Effects of ornithine α-ketoglutarate on growth performance and gut microbiota in a chronic oxidative stress pig model induced by D-galactose

Abstract

The aim of this study was to evaluate the protective effects and underlying mechanisms of ornithine α-ketoglutarate (OKG) on D-galactose (D-gal)-induced chronic oxidative stress in a pig model. A total of 40 castrated young pigs were randomly separated into five groups, including a control group, a model group treated with 5 mg per kg body weight (BW) D-gal, and three D-gal + OKG groups in which the pigs received 0.5%, 1%, and 2% OKG (n = 8). The experiment lasted for 28 days. The growth performance, serum oxidative stress index, expression of relative intestinal genes, gut microbiota, and serum amino acid pool were determined. The results demonstrated that administration of D-gal significantly affected growth performance and superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) levels including related mRNA expression suppression, malondialdehyde (MDA) levels enhancement, gut microbiota dysfunction, and serum amino acid alteration in pigs. However, treatment with 0.5% OKG markedly ameliorated the reduction in the growth performance, as evidenced by the reversed final body weight, average feed intake, and average body weight. Also, 0.5% OKG enhanced the SOD and GSH-Px levels including relative mRNA expression in the intestine and inhibited lipid oxidation subsequent to MDA generation. The intestinal abundances of Firmicutes were increased and those of Proteobacteria, Fusobacteria, Bacteriodetes, and Euryarchaeota were decreased in the pigs supplemented with 0.5% OKG. Meanwhile, 0.5% OKG increased the glutamate, proline, aspartate, threonine, valine, isoleucine and leucine levels in the serum. Collectively, these results indicate that D-gal induced chronic oxidative stress and also proved the positive effects of 0.5% OKG on altering the pig gut microbe, restoring serum amino acid and alleviating the growth-suppression induced by D-gal chronic oxidative stress.

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1. Introduction

With the growing population and the changing environment, chronic oxidative stress is increasingly becoming a major public health concern worldwide, affecting the health of humans and animals.¹ Chronic oxidative stress is an imbalance between reactive oxygen species (ROS) and antioxidant agents, and is implicated in the pathogenesis of chronic oxidative disease. D-Galactose (D-gal) is mainly derived from lactose in the milk of animals and can induce chronic oxidative stress and mild inflammation. For instance, D-gal readily reacts with free amino acid amines in proteins and peptides in vivo and in vitro to form advanced glycation end products² that elevate ROS production, decrease the activity of mitochondrial respiration complexes and cause chronic oxidation stress damage.^1,3,4 Previous studies have shown that D-gal in mice causes cognitive impairment,³ neurotoxicity,⁵ hepatic disorders,¹ and chronic kidney injury.⁶ Feng et al. investigated whether chronic subcutaneous administration with D-gal(100 mg kg⁻¹) induced the activation of oxidative stress and inflammation in the liver and kidneys.⁶ In addition, rats administered with D-gal(100 mg kg⁻¹) had increased oxidative DNA damage and renal podocyte loss.² In our previous study, treatment of weaned pigs for three weeks with 5 and 10 g kg⁻¹ day⁻¹D-gal induced oxidative stress and inhibited the growth performance (unpublished data).

Ornithine α-ketoglutarate (OKG) is a nutriment salt composed of two molecules of ornithine and one molecule of α-ketoglutarate, and has been used for centuries owing to its therapeutic and anabolic properties,^7,8 including: increasing bone mineral density,⁹ curing burns and trauma,¹⁰ and its anti-stress properties.^11,12 For example, short-term pretreatment with OKG before induction of ischemia-reperfusion decreased tissue damage, and increased pyruvate utilization for energy production in the Krebs cycle.¹³ OKG (0.5, 1.5 and 4.5 g kg⁻¹ day⁻¹) increased tissue glutamine concentration and N accumulation.¹⁴ Notably, OKG is not simply generated by AKG and Orn, but also the proline, arginine and glutamine levels precursor in vivo.^7,15 Previous studies have reported that OKG can more effectively induce insulin secretion and the growth hormone than either alone^16,17 and increases pyruvate utilization for energy production in the Krebs cycle.¹³ In a pig oxidative stress model, diquat, LPS or H₂O₂ are usually used to induce oxidative stress.^18–20 However, injection of diquat, LPS or H₂O₂ can cause severe stress that is difficult to control in the body. Moreover, the effect of OKG on the growth performance and gut microbiota in a chronic oxidative stress pig model remains to be elicited. Thus, this study aims to investigate whether OKG alleviates the damage caused by chronic oxidative stress in growing pigs challenged with D-gal.

