Supplementation of Lycium barbarum residue improves the animal performance through the interaction of rumen microbiota and metabolome of Tan sheep

doi:10.21203/rs.3.rs-1881657/v1

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Research Article

Supplementation of Lycium barbarum residue improves the animal performance through the interaction of rumen microbiota and metabolome of Tan sheep

https://doi.org/10.21203/rs.3.rs-1881657/v1

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Background

Lycium barbarum residue (LBR), a by-product of Lycium barbarum processing, contains bioactive components which can be used as a plant-derived feed additive in animal husbandry. This study aimed to investigate the effects of LBR on the rumen fermentation parameters, ruminal microbes and metabolites, and growth performance of Tan sheep.

Methods

Sixteen Tan sheep were fed with a basal diet (CON; n = 8) or a basal diet supplemented with 5% of LBR (LBR; n = 8). The experiment lasted for 70 d, with 10 d adaptation period and 60 d treatment period. After slaughter, rumen fluid samples were collected and analyzed by 16S rRNA sequencing and untargeted metabolomics to elucidate the alternations of rumen microbial community and rumen metabolites.

Results

Data showed that LBR enhanced the average daily feed intake, average daily gain, and total volatile fatty acids (P < 0.01) but decreased ammonia-nitrogen concentration (P < 0.01) and rumen pH (P < 0.05). Additionally, LBR supplementation improved the relative abundances of Prevotella, Succiniclasticum, Ruminococcus, Coprococcus, Selenomonas, and Butyrivibrio (P < 0.05) and reduced the abundances of Oscillospira and Succinivibrio (P < 0.05). Metabolomics analysis revealed that the LBR altered the ruminal metabolome (P < 0.01) by increasing the abundances of ruminal metabolites involved in amino acids (e.g., L-proline, L-phenylalanine, L-lysine, and L-tyrosine), pyrimidine metabolism (e.g., uridine, uracil, and thymidine), and microbial protein synthesis (e.g., xanthine and hypoxanthine). Correlation analysis provided potential relationships between major microbiota and metabolites in the rumen.

Conclusions

In conclusion, supplementation of LBR affects rumen fermentation by altering the rumen microbiome and metabolome, thereby improving the growth performance of Tan sheep.

Lycium barbarum residue

Rumen microbiome

Metabolomics

Growth performance

Tan sheep

With a ban on antibiotics as feed additives in many countries worldwide [1], there is growing interest worldwide in developing functional feed additives that could maintain health and improve the performance of animals [2]. Plant-derived bioactive compounds have shown promise as alternatives to growth-promoting antibiotics in ruminant production [3].

Lycium barbarum is a well-known traditional Chinese herbal medicine and edible plant species [4], commonly used as a supplement in Eastern and Western diets. Lycium barbarum has various biologically active components, including Lycium barbarum polysaccharides (LBPs), polyphenols, pigments, flavonoids, amino acids, vitamins, and trace elements [5], which can exert antioxidant, anti-inflammatory, anti-aging [6], gastrointestinal protective [7], cytoprotective as well as immunomodulatory [4] properties. The properties suggest that Lycium barbarum may be used as an alternative supplement for improving animal performance [8]. Previous studies found that dietary supplementation of Lycium barbarum could reduce pre-weaning mortality, improve feed conversion ratio (FCR) and growth rate [9]. Lycium barbarum promotes the growth of probiotics, such as Ruminococcaceae and Lachnospiraceae, alters the Firmicutes:Bacteroidetes ratio in the gut microbiota and modulates gastrointestinal microbiota composition and cecal fermentation in rabbits [10]. Lycium barbarum affects the secretion of leptin, insulin, and glucose, improving energy homeostasis in rabbits [11]. Additionally, Lycium barbarum residue (LBR), a by-product of Lycium barbarum processing, contains bioactive components of Lycium barbarum and is available as a feed additive. LBR as a dietary supplement improved animal performance and product quality [11, 12]. The addition of LBPs to pig diets improved growth performance [13], immune function [14], and gut microflora [15]. Moreover, the diet supplemented with LBPs improved body weight, average daily gain (ADG), average daily feed intake (ADFI), and FCR in broiler chickens [16]. As a popular functional food, LBPs can stimulate the growth of beneficial gut bacteria, inhibit the growth of E.coli, and enhance the immune status and antioxidant activity, thereby improving host growth performance [12].

Up to now, the effects of LBR on livestock animals have mainly been studied in monogastric animals, with few in ruminant animals. Feeding regimens and diets considerably impact on rumen microorganisms [17]. Rumen fermentation and rumen microorganisms have critical roles in utilizing dietary fibers and other components, thereby modulating host health [18]. In a recent study in lambs, the supplementation of a Chinese medicine polysaccharide comes from the mixture of Lycium barbarum and Astragalus membranaceus enhanced animal growth performance by changing rumen fermentation and bacterial community [19].

Thus, we hypothesized that there are LBR associated rumen microbes and the ruminal microbial communities and function contributed to the growth performance of Tan sheep. Therefore, this study investigated the effects of LBR on the ruminal microbiome and metabolome and revealed the interaction between them, thereby improving the rumen inner environment and growth performance of Tan sheep.

Animal ethics statement

The experimental protocols and treatments were approved by the Institutional Animal Care and Use Committee of Ningxia University (NXUC20200618).

Animals, diets, and experimental design

This study was conducted on a large-scale breeding farm in Ningxia, China. Sixteen ram Tan sheep (Ovis aries) at the age of 4 months old (local breed; 20.8 ± 0.34 kg) were randomly divided into 2 experimental groups, including the control and Lycium barbarum residue groups (n = 8 in each group). The sheep in the control group (CON) were fed with a basal diet, while those in the LBR groups were fed with a basal diet supplemented with 5% LBR. A 70 d feeding experiment (the first 10 d is the pre-trial period) was then carried out accordingly. The basal diets were offered to the Tan sheep twice at 08:00 and 16:00 daily with free access to drinking water. The ingredients and nutritional composition of the basal diet are listed in Table 1. The chemical compositions of the diets were analyzed based on the AOAC [20]. The metabolizable energy content was formulated based on the NRC [21]. Neutral detergent fiber (NDF) and acid detergent fiber (ADF) contents were assessed following the method described by Van Soest et al. [22]. The experimental design and workflow are summarized in Fig. 1.

Table 1

Ingredients and chemical composition of the basal diet (on a dry matter basis).
Ingredients	Diets¹
Ingredients	CON	LBR
Corn (%)	54.8	49.8
Soybean meal (%)	12.0	12.0
Linseed (%)	3.33	3.33
Flaxseed meal (%)	4.25	4.25
Soybean oil (%) Salt (%)	3.40 0.42	3.40 0.42
Baking soda (%)	0.42	0.42
Premix (%)²	4.25	4.25
Bone meal (%)	2.13	2.13
Straw (%) Lycium barbarum residue (%)	15.0 0	15.0 5.00
Chemical composition
DM (%)	81.7	80.4
DE (MJ·Kg^− 1)	12.9	12.7
CP (%)	10.2	10.2
EE (%)	12.4	12.0
NDF (%)	37.0	36.7
ADF (%)	19.1	19.0
Calcium (%)	0.23	0.23
Phosphorus (%)	0.30	0.30
¹ CON = control group; LBR = Lycium barbarum residue group.
² contained in each kilogram of premix: 100000 IU vitamin A, 20000 IU vitamin D3, 60 IU vitamin E, 1000 mg Fe, 1000 mg Mn, 780mg Zn, 270 mg Cu, 12 mg Se, 10 mg I.
DM = dry matter; DE = digestible energy; CP = crude protein; EE = ether extract; NDF = neutral detergent fiber; ADF = acid detergent fiber.

