https://orcid.org/0009-0005-1067-2489
Figures
Abstract
Intramuscular fat (IMF) content is crucial for meat quality, and genetic, environmental, and nutritional factors influence its deposition. This study aims to identify genes involved in the regulation of IMF deposition in Guizhou Yellow Chickens. Thirty Guizhou Yellow Chicken hens aged 120 days were selected, and their IMF content was measured using the Soxhlet extraction method. The IMF content was divided into a high IMF group (H group, n = 4) and a low IMF group (L group, n = 4). RNA-seq was performed on the H and L groups to screen out signaling pathways and key genes that affect IMF deposition. A total of 259 differentially expressed genes(DEGs) were screened, including 195 that were up-regulated and 64 that were down-regulated. Critical genes such as COL1A1, COL1A2, COL6A3, PLTP, LPIN1, ITGA8, and FN1 were identified as key influencers of IMF deposition in Guizhou Yellow Chickens. The slow virus interference vector has demonstrated that interfering with COL1A1 can inhibit the proliferation ability of chicken preadipocytes and reduce lipid droplet accumulation. In addition, interference with COL1A1 significantly inhibited the expression of PLTP, ABHD6, LPIN1, and PTGS2 and decreased the levels of triglycerides and cholesterol at 4 and 8 days. The results further showed that COL1A1 is a key gene in the gene regulatory network during fat deposition, and the interaction of these genes affects the proliferation and differentiation of fat cells, thereby reducing the accumulation of fat droplets in muscle fibers. This study indicates that the COL1A1 gene is associated with IMF formation in Guizhou Yellow Chickens, providing a critical gene for selecting markers to control IMF formation and laying a foundation for future poultry meat quality breeding.
Citation: Tian Y, Wang X, Rao Y, Zhou X, Jiang Y, Liu Q, et al. (2025) Identification and validation of differentially expressed genes in intramuscular fat metabolism in Guizhou yellow chickens using RNA-Seq analysis. PLoS One 20(6): e0326128. https://doi.org/10.1371/journal.pone.0326128
Editor: Birendra Mishra, University of Hawai'i at Manoa, UNITED STATES OF AMERICA
Received: August 6, 2024; Accepted: May 23, 2025; Published: June 16, 2025
Copyright: © 2025 Tian et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: This research was supported by Guizhou Science and Technology Plan Project (subject) (Contract No. :Guizhou Science and Technology Support [2022] Key 034).
Competing interests: No.
Introduction
Chicken meat is high in protein, low in calories, and low in cholesterol, making it a primary source of high-quality animal protein for humans. Consequently, broiler breeding has become a key area of focus in animal husbandry research. However, with social and economic development and rising living standards, consumers are increasingly demanding meat quality. IMF is a critical factor in meat quality, influencing attributes such as tenderness, water retention, shear force, and flavour [1]. The number of adipocytes and their lipid deposition capacity are closely related to the IMF content. In animals, the number of adipocytes is determined before birth, while lipid deposition capacity is influenced by various factors after birth. Moderately increasing IMF content enhances meat flavor, juiciness, and palatability, contributing to improved meat quality and the water-holding capacity of meat products. Additionally, higher IMF content can improve poultry meat quality by reducing drip loss and cooking loss [2].
Current broiler farming has mainly concentrated on augmenting lean meat output, expediting growth rates, and promoting the development of breast and leg muscles. This has resulted in a decrease in IMF content, thereby reducing the acceptability of the meat for consumers [3]. In chicken meat, the IMF content increases with age, promoting improvements in flavor and texture [4]. The heritability of IMF in chickens ranges from 0.21 to 0.81, and studies have shown that genetic breeding is an effective method to increase IMF content in chickens [5]. However, in practical production, IMF content can only be measured post-slaughter, posing challenges in breeding. Therefore, the use of molecular marker-assisted selection serves as an effective approach to enhance IMF content in livestock and poultry [6]. Numerous studies have shown that certain functional candidate genes can regulate the process of IMF deposition. For instance, the perilipin-1 (PLIN1) gene is significantly enriched in the peroxisome proliferator-activated receptor (PPAR) signaling pathway associated with lipid metabolism, with its expression level being markedly higher in pigs with high IMF content compared to those with low IMF content. Furthermore, PLIN1 gene knockout reduces triglyceride content and lipid droplet size in pig adipocytes [7]. As a key inhibitor of matrix metalloproteinases (MMPs), TIMP2 can regulate intramuscular fat deposition in chickens through the ECM-receptor interaction pathway under the influence of muscle satellite cells [8]. FABPs are involved in the uptake, intracellular metabolism, and transport of long-chain fatty acids, and all members of the FABP family are considered to be closely related to IMF deposition. Among them, H-FABP and A-FABP have been widely demonstrated as key genes influencing IMF in animals such as sheep, chickens, and pigs [9–11].
Despite the identification of genes associated with intramuscular fat deposition, the content of IMF is a quantitative trait governed by numerous genes with minor effects. A single or a few genetic markers are inadequate to precisely elucidate the genetic foundation of this trait. Furthermore, these marker loci are subject to variations across different breeds, complicating their application in breeding programs. In recent years, the rapid advancement of high-throughput technologies has enabled transcriptome sequencing (RNA-seq) to emerge as a robust molecular tool for comparing gene expression profiles at the transcriptome level across various physiological states, developmental stages, or environmental conditions. This allows researchers to screen out genes that show significant changes in expression [12]. Therefore, transcriptomics is widely recognized as an effective method to directly identify candidate genes and regulatory mechanisms associated with IMF deposition at the genomic level [13]. Li et al. performed RNA-seq on the breast muscle of Beijing-You chickens at different developmental stages and identified several DEGs related to energy metabolism, including ACOT9, CETP, LPIN1, DGAT2, RBP7, FBP1, and PHKA1. These genes were found to potentially regulate IMF deposition [14]. Genes such as L3MBTL1, TNIP1, HAT1, and BEND6 were also identified by RNA-seq as significantly positively correlated with high IMF and significantly negatively related to the low AFW, which could be relevant biomarkers for chicken breeding [15].