2. Materials and methods

2.1. Animals and groups

A total of 40 castrated young pigs (Landrace × Yorkshire, 28 days old, 7.68 ± 0.56 kg) were randomly assigned to one of five groups (n = 8 per group): a control group in which pigs were fed with a basal diet (see Table 1) according to NRC (2012), a model group in which pigs were fed with a basal diet with 5 mg per kg body weight D-galactose and another three groups in which pigs were fed with a basal diet with 5 mg per kg body weight D-galactose and 0.5% OKG (low OKG, SOKG), 1% OKG (middle OKG, MOKG), or 2% OKG (high OKG, LOKG), respectively. The test was started after pre-feeding for three days. Each pig was housed in a single cage and had free access to drinking water and the respective diet for 28 days. At the end of the feeding experiment, six pigs were randomly selected from each group and killed for sample collection. This study was carried out in accordance with the recommendations of the Declaration of Helsinki. All animal procedures were approved by the Committee on Animal Care of the Institute of Subtropical Agriculture, Chinese Academy of Sciences.

Table 1 Composition and nutrient levels of experimental diets for pigs (air-dried basis, %)

Feed ingredients (%)	Basal diet
a The premix provided the following per kg of diet: nicotinic acid 50 mg, pantothenic acid 5 mg, folic acid 2 mg, biotin 0.2 mg, VA 10 800 IU, VD3 4000 IU, VE 40 IU, VK 3 4 mg, VB1 6 mg, VB2 12 mg, VB6 6 mg, VB12 0.05 mg, Cu 45 mg, Fe 90 mg, Mn 37 mg, Zn 41 mg, and Se 0.3 mg.
Soybean meal, 43	25.50
Corn	70.00
L-Lysine	0.10
L-Cysteine	0.02
Choline	0.07
Betaine	0.02
Complex enzyme	0.06
Sweetener	0.04
Acidifier	0.26
Antioxidant	0.03
CaHPO4	0.63
Limestone	1.82
NaCl	0.45
Premix^a	1.00
Total	100.00
Nutritional level
Digestible energy MJ kg^−1	13.61
Crude protein	17.00
Lysine	1.56
Methionine	0.50
Threonine	0.91
Tryptophan	0.25
Calcium	0.94
Phosphorus	0.44

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2.2. Growth performance

During the entire experimental period, the daily feed intake from each pig was recorded and all pigs were weighed individually at the beginning and at the end of the experiment. The average daily feed intake (ADFI), average daily gain (ADG), and the ratios of the feed intake to weight gain (F/G) were calculated according to the feed consumption and weight of each pig.

2.3. Serum oxidative stress index

Blood samples were obtained from the vascular vein. Serum was prepared via centrifugation at 3000g and 4 °C for 10 min and stored at −80 °C until analysis.²¹ Malondialdehyde (MDA), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and catalase (CAT) in serum were measured using assay kits in accordance with the manufacturer's instructions (Beyotime Biotechnology, China)¹⁹

