Skip to main content
NCBI home page
Journal of Animal Science logoLink to Journal of Animal Science
. 2022 Nov 17;101:skac382. doi: 10.1093/jas/skac382

Characterization of fatty acid compositions in longissimus thoracis muscle and identification of candidate gene and SNPs related to polyunsaturated fatty acid in Hu sheep

Yuanyuan Kong 1, Chongyang Liu 2, Xueying Zhang 3, Xing Liu 4, Wenqiao Li 5, Fadi Li 6, Xinji Wang 7, Xiangpeng Yue 8,✉
PMCID: PMC9833039  PMID: 36394948

Abstract

Fatty acid (FA) composition contributes greatly to the quality and nutritional value of lamb meat. In the present study, FA was measured in longissimus thoracis (LT) muscles of 1,085 Hu sheep using gas chromatography. Comparative transcriptomic analysis was conducted in LT muscles to identify differentially expressed genes (DEGs) between six individuals with high polyunsaturated fatty acids (H-PUFA, 15.27% ± 0.42%) and six with low PUFA (L-PUFA, 5.22% ± 0.25%). Subsequently, the single nucleotide polymorphisms (SNPs) in a candidate gene PLIN2 were correlated with FA traits. The results showed a total of 29 FA compositions and 8 FA groups were identified, with the highest content of monounsaturated fatty acids (MUFA, 46.54%, mainly C18:1n9c), followed by saturated fatty acids (SFA, 44.32%, mainly C16:0), and PUFA (8.72%, mainly C18:2n6c), and significant correlations were observed among the most of FA traits. Transcriptomic analyses identified 110 upregulated and 302 downregulated DEGs between H-PUFA and L-PUFA groups. The functional enrichment analysis revealed three significant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 17 gene ontology (GO) terms, in which regulation of lipolysis in adipocytes, the AMPK signaling pathway, and the PPAR signaling pathway may play important roles in FA metabolism and biosynthesis. In addition, weighted gene co-expression network analysis (WGCNA) identified 37 module genes associated with PUFA-related traits. In general, PLIN1, LIPE, FABP4, LEP, ACACA, ADIPOQ, SCD, PCK2, FASN, PLIN2, LPL, FABP3, THRSP, and ACADVL may have a great impact on PUFA metabolism and lipid deposition. Four SNPs within PLIN2 were significantly associated with FA. Of those, SNP1 (g.287 G>A) was significantly associated with C18:1n9c and MUFA, and SNP4 (g.7807 T>C) was significantly correlated with PUFA (C18:3n3). In addition, the combined genotype of SNP1 (g.287 G>A), SNP3 (g.7664 T>C), and SNP4 (g.7807 T>C) were significantly correlated with C16:1, C17:0, C18:1C6, PUFA (C18:3n3, C22:6n3), and n-6/n-3 PUFA. These results contribute to the knowledge of the biological mechanisms and genetic markers involved in the composition of FA in Hu sheep.

Keywords: fatty acid composition, longissimus thoracis, RNA-seq, sheep

This study provides important information on the composition of fatty acids (FAs) in the longissimus thoracis muscles. The identification of candidate genes and genetic markers associated with polyunsaturated fatty acids will be worthwhile for marker-assisted selection in improving the FA composition of Hu sheep.

Introduction

Sheep is an important livestock worldwide that provides meat, milk, and wool for human consumption. The fatty acid (FA) composition of meat is important for its nutritional properties influencing human health, and is also critical for the physical properties of fat and oxidative stability, affecting the hardness, color, shelf life, flavor, and overall preference of sheep meat (Wood et al., 2004; Scollan et al., 2017). High intake of saturated fatty acids (SFAs) can increase the risk of cardiovascular disease and diabetes (Calder, 2015), while polyunsaturated fatty acids (PUFA) will be beneficial to human health (Schwingshackl and Hoffmann, 2012). FA is a kind of quantitative trait influenced by genetic and nutrition factors (Shi et al., 2019). The estimated heritability of the FA content in the longissimus thoracis (LT) muscle of sheep ranged from 0.25 to 0.46, suggesting that genetic regulation can be an effective pathway to change FA compositions (Rovadoscki et al., 2018). Therefore, exploring the synthetic mechanism of FA through genetic pathways to improve FA composition is beneficial to human health and has high economic value.

RNA-seq contributes to elucidating the genetic mechanisms that determine FA composition and identify genomic regions and metabolic pathways. The PPAR signaling pathway and AMPK signaling pathway are representative pathways of lipid metabolism (Liu et al., 2020). The PPAR signaling pathway, FA biosynthetic process and degradation, and AMPK signaling pathway are associated with FA composition in bovine chuck, neck, rump, tenderloin, and LT muscles, and SCD, LPL, FABP3, and PPARD are associated with lipid metabolism and adipocyte differentiation (Zhang et al., 2022). Silva-Vignato et al. (2017) identified RSAD2 associated with lipid droplet content and lipid biosynthesis in the bovine ribeye area and backfat thickness. Milk protein and FA synthesis in goat mammary tissue are associated with the AMPK signaling pathway (Zhang et al., 2018c; Cai et al., 2020). Corominas et al. (2013) and Ramayo-Caldas et al. (2012) identified APOB, THEM5, ME3, ACACA, FACN, and SCD genes related to lipid and FA metabolism in pig backfat and liver tissues. However, fewer studies have focused on the biological mechanisms in the FA composition of Hu sheep, therefore the mechanisms of genetic regulation of FA in sheep are worthy of study.

Marker-assisted selection is widely used in livestock breeding (Zou et al., 2020), and single nucleotide polymorphisms (SNPs) are becoming the first choice due to their low price, high specificity, ease of genotyping, and low error rate (Rafalski, 2002; Schlötterer, 2004). Previous studies have identified rs953413 in ELOVL2 and rs174547 in FADS1 are associated with PUFA (C20:5n3, C22:6n3, and n-6 PUFA) in human blood (Tanaka et al., 2009; Horiguchi et al., 2016). In pig, several SNPs, including g.3072 G>A in IGF2, g.43722547 A>G in IL-1A, and g.91508173 C>T in IL-6 were found to be significantly correlated with PUFA (C18:2n6, C18:3n3, C20:4n6) in pig backfat, C20:2 and C20:3n6 in pig muscle, respectively (Criado-Mesas et al., 2019; Pothakam et al., 2021). He et al. (2013) investigated the association of 77 SNPs in the PPAR signaling pathway with eight pig meat quality traits, revealing that TagSNP in RXRB was significantly associated with the trait of skin weight. Therefore, screening molecular markers in candidate genes related to FA compositions can be used for the genetic improvement of sheep meat quality traits.

Hu sheep is an excellent breed in China with the advantages of strong adaptability, high reproduction rate, high slaughter rate, fast growth, and resistance to rough feeding (Wang et al., 2020). Due to the implications of FA composition on meat flavor and human health, as well as the high human and economic costs of FA determination, it is necessary to understand the genetic mechanisms of FA composition in Hu sheep. Therefore, this study aimed to identify differentially expressed genes (DEGs) in the groups of extremely high PUFA (H-PUFA) and extremely low PUFA (L-PUFA) using RNA-seq. In addition, we searched for SNPs in DEGs and performed an association analysis between SNPs, combined genotypes and FA phenotype.

Materials and Method

Ethics statement

All animal procedures in this study were approved by the Ethics Committee of the College of Pastoral Agriculture Science and Technology, Lanzhou University.

Animal management and sample collection

A total of 1,085 male Hu lambs were reared from weaning to 6 months old using the same feed under the same management at Defu Agricultural Technology Co. Ltd (Minqin, Gansu, China) (Kong et al., 2022). A total of 173 sires were represented across the 1,085 individuals. Of these, 496 individuals were slaughtered at six-month to collect LT muscle in August 2018 (n = 166), August 2019 (n = 155), and August 2020 (n = 175). The remaining 589 individuals were slaughtered in January 2019 (n = 238), and January 2020 (n = 351). Approximately 20 g of LT muscle was packed in vacuum bags and stored at −20 °C refrigerator for FA composition analysis. Blood and LT muscle samples were collected to extract DNA and total RNA, respectively.

Determination of FA composition in LT muscle of Hu sheep

The fascia and fat were removed from the surface of the LT muscle and then freeze-dried (SCIENTZ-10ND, Xinzhi Biological Technology Co., Ltd, China). The FA of 1,085 LT muscle was extracted by chloroform/methanol method (Folch et al., 1957). The FA was analyzed by an HP88 capillary column (100 m × 0.25 mm × 0.20 μm; Agilent Technologies, Co., Ltd., Santa Clara, USA) in gas chromatography (Shimadzu, GC-2014, Japan) using nitrogen as carrier gas. FA determination procedure and standards product were based on the previous method (Zhang et al., 2020). Heneicosanoic acid (C21:0) was used as an internal standard to quantify the FA. The data were expressed as a percentage of total FA concentration (Hoopes et al., 2020).

RNA, DNA extraction, and cDNA synthesis

Total RNA was extracted from tissues using the TRIzol method according to the manufacturer’s instructions (Chomczynski and Sacchi, 1987). The concentration and purity of RNA were measured by NanoPhotometer® spectrophotometer (IMPLEN, CA, USA). Total RNA was reverse transcribed to cDNA with the TransScript One-Step gDNA Removal and cDNA Synthesis SuperMix (TransGen Biotech, Beijing, China). The integrity of RNA was checked using Agilent 2100 bioanalyzer (Agilent Technologies, CA, USA). The DNA of 1,085 Hu sheep was extracted from blood samples using a phenol-chloroform method (Adeli and Ogbonna, 1990).

RNA-seq and data analysis

Based on FA determination, six individuals with extremely high PUFA (H-PUFA, 15.27% ± 0.42%) and six with extremely low PUFA (L-PUFA, 5.22% ± 0.25%) were selected from the 351 samples in January 2020 for RNA-seq. In total, 1 μg of RNA per sample was used to prepare the library according to the protocol described in NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA). The constructed library was sequenced on the Illumina NovaSeq 6000 platform. Raw reads were obtained by CASAVA ver.1.8.2 (Illumina). Clean reads were obtained by removing reads containing adapter, reads containing ploy-N and low-quality reads from raw data. Calculating Q20, Q30, and GC content of clean reads. Aligning clean reads with Oar v3.1 genome (GCA_000298735.1) with Hisat2 v2.0.5. The expression levels of genes were measured by FPKM value.

Differential expression analysis between H-PUFA and L-PUFA was performed with the DESeq2 R package (1.20.0). To control the false discovery rate, the P-value was adjusted by Benjamini and Hochberg’s method. The screening criteria for DEGs were P ≦ 0.05, and |log2FoldChange| ≧ 1.2. GO and KEGG enrichment analysis of DEGs was performed using the clusterProfiler R package (3.4.4) (http://www.genome.jp/kegg/), where gene length bias was corrected. GO terms and KEGG pathways with Benjamini and Hochberg method-corrected Padj < 0.05 were considered significantly enriched.