Guizhou Yellow Chicken is a hybrid breed from the cross of New Hampshire, Plymouth Rock, and Weining chickens. It is highly adaptable to the climate of the Yunnan-Guizhou Plateau. The breed is characterized by a high feed conversion rate, rapid growth, abundant egg production, and bright feather coloration. Its meat is known for being fresh and waxy, making it an ideal ingredient for the renowned Guizhou dish, “Chicken with Chilies,” which is loved by many consumers. To increase the IMF content of Guizhou yellow chicken, this study analyzed RNA-seq data of the chest muscle of Guizhou Yellow Chickens with significant differences in IMF content to explore the regulatory mechanism of IMF metabolism and identify candidate genes affecting IMF deposition. A lentivirus interference vector was used to verify its function and explore its effect on adipocyte differentiation and its potential molecular mechanism. This research aims to provide a foundational basis and additional references for enhancing meat quality in poultry breeding and producing high-quality chicken meat.
Materials and methods
Ethics approval
All work in this experiment follows the Chinese Animal Welfare Guidelines and has been approved by the Subcommittee of Experimental Animal Ethics Guizhou University (Guiyang, People’s Republic of China) with approval number EAE-GZU-2022-E054.
Test material
The research chicken farm at Guizhou University provided the experimental animals for this study. Guizhou Yellow Chickens were raised from 1 to 120 days of age under the same environment and nutritional conditions, with free access to food and water. Thirty Guizhou Yellow Chickens of comparable body weight at 120 days of age were selected and subjected to slaughter via the carotid bleeding method following a 12-hour fasting period. The left pectoralis muscle was meticulously excised, and 2g samples were collected in RNase-free tubes. The remaining portion of the pectoralis muscle was appropriately labeled, sealed, and stored at −80°C for subsequent RNA-seq and IMF analysis. Additionally, abdominal adipose tissue from 10-day-old broilers was collected as the preadipocyte culture material.
IMF analyze and transcriptome sequencing
IMF content of Guizhou Yellow Chicken breast muscle was determined by utilizing the Soxhlet extraction method, according to the national standard GB5009.6−2016. In the study, highly significant differences in IMF content among individuals prompted the selection of two distinct groups: the high IMF group (H group, comprising four animals) and the low IMF group (L group, also consisting of four animals). Total RNA was extracted from the pectoral muscle of the H and L groups using a Trizol (Invitrogen, Carlsbad, USA) reagent according to the instructions of manufacturer. The RNA concentration and purity were measured using a micro UV-Vis spectrophotometer from Thermo Fisher Scientific. Subsequently passing quality control, libraries were prepared and sequenced on the Illumina platform. Sequencing and bioinformatics analysis were conducted by Qingdao BioMarker Technologies Company.
Bioinformatics analysis of sequencing data
To ensure data quality, sequences containing ligations and low-quality reads were removed from the sequencing data (Including Reads that remove >10% of N and reads that remove more than 50% of the entire read for bases with a quality value of Q ≤ 10). The Q30 and GC content of the remaining valid data were calculated. The alignment software HISAT2 was used to align the clean reads to the chicken reference genome (version: GRCg6a_NCBI), and the comparison flowchart can be found in S1 Fig.
Fragments per kilobase of transcript per million mapped reads (FPKM) were used as an index to calculate the expression level of each transcript in every library using the Cuffdiff program. The expression levels of the H and L groups were statistically analyzed using DESeq software (version 1.6.3). DEGs with significantly different expression levels between the two groups were identified based on the screening criteria of fold change ≥ 1.5 and P < 0.05. Pearson’s correlation coefficient was employed to assess the repeatability of samples both within and between groups [16], thereby eliminating expression differences attributed to biological variability [17,18]. The Gene Ontology (GO) database was employed for GO functional analysis of DEGs. P-values were corrected for false discovery rate (FDR), and enrichment was considered significant when FDR was ≤ 0.05. The KOBAS 2.0 software was employed to detect significant pathways associated with DEGs via the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The screening condition for DEGs in pathways was set as P ≤ 0.05.
QRT-PCR test
Six DEGs were randomly selected from the NCBI database (http://www.ncbi.nlm.nih.gov) based on the published mRNA sequence. The qRT-PCR primers were designed using Primer Premier 5 software and synthesized by Beijing Qingke Biotechnology Company (S1 Table). The RNA was reverse transcribed to cDNA using a Reverse Transcription Kit (Thermo Fisher, Shanghai), which confirmed the utilization of gel electrophoresis. Subsequently, quantitative PCR (qPCR) was performed using a Fluorescent PCR Kit (Thermo Fisher, Shanghai) according to the instructions of manufacturer. The reaction system (20 μL) included 10 μL of SYBR Green qPCR Mix, 0.5 μL each of upstream and downstream primers (10 μmol/L), 1 μL of cDNA, and 8 μL of double-distilled water. The PCR reaction conditions comprised predenaturation at 95°C for 2 min and denaturation at 95°C for 15 s, annealing at 60°C for 30 s, and extended to 72°C for 30 s over 40 cycles. Additionally, the default settings were utilized for the dissolving curve parameters while β-actin was employed as the internal reference gene, and gene expression was calculated using the 2-ΔΔCt formula. Analysis of each sample was repeated three times, with the SD of the threshold (Ct) value set at ≤0.5.
To understand the interaction between genes, qRT-PCR was used to detect the expression of PLTP, ABHD6, LAMA3, LPIN1, and PTGS2 genes related to fat deposition when the COL1A1 gene was interfered with. Primer information was provided by Anhui General Biol company synthesized primers (S2 Table).
