, protozoa, and archaea, all of which play distinct but important functions in their respective ecological niches [2]. Due to microbial interactions in the rumen, ruminants are capable of using crude fibers and converting nonprotein nitrogen into microbial protein. Therefore, ruminants utilize energy sources that monogastric animals may not, underlining the indispensable role of microbiota to the host. Recent research has shown that ruminal microbes participate in energy and protein metabolisms [3], affecting health and host immunity [4–6]. On the other hand, nutrient level and rumen ecological niche influence the composition of the rumen microbial community. Therefore, understanding the rumen bacterial community can guide on ways of improving ruminant productivity.

Rumen microbes are closely related to the host, participate in nutrient metabolism [7], and improve animal health [8]. In modern intensive farming mode, concentrated rations are used to increase production by providing high energy to the animal. An increase in dietary energy levels displayed promising results in the performance of lambs, average daily weight gains increased with metabolic energy elevating [9]. Under the high-energy dietary condition, the rumen micro-ecosystem has altered accordingly, which ultimately leads to performance traits change [10]. Since there had a complex symbiotic network between ruminants and rumen microbes, dietary energy content is a key factor affecting ruminal microbiota composition and structure [11]. Compared with a forage-rich diet, a grain-based diet lowers the bacterial diversity in cattle rumen [12]. Also, high-energy diets increase the relative abundance of Bacteroidetes and Prevotella but reduce that of Proteobacteria. By affecting the bacterial community structure, alters the synthesis of main metabolites important to animal health [13]. Despite this, little is known about how a high-energy diet that can promote daily weight gain greater than 200g influences rumen fermentation and microbiota.

In this study, the effects of dietary energy levels on rumen fermentation and microbiota in sheep were assessed across the fattening stages. The interactions between microbes and sheep energy absorption were also analyzed to understand the role of microorganisms in host energy metabolism. The findings of this study will provide solid scientific evidence for the best mutton production practice.

2.1. Ethical statement

The protocol for this study was approved by the Animal Care and Use Committee of the Northwest A&F University, China (Approval No. DK2022060).

2.2. Sheep management

The sheep were reared at Gansu Charming Sheep Breeder Co., Ltd., (Qingyang City, Gansu Province) between July 24, 2020, and October 10, 2020. The sheep were reared for 78 days, comprising 8 days of pretest and 70 days of testing. Thirty-six 2-month-old white-headed Suffolk sheep (♂) × Hu sheep (♀) crossbred lambs with an average body weight of 20.58±1.47 kg were selected from 2,000 lambs born at the same time (sex ratio of the lambs was 1:1). These lambs were randomly divided into low energy group (LE), standard group (CON), and high energy group (HE) based on diet. Every group had four replicates which had three sheep. The sheep feeds were prepared from China Agricultural Industry Standard- mutton sheep feeding standard (NY/T816-2004). At 20.0 kg, the weight gain of the sheep is 200 g/d. The digestible energy and crude protein in the CON diet were 10.38 MJ/kg and 14.36%, respectively. The LE diet contained 85% energy level as that of the standard group. The digestible energy (DE) was 8.67 MJ/kg. The energy in HE feeds was 15% higher than in the standard group, with a DE value of 12.31 MJ/kg. The dietary protein was equal in the LE, HE, and standard groups (CON). The lambs were fed on a mixed ration of dried pellets. The dietary composition and nutritional level of the feeds are shown in Table 1. The sheep were fed twice a day at 08:00 and 17:00 on enough feed and water.

