Effect of Pseudostellaria heterophylla polysaccharide on the growth and liver metabolism of chicks

Abstract

In this study, the effects of Pseudostellaria heterophylla polysaccharide (PHP) on the growth, development, and liver metabolism of chicks were investigated by feeding chicks diets. Four hundred 7-d-old Gushi roosters were selected and randomly divided into four groups, labeled A, B, C, and D. Group A was fed the basal diet, and Groups B, C, and D were fed 100, 200, and 400 mg PHP per kilogram of basal diet, respectively. At 14, 21, 28 and 35 d of age, five chicks were randomly selected from each group to collect samples for index detection. The results showed that compared with Group A, there were significant reduction in average daily feed intake (ADFI) and feed-to-weight ratio (F/G) at 14, 21, and 28 d (P < 0.05), significant increase in average daily gain (ADG) at 21, 28 d (P < 0.05), significantly increased levels of total protein (TP), albumin (ALB), insulin (INS), thyroxine (T3), growth hormone (GH) at 14, 28 d (P < 0.05), significantly decreased levels of glucose (GLU), total cholesterol (TC), glucagon (GC), and triglyceride (TG) at 28 d in Group C (P < 0.05). There were significantly increased levels of TP, ALB at 14, 21 d (P < 0.05), significantly increased level of TP at 35 d (P < 0.05), significantly increased level of GH at 28 d (P < 0.05), significantly decreased levels of GLU, GC at 28 d (P < 0.05), significant reduction in F/G at 14, 21 d in Groups B and D (P < 0.05). Based on the above results, the livers from chicks in Groups A and C at 28 d were selected for transcriptome sequencing. The sequencing results showed that significantly differentially expressed genes (SDEGs) were enriched in growth and development, oxidative phosphorylation, the PPAR signaling pathway and the lipid metabolism pathway. All these results revealed that the addition of 200 mg/kg PHP in the diet promoted the growth and development, lipid metabolism and energy metabolism of chicks, inhibit inflammation and tumor development, and improve the function of the liver.

The addition of PHP in the diet can promote the growth and development, lipid metabolism, and energy metabolism of chicks, inhibit inflammation and tumor development, and improve the function of the liver. The results provided a theoretical basis for PHP as a safe and effective nutritional feed additive in poultry production.

Introduction

With the great success of the poultry industry, the poultry meat and egg products have become an important component of human food, which provide humans with abundant animal derived proteins. In order to improve the quality and safety of poultry products, seeking green and healthy feed additives is imperative. Chinese herbal medicine not only has dual medicinal effects of promoting growth and enhancing immunity but also has the characteristics of natural, low residue, low toxicity, and low susceptibility to drug resistance (Ebrahim et al., 2020). Compared with chemical synthetic drug additives, Chinese herbal medicine or its extracts have significant advantages. It has been found that the addition of Chinese herbs to the ration can enhance the digestive function of laying hens and accelerate the absorption of nutrients (Huang et al., 2022).

Pseudostellaria heterophylla belongs to the dried root of the plant, also known as Child’s Ginseng, and the main component is polysaccharide. Plant polysaccharides have been reported to facilitate various activities such as growth promotion, appetite stimulation, immune enhancement, and antipathogen properties in animals (Chen et al., 2003; Long et al., 2020). The extract of prince ginseng beard polysaccharide could regulate the immune function of mice by increasing the immune activity of macrophages, thus enhancing the anti-inflammatory ability of the organism (Chen et al., 2020). Salvia miltiorrhiza polysaccharides could significantly increase the content of reduced glutathione in the liver and raise the level of total serum protein and albumin, thus improving antioxidant capacity and alleviating liver tissue damage (Han et al., 2019). Pseudostellaria heterophylla polysaccharide (PHP) has multiple bioactive effects, such as promoting growth and development and anti-glycemic, hypolipidemic, anti-inflammatory, antitumor, and protective effects on body tissues (Hu et al., 2013).

The liver is the main metabolic site and detoxification organ in the body, plays a key role in plasma protein synthesis, gluconeogenesis, cholesterol metabolism, bile acid synthesis, drug metabolism, and detoxification (Qian et al., 2021). When the diet is changed, the liver metabolic state is also changed. The changes in liver function are closely related to the body’s growth and development and glucose and lipid metabolism. Transcriptome sequencing technology has developed rapidly in recent years and has become an important approach for transcriptomic analysis and quantitative analysis of gene expression in organisms. Through transcriptome sequencing, it was found that anthocyanins from black fruit wolfberry altered the expression of the Cyp4a32 gene in mouse liver, possibly by regulating multiple signals, such as metabolic pathways or PPAR, to achieve lipid lowering (Liu, 2021). The transcriptomic investigation of the mechanism of polysaccharides in alleviating florfenicol-induced liver injury in broiler chicks revealed that polysaccharides could inhibit the harmful metabolites produced by florfenicol and promote the normal metabolism of hepatic lipids by regulating the cytochrome P450 metabolic pathway and the PPAR signaling pathway (Geng et al., 2021).

