Integrated Transcriptome and Metabolomics Analysis Reveals That Probiotics and Tea Polyphenols Synergetically Regulate Lipid Metabolism in Laying Hens

Abstract

Tea polyphenols (TP) and probiotics (PB) have been recognized for their ability to improve lipid metabolism and regulate immune function. However, their specific impact on lipid metabolism in laying hens has not been thoroughly elucidated. Therefore, this study sought to examine the effect of TP and Bacillus subtilis on lipid metabolism in laying hens through transcriptome and metabolome analyses. Two hundred Hy-line Brown layers were randomly allocated into four groups with supplemental dietary TP and PB alone and their combination for 8 weeks. Each treatment had 10 replicates of five birds. Supplementation with a TP and PB combination (TP-PB) increased redness (a*) (p < 0.05) compared to the control basal diet (CT). Dietary TP-PB decreased egg yolk and serum total cholesterol (TC) concentrations (p < 0.05) without affecting the content of total bile acid (TBA). The combined use of TP and PB significantly improved hepatic fatty acid synthetase (FAS) activity (p < 0.05) and reduced liver fat particles. Dietary TP-PB primarily influenced the transcript levels of genes involved in fat metabolic pathways. In particular, TP-PB supplementation reduced lipid storage by activating the Notch signaling pathway. Furthermore, the addition of TP-PB in the diet modulated the abundance of metabolic biomarkers associated with bile secretion and valine, leucine, and isoleucine degradation. An interaction network of mRNAs and metabolites was constructed associated with lipid metabolism, such as deoxycholic acid, TAG (14:3–14:3–20:5), PDK4, and HES4. Overall, these findings emphasized the potential health advantages of the TP and PB combination as a possible functional feed supplement in livestock nutrition.

1. Introduction

The main objectives of the commercial egg industry are to enhance both the quantity and quality of egg production while ensuring the health of laying hens. In order to achieve high egg production, it is necessary to provide adequate amounts of lipids in the diets of layers. Lipids are organic substances that perform several crucial functions within the body, including acting as structural elements, storing energy, signaling, serving as biomarkers, aiding in energy metabolism, and regulating hormones [1]. Previous studies have indicated that there is a restricted direct transfer of lipids synthesized by the ovaries to the developing oocytes. Consequently, the lipid storage in egg yolks relies on the availability of lipid substrates from plasma, which can come from dietary intake or in vivo lipogenesis [2]. Lipogenesis primarily occurs in the liver, where it produces and releases lipoproteins to facilitate lipid transport through the bloodstream in laying hens.

Various strategies have been explored to modulate the lipid metabolism of laying hens, including dietary supplementation such as fiber, herbal extracts, and direct-fed microbials. As natural antioxidants known as flavonoids, tea polyphenols are acknowledged for their various pharmacological benefits [3]. Research has shown that tea polyphenols possess antioxidant properties, offer protection against tumor development and liver injury, show anti-aging effects, modulate the immune response, and exhibit a wide range of antibacterial activities [4]. Extracts from plants have been observed to affect lipid metabolism in animals, with natural substances such as green tea demonstrating the ability to minimize weight gain and alter glycolipid metabolism in these animals [5]. Research indicates that green tea, particularly catechin (-)-epigallo-catechin-3-gallate (EGCG), can modify fat metabolism by upregulating carnitine palmitoyl transferase I (CPT-I), acyl-CoA oxidase 1 (ACOX1), and peroxisome proliferator-activated receptor-α gene expression in the liver [6,7]. Additionally, phytogenic compounds such as polysaccharides and flavonoids have also been linked to effects on lipid metabolism [8,9]. Live microorganisms known as probiotics offer health benefits to their hosts by improving the functionality of the intestinal epithelial barrier and facilitating digestion and absorption within the intestines [10,11], and regulating glucose and lipid metabolism [12]. Among probiotic species, Bacillus is a widely used feed additive due to its spore-forming capacity, which helps it withstand the extreme conditions and stresses faced by the host [13]. Supplementing the diet with B. subtilis positively influences productivity, enhances immune activity, and boosts antioxidant capacity in broiler chickens [14]. Adding probiotics to feed does not produce drug residues and has a positive impact on lipid metabolism in chickens. Shokryazdan et al. [15] and Hesong et al. [16] found that probiotics can promote the growth of broilers and reduce serum total cholesterol, triglycerides, and abdominal fat deposition. Moreover, supplemental 2.7 g/kg C. butyricum could accelerate hepatic fatty acid oxidation, as well as modulate the composition of gut microbes and their metabolite profiles, thereby favoring a reduction in lipid deposition in the livers of laying hens during the late phase of production [17].

The recent advancement in omics technologies has facilitated the widespread application of multi-omics in scientific research to reveal biological processes. RNA sequencing (RNA-Seq) is used to measure gene expression levels and is crucial for understanding the molecular mechanisms that underlie complex traits in organisms. The fast sequencing capabilities, high sensitivity, and cost-effectiveness of RNA-Seq have propelled advancements in studies of gene regulation [18]. Therefore, RNA-Seq serves as a valuable technical tool in biological research [19]. Metabolomics is an emerging approach used in precision medicine, food and nutritional analysis, crop characterization, and environmental monitoring to identify low-molecular-weight compounds. It allows for detailed phenotypic characterization of humans and model organisms, facilitating the analysis of metabolites to clarify gene–environment interactions [20].

