Dietary supplementation with grape seed extract improves energy metabolism by enhancing the production of inosine in the rumen of dairy cows

doi:10.21203/rs.3.rs-4724997/v1

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Dietary supplementation with grape seed extract improves energy metabolism by enhancing the production of inosine in the rumen of dairy cows

https://doi.org/10.21203/rs.3.rs-4724997/v1

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Background

The ruminal microbiota plays a key role in the supply of nutrients and energy-generating compounds to the animal. However, during the transition into lactation dairy cows experience dysbiosis of the microbial community and negative energy balance, both of which render animals prone to metabolic disorders and decreased milk production. Grape seed extract (GSE) can modulate the ruminal microbiota in vitro, but whether it could improve energy metabolism and inflammation during the transition period is unclear.

Results

Feeding GSE during the transition period led to greater milk yield and lower milk somatic cell count. In addition, GSE led to greater concentrations of glucose and lower concentrations of non-esterified fatty acids, β-hydroxybutyric acid, acute-phase proteins (haptoglobin and serum amyloid A), and the activity of alanine aminotransferase and aspartate aminotransferase in serum. The ruminal microbiota composition and their metabolites were altered, with the concentration of microbiota-derived inosine being greater both in serum and rumen due to feeding GSE. There was a positive correlation in cows fed GSE between inosine and abundance of differentially enriched genera, better milk performance and improved metabolic and inflammation-related markers. In vitro studies showed that inosine acted through adenosine receptors to reduce lipid accumulation, and increase insulin sensitivity and gluconeogenesis in hepatocytes, and inhibit lipolysis and inflammation in adipocytes. In dairy cows with ketosis, inosine treatment alleviated negative energy balance, liver injury, and hepatic lipid accumulation, enhanced insulin sensitivity, and decreased lipolysis and inflammatory response in adipose tissue.

Conclusions

GSE improves energy metabolism and inflammatory state around parturition by promoting the production of ruminal microbiota-derived inosine. Thus, feeding GSE and inosine can be a potential strategy to alleviate metabolic disorders and inflammation in dairy cows during the transition period.

Transition dairy cows

Ruminal microbiota

Inosine

Metabolic homeostasis

Ketosis

Milk provides comprehensive and balanced nourishment for humans, with global per capita consumption in the past five years exceeding 110 kg/year [1]. Sustained negative energy balance (NEB) during the peripartal period and associated metabolic and inflammation-related disorders contribute to decreased milk yield in dairy cows [2, 3]. The two-to-three fold increase in energy requirements after parturition leads to hypoglycemia and excessive lipolysis of adipose tissue stores [4–6]. In turn, these physiologic changes elevate concentrations of blood non-esterified fatty acids (NEFA), β-hydroxybutyric acid (BHBA), and increase the risk of hepatic steatosis and eventual culling from the herd [2, 3, 7]. Thus, finding effective management approaches to improve peripartal metabolic and inflammatory state in dairy cows is critical [8].

The close link between ruminal microbiota homeostasis and energy balance of the animal is exemplified by the fact that microbial fermentation of plant substrates results in production of absorbable nutrients such as volatile fatty acids (VFAs), which provide approximately 80% of the energy requirements, and amino acids [9–11]. In the postpartum period, dietary concentrate supplementation alters ruminal fermentation patterns, microbiota composition and production of VFAs [12–14]. For instance, propionate synthesis and the abundance of its synthesizing bacteria are reduced in the postpartum period [15–17]. As a precursor to hepatic gluconeogenesis, propionate deficiency in turn exacerbates the energy imbalance during transition [18]. It is noteworthy that the ruminal microbiome differed substantially between ketotic and healthy dairy cows, with negative correlations between serum BHBA concentrations and propionate-synthesizing bacteria such as Prevotella and Ruminococcus [16, 17]. These data suggested that improving NEB by manipulating the ruminal microbiota may be an effective strategy.

Microbial metabolites such as lipids, amino acids, VFAs, and nucleosides act as a bridge between the microbiota and host [19]. Inosine, as a key intermediate in the purine biosynthesis and degradation pathway [20, 21], is a common microbial metabolite and is involved in the regulation of host immunity and energy metabolism [22–24]. Although the function of inosine in dairy cows remains unclear, the expression of hepatic inosine receptors, i.e., adenosine receptors (ARs), decreased in dairy cows during the late prepartum period and appeared to be associated with peripartal metabolic disorders [25].

Grape seed extract (GSE) is a widely-used plant-derived polyphenolic extract approved by the European Union for use as an additive in human food and animal feed [26]. It can improve the composition of gut microbiota and its metabolites in humans and mice, and exerts anti-inflammatory, antioxidant, and lipid-regulating effects [27–29]. In lactating dairy cows, feeding grape seed and grape pomace extracts increased milk production and milk protein content, and reduced hepatic endoplasmic reticulum stress and inflammation [30, 31]. Whether GSE alters the ruminal microbiome and peripartal energy balance and inflammation are unknown.

In this study, feeding GSE to dairy cows altered the composition of ruminal microbiota, and increased inosine concentration in both rumen and serum. Further investigation revealed that inosine improved insulin sensitivity, hepatic lipid metabolism, and inhibited adipose tissue lipolysis in postpartum healthy and ketotic dairy cows. Our study provides new insights into the potential application of GSE for improving energy metabolism and inflammatory state during the peripartal period and highlights the potential of GSE and inosine in the prevention and treatment of ketosis.

Preparation of GSE and inosine

The GSE powder was purchased from Changsha Tianwei Biotechnology Co., Ltd (Changsha, China). It was quantified to contain 95.98% proanthocyanidins with a water content < 0.65% and crude ash < 0.27%. Inosine powder was purchased from Sigma-Aldrich (Louis, USA), purity ≥ 99.00% (HPLC). Inosine was dissolved in DMSO to 20 mM and stored at -80℃ until used in vitro when it was diluted with different concentrations of the corresponding culture medium. Inosine used for intramuscular injection was dissolved in saline (ready-to-use) at a concentration of 100 mg/mL.

