Screening and Characterization of Hypothalamic Differential lncRNA Associated with Residual Feed Intake in Beef Cattle

doi:10.7668/hbnxb.20193704

Abstract

Abstract:

Residual feed intake is one of the most important indicators used to evaluate the efficiency of feed utilization in livestock,which is regulated by various factors such as digestive metabolism,intestinal immunity,locomotion and hypothalamic feeding center.lncRNAs are a class of non-coding RNA molecules involved in numerous biological processes and have been extensively studied in phenotypic studies such as diseases,but it is still unclear about the characteristics of hypothalamic lncRNA expression and how it is linked to the regulation of feed intake in cattle.The aim of this study was to screen for RFI-related lncRNAs in the hypothalamus and to investigate the previous regulatory relationship between lncRNAs and feed utilization.The experiment selected extremely high and low RFI Angus cattle as the material,collected hypothalamic tissue samples,and carried out RFI-related differentially expressed lncRNA screening and regulatory pathway analysis after total RNA extraction,library construction and high-throughput sequencing.105 differentially expressed lncRNAs were identified,of which 46 lncRNAs were down-regulated and 59 were up-regulated in the low RFI group.These differentially expressed lncRNAs were predicted to target genes mainly enriched in mitochondria-related oxidative phosphorylation and thermogenesis pathways;these differential lncRNA predicted target genes were mainly enriched in mitochondria-related oxidative phosphorylation and thermogenesis pathways;meanwhile the related genes(ATPase,respiratory chain complex)in the above pathways were mainly associated with differentially expressed lncRNAs(LNC_006637,LNC_007569,LNC_012079,LNC_005449).Some genes(NDUFA4,SDHD,UQCRH)in the network constructed by protein interaction network analysis had also been reported to be associated with livestock feed efficiency.The above results suggested that the hypothalamic tissue associated with residual feed intake in cattle might regulate mitochondrial function and energy utilization in the body mainly through thermogenesis and oxidative phosphorylation signaling pathways,but more cellular experiments were needed to verify this.

Key words: Beef cattle, Residual feed intake, lncRNA, Hypothalamus, Bioinformatics

SU Zonghua, ZHOU Xiaonan, WANG Xiaowei, YANG Chaoyun, DING Yanling, ZHANG Yanfeng, LI Chenglong, ZENG Ling, MING Wenxuan, SHI Yuangang, KANG Xiaolong. Screening and Characterization of Hypothalamic Differential lncRNA Associated with Residual Feed Intake in Beef Cattle[J]. Acta Agriculturae Boreali-Sinica, 2023, 38(4): 213-224. doi: 10.7668/hbnxb.20193704.