All of the aforementioned studies have highlighted the advantages of incorporating tea polyphenols (TP) and probiotics (PB) as feed supplements in poultry farming. However, none of these studies have explored the effects of these compounds on the host’s transcriptome and metabolome. Thus, in addition to evaluating animal performance, it is essential to examine the molecular mechanisms and metabolic pathways underlying physiological metabolism to optimize nutritional management in livestock. In this paper, we hypothesized that dietary supplementation with a TP-PB diet could influence the expression of genes related to lipid metabolism and subsequently impact metabolism-related pathways. By integrating transcriptomics and metabolomics data, we examined the regulatory effects on the lipid metabolism of laying hens. Our findings offer a novel perspective on the lipid-lowering properties of TP and PB, which could potentially lead to the development of sustainable feed supplements that promote the production of healthier eggs for human consumption.

2. Materials and Methods

2.1. Preparation of TP and PB

TP, with a purity of 98.6%, was sourced from DONGYU BIO-TECH (Co. Ltd. Hanzhong, Shanxi, China) and contained 79.6% catechins, including 45.2% epigallocatechin gallate (ECGG). PB, obtained from B&B KOREA (Co. Ltd. Seoul, Korea), contained Bacillus subtilis at 1.34 × 10⁸ cfu/mL, with 98.9% water and 0.2% crude ash (CA). The dosages for TP and PB dietary supplements were determined according to the manufacturer’s commercial guidelines.

2.2. Experimental Birds, Diets, and Management

A total of 200 Hy-Line Brown commercial laying hens, aged 45 weeks and averaging 2.13 ± 0.03 kg, were randomly divided into four groups, each consisting of 10 replicates with 5 hens per replicate (50 hens per group): (1) CT = control group; (2) TP = 200 mg/kg; (3) PB = 300 mg/k PB; (4) TP-PB = 300 mg/kg TP + 300 mg/kg PB. The basal diet was formulated according to the breeding manual for Hy-Line Brown layers and met the National Research Council (NRC) requirements (1994), as outlined in

Table 1. Dietary composition and nutrient levels of experimental diet (as-fed basis).

Item, %	Proportion
Corn	63.50
Soybean meal	24.50
Soybean oil	1.20
Shell powder	4.50
Stone powder	3.80
NaCl	0.30
Mono-Dicalcium Phosphate (MDCP)	1.20
Premix	1.00
Total	100.00
Nutrient content, %
Crude protein	16.41
Crude lipid	3.20
Lysine	0.89
Methionine	0.65
Methionine + Cysteine (M + C)	0.47
Metabolizable energy, MJ/kg	11.56
Calcium	3.22
Total phosphorous	0.76
Available phosphorous	0.32

Note: Each kg of premix contains the following: vitamin A 108,000 IU, vitamin D3 3000 IU, vitamin E 20 IU, vitamin K 32 mg, vitamin B1 0.4 mg, vitamin B2 3.0 mg, vitamin B6 1.0 mg, vitamin B12 0.006 mg, biotin 0.05 mg, pantothenic acid 12 mg, folic acid 0.1 mg, nicotinic acid 7.0 mg, iron 80 mg, manganese 100 mg, zinc 75 mg, iodine 0.8 mg, selenium 0.35 mg.

. Each hen was housed in a cage measuring 60 cm × 60 cm × 45 cm, equipped with a feeder and nipple drinker. Water and feed were provided ad libitum throughout the 8-week experimental period. The temperature in the henhouse was maintained at 23 ± 2 °C, with a lighting schedule of 16 h of light (20 lx) and 8 h of darkness. All animal care and treatment procedures were approved by the Animal Ethics Committee of Shandong Agricultural University, China, and were conducted in accordance with the committee’s guidelines and regulations (Approval No.: 2004006).

2.3. Sample Collection

At 53 weeks of age, thirty eggs were randomly collected from each experimental group to evaluate egg quality and analyze the yolk biochemical parameters. The eggs were assessed for quality immediately after collection. Additionally, one hen per replicate was randomly selected for blood and tissue sampling. Blood samples were collected from the heart using coagulation-promoting tubes, with serum being separated by centrifugation at 3000 rpm for 15 min at 4 °C and stored at −80 °C until analysis. Following this, the hens were euthanized via cervical dislocation, and the liver and ovaries were promptly isolated. Liver segments measuring approximately 1 cm × 1 cm × 0.5 cm were excised, washed with normal saline, and fixed in 4% paraformaldehyde for histological analysis.