Animal experiments and sampling

Chinese Holstein cows used in this study received humane care according to the Guide for the Care and Use of Agricultural Animals in Research and Teaching (FASS, 2010). We conducted the GSE supplementation and inosine injection trial on the same farm, an 8,500-cow commercial intensive dairy farm in Inner Mongolia, China. All cows were selected to meet the basic criteria: clinically healthy, 2 ≤ parity < 6, previous 305-d mature equivalent milk yield > 8,000 kg, and with 3.0 ≤ body condition score (BCS) < 4.0. All cows had free access to water and total mixed ration (TMR) diet (Table S1).

For the GSE supplementation trial, 30 multiparous cows with similar expected calving dates (28 ± 3 d prior to calving) were selected and randomly assigned to two groups consisting of a non-GSE control (CON) and a GSE-fed group (0 or 15 g GSE/d per cow). Following a 7-day adaptation period (all cows were fed the same TMR diet), treatment began 21 days before expected calving and ended 21 days after calving. The GSE was fed individually to each cow during the morning while confined in head stalls for a 30 to 45 min until the supplement was consumed. During the treatment period, 8 cows (5 cows in CON and 3 cows in GSE) with less than 21 days of pre-calving feeding were excluded due to the gap between the expected and actual calving date, 2 cows in CON were excluded due to trauma-related lameness and metritis. This left 10 cows in CON and GSE groups that were included in the final analyses. A schematic diagram of the experiment is depicted in Fig. 1A. During the last 3 consecutive days, dry matter intake (DMI) and milk yield for each cow were recorded and the average of these 3 days used as representative for the experimental period. On the last day of the experiment, milk, serum, liver and subcutaneous adipose biopsies were collected and stored until use. The storage methods were as described in our previous work [32, 33].

Ruminal fluid was collected with a gastric tube rumen fluid sampler (A1141K, Anscitech, Winnipeg, CA) on the last day of the experiment, 1 h before the morning feeding. The first 2 tubes of fluid were discarded to avoid contamination, and 150 mL of fluid was then collected from each cow [34]. Ruminal fluid samples were filtered immediately through 4 layers of gauze and divided into 5 parts for the analysis of pH, VFA, ammonia nitrogen (NH₃-N), bacteria, and metabolites. The pH was measured immediately, and the remaining 4 parts of filtered rumen fluid were placed in liquid nitrogen immediately to preserve them for use.

Inosine injection trials were conducted in two stages: experiment Ⅰ (Fig. S5A) and experiment Ⅱ (Fig. 6A). For experiment Ⅰ, 15 postpartum cows without clinical diseases and serum BHBA < 1.2 mM (monitored for 3 consecutive days from day 5 to 7 after calving) were selected and randomly assigned to 3 groups: Control (saline), inosine (4 mg/kg), and inosine (8 mg/kg). From day 7 to 13 after calving, cows received an intramuscular injection of inosine (4, 8 mg/kg, dissolved in saline) before the morning feeding. Control cows were treated with an injection of vehicle (normal saline). Serum samples were collected on the next day before the morning feeding and stored for use. For experiment Ⅱ, 20 cows with serum BHBA < 1.2 mM (Healthy, n = 10) or ≥ 1.2 mM (Ketosis, n = 10) for 3 consecutive days were selected and assigned to 4 groups consisting of Healthy + Saline (n = 5), Healthy + Inosine (n = 5), Ketosis + Saline (n = 5) and Ketosis + Inosine (n = 5). From day 7 to 13 after calving, cows received an intramuscular injection of inosine (8 mg/kg, dissolved in saline) before the morning feeding. Control cows were treated with an injection of vehicle (saline). On the 14th day after calving, milk, serum, liver and subcutaneous adipose biopsies were collected and stored until use.

Milk and serum sample analyses

Milk protein, milk fat and SCC were analyzed by mid-infrared spectroscopy using a MilkoScan Minor FT3 (FOSS, Denmark). Serum concentrations of Glu, NEFA, and BHBA, as well as the activities of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were determined using a Hitachi 7170 autoanalyzer with commercially-available kits (Glu, GL3815; NFFA, FA115; BHB, RB1008; ALT, AL3801; AST, AS3804; Randox Laboratories, Crumlin, UK). Serum concentrations of haptoglobin (HP) and serum amyloid A (SAA) were determined using commercial ELISA kits (HP: GWB-A43096, GenWay Biotech, San Diego, CA; SAA: LS-F12552, LifeSpan BioSciences Inc., Seattle, WA) according to the manufacturer’s instructions. Serum concentrations of inosine, hypoxanthine, cholesterol, spermidine and 4-hydroxyphen were determined by HPLC [35].

Hematoxylin and Eosin (H&E) and Oil red O staining

Liver and subcutaneous adipose tissues were fixed in 4% formaldehyde neutral buffer solution (P1110, Solarbio, China) and embedded in paraffin, floated on a water bath, picked up onto glass slides, and placed in slide racks. Slides were dewaxed with xylene, rehydrated through descending concentrations of alcohol, and stained with H&E (G1120, Solarbio, China). For Oil red O staining, a portion of liver tissue was frozen in OCT compound (4583, Sakura, Japan), sectioned at -18°C and fixed with 75% alcohol at room temperature for 15 min. Then, the slides were stained with Oil red O (O0625, Sigma-Aldrich, USA) and counterstained with hematoxylin.