Most studies on PHP have focused on piglets and rats. Radix pseudostellariae stem and leaf polysaccharide could effectively increase ADG, decrease diarrhea rate, and improve immune function, antioxidant performance and biochemical indexes in serum of weaned piglets (Cai et al., 2016). PHP could enhance the cell-mediated immunity via improvements in macrophage phagocytosis, splenocyte proliferation, NK-cell activity and delayed type hypersensitivity, and improve humoral immunity through promoting the formation of serum hemolysin in immunosuppressed mice (Kan et al., 2022). At present, the effect of PHP on chicks growth and development has not been reported. In this study, the effects of PHP on the growth and development of chicks, the liver metabolic pathways and gene expression were investigated by adding in the diets. PHP may be a potential green and healthy feed additive applied in poultry production.

Materials and Methods

Ethics statement

The animal experiments in this study were in line with animal welfare and approved by the Animal Health Committee of the College of Animal Science and Veterinary Medicine, Henan Agricultural University (17-0126). Animals were euthanized with pentobarbital prior to tissue sampling. All protocols complied with animal welfare guidelines and minimized animal suffering.

Experimental material

PHP was provided by Shanyang Lianfeng Biotechnology Co., Ltd. With a purity of 90% and was stored at room temperature after dispensing. Gushi roosters were provided by the Poultry Germplasm Resource Farm of Henan Agricultural University.

Animal grouping

Randomly selected 400 healthy and weight-matched 7-d-old Gushi roosters were equally divided into 4 groups, namely, A, B, C, and D. Group A was fed the basic diet, and other 3 groups were supplemented with 100, 200, and 400 mg/kg of PHP in the basic diet, respectively. Breeding management was carried out according to the standards of the Poultry Germplasm Resource Farm of Henan Agricultural University. The health condition of the chicks was observed in a timely manner and recorded during the trial period, which was 4 weeks.

Growth performance

Ten chicks from each groups were randomly selected for weighing in the fasting state on the morning of the first day at the beginning of each test phase and on the evening of each weekend. Feeding was performed twice a day at 8:00 and 20:00 hours, and detailed records of the amount of feed given, the amount of remaining feed, and the number of dead chicks each day were kept. The average daily feed intake (ADFI), average daily gain (ADG), and feed-to-weight ratio (F/G) were also calculated for each group.

ADG (g/w) = weight of 10 chicks at the end of each phase of the test − average weight of 10 chicks at the beginning of each phase of the test (g)

$${\displaystyle \begin{array}{l}{\displaystyle \begin{array}{lllll}& {\displaystyle \mathrm{A}\mathrm{D}\mathrm{G}(\mathrm{g}/\mathrm{w})=\mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{o}\mathrm{f}10\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{c}\mathrm{k}\mathrm{s}}& {\displaystyle \mathrm{a}\mathrm{t}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{e}\mathrm{n}\mathrm{d}\mathrm{o}\mathrm{f}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{p}\mathrm{h}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}}& {\displaystyle -\mathrm{a}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{a}\mathrm{g}\mathrm{e}\mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}\mathrm{o}\mathrm{f}10\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{c}\mathrm{k}\mathrm{s}}& {\displaystyle \mathrm{a}\mathrm{t}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{b}\mathrm{e}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{o}\mathrm{f}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{p}\mathrm{h}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}(\mathrm{g})}\end{array}}\end{array}}$$

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{A}\mathrm{D}\mathrm{F}\mathrm{I}(\mathrm{g}/\mathrm{d})=\frac{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{p}\mathrm{h}\mathrm{a}\mathrm{s}\mathrm{e}\mathrm{o}\mathrm{f}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{t}\mathrm{e}\mathrm{s}\mathrm{t}(\mathrm{g})}{7(\mathrm{d})}}\end{array}}$$

$${\displaystyle \begin{array}{l}{\displaystyle \frac{\mathrm{F}}{\mathrm{G}}=\frac{\mathrm{A}\mathrm{D}\mathrm{F}\mathrm{I}(\mathrm{g})}{\mathrm{A}\mathrm{D}\mathrm{G}(\mathrm{g})}}\end{array}}$$

Digestive organ index measurement

Five chicks were randomly selected from each group at the ages of 14, 21, 28, and 35 d, and their live weight was recorded. The thoracic cavity was opened, the liver, pancreas, glandular stomach, and myogastric tissue were collected; the surfaces were washed with saline at 4 °C to remove the blood stain and connective tissue on the surface; and the tissue surfaces were blotted with filter paper. Weighing was performed using an analytical balance to calculate the digestive organ index.

$${\displaystyle \begin{array}{l}{\displaystyle \mathrm{D}\mathrm{i}\mathrm{g}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{o}\mathrm{r}\mathrm{g}\mathrm{a}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}(\mathrm{\%})=\frac{\mathrm{d}\mathrm{i}\mathrm{g}\mathrm{e}\mathrm{s}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{o}\mathrm{r}\mathrm{g}\mathrm{a}\mathrm{n}\mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}(\mathrm{g})}{\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{c}\mathrm{k}\mathrm{s}\mathrm{l}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}(\mathrm{k}\mathrm{g})}\times 100\mathrm{\%}}\end{array}}$$