A total of 18,006 genes with FPKM values > 0.1 were used to perform weighted gene co-expression network analysis (WGCNA) by using the WGCNA R package (https://cran.rstudio.com/web/packages/WGCNA/). Network construction and module identification using the topological overlap measure. Highly correlated modules were merged with a height cut-off of 0.4. We further studied the four modules (Melichthcyan, Medcorral, Medarkgray, and MedMediumorchid) that had a significant phenotypic correlation with PUFA-related phenotypes (P < 0.01). Association analysis was performed on all genes in the four modules and PUFA-related phenotypes. The genes with P < 0.01, r ≧ 0.8, and gene module membership ≧ 0.8 in the association analysis results were visualized using the Circlize R package (https://cran.r-project.org/web/packages/circlize/index.html).

Validation of RNA-seq results with qRT-PCR

Ten randomly selected DEGs were performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to validate the accuracy of RNA-seq results. Three technical replicates were performed for each sample. According to the mRNA sequence of ten DEGs in NCBI, primer-BLAST was used to design primers (Table S1). qRT-PCR reaction systems and procedures refer to previous methods (Kong et al., 2022). The results were calculated by the 2−∆∆Ct method using GAPDH as a reference gene (Michaelidou et al., 2013).

SNP identification and genotyping

We selected the perilipin-2 (PLIN2) gene significantly enriched in the PPAR signaling pathway as a candidate gene for SNP scanning. A total of 11 DNA pools were constructed. Each DNA pool consists of 15 individuals which were randomly selected from low, medium, and high PUFA percentage samples from five batches. According to the genomic DNA sequences of sheep PLIN2 (NC_056055.1), 10 pairs of primers were designed for its 9 exonic and flanking regions (Table S2). The PCR reaction systems and procedures refer to previous methods (Kong et al., 2022). Chromas (Technelysium Pty Ltd., Helensville, Australia) and DNAMAN (Lynnon Biosoft, Montreal, Canada) software were used to distinguish putative SNPs. A total of 1,085 DNA samples (30 ng/µL) were genotyped with an improved multiplex ligase detection reaction (iMLDR) method (Yuan et al., 2020). Six anonymous DNA samples were genotyped twice to validate the accuracy of the iMLDR method, along with three blank samples to eliminate cross-contamination.

Statistical analysis

Means and standard deviations of FA were calculated using Microsoft Excel. Batch effects (year and season) were removed from the FA phenotypes data for five batches using the lm function in R-4.2.1 (Vienna, Austria). FA phenotypes data after the removal of batch effects were subjected to Pearson correlation analysis using bivariate correlations in SPSS 24 (SPSS Inc. Chicago, IL, USA). The correlation matrix was plotted on R-4.2.1 using the corrplot package (https://github.com/taiyun/corrplot). Significant difference analysis of the PUFA percentage between H-PUFA and L-PUFA groups was performed in SPSS 24 using the t-test. Ten gene expression abundances were compared between H-PUFA and L-PUFA groups by multiple t-tests in Prism (GraphPad Prism 7.00).

Genetic diversity index, including the allelic frequencies, polymorphism information content (PIC), heterozygosity observed (Ho), and heterozygosity expected (He), Hardy–Weinberg equilibrium (HWE) testing P-value were calculated according to the methods of Zhao et al (2013). A linear regression model (1) was used to assess the correlation between SNP and FA traits in R-4.2.1.

Yijk=μ+Gj+T+e (1)

where Yijk is the FA percentage of the ijkth animal; μ is the overall population mean; Gj is the fixed effect associated with the jth genotype; T is the batch effect; and e is the random error. Differences in mean phenotypes between genotypes and combined genotypes were tested with the LSD multiple comparison test in the general linear model in SPSS 24. P < 0.05 was considered significant. Moreover, the SNPs were analyzed for linkage disequilibrium with HAPLOVIEW software (Barrett et al., 2005). The association analysis model between combined genotypes and 37 FA phenotypes was as follows the formula (1) except that Gj was replaced by combined genotypes. Combined genotypes with individuals less than 15 were excluded from the association analysis.

Results

FA profiles and phenotypic correlations of LT muscle in Hu sheep

A total of 29 FA compositions and 8 FA groups were identified in 1,085 LT muscles using gas chromatography in this study. The results showed C18:1n9c was the highest percentage (38.69%), followed by C16:0 (26.03%) and C18:0 (13.09%). The highest percentage of the FA group was MUFA (46.54%), followed by SFA (44.32%) and PUFA (8.72%). The ratio of n-6/n-3 PUFA, P/S (PUFA/SFA), and M/S (MUFA/SFA) were 3.48, 0.20, and 1.07, respectively (Table 1).

Table 1.

The fatty acid (FA) composition (g/100 g of total identified FA methyl esters) in the longissimus thoracis muscle of different batches of Hu sheep

Fatty acid (%) 2018.8 (n = 166) AVG ± SD 2019.1 (n = 238)
AVG ± SD		 2019.8 (n = 155)
AVG ± SD		 2020.1 (n = 351)
AVG ± SD		 2020.8 (n = 175) AVG ± SD Total (n = 1085)
AVG ± SD
C6:0 0.07 ± 0.05 0.04 ± 0.03 0.04 ± 0.04 0.01 ± 0.01 0.05 ± 0.05 0.03 ± 0.04
C8:0 0.04 ± 0.02 0.04 ± 0.02 0.05 ± 0.04 0.01 ± 0.01 0.01 ± 0.00 0.03 ± 0.02
C10:0 0.14 ± 0.05 0.15 ± 0.03 0.21 ± 0.19 0.14 ± 0.03 0.14 ± 0.03 0.15 ± 0.08
C12:0 0.09 ± 0.03 0.11 ± 0.03 0.12 ± 0.07 0.10 ± 0.04 0.08 ± 0.02 0.10 ± 0.04
C13:0 0.01 ± 0.01 0.02 ± 0.01 0.02 ± 0.02 0.01 ± 0.00 0.01 ± 0.01 0.01 ± 0.01
C14:0iso 0.07 ± 0.04 0.37 ± 0.20 0.21 ± 0.12 0.04 ± 0.02 0.03 ± 0.02 0.14 ± 0.17
C14:0 2.37 ± 0.66 2.71 ± 0.47 2.77 ± 0.65 2.41 ± 0.43 2.16 ± 0.50 2.48 ± 0.56
C15:0anteiso 0.10 ± 0.03 0.10 ± 0.04 0.13 ± 0.06 0.09 ± 0.04 0.13 ± 0.04 0.11 ± 0.05
C14:1 0.10 ± 0.08 0.10 ± 0.03 0.11 ± 0.07 0.12 ± 0.09 0.09 ± 0.03 0.11 ± 0.07
C15:0 0.33 ± 0.08 0.39 ± 0.21 0.41 ± 0.17 0.30 ± 0.09 0.30 ± 0.09 0.34 ± 0.14
C16:0iso 0.15 ± 0.04 0.21 ± 0.11 0.23 ± 0.07 0.16 ± 0.04 0.18 ± 0.05 0.23 ± 0.07
C16:0 25.20 ± 5.02 26.72 ± 2.44 25.49 ± 3.23 26.28 ± 2.82 25.87 ± 3.91 26.03 ± 3.45
C16:1 1.74 ± 0.33 1.67 ± 0.31 1.79 ± 0.35 2.28 ± 0.30 2.12 ± 0.42 1.97 ± 0.42
C17:0 1.74 ± 0.29 1.62 ± 0.37 1.78 ± 0.40 1.19 ± 0.22 1.28 ± 0.26 1.47 ± 0.39
C17:1 0.66 ± 0.12 0.61 ± 0.23 0.68 ± 0.18 0.73 ± 0.17 0.68 ± 0.13 0.68 ± 0.18
C18:0 13.57 ± 2.38 13.62 ± 2.05 14.36 ± 1.89 12.11 ± 1.41 12.72 ± 1.90 13.09 ± 2.04
C18:1n9t 4.13 ± 1.28 3.88 ± 1.10 4.21 ± 1.20 4.50 ± 1.03 3.62 ± 0.93 4.12 ± 1.14
C18:1n9c 38.49 ± 4.58 36.71 ± 3.93 37.22 ± 2.52 39.54 ± 3.32 41.18 ± 6.73 38.69 ± 4.55
C18:1C6 1.02 ± 0.17 0.86 ± 0.20 0.95 ± 0.15 0.77 ± 0.18 0.80 ± 0.12 0.86 ± 0.19
C18:2n6c 5.67 ± 1.17 6.58 ± 1.62 5.94 ± 1.25 6.75 ± 1.60 6.30 ± 1.38 6.36 ± 1.51
C18:3n6 0.06 ± 0.04 0.06 ± 0.02 0.07 ± 0.05 0.09 ± 0.04 0.06 ± 0.02 0.07 ± 0.04
C20:1 0.11 ± 0.04 0.10 ± 0.03 0.11 ± 0.06 0.15 ± 0.03 0.13 ± 0.03 0.13 ± 0.04
C18:3n3 0.19 ± 0.10 0.20 ± 0.05 0.19 ± 0.08 0.33 ± 0.12 0.32 ± 0.10 0.26 ± 0.12
C20:2 0.07 ± 0.06 0.07 ± 0.04 0.09 ± 0.09 0.10 ± 0.04 0.08 ± 0.02 0.08 ± 0.05
C22:0 0.03 ± 0.02 0.04 ± 0.03 0.07 ± 0.10 0.05 ± 0.02 0.05 ± 0.02 0.05 ± 0.04
C20:3n6 0.20 ± 0.12 0.27 ± 0.18 0.25 ± 0.24 0.13 ± 0.05 0.12 ± 0.04 0.19 ± 0.15
C20:3n3 1.62 ± 0.56 2.13 ± 0.85 1.87 ± 0.72 1.42 ± 0.51 1.39 ± 0.46 1.67 ± 0.70
C23:0 0.06 ± 0.03 0.07 ± 0.05 0.10 ± 0.12 0.02 ± 0.01 0.04 ± 0.03 0.05 ± 0.06
C22:6n3 0.17 ± 0.17 0.10 ± 0.05 0.17 ± 0.21 0.06 ± 0.04 0.05 ± 0.03 0.10 ± 0.11
SFA 43.99 ± 5.84 46.19 ± 3.16 46.33 ± 2.25 42.94 ± 2.94 43.06 ± 5.73 44.32 ± 4.29
PUFA 7.97 ± 1.76 9.41 ± 2.51 8.58 ± 2.02 8.87 ± 2.15 8.33 ± 1.86 8.72 ± 2.17
MUFA 46.25 ± 4.07 43.92 ± 3.41 45.06 ± 2.25 48.10 ± 2.78 48.61 ± 5.99 46.54 ± 4.16
n-3 PUFA 1.97 ± 0.64 2.44 ± 0.89 2.23 ± 0.79 1.81 ± 0.55 1.76 ± 0.50 2.02 ± 0.73
n-6 PUFA 5.94 ± 1.23 6.91 ± 1.70 6.26 ± 1.36 6.97 ± 1.64 6.48 ± 1.42 6.62 ± 1.57
n-6/n-3 PUFA 3.19 ± 0.85 3.03 ± 0.91 3.09 ± 1.44 3.97 ± 0.60 3.77 ± 0.59 3.48 ± 0.96
P/S (PUFA/SFA) 0.19 ± 0.06 0.21 ± 0.06 0.19 ± 0.05 0.21 ± 0.06 0.20 ± 0.05 0.20 ± 0.06
M/S (MUFA/SFA) 1.09 ± 0.36 0.95 ± 0.09 0.98 ± 0.08 1.13 ± 0.17 1.17 ± 0.33 1.07 ± 0.24
Open in a new tab

AVG ± SD, mean value of FA percentage ± standard deviation; SFA = C6:0 + C8:0 + C10:0 + C12:0 + C13:0 + C14:0iso + C14:0 + C15:0anteiso + C15:0 + C16:0 iso + C16:0 + C17:0 + C18:0 + C22:0 + C23:0; MUFA = C14:1 + C16:1 + C17:1 + C18:1n9t + C18:1n9tc + C18:1C6 + C20:1; PUFA = C18:2n6c + C18:3n6 + C18:3n3 + C20:2 + C20:3n6 + C20:3n3 + C22:6n3; n-3 PUFA= C18:3n3 + C20:3n3+ C22:6n3; n-6 PUFA = C18:2n6c + C18:3n6 + C20:3n6.