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Figures/Tables 14

References 56

[1]	Yang C Y, Zhu Y, Ding Y L, Huang Z W, Dan X G, Shi Y G, Kang X L. Identifying the key genes and functional enrichment pathways associated with feed efficiency in cattle[J]. Gene, 2022, 807:145934.doi:10.1016/j.gene.2021.145934. doi: 10.1016/j.gene.2021.145934 URL
[2]	Nkrumah J D, Okine E K, Mathison G W, Schmid K, Li C, Basarab J A, Price M A, Wang Z, Moore S S. Relationships of feedlot feed efficiency,performance,and feeding behavior with metabolic rate,methane production,and energy partitioning in beef cattle[J]. Journal of Animal Science, 2006, 84(1):145-153.doi:10.2527/2006.841145x. doi: 10.2527/2006.841145x pmid: 16361501
[3]	Koch R M, Swiger L A, Chambers D, Gregory K E. Efficiency of feed use in beef cattle[J]. Journal of Animal Science, 1963, 22(2):486-494.doi:10.2527/jas1963.222486x. doi: 10.2527/jas1963.222486x URL
[4]	Richardson E C, Herd R M. Biological basis for variation in residual feed intake in beef cattle.2.Synthesis of results following divergent selection[J]. Australian Journal of Experimental Agriculture, 2004, 44(5):431-440.doi:10.1071/EA02221. doi: 10.1071/EA02221 URL
[5]	Moraes G F, Abreu L R A, Toral F L B, Ferreira I C, Ventura H T, Bergmann J A G, Pereira I G. Selection for feed efficiency does not change the selection for growth and carcass traits in Nellore cattle[J]. Journal of Animal Breeding and Genetics, 2019, 136(6):464-473.doi:10.1111/jbg.12423. doi: 10.1111/jbg.12423 pmid: 31328836
[6]	Cafe L M, Robinson D L, Ferguson D M, McIntyre B L, Geesink G H, Greenwood P L. Cattle temperament:Persistence of assessments and associations with productivity,efficiency,carcass and meat quality traits[J]. Journal of Animal Science, 2011, 89(5):1452-1465.doi:10.2527/jas.2010-3304. doi: 10.2527/jas.2010-3304 pmid: 21169516
[7]	Basarab J A, Price M A, Aalhus J L, Okine E K, Snelling W M, Lyle K L. Residual feed intake and body composition in young growing cattle[J]. Canadian Journal of Animal Science, 2003, 83(2):189-204.doi:10.4141/a02-065. doi: 10.4141/a02-065 URL
[8]	Nkrumah J D, Crews D H, Basarab J A, Price M A, Okine E K, Wang Z, Li C, Moore S S. Genetic and phenotypic relationships of feeding behavior and temperament with performance,feed efficiency,ultrasound,and carcass merit of beef cattle[J]. Journal of Animal Science, 2007, 85(10):2382-2390.doi:10.2527/jas.2006-657. doi: 10.2527/jas.2006-657 pmid: 17591713
[9]	Trujillo A I, Casal A, Penagaricano F, Carriquiry M, Chilibroste P. Association of SNP of neuropeptide Y,leptin,and IGF-1 genes with residual feed intake in confinement and under grazing condition in Angus cattle[J]. Journal of Animal Science, 2013, 91(9):4235-4244.doi:10.2527/jas.2013-6254. doi: 10.2527/jas.2013-6254 pmid: 23881687
[10]	Kelly A K, Waters S M, McGee M, Fonseca R G, Carberry C, Kenny D A. mRNA expression of genes regulating oxidative phosphorylation in the muscle of beef cattle divergently ranked on residual feed intake[J]. Physiological Genomics, 2011, 43(1):12-23.doi:10.1152/physiolgenomics.00213.2009. doi: 10.1152/physiolgenomics.00213.2009 pmid: 20923863
[11]	Li W, Zheng M Q, Zhao G P, Wang J, Liu J, Wang S L, Feng F R, Liu D W, Zhu D, Li Q H, Guo L P, Guo Y M, Liu R R, Wen J. Identification of QTL regions and candidate genes for growth and feed efficiency in broilers[J]. Genet Sel Evol, 2021, 53(1):13.doi:10.1186/s12711-021-00608-3. doi: 10.1186/s12711-021-00608-3 pmid: 33549052
[12]	Li B, Fang L, Null D J, Hutchison J L, Connor E E, VanRaden P M, VandeHaar M J, Tempelman R J, Weigel K A, Cole J B. High-density genome-wide association study for residual feed intake in Holstein dairy cattle[J]. Journal of Dairy Science, 2019, 102(12):11067-11080.doi:10.3168/jds.2019-16645. doi: S0022-0302(19)30847-1 pmid: 31563317
[13]	Zhang T, Zhang X Q, Han K P, Zhang G X, Wang J Y, Xie K Z, Xue Q, Fan X M. Analysis of long noncoding RNA and mRNA using RNA sequencing during the differentiation of intramuscular preadipocytes in chicken[J]. PLoS One, 2017, 12(2):e0172389.doi:10.1371/journal.pone.0172389. doi: 10.1371/journal.pone.0172389 URL
