Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (6): 1239-1258.doi: 10.3864/j.issn.0578-1752.2025.06.014

• ANIMAL SCIENCE·VETERINARY SCIENCE • Previous Articles    

The Combination of Lipidome and Transcriptome Revealed the Differential Expression Patterns of Lipid Characteristics in Different Muscle Tissues for Nanyang Cattle

GAO YanHao1(), WANG TingTing1, BAI WeiWei1, DU XingJie1, LIU Xian2, QIN BenYuan2, FU Tong1, SUN Yu1, GAO TengYun1(), ZHANG TianLiu1()   

  1. 1 College of Animal Science and Technology, Henan Agricultural University, Zhengzhou 450046
    2 Animal Husbandry Technology Extension Station of Henan Province, Zhengzhou 450002
  • Received:2024-11-05 Accepted:2025-02-20 Online:2025-03-25 Published:2025-03-25
  • Contact: GAO TengYun, ZHANG TianLiu

Abstract:

【Objective】 The composition and content of intramuscular lipids are important factors affecting flavor and tenderness of beef, and are closely related to beef quality. In this study, by comparing the lipid characteristics and gene expression patterns of longissimus dorsi muscle and tenderloin tissue of Nanyang cattle, the potential candidate genes regulating lipid characteristics of different muscle tissues in Nanyang cattle were identified. 【Method】 The longissimus dorsi muscle and tenderloin tissues of adult Nanyang cattle with the same growth environment and genetic background were selected as experimental materials, and then the lipid profile and gene expression profile of muscle tissue were constructed by gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and transcriptome sequencing (RNA-seq) to identify differential lipid molecules (DLMs) and differentially expressed genes (DEGs) between different tissues. Functional enrichment analysis and protein-protein interaction network (PPI) were performed to screen potential candidate genes, and real-time fluorescent quantitative PCR (RT-qPCR) was used to verify their expression levels. 【Result】 In this study, 19 kinds of fatty acids were detected in muscle tissue, among which the content of C18:0, C14:1n5 and C16:1n7 were significantly different between tissues. A total of 2 106 lipid molecules were detected, of which Phosphatidylcholine (PC), Triacylglycerols (TG) and Phosphatidylethanolamine (PE) were the main components. A total of 39 DLMs and 3,424 DEGs were screened between two muscle tissues by difference analysis. According to receiver operating characteristic curve (ROC) analysis, 13 DLMs (e.g. DG(16:0_18:1), DG(18:0_18:1), DG(18:0_18:2)) could be used as potential lipid biomarkers between tissues. PPI and MCODE analysis obtained three core gene modules closely related to lipid metabolism. Pathway enrichment analysis showed that DEGs and DLMs were involved in Inositol phosphate metabolism, Glycerolipid metabolism and Glycerophospholipid metabolism. Integration analysis identified 19 potential candidate genes with different lipid characteristics, among which 7 genes (PLCG2, SYNJ1, LPIN1, DGKZ, DGAT1, LPL and SELENOI) were located in key positions in the pathway, and had direct regulatory effects on DLMs. RT-qPCR showed that the expression trend of six candidate genes was consistent with that of RNA-seq. 【Conclusion】 In this study, 13 potential lipid biomarkers were identified and 19 potential candidate genes were screened for the key metabolic pathways involved in the regulation of lipid characteristics between longissimus dorsi muscle and tenderloin tissue for Nanyang cattle, which provided a theoretical basis for further exploration of the regulatory mechanism for lipid difference formation in Nanyang cattle and molecular breeding for high-quality beef.

Key words: Nanyang cattle, lipid biomarkers, lipidome, transcriptome, candidate gene

Table 1

The table of gene primer sequences"

基因 Gene symbol 序列号 Accession number 引物序列 Primer sequence (5'-3') 产物长度 Product length (bp)
GAPDH NM_001034034.2 F:ATGCTGGTGCTGAGTATGTG
R:GTGTCGCTGTTGAAGTCG
604
LPL NM_001075120.1 F:GCCGCAGACAGGATTACAGG
R:CCAGGAATGAGGTGGCAAGT
107
ETNK1 XM_002687736.6 F:GCTACCACCACTTTCTGGGA
R:TGTTATCCCACTGTACCGCC
292
DGKZ NM_001278652.1 F:TTTTCGGGCACAGGAAAGC
R:GGGACACTGATTTCAGCATCTT
231
LYPLA1 NM_001034688.2 F:CACTGCCTTTACTGTGGATTGC
R:TACACAAGGCCTCTTGGTGG
293
GPCPD1 XM_002692364.7 F:GATCTCGGACAACCCCCATT
R:GGCTGTTCAGGCATCCAATC
241
PCYT1A NM_001105052.2 F:GTGCTGGTATATGGGTGCCT
R:TTGTCACTCTTGCTCGCTGG
166