Significant phenotypic correlations were observed among the most of FA traits (Figure 1). Of which, PUFA was positively correlated with C18:2n6c, C20:3n3, n-3 PUFA, n-6 PUFA, and P/S (r values ranged from 0.368 to 0.976, P < 0.01). While PUFA was negatively correlated with SFA, MUFA, n-6/n-3 PUFA, and M/S (r values ranged from −0.091 to −0.271, P < 0.01).

Figure 1.

Figure 1.

Open in a new tab

Correlation plots of phenotypic correlations among fatty acid (FA) phenotypes. * stands for P < 0.05, ** stands for P < 0.01, and *** stands for P < 0.001. Positive correlations are shown in blue and negative correlations are shown in red, the color intensity and size of the squares are proportional to the magnitude of the correlation coefficient.

RNA-seq analysis and DEGs screening

Based on FA determination, the PUFA content of the H-PUFA group (15.27% ± 0.42%) was significantly higher (P = 5.28E-13) than the L-PUFA group (5.22% ± 0.25%), accounting for 12 individuals, which were selected for RNA-seq (Figure S1). The Pearson’s correlation coefficient results of 12 samples of transcriptome data showed a range of 0.837 to 0.969 (Figure S2). The RNA-seq quality is shown in Table S3. The Q30 of 12 samples ranged from 93.27% to 94.73%. The clean reads were aligned with the Oar v3.1 (GCA_000298735.1) reference genome by HISAT2 software. The mapping rates ranged from 89.53% to 94.84% (Table S4). The results indicate that the quality of the data is sufficiently high to conduct subsequent analysis. FPKM > 1 was used as the standard to determine gene expression abundance. A total of 12,533 genes were co-expressed in H-PUFA and L-PUFA groups, 487 genes were expressed only in the L-PUFA group, and 426 genes were expressed only in the H-PUFA group (Figure 2a).

Figure 2.

Figure 2.

Open in a new tab

The differentially expressed genes (DEGs) identified between high polyunsaturated fatty acid (H-PUFA) and low PUFA (L-PUFA) groups. (a) Venn diagram of gene expression. (b) Volcano plot displaying DEGs within two different comparison groups in H-PUFA vs L-PUFA. (c) Clustering of DEGs lists. The correlation coefficient between H5_5 and other samples was less than 0.85, the sample H5_5 was excluded, and the subsequent analysis was based on five samples in the H-PUFA group and six samples in the L-PUFA group.

A total of 412 DEGs were identified between H-PUFA and L-PUFA groups using DESeq2 software, including 110 upregulated and 302 downregulated DEGs (Figure 2b). The expression pattern of DEGs was divided into two groups based on the high and low percentages of PUFA using hierarchical cluster analysis (Figure 2c). The accuracy and reproducibility of RNA-seq data were confirmed by 10 DEGs through qRT-PCR. The qRT-PCR results are shown in Figure 4a. The expression trends of ten genes in qRT-PCR were consistent with RNA-seq (Figure 4b).

Figure 4.

Figure 4.

Open in a new tab

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) validation of the RNA-seq data. (a) Differences in the mRNA expression level for 10 DEGs. (b) Difference of log2FoldChange between RNA-seq and qRT-PCR. Different lowercase letters indicate significant differences between tissues (P < 0.05). Different uppercase letters indicate extremely significant differences between tissues (P < 0.01).

Functional enrichment analyses of DEGs

Based on GO analysis, 412 DEGs were assigned to 395 subcategories of GO terms, including 54 cell components (CCs), 173 biological processes (BPs), and 168 molecular functions (MFs). Table S5 shows the significantly enriched GO terms (Padj < 0.05). They were involved in GO terms such as extracellular region, enzyme inhibitor activity, receptor binding, transmembrane receptor activity, and endopeptidase inhibitor activity. The three GO subgroups (top 10) are summarized in Figure 3a. The extracellular region in CC term and receptor binding in MF term, leptin (LEP) had been implicated in decreased lipogenesis, increases triglyceride hydrolysis, and FA oxidation (William et al., 2002).

Figure 3.

Figure 3.

Open in a new tab

Functional enrichment analysis of differentially expressed genes (DEGs). (a) Annotation of the top 10 gene ontology (GO) terms in biological process, cellular component, and molecular function categories for 412 DEGs. (b) Annotations of the top 20 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for 412 DEGs. P ≦ 0.05 and |log2FoldChange| ≧1.2 were used as standards for identifying the DEGs.

For KEGG analysis, a total of 232 metabolic pathways were enriched, of which regulation of lipolysis in adipocytes, the AMPK signaling pathway, and the PPAR signaling pathway were significantly enriched (Table S6). Summaries of the metabolic pathways (top 20) are presented in Figure 3b. The lipid-related metabolic pathways include regulation of lipolysis in adipocytes, the AMPK signaling pathway, the PPAR signaling pathway, fatty acid metabolism, fatty acid biosynthesis, and biosynthesis of unsaturated fatty acids. Remarkably, the genes that play a crucial role in the regulation of lipolysis in adipocytes (PLIN1, LIPE, FABP4, GNAI1, PDE3B, ADRB3, and ADRB1), the AMPK signaling pathway (LEP, ACACA, FBP1, LIPE, ADIPOQ, SCD, PCK2, HNF4A, and FASN), the PPAR signaling pathway (PLIN1, ADIPOQ, PLIN2, FABP4, SCD, PCK2, and MMP1) were downregulated.

Identification of genes related to PUFA-related phenotypes using WGCNA

To investigate the gene regulatory network affecting FA phenotypes, WGCNA was performed using 18,006 genes with FPKM > 0.1. According to the expression values, we ultimately obtained 17 modules (Figure 5a). The size of modules ranged from 71 (coral2 module) to 6,094 genes (turquoise module) (Table S7). We searched for significant correlations between FA phenotypes and module eigengene expression values, and many strong correlations were observed. Among them, genes clustered in the light cyan module (n = 425) were extremely significant correlation with C15:0anteiso, C18:1n9c, MUFA, n-6/n-3 PUFA (P-values ranged from 0.001 to 0.01); genes clustered in the dark gray module (n = 157) were extremely significant correlation with C18:0, C18:2n6c, C20:3n6, PUFA, n-3 PUFA, n-6 PUFA, and P/S (P-values ranged from 0.006 to 0.009) (Figure 5b). Then, among the four modules (Melichthyan, Medcorral, Medarkgray, and MedMediumorchid) that showed extremely significant correlation with PUFA-related traits, we identified 37 genes that showed extremely significant correlation with PUFA-related traits (P < 0.01, r ≧ 0.8, gene module membership ≧ 0.8) (Figure 6). Among which LEP, ADIPOQ, LPL, FABP3, THRSP, and ACADVL are involved in FA and lipid metabolism.

Figure 5.

Figure 5.

Open in a new tab

Weighted gene co-expression network analysis (WGCNA). (a) Cluster dendrogram of co-expression modules. (b) Correlations of FA traits with WGCNA co-expression modules. * stands for P < 0.05, ** stands for P < 0.01. The intensity and direction of correlations are indicated on the right side of the heat map (red, negatively correlated; blue, positively correlated).

Figure 6.

Figure 6.

Open in a new tab

Chord diagram of gene and polyunsaturated fatty acid (PUFA)-related traits. The marked genes in red are overlapped with the differentially expressed genes (DEGs) in the volcano map. PUFA-related traits are fatty acid (FA) phenotypes that are significantly associated with PUFA phenotypes.

SNP scanning of the PLIN2 gene

A total of four SNPs were found within the intron region of PLIN2 through pooled DNA sequencing (Figure S3). Four SNPs were genotyped with the iMLDR method. The genotype correlation coefficients for the six pairs of technical replicates were equal to 1, and no genotype signal was detected in the three blank samples, indicating that the iMLDR method is reliable. The genetic diversity index information of four SNPs is shown in Table S8, for SNP1 (g.287 G>A), the genotype frequencies of GG, GA, and AA were 0.30, 0.51, and 0.19, G was the dominant allele (0.55). The He, Ho, and PIC were 0.45, 0.51, and 0.30. All SNPs were in accordance with Hardy–Weinberg equilibrium (P > 0.05). All SNPs were moderately polymorphic (0.25 < PIC < 0.50), except SNP2 (g.714 G>A) which was low polymorphic (PIC < 0.25).

Association between SNPs in PLIN2 and FA traits

The general linear model was used to study the association between SNPs and FA phenotypes in R-4.2.1. The results showed that all four SNPs were significantly associated with at least one FA trait. Among them, SNP4 (g.7807 T>C) significantly affected PUFA (C18:3n3), with significantly higher levels in CT and CC genotypes than the TT genotype. The GG genotype of C18:1n9c and MUFA in SNP1 were significantly higher than the AA genotype. The GA genotype of C14:0iso in SNP2 was significantly higher than the AA genotype. In SNP3 (g.7664 T>C), the TT genotype of C16:0 was significantly lower than the CC genotype, the CC genotype of C16:1 was extremely significantly higher than TT and CT genotypes, the CC genotype of C17:0 was significantly lower than TT and CT genotypes. In SNP4, the TT genotype of C16:0 was significantly lower than the CC genotype, and the CC genotype of C16:1 was extremely significantly higher than TT and CT genotypes (Table 2).

Table 2.