[14]	赵丽玲, 王会, 柴志欣, 王吉坤, 王嘉博, 武志娟, 信金伟, 钟金城, 姬秋梅. 牦牛lncFAM200B的克隆鉴定、表达及生物信息学分析[J]. 华北农学报, 2020, 35(5):220-230.doi:10.7668/hbnxb.20191071. doi: 10.7668/hbnxb.20191071
	Zhao L L, Wang H, Chai Z X, Wang J K, Wang J B, Wu Z J, Xin J W, Zhong J C, Ji Q M. Cloning,expression and bioinformatics analysis of yak lncFAM200B[J]. Acta Agriculturae Boreali-Sinica, 2020, 35(5):220-230.
[15]	Zheng X, Ning C, Zhao P, Feng W, Jin Y, Zhou L, Yu Y, Liu J. Integrated analysis of long noncoding RNA and mRNA expression profiles reveals the potential role of long noncoding RNA in different bovine lactation stages[J]. Journal of Dairy Science, 2018, 101(12):11061-11073.doi:10.3168/jds.2018-14900. doi: S0022-0302(18)30892-0 pmid: 30268606
[16]	Wang H, Wang X X, Li X R, Wang Q W, Qing S Z, Zhang Y, Gao M Q. A novel long non-coding RNA regulates the immune response in MAC-T cells and contributes to bovine mastitis[J]. The FEBS Journal, 2019, 286(9):1780-1795.doi:10.1111/febs.14783. doi: 10.1111/febs.14783 URL
[17]	Sun X M, Li M X, Sun Y J, Cai H F, Lan X Y, Huang Y Z, Bai Y Y, Qi X L, Chen H. The developmental transcriptome sequencing of bovine skeletal muscle reveals a long noncoding RNA,lncMD,promotes muscle differentiation by sponging miR-125b[J]. Biochimica et Biophysica Acta, 2016, 1863(11):2835-2845.doi:10.1016/j.bbamcr.2016.08.014. doi: 10.1016/j.bbamcr.2016.08.014
[18]	Li Q L, Qiao J, Zhang Z F, Shang X L, Chu Z D, Fu Y J, Chu M X. Identification and analysis of differentially expressed long non-coding RNAs of Chinese Holstein cattle responses to heat stress[J]. Animal Biotechnology, 2020, 31(1):9-16.doi:10.1080/10495398.2018.1521337. doi: 10.1080/10495398.2018.1521337 pmid: 30589366
[19]	Mahmoudi B, Fayazi J, Roshanfekr H, Sari M, Bakhtiarizadeh M R. Genome-wide identification and characterization of novel long non-coding RNA in ruminal tissue affected with sub-acute ruminal acidosis from Holstein cattle[J]. Veterinary Research Communications, 2020, 44(1):19-27.doi:10.1007/s11259-020-09769-w. doi: 10.1007/s11259-020-09769-w pmid: 32043213
[20]	Gao Y, Li S P, Lai Z Y, Zhou Z H, Wu F, Huang Y Z, Lan X Y, Lei C Z, Chen H, Dang R H. Analysis of long non-coding RNA and mRNA expression profiling in immature and mature bovine(Bos taurus)testes[J]. Frontiers in Genetics, 2019, 10:646.doi:10.3389/fgene.2019.00646. doi: 10.3389/fgene.2019.00646 URL
[21]	Sartin J L, Whitlock B K, Daniel J A. TRIENNIAL GROWTH SYMPOSIUM:Neural regulation of feed intake:Modification by hormones,fasting,and disease[J]. Journal of Animal Science, 2011, 89(7):1991-2003.doi:10.2527/jas.2010-3399. doi: 10.2527/jas.2010-3399 pmid: 21148776
[22]	Perkins S D, Key C N, Garrett C F, Foradori C D, Bratcher C L, Kriese-Anderson L A, Brandebourg T D. Residual feed intake studies in Angus-sired cattle reveal a potential role for hypothalamic gene expression in regulating feed efficiency[J]. Journal of Animal Science, 2014, 92(2):549-560.doi:10.2527/jas.2013-7019. doi: 10.2527/jas.2013-7019 pmid: 24398827
[23]	Richards M P, Proszkowiec-Weglarz M. Mechanisms regulating feed intake,energy expenditure,and body weight in poultry[J]. Poultry Science, 2007, 86(7):1478-1490.doi:10.1093/ps/86.7.1478. doi: 10.1093/ps/86.7.1478 pmid: 17575199
[24]	Hou X H, Pu L, Wang L G, Liu X, Gao H M, Yan H, Zhang J S, Zhang Y B, Yue J W, Zhang L C, Wang L X. Transcriptome analysis of skeletal muscle in pigs with divergent residual feed intake phenotypes[J]. DNA and Cell Biology, 2020, 39(3):404-416.doi:10.1089/dna.2019.4878. doi: 10.1089/dna.2019.4878 pmid: 32004088
[25]	Zhang D Y, Zhang X X, Li G Z, Li X L, Zhang Y K, Zhao Y, Song Q Z, Wang W M. Transcriptome analysis of long noncoding RNAs ribonucleic acids from the livers of Hu sheep with different residual feed intake[J]. Animal, 2021, 15(2):100098.doi:10.1016/j.animal.2020.100098. doi: 10.1016/j.animal.2020.100098 URL
[26]	Karimi P, Bakhtiarizadeh M R, Salehi A, Izadnia H R. Transcriptome analysis reveals the potential roles of long non-coding RNAs in feed efficiency of chicken[J]. Scientific Reports, 2022, 12(1):2558.doi:10.1038/s41598-022-06528-6. doi: 10.1038/s41598-022-06528-6 pmid: 35169237