Quantitative reverse transcription PCR (qRT-PCR)

Total RNA was prepared from collected tissue using RNAiso Plus (TaKaRa Japan) and reverse-transcribed into cDNA using a reverse transcription kit (TaKaRa Japan) according to the manufacturer’s instructions. Quantitative reverse transcription (qRT)-PCR was performed on a 7500 Real-Time PCR System (Applied Biosystems) using the SYBR Green plus reagent kit (TaKaRa Japan) to analyze relative mRNA abundance of target genes. Relative transcription of each target gene was normalized against the geometric mean of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and β-actin (ACTB) and calculated with the 2^–ΔΔCT method. The primer pairs used in this study are listed in Supplemental Table S2.

Western blot analysis

Protein samples were separated on 10 or 15% Tris-glycine gels with a known pre-stained protein ladder (Shanghai Epizyme Biomedical Technology Co. Ltd, Shanghai, China). The membranes were blocked in Tris-buffered saline solution with 0.1% Tween-20 (TBST) containing 3% bovine serum albumin (Sigma-Aldrich Co. St. Louis, MO, USA) for 4 h at room temperature. The blocked membranes were incubated overnight at 4°C with primary antibodies against SREBP-1c (1:500; Cat. NB100-2215; Novus Biologicals, USA), PPARα (1:500; Cat. ab126285; Abcam, UK), p-HSL (1:1,000; Cat. 4139; Cell Signaling Technology; USA), HSL (1:1,000; Cat. 4107; Cell Signaling Technology, USA), ATGL (1:1,000; Cat. ab99532; Abcam, UK); p-AKT (1:1,000; Cat. 4060; Cell Signaling Technology, USA), AKT (1:1,000; Cat. 9272; Cell Signaling Technology, USA), p-NF-κB (1:1,000; Cat. 3033; Cell Signaling Technology, USA), NF-κB (1:1,000; Cat. 4764; Cell Signaling Technology, USA), p-IκBα(1:1,000; Cat. 2859; Cell Signaling Technology, USA), IκBα(1:1,000; Cat. 9242; Cell Signaling Technology, USA) and β-actin (1:1,000; Cat. sc-47778; Santa Cruz Biotechnology, USA). The primary and secondary antibodies were diluted in TBST. Removal of excess primary antibody was carried out by washing the membranes in TBST three times for 5 min each. Membranes were incubated with horseradish peroxidase-conjugated anti-rabbit (Proteintech, Chicago, USA) or anti-mouse (Proteintech, Chicago, USA) immunoglobulin at room temperature for 45 min. Excess secondary antibody was removed by washing the membranes in TBST three times for 5 min each. Lastly, immunoreactive bands were visualized via a Tanon Imaging System (Tanon 4600, Shanghai, China) using an enhanced chemiluminescence solution (Millipore, Bedford, USA). All bands were analyzed using Image-Pro Plus 6.0 (Media Cybernetics, Rockville, MD).

Ruminal fermentation parameters

Ruminal fluid pH was measured immediately after filtration using a portable pH meter (REX-PHBJ-260F, Shanghai, China). Ruminal NH₃-N was determined using a colorimetric method [36], and the VFAs were measured by gas chromatography-mass spectrometry (GC-MS) at Majorbio Bio-Pharm technology Co., Ltd (Beijing, China).

Ruminal bacterial DNA extraction, 16S rRNA gene amplification, sequencing and data analysis

The bacterial DNA from 20 ruminal fluid samples was extracted using the E.Z.N.A. ®Stool DNA Kit (D4015, Omega, Inc., USA) according to manufacturer ’s instructions. The extraction quality of DNA was assessed by 1% agarose gel electrophoresis, and the concentration and purity of DNA were determined by NanoDrop2000. The V4 region of 16S rRNA gene was amplified by PCR using 515F (5'-GTGYCAGCMGCCGCGGTAA-3') and 806R (5'-GGACTACNVGGGTWTCTAAT-3'). After mixing the PCR products of the same sample, they were recovered by using 2% agarose gel, purified by Axyprep DNA gel extraction kit (Axygen Biosciences, Union City, California, USA), detected by 2% agarose gel electrophoresis, and detected and quantified by Quantum Fluorometer (Promega, USA). The NEXTFLEX Rapid DNA-Seq Kit was used to build the database. High-throughput sequencing was conducted on the Illumina MiseqPE250 platform. After filtering, clean reads were obtained from the raw reads and merged into tags using FLASH. Using DADA2, bacterial tags were clustered into amplicon sequence variants (ASVs) with 100% sequence similarity. Based on the database of Silva 16S rRNA (v138), the bacterial ASVs were classified by the RDP classifier (threshold was set to 0.7).

Alpha diversity was calculated using Mothur, and rarefaction curves were generated by the R software. The Bray-Curtis distance algorithm was used to quantify the distances between samples, visualization was via Principal Coordinate Analysis (PCoA) and evaluation for statistical significance was done via ANOSIM testing. For differential abundance analysis, the Wilcoxon signed rank test was used to analyze paired samples and adjust for multiple comparisons by FDR.

Ruminal untargeted metabolomics and analysis by liquid chromategraphy-mass spectrometry (LC/MS)

After thawing at room temperature, 20 samples were extracted with acetonitrile: methanol = 1:1(v: v), and an internal standard (0.02 mg/mL, L-2-chlorophenylalanine) added. Samples were centrifuged for 15 min (4°C, 13000 g) and the supernatant removed and blown dry under nitrogen gas. The sample was then re-solubilized with solution (acetonitrile: water = 1:1) and extracted by low-temperature ultrasonication for 5 min (5°C, 40 KHz), followed by centrifugation at 13000 g and 4°C for 10 min. The supernatant was transferred to sample vials for LC-MS analysis. A quality control sample (QC) was prepared by mixing equal volumes of 20 samples. The QC samples were used to monitor the stability of the analysis, and were handled in the same manner as the test samples. Metabolites were identified by searching the HMDB (http://www.hmdb.ca/), Metlin (https://metlin.scripps.edu/) and Majorbio databases. The data matrix obtained by searching databases was uploaded to the Majorbio cloud platform (https://cloud.majorbio.com) for data analysis [37]. The variables from QC samples with relative standard deviation (RSD) > 30% were excluded and log10 logarithmicized to obtain the final data matrix for subsequent analysis.