Sample collection and measurements

On d 70, the rumen fluid was collected, filtered with 4 layers of sterile gauze, and divided into 10 mL sterile tubes. Each rumen fluid had 4 replicates and was stored at -80°C. The pH of the rumen fluid was measured immediately by a pH meter. Total volatile fatty acids (TVFAs) were determined using gas chromatography (Agilent 8890 GC, CA, USA) [23]. The ammonia-nitrogen (NH₃-N) concentration was measured using an enzyme-labeled instrument (Multiskan FC, Thermo Fisher, Beijing, China) [24]. During the trial, the body weight (BW) and leftover feed were measured daily for the calculation of ADG and ADFI. The gain feed ratio (F/G) was calculated by ADFI/ADG.

DNA extraction and sequencing

The QIAamp DNA Stool Mini Kit was used to extract the total DNA of rumen fluid samples. The DNA quality was determined by 1.2% agarose gel electrophoresis. The primers were designed, and PCR was used to amplify the V3 and V4 fragments of the 16S rRNA sequences. After purification, quantification, and homogenization of the PCR amplification products, high-throughput sequencing was performed by Illumina Miseq. The raw sequence data were demultiplexed using the demux plugin followed by primers cutting with the cutadapt plugin [25] and subjected to quality filtered, denoised, merged, and chimera removed using the DADA2 plugin. Taxonomy was assigned to amplicon sequence variants (ASVs) using the classify-sklearn naive Bayes taxonomy classifier in the feature-classifier plugin [26] against the SILVA Release 132 database. The raw sequence data have been uploaded to NCBI Sequence Read Archive (SRA) database (Accession Number: PRJNA837041).

Bioinformatics for rumen microbiota analysis

Sequence data analyses were mainly performed using QIIME2 and R packages (v3.2.0). The alpha diversity index (Shannon, Simpson, Chao1, and Good's coverage) of the rumen microbiota based on ASV level was analyzed using the ASV table in QIIME2 (version 2019.4) and visualized as box plots. Beta diversity (principal coordinates analysis, PCoA and nonmetric multidimensional scaling analysis, NMDS) was also conducted through QIIME2 (version 2019.4) and was assessed using Bray-Curtis dissimilarity. The Kruskal-Wallis sum rank test detected differentially abundant taxa between the groups. The differential bacteria were analyzed through linear discriminant analysis effect size (LEfSe) software (http://huttenhower.sph.harvard.edu/galaxy/). Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) was conducted to perform a functional prediction analysis of differential microbiota. The analysis was operated using the PICRUSt software package. The databases of MetaCyc (https://metacyc.org/) were conducted to map the enriched pathways of different levels.

Metabolite extraction and LC-MS/MS analysis

A 100 µL of rumen fluid sample was transferred to an EP tube. Each sample was mixed with 400 µL extract solution (acetonitrile:methanol = 1:1, v/v, containing isotopically-labeled internal standard mixture). Then, the mixture was vortexed for 30 s, sonicated for 10 min in an ice-water bath, and incubated for 1 h at -40°C. After that, the samples were centrifuged at 13,000 × g at 4°C for 15 min. The supernatant was pipetted into a sample bottle. Each sample with an equal volume of approximately 20 µL was mixed thoroughly to be a quality control (QC) sample [27].

A UHPLC system (Vanquish, Thermo Fisher Scientific) with a UPLC BEH Amide column (2.1 mm × 100 mm, 1.7 µm) coupled to Q Exactive HFX mass spectrometer (Orbitrap MS, Thermo) was utilized for the detection and analysis of metabolites in rumen fluid. The mobile phases were 25 mM ammonium acetate and 25 mM ammonium hydroxide in water (pH = 9.75, solvent A) and acetonitrile (solvent B). The auto-sampler temperature was 4°C, and each injection volume was 3 µL. The QE HFX mass spectrometer was used to acquire MS/MS spectra on information-dependent acquisition (IDA) mode in the control of the acquisition software (Xcalibur, Thermo).

Metabolomic data preprocessing and analysis

The original data were converted to the mz XML format by Proteo Wizard and processed by R packages of XCMS software to identify peaks and remove noise [28]. Then an in-house MS2 database (BiotreeDB) was applied in metabolite annotation. The cutoff for annotation was set at 0.3. The positive and negative data were combined. The data were conducted using Metaboanalyst 5.0 for principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and pathway enrichment analysis. Based on the OPLS-DA result, the differential metabolites with the variable importance in the projection (VIP) > 1.0 and the Student's t-test (P < 0.05) were identified as the significantly different metabolites between the two treatments. The fold change (FC) was used to evaluate the identification of significantly differential metabolites. In hierarchical clustering, hierarchical clustering analysis (HCA) for significantly different metabolites was used with Cluster 3.0 software. The online Human Metabolome Database (HMDB) (https://hmdb.ca), Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/) and Metabo Analyst 5.0 (http://www.metaboanalyst/) were conducted to map the enriched pathways of different metabolites.

Correlation analysis

The correlation between different rumen microbial genera (P < 0.05 and relative abundance > 0.1%), rumen fluid metabolites with VIP > 1.0, P < 0.05, rumen fermentation parameters and growth performance were assessed by Spearman's correlation analysis in R language package (Version 3.2.4, http://www.r-project.org). Spearman's rank correlation coefficient (r) was from − 1 to 1. The |r| value indicated the degree of correlation between variables. These correlations with Spearman's correlation value and P value were visualized using the heat map drawn by Cytoscape. The connections with P-value < 0.05 and |r| > 0.5 were considered statistically significant.

Statistical Analysis

All data were evaluated using the Student's t-test of SPSS Statistics software (version 23.0, IBM, Chicago, USA). Statistical significance was declared at P < 0.05, and 0.05 < P < 0.10 was considered to have a tendency.

Growth performance and rumen fermentation characteristics

As shown in Table 2, compared with the CON group, final BW, ADFI, and ADG were significantly increased in the LBR group (P < 0.01). Meanwhile, the F/G was decreased in the LBR group (P < 0.05).

Table 2

Average daily feed intake, growth performance, and rumen fermentation characteristics for the dietary treatments.
Item	Treatments¹		SEM²	P-value
Item	CON (n = 8)	LBR (n = 8)	SEM²	P-value
Growth performance
Initial BW, kg	20.9	20.8	0.09	0.644
Final BW, kg	31.1^b	33.0^a	0.28	< 0.01
ADG, g/d	170.3^b	203.4^a	4.78	< 0.001
ADFI, g/d	1496.8^b	1695.2^a	25.71	< 0.001
F/G	8.82^a	8.34^a	0.116	0.034
Rumen fermentation parameters
pH	6.56^a	6.30^b	0.015	0.040
NH₃-N, mg/dL	16.2^a	10.6^b	0.73	< 0.001
TVFAs, mmol/L	59.3^b	67.9^a	1.17	< 0.001
Acetate, mmol/L	38.7^b	40.7^a	0.32	< 0.001
Propionate, mmol/L	10.7^b	13.7^a	0.42	< 0.001
Butyrate, mmol/L	7.22^b	10.2^a	0.42	< 0.001
Isobutyric, mmol/L	0.91	0.97	0.022	0.207
Isovalerate, mmol/L	0.74^b	1.08^a	0.055	< 0.001
Valerate, mmol/L	1.01	1.11	0.031	0.099
A/P	3.62^a	2.96^b	0.094	< 0.001
¹ CON = control group; LBR = Lycium barbarum residue group.
² SEM = standard error of the mean.
BW = body weight; ADG = average daily gain; ADFI = average daily feed intake; F/G = ADFI to ADG; TVFAs = total volatile fatty acids; NH₃-N = ammonia-nitrogen; A/P = acetate to propionate.
^a,b Values within a row with different superscripts differ significantly at P < 0.05.