Table 2

Intramuscular fat content of longissimus dorsi muscle and tenderloin"

组别 Group n 最大值 Maximum (%) 最小值 Minimum (%) 平均值 Average(%) PP value
背最长肌 Longissimus_dorsi 5 10.63 9.46 9.97±0.006 0.208318
里脊 Tenderloin 5 15.65 9.38 13.18±3.338

Fig. 1

Fatty acid composition analysis between longissimus dorsi and tenderloin tissues"

Fig. 2

Comparative analysis of lipid composition characteristics between longissimus dorsi and tenderloin tissues A: PCA score map; B: OPLS-DA model score map; C: Histogram and pie chart of the quantity distribution of lipid subclasses; D: Comparative analysis of total lipid content between tissues; E: Comparative analysis of lipid subclass content between tissues"

Fig. 3

Lipidomics data analysis of longissimus dorsi and tenderloin A: Volcano map of DLMs between different muscle tissues; B: Clustering heat map of DLMs; C: Functional enrichment analysis of DLMs; D: ROC curve of potential lipid biomarkers"

Table 3

Detailed information on potential lipid biomarkers between muscle tissues"

脂质分子
Lipid molecule
脂质离子
Lipid ion
亚类
Subclass
质荷比
m/z
时间
Time (min)
P
P value
VIP值
VIP value
丰度高/丰度低
High-abundance/Low-abundance
DG(16:0_18:1) DG(16:0_18:1)+NH4 DG 612.5561505 13.08173 0.0053 2.64 丰度高 High-abundance
DG(18:0_18:1) DG(18:0_18:1)+NH4 DG 640.5874505 14.18853297 0.0000 3.51 丰度高 High-abundance
DG(18:0_18:2) DG(18:0_18:2)+NH4 DG 638.5718005 13.12742785 0.0020 3.40 丰度高 High-abundance
DG(37:4) DG(37:4)+H DG 631.5296015 13.45720732 0.0005 1.12 丰度高 High-abundance
DG(38:5) DG(38:5)+H DG 643.5296015 13.30231755 0.0137 2.13 丰度高 High-abundance
PC(11:0_22:1) PC(11:0_22:1)+Na PC 768.5513785 11.21609073 0.0039 2.04 丰度高 High-abundance
PC(35:4) PC(35:4)+H PC 768.5537835 11.2368014 0.0034 2.08 丰度高 High-abundance
PE(16:1e_18:2) PE(16:1e_18:2)-H PE 698.5130155 10.85689739 0.0088 2.37 丰度高 High-abundance
PE(18:0_18:1) PE(18:0_18:1)+Na PE 768.5513785 11.25022966 0.0055 4.24 丰度高 High-abundance
PE(18:0_20:4) PE(18:0_20:4)-H PE 766.5392305 11.23645543 0.0002 4.15 丰度高 High-abundance
PE(18:0e) PE(18:0e)+H PE 482.3241185 3.510563969 0.0097 2.60 丰度高 High-abundance
PE(18:2e_17:1) PE(18:2e_17:1)+H PE 714.5432185 11.53612853 0.0036 1.09 丰度高 High-abundance
SPH(d22:0) SPH(d22:0)+H SPH 358.3679555 3.081586854 0.0002 2.84 丰度低 Low-abundance

Table 4

Quality detection and analysis of transcriptome data"

样品
Sample
原始序列
Raw reads
原始碱基
Raw bases
(G)
质控序列
Clean reads
质控碱基
Clean bases
(G)
Q20
(%)
Q30
(%)
GC
(%)
序列比对率
Ratio of Mapped reads (%)
背最长肌_1
Longissimus_dorsi_1
44427252 6.71 43874518 6.61 96.97 94.64 48.38 97.56
背最长肌_2
Longissimus_dorsi_2
57740820 8.72 57298348 8.63 97.40 95.49 47.28 96.84
背最长肌_3
Longissimus_dorsi_3
57335318 8.66 56890740 8.56 97.39 95.48 50.01 97.37
背最长肌_4
Longissimus_dorsi_4
54104992 8.17 53663454 8.05 97.45 95.59 48.89 97.56
背最长肌_5
Longissimus_dorsi_5
55554150 8.39 55083896 8.22 97.38 95.46 49.48 96.66
里脊_1
Tenderloin_1
56825130 8.58 56433728 8.49 97.44 95.57 47.62 97.53
里脊_2
Tenderloin_2
56261048 8.50 55802264 8.40 97.42 95.53 47.81 97.51
里脊_3
Tenderloin_3
53378910 8.06 52998730 7.97 97.39 95.46 47.92 97.52
里脊_4
Tenderloin_4
53416466 8.07 52949120 7.98 97.23 95.14 47.10 96.86
里脊_5
Tenderloin_5
51869138 7.83 51266774 7.73 97.27 95.22 47.01 98.38
总和 All 540913224 81.69 536261572 80.64 - - - -
平均 Average 54091322.4 8.17 53626157.2 8.06 97.33 95.36 48.15 97.38