Table 1. Dietary composition, nutritional level, and energy of feeds fed to the experimental sheep

Item	LE	CON	HE
Ingredient composition, g/kg Dry matter basis
Corn	0.00	350.00	370.50
Soybean meal	91.60	144.60	86.50
Wheat barn	278.40	0.00	166.30
alfalfa hay	200.00	200.00	250.00
Oat hay	100.00	0.00	0.00
Corn stalk	300.00	275.40	66.70
Soybean oil	0.00	0.00	30.00
CaHPO₄	10.00	10.00	10.00
Premix ¹	20.00	20.00	20.00
Chemical composition ²
Digestible energy, MJ/kg	8.67	10.38	12.31
Crude protein, g/kg	143.60	143.60	144.00
Ether extract, g/kg	24.00	23.80	28.50
Neutral detergent fiber, g/kg	455.10	310.90	237.00
Acid detergent fiber, g/kg	232.60	163.20	92.20
Ca, g/kg	0.81	0.75	0.75
P, g/kg	0.67	0.52	0.62

^1.The premix comprised the following per kg of diet: VA 8000 IU, VD 212000 IU, VE 800 IU, Fe 5500 mg, Cu 500 mg, Zn 3500 mg, Mn 4000mg, I 60 mg, Se 20 mg, Co 33.5 mg. ^2.Chemical composition is calculated.

2.3. Sample collection and measurement

2.3.1. Rumen fermentation indexes

On the morning of the 70th day of the experiment, 1 sheep was randomly selected from each replicate (4 sheep in each group), plus 1 sheep was randomly selected from the remaining 8 sheep in each group, eventually, every group had 5 sheep for samples collection. The sheep were slaughtered after 12h of the fasting period. Rumen fluid was collected and immediately filtered with four layers of sterile gauze. The rumen fluid pH was measured using a portable pH probe (RPB10, Shanghai Haiheng Electromechanical Instrument Co., LTD, Shanghai, China). Each sample was divided into four parts, 1 part of rumen fluid was mixed with 0.2 mol/L hydrochloric acids and stored at -20 ℃ before the test to measure the ammonia nitrogen content, the rest samples were stored at -80 °C, pending subsequent tests.

The concentration of ammonia nitrogen was determined using the indophenol method after thawing and centrifuging the rumen fluid at 11292 ×g for 10 min at 4 ℃ [14]. For microbial protein (MCP), frozen rumen fluid was thawed at room temperature and centrifuged at 112.92 ×g for 8 min to remove unwanted debris. The supernatant was then centrifuged for 20 min at 70600 ×g before adding 100 µL of supernatant to 5 mL of Coomassie bright blue solution. The microbial protein concentration was measured using Synergy HT multifunctional microplate reader as previously described [15]. Volatile fatty acid (VFA) concentration was determined using Agilent 7820A gas chromatograph (Agilent Technologies, USA) as previously described [16]. Briefly, 5 mL of rumen fluid was incubated at -20 ℃, thawed, and centrifuged at 20600 g for 10 min. Thereafter, 400 μL of metaphosphoric acid was added to 2 mL of the sample supernatant in a 4 mL centrifuge tube, mixed thoroughly, and incubated for 3 - 4 h at 5 °C to precipitate the protein. The mixture was centrifuged at 20600 ×g for 15 min at 4℃ to separate protein and impurities. Crobituric acid (200 μL) was then added to 1 mL of supernatant. The samples were agitated at 0.5 °C for 0.5 - 1 h and analyzed using gas-liquid chromatography. The column temperature was 150 ℃, the sample temperature was 250 ℃, and the detector temperature was 250 ℃. The total flow rate of high purity nitrogen was 30 mL/min, the column flow rate was 1.4 mL/min, the hydrogen flow rate was 30 mL/min, and the air flow rate was 300 mL/min.

2.3.2. Rumen microorganism—metagenomics

(1) DNA extraction, library construction, and metagenomic sequencing

Total genomic DNA from rumen fluid samples was extracted using the MP-soil E.Z.N.A.® Soil DNA Kit (Omega Bio-Tek, Norcross, GA, U.S.) according to the manufacturer's instructions. The concentration and purity of extracted DNA were determined using TBS-380 and NanoDrop2000, respectively. The quality of the extracted DNA was checked on 1% agarose gel.