The R package “ropls” (Version 1.6.2) was used to perform PCA and orthogonal least partial squares discriminant analysis (OPLS-DA), and 7-cycle interactive validation evaluating the stability of the model. The metabolites with Variable importance in projection (VIP) > 1, P < 0.05 were deemed as significantly different based on the VIP obtained by the OPLS-DA model and the p-value generated by student’s t test.

Correlation analysis

The correlation analysis was conducted using the R package "psych" (version 1.8.12). The correlations between differential bacterial and metabolites, four types of data (the basic parameters, hepatic triglyceride, serum parameters, and ruminal fermentation indicators) and differential bacterial, as well as significantly differential metabolites and four types of data, were calculated via spearman's correlation coefficient. The correlation coefficients (r) ranged from − 1 to 1, with positive and negative values indicating positive and negative correlations. The absolute value of r indicated the strength of the correlation between variables. Specifically, r values of -1, 0, and 1 indicated a fully negative correlation, no correlation, and a fully positive correlation, respectively. Correlation significance was determined via P-values below 0.05 and 0.01, indicating significant and extremely significant correlations, respectively.

Cell isolation, culture and processing

Primary hepatocytes and pre-adipocytes were isolated using the collagenase (C0130 and V900893, Sigma-Aldrich, USA) digestion method from 5 Holstein calves (1 d old, female, 30–40 kg, fasting, rectal temperature: 38.7–39.7°C, healthy) purchased from a commercial dairy farm (Changchun, China). The culture procedure for both hepatocytes and pre-adipocytes and the method of pre-adipocyte maturation were the same as described previously [38, 39]. To determine the toxicological effect of inosine, hepatocytes and mature adipocytes were treated with different concentrations (0, 1, 2, 5, 10 µM) of inosine for 12 h and 24 h, respectively. To study the function of inosine in the metabolism of hepatocytes, cells were co-treated with 1.2 mM NEFA and different concentrations of inosine (0, 1, 2, 5, 10 µM) for 12 h. The method of NEFA preparation was described previously [40]. To activate the insulin signaling pathway, hepatocytes were treated with 100 nM bovine insulin (I6634, Sigma-Aldrich, USA) for 30 min before sampling. To investigate the effective receptor of inosine in hepatocyte metabolism, hepatocytes were pretreated separately with 100 nM SLV320 (A₁R inhibitor, HY-19533, MCE, USA), 1 µM SCH58261 (A_2aR inhibitor, HY-14858, MCE, USA), siRNA against A_2bR or A₃R, and subsequently incubated with inosine and NEFA.

To study the function of inosine in lipolysis of adipocytes, mature adipocytes were incubated with 10 µM ISO (S2566, Selleck, China) for 3 h and then treated with different concentrations (0, 1, 2, 5, 10 µM) of inosine for 12 h. To activate the insulin signaling pathway, adipocytes were treated with 100 nM insulin for 30 min before sampling. To investigate the function of inosine on inflammation in adipocytes, cells were co-treated with 1 µg/mL LPS (L2630, Sigma-Aldrich, USA) and inosine for 12 h. To investigate the effective receptor of inosine in adipocytes, adipocytes were pretreated separately with SLV320, SCH58261, siRNA against A_2bR or A₃R, and subsequently incubated with inosine and ISO or LPS.

Cell viability, triglyceride and glycerol content assessment

Cell viability was determined with the Cell Counting Kit (CCK)-8 assay according to the instructions (CK04, Dojindo, Japan). Liver tissue, hepatocytes and adipose were homogenized in lysis solution and then heated at 70℃ for 10 min. After cooling, the sample was vortexed and centrifuged at 2,000 × g for 5 min at room temperature. The supernatant was collected and analyzed using an enzymatic kit (E1013, Applygen, China) following the manufacturer’s instructions. Total protein concentration was measured with the BCA method (P1511, Applygen, China). The glycerol content of the adipocyte culture supernatant was measured using a glycerol measurement kit (E1002, Applygen, China) following the manufacturer’s instructions.

Nile red staining

A fluorescent quantitative method for determining Nile red-stained fat droplets was employed to determine the extent of triglyceride synthesis. Briefly, primary hepatocytes were fixed with 4% paraformaldehyde solution and stained with Nile red (1 µg/mL). After incubation for 30 min at 4°C, cells were washed with PBS and counterstained with 4′,6-diamidino-2-phenylindole (DAPI, Solarbio) for nuclear visualization. Then, cellular Nile red–stained lipid droplets were observed using a confocal scanning laser microscope (Leica).

Statistical analysis

GraphPad Prism 9.0 was used for the statistical analysis. All data were tested for normality of distribution with the Shapiro-Wilk test. Significant difference between two groups was evaluated by Mann-Whitney U test (non-parametric) or two-tailed unpaired Student’s t-test (parametric). For more than two groups of comparison, Kruskal-Walli’s test (non-parametric) or one-way analysis of variance (ANOVA) followed by Dunn’s test for non-parametric samples or Tukey test for parametric samples as a post hoc test. Data were expressed as the mean ± SEM, *P < 0.05, **P < 0.01. Other specific statistical analyses were mentioned in each methods section.