2.4. Laboratory Analysis

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Egg quality analysis

We assessed several egg quality traits, including yolk color, egg shape index, eggshell thickness, eggshell strength, albumen height, and Haugh units. The egg shape index (%) was calculated as [egg width (cm)/egg length (cm)] × 100%. Eggshell thickness was measured non-destructively using a P-1 thickness tester (FHK, Co., Ltd., Tokyo, Japan). Eggshell strength was evaluated with a KQ-1A eggshell strength tester (Tenovo international, Co., Ltd., Beijing, China). Shell color was measured using a CR-300 chroma meter (Minolta, Co., Ltd., Osaka, Japan). Egg weight, yolk color, albumen height, and Haugh units were determined with an EMT-5200 egg multi tester (Robotmation, Co., Ltd., Tokyo, Japan). Eggshell weights were recorded after the membranes of the broken eggshells were removed. These parameters were utilized to calculate the eggshell weight ratio (%). The egg shell color was reported in L*, a*, b* values, where L* represents lightness (0 = black, 100 = white), a* represents redness-greenness, and b* represents yellowness-blueness.

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Biochemical parameters analysis

Total cholesterol, triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and TBA levels were measured using enzyme-linked immunoassay (ELISA) kits (Shanghai Enzyme-linked Biotechnology Co., Ltd.). Absorbance readings were recorded at 450 nm with a microplate spectrophotometer. The parameters were analyzed rigorously and meticulously in accordance with the provided operating instructions.

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Histological examination of liver

Approximately 0.5 μm paraffin sections were stained with hematoxylin–eosin (HE) to examine steatosis, fibrosis, and inflammation using an Eclipse Ci-L microscope (Nikon, Tokyo, Japan). Frozen sections were prepared, and oil red O staining was performed to visualize lipid droplet distribution in liver cells (Servicebio technology, Co., Ltd., Wuhan, China). Each liver sample was analyzed independently in triplicate.

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RNA extraction and transcriptome sequencing analysis

Liver RNA samples were isolated using the Trizol reagent kit (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’ s instructions. RNA quality was assessed with an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA) and validated by RNase-free agarose gel electrophoresis. Eukaryotic mRNA was enriched using Oligo(dT) beads, fragmented, and then reverse-transcribed into cDNA with the NEBNext Ultra RNA Library Prep Kit for Illumina (NEB #7530, New England Biolabs, Ipswich, MA, USA). Following purification with AMPure XP beads (1.0×), the ligated fragments were size-selected using agarose gel electrophoresis and subsequently PCR amplified. The cDNA library was sequenced on an Illumina Novaseq6000 platform from Gene Denovo Biotechnology Co. (Guangzhou, China). Raw data were filtered with fastp (version 0.18.0, https://github.com/OpenGene/fastp, (accessed on 19 September 2022)) to trim the adapter sequences and obtain high-quality reads. The short-reads alignment tool Bowtie2 (version 2.2.8, http://bowtie-bio.sourceforge.net/bowtie2/index.shtml, (accessed on 30 September 2022)) was then used to map reads to the ribosomal RNA (rRNA) database. The remaining reads were employed for transcriptome assembly and analysis. An index of the reference genome was created using HISAT software v2.0.4 (http://ccb.jhu.edu/software/hisat/index.shtml, (accessed on 20 October 2022)), with clean reads aligned to Ensembl release 106 (https://ftp.ensembl.org/pub/release-106/gtf/gallus_gallus/, (accessed on 31 October 2022)). Gene expression levels were determined using the fragments per kilobase of transcript per million mapped reads (FPKM) method with StringTie software v2.2.1 (https://ccb.jhu.edu/software/stringtie/index.shtml, (accessed on 11 November 2022)). Differentially expressed genes (DEGs) were identified based on a fold change ≥ 2 and false a discovery rate (FDR) ≤ 0.05. Further analysis of DEGs was conducted using Gene Ontology (GO) categories via an online tool (http://www.geneontology.org/, (accessed on 25 November 2022)), while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were visualized in scatter plots (http://www.genome.jp/kegg/pathway.html, (accessed on 2 December 2022)).

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Untargeted Metabolomics analysis

Samples (100 μL) were placed in Eppendorf tubes and resuspended in 400 μL of pre-chilled 80% methanol, followed by thorough vortexing. They were then incubated on ice for 5 min and centrifuged at 15,000× g for 20 min at 4 °C. A portion of the supernatant was diluted to a final concentration of 53% methanol with LC-MS-grade water. The samples were transferred to new Eppendorf tubes and centrifuged again at 15,000× g for 20 min at 4 °C. Finally, the supernatant was injected into the LC-MS/MS system for analysis.

Metabolite extraction, endogenous metabolite identification, and data processing were conducted by Genedenovo Tech Co., Ltd. (Guangzhou, China). Raw data from UHPLC-MS/MS were analyzed using Compound Discoverer 3.1 (CD3.1, Thermo Fisher, Co., Ltd., Shanghai, China) for peak alignment, selection, and quantitation. The key parameters included a retention time tolerance of 0.2 min, a mass tolerance of 5 ppm, a signal intensity tolerance of 30%, a signal-to-noise ratio of 3, and a minimum intensity of 100,000. The peak intensities were normalized to the total spectral intensity, and the normalized data were used to predict the molecular formulas based on additive ions, molecular ion peaks, and fragment ions. The peaks were compared with the mzCloud, mzVault, and Mass List databases for accurate qualitative and quantitative results. Multidimensional statistical analysis included principal component analysis (PCA) using R package models, partial least squares-discriminant analysis (PLS-DA) for comparing groups with the R package ropls, and orthogonal partial least squares discriminant analysis (OPLS-DA) using the R package models. Differential metabolite analysis was based on a T-teset p-value < 0.05 and a variable importance in projection (VIP) value ≥1. Differential metabolites were annotated and enriched using the KEGG metabolome database, with pathways showing FDR < 0.05 considered significantly enriched. Pathway over-representation was evaluated through MSEA using the MetaboAnalyst module.