The DNA was fragmented into about 400 bp segments using Covaris M220 (Gene Company Limited, China) for paired-end library construction, performed using the NEXTflexTM Rapid DNA-Seq platform (Bioo Scientific, Austin, TX, USA). Adapters containing the full sequences complementary to the sites of interest were ligated to the blunt end of fragments. Paired-end sequencing was performed on the Illumina NovaSeq/Hiseq Xten platform (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China) using NovaSeq Reagent Kits/HiSeq X Reagent Kits, following the manufacturer's instructions (www.illumina.com). The generated sequence data for this project was deposited in the NCBI Short Read Archive database (Accession Number: PRJNA826547).

(2) Sequence quality control and genome assembly

The raw reads were trimmed and cleaned by removing adaptor sequences and low-quality reads (reads with N bases, a minimum length threshold of 50 bp, and a minimum quality threshold of 20) using the fast tool [17] (https://github.com/OpenGene/fastp, version 0.20.0) on the Majorbio Cloud Platform (cloud.majorbio.com). Clean reads were mapped to the Ovis aries (assembly Oar_rambouillet_v1.0) reference genome using BWA [18] (https://www.ncbi.nlm.nih.gov/genome/?term=SHEEP) tools to identify and remove the sheep host originated reads. These high-quality reads were then assembled to contigs using MEGAHIT [19] (Parameters: kmer_min=47，kmer_max=97，step=10) (https://github.com/voutcn/megahit, version 1.1.2), which utilizes succinct de Bruijn graphs. Contigs larger than 300 bp were selected for the final assembling.

(3) Gene prediction, taxonomy, and functional annotation

Open reading frames (ORFs) in contigs were identified using the MetaGene tool as previously described [20] (http://metagene.cb.k.u-tokyo.ac.jp/). The predicted ORFs larger than 100 bp were translated into amino acid sequences using the NCBI translation table (http://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=tgencodes#SG1.

(4) Non-redundant gene sets construct

A non-redundant gene catalog with 90% sequence identity and 90% coverage was constructed using the CD-HIT online tool (http://www.bioinformatics.org/cd-hit/, version 4.6.1) as described by Fu et al.[21]. Quality reads with 95% identity were mapped to the non-redundant genes using the SOAPaligner online tool [22] (http://soap.genomics.org.cn/, version 2.21). The gene abundance in each sample was then evaluated.

(5) Taxon and function annotation

Annotation of the non-redundant genes based on the NCBI NR database was performed using the blast tool in the DIAMOND software, v 0.9.19. For taxonomic annotations, the cutoff e-value was set at 1e-5 using the Diamond online tool [23] (http://www.diamondsearch.org/index.php, version 0.8.35). A cluster of orthologous groups of proteins (COG) annotation for selected sequences was performed using the same tool [23] (http://www.diamondsearch.org/index.php, version 0.8.35) based on data in the eggNOG database (version 4.5.1). The cutoff e-value was set at 1e-5. Differences in microbiota composition among groups were assessed using Analysis of similarity (ANOSIM) of (abund_jaccard) distances. Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation was performed using the Diamond tool [23] (http://www.diamondsearch.org/index.php, version 0.8.35) based on data in the KEGG database (http://www.genome.jp/keeg/, version 94.2). The cutoff e-value was set at 1e-5. Carbohydrate-active enzymes (CAZy) annotation was conducted using hmm can (http://hmmer.janelia.org/search/hmmscan) against the CAZy database (http://www.cazy.org/) with an e-value cutoff of 1e-5.

3.1. Rumen fermentation indexes

The rumen fermentation parameters are shown in Table 2. There was no significant difference in NH₃-N concentration and the pH of rumen fluid among the three groups. MCP concentration in rumen fluid was extremely significantly lower in CON and LE groups than that in the HE group (p < 0.01), but there was no significant difference in MCP concentration in the rumen fluid between the CON and LE groups.