Association analysis between single nucleotide polymorphisms (SNPs) in PLIN2 and fatty acid (FA) composition (g/100 g of total identified FA methyl esters)

SNP SNP1 (g.287 G>A) P-value SNP2 (g.714 G>A) P-value SNP3 (g.7664 T>C) P-value SNP4 (g.7807 T>C) P-value
Genotype AG GG AA AA GA CT TT CC CT TT CC
Number n = 526 n = 314 n = 201 n = 1034 n = 7 n = 410 n = 561 n = 66 n = 413 n = 555 n = 68
C6:0 0.04 ± 0.0022 0.04 ± 0.0028 0.03 ± 0.0028 0.316 0.04 ± 0.0016 0.01 ± 0.0076 0.224 0.04 ± 0.0025 0.04 ± 0.0021 0.03 ± 0.0062 0.627 0.04 ± 0.0025 0.04 ± 0.0021 0.03 ± 0.0061 0.583
C8:0 0.02 ± 0.0009 0.02 ± 0.0011 0.02 ± 0.0014 0.720 0.02 ± 0.0006 0.03 ± 0.0038 0.388 0.02 ± 0.0010 0.02 ± 0.0008 0.02 ± 0.0025 0.551 0.02 ± 0.0010 0.02 ± 0.0008 0.02 ± 0.0024 0.447
C10:0 0.14 ± 0.0035 0.14 ± 0.0034 0.15 ± 0.0071 0.157 0.15 ± 0.0025 0.14 ± 0.0113 0.810 0.15 ± 0.0054 0.14 ± 0.0025 0.15 ± 0.0074 0.215 0.15 ± 0.0054 0.14 ± 0.0025 0.15 ± 0.0073 0.261
C12:0 0.10 ± 0.0017 0.10 ± 0.0028 0.10 ± 0.0028 0.775 0.10 ± 0.0012 0.08 ± 0.0076 0.390 0.10 ± 0.0020 0.10 ± 0.0017 0.10 ± 0.0049 0.897 0.10 ± 0.0020 0.10 ± 0.0017 0.10 ± 0.0049 0.920
C13:0 0.01 ± 0.0004 0.01 ± 0.0006 0.01 ± 0.0007 0.695 0.01 ± 0.0003 0.01 ± 0.0038 0.898 0.01 ± 0.0005 0.01 ± 0.0004 0.01 ± 0.0025 0.423 0.01 ± 0.0005 0.01 ± 0.0004 0.01 ± 0.0024 0.506
C14:0iso 0.15 ± 0.0083 0.12 ± 0.0085 0.13 ± 0.0099 0.162 0.14 ± 0.0053a 0.28 ± 0.0680b 0.021 0.13 ± 0.0074 0.14 ± 0.0080 0.12 ± 0.0185 0.691 0.13 ± 0.0074 0.14 ± 0.0081 0.12 ± 0.0182 0.754
C14:0 2.47 ± 0.0235 2.44 ± 0.0322 2.54 ± 0.0437 0.135 2.47 ± 0.0177 2.48 ± 0.1021 0.965 2.51 ± 0.0282 2.45 ± 0.0232 2.54 ± 0.0800 0.161 2.51 ± 0.0280 2.45 ± 0.0233 2.52 ± 0.0776 0.231
C15:0anteiso 0.10 ± 0.0017 0.10 ± 0.0034 0.10 ± 0.0028 0.273 0.10 ± 0.0016 0.10 ± 0.0189 0.961 0.10 ± 0.002 0.10 ± 0.0021 0.10 ± 0.0049 0.835 0.10 ± 0.0020 0.10 ± 0.0021 0.10 ± 0.0049 0.929
C14:1 0.10 ± 0.0035 0.10 ± 0.0034 0.11 ± 0.0042 0.338 0.10 ± 0.0022 0.09 ± 0.0113 0.539 0.10 ± 0.0040 0.10 ± 0.0025 0.11 ± 0.0049 0.401 0.10 ± 0.0039 0.10 ± 0.0025 0.11 ± 0.0049 0.460
C15:0 0.34 ± 0.007 0.33 ± 0.0068 0.34 ± 0.0099 0.782 0.34 ± 0.0044 0.33 ± 0.0227 0.958 0.33 ± 0.0054 0.34 ± 0.0068 0.35 ± 0.0197 0.698 0.33 ± 0.0054 0.34 ± 0.0068 0.35 ± 0.0182 0.817
C16:0iso 0.17 ± 0.0035 0.34 ± 0.1163 0.17 ± 0.0049 0.100 0.22 ± 0.0355 0.25 ± 0.0567 0.956 0.17 ± 0.0035 0.27 ± 0.0654 0.18 ± 0.0098 0.405 0.17 ± 0.0034 0.27 ± 0.0658 0.17 ± 0.0085 0.392
C16:0 26.00 ± 0.1391 25.78 ± 0.2381 26.48 ± 0.1989 0.077 26.02 ± 0.1085 26.23 ± 0.4460 0.874 26.18 ± 0.1605ab 25.81 ± 0.1575a 26.98 ± 0.2954b 0.020 26.17 ± 0.1589ab 25.82 ± 0.1592a 26.92 ± 0.2886b 0.028
C16:1 1.94 ± 0.0187 1.97 ± 0.0220 2.00 ± 0.0310 0.088 1.96 ± 0.0131 1.77 ± 0.0869 0.180 1.96 ± 0.0217A 1.95 ± 0.0169A 2.14 ± 0.0542B 4.30E-04 1.96 ± 0.0212A 1.94 ± 0.0170A 2.12 ± 0.0546B 0.002
C17:0 1.47 ± 0.0166 1.44 ± 0.0203 1.44 ± 0.0282 0.310 1.46 ± 0.0118 1.44 ± 0.0869 0.901 1.46 ± 0.0188b 1.47 ± 0.0160b 1.37 ± 0.0505a 0.047 1.46 ± 0.0187 1.47 ± 0.0161 1.37 ± 0.0485 0.052
C17:1 0.67 ± 0.0087 0.67 ± 0.0090 0.66 ± 0.0134 0.687 0.67 ± 0.0056 0.56 ± 0.0378 0.097 0.66 ± 0.0079 0.67 ± 0.0080 0.70 ± 0.0271 0.357 0.67 ± 0.0079 0.67 ± 0.0081 0.69 ± 0.0267 0.534
C18:0 13.09 ± 0.0929 13.03 ± 0.1078 13.13 ± 0.1256 0.842 13.08 ± 0.0625 13.76 ± 0.5140 0.350 13.10 ± 0.1052 13.10 ± 0.0819 12.69 ± 0.2068 0.258 13.08 ± 0.1043 13.11 ± 0.0828 12.75 ± 0.2074 0.341
C18:1n9t 4.11 ± 0.048 4.08 ± 0.0615 4.20 ± 0.0719 0.438 4.11 ± 0.0336 4.24 ± 0.3439 0.751 4.13 ± 0.0563 4.10 ± 0.0443 4.19 ± 0.1219 0.781 4.12 ± 0.0561 4.10 ± 0.0446 4.20 ± 0.1201 0.774
C18:1n9c 38.63 ± 0.2106ab 39.22 ± 0.2365a 38.25 ± 0.2575b 0.036 38.74 ± 0.1384 37.73 ± 1.0999 0.541 38.52 ± 0.2440 38.94 ± 0.1731 38.26 ± 0.4985 0.215 38.52 ± 0.2421 38.94 ± 0.1745 38.29 ± 0.4851 0.234
C18:1C6 0.86 ± 0.0092 0.84 ± 0.0102 0.84 ± 0.0127 0.329 0.85 ± 0.0062 0.91 ± 0.0491 0.388 0.84 ± 0.0099 0.86 ± 0.0084 0.81 ± 0.0185 0.077 0.84 ± 0.0098 0.86 ± 0.0085 0.82 ± 0.0182 0.083
C18:2n6c 6.38 ± 0.0667 6.30 ± 0.0830 6.40 ± 0.1023 0.669 6.36 ± 0.0466 6.66 ± 0.4951 0.588 6.33 ± 0.0761 6.36 ± 0.0621 6.48 ± 0.1834 0.751 6.34 ± 0.0763 6.35 ± 0.0624 6.47 ± 0.1783 0.807
C18:3n6 0.06 ± 0.0017 0.07 ± 0.0023 0.06 ± 0.0021 0.324 0.06 ± 0.0012 0.05 ± 0.0076 0.467 0.06 ± 0.0015 0.07 ± 0.0017 0.06 ± 0.0037 0.474 0.06 ± 0.0015 0.06 ± 0.0017 0.06 ± 0.0036 0.638
C20:1 0.12 ± 0.0017 0.12 ± 0.0028 0.12 ± 0.0028 0.176 0.12 ± 0.0012 0.11 ± 0.0076 0.526 0.12 ± 0.0020 0.12 ± 0.0021 0.13 ± 0.0049 0.213 0.12 ± 0.0020 0.12 ± 0.0021 0.13 ± 0.0049 0.248
C18:3n3 0.25 ± 0.0048 0.26 ± 0.0068 0.26 ± 0.0085 0.655 0.25 ± 0.0037 0.20 ± 0.0189 0.170 0.26 ± 0.0059 0.25 ± 0.0046 0.28 ± 0.0135 0.052 0.26 ± 0.0059b 0.25 ± 0.0047a 0.27 ± 0.0133b 0.027
C20:2 0.07 ± 0.0022 0.08 ± 0.0034 0.08 ± 0.0035 0.786 0.08 ± 0.0016 0.07 ± 0.0076 0.547 0.07 ± 0.0025 0.08 ± 0.0025 0.07 ± 0.0037 0.607 0.07 ± 0.0025 0.08 ± 0.0025 0.07 ± 0.0036 0.560
C22:0 0.04 ± 0.0017 0.04 ± 0.0034 0.04 ± 0.0021 0.530 0.04 ± 0.0012 0.05 ± 0.0151 0.578 0.04 ± 0.0020 0.04 ± 0.0021 0.04 ± 0.0025 0.500 0.04 ± 0.0015 0.04 ± 0.0021 0.04 ± 0.0024 0.428
C20:3n6 0.18 ± 0.0061 0.17 ± 0.0062 0.18 ± 0.0078 0.315 0.18 ± 0.0040 0.20 ± 0.0302 0.570 0.18 ± 0.0069 0.18 ± 0.0046 0.16 ± 0.0098 0.456 0.18 ± 0.0069 0.18 ± 0.0047 0.16 ± 0.0097 0.475
C20:3n3 1.69 ± 0.0310 1.60 ± 0.0378 1.68 ± 0.0487 0.173 1.66 ± 0.0215 1.90 ± 0.2419 0.357 1.65 ± 0.0341 1.67 ± 0.0291 1.65 ± 0.0923 0.882 1.65 ± 0.0340 1.67 ± 0.0293 1.66 ± 0.0910 0.949
C23:0 0.05 ± 0.0022 0.04 ± 0.0023 0.05 ± 0.0035 0.513 0.05 ± 0.0016 0.07 ± 0.0189 0.214 0.05 ± 0.0030 0.04 ± 0.0017 0.05 ± 0.0062 0.647 0.05 ± 0.0030 0.05 ± 0.0017 0.05 ± 0.0061 0.741
C22:6n3 0.09 ± 0.0057 0.09 ± 0.0056 0.11 ± 0.0092 0.153 0.09 ± 0.0037 0.10 ± 0.0227 0.998 0.09 ± 0.0049 0.10 ± 0.0055 0.08 ± 0.0111 0.256 0.09 ± 0.0049 0.10 ± 0.0055 0.08 ± 0.0109 0.228
SFA 44.25 ± 0.1857 44.05 ± 0.2675 44.81 ± 0.2631 0.130 44.29 ± 0.1347 45.34 ± 0.6955 0.516 44.45 ± 0.2188 44.14 ± 0.1841 44.78 ± 0.5884 0.345 44.42 ± 0.217 44.16 ± 0.1859 44.76 ± 0.3929 0.418
PUFA 8.76 ± 0.0951 8.58 ± 0.1196 8.78 ± 0.1481 0.440 8.71 ± 0.0666 9.21 ± 0.7484 0.533 8.67 ± 0.1082 8.72 ± 0.0891 8.81 ± 0.2659 0.849 8.69 ± 0.1083 8.71 ± 0.0891 8.82 ± 0.2583 0.902
MUFA 46.45 ± 0.1932ab 47.04 ± 0.2246a 46.22 ± 0.2349b 0.039 46.59 ± 0.1281 45.44 ± 1.1528 0.445 46.36 ± 0.2232 46.77 ± 0.1625 46.37 ± 0.4542 0.246 46.36 ± 0.2219 46.77 ± 0.1638 46.38 ± 0.4426 0.271
n-3 PUFA 2.05 ± 0.0318 1.96 ± 0.0395 2.06 ± 0.0522 0.172 2.02 ± 0.0227 2.21 ± 0.2570 0.494 2.01 ± 0.0356 2.03 ± 0.0308 2.02 ± 0.0948 0.906 2.02 ± 0.0354 2.03 ± 0.0310 2.04 ± 0.0934 0.967
n-6 PUFA 6.64 ± 0.0693 6.55 ± 0.0863 6.66 ± 0.10580 0.641 6.61 ± 0.0482 6.93 ± 0.5216 0.583 6.59 ± 0.0790 6.62 ± 0.0646 6.71 ± 0.1896 0.817 6.60 ± 0.0787 6.61 ± 0.0645 6.71 ± 0.1843 0.868
n-6/n-3 PUFA 3.44 ± 0.0384 3.52 ± 0.0463 3.47 ± 0.0719 0.403 3.47 ± 0.0277 3.27 ± 0.2646 0.519 3.47 ± 0.0444 3.46 ± 0.0376 3.57 ± 0.1083 0.641 3.47 ± 0.0443 3.46 ± 0.0378 3.54 ± 0.1067 0.744
P/S 0.20 ± 0.0026 0.20 ± 0.0034 0.20 ± 0.0035 0.862 0.20 ± 0.0019 0.20 ± 0.0151 0.882 0.20 ± 0.0030 0.20 ± 0.0025 0.20 ± 0.0062 0.635 0.20 ± 0.0030 0.20 ± 0.0025 0.20 ± 0.0061 0.789
M/S 1.07 ± 0.0100 1.10 ± 0.0158 1.05 ± 0.0148 0.059 1.07 ± 0.0075 1.01 ± 0.0416 0.454 1.06 ± 0.0123 1.08 ± 0.0101 1.04 ± 0.0172 0.326 1.06 ± 0.0118 1.08 ± 0.0106 1.05 ± 0.0170 0.353
Open in a new tab