Measurement of serum biochemical indicators

Taking blood samples from the jugular vein of the same 5 chicks as above, 5 mL of fresh blood was collected in a procoagulant tube. The tube was placed on ice at 45 °C for 30 min at an angle and centrifuged at 4,000 r/min for 15 min. The serum was separated, transferred into EP tubes, and stored at −80 °C for testing. The levels of glucose (GLU), triglyceride (TG), total cholesterol (TC), total protein (TP), and albumin (ALB) in peripheral blood were measured using a fully automated biochemical analyzer. The levels of insulin (INS), glucagon (GC), thyroxine (T3), and growth hormone (GH) in the serum of chicks were determined by ELISA. The kits used were purchased from Jiangsu Meimian industrial Co., Ltd. The measurement method and operation steps were performed strictly in accordance with the kit instructions.

Liver tissue transcriptomic assay

Extraction and sequencing of total RNA from liver tissues

Based on the results of growth performance, serum hormone levels, serum biochemical indexes, and digestive organ indexes, three chicks from one of the adding PHP groups with the most significant differences in most indicators compared to group A at the unified sampling time point were selected. Liver tissues of these three chicks and three chicks in A group were noted as CS group (C1, C2, and C3) and AS group (A1, A2, and A3), and used for transcriptome sequencing. Total RNA was extracted using an RNAiso Plus kit. After the samples were tested, the final cDNA library construction was performed according to the NEBNextUltra RNA Library Construction Kit instructions. The different libraries were pooled according to the effective concentration and target downstream data volume requirements for Illumina sequencing.

Analysis of differentially expressed genes

Differential analysis of gene expression was performed by the mean normalized read counts using DESeq2R (v1.14.1) software (Wang et al., 2010). Genes detected by DESeq with P < 0.05, |log₂fold changes| ≥ 0 and FPKM > 1 were considered to be significantly expressed. FPKM values of differential genes under different experimental conditions were used for heatmap clustering analysis using the pheatmap R package.

GO and KEGG enrichment analysis

Gene Ontology (Trapnell et al., 2012) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of significantly differentially expressed genes (SDEGs) were used to perform bioinformatics analysis. GO enrichment analysis was performed using GOseq software, using the GOseq R (Young et al., 2010) package to identify GO entries that were significantly enriched for SDEGs. Analysis of the enrichment of SDEGs in the KEGG pathway was conducted using KOBAS (v2.0) software to identify enrichment pathways and elucidate intergroup differences in cellular pathways. The P-value was corrected using adjustment of P values (P_adj). The GO entries and KEGG pathways with P_adj < 0.05 were considered to be significantly enriched.

Fluorescence quantitative PCR technology detection of gene expression

To verify the accuracy and reliability of the sequencing data, nine SDEGs, including four upregulated genes and five downregulated genes, were randomly selected for quantitative validation using quantitative real-time PCR (qRT-PCR). RNA was extracted from chicks liver tissues using the TRIzol method. After extraction, RNA integrity and degradation were detected by 1% TAE agarose gel electrophoresis. Then, the RNA was reverse transcribed to cDNA using the PrimerScript RT kit. The cDNA concentration was measured and stored at −20 °C for backup. The specific primers for SDEGs were designed online by the Primer-BLAST program in NCBI, and the sequences were synthesized by Beijing QingKe Biotechnology Co., Ltd. GAPDH was as the internal reference gene. All primer sequences are shown in Table 1. LightCycler96 real-time quantitative fluorescent PCR instrument and SYBR PremixEx Taq kit were used. The relative expression of genes was calculated using the 2^−ΔΔCt method, with five biological replicates set for each group and two technical replicates for each biological replicate.

Table 1.

Information on the primers used for qRT-PCR analysis

Gene	Primer sequence (5ʹ–3ʹ)	Product size, bp
FABP1	CAGGAGAGAAGGCCAAGTGT CACGGATTTCAGCCCCTTCA	80
HSPA8	TGACCAGGGTAACAGGACCA ACGCCCAATCAACCGTTTTG	136
HSP90AA1	GTGCAAACACAGGACCAACC TTGCTCGGGTCAGTCAAACT	191
STAT3	TGAACAGCATGTCGTTTGCG CAGTAGCTTCTGAGTGTTCCTG	162
MyD88	AGCGTGCCAAAGACTTCAGA ACACGTTCCTGGCAAGACAT	201
SOX9	ACGTCAACGAGTTCGACCAA GTACCGCTGTAGGTGGTGAC	88
MGAT5B	GACTTCATCGGGAAGCCACA TGGCATGGTGCAGAAATCCT	183
DLL1	CACGAGAGAAGCAACCGCTA ACTGCGATCCAGGGAAACTG	158
ATP8	TTCTCTCTGCTTATCCAACCCA GGGGGTGGGTTTAGTTGTTGT	87
GAPDH	GAACATCATCCCAGCGTCCA CGGCAGGTCAGGTCAACAAC	132

Protein interaction network (PPI) analysis

The STRING database (https://string-db.org, Organism: chicken) was used for protein interaction network analysis of SDEGs. Cytoscape 3.6.1 software was used to make visual protein interaction network maps. The core proteins were identified by counting the number of intersections between each network node.