2.4. Isolation, characterization and bioinformatics of microbiota

Total DNA samples from the terminal ileum were extracted using the cetyltrimethylammonium bromide/sodium dodecylsulfate CTAB/SDS method and DNA concentration and purity was determined using 1% agarose gels.^22,23 16S rDNA genes of distinct regions were amplified using the specific primer with the barcode. Then, the products were purified with a GeneJETTM Gel Extraction Kit (Thermo Scientific).²⁴ Sequencing libraries were generated using the Ion Plus Fragment Library Kit 48 rxns (Thermo Scientific) following the manufacturer's recommendations. The library quality was assessed on the Qubit@2.0 Fluorometer (Thermo Scientific). Lastly, the library was sequenced on an Ion S5TM XL platform and 400 bp/600 bp single-end reads were generated.^25,26

Single-end reads were assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence.²⁷ Quality filtering on the raw reads was performed under specific filtering conditions to obtain the high-quality clean reads according to the Cutadapt^28,29 (V1.9.1, http://cutadapt.readthedocs.io/en/stable/) quality controlled process. The reads were compared with the reference database (Silva database, https://www.arb-silva.de/)³⁰ using the UCHIME algorithm (UCHIME Algorithm, http://www.drive5.com/usearch/manual/uchime_algo.html)^31,32 to detect chimera sequences, and then the chimera sequences were removed.³³ Then, the clean reads were finally obtained. Sequence analysis was performed using Uparse software (Uparse v7.0.1001, http://drive5.com/uparse/).³⁴ Sequences with greater than or equal to 97% similarity were assigned to the same operational taxonomic units (OTUs). A representative sequence for each operational taxonomic unit (OTU) was screened for further annotation. For each representative sequence, the Silva Database (https://www.arb-silva.de/)³⁰ was used based on the Mothur algorithm to annotate the taxonomic information. In order to study the phylogenetic relationship of different OTUs, and the difference between the dominant species in different samples (groups), multiple sequence alignments were conducted using the MUSCLE software (Version 3.8.31, http://www.drive5.com/muscle/).³⁵ The abundance information of the OTUs was normalized using a standard sequence number corresponding to the sample with the least sequences. Subsequent analysis of the alpha diversity and beta diversity were all performed based on this output normalized data. The alpha diversity is applied to analyze the complexity of the species diversity in a sample. All of the indices in our samples were calculated using QIIME (Version1.7.0) and displayed with R software (Version 2.15.3).³⁶

2.5. Amino acid determination

Eighteen amino acids (lysine, methionine, threonine, tryptophan, glutamate, aspartate, valine, isoleucine, leucine, phenylalanine, arginine, serine, histone, glycine, alanine, proline, cysteine, and tyrosine) in the serum were detected via a Hitachi L-8900 automatic amino acid analyzer according to the method used in our previous reports.¹⁹

2.6. Real-time quantitative polymerase chain reaction

The total RNA from intestine samples was isolated with TRIZOL reagent (Invitrogen, USA) and then treated with DNase I (Invitrogen, USA). Reverse transcription was performed at 37 °C for 15 min, and 95 °C for 5 s. The primers used in this study were designed using Primer 5.0 according to the pig gene sequence (Table 2). β-Actin was chosen as the house-keeping gene to normalize the target gene levels. The PCR cycling conditions were 36 cycles at 94 °C for 40 s, 60 °C for 30 s and 72 °C for 35 s.

Table 2 Primers used in this study^a

Gene	Gene bank no.	Sequence (5′–3′)
a F: Forward; R: reverse.
β-Actin	DQ845171.1	F: CTGCGGCATCCACGAAACT
		R: AGGGCCGTGATCTCCTTCTG
GPx1	NM_214201	F: TGGGGAGATCCTGAATTG
		R: GATAAACTTGGGGTCGGT
GPx4	NM_214407.1	F: GATTCTGGCCTTCCCTTGC
		R: TCCCCTTGGGCTGGACTTT
MnSOD	NM_214127	F: GGACAAATCTGAGCCCTAACG
		R: CCTTGTTGAAACCGAGCC
CuZnSOD	NM_001190422	F: CAGGTCCTCACTTCAATCC
		R: CCAAACGACTTCCASCAT

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The relative expression was expressed as the ratio of the target gene to the control gene using the formula 2^−(ΔΔCt), in which ΔΔCt = (Ct_Target − Ct_β-actin)_treatment − (Ct_Target − Ct_β-actin)_control. The relative expression was normalized and expressed relative to the expression in the control group according to our previous report.^19,37

2.7. Statistical analysis

All statistical analyses were performed using IBM SPSS 20 software. The data were subjected to one-way analysis of variance followed by Tukey's test. The values in the same row with different superscripts are significant (P < 0.05). Data were expressed as a mean ± standard error.