Statistical data are expressed as a mean value of FA percentage ± standard error; In the same row, different lowercase letters indicate significant differences (P < 0.05); Different capital letters indicate highly significant differences (P < 0.01).

Linkage disequilibrium analysis

The linkage disequilibrium analysis among four SNPs was calculated with HAPLOVIEW. The results showed that except for SNP2, which did not form a haplotype domain with SNP1, SNP3, and SNP4, the other SNPs were closely linked (Figure 7). The Dʹ and r2 values are factual indicators of linkage disequilibrium. The Dʹ (r2) was 1.0 (0.004) between SNP1 and SNP2. The Dʹ (r2) was 1.0 (0.008) between SNP2 and SNP3. The Dʹ (r2) was 1.0 (0.985) between SNP3 and SNP4. The high Dʹ (r2) values between these SNPs indicate that they are in strong linkage disequilibrium. In addition, three haplotypes were combined, TTG was the domain haplotype with the highest frequency of 0.552, CCA was on second with a frequency of 0.263, and TTA was with a frequency of 0.182.

Figure 7.

Figure 7.

Open in a new tab

Linkage disequilibrium analysis of four single nucleotide polymorphisms (SNPs) in the PLIN2 gene. SNP2 (g.714 G>A) is not in bold, indicating that SNP2 (g.714 G>A) does not form a haplotype block with other SNPs.

Since SNP2 did not form a haplotype domain with other SNPs, we performed genotype combinations for SNP1, SNP3, and SNP4. Six combined genotypes with a number greater than 15 were used for association analysis with FA phenotypes. The results showed that PUFA (C18:3n3, C22:6n3) and the ratio of n-6/n-3 PUFA were significantly different among different combined genotypes. The C18:3n3 with GACTCT, GGTTTT, and AACCCC combined genotypes were significantly higher than that with GATTTT combined genotype. The C22:6n3 with AATTTT combined genotype was significantly higher than that with GACTCT, GGTTTT, and AACCCC combined genotypes. The ratio of n-6/n-3 PUFA with the GATTTT combined genotype was significantly lower than other combined genotypes except for AACTCT (Table 3).

Table 3.

Association analysis of different combined genotypes of PLIN2 gene and fatty acid (FA) composition (g/100 g of total identified FA methyl esters)

Genotype combination GACTCT GGTTTT AACTCT AATTTT GATTTT AACCCC P-value
Number n = 307 n = 312 n = 100 n = 33 n = 210 n = 66
C6:0 0.04 ± 0.0029 0.04 ± 0.0028 0.04 ± 0.0040 0.04 ± 0.0087 0.04 ± 0.0035 0.04 ± 0.0062 0.711
C8:0 0.03 ± 0.0011 0.03 ± 0.0011 0.03 ± 0.0020 0.02 ± 0.0017 0.03 ± 0.0014 0.02 ± 0.0025 0.119
C10:0 0.15 ± 0.0057 0.14 ± 0.0034 0.17 ± 0.0130 0.15 ± 0.0087 0.15 ± 0.0048 0.16 ± 0.0074 0.395
C12:0 0.10 ± 0.0023 0.10 ± 0.0028 0.11 ± 0.0050 0.10 ± 0.0052 0.10 ± 0.0028 0.10 ± 0.0049 0.970
C13:0 0.01 ± 0.0006 0.01 ± 0.0006 0.01 ± 0.0010 0.01 ± 0.0017 0.01 ± 0.0007 0.01 ± 0.0025 0.738
C14:0 iso 0.14 ± 0.0086 0.13 ± 0.0085 0.15 ± 0.0140 0.13 ± 0.0191 0.17 ± 0.0166 0.13 ± 0.0197 0.076
C14:0 2.49 ± 0.0325 2.44 ± 0.0323 2.58 ± 0.0570 2.48 ± 0.1236 2.47 ± 0.0345 2.54 ± 0.0800 0.350
C15:0anteiso 0.11 ± 0.0023 0.11 ± 0.0034 0.11 ± 0.0040 0.10 ± 0.0070 0.10 ± 0.0028 0.11 ± 0.0049 0.434
C14:1 0.11 ± 0.0051 0.11 ± 0.0034 0.11 ± 0.0050 0.12 ± 0.0174 0.10 ± 0.0021 0.11 ± 0.0049 0.151
C15:0 0.33 ± 0.0051 0.34 ± 0.0068 0.35 ± 0.0140 0.31 ± 0.0157 0.35 ± 0.0152 0.35 ± 0.0197 0.366
C16:0iso 0.18 ± 0.0040 0.35 ± 0.1178 0.18 ± 0.0070 0.18 ± 0.0104 0.18 ± 0.0055 0.18 ± 0.0098 0.471
C16:0 26.06 ± 0.2026 25.78 ± 0.2400 26.57 ± 0.2040 25.34 ± 0.8356 25.96 ± 0.1808 26.98 ± 0.2954 0.067
C16:1 1.97 ± 0.0251ab 1.97 ± 0.0221ab 1.94 ± 0.0420ab 1.99 ± 0.0801ab 1.91 ± 0.0276b 2.14 ± 0.0542a 0.001
C17:0 1.45 ± 0.0205b 1.45 ± 0.0204b 1.49 ± 0.0410b 1.45 ± 0.0644ab 1.52 ± 0.0283b 1.37 ± 0.0505a 0.020
C17:1 0.67 ± 0.0091 0.68 ± 0.0085 0.66 ± 0.0180 0.63 ± 0.0226 0.68 ± 0.0166 0.70 ± 0.0271 0.447
C18:0 13.01 ± 0.1267 13.04 ± 0.1087 13.35 ± 0.1790 13.25 ± 0.3151 13.20 ± 0.1394 12.69 ± 0.2068 0.304
C18:1n9t 4.10 ± 0.0673 4.07 ± 0.0611 4.20 ± 0.1020 4.28 ± 0.1863 4.13 ± 0.0683 4.20 ± 0.1219 0.814
C18:1n9c 38.64 ± 0.3036 39.21 ± 0.2378 38.10 ± 0.3580 38.56 ± 0.5379 38.58 ± 0.2836 38.26 ± 0.4985 0.215
C18:1C6 0.85 ± 0.0108ab 0.85 ± 0.0102ab 0.86 ± 0.0190ab 0.87 ± 0.0348ab 0.89 ± 0.0152b 0.82 ± 0.0185a 0.034
C18:2n6c 6.32 ± 0.0902 6.30 ± 0.0838 6.35 ± 0.1430 6.43 ± 0.2646 6.42 ± 0.0994 6.48 ± 0.1834 0.922
C18:3n6 0.07 ± 0.0023 0.07 ± 0.0023 0.06 ± 0.0030 0.07 ± 0.0052 0.07 ± 0.0028 0.07 ± 0.0037 0.497
C20:1 0.12 ± 0.0023 0.13 ± 0.0028 0.12 ± 0.0040 0.13 ± 0.0087 0.12 ± 0.0028 0.13 ± 0.0049 0.249
C18:3n3 0.27 ± 0.0068b 0.26 ± 0.0068b 0.26 ± 0.0130ab 0.23 ± 0.0157ab 0.24 ± 0.0069a 0.28 ± 0.0135b 0.019
C20:2 0.08 ± 0.0023 0.08 ± 0.0034 0.09 ± 0.0060 0.08 ± 0.0070 0.08 ± 0.0041 0.08 ± 0.0037 0.644
C22:0 0.05 ± 0.0017 0.05 ± 0.0034 0.05 ± 0.0040 0.05 ± 0.0070 0.05 ± 0.0028 0.04 ± 0.0025 0.829
C20:3n6 0.19 ± 0.0086 0.18 ± 0.0062 0.19 ± 0.0100 0.21 ± 0.0279 0.19 ± 0.0076 0.17 ± 0.0098 0.340
C20:3n3 1.64 ± 0.0400 1.61 ± 0.0379 1.70 ± 0.0680 1.66 ± 0.1097 1.77 ± 0.0497 1.66 ± 0.0923 0.204
C23:0 0.05 ± 0.0034 0.05 ± 0.0023 0.06 ± 0.0050 0.05 ± 0.0052 0.05 ± 0.0028 0.05 ± 0.0062 0.735
C22:6n3 0.09 ± 0.0051b 0.09 ± 0.0057b 0.12 ± 0.0140ab 0.16 ± 0.0331a 0.12 ± 0.0117ab 0.09 ± 0.0111b 0.002
SFA 44.20 ± 0.2728 44.05 ± 0.2689 45.23 ± 0.3020 43.66 ± 1.0288 44.39 ± 0.2353 44.78 ± 0.4037 0.183
PUFA 8.65 ± 0.1273 8.60 ± 0.1200 8.76 ± 0.2110 8.84 ± 0.3603 8.88 ± 0.1428 8.82 ± 0.2659 0.739
MUFA 46.46 ± 0.2779 47.03 ± 0.2259 45.99 ± 0.3290 46.59 ± 0.4787 46.41 ± 0.2615 46.37 ± 0.4542 0.221
n-3 PUFA 1.99 ± 0.0405 1.96 ± 0.0402 2.07 ± 0.0740 2.05 ± 0.1271 2.12 ± 0.0518 2.02 ± 0.0948 0.217
n-6 PUFA 6.58 ± 0.0936 6.55 ± 0.0866 6.61 ± 0.1480 6.72 ± 0.2733 6.67 ± 0.1035 6.71 ± 0.1896 0.925
n-6/n-3 PUFA 3.50 ± 0.0548b 3.52 ± 0.0464b 3.35 ± 0.067ab 3.68 ± 0.3220b 3.34 ± 0.0518a 3.57 ± 0.1083b 0.047
P/S 0.20 ± 0.0034 0.20 ± 0.0034 0.20 ± 0.0050 0.21 ± 0.0122 0.20 ± 0.0035 0.20 ± 0.0062 0.846
M/S 1.08 ± 0.0154 1.10 ± 0.0159 1.03 ± 0.0140 1.12 ± 0.0696 1.05 ± 0.0097 1.04 ± 0.0172 0.062
Open in a new tab

Statistical data are expressed as a mean value of FA percentage ± standard error. In the same row, different lowercase letters indicate significant differences (P < 0.05).