Statistical analysis

Statistical analysis was performed using SPSS 26.0 software with one-way ANOVA and independent samples t test for significant differences between groups. Test data are expressed as the mean ± standard deviation, and P < 0.05 indicated a significant difference and was considered to be statistically significant. Bar graphs were plotted by GraphPad Prism 9.0 software.

Results

Growth performance

From the results of Figures 1, 2, and 3, it was found that compared with Group A, Group C had a significant reduction in ADFI and F/G at 14, 21, 28 d (P < 0.05), significant reduction in ADFI at 35 d (P < 0.05), and significant increase in ADG at 21 d, 28 d (P < 0.05); Group D showed significant increase in ADFI at 28 d (P < 0.05), significant increase in ADG at 14 d (P < 0.05), and significant reduction in F/G at 14, 21, 28 d (P < 0.05); Group B had a significant reduction in F/G at 14, 21 d (P < 0.05).

Figure 1.

Effect of PHP on the average daily intake of chicks. The x-axis is the age of the chicks and the y-axis is the average daily feed intake.^a,b,c Means of different superscripts within the same day of age differ significantly (P < 0.05).

Figure 2.

Effect of PHP on the average daily weight gain of chicks. The x-axis is the age of the chicks and the y-axis is the average daily weight gain, ^a,b,c Means of different superscripts within the same day of age differ significantly (P < 0.05).

Figure 3.

Effect of PHP on the feed-to-weight ratio of chicks. The x-axis is the age of the chicks and the y-axis is the ratio of average daily feed intake to average daily weight gain, ^a,b,c,d Means of different superscripts within the same day of age differ significantly (P < 0.05).

Digestive organ index

As shown in Table 2, compared with Group A, there were significant improvement in glandular stomach index at 21, 35 d and liver index at 28 d in Group C (P < 0.05), significant improvement in liver index in Group D at 35 d (P < 0.05), significant improvement in liver index in Group B at 28 d (P < 0.05). The pancreatic index and muscle stomach index tended to increase at 28 and 35 d in chicks in Groups B, C, and D, but the difference was not significant (P > 0.05). The differences in all digestive organ indices among groups at 14 d were not significant (P > 0.05).

Table 2.

Effect of PHP on the digestive organ index of chicks

Day of age	Items, g/kg	Groups				P-value
		A	B	C	D
14	Liver index	39.47 ± 3.08	36.97 ± 4.36	36.63 ± 3.65	35.79 ± 5.64	0.509
	Pancreatic index	5.59 ± 0.91	6.46 ± 0.76	5.32 ± 0.92	4.51 ± 0.46	0.062
	Glandular stomach index	9.53 ± 0.84	8.99 ± 1.51	9.40 ± 1.77	9.36 ± 0.38	0.913
	Muscle stomach index	54.20 ± 2.71	51.26 ± 5.37	55.62 ± 5.49	53.92 ± 4.54	0.622
21	Liver index	32.43 ± 4.63	30.82 ± 3.10	33.44 ± 1.83	34.53 ± 2.70	0.277
	Pancreatic index	5.12 ± 0.99	5.12 ± 1.04	4.05 ± 0.77	4.30 ± 0.59	0.094
	Glandular stomach index	8.19 ± 0.43b	8.07 ± 0.73b	9.43 ± 0.65a	8.02 ± 0.97b	0.088
	Muscle stomach index	53.76 ± 7.38	48.01 ± 5.03	51.35 ± 7.57	52.92 ± 4.57	0.421
28	Liver index	30.30 ± 0.91b	34.21 ± 2.01a	34.74 ± 3.78a	30.15 ± 1.36b	0.009
	Pancreatic index	4.25 ± 0.54	4.30 ± 0.63	4.33 ± 0.83	4.29 ± 0.13	0.998
	Glandular stomach index	7.29 ± 0.59	7.64 ± 1.08	7.55 ± 1.68	7.76 ± 0.80	0.903
	Muscle stomach index	45.04 ± 4.67	49.74 ± 4.24	49.75 ± 7.82	53.96 ± 6.56	0.181
35	Liver index	28.25 ± 1.16b	28.28 ± 1.65b	26.49 ± 1.05b	32.23 ± 1.94a	0.001
	Pancreatic index	3.63 ± 0.13	3.81 ± 0.19	3.60 ± 0.27	4.25 ± 0.91	0.370
	Glandular stomach index	6.04 ± 0.75b	7.53 ± 0.81ab	8.12 ± 2.32a	7.66 ± 1.28ab	0.102
	Muscle stomach index	37.14 ± 3.96	43.74 ± 2.78	41.66 ± 0.96	42.68 ± 5.78	0.137

^a,bMeans with different superscripts within the same row differ significantly (P < 0.05).