[27]	Alexandre P A, Reverter A, Berezin R B, Porto-Neto L R, Ribeiro G, Santana M H A, Ferraz J B S, Fukumasu H. Exploring the regulatory potential of long non-coding RNA in feed efficiency of indicine cattle[J]. Genes, 2020, 11(9):E997.doi:10.3390/genes11090997. doi: 10.3390/genes11090997
[28]	Nolte W, Weikard R, Brunner R M, Albrecht E, Hammon H M, Reverter A, Kühn C. Biological network approach for the identification of regulatory long non-coding RNAs associated with metabolic efficiency in cattle[J]. Frontiers in Genetics, 2019, 10:1130.doi:10.3389/fgene.2019.01130. doi: 10.3389/fgene.2019.01130 pmid: 31824560
[29]	杨朝云, 康晓龙, 淡新刚, 周靖航, 卢鑫, 叶连萌, 赵国丽, 李鹏, 史远刚. 不同剩余采食量水平的安格斯牛生长性状差异性分析[J]. 西南大学学报(自然科学版), 2020, 42(2):8-14.doi:10.13718/j.cnki.xdzk.2020.02.002. doi: 10.13718/j.cnki.xdzk.2020.02.002
	Yang C Y, Kang X L, Dan X G, Zhou J H, Lu X, Ye L M, Zhao G L, Li P, Shi Y G. Differential analysis of growth traits in Angus cattle on different levels of residual feed intake[J]. Journal of Southwest University (Natural Science), 2020, 42(2):8-14.
[30]	Xiao H M, Yuan Z T, Guo D H, Hou B F, Yin C L, Zhang W Q, Li F. Genome-wide identification of long noncoding RNA genes and their potential association with fecundity and virulence in rice brown planthopper,Nilaparvata lugens[J]. BMC Genomics, 2015, 16:749.doi:10.1186/s12864-015-1953-y. doi: 10.1186/s12864-015-1953-y URL
[31]	Pertea M, Kim D, Pertea G M, Leek J T, Salzberg S L. Transcript-level expression analysis of RNA-seq experiments with HISAT,StringTie and Ballgown[J]. Nature Protocols, 2016, 11(9):1650-1667.doi:10.1038/nprot.2016.095. doi: 10.1038/nprot.2016.095
[32]	Pertea M, Pertea G M, Antonescu C M, Chang T C, Mendell J T, Salzberg S L. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads[J]. Nature Biotechnology, 2015, 33(3):290-295.doi:10.1038/nbt.3122. doi: 10.1038/nbt.3122 pmid: 25690850
[33]	Trapnell C, Williams B A, Pertea G, Mortazavi A, Kwan G, van Baren M J, Salzberg S L, Wold B J, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation[J]. Nature Biotechnology, 2010, 28(5):511-515.doi:10.1038/nbt.1621. doi: 10.1038/nbt.1621 pmid: 20436464
[34]	Sun L, Luo H T, Bu D C, Zhao G G, Yu K T, Zhang C H, Liu Y N, Chen R S, Zhao Y. Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts[J]. Nucleic Acids Research, 2013, 41(17):e166.doi:10.1093/nar/gkt646. doi: 10.1093/nar/gkt646 URL
[35]	Kang Y J, Yang D C, Kong L, Hou M, Meng Y Q, Wei L P, Gao G. CPC2:A fast and accurate coding potential calculator based on sequence intrinsic features[J]. Nucleic Acids Research, 2017, 45(W1):W12-W16.doi:10.1093/nar/gkx428. doi: 10.1093/nar/gkx428 URL
[36]	Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar G A, Sonnhammer E L L, Tosatto S C E, Paladin L, Raj S, Richardson L J, Finn R D, Bateman A. Pfam:The protein families database in 2021[J]. Nucleic Acids Research, 2021, 49(D1):D412-D419.doi:10.1093/nar/gkaa913. doi: 10.1093/nar/gkaa913 pmid: 33125078
[37]	Wang J B, Chai Z X, Deng L, Wang J K, Wang H, Tang Y, Zhong J C, Ji Q M. Detection and integrated analysis of lncRNA and mRNA relevant to plateau adaptation of Yak[J]. Reproduction in Domestic Animals, 2020, 55(11):1461-1469.doi:10.1111/rda.13767. doi: 10.1111/rda.13767 URL
[38]	Wang Z B, Cotney J, Shadel G S. Human mitochondrial ribosomal protein MRPL12 interacts directly with mitochondrial RNA polymerase to modulate mitochondrial gene expression[J]. The Journal of Biological Chemistry, 2007, 282(17):12610-12618.doi:10.1074/jbc.m700461200. doi: 10.1074/jbc.m700461200 URL
[39]	Casal A, Garcia-Roche M, Navajas E A, Cassina A, Carriquiry M. Hepatic mitochondrial function in Hereford steers with divergent residual feed intake phenotypes[J]. Journal of Animal Science, 2018, 96(10):4431-4443.doi:10.1093/jas/sky285. doi: 10.1093/jas/sky285 pmid: 30032298
[40]	Liu X F, Ding X B, Li X, Jin C F, Yue Y W, Li G P, Guo H. An atlas and analysis of bovine skeletal muscle long noncoding RNAs[J]. Anim Genet, 2017, 48(3):278-286.doi:10.1111/age.12539. doi: 10.1111/age.12539 pmid: 28262958
[41]	Hou Y, Hu M Y, Zhou H H, Li C C, Li X Y, Liu X D, Zhao Y X, Zhao S H. Neuronal signal transduction-involved genes in pig hypothalamus affect feed efficiency as revealed by transcriptome analysis[J]. BioMed Research International, 2018, 2018: 5862571.doi:10.1155/2018/5862571. doi: 10.1155/2018/5862571