Discussion

FA compositions are essential for the quality and nutritional value of meat. As consumers become aware of the adverse health effects of higher SFA and lower PUFA, and MUFA in food, strategies to change the FA compositions of meat have been applied to livestock to produce healthier meat. Hu sheep had higher MUFA, PUFA, and lower SFA than Yunling cattle (Zhang et al., 2018a). Hu sheep had higher SFA, lower PUFA, and MUFA than Duroc pigs (Ovilo et al., 2014). Hu sheep had higher SFA, MUFA, and lower PUFA than Merino, Damara, and Dorper, Dorper sheep had significantly higher SFA, MUFA, and lower PUFA than Merino and Damara sheep (Van Harten et al., 2016). The most abundant FA in this study was C18:1n-9c, followed by C16:0 and C18:0, which is consistent with the previous study (Andrade et al., 2014). The ratios of n-6/n-3 PUFA and P/S were 3.43 and 0.2 in this study, respectively. Ye et al. (2016) reported the ratios of P/S and n-6/n-3 PUFA were 0.8548 and 2.9695, respectively. The ratio of n-6/n-3 PUFA was within the optimum nutritional standard of 4, but P/S did not exceed the optimum nutritional standard of 0.4 (Wood et al., 2004). The P/S and n-6/n-3 PUFA ratios are an indicator to evaluate the nutritional value of meat, but the P/S ratio of many kinds of meat is around 0.1 and the n-6/n-3 PUFA ratio is also higher than 4, which fail to meet the prescribed nutritional health index and lead to the imbalance of FA intake by consumers. Therefore, it is necessary to adjust the FA to a more favorable P/S and n-6/n-3 PUFA ratio to meet human health needs.

Studies have shown that there are phenotypic correlations among most of the FA compositions. C18:2n6c was negatively correlated with C16:0, PUFA was negatively correlated with SFA, MUFA, n-6/n-3 PUFA, and M/S, PUFA was positively correlated with n-6 PUFA, n-3 PUFA, P/S, which are consistent with the results reported in the study of Italian Large White Pig (Zappaterra et al., 2019). Wang et al. (2015) reported a significant positive correlation between C18:3 and C16:0 in Brassica napus, however, C18:3 was positively but not significantly correlated with C16:0 in this study. C18:3n3 was significantly negatively correlated with C18:0 in soybean (Zhou et al., 2019), and our study is consistent with it. As PUFA plays an important role in various physiological processes. We analyzed the gene expression patterns between H-PUFA and L-PUFA groups to gain insight into the signaling pathways and biological processes associated with PUFA and identified key genes related to PUFA.

Transcriptome sequencing of fat and liver tissues with extremely high and extremely low PUFA content in pig muscle has been performed previously, APOB, THEM5, ME3, ACACA, FACN, and SCD genes associated with lipid and FA metabolism are identified (Ramayo-Caldas et al., 2012; Corominas et al., 2013). Zhang et al. (2022) and Silva-Vignato et al. (2017) identified fat synthesis, metabolism pathways, and genes that affect FA composition in the muscle and adipose tissues of cattle. CD36, FADS2, ACSL1, ELOVL6, and CTBP2 genes are associated with PUFA in fish muscle, liver, and brain tissues (Zhang et al., 2019). However, less attention has been paid to transcriptome sequencing to analyze FA in sheep. We identified 412 DEGs between H-PUFA and L-PUFA groups, the DEGs were significantly enriched in the regulation of lipolysis in adipocytes, the AMPK signaling pathway, and the PPAR signaling pathway that regulate fat metabolism and deposition. PLIN1, LIPE, FABP4, LEP, ACACA, ADIPOQ, SCD, PCK2, FASN, and PLIN2 are key genes affecting PUFA. Perilipin 1 (PLIN1) regulates lipolysis and lipid storage in adipocytes and is involved in the regulation of lipolysis by interacting with hormone-sensitive lipase (HSL) and adipose triglyceride lipase (ATGL) (Contreras et al., 2017). Overexpression of PLIN1 upregulated the expression of FA and triglyceride (TAG) synthesis molecule sterol regulator element-binding protein-1c (SREBP-1c), and its target genes diacylglycerol acyltransferase 1 (DGAT1) and DGAT2, however, the expression of comparative gene identification-58 (CGI-58) for ATGL and HSL were inhibited, thereby increasing FA and TAG synthesis and inhibiting lipolysis (Zhang et al., 2018b). Hormone-sensitive lipase (LIPE) affects lipid metabolism, hydrolyzing FA stored in TAG in adipose tissues to release free FA (Fang et al., 2017). Acetyl-CoA carboxylase alpha (ACACA) is involved in FA biosynthesis, and ACACA encodes an enzyme that catalyzes the carboxylation of acetyl-CoA to malonyl-CoA, leading to the biosynthesis of de novo FA from C4:0 to C16:0 (Chilliard et al., 2000). Stearoyl-CoA desaturase (SCD) is located in endoplasmic reticulum and membrane cells, catalyzing the biosynthesis of MUFA from SFA (Ntambi and Miyazaki, 2004). Fatty acid synthase (FASN) affects fat and FA content, plays a key role in lipid metabolism, and catalyzes the de novo synthesis of short- and medium-chain FA (Pecka-Kiełb et al., 2021). Polymorphisms in LIPE, ACACA, SCD, and FASN were significantly associated with FA compositions, which are promising marker genes for improving FA compositions (Henriquez-Rodriguez et al., 2015; Pegolo et al., 2016; Fang et al., 2017; Zappaterra et al., 2019). PLIN2 and fatty acid binding protein 4 (FABP4) are target genes of peroxisome proliferators-activated receptor γ (PPARγ), FABP4 is associated with fat deposition and is involved in the binding and transport of long-chain FA (Yan et al., 2018). Hoashi et al. (2008) reported that C16:1 and C18:2 in Japanese black cattle LT muscle were significantly associated with a missense mutation in FABP4. In general, the significantly enriched signaling pathways identified in this study all affect lipid and FA metabolism, and these DEGs described above are key genes that play an important role in FA composition.

Six modules associated with FA traits were observed by WGCNA, among them, four modules were associated with PUFA-related traits. We found some key genes affecting lipid and FA metabolism, including LEP, ADIPOQ, LPL, FABP3, THRSP, and ACADVL. Among them, LEP, ADIPOQ, and THRSP are DEGs. Adiponectin (ADIPOQ) is significantly enriched in PPAR signaling pathways. LEP is significantly enriched in AMPK signaling pathways, GO:0005576 and GO:0005102, respectively. LEP is an adipocyte-derived hormone that regulates energy homeostasis, metabolism, and neuroendocrine responses to nutritional alterations. SNPs in LEP were significantly associated with PUFA (C18:3n3, C20:5n3), SFA, and MUFA in the LT muscle of Simmental bulls (Orrù et al., 2011). ADIPOQ is an adipose tissue-specific protein, which is an adipocytokine produced in large quantities by adipose tissue (Ahima, 2006). It was found that ADIPOQ affects lipid metabolism by promoting FA oxidation and inhibiting lipid synthesis (Díez and Iglesias, 2003). The results of DEGs functional enrichment and WGCNA analysis provide important candidate genes for FA metabolism and deposition. Based on these findings, there is a need to continue the search for molecular markers to provide a theoretical basis for the selection of sheep FA composition and the breeding of new breeds.

In this study, the DEGs were significantly enriched in signaling pathways that regulate lipid and FA metabolism. The PPAR signaling pathway is a pathway related to the FA metabolism and the differentiation of adipogenesis, more long-chain fatty acids may have an important influence on the signal transduction of the PPAR signaling pathway (Huang et al., 2017). PLIN2 is significantly enriched in the PPAR signaling pathway. PLIN2 is an important lipid droplet protein that prevents lipase from entering the lipid droplet and maintains fat deposition (Huang et al., 2017). PLIN2 knockdown marginally increases FA oxidation and decreases lipid droplet formation (Bosma et al., 2012). PLIN2 promotes the uptake of long-chain fatty acids. PLIN2 mRNA expression is affected by the dose and timing of long-chain fatty acids, while short-chain fatty acids have no significant effect on it (Gao et al., 2000). Protein expression of PLIN2 is significantly increased after oleic acid treatment of glioblastoma multiforme cells (Taïb et al., 2019). PLIN2 (g.98G>A) was significantly correlated with the intramuscular fat content and water-holding capacity of pig muscle (Polasik et al., 2019). Li et al. (2020) reported that c.302T>C in PLIN2 significantly affects medium-chain fatty acid and long-chain fatty acid in cow’s milk. However, there are no reports of molecular markers for the PLIN2 gene in sheep. In this study, the SNPs that significantly affected FA phenotype were obtained by a large sample size of genotyping data. We found that the FA types significantly affected by four SNPs and genotype combinations were all long-chain fatty acids. Of which, SNP4 significantly affected C18:3n3, and genotype combinations significantly affected C18:3n3 and C22:6n3. It was confirmed that PLIN2 acts on long-chain fatty acids and also affects PUFA (C18:3n3, C22:6n3). Therefore, based on association analysis with a large sample size, we obtained SNPs that significantly affected fatty acids. We conclude that the SNPs in PLIN2 may affect sheep FA component levels, and the four SNPs in PLIN2 could be used as potential genetic markers for improving FA composition in sheep.

Conclusion

This study identified 29 FA compositions and 8 FA groups in 1,085 Hu sheep, with the highest content of MUFA (46.54%, mainly C18:1n9c), followed by SFA (44.32%, mainly C16:0) and PUFA (8.72%, mainly C18:2n6c), and significant correlations were observed among most FA traits. Based on the transcriptomic analysis between H-PUFA and L-PUFA groups, 3 KEGG pathways and 17 GO terms were found to have critical roles in adipogenesis and lipometabolism, and 37 module genes associated with PUFA-related traits were identified by WGCNA. In general, PLIN1, LIPE, FABP4, LEP, ACACA, ADIPOQ, SCD, PCK2, FASN, PLIN2, LPL, FABP3, THRSP, and ACADVL may have great impacts on PUFA metabolism and lipidosis. In addition, four SNPs were identified in PLIN2, of which SNP4 (g.7807 T>C) significantly affected PUFA (C18:3n3), and genotype combinations significantly affected PUFA (C18:3n3, C22:6n3). These results provide new insights into improving FA composition in sheep.