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Integrative analysis of metabolome and transcriptome

Differentially expressed genes (DEGs) and differentially abundant metabolites (DAMs) were integrated through two-way orthogonal partial least squares (O2PLS) analysis to identify lipid metabolism-related genes and pathways from metabolome and transcriptome datasets. Pearson correlation coefficients were calculated to integrate metabolome and transcriptome data. Common gene–metabolite pairs within metabolic pathways were visualized using Cytoscape (V3.3.0), selecting pairs with an absolute Pearson correlation coefficient > 0.995 and p < 0.05. Heatmap analysis was performed on the top 50 gene-metabolite pairs. Additionally, a metabolite–transcript network analysis was conducted using the igraph package for the top 250 gene–metabolite pairs exhibiting an absolute Pearson correlation > 0.5.

2.5. Statistical Analysis

The data are expressed as means ± standard errors of the means (SEM). Statistical analysis was performed using one-way analysis of variance (ANOVA). Mean comparisons were conducted with Duncan’s multiple range test using SAS 9.2 software (SAS Institute Inc., Cary, NC, USA). Statistical significance is indicated by different letters (a–d) in the columns, denoting significant differences between means (p < 0.05).

3. Results

3.1. Egg Quality

In comparison to the CT group, no significant differences (

Table 2. Effects of dietary TP and PB on the egg quality of Hy-line Brown laying hens.

Items	Groups				p-Value
	CT	PB	TP	PB-TP
Yolk color	8.72 ± 0.37	8.25 ± 0.25	7.82 ± 0.64	8.68 ± 0.55	0.513
Egg shape index	1.28 ± 0.01	1.27 ± 0.01	1.29 ± 0.01	1.29 ± 0.01	0.509
Eggshell thickness, mm2	0.49 ± 0.01	0.50 ± 0.01	0.51 ± 0.01	0.51 ± 0.01	0.167
Eggshell strength, kg/cm2	4.74 ± 0.13	4.88 ± 0.12	4.82 ± 0.13	4.63 ± 0.13	0.535
Shell ratio, %	10.86 ± 0.13 b	11.21 ± 0.11 a	11.21 ± 0.08 a	11.10 ± 0.10 ab	0.062
Yolk ratio, %	26.55 ± 0.31	26.08 ± 0.34	26.32 ± 0.24	26.36 ± 0.25	0.713
Albumen height, mm	7.99 ± 0.20	8.02 ± 0.25	7.48 ± 0.29	7.96 ± 0.21	0.337
Haugh units	88.15 ± 1.12	87.25 ± 1.68	83.89 ± 2.48	87.77 ± 1.36	0.289
Eggshell color L*	63.16 ± 0.62	61.02 ± 0.92	63.01 ± 0.73	61.18 ± 0.50	0.053
Eggshell color a*	21.53 ± 0.30 b	22.08 ± 0.41 ab	21.90 ± 0.35 ab	22.59 ± 0.28 a	0.173
Eggshell color b*	27.64 ± 0.24	26.96 ± 0.37	27.53 ± 0.34	28.27 ± 0.45	0.088

Data are presented as mean ± SEM of thirty eggs per treatment (n = 30). Within a row, means without a common superscript differ significantly (p < 0.05). CT, basal diet; TP, supplementation with 300 mg/kg tea polyphenols; PB, supplementation with 300 mg/kg bacillus subtilis; TP-PB, supplementation with a combination of 300 mg/kg tea polyphenols and 300 mg/kg bacillus subtilis.

) were observed in yolk color, egg shape index, eggshell thickness, eggshell strength, albumen height, and Haugh units among the other three dietary groups.

3.2. Lipid Metabolism Indicator

An analysis of lipid-related indicators, the results of which are shown in

Table 3. Effects of dietary TP and PB on lipid metabolism of Hy-line Brown laying hens.