The ruminal pH differed (P < 0.05) between the LBR and CON groups. However, the NH₃-N concentration in the rumen decreased (P < 0.01) in the LBR group. The concentrations of TVFAs (P < 0.01), acetate (P < 0.01), propionate (P < 0.01), butyrate (P < 0.01) and isovalerate (P < 0.01) increased in the LBR group in comparison with the CON group. In addition, a significant decline in the acetate/propionate (A/P) ratio (P < 0.01) was noticed in the LBR group (Table 2).

Rumen microbe diversity and composition

1,277,062 raw reads were obtained from the Illumina MiSeq platform sequencing runs, averaging 63,853 sequences per sample. After quality control, 658,124 filtered sequences were retained, with a mean of 32,906 filtered sequences per sample. The distributed range in length of all sequences was 51 to 439 base pairs. The rarefaction curve (Additional file 2: Fig. S1A) illustrated that the sequencing was sufficient to give the diversity of the rumen bacterial community. A Venn diagram demonstrated the shared and unique ASVs among the two treatment groups (Additional file 2: Fig. S1B). A total of 15,282 ASVs were identified in two groups, of which 2,330 ASVs were presented in all groups, accounting for 15.2% of the total number of ASVs. Alpha diversity indices analysis, including Chao1 index, Good's coverage, and Simpson and Shannon index, revealed that there were significant differences (P < 0.01) in all indices except Simpson between the LBR and CON groups (Fig. 2).

The taxonomical distributions of LBR and CON groups at phylum and genus levels for the rumen microbial samples are presented in Fig. 3. Ten bacterial phyla were identified in the rumen samples (Fig. 3A and Additional file 1: Table S1). Among these species, Bacteroidetes were the most dominant phylum (51.89% and 53.47%), followed by Firmicutes (35.08% and 42.05%) and Proteobacteria (10.16% and 3.0%) in the CON and LBR groups, respectively. The relative abundances of Bacteroidetes, Firmicutes, Tenericutes, Cyanobacteria, and TM7 were significantly higher (P < 0.01) in the LBR group than in the CON group. However, the relative abundances of Proteobacteria, Synergistetes, and Spirochaetes were significantly lower (P < 0.01) in the LBR group than in the CON group. 169 bacterial taxa were identified at the genus level, and 97 genera were observed in all samples. The top 10 genera presented in the samples were an indicator of the core microbiome in this study (Fig. 3B and Additional file 1: Table S2). Among these genera, Prevotella (41.79% and 43.74%), Succiniclasticum (3.82% and 9.14%), Oscillospira (5.72% and 5.28%), Succinivibrio (6.72% and 2.03%), Ruminococcus (2.29% and 2.74%) and Coprococcus (1.55% and 2.26%) were the dominant genera in the CON and LBR groups, respectively. Addition of LBR significantly increased the relative abundances of Prevotella (P < 0.05), Succiniclasticum (P < 0.01), Ruminococcus (P < 0.01), Coprococcus (P < 0.01), Selenomonas (P < 0.01) and Butyrivibrio (P < 0.01) in the treated group, whereas three genera (Oscillospira, Succinivibrio, and Treponema) were significantly higher (P < 0.01) in the CON group.

Alteration of ruminal microbiota

The PCoA and NMDS based on Bray-Curtis distance were used to identify beta diversity of the rumen microbial community across all the samples in CON and LBR groups (Fig. 4A and B). The rumen microbial communities of the LBR group and the CON group were separated from each other, demonstrating that LBR could affect the species and abundance of rumen microorganisms. The differences in rumen microorganisms between the LBR and CON groups were performed using LEfSe analysis and LDA (Fig. 5A and B). The relatively high abundance in the LBR group was mainly Succiniclasticum, Selenomonas, Coprococcus, Ruminococcus, Butyrivibrio, Prevotella, and Anaerovibrio. By comparison, Succinivibrio, Treponema, Oscillospira, Alistipes, Pseudobutyrivibrio, Ruminobacter, were more abundant in the CON group.

Prediction of rumen microbial function

Based on the 16S rRNA data, PICRUSt2 was conducted to predict the function of rumen microbiota in the LBR and CON groups. As shown in Additional file 2: Fig. S3A, biosynthesis, degradation/utilization/assimilation, detoxification, generation of precursor metabolite and energy, glycan pathways, macromolecule modification, and metabolic clusters were observed in the function of rumen microbe among the two groups at level 1. A total of 57 pathways were identified, and 21 were different among the LBR and CON groups at level 2. Compared with the CON group, supplemented with LBR was predicted to have a lower (P < 0.05) capability of controlling cell structure biosynthesis, secondary metabolite degradation, carbohydrate degradation, and metabolic regulator biosynthesis but a higher (P < 0.05) capability of influencing fatty acid and lipid biosynthesis, amino acid biosynthesis, nucleoside, and nucleotide biosynthesis, glycolysis, L-glutamate, and L-glutamine biosynthesis and nucleoside and nucleotide degradation (Additional file 2: Fig. S3A and B). Furthermore, flora correlated with D-arginine and D-ornithine metabolism, D-glutamine, and D-glutamate metabolism, fatty acid biosynthesis, D-alanine metabolism, biotin metabolism, Vitamin B6 metabolism, pyrimidine metabolism, arginine, and proline metabolism (P < 0.05), were significantly higher in the LBR group at level 3 (Additional file 2: Fig. S3C).

Rumen fluid metabolome analysis

The untargeted LC-MS/MS approach was used to analyze the rumen fluid metabolome. The total ion chromatogram (TIC) curves of QC samples in positive and negative ion modes showed high stability, large peak identification capacity, and consistent retention time (Additional file 2: Fig. S4). The PCA revealed the overall difference and the degree of variation among rumen samples within the CON and LBR groups (Fig. 6A). All the pieces were within the 95% confidence interval (Hotelling's T-squared ellipse). The R²X for PCA was 0.693, showing the reliability of the PCA model (Additional file 1: Table S3). The PCA indicated that the two groups were distributed in different areas. Furthermore, OPLS-DA score plots exhibited the differences in ruminal metabolites between the CON and LBR groups (Fig. 6B). As shown in Additional file 1: Table S3, the OPLS-DA model parameters R²Y (cum) and Q² (cum) were above 0.90, explaining that the model remained stable and reliable. Furthermore, the value of Q² (-1.1 < 0) was negative in response to permutation testing, indicating that the models did not overfit (Fig. 6C).

Ruminal metabolites affected by LBR supplementation

In total, 431 differential metabolites were acquired with VIP > 1.0 and P < 0.05 from the rumen fluid samples between the CON and LBR groups. Among these metabolites, 287 were up-regulated by LBR (Fig. 7A), which included 94 organic acids and derivatives, 52 organoheterocyclic compounds, 51 lipids and lipid-like molecules, 24 nucleosides, nucleotides, and analogs, 20 organic oxygen compounds and 14 benzenoids. 144 metabolites were down-regulated in the LBR group (Fig. 7A), including 38 lipids and lipid-like molecules, 36 organic acids and derivatives, 26 organo-heterocyclic compounds, and 12 benzenoids. The HCA analysis was conducted on the top 100 different metabolites to visualize the differences in the rumen metabolome between the two groups (Fig. 7B). The differential metabolites were clustered separately between the CON and LBR groups. In addition, 12 significantly differential ruminal metabolites with VIP > 1.0 and P < 0.01 between CON and LBR were picked out. These metabolites were all elevated in the LBR group, except for 3-aminoisobutanoic acid (Table 3).