Fig. 4

The gene expression pattern analysis between longissimus dorsi and tenderloin tissues A: PCA score map; B: The clustering heat map of muscle samples; C: Volcano map of DEGs between different muscle tissues; D: Functional enrichment analysis of DEGs; E: PPI network analysis of genes related to lipid metabolism; F: GO functional annotation of gene module; G: KEGG enrichment analysis of gene modules"

Table 5

Gene expression and lipid molecule abundance in candidate pathways"

通路
Pathway
基因数目 Gene number 脂质数目 Lipid number
上调
Up-regulated
下调
Down-regulated
丰度高
High-abundance
丰度低
Low-abundance
肌醇磷酸代谢 Inositol phosphate metabolism 12 6 11 0
甘油酯代谢 Glycerolipid metabolism 12 5 11 2
甘油磷脂代谢 Glycerophospholipid metabolism 17 6 32 0

Fig. 5

Joint analysis of lipidomic and transcriptomic data between longissimus dorsi and tenderloin tissues A: Correlation analysis of DLMs and DEGs in Inositol phosphate metabolism pathway; B: Correlation analysis of DLMs and DEGs in Glycerolipid metabolism pathway; C: Correlation analysis of DLMs and DEGs in Glycerophospholipid metabolism pathway; D: The regulatory relationship between DLMs and DEGs in lipid metabolism pathway"

Fig. 6

Analysis of fatty acid synthesis and metabolism and functional verification of candidate genes A: Analysis of fatty acid synthesis and metabolism; B: RT-qPCR of validation of candidate genes"

[1]
王中波, 刘爽, 贺丽霞, 冯雪, 杨梦丽, 汪书哲, 刘源, 冯兰, 丁晓玲, 冀国尚, 杨润军, 张路培, 马云. 固原黄牛不同部位肌肉组织代谢组学分析. 畜牧兽医学报, 2024, 55(4): 1565-1578.

doi: 10.11843/j.issn.0366-6964.2024.04.020
WANG Z B, LIU S, HE L X, FENG X, YANG M L, WANG S Z, LIU Y, FENG L, DING X L, JI G S, YANG R J, ZHANG L P, MA Y. Metabolomics analysis on different muscle tissues of Guyuan cattle. Acta Veterinaria et Zootechnica Sinica, 2024, 55(4): 1565-1578. (in Chinese)

doi: 10.11843/j.issn.0366-6964.2024.04.020
[2]
孙晓明, 张佳程, 卢凌, 张松山, 孙宝忠. 牛胴体部位肉营养成分和理化指标差异性分析. 中国畜牧兽医, 2011, 38(2): 205-208.
SUN X M, ZHANG J C, LU L, ZHANG S S, SUN B Z. Analyzing nutrients and physicochemical index of beef carcass cuts. China Animal Husbandry & Veterinary Medicine, 2011, 38(2): 205-208. (in Chinese)
[3]
RAZA S H A, KHAN R, ABDELNOUR S A, ABD EL-HACK M E, KHAFAGA A F, TAHA A, OHRAN H, MEI C G, SCHREURS N M, ZAN L S. Advances of molecular markers and their application for body variables and carcass traits in Qinchuan cattle. Genes, 2019, 10(9): 717.
[4]
张润, 杨曼, 王立贤, 张龙超. 畜禽肉中代谢物质对肉品质的影响及相关基因研究进展. 畜牧兽医学报, 2022, 53(8): 2444-2452.

doi: 10.11843/j.issn.0366-6964.2022.08.004
ZHANG R, YANG M, WANG L X, ZHANG L C. Research progress of effects of metabolic substances in meat of livestock and poultry on meat quality and the related genes. Acta Veterinaria et Zootechnica Sinica, 2022, 53(8): 2444-2452. (in Chinese)

doi: 10.11843/j.issn.0366-6964.2022.08.004
[5]
PARK S J, BEAK S H, JUNG D J S, KIM S Y, JEONG I H, PIAO M Y, KANG H J, FASSAH D M, NA S W, YOO S P, BAIK M. Genetic, management, and nutritional factors affecting intramuscular fat deposition in beef cattle: A review. Asian-Australasian Journal of Animal Sciences, 2018, 31(7): 1043-1061.
[6]
杨玉莹, 张一敏, 毛衍伟, 梁荣蓉, 董鹏程, 杨啸吟, 朱立贤, 罗欣, 张文华, 曹晖. 不同部位牦牛肉肌纤维特性与肉品质差异. 食品科学, 2019, 40(21): 72-77.