Table 2. Rumen fermentation parameters of sheep fed on different energy diets

Item	LE	CON	HE	p-value
Ammonia nitrogen, mmol/L	8.8 ± 0.39	10.09 ± 0.87	8.38 ± 1.23	0.748
pH	5.98 ± 0.05	5.78 ± 0.20	5.70 ± 0.21	0.251
MCP, μg/mL	1149.88 ± 120.94^a	1224.08 ± 98.95^a	2777.78 ± 163.53^B	0.001
Acetic acid, mmol/L	44.54 ± 3.12^b	35.37 ± 3.88^ab	32.09 ± 3.48^a	0.027
Propionic acid, mmol/L	9.57 ± 3.12^ab	12.07 ± 3.88^b	7.22 ± 3.48^a	0.011
Isobutyric acid, mmol/L	0.83 ± 0.09^b	0.94 ± 0.09^B	0.57 ± 0.06^a	0.005
Butyric acid, mmol/L	6.48 ± 0.39^a	6.75 ± 0.96^a	12.26 ± 1.01^B	0.011
Isovaleric acid, mmol/L	0.97 ± 0.11^B	1.18 ± 0.08^B	0.58 ± 0.08^a	0.009
Valeric acid, mmol/L	0.47 ± 0.04^a	0.78 ± 0.12^b	0.60 ± 0.08	0.019
Acetic / Propionic ratio	4.70 ± 0.35^ab	3.10± 0.37^a	4.82 ± 0.81^b	0.047
Total VFA, mmol/L	62.83 ± 3.26	57.07 ± 5.47	53.3 ± 3.67	0.751

Different superscript letters (A, B, C) denote significant difference p < 0.05, different superscript letters and case (example: A, b) represent extremely significant p < 0.01.

The dietary energy level impacted the rumen fluid VFAs concentration in sheep. The acetic acid concentration was no significantly difference between the CON and HE groups, but there was significantly higher in the LE group than in the HE group (p < 0.05) in this metabolite. The propionic acid concentration was significantly higher in the CON than in the HE group (p < 0.05), but there was no significant difference in this metabolite between the HE and LE groups. The butyric acid concentration was extremely significantly lower in the CON and LE groups than HE group (p < 0.01). The valeric acid concentration was significantly higher in CON than LE group (p < 0.05). The concentration of Isobutyric and Isovaleric acids was significantly higher in the CON and LE groups than in HE group (p < 0.01). Acetic / Propionic ratio was significantly lower in CON than HE group (p < 0.05). There was no significant difference in the total VFA concentration among the three groups

3.2. Metagenomic sequence data

The diversity index at the species level. (B) ANOSIM analyses (abund_jaccard) of rumen microbial community composition. *represents p < 0.05, ** represents p < 0.01.

A total of 139.7 Gb of Illumina HiSeq metagenomic sequences for 15 rumen fluid samples was generated. After eliminating low-quality reads and host contaminants, an average of 9.07 Gb of data for each sample was obtained. Based on the assembled contigs with an N50 contig length of 1174 bp, 2.80 million non-redundant genes with an average ORF length of 629 bp were identified. The metagenomic sequencing data were annotated at different taxon levels (137 Phyla, 226 Classes, 388 Orders, 754 Families, 2888 Genera, and 15883 Species in CON group;145 Phyla, 234 Classes, 395 Orders, 762 Families, 2926 Genera, and 16079 Species in LE group; 126 Phyla, 204 Classes, 351 Orders, 681 Families, 2438 Genera, and 11840 Species in HE group).

The diversity indices of ruminal microflora in sheep fed on diets with different energy levels are shown in figure 1. The ACE and Chao indexes were significantly higher in the CON and LE groups than that in HE group (p < 0.01). ANOSIM analysis revealed a significant difference in microbial community composition (ANOSIM=0.328, p < 0.01) in the HE group.