Preadipocyte culture and differentiation
Abdominal fat was collected from 10-day Guizhou Yellow chicken under aseptic conditions. The fat was rinsed with PBS containing 2% double antibodies and placed in sterilized petri dishes. The fat was then cut into small pieces and digested in a type I collagenase solution at 37°C for 65 min. After digestion, the solution was neutralized with an equal volume of complete medium [Dulbecco’s Modified Eagle Medium (DMEM)/F12 + 10% fetal bovine serum (FBS) + 100 IU/mL penicillin]. The digested solution was filtered through a 400-mesh sieve to remove undigested tissue and debris, and the filtrate was centrifuged at 1600 γ/min for 10 min. The supernatant was discarded, and the precipitate was rinsed with a complete medium and centrifuged again at 1600 γ/min for 10 min. The resulting precipitate was resuspended in a complete medium and incubated at 37°C with 5% CO2. The cells were then incubated for an hour, the medium was discarded, and the cells were washed twice with PBS to obtain preadipocytes. Finally, the medium was added to the complete medium and cultured, with the medium being changed every 1 or 2 days.
Lentivirus transduction
The results of RNA-seq showed that genes such as COL1A1, COL1A2, CAPN2, LPIN1, PLTP, and others involved in focal adhesion, ECM-receptor interaction, TGF-β and PPAR signalling pathways may be the key roles affecting IMF deposition in Guizhou Yellow Chickens. Among them, the COL1A1 gene has been identified as a candidate gene for IMF, and its role has been confirmed by many studies [19–25]. However, the lack of studies on knocking down the COL1A1 gene to explore its molecular mechanics significantly increases our research interest in COL1A1 gene. Therefore, in this study Genepharma Designer3.0 was used to design the RNA interference sequence of the chicken COL1A1 gene (CAGCCAACAGATCGAGAACAT). The interference sequence was synthesised by Gemma Suzhou. Human embryonic kidney cells (293T) were cultured in a T25 flask until they reached a concordance of about 80–90 percent. Subsequently, recombinant shuttle plasmid and package plasmid containing target sequence were transfected into 293T cells using RNAi-Mate transfection reagent according to the instructions of the manufacturer. Finally, lentivirus overexpressing COL1A1 (sh-COL1A1) was obtained, and blank vector control lentivirus was prepared. Furthermore, chicken preadipose cells were transfected using the Lipofectamine 2000 reagent. The transfected chicken preadipocyte suspension was inoculated in a 6-well plate and incubated in an incubator at 37°C with 5% CO2 for 48 hours. After washing twice with a PBS solution containing double antibodies, the medium was replaced with an induced differentiation medium (10% FBS + 1 μmol/L dexamethasone + 5 μg/mL insulin + 0.5 mmol/L IBMX) and labelled Day 0. After 2 days of incubation, the medium was replaced with a maintenance differentiation medium (10% FBS + 1 μg/mL insulin). In addition, the medium was changed every 2 days.
Oil Red O staining and quantitative analysis
Following virus infection and differentiation induction, cells were removed from the medium on Day 4 and Day 8 of differentiation. They were then washed three times with PBS and fixed with 4% paraformaldehyde for 30 minutes, then washed three more times with PBS (5 minutes each wash) and permeabilized with 60% isopropanol. Moreover, the Oil Red O staining solution was added to cover the bottom of the cell culture plate completely and stain the cells for 10 min to 30 min. The staining solution was removed, and the cells were washed three times with PBS for 5 min per wash. Subsequently, hematoxylin staining was performed for 1 min, and the cells were washed three times with PBS for 5 min per wash. An appropriate amount of PBS was added to preserve lipid droplets, and images were promptly captured using a fluorescent inverted microscope.
Triglyceride and cholesterol measurement
IMF refers to the fat content within muscle tissue, primarily composed of triglycerides (TG) but also encompassing phospholipids (FLIP) and cholesterol (CH). IMF is a complex trait, thus identifying the key genes involved in its deposition presents significant challenges. The analysis of TG, FLIP, and CH can facilitate a more streamlined phenotype characterization and aid in uncovering the mechanisms underlying fat deposition [26,27]. In this study, the detection of TG and CH was performed to reflect changes in lipid levels. Triglyceride and cholesterol concentrations in the medium were quantified using kits on Days 4 and 8 of COL1A1 interference-induced differentiation. Experiments were conducted following the operating instructions of the Glycerol Test Kit and Cholesterol Test Kit provided by Shanghai EnzymeLink Biologicals.
Statistics and data analysis
Data were processed using Excel 2017 for statistical analysis. The expression levels of fat deposition-related genes and the internal reference gene GAPDH in chicken preadipocytes were determined using the 2-ΔΔCt method. The results were presented as means ± SD. To compare gene expression levels between groups, an independent samples t-test was performed using SPSS version 20 software, while Graphpad Prism 8.0 was used for data visualization.
Results
RNA-seq data analysis
As shown in Table 1, thirty Guizhou Yellow Chickens were selected and categorized into two groups based on their IMF content, which included a high-IMF group (n = 4) and a low-IMF group (n = 4). The high-IMF group (Group H) had an average IMF content of 2.10% ± 0.29%, which was significantly higher (P < 0.01) than the average IMF content in the low-IMF group (Group L), measured at 1.42% ± 0.13%. RNA-seq was conducted on the high-IMF and low-IMF groups using the Illumina platform to investigate the differential genes influencing IMF content in Guizhou Yellow Chickens. After quality control, the sequencing produced 5.427 GB of clean data, as detailed in Table 2. Each sample had an average GC content of 52.62%, and the Q30 score was > 92.25%. The comparison rate of the sequencing reads relative to the chicken reference genome was > 82.27%, indicating high sequencing accuracy, while the comparison rate relative to the genome’s exons was 88.33%, further confirming the reliability and quality of the data for subsequent analyses.
Differential expression analysis and qRT-PCR validation
Fig 1 shows the correlation between the gene expression levels of biological replicates within each group. The R-values for the four replicates in groups L and H were all > 0.9, indicating the reproducibility of the samples and the absence of significant biological variability [referencing Materials and Methods, reference. EdgeR [28] was used to identify the DEGs in groups H and L. A total of 259 DEGs were identified between the two groups, with 64 genes up-regulated and 195 down-regulated (Fig 2 and S3 Table). To validate the RNA-seq data of the H and L IMF groups in Guizhou Yellow Chicken, a subset of DEGs was randomly selected including up-regulated (CAPN2, PLTP, and ABHD6) and down-regulated genes (LPIN1, ANKRD2, and MCEE). RT-qPCR was performed to measure the expression of these six genes in pectoral muscle tissue. The qPCR validation results (Fig 3) revealed a consistent expression trend for these DEGs in the H and L IMF groups, confirming the reliability and reproducibility of the RNA-seq data.