Items	Groups				p-Value
	CT	PB	TP	TP-PB
Egg yolk
TC (μmol/g)	7.78 ± 0.09 a	7.73 ± 0.10 a	7.26 ± 0.09 b	6.86 ± 0.08 c	0.000
TG (mg/g)	7.15 ± 0.50	7.27 ± 0.44	7.56 ± 0.15	6.83 ± 0.37	0.635
LDL-C (mmol/L)	4.04 ± 0.46	4.12 ± 0.46	3.68 ± 0.52	4.64 ± 0.40	0.556
HDL-C (μmol/L)	913.81 ± 165.33	791.22 ± 99.09	904.04 ± 72.48	989.98 ± 26.38	0.620
Serum
TC (μmol/dL)	50.85 ± 1.63 a	46.06 ± 2.86 ab	43.80 ± 1.83 ab	41.74 ± 3.01 b	0.085
TG (mg/mL)	0.89 ± 0.05 ab	0.92 ± 0.05 a	0.75 ± 0.04 b	0.77 ± 0.05 ab	0.072
LDL-C (mmol/L)	5.91 ± 0.27 a	5.28 ± 0.36 ab	4.45 ± 0.37 b	4.29 ± 0.34 b	0.012
HDL-C (μmol/L)	976.24 ± 61.38 bc	941.57 ± 42.60 c	1178.60 ± 90.69 ab	1322.97 ± 72.96 a	0.004
TBA (μmol/L)	32.48 ± 3.72	32.32 ± 2.87	30.80 ± 4.07	25.52 ± 3.04	0.468
Ovaries
TC (μmol/g)	6.98 ± 0.41 a	5.63 ± 0.31 b	7.66 ± 0.35 a	7.27 ± 0.22 a	0.003
TG (mg/g)	6.76 ± 0.14 ab	7.91 ± 0.55 a	6.18 ± 0.48 b	7.05 ± 0.42 ab	0.070
LDL-C (mmol/g)	0.048 ± 0.002 a	0.047 ± 0.003 a	0.036 ± 0.003 b	0.040 ± 0.003 ab	0.032
HDL-C (μmol/g)	8.61 ± 0.85	7.76 ± 0.75	9.98 ± 0.78	10.10 ± 0.82	0.157
TBA (nmol/g)	32.48 ± 3.72	32.32 ± 2.87	30.80 ± 4.07	25.52 ± 3.04	0.468
Liver
TC (μmol/g)	6.43 ± 0.36	6.42 ± 0.50	6.89 ± 0.49	7.06 ± 0.37	0.646
TG (mg/g)	8.82 ± 0.35 a	8.25 ± 0.53 a	6.61 ± 0.56 b	7.79 ± 0.56 ab	0.042
LDL-C (mmol/g)	0.048 ± 0.003	0.051 ± 0.003	0.046 ± 0.002	0.046 ± 0.005	0.674
HDL-C (μmol/g)	9.00 ± 0.52	9.08 ± 0.68	10.14 ± 0.87	10.08 ± 0.87	0.576
TBA (nmol/g)	292.84 ± 27.04	235.45 ± 22.91	273.48 ± 38.50	289.23 ± 18.07	0.463
AMPK (U/g)	0.41 ± 0.04	0.49 ± 0.03	0.50 ± 0.05	0.44 ± 0.03	0.374
FAS (U/g)	8619.85 ± 454.04 c	9665.18 ± 954.03 bc	11,522.35 ± 747.48 ab	12,516.20 ± 796.60 a	0.009
LPL (U/g)	5.71 ± 0.35	5.00 ± 0.26	5.86 ± 0.27	5.01 ± 0.24	0.083
ACC (U/g)	296.23 ± 20.30 b	284.87 ± 23.92 b	389.53 ± 13.29 a	339.75 ± 10.79 ab	0.003

Data are presented as mean ± SEM of ten samples per treatment (n = 10). Within a row, means without a common superscript differ significantly (p < 0.05). CT, basal diet; TP, supplementation with 300 mg/kg tea polyphenols; PB, supplementation with 300 mg/kg bacillus subtilis; TP-PB, supplementation with a combination of 300 mg/kg tea polyphenols and 300 mg/kg bacillus subtilis. TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; TBA, total bile acids; AMPK, Adenosine 5′ -monophosphate-activated protein kinase; FAS, fatty acid synthase; LPL, lipoprotein lipase; ACC, acetyl-coa carboxylase.

, revealed that the combined use of TP and PB significantly decreased egg yolk and serum TC content compared to the basal diet on week 8 (p < 0.05). The content of serum LDL-C was decreased, whereas the content of HDL-C was increased, in the TP-PB group compared to those in the CT group (p < 0.05). Table 3 demonstrates that birds fed a diet containing only TP exhibited significantly lower TG content in the liver (p < 0.05). Furthermore, compared to the CT group, dietary PB and TP led to decreased TC and TG levels in the ovaries, respectively (p < 0.05). No significant changes in TBA were observed when comparing the CT group to the other dietary supplementation groups.

The combination of TP and PB supplementation improved liver FAS enzyme activity (p < 0.05) but did not have a notable effect on the other indicators. Furthermore, liver ACC activity in the TP group was obviously higher than that in the Ct and PB groups (p < 0.05).

3.3. Observation of Morphology of Liver Tissues

Compared with the CT group, the structural arrangement and morphological features were not affected by dietary TP or PB alone. The red lipid droplets in the TP-PB group were significantly fewer than those in the CT group and other treatment groups (Figure 1).

3.4. Transcriptome Analysis

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Identification of DEGs

Table 4. An overview of the reads and quality filtering of the twelve libraries.