doi: 10.7506/spkx1002-6630-20181025-296
YANG Y Y, ZHANG Y M, MAO Y W, LIANG R R, DONG P C, YANG X Y, ZHU L X, LUO X, ZHANG W H, CAO H. Differences in myofiber characteristics and meat quality of different yak muscles. Food Science, 2019, 40(21): 72-77. (in Chinese)

doi: 10.7506/spkx1002-6630-20181025-296
[7]
SCOZZAFAVA G, CORSI A M, CASINI L, CONTINI C, LOOSE S M. Using the animal to the last bit: consumer preferences for different beef cuts. Appetite, 2016, 96: 70-79.

doi: S0195-6663(15)30015-5 pmid: 26363423
[8]
王思伟, 彭朋, 刘婷婷, 肖延仁, 王昆, 周荣艳. 调控肉牛肌内脂肪沉积的分子机制研究进展. 黑龙江畜牧兽医, 2024(14): 23-31.
WANG S W, PENG P, LIU T T, XIAO Y R, WANG K, ZHOU R Y. Research progress on molecular mechanisms regulating intramuscular fat deposition in beef cattle. Heilongjiang Animal Science and Veterinary Medicine, 2024(14): 23-31. (in Chinese)
[9]
YU H W, YU S C, GUO J T, WANG J F, MEI C G, ABBAS RAZA S H, CHENG G, ZAN L S. Comprehensive analysis of transcriptome and metabolome reveals regulatory mechanism of intramuscular fat content in beef cattle. Journal of Agricultural and Food Chemistry, 2024, 72(6): 2911-2924.

doi: 10.1021/acs.jafc.3c07844 pmid: 38303491
[10]
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, LI J Y, XU L Y. Comparative transcriptomic analysis reveals diverse expression pattern underlying fatty acid composition among different beef cuts. Foods, 2022, 11(1): 117.
[11]
SHEET S, JANG S S, LIM J A, PARK W, KIM D. Molecular signatures diversity unveiled through a comparative transcriptome analysis of longissimus dorsi and psoas major muscles in Hanwoo cattle. Animal Biotechnology, 2024, 35(1): 2379883.
[12]
李敬, 李娜, 孙宝忠, 李海鹏, 谢鹏, 郎玉苗, 丰永红, 刘亚娜, 郭江南, 王勇峰. 中国五大良种黄牛品质特性的研究进展. 肉类研究, 2015, 29(9): 34-37.
LI J, LI N, SUN B Z, LI H P, XIE P, LANG Y M, FENG Y H, LIU Y N, GUO J N, WANG Y F. Recent progress in the study of meat quality characteristics of five dominant cattle breeds. Meat Research, 2015, 29(9): 34-37. (in Chinese)
[13]
赵改名, 候建彪, 祝超智, 许龙, 崔文明, 祁兴山. 南阳黄牛不同部位肉品质特性差异分析. 肉类研究, 2022, 36(9): 1-6.
ZHAO G M, HOU J B, ZHU C Z, XU L, CUI W M, QI X S. Differences in meat quality characteristics of different carcass parts of Nanyang cattle. Meat Research, 2022, 36(9): 1-6. (in Chinese)
[14]
鲁云风, 高雪琴. 南阳牛肉品品质研究. 黑龙江畜牧兽医, 2007(5): 101.
LU Y F, GAO X Q. Study on the quality of Nanyang beef products. Heilongjiang Animal Science and Veterinary Medicine, 2007(5): 101. (in Chinese)
[15]
王复龙, 卢桂松, 王娜, 彭增起, 祁兴磊, 李玉林, 姚瑶, 李君珂, 朱易, 万可慧, 等. 南阳牛与延边牛牛肉感官品质和加工特性比较研究. 食品科学, 2013, 34(23): 62-66.

doi: 10.7506/spkx1002-6630-201323014
WANG F L, LU G S, WANG N, PENG Z Q, QI X L, LI Y L, YAO Y, LI J K, ZHU Y, WAN K H, et al. Comparative studies on beef sensory and processing characteristics of Nanyang and Yanbian yellow cattles. Food Science, 2013, 34(23): 62-66. (in Chinese)
[16]
THÉVENOT E A, ROUX A, XU Y, EZAN E, JUNOT C. Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. Journal of Proteome Research, 2015, 14(8): 3322-3335.

doi: 10.1021/acs.jproteome.5b00354 pmid: 26088811
[17]
CHEN S F, ZHOU Y Q, CHEN Y R, GU J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 2018, 34(17): i884-i890.
[18]
KIM D, LANGMEAD B, SALZBERG S L. HISAT: A fast spliced aligner with low memory requirements. Nature Methods, 2015, 12: 357-360.