Green dots represent down-regulated DEGs, red dots represent up-regulated DEGs, and black dots represent non-DEGs.
GO enrichment and KEGG pathway analysis
The functionality of 200 DEGs was analyzed through GO. The results show that most DEGs are primarily involved in binding, metabolic processes, biological regulation, responses to stimuli, catalytic activities, and cellular regulation of processes (Fig 4 and S4 Table). These DEGs may directly or indirectly participate in fat formation, such as lipoprotein metabolism, triglyceride synthesis, lipid oxidation and stress regulation of fat energy storage. KEGG pathway analysis demonstrated the top 20 pathways exhibiting the lowest P-values, which are illustrated in the scatter plots in Fig 5. DEGs showed significant enrichment in pathways related to focal adhesion and ECM-receptor interactions. Furthermore, notable enrichment was found in pathways associated with the regulation of actin cytoskeleton, cell adhesion molecules, and the TGF-β signaling pathway. These pathways are likely implicated in fat metabolism and IMF deposition. Additionally, DEGs (including LAMA3, CHAD, COL1A1, COL1A2, COL6A1, COMP, FN1, and ITGA8) were enriched within ECM-receptor interaction and focal adhesion. Several pathways related to lipid deposition, including arachidonic acid metabolism, PPAR signaling pathway, and linoleic acid metabolic pathways, showed varying degrees of enrichment. (Table 3 and S2 Fig).
X-axis represents GO terms and classifications; Y-axis represents number of DEGs annotated to the term (right) and percentage of that over total annotated genes (left).
The size of the dots indicates the number of expressed genes within each pathway, while the color of the dots represents the P-values of the significantly enriched pathways.
Sh-COL1A1 interference efficiency test
A lentivirus vector system was used to interfere with the COL1A1 gene by expressing short hairpin RNAs (shRNAs). In order to further determine the interference efficiency of the COL1A1 gene, the NC group was used as a calibration, and GAPDH was used as the internal reference gene. After 72h inoculation of chicken intramuscular adipocytes, total RNA was extracted, and qPCR was performed. As shown in Fig 6, compared with the NC group, the interference efficiency of the COL1A1 gene reached 72%, indicating that the interference efficiency was satisfactory.
Effect of COL1A1 gene interference on proadipocyte proliferation and adipocyte differentiation
Chicken preadipocytes were transfected with the COL1A1 interfering vector and the NC group when cells reached approximately 80% confluence. Cell proliferation was assessed using a CCK-8 Kit, and the absorbance was measured at 450 nm. Cell growth curves were plotted using time (h) on the x-axis and the measured absorbance values (OD) of different treatment groups at various time points on the y-axis. As shown in (Fig 7), the proliferation capacity of chicken preadipocytes was lower at 48, 72, and 96 h after interference compared with the control group. Suggesting that the COL1A1 genetic factor promotes the proliferative capacity of precursor adipocytes.
Oil Red O staining was used to observe changes in adipocyte lipid droplet deposition after lentivirus-mediated interference with sh-COL1A1 in chicken preadipocytes at two-time points, including Day 4 and Day 8. Cell differentiation and lipid droplet deposition decreased significantly in the COL1A1-interference group at both time points compared with the control group, which indicated inhibited lipid droplet accumulation (Fig 8).
Testing of triglyceride and cholesterol levels
Kits detected triglyceride and cholesterol level changes in adipocytes following COL1A1 gene interference. As shown in Fig 9A and 9B, the glycerol and cholesterol levels in the medium decreased significantly (P < 0.05) after transfection with sh-COL1A1 on both Day 4 and Day 8 compared with the control group. Moreover, on Day 8, a highly significant decrease (P < 0.01) was observed. The results indicated that the formation of COL1A1 gene in adipocytes was inhibited.
Testing of fat deposition-related genes
In order to study the effect of COL1A1 gene interference on RNA expression levels of adipose-deposition-related genes, qRT-PCR was used to verify the relative expression levels of PLTP, ABHD6, LPIN1, LAMA3 and PTGS2 genes. As can be seen from Fig 10A and 10B, after transfection with the interfering COL1A1 gene on Day 4 and Day 8, the mRNA expression levels of the PLTP and LPIN1 genes were lower compared to the control group. Additionally, the mRNA expression of the ABHD6, LAMA3, and PTGS2 genes was significantly reduced (P < 0.05) compared to the control group. After 8 days, the PLTP gene expression was significantly lower (P < 0.05), while the ABHD6, LPIN1, and PTGS2 gene expressions were significantly lower (P < 0.01) than those in the control group. These results indicated that interference with the COL1A1 gene could inhibit the expression level of adipose-related genes.
