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Poult Sci
Poult Sci
Poultry Science
0032-5791
1525-3171
Elsevier

S0032-5791(24)00827-7
10.1016/j.psj.2024.104248
104248
GENETICS AND MOLECULAR BIOLOGY
Research Note: Integrative analysis of transcriptome and gut microbiome reveals foie gras capacity difference between cage and floor rearing systems
Yu Yin *†1
Wei Rongxue *†1
Yi Shuang *†
Teng Yongqiang *†
Ning Rong *†
Wei Shouhai *†
Bai Lili *†
Liu Hehe *†
Li Liang *†
Xu Hengyong *†
Han Chunchun chunchunhai_510@163.com
*†2
⁎ Key Laboratory of Livestock and Poultry Multi-omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, Sichuan, 611130, China
† Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, China
2 Corresponding author: chunchunhai_510@163.com
1 These authors contributed equally to the work.

22 8 2024
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© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
To explore the differences in foie gras performance between geese raised in cages and on the ground, we conducted an integrative analysis of liver transcriptome and gut microbial metagenomes. The results showed extremely significant differences in the liver weight (P < 0.01) and liver lipid accumulation of FRS and CRS groups. The levels of triglyceride (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) of CRS were significantly higher than those of FRS (P < 0.05). Transcriptome analysis showed that 3,917 upregulated and 1,395 downregulated genes were identified, and lipid metabolism pathway and fatty acid metabolism were significantly enriched. Analysis of cecum microbiota revealed that several inflammation-related bacteria (including Gallibacterium, Escherichia-Shigella, Desulfovibrio, Alistipes, and Fournierella) were enriched in CRS, while beneficial bacteria (including Lactobacillus, Limosilactobacillus, and Ligilactobacillus) were significantly enriched in FRS. In conclusion, CRS was better than FRS in foie gras production, which was more conducive to lipid deposition in the goose liver.

Key words

foie gras
transcriptome
gut microbial metagenome
cage rearing system
floor rearing system
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pmcINTRODUCTION

Foie gras, a French culinary delicacy, is the fatty liver of geese or ducks that have been fattened by a process of force-feeding. In foie gras production, the primary rearing systems for overfed geese or ducks include net, floor and cage. However, there are still important unfilled gaps in research and evidence about the influence of different rearing systems on the production of foie gras. Previous studies examining the formation of foie gras have reported that the accumulation of excessive fat in the goose liver was attributed to the disbalance between the storage and secretion of exogenous lipids and endogenous lipids synthesized de novo when the waterfowl was overfed with large amounts of food (Lu et al., 2015). Nevertheless, whether the rearing system, as an important nongenetic factor influencing poultry production performance, affects the formation process of goose liver still requires further investigation.

The intestinal microbiota, known as the “second genome” of the host, is crucial for nutrient digestion, immune regulation, and overall physiological functions. Although floor rearing system (FRS) may be more conducive to the colonization of gut microbiota, it is concurrently associated with elevated levels of potentially harmful bacteria. Li et al. found that compared to FRS, Megamonas and Anaerobiospirillum were significantly enriched in cage rearing system (CRS), which may be beneficial for fat deposition (Li et al., 2022). However, the influence of various rearing systems on the intestinal microbiota in foie gras production remains unclear. Compared with FRS, CRS significantly reduces direct contact between poultry and the external environment, significantly lowering the risk of disease transmission (De Vylder et al., 2011); and the restriction of movement within cages leads to decreased energy consumption, which leads to more lipid deposit in goose liver. While cage-rearing and floor-rearing each have their unique advantages and disadvantages, it is undeniable that both of these rearing systems restrict geese's natural behaviors to a certain extent, especially swimming and bathing. We hypothesize that the liver transcriptome and gut microbial metagenomes play a significant role in shaping the differences of foie gras performance via the lipid metabolism pathway. To validate our hypothesis, we conducted an integrative analysis encompassing both the liver transcriptome and gut microbial metagenomes. Not only will addressing the hypothesis explain foie gras performance difference induced by FRS and CRS, it is also conducive to improving the production efficiency and foie gras quality.

METHODS AND MATERIALS

Ethics Statement

All experimental procedures that involved in animal manipulation were approved by the Institutional Animal Care and Use Committee (IACUC) of Sichuan Agricultural University (Permit No. DKY-B20141401), and they were implemented strictly in accordance with the established guidelines.

Birds and Experiment Design and Sampling

Eighty 13-wk-old male Tianfu meat geese were selected from the Experimental Farm for Waterfowl Breeding at Sichuan Agricultural University (Ya'an, China) and randomly divided into 2 groups: FRS and CRS. The geese in FRS group were reared on the ground with a density of 2 birds /m2, while the geese in CRS group were reared in cages with a density of 7 birds /m2. The overfeeding procedure and dietary regimen followed the protocol outlined in a previous study (Wei et al., 2022). The daily overfeeding intake of geese reached 1600 g of dry matter (4 meals per day; dry matter: water = 1:0.75), which continued for 3 wk. At 16 wk of age, all male geese were slaughtered after a 12-h fast. Blood samples were collected from the wing vein, and the geese were euthanized. The blood samples were centrifuged at 4000 r/min for 10 minutes at 4°C to obtain serum, which was then stored at −20°C for subsequent analysis.