Serum biochemical indicators

As shown in Table 3, compared with Group A, there were significantly increased levels of TP and ALB at 14, 21, 28 d (P < 0.05), significantly increased levels of TP at 35 d (P < 0.05), significantly decreased levels of GLU and TC at 28 d in Group C (P < 0.05). There were significantly increased levels of TP and ALB at 14 d, 21 d (P < 0.05), significantly increased level of TP at 35 d (P < 0.05), and a significantly decreased level of GLU at 28 d in Groups B and D (P < 0.05). There was significantly reduced level of ALB at 28 d in Group B (P < 0.05).

Table 3.

Influence of PHP on the serum biochemical indices of chicks

Day of age	Items	Groups				P-value
		A	B	C	D
14	TP, g/L	24.08 ± 3.33b	31.88 ± 1.14a	28.48 ± 1.81a	29.48 ± 3.02a	0.006
	ALB, g/L	9.30 ± 0.92c	12.80 ± 0.29a	11.40 ± 0.58b	11.65 ± 0.94b	<0.001
	GLU, mmol/L	7.97 ± 2.24	7.05 ± 2.37	7.79 ± 0.97	7.83 ± 1.48	0.885
	TG, mmol/L	0.51 ± 0.21	0.43 ± 0.12	0.44 ± 0.03	0.39 ± 0.07	0.476
	TC, mmol/L	3.06 ± 0.09	2.78 ± 0.30	3.06 ± 0.18	2.86 ± 0.16	0.273
21	TP, g/L	24.38 ± 3.15b	33.93 ± 4.47a	35.68 ± 2.37a	34.60 ± 1.44a	0.001
	ALB, g/L	9.68 ± 0.86c	11.97 ± 1.14b	12.55 ± 1.12ab	13.73 ± 0.38a	<0.001
	GLU, mmol/L	11.47 ± 2.41	13.23 ± 0.17	11.92 ± 0.64	12.06 ± 0.28	0.412
	TG, mmol/L	0.38 ± 0.14	0.43 ± 0.09	0.45 ± 0.06	0.50 ± 0.02	0.383
	TC, mmol/L	2.73 ± 0.41	2.32 ± 0.08	2.57 ± 0.39	2.58 ± 0.01	0.375
28	TP, g/L	37.40 ± 1.44b	32.60 ± 0.79b	42.53 ± 0.25a	37.70 ± 4.55ab	0.012
	ALB, g/L	13.02 ± 0.66b	11.66 ± 0.97c	14.45 ± 0.33a	12.37 ± 1.12bc	0.001
	GLU, mmol/L	11.41 ± 1.00a	9.28 ± 0.34b	9.95 ± 0.56b	8.78 ± 0.99b	0.004
	TG, mmol/L	0.39 ± 0.07	0.43 ± 0.07	0.34 ± 0.02	0.34 ± 0.06	0.178
	TC, mmol/L	3.43 ± 0.34a	3.11 ± 0.63ab	2.64 ± 0.39b	3.27 ± 0.30ab	0.146
35	TP, g/L	29.13 ± 0.93c	33.57 ± 2.03b	39.13 ± 2.64a	35.20 ± 1.20b	0.001
	ALB, g/L	12.93 ± 1.01	12.60 ± 1.28	12.48 ± 1.37	11.83 ± 0.79	0.633
	GLU, mmol/L	9.18 ± 1.23	9.41 ± 0.99	9.84 ± 0.65	8.82 ± 1.52	0.563
	TG, mmol/L	0.38 ± 0.06	0.39 ± 0.03	0.41 ± 0.03	0.40 ± 0.06	0.904
	TC, mmol/L	2.80 ± 0.52	2.93 ± 0.56	2.86 ± 0.02	2.88 ± 0.27	0.974

^a,b,cMeans with different superscripts within the same row differ significantly (P < 0.05).

As shown in Table 4, compared with Group A, there were significantly increased levels of INS, T3, and GH at 14, 28 d (P < 0.05), significantly increased levels of INS at 21 d (P < 0.05), significantly increased levels of T3 at 35 d (P < 0.05), and significantly decreased levels of GC at 21, 28, 35 d in Group C (P < 0.05). There were significantly increased levels of T3 and GH at 28 d (P < 0.05), significantly increased level of INS at 21 d (P < 0.05), significantly increased level of GH at 14 d (P < 0.05), and significantly decreased level of GC at 28 d in Group D (P < 0.05). There were significantly increased level of GH at 28 d (P < 0.05), and significantly decreased level of GC at 28 d in Group B (P < 0.05).

Table 4.