3. Results

3.1. Growth performance

Compared with the control group, the D-gal treatment group showed a markedly reduced final body weight, ADG, ADFI and increased F/G (P < 0.05, Fig. 1). Interestingly, dietary supplementation with 0.5% OKG significantly reversed the final body weight, ADG, ADFI, and F/G compared with the model group (P < 0.05). However, we failed to notice any significant changes in the 1% OKG and 2% OKG group compared with the model group (P > 0.05).

Fig. 1 Effects of D-gal and OKG on growth performance. (A) Initial body weight and final body weight; (B) The ratios of the feed intake to weight gain; (C) Average body weight; (D) Average feed intake. Values are expressed as the mean ± SEM (n = 8).

3.2. Serum oxidative stress index and intestine relative gene expressions

Data on the serum oxidative stress-related indexes, including the MDA, SOD, GSH-Px, CAT and intestine mRNA expression, including Gpx1, Gpx4, MnSOD, and CuZnSOD are summarized in Fig. 2. The results showed that the serum CAT activity was unchanged after dietary D-gal and OKG (P > 0.05). D-Gal treatment significantly increased the MDA content and reduced the serum SOD activity (P < 0.05), while the SOKG and MOKG group showed markedly reduced MDA levels (P < 0.05). Compared with the control group, the LOKG group showed markedly reduced serum GSH-Px activity (P < 0.05).

Fig. 2 Effects of D-gal and OKG on serum oxidative stress index and relative intestinal gene expressions. (A–D) Plasm malondialdehyde, superoxide dismutase, glutathione peroxidase, and catalase; (E–L) mRNA abundance of Gpx1, Gpx4, MnSOD, and CuZnSOD in the jejunum and ileum. Values are expressed as the mean ± SEM (n = 8).

We next preformed a real-time polymerase chain reaction (RT-PCR) to test the expression of Gpx1, Gpx4, MnSOD, and CuZnSOD. The results revealed that D-gal and LOKG treatment markedly decreased the gene expression of these in the intestine (P < 0.05). However, SOKG and MOKG ameliorated the reduction of the relative gene expression, including Gpx1 and CuZnSOD in the jejunum and ileum (P < 0.05).

3.3. Alpha-diversity of intestinal microbial communities

As mentioned above, SOKG alleviated the D-gal induced chronic oxidative stress via the growth performance and oxidative stress index. Thus, we further investigated the influence of 0.5% OKG and D-gal on the intestinal microbiota. The number of OTUs (observed) and the estimators of community evenness, richness, and diversity (chao1, Simpson, Shannon, and phylogenetic diversity) were examined (Fig. 3). Dietary 0.5% OKG significantly reduced the community evenness, richness, and diversity, as evidenced by the decreased Shannon, Simpson, chao1, and ACE indices compared with those of the control group (P < 0.05). However, treatment with D-gal only markedly reduced the Shannon and Simpson indices compared with those of the control group (P < 0.05).

Fig. 3 Effects of D-gal and OKG on gut microbial diversity. (A) Observed specied; (B) Shannon index; (C) Simpson index; (D) Chao1 index; (E) ACE index; (F) Phylogenetic Diversity. Values are expressed as the mean ± SEM (n = 8).