Table 3

HMDB compound classification of significantly different metabolites in Tan sheep among CON and LBR groups.
HMDB superclass¹	HMDB class¹	HMDB subclass¹	Metabolites	VIP²	Log₂FC³	P-value
Organic acids and derivatives	Carboxylic acids and derivatives	Amino acids, peptides, and analogs	L-Lysine	1.56	2.34	< 0.001
			L-Tyrosine	1.47	2.33	0.003
			L-Phenylalanine	1.40	1.43	0.002
			L-Proline	1.44	1.26	< 0.001
			D-Proline	1.22	0.59	< 0.001
			3-Aminoisobutanoic acid	1.43	-0.60	< 0.001
Organoheterocyclic compounds	Diazines	Pyrimidines and pyrimidine derivatives	Uracil	1.51	1.54	< 0.001
	Imidazopyrimidines	Purines and purine derivatives	Hypoxanthine	1.68	2.95	< 0.001
	Imidazopyrimidines	Purines and purine derivatives	Xanthine	1.11	1.20	< 0.001
Nucleosides, nucleotides, and analogues	Pyrimidine nucleosides	Pyrimidine 2'-deoxyribonucleosides	Thymidine	1.66	3.81	< 0.001
Nucleosides, nucleotides, and analogues	Pyrimidine nucleosides	–⁴	Uridine	1.59	1.74	< 0.001
Phenylpropanoids and polyketides	Phenylpropanoic acids	–	Hydrocinnamic acid	1.17	1.15	< 0.001
CON = control group; LBR = Lycium barbarum residue group.
¹ HMDB = Human metabolome database.
² VIP = Variable importance in the projection; VIP: the contribution value of metabolites to the difference between the two groups (VIP > 1).
³ FC = Fold change; log₂FC > 0 represents the upregulated compounds, while log₂FC < 0 represents the downregulated compounds.
⁴–: no pathway information.

Metabolic pathway enrichment analysis of differential metabolites

The metabolic pathway enrichment analysis was performed based on the different metabolites identified between CON and LBR groups (P < 0.05). Given the high impact and P-value (Table 4), pyrimidine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, arginine and proline metabolism, and phenylalanine metabolism were regarded as key metabolic pathways altered due to LBR supplementation (Additional file2: Fig. S5).

Table 4

Metabolic pathway enrichment analysis of significantly different metabolites among CON and LBR groups.
KEGG pathway	Metabolites	P-value
Pyrimidine metabolism	Uridine; Cytidine monophosphate; Cytidine; dCMP; Deoxyuridine; Thymidine; Dihydrothymine Ureidoisobutyric acid; Orotic acid Uracil; 3-Aminoisobutanoic acid	0.002
Phenylalanine, tyrosine, and tryptophan biosynthesis	Phenylpyruvic acid L-Phenylalanine; L-Tyrosine	0.006
Arginine and proline metabolism	Ornithine; Citrulline; L-Proline; L-Aspartic acid; L-Glutamic acid; N-Acetylornithine; D-Proline; 4-Aminobutyraldehyde N-Acetylputrescine; N4-Acetylaminobutanal 1-Pyrroline-2-carboxylic acid	0.009
Phenylalanine metabolism	L-Phenylalanine; 2-Phenylacetamide; L-Tyrosine; Phenylpyruvic acid	0.014
CON = control group; LBR = Lycium barbarum residue group.

Correlation between the differential rumen microbiome, metabolites, and rumen fermentation parameters

The Spearman correlation coefficient was used to conduct correlation analysis among several indicators (Fig. 8). As illustrated in Fig. 8A, the correlation between significantly differential bacteria and rumen fermentation parameters showed that the TVFAs, acetate, propionate, butyrate, isovalerate were most positively correlated to Succiniclasticum, Coprococcus, Selenomonas, Ruminococcus, Butyrivibrio and YRC22 (r > 0.7, P < 0.001), but negatively correlated with Succinivibrio and Treponema (r < -0.7, P < 0.001). Nevertheless, NH₃-N concentration was most positively correlated to Succinivibrio and Treponema (r > 0.9, P < 0.001), but negatively correlated to Succiniclasticum, Coprococcus, Selenomonas, Butyrivibrio and YRC22 (r < -0.7, P < 0.001). There was no correlation between pH, valerate, and isobutyric parameters with different bacteria.

Furthermore, the correlation analysis between significantly differential bacteria and metabolites manifested that Succiniclasticum, Ruminococcus, YRC22, Selenomonas, and Coprococcus were most positively correlated to L-proline, L-phenylalanine, L-lysine, L-tyrosine, D-proline, uracil, hypoxanthine, xanthine, thymidine, uridine and hydrocinnamic acid (r > 0.7, P < 0.001), but negatively correlated to 3-aminoisobutanoic acid (r < -0.6, P < 0.01). 3-aminoisobutanoic acid was most positively correlated to Treponema and Succinivibrio (r > 0.7, P < 0.01). Prevotella was positively associated with uracil, hypoxanthine, xanthine, thymidine, and uridine (r > 0.5, P < 0.05). Nevertheless, these metabolites were negatively related with Oscillospira (r < -0.5, P < 0.05). Additionally, Butyrivibrio was positively correlated with L-proline, L-lysine, D-proline, uracil, hypoxanthine, xanthine, thymidine, uridine and hydrocinnamic acid (r > 0.5, P < 0.05), but negatively correlated with 3-aminoisobutanoic acid (r < -0.7, P < 0.001) (Fig. 8B).

As shown in Fig. 8C, the relevance between significantly differential metabolites and rumen fermentation parameters revealed that TVFAs, propionate, and butyrate were positively correlated to L-proline, L-phenylalanine, L-lysine, L-tyrosine, D-proline, uracil, hypoxanthine, xanthine, thymidine, uridine and hydrocinnamic acid (r > 0.6, P < 0.001), but negatively correlated to 3-aminoisobutanoic acid (r < -0.7, P < 0.001). Nevertheless, NH₃-N was most positively correlated to 3-aminoisobutanoic acid (P < 0.001). The acetate and isovalerate were positively associated with L-proline, L-phenylalanine, L-lysine, L-tyrosine, uracil, hypoxanthine, xanthine, thymidine, uridine, and hydrocinnamic acid (r > 0.6, P < 0.05) but negatively correlated with 3-aminoisobutanoic acid (r < -0.7, P < 0.001). D-proline was negatively correlated with pH (r < -0.6, P < 0.05). Moreover, there was no correlation between valerate and isobutyric with different metabolites.

Growth performance

The beneficial effects of Lycium barbarum supplementation have been demonstrated through human clinical trials and laboratory animal studies [29] and further studied in livestock animals [12, 16]. In the present study, dietary supplementation of LBR increased the final BW, ADG, and ADFI of Tan sheep but decreased the F/G, which could be due to the presence of LBPs, polyphenols, amino acids, and vitamins in LBR, which could improve the growth performance of Tan sheep. Previous studies have used polysaccharides from Chinese medicinal herbs, such as Astragalus membranaceus polysaccharide (AMP), Aloe vera polysaccharides, Astragalus polysaccharides (APS), Ginseng polysaccharide, and LBPs, to improve growth performance in piglets, chickens, and rabbits [11, 14, 30]. A diet with a mixture of Lycium barbarum and Astragalus. membranaceus increased the ADG and decreased the F/G of lambs [17]. Another study showed that diets with bee pollen polysaccharides improved the nutrient digestibility of calves [31]. Polyphenols are considered as the main bioactive components of Lycium barbarum, which positively regulates energy metabolism [32]. Consequently, the improvement of growth performance in the LBR group could be due to the elevated digestive enzymes, improved bioactive nutrient utilization, and enhanced antioxidant capacity of Tan sheep.