Effect of PHP on the serum hormone content of chicks

Day of age	Items	Groups				P-value
		A	B	C	D
14	INS, mU/L	17.75 ± 2.09b	21.95 ± 4.27b	27.83 ± 4.22a	22.96 ± 3.27ab	0.014
	GC, pg/mL	270.05 ± 36.98	293.94 ± 42.86	250.62 ± 37.58	253.69 ± 35.58	0.297
	T3, nmol/L	3.75 ± 0.64b	3.74 ± 0.71b	5.19 ± 0.29a	4.12 ± 0.59b	0.003
	GH, ng/mL	8.44 ± 1.50c	10.17 ± 1.32bc	12.17 ± 1.48a	11.45 ± 1.35ab	0.004
21	INS, mU/L	26.10 ± 0.80c	25.93 ± 2.13c	36.11 ± 1.28a	31.58 ± 2.42b	<0.001
	GC, pg/mL	277.33 ± 46.09a	227.77 ± 42.53ab	204.36 ± 39.96b	243.77 ± 60.18ab	0.147
	T3, nmol/L	5.13 ± 0.67	5.56 ± 074	5.84 ± 0.59	5.83 ± 0.56	0.469
	GH, ng/mL	12.01 ± 1.10	11.03 ± 2.47	13.05 ± 0.70	13.74 ± 0.34	0.185
28	INS, mU/L	26.93 ± 0.60b	28.12 ± 2.50b	39.40 ± 1.88a	28.37 ± 1.82b	<0.001
	GC, pg/mL	269.41 ± 34.21a	207.04 ± 41.34b	176.12 ± 46.10b	194.09 ± 44.23b	0.015
	T3, nmol/L	4.98 ± 0.94b	5.59 ± 0.83ab	6.65 ± 0.45a	6.33 ± 1.12a	0.034
	GH, ng/mL	10.52 ± 1.13c	14.62 ± 0.87a	12.29 ± 0.23b	15.84 ± 0.27a	<0.001
35	INS, mU/L	23.73 ± 6.38	24.75 ± 4.14	28.83 ± 3.36	27.24 ± 4.47	0.338
	GC, pg/mL	234.21 ± 26.34a	225.84 ± 20.41ab	180.45 ± 42.58b	185.40 ± 27.12ab	0.077
	T3, nmol/L	5.17 ± 0.35bc	5.88 ± 0.33ab	6.30 ± 0.93a	4.83 ± 0.60c	0.021
	GH, ng/mL	11.48 ± 1.45ab	9.64 ± 1.00b	12.88 ± 2.58a	12.63 ± 2.36a	0.070

^a,b,cMeans with different superscripts within the same row differ significantly (P < 0.05).

Identification of SDEGs

A total of 18,457 genes were detected in the AS and CS groups, of which 1,054 SDEGs were screened (P < 0.05, |log₂fold changes| ≥ 0, and FPKM > 1), including 422 upregulated genes and 632 downregulated genes (Figure 4).

Figure 4.

Volcano map of the differentially expressed genes between the AS group and the CS group of the livers. The significantly differentially expressed genes (SDEGs) are shown as green (down) and red (up) dots, and blue dots indicate a lack of significance. The horizontal black line indicates the suggestive significance thresholds of differentially expressed genes at P < 0.05; the vertical black lines indicate the threshold of log₂fold change ≥ 0. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

GO and KEGG enrichment analysis

A total of 4,803 GO terms were screened and categorized into molecular function (MF), cellular component (CC), and biological process (BP). SDEGs were mainly enriched in BP. It was found that PHP mainly affected negative regulation of blood coagulation, lipid metabolism, and mitochondrial inner membrane (Figure 5). The upregulated genes FGG and FGB were enriched in negative regulation of blood coagulation. The upregulated gene CYP7A1 and the downregulated genes FASN and FABP1 were enriched in lipid metabolism. ATP8, COX3, and SOX9 were significantly upregulated in mitochondrial inner membrane.

Figure 5.

Top 30 GO-enriched entries of differential genes. The X-axis indicates GO entries enriched for SDEGs, and the Y-axis is the −log₁₀Padj.

Based on the results of KEGG enrichment analysis of SDEGs, a total of 138 enriched pathways were screened, and the top 20 pathways with the most significant enrichment were showed (Figure 6). The top 20 pathways with the ­highest percentage of SDEGs were mainly distributed in protein processing in the endoplasmic reticulum, the PPAR signaling pathway, and the oxidative phosphorylation signaling pathway. HSPA8 and HSP90AA1 were significantly downregulated in the protein processing in the endoplasmic reticulum pathway; the PPAR signaling pathway included the upregulated gene CYP7A1 and the downregulated gene FABP1. ATP8 and COX3 were significantly upregulated in the oxidative phosphorylation pathway.

Figure 6.

Top 20 enriched KEGG pathways of the SDEGs of the AS group and CS group. The X-axis shows the rich factor, the Y-axis shows the KEGG pathway. The colour of the dot represents the Padj, and the size of the dot represents the number of DEGs enriched in the reference pathway.