3.3. Composition of ruminal microbiota

Diet influenced the diversity of the ruminal microbiota community in sheep. At the phylum level, Bacteroidetes, Firmicutes, Actinobacteria, Proteobacteria, and Lentisphaerae were the dominant phyla in sheep rumen with a relative abundance of 65.33%, 27.19%, 2.74%, 1.69%, and 0.12% in the CON group, 73.18%, 20.40%, 0.73%, 1.46%, 1.03% in the LE group, and 50.74%, 40.17%, 5.11%, 2.37%, and 0.02% in the HE group, respectively (Fig. 2A). The dominant genera across the feed energy groups are shown in Figure 2A. The top 10 most dominant genera of three groups were Prevotella, unclassified Lachnospiraceae, unclassified Bacteroidales, unclassified Clostridiales, and unclassified Prevotellaceae, unclassified Rikenellaceae, Bacteroides, unclassified Ruminococcaceae, Clostridium, and Olsenella. The relative abundance of unclassified Lachnospiraceae was significantly lower (p < 0.05) in the LE group than in the HE group, whereas the relative abundance of unclassified Bacteroidales and Bacteroides in CON and LE group were significantly higher than in the HE group (p < 0.01, p < 0.05). The relative abundance of unclassified Rikenellaceae was significantly higher in the CON group than that in the HE group (p < 0.05) (Fig. 2B). At the species level, the top 10 most dominant species of three groups were Prevotella ne3005, Prevotella tf2-5, Prevotella_ruminicola, Rikenellaceae_bacterium, Lachnospiraceae_bacterium, Clostridiales_bacterium, unclassified Prevotella, Prevotella tc2-28, Bacteroidales_bacterium, and Prevotella_sp. (Fig. 2A). The relative abundance of Rikenellaceae_bacterium was significantly higher in the CON group than HE group (p < 0.05), whereas the relative abundance of Lachnospiraceae_bacterium was significantly higher in the HE group than that in the LE group (p < 0.05). Besides, the relative abundance of Bacteroidales_bacterium was significantly lower in the HE group than that in CON (p < 0.05) and LE (p < 0.01) groups (Fig. 2B).

3.3. Correlation network between microbial and ruminal fermentation indexes

The relationship between rumen microbiota and fermentation was analyzed based on the Spearman correlation coefficient. We observed that only ammonia nitrogen, acetic acid, propionic acid, isobutyric acid, isovaleric acid, and valeric acid levels are associated with the abundance of the rumen microbes (Fig. 3A). Ammonia nitrogen levels positively correlated with Prevotella_sp._tc2-28 abundance, but negatively correlated with Lachnospiraceae_bacterium abundance. The acetic acid level was positively correlated with Prevotella_sp._tc2-28 and Prevotella_brevis abundance but negatively correlated with Lachnospiraceae_bacterium abundance. The propionic acid level was positively correlated with Prevotellaceae_bacterium and Succiniclasticum_ruminis abundance. Similar to propionic acid, isobutyric acid and isovaleric acid contents positively correlated with Prevotellaceae_bacterium abundance. Valeric acid level positively correlated with Prevotellaceae_bacterium_LKV-178 and Acidaminococcaceae_bacterium abundance. The relative abundance of rumen microbiota was further calculated to explore the variation in correlation network related microbial (Fig. 3B). The relative abundance of Lachnospiraceae_bacterium was significantly lower in group LE than in the HE group (p < 0.05). However, the relative abundance of Prevotella_brevis, Prevotellaceae_bacterium, and Succiniclasticum_ruminis were significantly higher in groups CON and LE than in group HE (p < 0.05).

3.4. Rumen microbial function

KEGG pathway analysis was performed to investigate the function of the different rumen microbiota. The function of the rumen microbiota at KEGG level 2 is shown in Figure 4. KEGG level 2 pathway analysis revealed a significant difference in glycan biosynthesis and metabolism, membrane transport, endocrine system, lipid metabolism, xenobiotics biodegradation and metabolism, metabolism of terpenoids, and polyketides across the three groups. KEGG level 3 pathway enrichment analysis revealed that glycosaminoglycan degradation, biosynthesis of numerous N-glycans, glycosphingolipid biosynthesis - ganglio series, and renin and steroid hormone biosynthesis significantly decreased (p < 0.05) with an increase in diet energy level, whereas Phosphotransferase system (PTS), Primary bile acid biosynthesis, Secondary bile acid biosynthesis, and Geraniol degradation were significantly higher in group HE (p < 0.05) (Fig. 4).