3.4. 16S rDNA bacterial sequences represented in terminal ileum samples

The overall microbial compositions from the three groups were altered at the phylum and genus levels (Fig. 4–6). The Firmicutes and Proteobacteria mainly accounted for the phylum level and the top 10 microbes changed in response to the dietary OKG and D-galactose (Fig. 4 and 5). Compared with the results for the control group, SOKG markedly enhanced the abundance of the Firmicutes and Cyanobacteria, while the abundance of the Proteobacteria, Fusobacteria, Bacteroidetes, and Euryachaeota decreased (P < 0.05). Lactobacillus, Streptococcus, and Stenotrophomonas mainly accounted for the genus level and the top 10 microbes changed in response to the dietary OKG and D-galactose (Fig. 4 and 6). The abundance of Lactobacillus and Romboutsia in the SOKG group was enhanced, while the abundance of Streptococcus and Fusobacterium was reduced (P < 0.05). In addition, the model group significantly decreased the abundance of Fusobacterium but increased the Turicibater (P < 0.05).

Fig. 4 16S rRNA bacterial sequences represented in terminal ileum samples. Pie chart of average values of the relative abundances (percentage of sequences) of the most abundant bacterial groups: phylum and genus levels found in the ileal microbiota (n = 8).

Fig. 5 Altered ileal microbiota at the phylum level in response to dietary D-gal and OKG in piglets. Values are expressed as the mean ± SEM (n = 8).

Fig. 6 Altered ileal microbiota at the genus level in response to dietary D-gal and OKG in piglets. Values are expressed as the mean ± SEM (n = 8).

3.5. Biofunction prediction of the intestinal microbial flora

In this study, PICRUSt is based on the OTU tree in the Greengene database, and the genetic information on the OTU, inferring the gene function spectrum of their common ancestors, and inferring the gene function spectrum of other untested species in the Greengenes database to construct the archaeal and bacterial domain full spectrum of the gene function prediction spectrum, and finally “map” the sequenced microbial composition into the database to achieve prediction of the metabolic function of the flora.

The predicted results can be enriched at two different levels of the KEGG pathways, in which the 1 and 2 level impressions are used for histograms (Fig. 7). The results showed that the metagenome has a highly regulated response to OKG and D-gal and can differentiate the microbiomes that mainly contribute to the metabolism (46%) at level 1. At the KEGG pathway level 2, the membrane transport, carbohydrate metabolism, amino acid metabolism, replication and repair, translation, and energy metabolism changed in response to OKG and D-gal.

Fig. 7 Predictive functional profiling of microbial communities by PICRUSt. KEGG pathway annotations in level 1 and level 2.

3.6. Serum amino acid profiles

Compared with the results of the control group, the model group showed a significantly decreased serum glycine and proline content, but an increased phenylalanine content (P < 0.05). However, the SOKG group showed a markedly enhanced aspartate, glutamate, alanine, valine, leucine, tyrosine, and phenylalanine content, the MOKG group also showed an increased alanine, isoleucine, leucine, tyrosine, phenylalanine, lysine, and proline content (P < 0.05). In addition, the LOKG group showed a markedly enhanced phenylalanine, but decreased glycine, content (P < 0.05) (see Table 3).