Supplementary Material

skac382_suppl_Supplementary_Figures
Click here for additional data file. (287.9KB, docx)
skac382_suppl_Supplementary_Tables
Click here for additional data file. (30.8KB, xlsx)

Acknowledgments

This research was supported by the National Natural Science Foundation of China (32072713, 31872319), the earmarked fund for the China Agriculture Research System (CARS-38), Gansu Key R&D Project (20YF8NH158, 21YF5NH213).

Glossary

Abbreviations

LT

longissimus thoracis

FA

fatty acid

SFA

saturated fatty acid

MUFA

monounsaturated fatty acid

PUFA

polyunsaturated fatty acid

SNP

single nucleotide polymorphism

DEG

differentially expressed gene

WGCNA

weighted gene co-expression network analysis

iMLDR

improved multiplex ligase detection reaction

PIC

polymorphism information content

Ho

heterozygosity observed

He

heterozygosity expected

HWE

Hardy–Weinberg equilibrium

CC

cell components

BP

biological processes

MF

molecular functions

Contributor Information

Yuanyuan Kong, State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China.

Chongyang Liu, State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China.

Xueying Zhang, State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China.

Xing Liu, State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China.

Wenqiao Li, State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China.

Fadi Li, State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China.

Xinji Wang, Extension Station of Animal Husbandry and Veterinary Medicine in Minqin, Minqin County 733300, China.

Xiangpeng Yue, State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems; Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs; Engineering Research Center of Grassland Industry, Ministry of Education; College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China.

Data Availability Statement

All sequencing data are available through the NCBI Sequence Read Archive (Bio Project ID: PRJNA885411).

Conflict of Interest Statement

The authors declare that they have no conflict of interest.