The expression of the CAZy gene encoding carbohydrate catabolism enzymes in the sheep rumen microbiota. At the class level, the relative expression of the GTs gene was significantly high in the HE group (p < 0.05) (Fig.5 A). At the family level, the relative abundance of GT2_Glycos_transf_2, GH77, CE2, and GT28 genes increased with dietary energy level (p < 0.05). These genes encode energy metabolism genes, such as 4-α-glucanotransferase (EC 2.4.1.25), acetyl xylan esterase (EC 3.1.1.72), 1,2-diacylglycerol, and 3-β-glucosyltransferase (EC 2.4.1.157). The expression of GH31, GH92, GH9, GH146, and CE15 decreased with an increase in the dietary energy level (p < 0.05) (Fig.5 B). These genes encode plant cell wall degradation enzymes, such as α-glucosidase (EC 3.2.1.20), α-1,2-mannosidase (EC 3.2.1.-), and endoglucanase (EC 3.2.1.4), β-L-arabinofuranosidase (EC 3.2.1.185), and 4-O-methyl-glucuronoyl methylesterase (EC 3.1.1.-).

The rumen is the most efficient bioreactor. Besides, an indivisible relationship exists between rumen microbes and the host. The rumen microbiota comprises bacteria, protozoa, fungi, and archaea. These microorganisms exist in symbiotic, competitive, predation, or antagonistic relationships and are jointly responsible for the microbial fermentation in the rumen [24]. Research on rumen microbiota and the host mainly focuses on how nutrition strategies influence the rumen microbiome and how alteration of rumen microbiota affects the host.

Dietary energy is an important factor affecting nutrient intake, digestion, metabolic efficiency, and production performance. This research belongs to part of the previous experiment, in our previous research, we found that high dietary energy levels increase the final weight, live weight, average daily gain and feed rewards. The increase of dietary energy levels significantly increased (p < 0.05) the mRNA expression of fat deposition-related genes in subcutaneous fat (HSL), tail fat (FASN) andlongissimus dorsi(FABP4) [25]. As rumen fermentation and microbiota play a key role in nutrient digestion and utilization of ruminants, therefore, the influence of dietary energy on rumen fermentation and microbiota was further investigated in current research.

4.1. High -energy diet affects ruminal fermentation

VFA, NH₃-N, and MCP are the critical rumen fermentation pattern indices. Feed in ruminants first undergoes microbial fermentation in the rumen. Ruminal microbiota breaks down carbohydrates into VFA, the main metabolite of microbial fermentation [26]. Microbes first break down carbohydrates (crude fiber, starch, and soluble sugar) to pyruvate. The pyruvate is then metabolized to different VFAs (acetic acid, propionic acid, butyric acid, isobutyric acid, valeric acid, and isovaleric acid) through different metabolic pathways. Proteins and fats can also be degraded to VFA (mostly brunch chain fatty acids). Thus, ruminants utilize VFA which degrade from carbohydrates by ruminal microbes rather than glucose, which account for 70%~80% metabolizable energy of ruminant energy needs[27]. ATP for microbial growth is released during the VFA synthesis processes.

Rumen fermentation patterns usually change under different nutrition conditions. VFA concentration increases with energy level or a high concentrate diet. Also, high metabolizable energy (ME) significantly increases the proportions of butyrate and valerate [10]. Furthermore, high acetate and butyrate concentrations have been observed after intake of high grain diet [28]. In the present study, butyric acid levels in the rumen fluid increased with diet energy concentration. A significant increase in MCP with energy intake was also observed, consistent with previous findings [29]. These findings show that feed energy level is directly proportional to the rumen MCP yield. However, a positive correlation between acetic acid and propionic acid and daily feed energy was not observed in this study, which may be due to a strong correlation between rumen VFA concentration and feed type, and nutrient level [30]. The LE group diet contained more crude fibers as the substrate for microbial fermentation to produce VFA. Our result indicates that a high-energy diet had limited influences on the rumen fermentation pattern, and provided sufficient energy for microorganisms to synthesize MCP, giving sheep more rumen-protected proteins to metabolize.