Validation of the transcriptome sequencing results by qRT-PCR

Nine SDEGs (FABP1, HSPA8, HSP90AA1, STAT3, MyD88, SOX9, MGAT5B, DLL1, and ATP8) were randomly selected from the RNA-seq results for qRT-PCR validation (Figure 7). The results showed that compared with the AS group, SOX9, DLL1, MGAT5B, and ATP8 were significantly upregulated in the CS group (P < 0.05), and FABP1, HSPA8, HSP90AA1, STAT3, and MyD88 were significantly downregulated in the CS group (P < 0.05), which was consistent with the RNA-seq results. This indicated that the sequencing results were accurate and reliable.

Figure 7.

qRT-PCR validation of the RNA-seq results. (A) RNA-seq results; (B) qRT-PCR results. The Pink column is the AS group, and the green column is the CS group. * indicates P < 0.05. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

PPI analysis

A protein interaction network map was generated for SDEGs (Figure 8). A total of 982 pairs of protein interactions were obtained from 1054 SDEGs. The results showed that the COII, ND1, CYP7A1, and STAT3 genes have more than five key core node interactions. Among them, STAT3 is mainly enriched in the growth and development signaling pathway and adipocytokine signaling pathway.

Figure 8.

PPI diagram of the differentially expressed genes. The connection between proteins indicates that they interact, and the confidence interval is greater than 0.4.

Discussion

Growth performance plays a crucial role in the actual production of livestock and poultry. Many plant polysaccharides have been shown to have growth-promoting effects in animals (Ebrahim et al., 2020). The compound polysaccharides from herbs could effectively stimulate gastric acid secretion, promote the secretion of digestive gland enzymes and the absorption of nutrients in the diet, and facilitate the growth and development of piglets (Liu, 2007). The addition of comfrey polysaccharide to the diet could effectively promote the digestion and absorption of laying hens and improve their production performance (Zhou et al., 2022). Acanthopanax polysaccharides in the diet could promote intestinal health and improve the disease resistance and growth performance of weaned piglets (Yang et al., 2013). PHP in the diet could increase the ADFI and ADG, promote nutrient digestion, reduce the rate of diarrhea, and improve the growth performance of piglets (Cai et al., 2016). In this study, compared with Group A, there were significant reduction in ADFI and F/G at 14, 21, 28 d (P < 0.05), significant increase in ADG at 21, 28 d in Group C (P < 0.05), which indicated that PHP could promote the absorption of nutrients in the small intestine and improve the growth performance of chicks.

The weight of the visceral organs reflects the growth and development as well as the health status of the animal (Smith et al., 2011). It has been shown that the weight of the organs was related to age during the animal growth and development (Scholz et al., 1988). In this study, compared with Group A, there were significant improvement in glandular stomach index at 21, 35 d and liver index at 28 d in Group C (P < 0.05), which indicated that PHP could promote the development of digestive organs and improve the digestive function of chicks.

The serum biochemical indices are important indicators of the distribution of nutrient digestion and metabolism, the metabolic function of the organism and the normal or abnormal health status in animal (Wang et al., 2021). The serum TP and ALB contents reflect the state of protein synthesis and metabolism by the liver (Wang et al., 2015). Within the normal range, the increase in the TP content indicates the ability to digest and absorb nutrients is better, thereby improving the animal’s feed utilization rate (Wu et al., 2021). Serum lipids, mainly including TG and TC, are important indicators reflecting the level of lipid metabolism in the body. High levels of TG indicates lipid metabolism disorders (Wang et al., 2020). The blood GLU is a biological indicator of the energy balance of animals, which is closely related to the level of INS. The high concentration of INS promotes the synthesis of ­protein and inhibits its catabolic transformation. GH ­regulates protein and fat metabolism and promotes the growth and development of animals by promoting cell division and proliferation and increasing protein synthesis in muscle and other tissues (Dong et al., 2021). T3 and T4 are important hormones that promote the efficiency of material and energy metabolism in animals (Huang et al., 2011). In this study, the addition of 200 mg/kg PHP could significantly increase the contents of TP, ALB, INS, T3, GH (P < 0.05) and significantly decrease the contents of GLU, TC, and GC (P < 0.05), which indicated that PHP could improve the metabolic absorption of nutrients, promote protein synthesis and lipid metabolism, increase the level of growth-related hormones and improve the growth performance of chicks.

The liver is the largest digestive gland in the body and is the central hub for the metabolism of substances and energy in the body. Recent studies have shown that polysaccharides could improve broiler growth performance, promote organism metabolism, and improve liver antioxidant potential (Hou et al., 2023). Polysaccharides could also improve hepatic lipid deposition in mice by regulating lipid levels and the reverse cholesterol transport process (Wang et al., 2022). Polysaccharides effectively modulated hepatic inflammatory factors in immune liver-injured mice and inhibit the development of hepatic inflammatory responses (Wang, 2019). Therefore, we chosed liver transcriptome to further evaluate the effects of PHP on chicks.