doi: 10.1038/nmeth.3317 pmid: 25751142
[19]
LIAO Y, SMYTH G K, SHI W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 2014, 30(7): 923-930.

doi: 10.1093/bioinformatics/btt656 pmid: 24227677
[20]
LOVE M I, HUBER W, ANDERS S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 2014, 15(12): 550.
[21]
SU G, MORRIS J H, DEMCHAK B, BADER G D. Biological network exploration with cytoscape 3. Current Protocols in Bioinformatics, 2014, 47(1): 8-13.
[22]
BADER G D, HOGUE C W. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 2003, 4(1): 2.
[23]
OH M, KIM E K, JEON B T, TANG Y J, KIM M S, SEONG H J, MOON S H. Chemical compositions, free amino acid contents and antioxidant activities of Hanwoo (Bos taurus coreanae) beef by cut. Meat Science, 2016, 119: 16-21.
[24]
杜丽丽. 基于转录组与脂质代谢组联合分析鉴定华西牛脂肪沉积候选基因[D]. 北京: 中国农业科学院, 2022.
DU L L. Integrating transcriptomics and lipid metabolomics to identify candidate genes for fat deposition in Huaxi Cattle[D]. Beijing: Chinese Academy of Agricultural Sciences, 2022. (in Chinese)
[25]
SON Y, KENNY T C, KHAN A, BIRSOY K, HITE R K. Structural basis of lipid head group entry to the Kennedy pathway by FLVCR1. Nature, 2024, 629: 710-716.
[26]
LI Z Y, VANCE D E. Thematic review series: Glycerolipids. phosphatidylcholine and choline homeostasis. Journal of Lipid Research, 2008, 49(6): 1187-1194.

doi: 10.1194/jlr.R700019-JLR200 pmid: 18204095
[27]
PATEL D, WITT S N. Ethanolamine and phosphatidylethanolamine: Partners in health and disease. Oxidative Medicine and Cellular Longevity, 2017, 2017(1): 4829180.
[28]
ALVES-BEZERRA M, COHEN D E. Triglyceride metabolism in the liver. Comprehensive Physiology, 2017, 8(1): 1-8.
[29]
LI M M, ZHU M X, CHAI W Q, WANG Y H, FAN D M, LV M Q, JIANG X J, LIU Y X, WEI Q X, WANG C F. Determination of lipid profiles of Dezhou donkey meat using an LC-MS-based lipidomics method. Journal of Food Science, 2021, 86(10): 4511-4521.
[30]
GU X D, SUN W J, YI K G, YANG L, CHI F M, LUO Z, WANG J Q, ZHANG J M, WANG W, YANG T, GENG F. Comparison of muscle lipidomes between cattle-yak, yak, and cattle using UPLC-MS/MS. Journal of Food Composition and Analysis, 2021, 103: 104113.
[31]
李孟孟, 宋英华, 刘宝秀, 薛鹏, 任薇, 柴文琼, 刘桂芹, 朱明霞, 王长法. 基于液相色谱-质谱法脂质组学研究德州驴肌肉的脂质组成. 食品科学, 2022, 43(14): 249-255.
LI M M, SONG Y H, LIU B X, XUE P, REN W, CHAI W Q, LIU G Q, ZHU M X, WANG C F. Lipid profiles of meat from donkeys in Dezhou analyzed by liquid chromatography-mass spectrometry-based lipidomics. Food Science, 2022, 43(14): 249-255. (in Chinese)

doi: 10.7506/spkx1002-6630-20210816-200
[32]
WENK M R. Lipidomics: New tools and applications. Cell, 2010, 143(6): 888-895.

doi: 10.1016/j.cell.2010.11.033 pmid: 21145456
[33]
ONOGI A, OGINO A, KOMATSU T, SHOJI N, SHIMIZU K, KUROGI K, YASUMORI T, TOGASHI K, IWATA H. Whole-genome prediction of fatty acid composition in meat of Japanese Black cattle. Animal Genetics, 2015, 46(5): 557-559.

doi: 10.1111/age.12300 pmid: 25997367
[34]
BALLA T. Phosphoinositides: Tiny lipids with giant impact on cell regulation. Physiological Reviews, 2013, 93(3): 1019-1137.

doi: 10.1152/physrev.00028.2012 pmid: 23899561
[35]
LI S H, GHOSH C, XING Y L, SUN Y. Phosphatidylinositol 4, 5-bisphosphate in the control of membrane trafficking. International Journal of Biological Sciences, 16(15): 2761-2774.
[36]
DE CRAENE J O, BERTAZZI D, BÄR S, FRIANT S. Phosphoinositides, major actors in membrane trafficking and lipid signaling pathways. International Journal of Molecular Sciences, 2017, 18(3): 634.
[37]
POSOR Y, JANG W, HAUCKE V. Phosphoinositides as membrane organizers. Nature Reviews Molecular Cell Biology, 2022, 23: 797-816.