Literature Cited

  1. Adeli, K., and Ogbonna G.. . 1990. Rapid purification of human DNA from whole blood for potential application in clinical chemistry laboratories. Clin. Chem. 36:261–264. doi: 10.1093/clinchem/36.2.261. [DOI] [PubMed] [Google Scholar]
  2. Ahima, R. S. 2006. Metabolic actions of adipocyte hormones: focus on adiponectin. Obesity (Silver Spring) 14:9s–15s. doi: 10.1038/oby.2006.276. [DOI] [PubMed] [Google Scholar]
  3. Andrade, E. N., Polizel Neto A., Roça R. O., Faria M. H., Resende F. D., Siqueira G. R., and Pinheiro R. S.. . 2014. Beef quality of young Angus×Nellore cattle supplemented with rumen-protected lipids during rearing and fatting periods. Meat Sci. 98:591–598. doi: 10.1016/j.meatsci.2014.05.028. [DOI] [PubMed] [Google Scholar]
  4. Barrett, J. C., Fry B., Maller J., and Daly M. J.. . 2005. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21:263–265. doi: 10.1093/bioinformatics/bth457. [DOI] [PubMed] [Google Scholar]
  5. Bosma, M., Hesselink M. K., Sparks L. M., Timmers S., Ferraz M. J., Mattijssen F., van Beurden D., Schaart G., de Baets M. H., Verheyen F. K., . et al. 2012. Perilipin 2 improves insulin sensitivity in skeletal muscle despite elevated intramuscular lipid levels. Diabetes 61:2679–2690. doi: 10.2337/db11-1402. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cai, J., Wang D. M., Zhao F. Q., Liang S. L., and Liu J. X.. . 2020. AMPK-mTOR pathway is involved in glucose-modulated amino acid sensing and utilization in the mammary glands of lactating goats. J. Anim. Sci. Biotechnol. 11:32. doi: 10.1186/s40104-020-0434-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Calder, P. C. 2015. Functional roles of fatty acids and their effects on human health. J Parenter. Enteral. Nutr. 39(1 Suppl):18s–32s. doi: 10.1177/0148607115595980. [DOI] [PubMed] [Google Scholar]
  8. Chilliard, Y., Ferlay A., Faulconnier Y., Bonnet M., Rouel J., and Bocquier F.. . 2000. Adipose tissue metabolism and its role in adaptations to undernutrition in ruminants. Proc. Nutr. Soc. 59:127–134. doi: 10.1017/s002966510000015x. [DOI] [PubMed] [Google Scholar]
  9. Chomczynski, P., and Sacchi N.. . 1987. Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction. Anal. Biochem. 162:156–159. doi: 10.1006/abio.1987.9999. [DOI] [PubMed] [Google Scholar]
  10. Contreras, G. A., Strieder-Barboza C., and Raphael W.. . 2017. Adipose tissue lipolysis and remodeling during the transition period of dairy cows. J. Anim. Sci. Biotechnol. 8:41. doi: 10.1186/s40104-017-0174-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Corominas, J., Ramayo-Caldas Y., Puig-Oliveras A., Estellé J., Castelló A., Alves E., Pena R. N., Ballester M., and Folch J. M.. . 2013. Analysis of porcine adipose tissue transcriptome reveals differences in de novo fatty acid synthesis in pigs with divergent muscle fatty acid composition. BMC Genom. 14:843. doi: 10.1186/1471-2164-14-843. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Criado-Mesas, L., Ballester M., Crespo-Piazuelo D., Castelló A., Benítez R., Fernández A. I., and Folch J. M.. . 2019. Analysis of porcine IGF2 gene expression in adipose tissue and its effect on fatty acid composition. PLoS One 14:e0220708. doi: 10.1371/journal.pone.0220708. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Díez, J. J., and Iglesias P.. . 2003. The role of the novel adipocyte-derived hormone adiponectin in human disease. Eur. J. Endocrinol. 148:293–300. doi: 10.1530/eje.0.1480293. [DOI] [PubMed] [Google Scholar]
  14. Fang, X. B., Zhao Z. H., Jiang P., Yu H. B., Xiao H., and Yang R. J.. . 2017. Identification of the bovine HSL gene expression profiles and its association with fatty acid composition and fat deposition traits. Meat Sci. 131:107–118. doi: 10.1016/j.meatsci.2017.05.003. [DOI] [PubMed] [Google Scholar]
  15. Folch, J., Lees M., and Sloane Stanley G. H.. . 1957. A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 226:497–509. doi: 10.1016/s0021-9258(18)64849-5. [DOI] [PubMed] [Google Scholar]
  16. Gao, J., Ye H., and Serrero G.. . 2000. Stimulation of adipose differentiation related protein (ADRP) expression in adipocyte precursors by long-chain fatty acids. J. Cell. Physiol. 182:297–302. doi: 10.1002/(SICI)1097-4652(200002)182:2<297::AID-JCP19>3.0.CO;2-Z. [DOI] [PubMed] [Google Scholar]
  17. He, K., Wang Q. S., Wang Z., and Pan Y. C.. . 2013. Association study between gene polymorphisms in PPAR signaling pathway and porcine meat quality traits. Mamm. Genome 24:322–331. doi: 10.1007/s00335-013-9460-4. [DOI] [PubMed] [Google Scholar]
  18. Henriquez-Rodriguez, E., Tor M., Pena R. N., and Estany J.. . 2015. A polymorphism in the stearoyl-CoA desaturase gene promoter increases monounsaturated fatty acid content in dry-cured ham. Meat Sci. 106:38–43. doi: 10.1016/j.meatsci.2015.03.019. [DOI] [PubMed] [Google Scholar]
  19. Hoashi, S., Hinenoya T., Tanaka A., Ohsaki H., Sasazaki S., Taniguchi M., Oyama K., Mukai F., and Mannen H.. . 2008. Association between fatty acid compositions and genotypes of FABP4 and LXR-alpha in Japanese black cattle. BMC Genet. 9:84. doi: 10.1186/1471-2156-9-84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hoopes, L. A., Clauss T. M., Browning N. E., Delaune A. J., Wetherbee B. M., Shivji M., Harvey J. C., and Harvey G. C. M.. . 2020. Seasonal patterns in stable isotope and fatty acid profiles of southern stingrays (Hypanus americana) at Stingray City Sandbar, Grand Cayman. Sci. Rep. 10:19753. doi: 10.1038/s41598-020-76858-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Horiguchi, S., Nakayama K., Iwamoto S., Ishijima A., Minezaki T., Baba M., Kontai Y., Horikawa C., Kawashima H., Shibata H., . et al. 2016. Associations between a fatty acid desaturase gene polymorphism and blood arachidonic acid compositions in Japanese elderly. Prostaglandins Leukot. Essent. Fatty Acids 105:9–14. doi: 10.1016/j.plefa.2015.11.005. [DOI] [PubMed] [Google Scholar]
  22. Huang, W. L., Guo Y. T., Du W. H., Zhang X. X., Li A., and Miao X. Y.. . 2017. Global transcriptome analysis identifies differentially expressed genes related to lipid metabolism in Wagyu and Holstein cattle. Sci. Rep. 7:5278. doi: 10.1038/s41598-017-05702-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kong, Y. Y., Yuan Z. H., Liu X., Li F. D., and Yue X. P.. . 2022. A novel SNP within LIPE gene is highly associated with sheep intramuscular fat content. Small Rumin. Res. 209:106658. doi: 10.1016/j.smallrumres.2022.106658. [DOI] [Google Scholar]
  24. Liu, R. L., Liu X. X., Bai X. J., Xiao C. Z., and Dong Y. J.. . 2020. Different expression of lipid metabolism-related genes in Shandong black cattle and Luxi cattle based on transcriptome analysis. Sci. Rep. 10:21915. doi: 10.1038/s41598-020-79086-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Li, Y. H., Zhou H., Cheng L., Zhao J., and Hickford J. G. H.. . 2020. Variation in PLIN2 and its association with milk traits and milk fat composition in dairy cows. J. Agric. Sci 158:774–780. doi: 10.1017/S0021859621000174. [DOI] [Google Scholar]
  26. Michaelidou, K., Tzovaras A., Missitzis I., Ardavanis A., and Scorilas A.. . 2013. The expression of the CEACAM19 gene, a novel member of the CEA family, is associated with breast cancer progression. Int. J. Oncol. 42:1770–1777. doi: 10.3892/ijo.2013.1860. [DOI] [PubMed] [Google Scholar]
  27. Ntambi, J. M., and Miyazaki M.. . 2004. Regulation of stearoyl-CoA desaturases and role in metabolism. Prog. Lipid Res. 43:91–104. doi: 10.1016/s0163-7827(03)00039-0. [DOI] [PubMed] [Google Scholar]
  28. Orrù, L., Cifuni G. F., Piasentier E., Corazzin M., Bovolenta S., and Moioli B.. . 2011. Association analyses of single nucleotide polymorphisms in the LEP and SCD1 genes on the fatty acid profile of muscle fat in Simmental bulls. Meat Sci. 87:344–348. doi: 10.1016/j.meatsci.2010.11.009. [DOI] [PubMed] [Google Scholar]
  29. Ovilo, C., Benítez R., Fernández A., Núñez Y., Ayuso M., Fernández A. I., Rodríguez C., Isabel B., Rey A. I., López-Bote C., . et al. 2014. Longissimus dorsi transcriptome analysis of purebred and crossbred Iberian pigs differing in muscle characteristics. BMC Genomics. 15:413. doi: 10.1186/1471-2164-15-413. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Pecka-Kiełb, E., Kowalewska-Łuczak I., Czerniawska-Piątkowska E., and Króliczewska B.. . 2021. FASN, SCD1 and ANXA9 gene polymorphism as genetic predictors of the fatty acid profile of sheep milk. Sci. Rep. 11:23761. doi: 10.1038/s41598-021-03186-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Pegolo, S., Cecchinato A., Mele M., Conte G., Schiavon S., and Bittante G.. . 2016. Effects of candidate gene polymorphisms on the detailed fatty acids profile determined by gas chromatography in bovine milk. J. Dairy Sci. 99:4558–4573. doi: 10.3168/jds.2015-10420. [DOI] [PubMed] [Google Scholar]
  32. Pothakam, N., Supakankul P., Norseeda W., Liu G., Teltathum T., Naraballobh W., Khamlor T., Sringarm K., and Mekchay S.. . 2021. Association of adipocytokine IL-1A and IL-6 genes with intramuscular fat content and fatty acid composition in pigs. Meat Sci. 179:108554. doi: 10.1016/j.meatsci.2021.108554. [DOI] [PubMed] [Google Scholar]
  33. Polasik, D., Eva-Maria K., Mirosław T., Grzegorz Z., and Arkadiusz T.. . 2019. Analysis of FTO and PLIN2 polymorphisms in relation to carcass and meat quality traits in pigs. Ann. Anim. Sci. 19:71–83. doi: 10.2478/aoas-2018-0053. [DOI] [Google Scholar]
  34. Rafalski, A. 2002. Applications of single nucleotide polymorphisms in crop genetics. Curr. Opin Plant Biol. 5:94–100. doi: 10.1016/s1369-5266(02)00240-6. [DOI] [PubMed] [Google Scholar]
  35. Ramayo-Caldas, Y., Mach N., Esteve-Codina A., Corominas J., Castelló A., Ballester M., Estellé J., Ibáñez-Escriche N., Fernández A. I., Pérez-Enciso M., . et al. 2012. Liver transcriptome profile in pigs with extreme phenotypes of intramuscular fatty acid composition. BMC Genomics. 13:547. doi: 10.1186/1471-2164-13-547. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Rovadoscki, G. A., Pertile S. F. N., Alvarenga A. B., Cesar A. S. M., Pértille F., Petrini J., Franzo V., Soares W. V. B., Morota G., Spangler M. L., . et al. 2018. Estimates of genomic heritability and genome-wide association study for fatty acids profile in Santa Inês sheep. BMC Genomics. 19:375. doi: 10.1186/s12864-018-4777-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Schlötterer, C. 2004. The evolution of molecular markers—just a matter of fashion? Nat. Rev. Genet. 5:63–69. doi: 10.1038/nrg1249. [DOI] [PubMed] [Google Scholar]
  38. Schwingshackl, L., and Hoffmann G.. . 2012. Monounsaturated fatty acids and risk of cardiovascular disease: synopsis of the evidence available from systematic reviews and meta-analyses. Nutrients 4:1989–2007. doi: 10.3390/nu4121989. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Scollan, N. D., Price E. M., Morgan S. A., Huws S. A., and Shingfield K. J.. . 2017. Can we improve the nutritional quality of meat? Proc. Nutr. Soc. 76:603–618. doi: 10.1017/S0029665117001112. [DOI] [PubMed] [Google Scholar]
  40. Shi, L. J., Han B., Liu L., Lv X. Q., Ma Z., Li C., Xu L. N., Li Y. H., Zhao F., Yang Y. Z., . et al. 2019. Determination of genetic effects of LIPK and LIPJ genes on milk fatty acids in dairy cattle. Genes 10:86. doi: 10.3390/genes10020086. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Silva-Vignato, B., Coutinho L. L., Cesar A. S. M., Poleti M. D., Regitano L. C. A., and Balieiro J. C. C.. . 2017. Comparative muscle transcriptome associated with carcass traits of Nellore cattle. BMC Genomics. 18:506. doi: 10.1186/s12864-017-3897-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Taïb, B., Aboussalah A. M., Moniruzzaman M., Chen S., Haughey N. J., Kim S. F., and Ahima R. S.. . 2019. Lipid accumulation and oxidation in glioblastoma multiforme. Sci. Rep. 9:19593. doi: 10.1038/s41598-019-55985-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Tanaka, T., Shen J., Abecasis G. R., Kisialiou A., Ordovas J. M., Guralnik J. M., Singleton A., Bandinelli S., Cherubini A., Arnett D., . et al. 2009. Genome-wide association study of plasma polyunsaturated fatty acids in the InCHIANTI Study. PLoS Genet. 5:e1000338. doi: 10.1371/journal.pgen.1000338. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Van Harten, S., Kilminster T., Scanlon T., Milton J., Oldham C., Greeff J., and Almeida A. M.. . 2016. Fatty acid composition of the ovine longissimus dorsi muscle: effect of feed restriction in three breeds of different origin. J. Sci. Food Agric. 96:1777–1782. doi: 10.1002/jsfa.7285. [DOI] [PubMed] [Google Scholar]
  45. Wang, Q. Y., Wang Y. C., Hussain T., Dai C. P., Li J. Z., Huang P. F., Li Y. L., Ding X. Q., Huang J., Ji F. J., . et al. 2020. Effects of dietary energy level on growth performance, blood parameters and meat quality in fattening male Hu lambs. J. Anim. Physiol. Anim. Nutr 104:418–430. doi: 10.1111/jpn.13278. [DOI] [PubMed] [Google Scholar]
  46. Wang, X. D., Long Y., Yin Y. T., Zhang C. Y., Gan L., Liu L. Z., Yu L. J., Meng J. L., and Li M. T.. . 2015. New insights into the genetic networks affecting seed fatty acid concentrations in Brassica napus. BMC Plant Biol. 15:91. doi: 10.1186/s12870-015-0475-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. William, W. N., Jr., Ceddia R. B., and Curi R.. . 2002. Leptin controls the fate of fatty acids in isolated rat white adipocytes. J. Endocrinol. 175:735–744. doi: 10.1677/joe.0.1750735. [DOI] [PubMed] [Google Scholar]
  48. Wood, J. D., Richardson R. I., Nute G. R., Fisher A. V., Campo M. M., Kasapidou E., Sheard P. R., and Enser M.. . 2004. Effects of fatty acids on meat quality: a review. Meat Sci. 66:21–32. doi: 10.1016/S0309-1740(03)00022-6. [DOI] [PubMed] [Google Scholar]
  49. Yan, W., Zhou H. T., Hu J., Luo Y. Z., and Hickford J. G. H.. . 2018. Variation in the FABP4 gene affects carcass and growth traits in sheep. Meat Sci. 145:334–339. doi: 10.1016/j.meatsci.2018.07.007. [DOI] [PubMed] [Google Scholar]
  50. Ye, M. H., Zhou B., Wei S. S., Ding M. M., Lu X. H., Shi X. H., Ding J. T., Yang S. M., and Wei W. H.. . 2016. Transcriptomic analysis identifies candidate genes related to intramuscular fat deposition and fatty acid composition in the breast muscle of squabs (columba). G3 (Bethesda) 6:2081–2090. doi: 10.1534/g3.116.029793. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Yuan, Z. H., Luo J., Wang L., Li F. D., Li W. H., and Yue X. P.. . 2020. Expression of DAZL gene in selected tissues and association of its polymorphisms with testicular size in Hu sheep. Animals 10:740. doi: 10.3390/ani10040740. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Zappaterra, M., Luise D., Zambonelli P., Mele M., Serra A., Costa L. N., and Davoli R.. . 2019. Association study between backfat fatty acid composition and SNPs in candidate genes highlights the effect of FASN polymorphism in large white pigs. Meat Sci. 156:75–84. doi: 10.1016/j.meatsci.2019.05.013. [DOI] [PubMed] [Google Scholar]
  53. Zhang, H. Y., Xu P., Jiang Y. L., Zhao Z. X., Feng J. X., Tai R. Y., Dong C. J., and Xu J.. . 2019. Genomic, transcriptomic, and epigenomic features differentiate genes that are relevant for muscular polyunsaturated fatty acids in the common carp. Front. Genet. 10:217. doi: 10.3389/fgene.2019.00217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Zhang, H. M., Xia H. L., Jiang H. R., Mao Y. J., Qu K. X., Huang B. Z., Gong Y. C., and Yang Z. P.. . 2018a. Longissimus dorsi muscle transcriptomic analysis of Yunling and Chinese simmental cattle differing in intramuscular fat content and fatty acid composition. Genome 61:549–558. doi: 10.1139/gen-2017-0164. [DOI] [PubMed] [Google Scholar]
  55. Zhang, S. Q., Liu G. W., Xu C., Liu L., Zhang Q., Xu Q. S., Jia H. D., Li X. B., and Li X. W.. . 2018b. Perilipin 1 mediates lipid metabolism homeostasis and inhibits inflammatory cytokine synthesis in bovine adipocytes. Front. Immunol. 9:467. doi: 10.3389/fimmu.2018.00467. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Zhang, T. L., Niu Q. H., Wang T. Z., Zheng X., Li H. P., Gao X., Chen Y., Gao H. J., Zhang L. P., Liu G. E., . et al. 2022. Comparative transcriptomic analysis reveals diverse expression pattern underlying fatty acid composition among different beef cuts. Foods 11:117. doi: 10.3390/foods11010117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Zhang, T. Y., Huang J. T., Tian H. B., Ma Y., Chen Z., Wang J. J., Shi H. P., and Luo J.. . 2018c. Trans-10, cis-12 conjugated linoleic acid alters lipid metabolism of goat mammary epithelial cells by regulation of de novo synthesis and the AMPK signaling pathway. J. Dairy Sci. 101:5571–5581. doi: 10.3168/jds.2017-12822. [DOI] [PubMed] [Google Scholar]
  58. Zhang, Z. A., Niu X. L., Li F., Li F. D., and Guo L.. . 2020. Ruminal cellulolytic bacteria abundance leads to the variation in fatty acids in the rumen digesta and meat of fattening lambs. J. Anim. Sci. 98:skaa228. doi: 10.1093/jas/skaa228. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Zhao, H. Y., Wu X. F., Cai H. F., Pan C. Y., Lei C. Z., Chen H., and Lan X. Y.. . 2013. Genetic variants and effects on milk traits of the caprine paired-like homeodomain transcription factor 2 (PITX2) gene in dairy goats. Gene 532:203–210. doi: 10.1016/j.gene.2013.09.062. [DOI] [PubMed] [Google Scholar]
  60. Zhou, Z., Lakhssassi N., Cullen M. A., El Baz A., Vuong T. D., Nguyen H. T., and Meksem K.. . 2019. Assessment of phenotypic variations and correlation among seed composition traits in mutagenized soybean populations. Genes (Basel) 10:975. doi: 10.3390/genes10120975. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Zou, C., Karn A., Reisch B., Nguyen A., Sun Y., Bao Y., Campbell M. S., Church D., Williams S., Xu X., . et al. 2020. Haplotyping the Vitis collinear core genome with rhAmpSeq improves marker transferability in a diverse genus. Nat. Commun. 11:413. doi: 10.1038/s41467-019-14280-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

skac382_suppl_Supplementary_Figures
Click here for additional data file. (287.9KB, docx)
skac382_suppl_Supplementary_Tables
Click here for additional data file. (30.8KB, xlsx)

Data Availability Statement

All sequencing data are available through the NCBI Sequence Read Archive (Bio Project ID: PRJNA885411).

Articles from Journal of Animal Science are provided here courtesy of Oxford University Press

RESOURCES