[42]	Xu Y Y, Qi X L, Hu M Y, Lin R Y, Hou Y, Wang Z X, Zhou H H, Zhao Y X, Luan Y, Zhao S H, Li X Y. Transcriptome analysis of adipose tissue indicates that the cAMP signaling pathway affects the feed efficiency of pigs[J]. Genes, 2018, 9(7):336.doi:10.3390/genes9070336. doi: 10.3390/genes9070336 URL
[43]	Han S Y, Liang Y C, Li Y, Du W. Long noncoding RNA identification:Comparing machine learning based tools for long noncoding transcripts discrimination[J]. BioMed Research International, 2016, 2016:1-14.doi:10.1155/2016/8496165. doi: 10.1155/2016/8496165
[44]	Ferrè F, Colantoni A, Helmer-Citterich M. Revealing protein-lncRNA interaction[J]. Briefings in Bioinformatics, 2016, 17(1):106-116.doi:10.1093/bib/bbv031. doi: 10.1093/bib/bbv031 pmid: 26041786
[45]	Bottje W G, Carstens G E. Association of mitochondrial function and feed efficiency in poultry and livestock speciesl[J]. Journal of Animal Sciencel, 2009, 87(S14):E48-E63.doi:10.2527/jas.2008-1379. doi: 10.2527/jas.2008-1379
[46]	Westphal N J, Seasholtz A F. CRH-BP:The regulation and function of a phylogenetically conserved binding protein[J]. Frontiers in Bioscience, 2006, 11(2):1878-1891.doi:10.2741/1931. doi: 10.2741/1931 URL
[47]	DiGiacomo K, Norris E, Dunshea F R, Hayes B J, Marett L C, Wales W J, Leury B J. Responses of dairy cows with divergent residual feed intake as calves to metabolic challenges during midlactation and the nonlactating period[J]. Journal of Dairy Science, 2018, 101(7):6474-6485.doi:10.3168/jds.2017-12569. doi: S0022-0302(18)30272-8 pmid: 29605310
[48]	Sapolsky R M, Romero L M, Munck A U. How do glucocorticoids influence stress responses? Integrating permissive,suppressive,stimulatory,and preparative actions[J]. Endocrine Reviews, 2000, 21(1):55-89.doi:10.1210/edrv.21.1.0389. doi: 10.1210/edrv.21.1.0389 pmid: 10696570
[49]	袁章琴, 谭支良, 曾军英, 沈辰辰. 饲料转换效率与细胞能量代谢[J]. 华北农学报, 2009, 24(S1):184-190.doi:10.7668/hbnxb.2009.S1.044. doi: 10.7668/hbnxb.2009.S1.044
	Yuan Z Q, Tan Z L, Zeng J Y, Shen C C. Feed efficiency and cellular energy metabolism[J]. Acta Agriculturae Boreali-Sinica, 2009, 24(S1):184-190.
[50]	Bayir H, Kagan V E. Bench-to-bedside review:Mitochondrial injury,oxidative stress and apoptosis-there is nothing more practical than a good theory[J]. Critical Care, 2008, 12(1):206.doi:10.1186/cc6779. doi: 10.1186/cc6779 URL
[51]	Kolath W H, Kerley M S, Golden J W, Keisler D H. The relationship between mitochondrial function and residual feed intake in Angus steers[J]. Journal of Animal Science, 2006, 84(4):861-865.doi:10.2527/2006.844861x. doi: 10.2527/2006.844861x pmid: 16543563
[52]	Iqbal M, Pumford N R, Tang Z X, Lassiter K, Ojano-Dirain C, Wing T, Cooper M, Bottje W. Compromised liver mitochondrial function and complex activity in low feed efficient broilers are associated with higher oxidative stress and differential protein expression[J]. Poultry Science, 2005, 84(6):933-941.doi:10.1093/ps/84.6.933. doi: 10.1093/ps/84.6.933 pmid: 15971533
[53]	Iqbal M, Pumford N R, Tang Z X, Lassiter K, Wing T, Cooper M, Bottje W. Low feed efficient broilers within a single genetic line exhibit higher oxidative stress and protein expression in breast muscle with lower mitochondrial complex activity[J]. Poultry Science, 2004, 83(3):474-484.doi:10.1093/ps/83.3.474. doi: 10.1093/ps/83.3.474 pmid: 15049502
[54]	Poyton R O, McEwen J E. Crosstalk between nuclear and mitochondrial genomes[J]. Annual Review of Biochemistry, 1996, 65:563-607.doi:10.1146/annurev.bi.65.070196.003023. doi: 10.1146/annurev.bi.65.070196.003023 pmid: 8811190
[55]	Kong R S G, Liang G X, Chen Y H, Stothard P, Guan L L. Transcriptome profiling of the rumen epithelium of beef cattle differing in residual feed intake[J]. BMC Genomics, 2016, 17:592.doi:10.1186/s12864-016-2935-4. doi: 10.1186/s12864-016-2935-4 pmid: 27506548
[56]	Cota D, Proulx K, Smith K A B, Kozma S C, Thomas G, Woods S C, Seeley R J. Hypothalamic mTOR signaling regulates food intake[J]. Science, 2006, 312(5775):927-930.doi:10.1126/science.1124147. doi: 10.1126/science.1124147 pmid: 16690869