doi: 10.1038/s41580-022-00490-x pmid: 35589852
[38]
MURPHY R C, LEIKER T J, BARKLEY R M. Glycerolipid and cholesterol ester analyses in biological samples by mass spectrometry. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids, 2011, 1811(11): 776-783.
[39]
CARRIZO D, CHEVALLIER O P, WOODSIDE J V, BRENNAN S F, CANTWELL M M, CUSKELLY G, ELLIOTT C T. Untargeted metabolomic analysis of human serum samples associated with exposure levels of Persistent organic pollutants indicate important perturbations in Sphingolipids and Glycerophospholipids levels. Chemosphere, 2017, 168: 731-738.

doi: S0045-6535(16)31535-1 pmid: 27825712
[40]
YE J J, BIAN X, LIM J, MEDZHITOV R. Adiponectin and related C1q/TNF-related proteins bind selectively to anionic phospholipids and sphingolipids. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(29): 17381-17388.
[41]
WANG X Y, LIANG C C, LI A N, CHENG G, LONG F, KHAN R, WANG J F, ZHANG Y, WU S, WANG Y J, et al. RNA-Seq and lipidomics reveal different adipogenic processes between bovine perirenal and intramuscular adipocytes. Adipocyte, 2022, 11(1): 448-462.

doi: 10.1080/21623945.2022.2106051 pmid: 35941812
[42]
ZHANG Z W, LIAO Q C, SUN Y, PAN T L, LIU S Q, MIAO W W, LI Y X, ZHOU L, XU G X. Lipidomic and transcriptomic analysis of the longissimus muscle of Luchuan and duroc pigs. Frontiers in Nutrition, 2021, 8: 667622.
[43]
LI J J, HUANG Q K, YANG C W, YU C L, ZHANG Z R, CHEN M Y, REN P, QIU M H. Molecular regulation of differential lipid molecule accumulation in the intramuscular fat and abdominal fat of chickens. Genes, 2023, 14(7): 1457.
[44]
XU M D, ZHANG Y Y, ZHANG Y, XU Q, ZHANG Y, CHEN G H. Integrated lipidomics and transcriptomics analyses reveal key regulators of fat deposition in different adipose tissues of geese (Anser cygnoides). Animals, 2024, 14(13): 1990.
[45]
TORO C A, HANSEN J, SIDDIQ M M, JOHNSON K, CAO J Q, PERO A, IYENGAR R, CAI D M, CARDOZO C P. Synaptojanin 1 modulates functional recovery after incomplete spinal cord injury in male apolipoprotein E Epsilon 4 mice. Neurotrauma Reports, 2023, 4(1): 464-477.
[46]
WANG S Y, LIU J, ZHAO W M, WANG G F, GAO S X. Selection of candidate genes for differences in fat metabolism between cattle subcutaneous and perirenal adipose tissue based on RNA-seq. Animal Biotechnology, 2023, 34(3): 633-644.
[47]
VALDÉS-HERNÁNDEZ J, FOLCH J M, CRESPO-PIAZUELO D, PASSOLS M, SEBASTIÀ C, CRIADO-MESAS L, CASTELLÓ A, SÁNCHEZ A, RAMAYO-CALDAS Y. Identification of candidate regulatory genes for intramuscular fatty acid composition in pigs by transcriptome analysis. Genetics Selection Evolution, 2024, 56(1): 12.
[48]
ZHENG C B, ZHONG Y Z, ZHANG P W, GUO Q P, LI F N, DUAN Y H. Dynamic transcriptome profiles of skeletal muscle growth and development in Shaziling and Yorkshire pigs using RNA-sequencing. Journal of the Science of Food and Agriculture, 2024, 104(12): 7301-7314.
[49]
DAI R, ZHOU H T, FANG Q, ZHOU P, YANG Y, JIANG S, HICKFORD J G H. Variation in ovine DGAT1 and its association with carcass muscle traits in southdown sheep. Genes, 2022, 13(9): 1670.
[50]
THALLER G, KÜHN C, WINTER A, EWALD G, BELLMANN O, WEGNER J, ZÜHLKE H, FRIES R. DGAT1, a new positional and functional candidate gene for intramuscular fat deposition in cattle. Animal Genetics, 2003, 34(5): 354-357.

pmid: 14510671
[51]
YUAN Z R, LI J Y, LI J, GAO X, GAO H J, XU S Z. Effects of DGAT1 gene on meat and carcass fatness quality in Chinese commercial cattle. Molecular Biology Reports, 2013, 40(2): 1947-1954.