Discussion
IMF deposition is a highly complex process that can be affected by genetic, managerial, and nutritional factors. Among these factors, genetics is a significant driver [29]. In this study, to identify candidate genes associated with IMF deposition, 259 DEGs were screened from pectoral muscle tissue samples with significant differences in IMF deposition. These DEGs were analyzed using GO and KEGG pathway analyses. The results revealed that the DEGs PLTP and LPIN1 were enriched in the PPAR and TGF-β signaling pathways, respectively, which have been shown to play crucial roles in regulating fat deposition and muscle development in chicken meat breeding. This is consistent with study by Liu L and Cheng J [26,30], which have shown PPAR and TGF-β signaling pathways play critical roles in IMF deposition. Similarly, the activation of the transcription factor PPARG in the PPAR pathway promotes the interaction between PLIN1 and CIDEC, leading to accelerated lipid droplet formation [31]. During preadipocyte differentiation, LPIN1 is negatively correlated with PPARγ and C/EBPα expression, promoting the proliferation and differentiation of preadipocytes [32]. Essentially, LPIN1 functions as a negative regulator of adipogenesis by inhibiting the expression of key differentiation markers. LPIN1 is consistently highly expressed in adipose tissue, skeletal muscle, and liver, which plays a role in lipid metabolism and energy regulation [33]. In the pituitary and hamstring muscles’ DEGs, the PLTP gene participates in the PPAR signaling pathway, affecting lipid metabolism [34]. In this study, PLTP was up-regulated in group H vs group L and is known to be highly expressed in adipose tissue, where it regulates glucose and lipid homeostasis and promotes CH efflux [35]. These findings suggest that PLTP and LPIN1 positively regulate fat deposition. Interestingly, the focal adhesion pathway and ECM-receptor interaction were all significantly enriched by genes such as COL1A1, COL1A2, COL6A1, COL6A2, COL6A3, CHAD, FN1, and ITGA8. Cui Et al. also identified common collagen family genes (COL6A2, COL6A3, COL5A2) in ECM-receptor interactions and plaque adhesion pathways [36], which is similar to our findings. Furthermore, the ECM-receptor interaction and focal adhesion pathways have points of interaction where they play an essential role in maintaining tissue integrity, and these two pathways, along with the PPAR signaling pathway, may contribute to IMF metabolism in chickens [37]. COL1A1 regulates hormone-induced expression of proteolytic enzymes and clearance of intracellular matrix deposition, suggesting its role in cytoplasmic matrix stabilization [38]. The COL1A1 gene is up-regulated in IMF cell populations, and both COL1A1 and COL1A2 are associated with meat quality traits [39,40]. COL6A3 plays a role in ECM remodeling and integrin-mediated signaling, impacting IMF deposition [41]. ITGA8, a member of the integrin family involved in cell adhesion and ECM signaling, modulates mechanotransduction pathways through its role in integrin-ECM interactions, influencing adipocyte development and lipid storage [42]. FN1 interactions with integrins, particularly integrin α5β1, facilitate ECM assembly and mechanotransduction, promoting structural support and functional signaling essential for lipid metabolism [43]. In summary, the above evidence suggests that COL1A1, COL1A2, COL6A3, PLTP, LPIN1, ITGA8 and FN1 are key genes involved in IMF deposition in Guizhou yellow chickens. Future research will focus on utilizing molecular-assisted breeding techniques to identify and select genes associated with improved IMF content in chickens.
Previous studies on COL1A1 have primarily focused on human diseases and cancers [44], suggesting that the COL1A1 gene may serve as a biomarker and therapeutic target for hepatocellular carcinoma and metastasis [45]. COL1A1 gene was also identified as a key gene in the IMF deposition capacity [19–25], which is consistent with results of this study. However, the mechanism of the COL1A1 gene affecting the formation and deposition of IMF cells is still unclear, so this gene has generated significant interest for this study. In this study, it was hypothesized that the COL1A1 gene influences the proliferation and differentiation of preadipocytes and adipocytes, with other genes potentially contributing to this process. The results demonstrated that the knockdown of COL1A1 reduced the proliferative capacity of chicken preadipocytes at 48, 72, and 96 hours post-intervention. Cell differentiation and lipid droplet formation were notably decreased at 4 and 8 days post-COL1A1 knockdown. These findings indicate that COL1A1 is a key regulator of adipogenesis, impacting lipid metabolism by modulating the secretion or accumulation of glycerol and CH. The reduced differentiation and lipid accumulation observed could be attributed to the impaired function of fibro/adipogenic progenitors (FAPs), the common progenitor cells from which intramuscular adipocytes and fibroblasts originate [46]. The association between lipid metabolism and ECM remodeling offers insights into the interplay between adipogenesis and fibrogenesis. Adipogenesis and fibrogenesis are interdependent processes, making the role of COL1A1 crucial in maintaining the balance between these pathways. Furthermore adipose tissue formation is closely linked to the extracellular matrix (ECM), in which COL1A1 serves as a fundamental component [47]. Disruption of COL1A1 likely alters the ECM’s structure and function, which may impair the microenvironment necessary for preadipocyte differentiation and lipid deposition [48,49]. COL1A1 impacts lipid metabolism through structural and signaling roles within the ECM, particularly by interacting with integrin receptors, TGF-β, PPARγ, and Hippo pathways [50,51]. Dysregulation of COL1A1 expression leads to ECM stiffness, impaired adipogenesis, and disrupted lipid metabolism, contributing to metabolic diseases such as obesity and insulin resistance [52]. The observed reduction in lipid deposition could be explained by a downstream effect of COL1A1 knockdown on lipid metabolic enzymes or transporters, which deserves further investigation. The findings of this study demonstrate that COL1A1 knockdown not only impacts the proliferation and differentiation of chicken preadipocytes but also significantly suppresses the expression of several key adipose-related genes, including PLTP, LPIN1, ABHD6, LAMA3, and PTGS2. The down-regulation of these genes provides insight into the molecular mechanisms by which COL1A1 influences lipid metabolism and adipogenesis. At both 4 and 8 days post-transfection, the mRNA expression levels of PLTP and LPIN1 were significantly lower in the COL1A1 knockdown group compared to the control, with PLTP showing particularly pronounced suppression at 8 days (P < 0.05). These genes play crucial roles in lipid metabolism; PLTP is involved in lipid transport and remodeling [53], while LPIN1 is a key regulator of TG synthesis and adipocyte differentiation [54]. The suppression of these genes suggests that the disruption of COL1A1 impairs critical pathways necessary for lipid storage and adipocyte function. Furthermore, the mRNA expression levels of ABHD6, LAMA3, and PTGS2 were significantly reduced after COL1A1 knockdown. Notably, at 8 days post-transfection, ABHD6, LPIN1, and PTGS2 exhibited highly significant down-regulation (P < 0.01), indicating a cumulative effect over time. ABHD6 plays a role in lipid hydrolysis, contributing to lipid droplet turnover [55], while PTGS2 (also known as COX-2) is involved in inflammation and lipid metabolism [56]. LAMA3, a laminin family member, is critical for ECM integrity and cellular interactions [57]. The suppression of these genes highlights the interconnected roles of ECM organization, lipid metabolism, and cellular signaling in adipogenesis. These results further reinforce the concept that adipogenesis and fibrogenesis are intricately linked processes. As COL1A1 is essential for the formation and maintenance of the ECM, its knockdown likely disrupts the microenvironment required for the proper expression of adipose-related genes. This aligns with our earlier findings that COL1A1 knockdown reduces adipocyte proliferation, differentiation, and lipid droplet accumulation, collectively contributing to impaired adipose tissue formation. In summary, the significant down-regulation of adipose-related genes following COL1A1 knockdown indicates that this gene is a central regulator of adipogenesis and lipid metabolism in chicken adipocytes. These findings provide valuable insights into the molecular mechanisms governing IMF deposition and highlight potential genetic targets for improving meat quality. Future research should focus on elucidating the upstream regulators and downstream pathways of COL1A1 to clarify further its role in adipocyte biology and meat production traits.