lncRNA名称 lncRNA name	引物序列(5'—3') Primer sequence(5'—3')	lncRNA名称 lncRNA name	引物序列(5'—3') Primer sequence(5'—3')
LNC_001283	F:AACAGTGCTCACCCTCTA	LNC_015216	F:TCTGCTTGGAGCCTCTGT
	R:TTCCTGGTTACATCTTCC		R:GTGTTTGTGGGCGATGTA
LNC_014918	F:TAACACGCTCATAAACAAAG	LNC_015680	F:AACTTTGTGGGTCGCATCA
	R:ACCTGCACGTAAAGAAAA		R:GCACTTGGCACCCTTGAT
LNC_007183	F:TTTTACAGCAGCGGATGG	LNC_017103	F:CAACCATAGGCACAAAGT
	R:AGAAGGAAGGGAGGAGGC		R:CTACAAGGACCAAGCATC
LNC_002434	F:CCAGTAGGACGCTGAATC	LNC_001739	F:AAGCCAGGTAATGCGTAG
	R:AATGTCGGAGGTAGGAGG		R:AGAAGGAGGTCAAAGGAGA

lncRNA名称 lncRNA name	引物序列(5'—3') Primer sequence(5'—3')	lncRNA名称 lncRNA name	引物序列(5'—3') Primer sequence(5'—3')
LNC_001283	F:AACAGTGCTCACCCTCTA	LNC_015216	F:TCTGCTTGGAGCCTCTGT
	R:TTCCTGGTTACATCTTCC		R:GTGTTTGTGGGCGATGTA
LNC_014918	F:TAACACGCTCATAAACAAAG	LNC_015680	F:AACTTTGTGGGTCGCATCA
	R:ACCTGCACGTAAAGAAAA		R:GCACTTGGCACCCTTGAT
LNC_007183	F:TTTTACAGCAGCGGATGG	LNC_017103	F:CAACCATAGGCACAAAGT
	R:AGAAGGAAGGGAGGAGGC		R:CTACAAGGACCAAGCATC
LNC_002434	F:CCAGTAGGACGCTGAATC	LNC_001739	F:AAGCCAGGTAATGCGTAG
	R:AATGTCGGAGGTAGGAGG		R:AGAAGGAGGTCAAAGGAGA