doi: 10.1007/s11033-012-2251-2 pmid: 23143182
[52]
乔永, 黄治国, 李齐发, 刘振山, 代蓉, 谢庄, 郝称莉, 刘红林. 绵羊肌肉LPL基因表达的发育性变化及其对肌内脂肪含量的影响. 中国农业科学, 2007, 40(10): 2323-2330.
QIAO Y, HUANG Z G, LI Q F, LIU Z S, DAI R, XIE Z, HAO C L, LIU H L. Developmental changes of the LPL mRNA expression and the effect on IMF content in sheep muscle. Scientia Agricultura Sinica, 2007, 40(10): 2323-2330. (in Chinese)
[53]
MA C, HOFFMANN F W, SHAY A E, KOO I, GREEN K A, GREEN W R, HOFFMANN P R. Upregulated selenoprotein I during lipopolysaccharide-induced B cell activation promotes lipidomic changes and is required for effective differentiation into IgM-secreting plasma B cells. Journal of Leukocyte Biology, 2024, 116(1): 6-17.
[54]
王秀娟, 高翰, 李海鹏, 高雪, 孙宝忠, 程强, 徐磊, 张亚朋, 雷元华, 魏萌, 李三禄, 胡俊伟, 张长庆, 高会江, 李俊雅, 张路培, 陈燕. 平凉红牛生长性能、胴体及肉质性状分析. 中国农业科学, 2023, 56(3): 559-571. doi: 10.3864/j.issn.0578-1752.2023.03.013.
WANG X J, GAO H, LI H P, GAO X, SUN B Z, CHENG Q, XU L, ZHANG Y P, LEI Y H, WEI M, LI S L, HU J W, ZHANG C Q, GAO H J, LI J Y, ZHANG L P, CHEN Y. Analysis of growth performance as well as carcass and meat quality traits in Pingliang red cattle. Scientia Agricultura Sinica, 2023, 56(3): 559-571. doi: 10.3864/j.issn.0578-1752.2023.03.013. (in Chinese)
[55]
KELLY F D, SINCLAIR A J, MANN N J, TURNER A H, ABEDIN L, LI D. A stearic acid-rich diet improves thrombogenic and atherogenic risk factor profiles in healthy males. European Journal of Clinical Nutrition, 2001, 55(2): 88-96.