GSE supplementation improved milk performance, energy metabolism, liver function, and inflammatory state in dairy cows during the transition period

During the transition period (-3 weeks to 3 weeks), Chinese Holstein cows were supplemented with 15 g/d of GSE (Fig. 1A). GSE supplementation did not alter DMI and BCS (Fig. S1A), but significantly improved lactation performance characterized by greater milk yield, milk protein, and milk fat and lower somatic cell count (SCC) (Fig. 1B, Fig. S1B). The serum concentration of Glucose (Glu) was significantly greater with GSE supplementation, while NEFA and BHBA were lower (Fig. 1C). The serum activities of ALT and AST, and serum concentrations of HP and SAA were significantly lower with GSE supplementation (Fig. 1D). In the liver, triglyceride content was significantly lower with GSE supplementation (Fig. S1C), consistent with the alleviation of lipid accumulation revealed by H&E and Oil red O staining (Fig. 1E). Further, the expression of the transcription factor SREBP-1c and its target genes ACC1, SCD1, and FASN, which regulate hepatic lipid synthesis, was significantly lower (Fig. 1F; Fig. S1D) in the GSE group. In contrast, the expression of the transcription factor PPARα and its target genes ACO1 and CPT1A, which regulate fatty acid oxidation, was significantly greater in the liver of cows fed GSE (Fig. 1F; Fig. S1E). Additionally, dairy cows fed GSE had greater phosphorylation of AKT (Fig. 1F) and mRNA abundance of the gluconeogenesis-related genes G6PC1 and PCK1 in the liver (Fig. S1F). These data indicated that GSE improved NEB and alleviated hepatic triglyceride accumulation and triggered insulin signaling in dairy cows during the transition period.

Excessive lipid mobilization leads to an increase in NEFA concentration, which are linked to greater incidence of ketosis. The H&E staining revealed that feeding GSE led to significantly greater subcutaneous adipocyte volume (Fig. 1E) while it inhibited lipolysis in adipocytes, as evidenced by the reduction in phosphorylation of HSL and the expression of ATGL, and the upregulation in PLIN1 abundance (Fig. 1G; Fig. S1G). Additionally, GSE supplementation significantly led to lower phosphorylation of NF-κB and IκBα in adipocytes (Fig. 1H), accompanied by a downregulation of the pro-inflammatory cytokines TNFA and IL1B along with upregulation of the anti-inflammatory cytokine IL10 (Fig. S1H). Taken together, these results suggest that GSE supplementation reduces lipolysis and inflammatory response of adipose tissue in perinatal dairy cows.

GSE supplementation altered ruminal fermentation and microbiota composition

To further delineate the underlying mechanisms driving improvements in milk performance, energy metabolism, and inflammatory state in GSE-supplemented cows, we assessed ruminal fermentation parameters and microbiota. Ruminal pH, NH₃-N, total VFAs, acetate, and propionate were significantly greater in the GSE group (Fig. 2A-B). By sequencing and analyzing of the 16S rRNA genes in ruminal fluid, we obtained a total of 2,489,920 effective sequences and 16,996 amplicon variant sequences (ASVs). The rarefaction curve (Fig. S2A) and core curve (Fig. S2B) indicated that sequencing depth and sample size were appropriate. There was no significant difference in species richness and diversity between CON and GSE groups (Fig. S2C-D). However, the PCoA (Fig. 2C) and taxonomy analysis (Fig. 2D, Fig. S2E) of ruminal microbiota composition highlight significant differences. Compared with the CON group, linear discriminant analysis Effect Size (LEfSe) analysis revealed that 7 bacterial genera including Succiniclasticum, norank_f_Muribaculaceae, norank_f_Bacteroidales_RF16_group, Schwartzia, Ruminococcus, Bifidobacterium and Candidatus Saccharimonas were enriched in the GSE group while 6 different bacterial genera were decreased (Fig. 2E-F, Fig. S2F). Furthermore, Spearman correlation between differential bacterial genera and phenotypic indicators indicated that bacterial genera enriched in the GSE group including Succiniclasticum, norank_f_Muribaculaceae, Candidatus_Saccharimonas, Schwartzia, and Bifidobacterium were significantly positively correlated with milk yield, serum Glu, and ruminal content of VFAs, and negatively correlated with NEB and liver injury parameters (Fig. 2G). In contrast, the correlation coefficient for depleted bacterial genera was the opposite (Fig. S2G). These results indicate that GSE supplementation significantly altered ruminal fermentation and the microbiota composition in peripartal dairy cows, which are strongly correlated with improved performance.

GSE supplementation altered ruminal microbial metabolites and increased inosine production

Growing evidence suggested that ruminal microbial metabolites are a crucial link driving the host-gut microbiota interaction. Thus, we performed a metabolomic analysis to identify candidate ruminal metabolites involved in the GSE-mediated beneficial effects on peripartal metabolism and inflammatory state. OPLS-DA revealed a clear difference in ruminal metabolite composition between CON and GSE groups in both positive and negative modes, excluding model overfitting (Fig. S3A-B). A total of 2,087 metabolites were identified in 20 samples (Fig. 3A). Among the identified metabolites, 172 were greater increased and 351 lower in the GSE group (Fig. 3A). Trace origin analysis of the identified metabolites indicated that 209 were of host origin and 425 of microbiota origin (Fig. 3B). Host and microbiota shared 185 co-metabolites (Fig. 3B). Pathway enrichment analysis revealed that the top 5 co-metabolite pathways were significantly enriched in tyrosine metabolism, drug metabolism, tryptophan metabolism, purine metabolism, and lysine degradation (Fig. 3C). The clustering heatmap further revealed that among the top 20 differential ruminal metabolites were 7 that were greater and 13 that were lower in cows fed GSE (Fig. 3D). Spearman correlation between differential metabolites and differential bacterial genera enriched in the GSE group indicated that the metabolites had a positive correlation with bacterial genera including Succiniclasticum, norank_f_Muribaculaceae, Candidatus_Saccharimonas, Schwartzia, and Bifidobacterium but excluding Ruminococcus (Fig. 3E). Furthermore, Spearman correlation between differential metabolites and phenotypic parameters indicated that metabolites enriched in the GSE group including inosine, hypoxanthine, deoxycholic acid 3-glucuronide, cholesterol, 5-hydroxyindoleacetaldehyde and spermidine had a positive correlation with milk yield, serum Glu, and ruminal content of VFAs, but were negatively correlated with hepatic triglyceride and serum NEFA, BHBA and ALT (Fig. 2F). Notably, inosine had the highest relative abundance (Fig. S3C) and was also significantly elevated in the serum (Fig. 3G). Ruminal and serum inosine levels had a strong positive correlation (R = 0.90, P < 0.05) (Fig. 3H). These results suggest that GSE increases the purine metabolite inosine in the rumen and its subsequent transport into the circulation. Changes in ruminal microbial metabolites are associated with improved performance and energy metabolism in dairy cows.