4.2. High-energy diet changes the composition of ruminal microbiota

Bacteria are the most abundant and diverse microbiota in the rumen, attributable to diet [31]. On the other hand, the microbiota affects animal health. Therefore, understanding the effect of dietary energy on microflora is of great significance to animal husbandry. A more diverse microbial community increases the resilience, resistance, and stability of the rumen ecosystem [32]. Diet energy affects the overall rumen multi-kingdom microbiota [33]. Mostly, ruminal microbiota diversity decreases with dietary energy [34, 35]. In the present study, ACE and Chao indices were significantly higher in the LE group than in the CON and HE groups, consistent with previous studies [36, 37], which indicates that a high-energy diet reduces richness of the microbiome.

Despite the complexity of the rumen ecosystem, the (composition and abundance of) rumen microbiota are relatively stable at the phylum level. Firmicutes, Bacteroidetes, Fibrobacteres, and Proteobacteria are the four most abundant phyla in the sheep rumen, whereas the dominant genera include unidentified Prevotellaceae, Fibrobacter, unidentified Lachnospiraceae, Saccharofermentans, and Succinivibrio [36]. In the present study, the dominant gut microbial phyla were Bacteroidetes, Firmicutes, Actinobacteria, and Proteobacteria, whereas the dominant genera were Lentisphaera, Prevotella, unclassified Lachnospiraceae, unclassified Bacteroidales, unclassified Clostridiales, and unclassified Prevotellaceae.

More research continues to reveal the functions of ruminal microbiota in ruminants. For instance, Bacteroidetes degrade carbohydrates and polysaccharides more efficiently than Proteobacteria [38]. At the genus level, Butyrivibrio, Fibrobacter, Olsenella, and Prevotella are critical in degrading cellulose [39]. Overall, studies [40-42] demonstrate that a high-energy diet reduces the relative abundance of ruminal microbiota that participate in crude fiber fermentation. A former study revealed that Bacteroidetes utilize crude fiber in the form of glycans (oligomeric and polymeric glycans), and the abundance of this bacteria is influenced by the dietary intake of these indigestible carbohydrates [43]. We found that the relative abundance of Bacteroides in the HE group was relatively low (feed of sheep in the HE group contained little crude fiber). Furthermore, at the species level, the relative abundance of Rikenellaceae_bacterium and Bacteroidales_bacterium decreased with an increase in the dietary energy level. Rikenellaceae is one of the main producers of VFA. Previous studies have found that a high-fat diet drastically decreased the Rikenellaceae composition in the rumen [44]. Accordingly, our research demonstrated that the relative abundance of crude fiber metabolizing bacteria in rumen decreased with an increase in energy diet.

The rumen microbiota produces numerous metabolites, including VFA and polyamines, through anaerobic fermentation. Notably, 10% of metabolites in the mammalian blood are derived from microbes or microbial activities [45]. The blood metabolites participate in developing and regulating host physiology and immunity and can be modified to other novel metabolites [46]. Relationships between some microorganisms and rumen metabolites have been well established. Members of the Lachnospiraceae family are among the main producers of short-chain fatty acids [47]. Bacteroidetes produce enzymes that degrade plant cell wall compounds (e.g., cellulose and pectin) to release VFA (mainly acetate, propionate, and butyrate) [48]. The phylum Lentisphaerae is associated with changes in feed efficiency [49]. Succiniclasticum degrades starch into acetic and succinic acids and further converts succinic acid to propionic acid [50]. The abundance of Prevotellaceae UCG-003 positively correlated with the rumen VFA content [51]. In the present study, correlation analysis revealed that at the species level, only the relative abundance of Lachnospiraceae_bacterium, Prevotella_brevis, Prevotellaceae_bacterium, and Succiniclasticum_ruminis was significantly different among the groups. Further analyses revealed that Prevotella_brevis promotes acetic acid synthesis, Succiniclasticum_ruminis promotes synthesis, Prevotellaceae_bacterium promotes propionic acid, isobutyric acid, and isovaleric acid synthesis, whereas Lachnospiraceae_bacterium inhibit ammonia nitrogen and propionic acid synthesis.