Conclusions
In this study, candidate genes affecting IMF were identified as COL1A1, COL1A2, COL6A3, PLTP, LPIN1, ITGA8 and FN1 by RNA-seq analysis of high and low IMF groups of Guizhou yellow chickens. Further investigation revealed that COL1A1 is a positive regulator of chicken adipocyte proliferation and differentiation. Moreover, reduced COL1A1 expression may decrease IMF content via the ECM−receptor interaction and Focal adhesion signaling pathways. These findings provide a theoretical basis for elucidating the molecular regulatory mechanisms of IMF in Guizhou Yellow chickens and offer valuable insights for developing genetic improvement and molecular breeding strategies in poultry.
Supporting information
S1 Fig. Flow chart of sequencing data comparison.
https://doi.org/10.1371/journal.pone.0326128.s001
(PNG)
S2 Fig. KEGG enrichment network map of DEGs.
The color of the edge represents different pathways, and the color of the gene node represents the difference multiple. The larger the pathway node is, the more genes are enriched into the pathway.
https://doi.org/10.1371/journal.pone.0326128.s002
(PNG)
S1 Table. Primer information table for 6 genes for qPCR validation.
https://doi.org/10.1371/journal.pone.0326128.s003
(XLS)
S2 Table. 6 DEGs primer information tables related to IMF content.
https://doi.org/10.1371/journal.pone.0326128.s004
(XLS)
S3 Table. DEGs information in H and L groups of Guizhou yellow chickens.
https://doi.org/10.1371/journal.pone.0326128.s005
(XLSX)
References
- 1. Wang B, Wang Z, Chen Y, Liu X, Liu K, Zhang Y, et al. Carcass Traits, Meat Quality, and Volatile Compounds of Lamb Meat from Different Restricted Grazing Time and Indoor Supplementary Feeding Systems. Foods. 2021 Nov 16;10(11):2822. pmid:34829104.
- 2. Matitaputty PR, Wijaya CH, Bansi H, Vincenzo T. Influence of duck species and cross-breeding on sensory and quality characteristics of Alabio and Cihateup duck meat. CyTA - Journal of Food, 2015;13(4):522–6.
- 3. Li DL, Chen JL, Wen J, Zhao GP, Zheng MQ, Liu C. Growth, carcase and meat traits and gene expression in chickens divergently selected for intramuscular fat content. Br Poult Sci. 2013;54(2):183–9. pmid:23647181.
- 4. Wang B, Du Y, Shen Q, Liu X, Xie C, Geng Z, et al. Comparative study of growth performance and meat quality of three-line crossbred commercial group from Shanzhongxian and W-line chicken. Italian Journal of Animal Science. 2018;18(1):1–7.
- 5. Liu L, Cui H, Xing S, Zhao G, Wen J. Effect of Divergent Selection for Intramuscular Fat Content on Muscle Lipid Metabolism in Chickens. Animals (Basel). 2019;10(1):4. pmid:31861430
- 6. Hocquette JF, Gondret F, Baéza E, Médale F, Jurie C, Pethick DW. Intramuscular fat content in meat-producing animals: development, genetic and nutritional control, and identification of putative markers. Animal. 2010;4(2):303–19. pmid:22443885
- 7. Li B, Weng Q, Dong C, Zhang Z, Li R, Liu J, et al. A Key Gene, PLIN1, Can Affect Porcine Intramuscular Fat Content Based on Transcriptome Analysis. Genes (Basel). 2018;9(4):194. pmid:29617344
- 8. Cui H, Luo N, Guo L, Liu L, Xing S, Zhao G, et al. TIMP2 promotes intramuscular fat deposition by regulating the extracellular matrix in chicken. Journal of Integrative Agriculture. 2023;22(3):853–63.