Sample	Raw Data (bp)	Clean Data (bp)	AF_Q20 (%)	AF_Q30 (%)	AF_GC (%)	Total_Mapped (%)
CT-1	6,943,828,200	6,809,432,486	97.69	93.41	47.07	43,757,136 (95.79)
CT-2	7,961,733,600	7,810,166,250	97.18	92.37	46.62	49,931,048 (95.33)
CT-3	6,214,077,300	6,127,075,869	97.74	93.61	46.02	39,252,483 (96.01)
TP-1	7,296,584,700	7,203,081,201	97.63	93.21	46.32	46,245,939 (96.19)
TP-2	6,562,045,500	6,487,758,405	97.36	92.62	46.11	41,589,933 (96.02)
TP-3	7,580,837,100	7,464,044,444	97.40	92.74	46.18	47,781,096 (95.84)
PB-1	7,510,672,800	7,407,463,379	97.62	93.20	46.84	47,460,828 (95.98)
PB-2	5,823,915,600	5,720,173,658	97.22	92.31	46.60	36,780,809 (95.76)
PB-3	7,719,579,300	7,601,061,060	97.27	92.54	46.80	48,346,760 (95.49)
PB-TP-1	7,079,481,600	6,966,470,849	97.17	92.35	46.59	44,421,268 (95.31)
PB-TP-2	8,075,640,300	7,947,831,467	97.18	92.33	46.30	50,711,059 (95.41)
PB-TP-3	7,526,127,000	7,366,471,758	97.21	92.40	46.46	47,271,368 (95.54)

displays the RNA-Seq analysis results for twelve liver samples from laying hens. The data include the number of mapped reads, the alignment details, the alignment rates with the reference genome (ranging from 95.31% to 96.19%), confirmation of no contamination, accurate genome alignment, the percentage of bases with a base-calling accuracy exceeding 99.9% (>92.31%), and the GC content percentage (46.02% to 47.07%). There was no observed separation phenomenon, indicating high sequencing data quality that met the requirements. Overall, 19 (14 upregulated; 5 downregulated), 17 (12 upregulated; 5 downregulated) and 14 (8 upregulated; 6 downregulated) DEGs were identified for CT vs. PB, CT vs. TP, and CT vs. TP-PB, respectively (Supplementary Materials, Table S1). The clustering heatmap analysis of these DEGs exhibited strong repeatability and consistent gene expression profiles across the four groups (Figure 2).

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Enrichment and functional annotation of differential gene expression

According to GO annotation, the DEGs in the four groups were categorized into three major categories: biological process, cellular component, and molecular function (Figure 3a). A comparison of the DEGs between the CT and TP-PB groups showed that the biological process category was the most enriched, comprising 13 terms, followed by cellular component and molecular function. The terms biological regulation, cell, and binding were the most prevalent in their respective categories. Notably, six DEGs in the CT vs. TP-PB comparison were predominantly enriched in the cellular process term (GO: 0009987) within the biological process category, while GO: 0005488, related to binding, was the most significantly enriched term overall. Additionally, the top 20 pathways with the most significant enrichment among the four groups were identified. Multiple DEGs between CT and TP-PB were enriched-KEGG-pathway categories, with the most significant being maturity-onset diabetes of the young, followed by the Fanconi anemia pathway and then the Notch signaling pathway (Figure 3b). Furthermore, insulin resistance and the insulin signaling pathway were also among the most enriched pathways.

3.5. Differentially Accumulated Metabolite Analysis

Nontarget metabolomics was employed to investigate the potential effects of supplementing TP and PB on the serum metabolome. PCA score plots were generated to compare the CT and TP-PB groups in both positive (POS) mode (PC1 = 20.4%, PC2 = 14.5%) and negative (NEG) mode (PC1 = 23.8%, PC2 = 18.4%) (Figure S1). The PCA results clearly showed distinct separation between the CT and TP-PB groups. OPLS-DA further confirmed the clear differentiation among the CT, TP, PB, and TP-PB groups, indicating significant differences in metabolite accumulation (Figure 4). A total of 14 DAMs (12 upregulated and 2 downregulated) and 19 DAMs (18 upregulated and 1 downregulated) were found between the CT and TP-PB groups in the POS and NEG modes, respectively (Figure S2; Supplementary Materials, Table S2). KEGG enrichment analysis highlighted significant enrichment of these metabolites in pathways such as phospholipase D signaling, the biosynthesis of unsaturated fatty acids, and bile secretion for CT vs. PB, CT vs. TP, and CT vs. TP-PB, respectively (Figure 5).

3.6. Integrated Transcriptome and Metabolome Analysis

A correlation analysis employing the Pearson method was conduct to explore the relationship between transcriptomic and metabolomic data. Figure 6 summarizes the DEGs and DAMs identified among the CT group and three other dietary groups. This figure illustrates strong correlations between individual transcripts and metabolites, emphasizing the specific association of each transcript with certain metabolites. Furthermore, gene–metabolite pairs with an absolute Pearson correlation > 0.5 were used to construct gene–metabolite regulatory networks. Three integrated analysis and co-expression networks were developed for DEGs between the CT and other dietary groups and DAMs, and the top 250 relationship pair network diagrams are presented (Figure 7; Supplementary Materials, Table S3). D-(+)-Malic acid (com_30_neg), N-Acetyl-L-phenylalanine (com_426_neg), and Isoleucine (com_198_neg) demonstrated significant correlations with a substantial number of different transcripts between the CT and TP-PB groups. On the contrary, HHLA2 (ENSGALG00000022871) and PPP1R3B (ENSGALG00000030303) exhibited significant correlations with a wide range of metabolites.