4.3. High-energy diet alters rumen microbial function

Changes in the KEGG pathway reflect an alteration in rumen microbial function under specific conditions, and the changes in enzyme function modify microbial function. Rumen microbial function changes with dietary energy concentration. Zhang et.al [52] investigated the effect of feeding sheep with caragana, corn straw and alfalfa, they found that sheep with caragana (this group had a higher digestible energy) mainly enhanced the microbial function about starch and sucrose metabolism, fructose and mannose metabolism, photosynthesis, and D-alanine metabolism in the rumen. A separate study [10] further confirmed that higher energy digestible diet increased the abundance of bacteria related to carbohydrate metabolism. In the current study, we found that the expression of genes related to lipid metabolism increased with dietary energy, but a reverse trend was observed for genes related to glycan biosynthesis and metabolism. Microbial CAZy regulates carbohydrate metabolism and, thus, is critical to the energy available to the host. In the present study, we found that a high-energy diet increased the expression of these genes involved in energy metabolism. However, the expression of genes that encode enzymes that catalyze plant cell wall degradation decreased.

KEGG and CAZy enrichment data further revealed that a high-energy diet improved lipid metabolism in sheep by promoting the expression of related genes. Meanwhile, low-energy diets enhance glycan biosynthesis and metabolism by promoting the expression of enzymes involved in plant cell wall degradation to meet the animals' energy needs.

We found that high dietary energy had limited influences on the rumen fermentation pattern, whereas, increase MCP synthesis. Also, dietary energy changes the rumen microbial composition and microbial energy metabolism pattern. In particular, high dietary energy reduces the metabolism of crude fiber but strengthens the energy metabolism pathway of microorganisms. Baes on our research, the recommend fattening dietary energy level is 12.31 MJ/kg for 2-month-old lambs.

ANOSIM: Analysis of similarity; CAZy: Carbohydrate-Active enzymes; CEs: Carbohydrate Esterases; GTs: Glycosyl Transferases; GHs: Glycoside Hydrolases; MCP: Microbial cell protein; KEGG: Kyoto Encyclopedia of Genes and Genomes; VFA: Volatile fatty acid.

Author Contributions

Conceptualization, Xiao Yu Huang; Data curation, Bo Li; Methodology, Chen Yang, Lei Yun Zhao, Xiao Qiang Zhang and Lin Tao Tian; Supervision, Ping En Zhang; Writing – original draft, Ting Ge; Writing – review & editing, Ting Ge. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

In this research, we acknowledge all members of the Innovative research team of sheep and goat of Northwest Agriculture & Forestry University.

Funding

This research was supported by the mutton sheep industry technology system construction project of Shaanxi Province (NYKJ-2021-YL（XN）43).

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Ethics approval and consent to participate

The protocol for this study was approved by the Animal Care and Use Committee of the Northwest A&F University, China (Approval No. DK2022060).

Consent for publications

Not applicable.

Competing interests

The authors declare that they have no competing interests

Author details

College of Animal Science and Technology, Northwest A&F University, Xianyang 712100 Shaanxi, China

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Response of rumen fermentation and microbiota to high-energy diet in fattening sheep

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Abstract

Background

Results

Conclusions

Figures

1. Background

2. Materials And Methods

2.1. Ethical statement

2.2. Sheep management

2.3. Sample collection and measurement

3. Results

3.1. Rumen fermentation indexes

3.2. Metagenomic sequence data

3.3. Composition of ruminal microbiota

3.3. Correlation network between microbial and ruminal fermentation indexes

3.4. Rumen microbial function

4. Discussion

4.1. High -energy diet affects ruminal fermentation

4.2. High-energy diet changes the composition of ruminal microbiota

4.3. High-energy diet alters rumen microbial function

5. Conclusion

Abbreviations

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