- 9. Huang Z-G, Xiong L, Liu Z-S, Qiao Y, Liu S-R, Ren H-X, et al. The developmental changes and effect on IMF content of H-FABP and PPARgamma mRNA expression in sheep muscle. Yi Chuan Xue Bao. 2006;33(6):507–14. pmid:16800381
- 10. Chen Q-M, Wang H, Zeng Y-Q, Chen W. Developmental changes and effect on intramuscular fat content of H-FABP and A-FABP mRNA expression in pigs. J Appl Genet. 2013;54(1):119–23. pmid:23135696
- 11. Ye MH, Chen JL, Zhao GP, Zheng MQ, Wen J. Associations of A-FABP and H-FABP markers with the content of intramuscular fat in Beijing-You chicken. Anim Biotechnol. 2010;21(1):14–24. pmid:20024783
- 12. Aziz Z, Moss JD, Jabbarzadeh M, Hellstrom J, Balkhy H, Tung R. Totally endoscopic robotic epicardial ablation of refractory left ventricular summit arrhythmia: First-in-man. Heart Rhythm. 2017;14(1):135–8. pmid:27614027
- 13. Cao Y, Xing Y, Guan H, Ma C, Jia Q, Tian W, et al. Genomic Insights into Molecular Regulation Mechanisms of Intramuscular Fat Deposition in Chicken. Genes (Basel). 2023;14(12):2197. pmid:38137019
- 14. Li D, Pan Z, Zhang K, Yu M, Yu D, Lu Y, et al. Identification of the Differentially Expressed Genes of Muscle Growth and Intramuscular Fat Metabolism in the Development Stage of Yellow Broilers. Genes (Basel). 2020;11(3):244. pmid:32110997
- 15. Xing S, Liu R, Zhao G, Liu L, Groenen MAM, Madsen O, et al. RNA-Seq Analysis Reveals Hub Genes Involved in Chicken Intramuscular Fat and Abdominal Fat Deposition During Development. Front Genet. 2020;11:1009. pmid:33117416
- 16. Schulze SK, Kanwar R, Gölzenleuchter M, Therneau TM, Beutler AS. SERE: single-parameter quality control and sample comparison for RNA-Seq. BMC Genomics. 2012;13:524. pmid:23033915
- 17. Robasky K, Lewis NE, Church GM. The role of replicates for error mitigation in next-generation sequencing. Nat Rev Genet. 2014;15(1):56–62. pmid:24322726
- 18. Hansen KD, Wu Z, Irizarry RA, Leek JT. Sequencing technology does not eliminate biological variability. Nat Biotechnol. 2011;29(7):572–3. pmid:21747377
- 19. Li J, Xing S, Zhao G, Zheng M, Yang X, Sun J, et al. Identification of diverse cell populations in skeletal muscles and biomarkers for intramuscular fat of chicken by single-cell RNA sequencing. BMC Genomics. 2020;21(1):752. pmid:33129271
- 20. Park SJ, Kang HJ, Na S, Lee SH, Baik M. Differential expression of extracellular matrix and integrin genes in the longissimus thoracis between bulls and steers and their association with intramuscular fat contents. Meat Sci. 2018;136:35–43. pmid:29065314
- 21. Fontes MMDS, Costa TC, Lopes MM, Souza RO, Carneiro LS, Paulino PVR, et al. Intramuscular collagen characteristics and expression of related genes in skeletal muscle of cull cows receiving a high-energy diet. Meat Sci. 2021;177:108495. pmid:33756247
- 22. Zhang Y-Y, Wang H-B, Wang Y-N, Wang H-C, Zhang S, Hong J-Y, et al. Transcriptome analysis of mRNA and microRNAs in intramuscular fat tissues of castrated and intact male Chinese Qinchuan cattle. PLoS One. 2017;12(10):e0185961. pmid:29073274
- 23. Lim KS, Lee KT, Park JE, Chung WH, Jang GW, Choi BH, et al. Identification of differentially expressed genes in longissimus muscle of pigs with high and low intramuscular fat content using RNA sequencing. Anim Genet. 2017;48(2):166–74. pmid:27928823
- 24. Martínez Del Pino L, Urrutia O, Arana A, Alfonso L, Mendizabal JA, Soret B. Expression of key myogenic, fibrogenic and adipogenic genes in Longissimus thoracis and Masseter muscles in cattle. Animal. 2020;14(7):1510–9. pmid:31996275
- 25. Anthony NB. A review of genetic practices in poultry: efforts to improve meat quality1. Journal of Muscle Foods. 1998;9(1):25–33.
- 26. Liu L, Liu X, Cui H, Liu R, Zhao G, Wen J. Transcriptional insights into key genes and pathways controlling muscle lipid metabolism in broiler chickens. BMC Genomics. 2019;20(1):863. pmid:31729950
- 27. Cui H, Liu L, Liu X, Wang Y, Luo N, Tan X, et al. A selected population study reveals the biochemical mechanism of intramuscular fat deposition in chicken meat. J Anim Sci Biotechnol. 2022;13(1):54. pmid:35546408
- 28. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139–40. pmid:19910308
- 29. Ayuso M, Fernández A, Núñez Y, Benítez R, Isabel B, Fernández AI, et al. Developmental Stage, Muscle and Genetic Type Modify Muscle Transcriptome in Pigs: Effects on Gene Expression and Regulatory Factors Involved in Growth and Metabolism. PLoS One. 2016;11(12):e0167858. pmid:27936208
- 30. Cheng J, Wang L, Wang S, Chen R, Zhang T, Ma H, et al. Transcriptomic analysis of thigh muscle of Lueyang black-bone chicken in free-range and caged feeding. Anim Biotechnol. 2023;34(4):785–95. pmid:34965837
- 31. Qiu F, Xie L, Ma J-E, Luo W, Zhang L, Chao Z, et al. Lower Expression of SLC27A1 Enhances Intramuscular Fat Deposition in Chicken via Down-Regulated Fatty Acid Oxidation Mediated by CPT1A. Front Physiol. 2017;8:449. pmid:28706492
- 32. Chao X, Guo L, Wang Q, Huang W, Liu M, Luan K, et al. miR-429-3p/LPIN1 Axis Promotes Chicken Abdominal Fat Deposition via PPARγ Pathway. Front Cell Dev Biol. 2020;8:595637. pmid:33425901
- 33. Zhang W, Zhong W, Sun Q, Sun X, Zhou Z. Adipose-specific lipin1 overexpression in mice protects against alcohol-induced liver injury. Sci Rep. 2018;8(1):408. pmid:29323242
- 34. Shao M, Shi K, Zhao Q, Duan Y, Shen Y, Tian J, et al. Transcriptome Analysis Reveals the Differentially Expressed Genes Associated with Growth in Guangxi Partridge Chickens. Genes (Basel). 2022;13(5):798. pmid:35627183
- 35. Sponton CH, Hosono T, Taura J, Jedrychowski MP, Yoneshiro T, Wang Q, et al. The regulation of glucose and lipid homeostasis via PLTP as a mediator of BAT-liver communication. EMBO Rep. 2020;21(9):e49828. pmid:32672883
- 36. Cui H, Zheng M, Zhao G, Liu R, Wen J. Identification of differentially expressed genes and pathways for intramuscular fat metabolism between breast and thigh tissues of chickens. BMC Genomics. 2018;19(1):55. pmid:29338766
- 37. Zhang M, Li F, Ma X-F, Li W-T, Jiang R-R, Han R-L, et al. Identification of differentially expressed genes and pathways between intramuscular and abdominal fat-derived preadipocyte differentiation of chickens in vitro. BMC Genomics. 2019;20(1):743. pmid:31615399
- 38. Diógenes MN, Guimarães ALS, Leme LO, Maurício MF, Dode MAN. Effect of prematuration and maturation with fibroblast growth factor 10 (FGF10) on in vitro development of bovine oocytes. Theriogenology. 2017 Oct 15;102:190–8. pmid:28826105.