Rumen fermentation parameters

The ruminal pH can influence the rumen microbial community structure and change the production pathways of VFA [33]. The optimum pH value of a healthy rumen ranges between 5.9 and 7.2, in which the fibrinolytic microorganisms are most active [34]. In the present study, a lower ruminal pH was discovered in the LBR group, but the pH value was within the optimal pH, indicating that feeding LBR maintained a suitable acid-base status. In line with a previous study, the ruminal fluid with the lowest pH had the highest concentration of TVFAs, one of the main pH drivers [35]. Similar to the data reported by Ren et al. [36], the administration of LBPs could reduce the pH of rat caecal contents. The concentration of TVFAs in the LBR group was higher than that in the CON group, which could provide energy for ruminants. In addition, with supplementation of LBR, the concentration of acetate, propionate, and butyrate increased, whereas the A/P ratio decreased, as observed in this study. Acetate and butyrate are used to synthesize fatty precursors, and propionate is the major VFA used for gluconeogenesis in ruminants [37]. The concentration of NH₃-N was positively correlated with protein degradation.

Consequently, supplementation of LBR increased ADG and feed efficiency by offering more substrates for body protein and fatty acid syntheses in sheep. Furthermore, feeding LBR increased the fermentation of rumen microorganisms, increasing propionic production. Good fermentation characteristics facilitate the proliferation of major beneficial microorganisms in the rumen.

Rumen microbiome

Bacteria are the most abundant, diverse, and metabolically active microorganisms in ruminants, and were affected by diets, as reported previously [19]. There was a negative effect on Chao 1 value and Shannon index among LBR groups, which was similar to previous studies [38]. These results could be attributed to the LBR having numerous potential health benefits, including antioxidative, antibacterial and antitumor activities [39], and immunoenhancing effects [40]. Also, LBR contains abundant polyphenols, stimulating the growth of beneficial bacteria and inhibiting pathogenic bacteria [41]. Therefore, LBR supplementation increased performance-enhancing microorganisms. In the present study, Bacteroidetes, Firmicutes, and Proteobacteria were the main microbes at the phylum level of Tan sheep, which was in accordance with the findings of a previous report [42]. Compared to the CON group, intake of LBR significantly increased the relative abundance of Bacteroidetes and Firmicutes in the treated group. Among these microorganisms, Bacteroidetes could assist the host in degrading a variety of complex polysaccharides, thereby improving nutrient absorption [43], promoting immune system development, and maintaining intestinal microecological balance [44], and the high LBPs content of the diet could explain the high abundance of Bacteroidetes. Firmicutes are important in the degradation of fibers and celluloses and are involved in many fiber-decomposing genera [45]. Enhancing cellulose degradation can improve animal feed utilization efficiency and promote animal production. The Firmicutes to Bacteroidetes ratio increased with supplementation of LBR. These findings are consistent with a report that obesity is positively associated with a high Firmicutes to Bacteroidetes ratio, which affects energy harvesting and fat deposition in the guts of mice [45]. In addition, the significantly decreased rumen microbes induced by LBR, including Proteobacteria and Spirochaetes, were mainly Gram-negative bacteria, including several potential pathogens.

In this study, Prevotella, Succiniclasticum, Ruminococcus, Coprococcus, Selenomonas, and Butyrivibrio were core microbiomes at the genus level, in line with previous reports [46, 47]. Prevotella plays a pivotal role in fiber degradation [48], which has the function of degrading non-fibrous polysaccharides and proteins, digesting and utilizing starch, xylan, and pectin, and producing short-chain fatty acids (SCFA), such as acetate, propionate, and succinate [49]. Hence, the increased VFA, acetate, and propionate concentrations in the LBR group could be attributable to the degradation of carbohydrates and fibers. Consistent with a previous study [50], Ruminococcus was abundant in the mammalian gut, which is beneficial for gut health, and its abundance was increased by LBR supplementation. Ruminococcus is rich in carbohydrate-active enzymes, which can degrade a wide variety of recalcitrant substrates in feed [51]. In the gut environment, the degradability of cellulose and hemicellulose components in plant materials enables Ruminococcus to break down these substrates, which are otherwise indigestible [52]. Furthermore, these substrates are fermented and produced short-chain VFA (acetate, propionate, and butyrate) that can be absorbed through the rumen epithelium and used as the primary carbon and energy sources supporting animal growth [53]. Succiniclasticum and Selenomonas specialized in fermenting succinate and converting it to propionate [54]. Butyrivibrio, a main butyrate-producing bacterium, also increased in the LBR group [55]. These findings showed that LBR significantly enhanced the growth of Prevotella, Succiniclasticum, Ruminococcus, and Butyrivibrio, leading to greater production of VFA and greater utilization of proteins in rumen fluid. Consistently, we found that numerous rumen microbes in the LBR were positively associated with TVFAs, acetate, propionate, and butyrate and negatively related to NH₃-N. Increased VFA production improved sheep's energy supply while inhibiting the proliferation of pathogens in the gastrointestinal tract [56]. Among these acids, propionate could improve colonization resistance of Salmonella by altering the pH, and butyrate could control the growth of pathogenic bacteria [57]. The rumen microbiota provides diverse metabolites required by the sheep.

In summary, LBR dietary supplement increased the abundance of SCFA-producing bacteria, increasing VFA and reducing pH, which would provide an acidic environment in the rumen and diminish pathogenic populations.

Rumen fluid metabolome

The ruminal microbial metabolites influence tissue and organ development, maintain the homeostasis of the gastrointestinal tract ecosystem and ensure the health of animals. In light of a previous report [58], the variations in the contents of various rumen metabolites could affect the rumen and host health. We used metabolomics analysis to uncover the differences in rumen metabolites between the LBR and CON groups. According to a previous study, the metabolomics data revealed that LBR supplementation significantly altered metabolites, including amino acids, peptides, analogs, fatty acids and conjugates, and purines and purine derivatives [59]. Among the significantly altered metabolites, most of them were amino acids, which are from the degradation of proteins and microorganisms by rumen microbiota and acted as the key precursors for synthesizing proteins and polypeptides [60], such as 3-aminoisobutanoic acid, lysine and proline.

Meanwhile, an aromatic compound, hydrocinnamic acid [61], is a degradation product of amino acids. VFA and other substances are used as a carbon source, and nitrogen compounds, like ammonia, as a nitrogen source by the rumen microbe for de novo synthesis of amino acids [62]. In our study, higher concentrations of VFA and lower pH in the LBR group might promote amino acid synthesis in rumen.

Notably, most amino acids were positively related to SCFA-producing bacteria (Succiniclasticum, Selenomonas, Butyrivibrio) abundant in the LBR group. Therefore, LBR supplementation had positive effects on ruminal amino acid metabolism. Among these amino acids, L-proline, L-phenylalanine, L-lysine, L-tyrosine, and D-proline increased except for 3-aminoisobutanoic acid in the LBR group. In agreement, a previous study reported that 3-aminoisobutanoic acids and branched- chain fatty acids were competitive and negatively correlated with cellulolytic bacteria in the rumen [19]. Also, phenylalanine, tyrosine, and lysine could be used as precursors of acetate and butyrate [63]. Butyrate stems from lysine and glutamic acid, and proline is degraded to produce isoacids such as isobutyric acid, isovaleric acid, and valeric acid [64]. Thus, L-lysine, L-proline, L-phenylalanine, and L-tyrosine were positively correlated with SCFA-producing bacteria, TVFAs, propionic, butyric and isovaleric acid. L-proline is the main amino acid that maintains cell structure and function, and is an important regulator of cell metabolism and physiology [65]. Thus, LBR supplementation might promote gastrointestinal microbiota homeostasis and the synthesis of bacterial proteins.