Table 3 Effect of D-gal and OKG on amino acid pool^a

Item	Control	Model	SOKG	MOKG	LOKG	P-Value
a Data are expressed as the mean ± SEM (n = 8). Values in the same row with different superscripts are significantly different.
Asp	8.73 ± 0.70b	10.99 ± 0.37ab	12.06 ± 1.00a	10.66 ± 0.73ab	10.83 ± 0.75ab	0.049
Thr	21.33 ± 2.28ab	16.80 ± 2.64b	28.73 ± 4.68a	30.05 ± 1.30a	16.51 ± 2.55b	0.017
Ser	9.69 ± 0.85	11.61 ± 0.19	12.45 ± 0.71	10.95 ± 1.48	8.44 ± 0.11	0.056
Glu	39.50 ± 1.44b	51.07 ± 1.72ab	53.10 ± 5.64a	51.85 ± 5.00ab	43.09 ± 2.02ab	0.037
Gly	54.48 ± 3.92a	39.10 ± 1.53b	49.61 ± 3.52ab	40.66 ± 6.61ab	35.46 ± 6.27b	0.039
Ala	24.78 ± 1.13b	26.71 ± 0.80b	35.35 ± 4.31ab	39.06 ± 5.52a	33.43 ± 3.68ab	0.049
Cys	9.56 ± 0.39	10.86 ± 0.06	9.77 ± 0.30	10.41 ± 0.25	9.73 ± 0.26	0.052
Val	11.75 ± 0.67b	13.53 ± 0.96ab	14.72 ± 0.82a	13.34 ± 0.82ab	11.23 ± 0.38b	0.045
Met	4.52 ± 0.24	4.88 ± 0.26	4.52 ± 0.31	4.9 ± 0.35	4.03 ± 0.19	0.236
Ile	4.62 ± 0.27c	6.04 ± 0.31bc	8.06 ± 0.87ab	9.57 ± 1.24a	7.91 ± 0.78ab	0.001
Leu	12.55 ± 1.17b	15.95 ± 0.46ab	17.12 ± 1.02a	20.18 ± 1.40a	16.69 ± 0.24ab	0.001
Tyr	9.72 ± 2.42b	13.64 ± 0.77ab	17.14 ± 1.58a	19.66 ± 1.14a	16.48 ± 0.76ab	0.005
Phe	7.68 ± 0.78b	11.44 ± 0.44a	12.57 ± 0.92a	13.24 ± 0.69a	11.51 ± 0.64a	0.001
Lys	14.43 ± 0.54b	13.33 ± 1.65b	17.87 ± 1.51ab	24.25 ± 2.37a	14.50 ± 1.49b	0.001
His	5.11 ± 0.34	6.08 ± 0.68	5.32 ± 0.53	6.23 ± 0.68	5.11 ± 0.39	0.454
Arg	11.80 ± 0.79	11.10 ± 0.34	13.88 ± 2.01	15.66 ± 1.76	12.6 ± 0.57	0.271
Pro	14.52 ± 0.32b	16.42 ± 2.35a	19.02 ± 1.81ab	24.59 ± 3.04a	20.37 ± 1.31ab	0.023

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4. Discussion

D-Galactose is a nutrient and reducing sugar that reacts with free amino acids in proteins to produce ROS, and thus causes chronic oxidative stress.³ Accumulating evidence has shown that OKG possesses beneficial and protective activities in animals and humans under chronic and pathological conditions, owing to its regulatory roles in oxidative stress, burns, injury and metabolism.^7,15,38,39 However, evidence of the protective effects of OKG on chronic oxidative stress is still limited. Thus, in the current study, we further discovered that D-gal model pigs alleviated growth-suppression and restored serum amino acid profiles by altering the gut microbiome. It is of note that growth performance was inhibited in D-gal model pigs and 0.5% OKG alleviated growth-suppression, which is similar to the effect of OKG on tumor rats⁴⁰ and D-gal induced ageing mice, in which D-gal inhibited body weight in mice.⁴¹ Also, OKG alleviated D-gal induced chronic oxidative stress via enhancing the SOD and GSH-Px levels including the relative mRNA expression in the intestine and inhibiting lipid oxidation subsequent to MDA generation, similar to the results found in our previous study on glutamate restored diquat-induced oxidative stress in piglets.^19,42