Inosine alleviated lipid accumulation and improved insulin sensitivity and gluconeogenesis in bovine hepatocytes

Given the high correlation of microbiota-derived inosine with improved energy metabolism, we next investigated the effect of inosine on the regulation of energy metabolism in hepatocytes. The cytotoxicity assay indicated that inosine (1, 2, 5, and 10 µM) had no significant toxic effect on bovine hepatocytes (Fig. S4A-B). Our previous studies have demonstrated that 1.2 mM NEFA can induce steatosis and insulin resistance in cultured bovine hepatocytes [41]. Different concentrations of inosine (1, 2, 5, and 10 µM) treatment attenuated NEFA-induced intracellular triglyceride accumulation in hepatocytes (Fig. 4A; Fig. S4C). The mRNA abundance of FASN and ACC1 and protein level of SREBP-1c were significantly lower (Fig. S4D; Fig. 4B), whereas the mRNA abundance of CPT1A (Fig. S4E) and protein level of PPARα (Fig. 4B) were significantly greater in the inosine and NEFA co-treatment group compared with NEFA treatment alone. Of note, inosine led to greater phosphorylation of AKT and mRNA abundance of G6PC1 and PCK1 (Fig. 4C; Fig. S4F). These data indicate that inosine alleviates lipid accumulation, activates insulin signaling, and seems to improve gluconeogenic capacity in NEFA-treated hepatocytes.

Inosine exerts its function through ARs, including A₁, A_2a, A_2b, and A₃ G-protein coupled receptors [22]. To investigate whether inosine acts through a specific receptor, we used inhibitors (SLV320, SCH58261) and siRNAs to interfere with the A₁R, A_2aR, A_2bR, and A₃R. In the presence of inosine, inhibition of A_2aR but not A₁R, A_2bR, and A₃R increased triglyceride levels in hepatocytes. Furthermore, inhibition of A_2aR blocked the effects of inosine on SREBP-1c and PPARα (Fig. 4F). However, inhibition of A₁R, but not A_2aR, hindered the alleviation effects of inosine on insulin signaling and gluconeogenic capacity (Fig. 4G-H; Fig. S4G). These findings indicate that the receptors A_2aR and A₁R are central to the positive effect of inosine on lipid accumulation, insulin signaling and gluconeogenic capacity in hepatocytes.

Inosine inhibited lipolysis and inflammation in bovine adipocytes

We further evaluated the effects of inosine on lipolysis and inflammation of adipocytes. Inosine at concentrations of 1, 2, 5, and 10 µM did not have a significant cytotoxic effect on bovine adipocytes (Fig. S4H). Treatment with different concentrations (1, 2, 5, and 10 µM) of inosine significantly increased intracellular triglyceride content and decreased GC concentration in the supernatant compared to ISO treatment alone (lipolytic inducer) (Fig. 5A). The expression of ATGL and phosphorylated HSL were significantly reduced in the group of inosine and ISO co-treatment compared with ISO treatment alone (Fig. 5B). These data indicate that inosine contributes to inhibition of lipolysis in adipocytes.

Sustained lipolysis is associated with inflammation in bovine adipocytes [39]. To investigate whether inosine alleviates inflammation, we detected the expression of p-NF-κB and p-IκBα. Compared with LPS treatment alone, inosine and LPS co-treatment significantly decreased phosphorylated NF-κB and IκBα expression (Fig. 5C). Further, inhibition of A₁R weakened the antilipolytic and anti-inflammatory effect of inosine (Fig. 5D-F). Taken together, a functional A₁R is needed for inosine to inhibit lipolysis and inflammation in adipocytes.

Inosine injection improved energy metabolism and alleviated hyperketonemia in postpartum dairy cows

Next, we investigated whether inosine improves energy metabolism and its effect on hyperketonemia in postpartum dairy cows. As depicted in Fig. S5A, we selected healthy early lactation dairy cows for injections with a low-dose (4 mg/kg/day) or high-dose (8 mg/kg/day) of inosine for 7 consecutive days. Compared with the control group, the high-dose of inosine significantly increased serum Glu (3–7 days) and significantly decreased serum NEFA (5–7 days) and BHBA (2–7 days) (Fig. S5B). ALT and AST were also significantly decreased in the high-dose group (Fig. S5C). These results indicate that inosine injected at 8 mg/kg/day ameliorates NEB in postpartum cows.

To determine whether inosine alleviates hyperketonemia in postpartum dairy cows, we selected healthy and ketotic cows, and injected them with inosine at 8 mg/kg/day for 7 consecutive days (Fig. 6A). Inosine injection significantly increased the serum Glu and milk yield in both healthy and ketotic cows, while serum NEFA, BHBA, ALT, and AST were significantly reduced (Fig. 6B-D). H&E and Oil red O staining (Fig. 6E) and triglyceride analysis (Fig. S5C) revealed that inosine administration markedly mitigated hepatic steatosis in both healthy and ketotic cows. Mechanistically, the hepatic expression of SREBP-1c and SCD1 was significantly lower, phosphorylation of AKT, protein abundance of PPARα, and mRNA abundance of ACO1 and CPT1A were significantly greater (Fig. 6F; Fig. S5D-E) with inosine treatment. Compared with saline treatment, inosine significantly upregulated hepatic G6PC1 and PCK1 in both healthy and ketotic cows (Fig. 6I). In adipose, H&E staining (Fig. 6E) and Western-blotting (Fig. 6G) the large adipocyte size and significantly lower expression of p-HSL and ATGL indicated that inosine administration had an antilipolytic effect. Furthermore, p-NF-κB, p-IκBα, TNF-ɑ and IL-1β were significantly lower, and IL-10 was significantly greater with inosine treatment (Fig. 6H). These results indicate that inosine treatment induced an antilipolytic and anti-inflammatory effect in adipose tissue. These findings suggest that injection of inosine at 8 mg/kg/day could alleviate hyperketonemia and metabolic disorders in postpartum dairy cows.