Ten male geese per group were anesthetized with sodium pentobarbital (60 mg/kg) before slaughter. Immediately after slaughter, the liver was collected and separated into 2 distinct parts. One part was frozen in liquid nitrogen at −80°C for transcriptome sequencing (n = 3), while the other part was fixed in 4% formaldehyde-phosphate buffer for histomorphology examination after washing in ice-cold saline (0.9% NaCl; 4°C) (n = 3). Furthermore, the liver was weighed immediately after slaughter (n = 40).

Biochemical Index Examinations of Serum

Ten blood samples were randomly selected from each group for the quantification of serum biochemical indices in the whole serum. Triglyceride (TG), cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels were detected using assay kits that were provided by Nanjing Jiancheng Bioengineering Institute (Nanjing, China).

Foie Gras Hepatic Steatosis Examination

The crude fat content of liver samples was determined using the Soxhlet extraction method. The detailed method was performed according to our previous study (Luo et al., 2022).

The cross-sections from the middle of liver were preserved in 4% formaldehyde-phosphate buffer, and prepared using standard paraffin embedding techniques. Tissue sectioned at 5μm was stained with hematoxylin and eosin (HE), sealed with neutral resin, and examined using a microscope photography system (Olympus, Tokyo, Japan), each slice was observed and 5 visual fields were randomly selected at 40× magnification.

Transcriptome Sequencing and Analysis

1 μg of RNA from liver samples in FRS and CRS groups was used for RNA sample preparations following the manufacturer's instructions. PCR products were purified with AMPure XP system (Beckman Coulter, Beverly) and assessed for library quality on the Agilent Bioanalyzer 2100 system. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v4-cBot-HS (Illumina) according to the manufacturer's instructions. After cluster generation, the library preparations were sequenced on the Illumina platform, and paired-end reads were generated. The sequencing was conducted by Baimike Biological Technology Co., LTD (Beijing, China).

Raw reads in fastq format were first processed through an in-house Perl script to remove adapter sequences and reads containing ploy-N and with low quality. Reference genomes were downloaded from the NCBI genome database, and paired-end clean reads were aligned to the reference genome using HISAT2. DEseq R package was used to identify differentially expressed genes (DEG) between the 2 groups. DEG were identified based on the criteria of |log2FoldChange| > 2 and an adjusted P-value of < 0.1. In addition, KOBAS (v3.0) software was used to predict the significantly enriched KEGG pathways (Bu et al., 2021).

Analysis of Intestinal Flora

Total genomic DNA was extracted from cecal contents of each sample using the E.Z.N.A. soil DNA Kit (Omega Bio-tek, Norcross, GA) according to the manufacturer's instructions. The V3-V4 region of bacterial 16S rDNA genes was amplified with the primer pairs 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). Bacterial genomes were then sequenced on the Illumina HiSeq platform (Illumina, San Diego, CA). A more detailed method was described earlier.

Raw sequencing reads were denoised, joined, delineated into amplicon sequence variants (ASV), and assigned taxonomy in QIIME 2 (v.2021.2). From raw sequences data, the ASVs were obtained using the Divisive Amplicon Denoising Algorithm 2 (DADA2). The representative sequences, which were generated after denoising were used to assign bacterial taxonomy using a Naïve-Bayes classifier trained on the SILVA v138.1 99% 16S full-length database. Linear Discriminant Analysis Effect Size (LEfSe) was applied (P-value <0.05, LDA score > 2) to calculate the statistical differences between 2 rearing systems at different taxonomic assignments.

Statistical Analysis

The collected data were analyzed using IBM SPSS statistical (v.27.0). T test was used to compare the difference between 2 groups, and the data are presented as the means ± standard deviation (SD). Statistically, P-value <0.05 represents a significant difference, and P-value <0.01 indicates an extremely significant difference. The KEGG database was used to search for the related KEGG pathways of the transcriptome DEGs and gut microorganic genome, the co-involved pathways were applied for integrative analysis.