In this study, we found that LBR supplementation affected pyrimidine and purine metabolism, characterized by elevated levels of uridine, thymidine, uracil, and xanthine hypoxanthine. Hypoxanthine is a purine derivative, while uracil, xanthine, and thymine are nucleic acid bases found in RNA and DNA, respectively [66]. A previous study showed that greater amounts of nucleic acids in the rumen could indicate greater availability of crude microbial protein (MCP) [67]. Among these, xanthine and hypoxanthine have been frequently used as biomarkers of microbial protein synthesis in the rumen [58]. The decomposition product of uracil is β-alanine, which can be further converted into propionic acid, explaining the positive correlation between uracil and Succiniclasticum. The above findings further suggested that supplementation with LBR might increase microbial protein synthesis in the rumen and improve health.

The integrative microbiome and metabolomics analysis investigated the effects of LBR on ruminal microbial communities, metabolites, and their associations with the growth performance of Tan sheep. The LBR increased the abundance of ruminal beneficial bacteria (such as Prevotella, Succiniclasticum, and Ruminococcus), and promoted amino acids synthesis and metabolism, pyrimidine metabolism, and organic acid production pathways in the rumen. These changed LBR associated rumen microbial communities and functions which in turn enhanced the growth performance and feed efficiency of Tan sheep.

LBR: Lycium barbarum residue, LBPs: Lycium barbarum polysaccharides, AMP: Astragalus membranaceus polysaccharide, APS: Astragalus polysaccharides, ADG: Average daily gain, ADFI: Average daily feed intake, NDF: Neutral detergent fiber, ADF: Acid detergent fiber, TVFAs: Total volatile fatty acids, VFAs: Volatile fatty acids, NH₃-N: Ammonia nitrogen, BW: Body weight, A/P: Acetate to propionate ratio, DM: Dry matter, DE: Digestible energy, CP: Crude protein, EE: Ether extract, FCR: Feed conversion ratio, F/G: Gain feed ratio, ASVs: Amplicon sequence variants, PCoA: Principal coordinates analysis, NMDS: Nonmetric multidimensional scaling analysis, LEfSe: Linear discriminant analysis effect, LDA: Linear discriminant analysis, FC: Fold change, HCA: Hierarchical clustering analysis, VIP: Variable importance in the projection, PCA: principal component analysis, HMDB: Human Metabolome Database, PICRUSt: Phylogenetic Investigation of Communities by Reconstruction of Unobserved States, OPLS-DA: Orthogonal partial least squares discriminant analysis, KEGG: Kyoto Encyclopedia of Genes and Genomes, RPT: Response permutation testing, MCP: Crude microbial protein, SCFA: Short-chain fatty acids, TIC: Total ion chromatogram, IDA: Information-dependent acquisition, QC: Quality control

Acknowledgements

This study was supported by the National Natural Science Foundation of China (31960672), Top Discipline Construction Project of Pratacultural Science (NXYLXK2017A01), and the Key Research and Development Program of Ningxia Hui Autonomous Region (2021BBF02034 and 2021BEG02042).

Authors' contributions

YJZ: Performed the study, data collection and analysis, drafted the manuscript. BW, DWW and YSG: Assisted in data analyses and manuscript preparation. MD: Participated in data intepration and manuscript preparation. GJZ: Designed the study, data interpretation and finalized the manuscript.

Availability of data and materials

The data analyzed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

The experimental protocols and treatments were approved by the Institutional Animal Care and Use Committee of Ningxia University (NXUC20200618).

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

¹Departments of Animal Science, School of Agriculture, Ningxia University, Yinchuan 750021, P.R. China. ²State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100193, P. R. China. ³Nutrigenomics and Growth Biology Laboratory, Department of Animal Sciences, Washington State University, Pullman, WA 99164, USA.