样本编号 Sample number	采食量(平均值) FI(mean)	中期代谢体质量(平均值) MMBW^0.75(mean)	平均日增质量 (平均值) ADG(mean)	剩余采食量 RFI
A1	14.357 777 78	89.297 890 72	1.566 485 507	0.531 063 695
A2	13.576 805 56	91.201 178 77	1.717 210 145	-0.496 870 179
A3	13.205 555 56	80.218 759 85	1.631 884 058	-0.027 798 067
A4	13.585 277 78	72.533 320 34	1.263 768 116	1.169 870 266
A5	13.527 916 67	85.755 069 81	1.214 311 594	0.214 283 569
A6	12.718 708 33	72.999 303 48	1.561 413 043	0.048 472 642
A7	13.539 555 56	80.292 557 63	1.836 775 362	0.148 251 484
A8	12.701 166 67	84.021 335 45	1.663 586 957	-0.824 751 519
A9	11.808 250 00	75.032 672 68	1.518 840 580	-0.974 058 594
A10	12.010 000 00	75.052 755 57	1.459 782 609	-0.729 705 616
A11	13.880 000 00	72.586 165 78	1.478 079 710	1.301 101 744
A12	13.124 444 44	77.645 281 30	1.255 072 464	0.353 981 832
A13	12.986 666 67	82.325 233 60	1.315 036 232	-0.159 478 252
A14	13.513 055 56	84.200 176 56	1.354 166 667	0.205 138 813
A15	14.645 791 67	85.254 319 56	1.683 695 652	1.017 682 685
A16	12.447 777 78	85.372 530 40	1.498 188 406	-1.050 409 696
A17	12.974 444 44	81.298 301 29	1.562 318 841	-0.283 402 717
A18	10.300 833 33	72.038 443 25	0.796 920 290	-1.731 574 365
A19	13.038 722 22	69.802 216 96	1.436 050 725	0.688 044 748
A20	13.026 805 56	73.329 001 31	0.722 282 609	0.958 761 575
A21	13.213 472 22	80.114 927 44	1.478 623 188	0.101 706 569
A22	12.748 250 00	73.921 466 98	1.060 326 087	0.386 318 414
A23	11.653 166 67	69.850 766 61	1.526 449 275	-0.768 329 801
A24	15.121 388 89	87.812 641 76	1.940 942 029	1.120 587 767
A25	13.350 833 33	91.308 253 03	1.759 420 290	-0.761 879 611
A26	13.465 638 89	78.189 288 71	1.500 543 478	0.473 721 690
A27	12.884 722 22	71.581 825 45	1.356 521 739	0.467 467 088
A28	11.368 750 00	70.647 783 05	1.537 500 000	-1.117 351 952
A29	12.211 666 67	71.099 083 89	1.476 449 275	-0.260 844 210