4. Discussion

The tea polyphenols and probiotics positively influenced the production performance of laying hens, potentially improving egg quality [21,22]. Egg quality is a crucial factor in the egg-food industry that impacts economic profitability. In egg-laying birds, attributes such as eggshell strength, yolk color, and cholesterol levels are important indicators of egg quality and hatchability. The present study found no significant differences in the aforementioned indicators between laying hens fed diets supplemented with TP or PB and control hens at 45 to 53 weeks of age. In accordance with our results, egg quality aspects such as yolk shape index, eggshell thickness, eggshell strength, albumen height, and Haugh units were unaffected by dietary green tea extract [23] and probiotic [24] treatments. However, Li et al. (2023) reported that incorporating green tea leaves into hens’ diet improved eggshell thickness compared to the control group [25]. Similarly, 3% GTP supplementation significantly increased albumen height and Haugh units in Xianju chickens from 20 to 30 weeks of age [26]. Dietary inclusion of B. subtilis CGMCC 1.921 enhanced eggshell strength [27]. Lei et al. (2013) noted that different levels of B. licheniformis supplementation improved egg quality by increasing eggshell thickness and strength, though its impact on egg yolk was uncertain [28]. The discrepancies among these studies may stem from variations in the concentrations of tea polyphenols and probiotics in the hens’ diets, the breed and age of the hens, or other factors. One of the important indicators influencing egg consumption is eggshell color, which is strongly correlated with customer preferences. Interestingly, we observed that the dietary the combination of TP and PB supplementation increased the redness (a*) of eggshell color, a phenomenon not previously reported. Although the mechanism behind this is unclear, it is possible that the combined addition of TP and PB enhanced eggshell quality by increasing oxidation levels in the uterus (the site of eggshell formation), which, in turn, oxidized the eggshell pigment [29].

This study’s findings align with earlier research suggesting that supplementation with probiotics and tea polyphenols could enhance lipid metabolism [30,31,32]. Additionally, our earlier results demonstrated that TP-PB notably altered the abdominal fat mass of laying hens [33]. Those findings prompt us to further investigate the regulatory effect of TP combined with PB on lipid-lowering capacities in laying hens. Supplementation with TP and PB did not significantly affect TBA content in the serum, liver, and ovaries throughout the experimental period. Our results indicated that the combined use of 300 mg/kg TP and 300 mg/kg PB resulted in a reduction in TC in both egg yolk and serum. Blood lipids are the primary source of lipids in egg yolk; therefore, the decrease in lipids observed in the egg yolks of layers supplemented with TP and PB may be attributed to the lower serum lipid levels. HDLC, primarily produced in the liver and small intestine, plays a crucial role in the removal of serum cholesterol [34]. Elevated TC, TG, and LDL-C levels, coupled with decreased HDL-C levels, are commonly used indicators of dyslipidemia, often associated with obesity [35]. Our serum metabolic indicators were exactly the opposite, suggesting that TP-PB might have a more favorable impact on cholesterol metabolism than the other three groups. The changes observed in egg yolk, liver, and serum lipids may be associated with endocrine alterations. Further research is necessary to deepen our understanding of the effects of TP-PB on the amino acid and fatty acid composition in the layers.

The regulation of lipid metabolic enzymes is essential for adapting to changes in dietary, nutritional, and physiological conditions. ACC and FAS are key rate-limiting enzymes for hepatic triglyceride synthesis [36,37]. Interestingly, the increase in FAS observed in the liver does not align with the changes in lipid indices in serum and eggs following TP-PB supplementation. Perhaps as a result of the negative feedback regulation of dietary TP-PB supplementation brought on by a drop in total cholesterol, the hepatic activity of FAS and ACC was increased. Previous research suggests that the combination of TP and PB may influence immune function, establishing a link between the immune system and lipid metabolism [38]. Certain cytokines, including IL-1, IL-6, and TNF-α, are known to regulate lipid metabolism, with TNF-α specifically inhibiting fatty acid synthesis [39]. Therefore, it is hypothesized that the observed reductions in serum and egg lipids may be mediated through a pathway that influences immune status. The mechanism of this phenomenon deserves further study.