Negative energy balance and related metabolic disorders during the transition period reduce milk production [2, 3]. Ruminal microbiota and its metabolites play an important role in maintaining host energy metabolism homeostasis and milk production [42]. In this study, we demonstrate that GSE supplementation altered the ruminal microbiome, alleviated NEB, hepatic lipid accumulation, and enhanced insulin signaling while reducing adipose tissue lipolysis and inflammation in dairy cows. Further, GSE supplementation increased synthesis of the microbial co-metabolite inosine in the rumen leading to increases in serum. In dairy cows with ketosis, inosine enhanced insulin signaling, gluconeogenic capacity and reduced lipid accumulation in the liver while it inhibited adipose tissue lipolysis and inflammation. These data suggest that GSE and inosine may be a potential preventive or treatment strategy to reduce incidence of metabolic disorders in dairy cows during the transition period.

The GSE consists of monomers, dimers, trimers, oligomers, and polymers of polyphenolic proanthocyanidins that in non-ruminants exert prebiotic, lipid-regulating, insulin-sensitizer and anti-inflammatory properties [28]. These features were further confirmed in our study. During the transition period, the state of NEB leads to intense adipose tissue lipolysis and the release of NEFA into the blood that can reach pathological levels in the blood [2]. Approximately one to third of circulating NEFA is absorbed by the liver for β-oxidation, ketogenesis, or re-esterification to triglyceride [31]. The latter are associated with development of fatty liver and ketosis, thus, our findings of beneficial effects of GSE on insulin signaling and hypoglycemia during the transition period are noteworthy. Previous studies in dairy cows and non-ruminant models did not observe positive effects of GSE on gluconeogenic capacity; but some data underscored its potency for alleviating insulin resistance [29, 43]. The increase in gluconeogenic capacity and the subsequent increase in serum Glu may also be related to alterations in the ruminal microbiota and fermentation pathways, i.e., a shift toward increases in propionate synthesis to provide more glucose precursors to the liver [15]. Further, GSE supplementation also can alleviate lipolysis, adipose tissue inflammation and hepatic steatosis during the transition period, observations that are consistent with previous studies in humans and rodents [27, 28]. Those studies established a prebiotic effect of GSE leading to its lipid-lowering and metabolic disorder-reversing effects.

Feeding GSE enhanced several genera in the rumen, including Succiniclasticum, norank_f_Muribaculaceae, norank_f_Bacteroidales_RF16_group, Schwartzia, Ruminococcus, Bifidobacterium, and Candidatus Saccharimonas. Among them, norank_f_Muribaculaceae and Ruminococcus are positively correlated with milk performance and feed efficiency in dairy cows [44, 45], which is consistent with the present study, although a clear mechanism is unknown. Importantly, Succiniclasticum, norank_f_Bacteroidales_RF16_group, Schwartzia, and Candidatus Saccharimonas are known to be highly positively correlated with ruminal propionate concentration [45, 46], a link that is supported by our observations. By competing for hydrogen with hydrogenotrophic methanogens, Succiniclasticum and Schwartzia are particularly important in the context of fermentation of succinate to propionate [47, 48]. These microbes are specialized propionate producers, and the former is considered the main ruminal microbiota involved in propionate production [47]. Propionate is the only gluconeogenic VFA produced in the rumen and because of its high production relative to acetate and butyrate it also provides high levels of ATP to sustain hepatic metabolism. Thus, it is possible that the association between propionate and these microbial genera are involved in the elevation of serum Glu and alleviation of NEB during the transition period.

It is well-established that GSE supplementation increases the abundance of Bifidobacterium in the intestine, a recognized probiotic that in non-ruminants contributes to improved insulin sensitivity and anti-inflammatory effects [28]. In the present study, integrated microbiomics and metabolomics revealed that the up-regulation of inosine in the rumen and serum had the strongest positive correlation with Bifidobacteria. This is noteworthy because Bifidobacteria increases the production of inosine to enhance immunotherapy response [23]. Inosine, a purine nucleoside and ARs activator [49], plays an important role in cellular energy metabolism [50, 51]. Because inosine, among other differentially altered metabolites (Fig. S3D), was significantly elevated in serum, we speculate that microbiota-derived inosine may regulate host metabolism via ruminal epithelial uptake and transport into the bloodstream.

In bovine hepatocytes, we found that inosine inhibited lipid synthesis and increased gluconeogenesis via its receptor A_2aR. However, inosine increased insulin sensitivity in bovine hepatocytes, and inhibited lipolysis and inflammation in bovine adipocytes, via A₁R. In mice, A₁R negatively regulates lipolysis of adipose tissue [52], A_2aR activation alleviates NEFA-induced lipo-toxicity and SREBP-1c activity, and improves glucose homeostasis and insulin resistance [53]. Thus, inosine alleviates lipid and glucose metabolism in hepatocytes and adipocytes via its receptors.