RESULTS AND DISCUSSION

Foie Gras Performance Induced by FRS and CRS

As shown in Figure 1A, the liver weight and liver/body weight ratio of CRS were significantly higher than those of FRS (P < 0.05). However, the liver crude fat percentage from CRS was significantly higher than that from FRS (P < 0.05, Figure 1A). Compared with FRS, CRS was higher in the level of TC, HDL-C and LDL-C (P < 0.05, Figure 1B). However, the rearing systems did not significantly affect the TG level (P > 0.05). Liver tissue slices stained with H&E also showed that more lipid droplets were deposited in the hepatocytes of the CRS (Figure 1C). Overfed geese that were raised on CRS exhibited an increased propensity for hepatic lipid accumulation.Figure 1 Foie gras performance between different rearing systems (FRS vs CRS). (A) Comparison of liver weight, liver/body weight ratio (n = 40) and crude fat content of liver (n = 20); (B) Comparison of serum biochemical indexes (n = 10); C, Comparison of livers and liver tissue sections (HE staining, 200×) (n = 3).

Figure 1

Liver Transcriptome Analysis Reveals Foie Gras Performance Between CRS and FRS

By performing transcriptome sequencing on 3 liver samples from each group, a total of 84.91 Gb of clean data was obtained, with an average of 14.15 Gb per sample. The Q30 base percentage exceeded 93.38%, and the mapping efficiency of clean reads to the reference genome was 74.04 - 88.60%, and a total of 15,721 genes were annotated. In comparison to the FRS group, the CRS group had 5312 DEGs (3917 up-regulated and 1395 down-regulated; Figure 2A). Pathway enrichment analysis revealed that DEGs were enriched in 65 pathways, mainly including metabolic pathways, inositol phosphate metabolism, endocytosis, phosphatidylinositol signaling system, MAPK signaling pathway, fatty acid metabolism, and other signaling pathways, indicating that these pathways are critical for foie gras formation (Figure 2B). Genes related to lipid synthesis (PPARγ, ACSL, ELOVL, PCK1, FADS1, HMGCR, and ACSBG), lipid oxidation (ACOX1, PPARα/β), and lipid transport (CD36) were upregulated in the liver of cage-reared geese. ACSL1 regulates hepatic TG synthesis; ACSL3 aids lipid droplet formation for homeostasis (Ding et al., 2023; Parkes et al., 2006). This suggests a potential molecular mechanism underlying the observed differences in lipid metabolism between cage-reared and floor-reared geese.Figure 2 Transcriptome analysis of goose fatty liver and gut microbial metagenomes analysis (n = 3) (FRS vs CRS). (A) Volcanic map of differentially expressed genes; (B) KEGG analysis of liver tissue; (C) Linear discriminant analysis (LDA) score distribution of cecal microorganisms between FRS (n = 8) and CRS (n = 12); (D) Integrative analysis of transcriptome and gut microbiome reveals foie gras performance difference between FRS and CRS.

Figure 2

Gut Microbial Metagenomes Analysis Reveals Foie Gras Performance between CRS and FRS

After quality control and filtering, a total of 743,058 high-quality reads were generated from 20 samples, with an average of 37,153 reads per sample. The reads were assigned using the DADA2 analysis pipeline in QIIME2, resulting in the identification of 1,088 ASVs. Subsequently, these ASVs were taxonomically classified into 19 phyla, 28 classes, 60 orders, 106 families, 237 genera, and 549 species. Using LEfSe analysis, we identified 37 differentially abundant taxa (P < 0.05, LDA score > 4), including Bacteroides and Limosilactobacillus (Figure 2C). Among these, several inflammation-related bacteria (including Gallibacterium, Escherichia-Shigella, Desulfovibrio, Alistipes, and Fournierella) were enriched in CRS, while beneficial bacteria (including Lactobacillus, Limosilactobacillus, and Ligilactobacillus) were significantly enriched in FRS (Figure 2C). The reason may be that the overfed geese density was higher in CRS than in FRS, the overfed geese of FRS had a wider space of motion, and more healthy feeding environment, therefore leading to healthier intestinal microbiota construction (Ramos et al., 2022). On the other hand, more amount of exercise induced more energy consumption, thus resulting in the liver weight of FRS being lower than that of CRS.

Integrative Analysis Reveals Foie Gras Performance Between CRS and FRS

In order to investigate whether the gut microbiota contributed to the liver transcriptome, we performed pathway analysis on gut microbiota that were significantly affected by rearing system. Figure 2D integrated the transcriptome analysis and microbiome analysis through KEGG annotation. The analysis revealed that the gut microbiota could synergize the response of goose liver to rearing system through some pathways. For example, the ‘metabolism’ pathway featured in the liver transcriptome overlapped with those revealed by genes of gut microbes that were prominently influenced by rearing system., particularly those involved in lipid metabolism and metabolism of other amino acids. Furthermore, 3 other pathways were also significantly influenced by rearing system in both liver transcriptomes and gut microbiome, including those involved in cellular community-eukaryotes, transport and catabolism, and infectious disease: bacterial.

DISCLOSURES

No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All authors are in agreement with the content of the manuscript.

ACKNOWLEDGMENTS

The work was supported by the National Natural Science Funds of China (No. 31672413 ), and the Natural Science Funds of Sichuan Province (No. 2022NSFSC0059 ).
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