Transcriptome analysis was performed to explore the impact of TP and PB on metabolic processes in laying hens. Serum lipids originate from either dietary sources or the liver, which is the main site for fatty acid biosynthesis. This process is regulated by a complex molecular network that manages hepatic lipid composition and systemic lipid metabolism [40]. TP-PB may alleviate fat accumulation in laying hens by modulating genes primarily associated with immune regulatory pathways, according to enrichment analysis of the KEGG pathway TP-PB/CT. Specifically, TP-PB decreased lipid storage in laying hens by upregulating HES4. The Notch signaling pathway facilitates the release of the Notch intracellular domain into the nucleus upon ligand–receptor binding between neighboring cells, activating downstream transcription factors such as Hes-1, Hes-4, and Hey-1 [41]. These transcription factors interact with target gene DNA sequences to exert their effects. The influence of the Notch family on lipid metabolism in non-alcoholic fatty liver disease may be partly mediated through the insulin signaling pathway [42]. The Notch1 signal may inhibit the process of adipogenesis by inducing homologous dimerization of the Hes1 protein to inhibit the transcriptional expression of key target genes [43]. The role of Notch signaling in the development and progression of liver lipid metabolism in laying hens remains undefined. Additionally, TP-PB was found to upregulate PPP1R3B expression within the insulin signaling pathway. This signaling system is essential for regulating numerous physiological processes, including carbohydrate and lipid metabolism, as well as cell growth and survival [44]. Miao et al. reported that baicalin improves insulin resistance and regulates hepatic glucose metabolism by activating the insulin signaling pathway in obese pre-diabetic mice [45]. The combination of a prebiotic with two probiotic strains in a food matrix may provide protection against obesity-related inflammation and enhance insulin resistance, even in the short term [46]. Taken together, TP-PB supplementation can alleviate fat accumulation by activating the Notch signaling and Insulin signaling pathways in laying hens.

Metabolomics is a technique utilized for the detection and identification of metabolites within cells and tissues, playing a vital role in biological research [47]. This study identified five KEGG pathways that were significantly enriched in TP-PB/CT, specially bile secretion, valine, leucine and isoleucine degradation, taste transduction, purine metabolism, and ABC transporters, indicating that TP-PB supplementation has a significant impact on both bile metabolism and amino acid metabolism. Additionally, dietary TP alone had a significant impact on the biosynthesis of unsaturated fatty acids. Supplementation with unsaturated fatty acids alleviated hepatic steatosis, reduced liver oxidative stress, and improved serum lipid levels in mice on a high-fat diet, indicating its potential for preventing hyperlipidemia and obesity [48]. Among these differences, we place greater emphasis on bile secretion, as it is crucial for the digestion and absorption of fats. Bile salts, cholesterol, and lecithin in bile can promote the emulsification of fats into many tiny droplets, which facilitates the body’ s digestion of fats. Bile salts promote the absorption of lipid defense digestion products by combining with fatty acid glycerol esters to form water-soluble complexes. Bile acids, which are specific products of cholesterol breakdown in the liver, are the main components of bile in humans and animals. They can lower the surface tension between oil and water phases, facilitating lipid emulsification [49]. Bile acids, as metabolites of cholesterol, have a feedback role in cholesterol synthesis. They increase the expression of low-density lipoprotein receptor genes on cell membranes, aiding in the uptake and conversion of LDL-C [50]. The current study demonstrated that bile acids can lower serum levels of TC, TG, and LDL-C, which may contribute to a reduction in adipose tissue weight [51,52]. Huo reported that the functional genes of the gut microbiota in low-abdominal-fat broiler chickens are mainly involved in the metabolic pathway of bile acid secretion and fatty acid biosynthesis [53].

Transcriptomics and metabolomics are valuable techniques for examining changes in genotype and phenotype, offering insights into genetic alterations, protein synthesis, metabolism, and cellular function [54]. Our study specifically examined processes potentially related to fat metabolism obtained through integrative analyses containing significantly correlated DEGs and DAMs. For example, the gene PDK4 and its associated metabolites, Com_373_pos (Uric acid) and Com_90_pos (Betaine), were related to the regulation of glucose metabolic process, and it could be considered that the gene PDK4 might be related to TP-PB supplementation affecting lipid metabolism in laying hens. In this study, PDK4 expression was significantly lower in the TP-PB group compared to the CT group. This result is consistent with prior research in which the interference of goat PDK4 promotes the downregulation of lipid droplet accumulation and lipid deposition-related gene expression in adipocytes [55]. In addition, the overexpression of PGC-1α led to increased levels of PDK4 and PPARγ proteins in the muscle tissue of mice and pigs, potentially leading to increased glycogen deposition and fatty acid oxidation [56]. In transcriptome analysis, dietary supplementation with TP-PB obviously upregulated the transcription of the HES4 gene. The results of our transcriptomic and metabolomic correlation analysis indicated that HES4 was involved in various metabolic pathways. Interestingly, we also found that HES4 and metabolites in the bile acid metabolism pathway are correlated to deoxycholic acid (r = 0.60). Therefore, TP-PB supplementation alters fatty acid and bile acid metabolism, possibly by affecting the gene expression level of PDK4 and HES4, which helps alleviate fat accumulation in the liver. However, it should be noted that there are limitations in this study, such as the limited understanding of the functions of many DEGs and DAMs in the existing literature, as well as the small sample size in the metabolomic analysis. Further accurate experimental verification will be needed to determine a more comprehensive understanding of TP-PB utilization in vivo.

5. Conclusions

In summary, the dietary combination of TP and PB improved lipid metabolism by decreasing the egg yolk and serum TC concentrations of laying hens. An in-depth analysis integrating transcriptome and metabolome data visualized the altered metabolic pathways. The pathway of Notch signaling and bile secretion might represent the main mechanisms of action of TB-PB. These findings provide valuable insights into the lipid-lowering efficacy of tea polyphenols and probiotics, which could be further utilized in the development of feed for producing healthier eggs for human consumption.