pmid: 11305631
[56]
巨晓军, 章明, 单艳菊, 姬改革, 屠云洁, 刘一帆, 邹剑敏, 束婧婷. 鸡肉品质分析及关键风味物质和基因的筛选. 中国农业科学, 2023, 56(9): 1813-1826. doi: 10.3864/j.issn.0578-1752.2023.09.016.
JU X J, ZHANG M, SHAN Y J, JI G G, TU Y J, LIU Y F, ZOU J M, SHU J T. Chicken quality analysis and screening of key flavor substances and genes. Scientia Agricultura Sinica, 2023, 56(9): 1813-1826. doi: 10.3864/j.issn.0578-1752.2023.09.016. (in Chinese)
[57]
PENG J Y, CAI D K, ZENG R L, ZHANG C Y, LI G C, CHEN S F, YUAN X Q, PENG L. Upregulation of superenhancer-driven LncRNA FASRL by USF1 promotes de novo fatty acid biosynthesis to exacerbate hepatocellular carcinoma (adv. sci. 1/2023). Advanced Science, 2023, 10(1): 2370004.
[58]
PATON C M, NTAMBI J M. Biochemical and physiological function of stearoyl-CoA desaturase. American Journal of Physiology- Endocrinology and Metabolism, 2009, 297(1): E28-E37.
[59]
GALLARDO D, QUINTANILLA R, VARONA L, DÍAZ I, RAMÍREZ O, PENA R N, AMILLS M. Polymorphism of the pig acetyl-coenzyme A carboxylase α gene is associated with fatty acid composition in a Duroc commercial line. Animal Genetics, 2009, 40(4): 410-417.
[1] ZOU XiaoWei, XIA Lei, ZHU XiaoMin, SUN Hui, ZHOU Qi, QI Ji, ZHANG YaFeng, ZHENG Yan, JIANG ZhaoYuan. Analysis of Disease Resistance Induced by Ustilago maydis Strain with Overexpressed UM01240 Based on Transcriptome Sequencing [J]. Scientia Agricultura Sinica, 2025, 58(6): 1116-1130.
[2] SUN Ping, ZHU WenCan, LIN XianRui, WU JiaQi, CAO YiWen, CHEN ChenFei, WANG Yi, ZHU JianXi, JIA HuiJuan, QIAN MinJie, SHEN JianSheng. Effects of Rainy and Low Light Conditions on Coloration and Flavonoid Accumulation in Peach Peel Based on Metabolomic and Transcriptomic Analyses [J]. Scientia Agricultura Sinica, 2025, 58(6): 1173-1194.
[3] XIE LuLu, LI Fu, ZHANG SiYuan, GAO JianChang. Analysis of Conserved Genes in Adventitious Root Formation Based on Cross Species Transcriptomes [J]. Scientia Agricultura Sinica, 2025, 58(6): 1195-1209.
[4] ZHOU GuangFei, MA Liang, MA Lu, ZHANG ShuYu, ZHANG HuiMin, SONG XuDong, ZHANG ZhenLiang, LU HuHua, HAO DeRong, MAO YuXiang, XUE Lin, CHEN GuoQing. Genome-Wide Association Study of Husk Traits in Maize [J]. Scientia Agricultura Sinica, 2025, 58(3): 431-442.
[5] PAN Yuan, WANG De, LIU Nan, MENG XiangLong, DAI PengBo, LI Bo, HU TongLe, WANG ShuTong, CAO KeQiang, WANG YaNan. Evaluation of the Effectiveness of Two High-Throughput Sequencing Techniques in Identifying Apple Viruses and Identification of Two Novel Viruses [J]. Scientia Agricultura Sinica, 2025, 58(2): 266-280.
[6] CAO YanYong, CHENG ZeQiang, MA Juan, YANG WenBo, ZHU WeiHong, SUN XinYan, LI HuiMin, XIA LaiKun, DUAN CanXing. Integrating Transcriptomic and Metabolomic Analyses Reveals Maize Responses to Stalk Rot Caused by Fusarium proliferatum [J]. Scientia Agricultura Sinica, 2025, 58(1): 75-90.
[7] ZHANG Ying, SHI TingRui, CAO Rui, PAN WenQiu, SONG WeiNing, WANG Li, NIE XiaoJun. Genome-Wide Association Study of Drought Tolerance at Seedling Stage in ICARDA-Introduced Wheat [J]. Scientia Agricultura Sinica, 2024, 57(9): 1658-1673.
[8] QI RenJie, NING Yu, LIU Jing, LIU ZhiYang, XU Hai, LUO ZhiDan, CHEN LongZheng. Identification and Analysis of Genes Related to Bitter Gourd Saponin Synthesis Based on Transcriptome Sequencing [J]. Scientia Agricultura Sinica, 2024, 57(9): 1779-1793.
[9] LIN Wei, WU ShuiJin, LI YueSen. Transcriptome and Proteome Association Analysis to Revealthe Molecular Mechanism of Baxi Banana Seedlings in Response to Low Temperature [J]. Scientia Agricultura Sinica, 2024, 57(8): 1575-1591.
[10] XU Na, TANG Ying, XU ZhengJin, SUN Jian, XU Quan. Genetic Analysis and Candidate Gene Identification on Fertility and Inheritance of Hybrid Sterility of XI and GJ Cross [J]. Scientia Agricultura Sinica, 2024, 57(8): 1417-1429.
[11] GAO ChenXi, HAO LuYang, HU Yue, LI YongXiang, ZHANG DengFeng, LI ChunHui, SONG YanChun, SHI YunSu, WANG TianYu, LI Yu, LIU XuYang. Analysis of Transposable Element Associated Epigenetic Regulation under Drought in Maize [J]. Scientia Agricultura Sinica, 2024, 57(6): 1034-1048.
[12] ZHANG BiDong, LIN Hong, ZHU SiYing, LI ZhongCheng, ZHUANG Hui, LI YunFeng. Identification and Candidate Gene Analysis of the ABNORMAL HULL 1 (ah1) Mutant in Rice (Oryza sativa L.) [J]. Scientia Agricultura Sinica, 2024, 57(3): 429-441.
[13] HAN XuDong, YANG ChuanQi, ZHANG Qing, LI YaWei, YANG XiaXia, HE JiaTian, XUE JiQuan, ZHANG XingHua, XU ShuTu, LIU JianChao. QTL Mapping and Candidate Gene Screening for Nitrogen Use Efficiency in Maize [J]. Scientia Agricultura Sinica, 2024, 57(21): 4175-4191.
[14] MA JingE, XIONG XinWei, ZHOU Min, WU SiQi, HAN Tian, RAO YouSheng, WANG ZhangFeng, XU JiGuo. Full-Length Transcriptomic Analysis of Chicken Pituitary Reveals Candidate Genes for Testicular Trait [J]. Scientia Agricultura Sinica, 2024, 57(20): 4130-4144.
[15] YIN JunLiang, LI JingYi, HAN Shuo, YANG PeiHua, MA JiaWei, LIU YiQing, HU HaiJun, ZHU YongXing. Identification of Ginger (Zingiber officinale Roscoe) NHX Gene Family Members and Characterization of Their Expression Patterns in Silicon Alleviating Salt Stress [J]. Scientia Agricultura Sinica, 2024, 57(19): 3848-3869.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!