样本编号 Sample number	采食量(平均值) FI(mean)	中期代谢体质量(平均值) MMBW^0.75(mean)	平均日增质量 (平均值) ADG(mean)	剩余采食量 RFI
A1	14.357 777 78	89.297 890 72	1.566 485 507	0.531 063 695
A2	13.576 805 56	91.201 178 77	1.717 210 145	-0.496 870 179
A3	13.205 555 56	80.218 759 85	1.631 884 058	-0.027 798 067
A4	13.585 277 78	72.533 320 34	1.263 768 116	1.169 870 266
A5	13.527 916 67	85.755 069 81	1.214 311 594	0.214 283 569
A6	12.718 708 33	72.999 303 48	1.561 413 043	0.048 472 642
A7	13.539 555 56	80.292 557 63	1.836 775 362	0.148 251 484
A8	12.701 166 67	84.021 335 45	1.663 586 957	-0.824 751 519
A9	11.808 250 00	75.032 672 68	1.518 840 580	-0.974 058 594
A10	12.010 000 00	75.052 755 57	1.459 782 609	-0.729 705 616
A11	13.880 000 00	72.586 165 78	1.478 079 710	1.301 101 744
A12	13.124 444 44	77.645 281 30	1.255 072 464	0.353 981 832
A13	12.986 666 67	82.325 233 60	1.315 036 232	-0.159 478 252
A14	13.513 055 56	84.200 176 56	1.354 166 667	0.205 138 813
A15	14.645 791 67	85.254 319 56	1.683 695 652	1.017 682 685
A16	12.447 777 78	85.372 530 40	1.498 188 406	-1.050 409 696
A17	12.974 444 44	81.298 301 29	1.562 318 841	-0.283 402 717
A18	10.300 833 33	72.038 443 25	0.796 920 290	-1.731 574 365
A19	13.038 722 22	69.802 216 96	1.436 050 725	0.688 044 748
A20	13.026 805 56	73.329 001 31	0.722 282 609	0.958 761 575
A21	13.213 472 22	80.114 927 44	1.478 623 188	0.101 706 569
A22	12.748 250 00	73.921 466 98	1.060 326 087	0.386 318 414
A23	11.653 166 67	69.850 766 61	1.526 449 275	-0.768 329 801
A24	15.121 388 89	87.812 641 76	1.940 942 029	1.120 587 767
A25	13.350 833 33	91.308 253 03	1.759 420 290	-0.761 879 611
A26	13.465 638 89	78.189 288 71	1.500 543 478	0.473 721 690
A27	12.884 722 22	71.581 825 45	1.356 521 739	0.467 467 088
A28	11.368 750 00	70.647 783 05	1.537 500 000	-1.117 351 952
A29	12.211 666 67	71.099 083 89	1.476 449 275	-0.260 844 210

样本名称 Sample name	剩余采食量值 RFI value	样本名称 Sample name	剩余采食量值 RFI value
H1	0.96	L1	-1.73
H2	1.02	L2	-1.12
H3	1.12	L3	-1.05
H4	1.17	L4	-0.97
H5	1.30	L5	-0.82

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