In this study, the livers from chicks in Groups A and C at 28 d were selected for transcriptome sequencing. A total of 1,054 SDEGs were screened, including 422 upregulated genes and 632 downregulated genes. SMAD7b was the most significantly upregulated gene and enriched in the growth and development signaling pathway. SMAD7b is an important member of the SMAD family. SMAD7 is a negative feedback regulatory molecule of the TGF-β1/SMAD signaling pathway involved in regulating cell proliferation, transformation, synthesis, secretion and apoptosis (Zhou, 2014). Silencing of the KLF4 gene promoted upregulation of SMAD7 mRNA and protein expression in rat hepatic stellate cells, which resulted in possible resistance to liver fibrosis (Li et al., 2016). ­Furthermore, significantly upregulated genes including SOX9, ANXA1, and TBX20 were also enriched in the growth and development signaling pathway. The SOX9 gene is an important member of the SOX gene family that plays an important role in vertebrate growth and development, and its high expression promotes early tissue development (Yuan et al., 2007). SOX9-labeled progenitor cell populations contributed to self-renewal and repair of the liver and have stem cell properties (Huch and Clevers, 2011). ANXA1 acted as a signal amplifying factor that promotes the release of PGE2, which caused cell proliferation (Hirata et al., 2010). TBX20 played an important role in maintaining angiogenesis and development in animal tissues (Chen et al., 2021). TBX20 expression was involved in early heart development, and numerous studies have shown that reducing TBX20 gene expression will affect heart formation (Shelton and Yutzey, 2007).

CYP7A1, FASN, ATP8, COX3, and FABP1 were enriched in lipid metabolism, energy metabolism, and PPAR signaling pathways. Cyp is involved in the synthesis and metabolism of drugs, fatty acids, and cholesterol in the liver. CYP7A1 was closely related to fatty acid metabolism (Nebert and Russell, 2002). Increased expression of the CYP7A1 signaling pathway promoted hepatic reverse cholesterol transport (RCT) and bile acid synthesis, thereby reduced cholesterol levels and improved visceral lipid metabolism (Chambers et al., 2019). The inhibition of FASN activity reduced fat synthesis and caused an increase in the malonyl-CoA concentration, thus reduced diet consumption (Wu et al., 2011). ATP8 and COX3 could be independently transcribed and translated to form proteins that provide energy for the body (Eipel et al., 2011; Hejzlarova et al., 2015). Fatty acid binding protein 1 (FABP1) mainly plays a role in fatty acid uptake, transport, metabolism, and intracellular transport. The overexpression of the chicks FABP gene induced a high lipolysis rate and a reduction in fat content (Shi et al., 2010). FABP could inhibit PPAR-γ activity. The downregulation of FABP may activate the PPAR signaling pathway and regulate the metabolism of fatty substances (Abbott, 2009).

The results of PPI analysis showed that STAT3 gene was the core node, and had 5 interactions. STAT3 was mainly enriched in the growth and development signaling pathway and adipocytokine signaling pathway. STAT3 as an important intracellular transcription factor involved in the regulation of cell growth, differentiation and cell cycle processes and played a crucial role in stimulating the expression of hepatic innate immune mediators (Hillmer et al., 2016). It has been suggested that herbal medicine could inhibit the metastatic invasion of cancer cells by inhibiting JAK2/STAT3 signaling pathway activation, inhibit tumor cell cycle progression, and promote apoptosis and anti-neoangiogenesis (Song et al., 2022). In this study, PHP significantly downregulated the STAT3 gene in the liver, and may play an anti-inflammatory and antitumor role by inhibiting STAT3 signaling pathway activation.

Conclusion

The results revealed that the addition of 200 mg/kg PHP to the diet promoted the growth and development, lipid metabolism of chicks, inhibit inflammation, and tumor development, and improve the function of the liver. The results provided a theoretical basis for PHP as a safe and effective nutritional feed additive in poultry production.

Glossary

Abbreviations

ADFI

average daily feed intake

ADG

average daily gain

ALB

albumin

ELISA

enzyme-linked immunosorbent assay

F/G

feed-to-weight ratio

GC

glucagon

GH

growth hormone

GLU

glucose

GO

Gene Ontology

INS

insulin

KEGG

Kyoto Encyclopedia of Genes and Genomes

PHP

Pseudostellaria heterophylla polysaccharide

qRT-PCR

quantitative real-time PCR

SDEGs

significantly differentially expressed genes

T3

thyroxine

TC

total cholesterol

TG

triglyceride

TP

total protein

Conflict of Interest Statement

The authors declare that they have no conflicts of interest.

Author Contributions

Yange Yu: Conceptualization, methodology, and writing—original draft. Zhaoyan Zhu and Mengxia Ding: Investigation and software. Bingxin Wang: Formal analysis. Yadong Tian and Ruirui Jiang: Validation and data curation. Guirong Sun and Ruili Han: Methodology and software. Xiangtao Kang: Resources, supervision and project administration. Fengbin Yan: Writing—review and editing. Yujie Guo: Investigation and data curation.