Intestinal microbial communities play an important role in the status of the host, including the decomposition of food, metabolism, and defense against pathogen invasion.^43–49 Diet rapidly and reproducibly influences gut microbial composition, structure, and metabolism.^23,50–52 It was found that OKG can prevent bacterial translocation and dissemination and thus reduce gut-derived sepsis.⁵³ Zhao et al. reported that D-gal markedly increased Firmicutes and decreased Bacteroidetes. In this study, we reported for the first time that 0.5% OKG suppressed alpha diversity, which refers to the diversity of species within a community including the Shannon, Simpson, Chao1, and ACE indices.⁵⁴ We further discovered that OKG enhanced the intestinal abundance of Firmicutes and Cyanobacteria but decreased the Proteobacteria, Fusobacteria, Bacteriodetes, and Euryarchaeota at the phylum level. Vaughn et al. and Yin et al. reported that rats fed a high energy-dense diet and showed an increase in the Firmicutes/Bacteriodetes ratio may be associated with being overweight and with body fat accumulation.^46,55Proteobacteria, Fusobacteria, and Euryarchaeota are harmful intestinal bacteria. Proteobacteria promotes chronic gut inflammation.⁵⁶Fusobacteria are members of the oral and gastrointestinal flora and are important potential pathogens in human.^57,58 We also observed an increase in the genus Lactobacillus and Romboutsia, but a decrease in the Streptococcus, Fusobacterium and Turicibacter in the 0.5% OKG group. Lactobacillus has a protective effect on the intestinal morphology, and the integrity of the intestinal epithelium of pigs and humans.⁵⁹Romboutsia is commonly identified in the human gut and is often associated with a healthy status in patients, it is also a potential microbial indicator of disease.⁶⁰ This result indicated that 0.5% OKG may alter bacteria and could further increase feed intake, body weight and the health of pigs.

PICRUSt was used to predict the metabolic function of the flora based on the OTU tree in the Greengene database, and by constructing the archaeal and bacterial domain full spectrum of the gene function prediction spectrum.⁶¹ The results showed that the altered microbes were mainly involved in membrane transport, carbohydrate metabolism, amino acid metabolism, replication and repair, translation and energy metabolism. Similarly, a previous study also indicated that OKG influenced amino acid metabolism.^7,62 Thus, to explore the effect of D-gal and OKG on altering the gut microbiome, by modulating host physiological functions we further determined the serum amino acid pool to confirm the role of OKG on amino acid metabolism. The serum amino acid pool reflects the condition of the body and some diseases dynamically.⁶³ Diquat or mycotoxin exposure reduced the level of most serum amino acids.^19,64 Similarly, our results indicated that the concentration of most serum amino acids was reduced in the D-gal induced pigs. OKG can transform proline, arginine, glutamate, glutamine and other precursor which can respond to oxidative reactions and improve the immune status during stress.^{7,11,12,15,65} Meanwhile, aspartate, glutamate and threonine are the main sources of energy for the intestines and mainly metabolized in the intestines to synthesize intestinal mucosal proteins and improve the integrity of the intestinal epithelium.^66,67 In our study dietary 0.5% and 1% OKG increased glutamate, proline, aspartate, and threonine levels, suggesting that OKG may convert glutamate, proline, aspartate, and threonine to alleviate D-gal induced intestinal oxidative stress. Branched-chain amino acids, including leucine, isoleucine, and valine, are essential dietary nutrients for the normal growth of pigs and can improve intestinal morphology and cell proliferation.⁶⁸ In our study, 0.5% and 1% OKG markedly increased the concentration of the serum branched-chain amino acids.

In conclusion, dietary supplementation with 0.5% OKG restores serum amino acids and alleviates the growth-suppression induced by D-gal chronic oxidative stress and alters the composition of gut microbiota, especially for the Firmicutes and Bacteriodetes at the phylum level. Therefore, OKG may decreased the damage caused by D-gal in pigs and could be used as a nutritional additive. Further studies with prolonged supplementation of OKG in healthy animals and humans should be performed.

Conflicts of interest

The authors have declared no conflict of interest.

Funding

This study was supported by the National Science Foundation for Distinguished Young Scholars of Hunan Province (2016JJ1015), the National Natural Science Foundation of China (31872371, 31470132, 31772642), the Chinese Academy of Sciences “Hundred Talent” award, and the Open Foundation of Key Laboratory of Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences (ISA2016101).

Acknowledgements

We would like to thank Special Fund for Taishan Industry Leading Talent for the funding support. We are grateful to the Public Service Technology Center, Institute of Subtropical Agriculture, Chinese Academy of Sciences for technical support.

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