The positive effects of inosine supply in vivo on milk yield, metabolic homeostasis, hepatic lipid accumulation during ketosis, insulin signaling, and gluconeogenic capacity confirmed the in vitro data. In humans and rodents with metabolic dysfunction-associated diseases such as obesity and type 2 diabetes, inosine had similar positive effects on lipid metabolism dysregulation and inflammation [22, 54]. Further validation of whether inosine regulates through tissue adenosine receptor activation in this experiment is hindered by the high costs of the dairy cows involved. However, the combined data confirm the efficacy of inosine treatment on metabolic homeostasis and ketosis in postpartum cows.

Although the uptake and metabolism of GSE in ruminants are likely complex and relatively unknown [30], it is possible that its phenolic hydroxyls act directly on the host to exert antioxidant and lipid metabolism-modulating effects [43]. Further, the present data strongly suggests a synergistic effect between GSE and rumen-derived inosine. Some of the limitations in the present study could be addressed in future studies with dairy cows by supplementing Bifidobacterium or comparing the separate therapeutic effects or compound-specific effects of GSE with inosine or Bifidobacterium in cows with ketosis. Overall, GSE supplementation, which increases microbiota-derived inosine production, may be an alternative or synergistic approach to treating metabolic disorders in periparturient cows.

Feeding GSE improves metabolic homeostasis in transition dairy cows by regulating the ruminal microbiota and increasing the production of inosine, which can alleviate lipid- and glucose-related metabolic disorders in hepatocytes and adipocytes (Fig. 7). Thus, the use of GSE and inosine contributes to facilitating the development of strategies to prevent and treat metabolic disorders of dairy cows during the transition period.

NEB: Negative energy balance; GSE: Grape seed extract; TMR: total mixed ration; Glu: Glucose; NEFA: Non-esterified fatty acid; BHBA: β-hydroxybutyric acid; SCC: Somatic cell count; NH3-N: ammonia nitrogen; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; HP: haptoglobin; SAA: serum amyloid A; SREBP-1c: Sterol regulatory element binding transcription factor 1; PPARα: Peroxisome proliferator activated receptor alpha; AKT: Protein kinase B; p-AKT: Phosphorylation of AKT; HSL: hormone-sensitive lipase; p-HSL: Phosphorylation of HSL; ATGL: Adipose triglyceride lipase; NF-κB: Nuclear factor kappa B; p-NF-κB: Phosphorylation of NF-κB; IκBα: NF-κB inhibitor alpha; p-IκBα: Phosphorylation of IκBα; SCD1: Stearoyl-CoA desaturase 1; ACC1: Acetyl-CoA carboxylase 1; FASN: Fatty acid synthase; ACO1: Aconitase 1; CPT1A: Carnitine palmitoyltransferase 1A; PCK1: Phosphoenolpyruvate carboxykinase 1; G6PC1: Glucose-6-phosphatase catalytic subunit 1; PLIN1: Perilipin 1; TNFA: Tumor necrosis factor alpha; IL1B: Interleukin 1 beta; IL10: Interleukin 10; ACTB: Actin beta; GAPDH: Glyceraldehyde-3-phosphate dehydrogenase; ISO: isoprenaline; ARs: adenosine receptors; VFAs: volatile fatty acids; qRT-PCR: Quantitative reverse transcription PCR; GC/MS: Gas chromategraphy-mass spectrometry; LC/MS: Liquid chromategraphy-mass spectrometry; PcoA: Principal coordinate analysis; LEfSe: Linear discriminant analysis Effect Size, i.e. LDA Effect Size; OPLS-DA: Orthogonal Partial Least Squares Discriminant Analysis.

Ethics approval

This study was approved by the Animal Ethics Committee of Jilin University.

Consent for publication

Not applicable.

Availability of data and materials

The raw sequences from 16S rRNA sequencing for this study are available at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under the BioProject accession numbers PRJNA1115928 (https://www.ncbi.nlm. nih.gov/).

Competing interests

The authors declare no financial and nonfinancial competing interests.

Funding

This work was supported by the National Key R&D Program of China (Beijing, China; grant no. 2023YFE0116900 and 2023YFD1801400), National Natural Science Foundation of China (Beijing, China; grant nos. 32302943, 32302941 and 32273059), and Postdoctoral Fellowship Program of CPSF (Beijing, China; grant no. GZC20230951).

Authors’ contributions

Conceptualization: X.L., X.D., and H.F.; methodology, investigation, formal analysis, visualization, and writing: Q.S., G.L., Z.F., M.C and H.Y.; experimental animal feeding and sampling: C.L., L.J., X.H., J.J.L., M.U., L.L., W.G., Y.S., G.L., C.Z., and Y.L. checked and edited the manuscript. All authors read and approved the final manuscript.

Acknowledgments

The authors would like to thank the individuals for participating in this study.

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Dietary supplementation with grape seed extract improves energy metabolism by enhancing the production of inosine in the rumen of dairy cows

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Abstract

Background

Results

Conclusions

Figures

Background

Methods

Preparation of GSE and inosine

Animal experiments and sampling

Milk and serum sample analyses

Hematoxylin and Eosin (H&E) and Oil red O staining

Quantitative reverse transcription PCR (qRT-PCR)

Western blot analysis

Ruminal fermentation parameters

Ruminal bacterial DNA extraction, 16S rRNA gene amplification, sequencing and data analysis

Ruminal untargeted metabolomics and analysis by liquid chromategraphy-mass spectrometry (LC/MS)

Correlation analysis

Cell isolation, culture and processing

Cell viability, triglyceride and glycerol content assessment

Nile red staining

Statistical analysis

Results

GSE supplementation altered ruminal fermentation and microbiota composition

GSE supplementation altered ruminal microbial metabolites and increased inosine production

Inosine alleviated lipid accumulation and improved insulin sensitivity and gluconeogenesis in bovine hepatocytes

Inosine inhibited lipolysis and inflammation in bovine adipocytes

Inosine injection improved energy metabolism and alleviated hyperketonemia in postpartum dairy cows

Discussion

Conclusion

Abbreviations

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References

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