Castanon JIR. History of the use of antibiotic as growth promoters in European poultry feeds. Poult. Sci. 2007,86(11):2466-2471. https://doi.org/10.3382/ps.2007-00249.
Ma XK, Shang QH, Wang QQ, Hu JX, Piao XS. Comparative effects of enzymolytic soybean meal and antibiotics in diets on growth performance, antioxidant capacity, immunity, and intestinal barrier function in weaned pigs. Anim Feed Sci Technol. 2019,248:47-58. https://doi.org/10.1016/j.anifeedsci.2018.12.003.
Wang B, Ma MP, Diao QY, Tu Y. Saponin-induced shifts in the rumen microbiome and metabolome of young cattle. Front Microbiol. 2019,10. https://doi.org/10.3389/fmicb.2019.00356.
Gao Y, Wei Y, Wang Y, Gao F, Chen Z. Lycium barbarum: A traditional Chinese herb and a promising anti-aging agent. Aging Dis. 2017,8(6):778-791. https://doi.org/10.14336/AD.2017.0725.
Lu Y, Guo S, Zhang F, Yan H, Qian DW, Wang HQ, et al. Comparison of functional components and antioxidant activity of lycium barbarum L. Fruits from different regions in China. Molecules. 2019,24(12). https://doi.org/10.3390/molecules24122228.
Yi R, Liu XM, Dong Q. A study of Lycium barbarum polysaccharides (LBP) extraction technology and its anti-aging effect. African J Tradit Complement Altern Med AJTCAM. 2013,10(4):171-174. https://doi.org/10.4314/ajtcam.v10i4.27.
Lian YZ, Lin IH, Yang YC, Chao JCJ. Gastroprotective effect of Lycium barbarum polysaccharides and C-phyocyanin in rats with ethanol-induced gastric ulcer. Int J Biol Macromol. 2020,165:1519-1528. https://doi.org/10.1016/j.ijbiomac.2020.10.037.
Cheng D, Kong H. The effect of lycium barbarum polysaccharide on alcohol-induced oxidative stress in rats. Molecules. 2011,16(3)2542-2550. https://doi.org/10.3390/molecules16032542.
Menchetti L, Vecchione L, Filipescu I, Petrescu VF, Fioretti B, Beccari T, et al. Effects of Goji berries supplementation on the productive performance of rabbit. Livest Sci. 2019,220:123-128. https://doi.org/10.1016/j.livsci.2018.12.016.
Cremonesi P, Curone G, Biscarini F, Cotozzolo E, Menchetti L, Riva F, et al. Dietary Supplementation with Goji Berries (Lycium barbarum) modulates the microbiota of digestive tract and caecal metabolites in rabbits. Animals. 2022,12(1). https://doi.org/10.3390/ani12010121.
Menchetti L, Curone G, Andoni E, Barbato O, Troisi A, Fioretti B, et al. Impact of goji berries (Lycium barbarum) supplementation on the energy homeostasis of rabbit does: Uni‐ and multivariate approach. Animals. 2020,10(11):1-19. https://doi.org/10.3390/ani10112000.
Chen J, Long L, Jiang Q, Kang B, Li Y, Yin J. Effects of dietary supplementation of Lycium barbarum polysaccharides on growth performance, immune status, antioxidant capacity and selected microbial populations of weaned piglets. J Anim Physiol Anim Nutr (Berl). 2020,104(4):1106-1115. https://doi.org/10.1111/jpn.13247.
Yin FG, Liu YL, Yin YL, Kong XF, Huang RL, Li TJ, et al. Dietary supplementation with Astragalus polysaccharide enhances ileal digestibilities and serum concentrations of amino acids in early weaned piglets. Amino Acids. 2009,37. https://doi.org/10.1007/s00726-008-0142-6.
Qiao J, Li HH, Zheng CJ, Feng ZY, Wang W. Dietary supplementation with Aloe vera polysaccharide enhances the growth performance and immune function of weaned piglets. J Anim Feed Sci. 2013,22(4):329-334. https://doi.org/10.22358/jafs/65921/2013.
Yin F, Yin Y, Kong X, Liu Y, He Q, Li T, et al. Dietary supplementation with Acanthopanax senticosus extract modulates gut microflora in weaned piglets. Asian-Australasian J Anim Sci. 2008,21(9):1330-1338. https://doi.org/10.5713/ajas.2008.70583.
Long LN, Kang BJ, Jiang Q, Chen JS. Effects of dietary Lycium barbarum polysaccharides on growth performance, digestive enzyme activities, antioxidant status, and immunity of broiler chickens. Poult Sci. 2020,99(2):744-751. https://doi.org/10.1016/j.psj.2019.10.043.
Zhang J, Shi H, Wang Y, Li S, Cao Z, Ji S, et al. Effect of dietary forage to concentrate ratios on dynamic profile changes and interactions of ruminal microbiota and metabolites in holstein heifers. Front Microbiol. 2017,8. https://doi.org/10.3389/fmicb.2017.02206.
Saleem F, Bouatra S, Guo AC, Psychogios N, Mandal R, Dunn SM, et al. The bovine ruminal fluid metabolome. Metabolomics. 2013,9(2):360-378. https://doi.org/10.1007/s11306-012-0458-9.
Chen H, Guo B, Yang M, Luo J, Hu Y, Qu M, et al. Response of growth performance, blood biochemistry indices, and rumen bacterial diversity in lambs to diets containing supplemental probiotics and Chinese medicine polysaccharides. Front Vet Sci. 2021,8:681389. https://doi.org/10.3389/fvets.2021.681389.
AOAC. Official methods of analysis of the association of official's analytical chemists. Arlington, Virginia: Association of Official Analytical Chemists, 2003.
NRC. Nutrient requirements of dairy cattle. Washington, DC: National Academy Press, 2001.
Van Soest PJ, Robertson JB, Lewis BA. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J Dairy Sci. 1991,74(10):3583-3597. https://doi.org/10.3168/jds.S0022-0302(91)78551-2.
Liu C, Wu H, Liu S, Chai S, Meng Q, Zhou Z. Dynamic alterations in yak rumen bacteria community and metabolome characteristics in response to feed type. Front Microbiol. 2019,10. https://doi.org/10.3389/fmicb.2019.01116.
Hu WL, Liu JX, Ye JA, Wu YM, Guo YQ. Effect of tea saponin on rumen fermentation in vitro. Anim Feed Sci Technol. 2005,120(3-4):333-339. https://doi.org/10.1016/j.anifeedsci.2005.02.029.
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetJournal. 2011,17(1):10. https://doi.org/10.14806/ej.17.1.200.
Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018,6(1):90. https://doi.org/10.1186/s40168-018-0470-z.
Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-Mcintyre S, Anderson N, et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc. 2011,6(7):1060-1083. https://doi.org/10.1038/nprot.2011.335.
Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem. 2006,78(3):779-787. https://doi.org/10.1021/ac051437y.
Ruíz-Salinas AK, Vázquez-Roque RA, Díaz A, Pulido G, Treviño S, Floran B, et al. The treatment of Goji berry (Lycium barbarum) improves the neuroplasticity of the prefrontal cortex and hippocampus in aged rats. J Nutr Biochem. 2020,83. https://doi.org/10.1016/j.jnutbio.2020.108416.
Liu YL, Yin RQ, Liang SS, Duan YL, Yao JH, Duan YL, et al. Effect of dietary Lycium barbarum polysaccharide on growth performance and immune function of broilers. J Appl Poult Res. 2017,26(2):200-208. https://doi.org/10.3382/japr/pfw063.
Tu Y, Zhang GF, Deng KD, Zhang NF, Diao QY. Effects of supplementary bee pollen and its polysaccharides on nutrient digestibility and serum biochemical parameters in Holstein calves. Anim Prod Sci. 2015,55(10):1318-1323. https://doi.org/10.1071/AN14684.
Lan W, Yang C. Ruminal methane production: Associated microorganisms and the potential of applying hydrogen-utilizing bacteria for mitigation. Sci Total Environ. 2019,654:1270-1283. https://doi.org/10.1016/j.scitotenv.2018.11.180.https://doi.org/10.1016/j.scitotenv.2018.11.180.
Dijkstra J, Ellis JL, Kebreab E, Strathe AB, López S, France J, et al. Ruminal pH regulation and nutritional consequences of low pH. Anim Feed Sci Technol. 2012,172(1-2):22-33. https://doi.org/10.1016/j.anifeedsci.2011.12.005.
Farghaly MM, Abdullah MAM, Youssef IMI, Abdel-Rahim IR, Abouelezz K. Effect of feeding hydroponic barley sprouts to sheep on feed intake, nutrient digestibility, nitrogen retention, rumen fermentation and ruminal enzymes activity. Livest Sci. 2019,228:31-37. https://doi.org/10.1016/j.livsci.2019.07.022.
Ceconi I, Ruiz-Moreno MJ, Dilorenzo N, Dicostanzo A, Crawford GI. Effect of urea inclusion in diets containing corn dried distillers grains on feedlot cattle performance, carcass characteristics, ruminal fermentation, total tract digestibility, and purine derivatives-to-creatinine index. J Anim Sci. 2015,93(1):357-369. https://doi.org/10.2527/jas.2014-8214.
Ren L, Li J, Xiao Y, Zhang Y, Fan J, Zhang B, et al. Polysaccharide from Lycium barbarum L. leaves enhances absorption of endogenous calcium, and elevates cecal calcium transport protein levels and serum cytokine levels in rats. J Funct Foods. 2017,33:227-234. https://doi.org/10.1016/j.jff.2017.03.053.
Yu S, Shi W, Yang B, Gao G, Chen H, Cao L, et al. Effects of repeated oral inoculation of artificially fed lambs with lyophilized rumen fluid on growth performance, rumen fermentation, microbial population and organ development. Anim Feed Sci Technol. 2020,264. https://doi.org/10.1016/j.anifeedsci.2020.114465.