- 39. Liu Y, Liang S, Wang B, Zhao J, Zi X, Yan S, et al. Advances in Single-Cell Sequencing Technology and Its Application in Poultry Science. Genes (Basel). 2022;13(12):2211. pmid:36553479
- 40. He X, Wu Q, Xue W, Wu R, Huang Y, Chen L, et al. Characterization of Type I and Type III Collagen in the Intramuscular Connective Tissue of Wuzhumuqin Sheep. Animals (Basel). 2023;13(3):395. pmid:36766284
- 41. McCulloch LJ, Rawling TJ, Sjöholm K, Franck N, Dankel SN, Price EJ, et al. COL6A3 is regulated by leptin in human adipose tissue and reduced in obesity. Endocrinology. 2015;156(1):134–46. pmid:25337653
- 42. Marek I, Hilgers KF, Rascher W, Woelfle J, Hartner A. A role for the alpha-8 integrin chain (itga8) in glomerular homeostasis of the kidney. Mol Cell Pediatr. 2020;7(1):13. pmid:33000355
- 43. Spada S, Tocci A, Di Modugno F, Nisticò P. Fibronectin as a multiregulatory molecule crucial in tumor matrisome: from structural and functional features to clinical practice in oncology. J Exp Clin Cancer Res. 2021;40(1):102. pmid:33731188
- 44. Liu J, Shen J-X, Wu H-T, Li X-L, Wen X-F, Du C-W, et al. Collagen 1A1 (COL1A1) promotes metastasis of breast cancer and is a potential therapeutic target. Discov Med. 2018;25(139):211–23. pmid:29906404
- 45. Ma H-P, Chang H-L, Bamodu OA, Yadav VK, Huang T-Y, Wu ATH, et al. Collagen 1A1 (COL1A1) Is a Reliable Biomarker and Putative Therapeutic Target for Hepatocellular Carcinogenesis and Metastasis. Cancers (Basel). 2019;11(6):786. pmid:31181620
- 46. Uezumi A, Ito T, Morikawa D, Shimizu N, Yoneda T, Segawa M, et al. Fibrosis and adipogenesis originate from a common mesenchymal progenitor in skeletal muscle. J Cell Sci. 2011;124(Pt 21):3654–64. pmid:22045730
- 47. Liu L, Cao P, Zhang L, Qi M, Wang L, Li Z, et al. Comparisons of adipogenesis- and lipid metabolism-related gene expression levels in muscle, adipose tissue and liver from Wagyu-cross and Holstein steers. PLoS One. 2021;16(2):e0247559. pmid:33626085
- 48. Mariman ECM, Wang P. Adipocyte extracellular matrix composition, dynamics and role in obesity. Cell Mol Life Sci. 2010;67(8):1277–92. pmid:20107860
- 49. Mor-Yossef Moldovan L, Lustig M, Naftaly A, Mardamshina M, Geiger T, Gefen A, et al. Cell shape alteration during adipogenesis is associated with coordinated matrix cues. J Cell Physiol. 2019;234(4):3850–63. pmid:30191963
- 50. Jääskeläinen I, Petäistö T, Mirzarazi Dahagi E, Mahmoodi M, Pihlajaniemi T, Kaartinen MT, et al. Collagens Regulating Adipose Tissue Formation and Functions. Biomedicines. 2023;11(5):1412. pmid:37239083
- 51. Devos H, Zoidakis J, Roubelakis MG, Latosinska A, Vlahou A. Reviewing the Regulators of COL1A1. Int J Mol Sci. 2023;24(12):10004. pmid:37373151
- 52. Bi X, Li Y, Dong Z, Zhao J, Wu W, Zou J, et al. Recent Developments in Extracellular Matrix Remodeling for Fat Grafting. Front Cell Dev Biol. 2021;9:767362. pmid:34977018
- 53. Jiang H, Yazdanyar A, Lou B, Chen Y, Zhao X, Li R, et al. Adipocyte phospholipid transfer protein and lipoprotein metabolism. Arterioscler Thromb Vasc Biol. 2015;35(2):316–22. pmid:25477345
- 54. Miranda M, Chacón MR, Gómez J, Megía A, Ceperuelo-Mallafré V, Veloso S, et al. Human subcutaneous adipose tissue LPIN1 expression in obesity, type 2 diabetes mellitus, and human immunodeficiency virus--associated lipodystrophy syndrome. Metabolism. 2007;56(11):1518–26. pmid:17950103
- 55. Zhang H, Li X, Liao D, Luo P, Jiang X. Alpha/Beta-Hydrolase Domain-Containing 6: Signaling and Function in the Central Nervous System. Front Pharmacol. 2021;12:784202. pmid:34925039
- 56. Zhu Q, Han Y, He Y, Meng P, Fu Y, Yang H, et al. Quercetin inhibits neuronal Ferroptosis and promotes immune response by targeting lipid metabolism-related gene PTGS2 to alleviate breast cancer-related depression. Phytomedicine. 2024;130:155560. pmid:38815404
- 57. Chavda ND, Sari B, Asiri FM, Hamill KJ. Laminin N-terminus (LaNt) proteins, laminins and basement membrane regulation. Biochem Soc Trans. 2022;50(6):1541–53. pmid:36355367