Kruger Ben Shabat S, Sasson G, Doron-Faigenboim A, Durman T, Yaacoby S, Berg Miller ME, et al. Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. ISME J. 2016,10(12):2958-2972. https://doi.org/10.1038/ismej.2016.62.
Lin CL, Wang CC, Chang SC, Inbaraj BS, Chen BH. Antioxidative activity of polysaccharide fractions isolated from Lycium barbarum Linnaeus. Int J Biol Macromol. 2009,45(2):146-151. https://doi.org/10.1016/j.ijbiomac.2009.04.014.
Chen JR, Li EQ, Dai CQ, Yu B, Wu XL, Huang CR, et al. The inducible effect of LBP on maturation of dendritic cells and the related immune signaling pathways in hepatocellular carcinoma (HCC). Curr Drug Deliv 2012,9(4):414-420. https://doi.org/10.2174/156720112801323107.
Valdés L, Cuervo A, Salazar N, Ruas-Madiedo P, Gueimonde M, González S. The relationship between phenolic compounds from diet and microbiota: Impact on human health. Food Funct. 2015,8. https://doi.org/10.1039/c5fo00322a.
Klevenhusen F, Petri RM, Kleefisch MT, Khiaosa-ard R, Metzler-Zebeli BU, Zebeli Q. Changes in fibre-adherent and fluid-associated microbial communities and fermentation profiles in the rumen of cattle fed diets differing in hay quality and concentrate amount. FEMS Microbiol Ecol. 2017,93(9). https://doi.org/10.1093/femsec/fix100.
Sóki J, Wybo I, Hajdú E, Toprak NU, Jeverica S, Stingu CS, et al. A Europe-wide assessment of antibiotic resistance rates in Bacteroides and Parabacteroides isolates from intestinal microbiota of healthy subjects. Anaerobe. 2020,62. https://doi.org/10.1016/j.anaerobe.2020.102182.
Sears CL. A dynamic partnership: Celebrating our gut flora. Anaerobe. 2005,11(5). https://doi.org/10.1016/j.anaerobe.2005.05.001.
Flint HJ, Scott KP, Duncan SH, Louis P, Forano E. Microbial degradation of complex carbohydrates in the gut. Gut Microbes. 2012,3(4):289-306. https://doi.org/10.4161/gmic.19897.
Jami E, Mizrahi I. Composition and similarity of bovine rumen microbiota across individual animals. PLoS One. 2012,7(3). https://doi.org/10.1371/journal.pone.0033306.
Zhou Z, Fang L, Meng Q, Li S, Chai S, Liu S, et al. Assessment of ruminal bacterial and archaeal community structure in Yak (Bos grunniens). Front Microbiol. 2017,8. https://doi.org/10.3389/fmicb.2017.00179.
Emerson EL, Weimer PJ. Fermentation of model hemicelluloses by Prevotella strains and Butyrivibrio fibrisolvens in pure culture and in ruminal enrichment cultures. Appl Microbiol Biotechnol. 2017,101(10):4269-4278. https://doi.org/10.1007/s00253-017-8150-7.
Carberry CA, Kenny DA, Han S, McCabe MS, Waters SM. Effect of phenotypic residual feed intake and dietary forage content on the rumen microbial community of beef cattle. Appl Environ Microbiol. 2012,78:4949-58. https://doi.org/10.1128/AEM.07759-11.
Jalanka-Tuovinen J, Salonen A, Nikkilä J, Immonen O, Kekkonen R, Lahti L, et al. Intestinal microbiota in healthy adults: Temporal analysis reveals individual and common core and relation to intestinal symptoms. PLoS One. 2011,6(7). https://doi.org/10.1371/journal.pone.0023035.
Brulc JM, Antonopoulos DA, Berg Miller ME, Wilson MK, Yannarell AC, Dinsdale EA, et al. Gene-centric metagenomics of the fiber-adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc Natl Acad Sci U S A. 2009,106(6):1948-53. https://doi.org/10.1073/pnas.0806191105.
Biddle A, Stewart L, Blanchard J, Leschine S. Untangling the genetic basis of fibrolytic specialization by lachnospiraceae and ruminococcaceae in diverse gut communities. Diversity. 2013,5(3):627-640. https://doi.org/10.3390/d5030627.
Singh KM, Reddy B, Patel AK, Panchasara H, Parmar N, Patel AB, et al. Metagenomic analysis of buffalo rumen microbiome: Effect of roughage diet on Dormancy and Sporulation genes. Meta Gene. 2014,2(1):252-268. https://doi.org/10.1016/j.mgene.2014.01.005.
Fernando SC, Purvis HT, Najar FZ, Sukharnikov LO, Krehbiel CR, Nagaraja TG, et al. Rumen microbial population dynamics during adaptation to a high-grain diet. Appl Environ Microbiol. 2010,76(22):7482-7490. https://doi.org/10.1128/AEM.00388-10.
Kopečný J, Zorec M, Mrázek J, Kobayashi Y, Marinšek-Logar R. Butyrivibrio hungatei sp. nov. and Pseudobutyrivibrio xylanivorans sp. nov., butyrate-producing bacteria from the rumen. Int J Syst Evol Microbiol. 2003,53(1):201-209. https://doi.org/10.1099/ijs.0.02345-0.
Hippe B, Remely M, Aumueller E, Pointner A, Haslberger AG. SCFA producing gut microbiota and its effects on the epigenetic regulation of inflammation. In: Liong, MT. (eds) Beneficial Microorganisms in Medical and Health Applications. Microbiology Monographs, vol 28,2015. p. 181-197.
Rosser EC, Piper CJM, Matei DE, Blair PA, Rendeiro AF, Orford M, et al. Microbiota-derived metabolites suppress arthritis by amplifying aryl-hydrocarbon receptor activation in regulatory B cells. Cell Metab 2020,31(4):837-851. https://doi.org/10.1016/j.cmet.2020.03.003.
Ametaj BN, Zebeli Q, Saleem F, Psychogios N, Lewis MJ, Dunn SM, et al. Metabolomics reveals unhealthy alterations in rumen metabolism with increased proportion of cereal grain in the diet of dairy cows. Metabolomics. 2010,6:583-94. https://doi.org/10.1007/s11306-010-0227-6.
Li YQ, Xi YM, Wang ZD, Zeng HF, Han Z. Combined signature of rumen microbiome and metabolome in dairy cows with different feed intake levels. J Anim Sci. 2020,98(3). https://doi.org/10.1093/JAS/SKAA070.
Mariz LDS, Amaral PM, Valadares Filho SC, Santos SA, Detmann E, Marcondes MI, et al. Dietary protein reduction on microbial protein, amino acid digestibility, and body retention in beef cattle: 2. amino acid intestinal absorption and their efficiency for whole-body deposition. J Anim Sci. 2018,96(2):670-683. https://doi.org/10.1093/jas/sky018.
Moss CW, Lambert MA, Goldsmith DJ. Production of hydrocinnamic acid by clostridia. Appl Microbiol. 1970,19(2):375-378. https://doi.org/10.1128/aem.19.2.375-378.1970.
Kajikawa H, Mitsumori M, Ohmomo S. Stimulatory and inhibitory effects of protein amino acids on growth rate and efficiency of mixed ruminal bacteria. J Dairy Sci. 2002,85(8):2015-2022. https://doi.org/10.3168/jds.S0022-0302(02)74278-1.
Wang Y, Nan X, Zhao Y, Jiang L, Wang H, Hua D, et al. Dietary supplementation with inulin improves lactation performance and serum lipids by regulating the rumen microbiome and metabolome in dairy cows. Anim Nutr. 2021,7(4):1189-204. https://doi.org/10.1016/j.aninu.2021.09.007.
Andries JI, Buysse FX, De Brabander DL, Cottyn BG. Isoacids in ruminant nutrition: Their role in ruminal and intermediary metabolism and possible influences on performances - A review. Anim Feed Sci Technol. 1987,18(3):169-80. https://doi.org/10.1016/0377-8401(87)90069-1.
Ji Y, Guo Q, Yin Y, Blachier F, Kong X. Dietary proline supplementation alters colonic luminal microbiota and bacterial metabolite composition between days 45 and 70 of pregnancy in Huanjiang mini-pigs. J Anim Sci Biotechnol. 2018,9(1). https://doi.org/10.1186/s40104-018-0233-5.
Clemmons BA, Powers JB, Campagna SR, Seay TB, Embree MM, Myer PR. Rumen fluid metabolomics of beef steers differing in feed efficiency. Metabolomics. 2020,16(2). https://doi.org/10.1007/s11306-020-1643-x.
Bach A, Calsamiglia S, Stern MD. Nitrogen metabolism in the rumen. J Dairy Sci. 2005, 88 (E. Suppl.):E9-E21. https://doi.org/10.3168/jds.S0022-0302(05)73133-7.

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Supplementation of Lycium barbarum residue improves the animal performance through the interaction of rumen microbiota and metabolome of Tan sheep

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Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials And Methods

Animal ethics statement

Animals, diets, and experimental design

Sample collection and measurements

DNA extraction and sequencing

Bioinformatics for rumen microbiota analysis

Metabolite extraction and LC-MS/MS analysis

Metabolomic data preprocessing and analysis

Correlation analysis

Statistical Analysis

Results

Growth performance and rumen fermentation characteristics

Rumen microbe diversity and composition

Alteration of ruminal microbiota

Prediction of rumen microbial function

Rumen fluid metabolome analysis

Ruminal metabolites affected by LBR supplementation

Metabolic pathway enrichment analysis of differential metabolites

Correlation between the differential rumen microbiome, metabolites, and rumen fermentation parameters

Discussion

Growth performance

Rumen fermentation parameters

Rumen microbiome

Rumen fluid metabolome

Conclusions

Abbreviations

Declarations

References

Supplementary Files

Status:

Version 1