Survey of allele specific expression in bovine muscle.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
12 03 2019
12 03 2019
Historique:
received:
18
07
2018
accepted:
22
02
2019
entrez:
14
3
2019
pubmed:
14
3
2019
medline:
29
9
2020
Statut:
epublish
Résumé
Allelic imbalance is a common phenomenon in mammals that plays an important role in gene regulation. An Allele Specific Expression (ASE) approach can be used to detect variants with a cis-regulatory effect on gene expression. In cattle, this type of study has only been done once in Holstein. In our study we performed a genome-wide analysis of ASE in 19 Limousine muscle samples. We identified 5,658 ASE SNPs (Single Nucleotide Polymorphisms showing allele specific expression) in 13% of genes with detectable expression in the Longissimus thoraci muscle. Interestingly we found allelic imbalance in AOX1, PALLD and CAST genes. We also found 2,107 ASE SNPs located within genomic regions associated with meat or carcass traits. In order to identify causative cis-regulatory variants explaining ASE we searched for SNPs altering binding sites of transcription factors or microRNAs. We identified one SNP in the 3'UTR region of PRNP that could be a causal regulatory variant modifying binding sites of several miRNAs. We showed that ASE is frequent within our muscle samples. Our data could be used to elucidate the molecular mechanisms underlying gene expression imbalance.
Identifiants
pubmed: 30862965
doi: 10.1038/s41598-019-40781-6
pii: 10.1038/s41598-019-40781-6
pmc: PMC6414783
doi:
Substances chimiques
3' Untranslated Regions
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
4297Références
Segal, E. et al. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34, 166–176 (2003).
pubmed: 12740579
doi: 10.1038/ng1165
Amit, I. et al. Unbiased reconstruction of a mammalian transcriptional network mediating the differential response to pathogens. Science 326, 257–263 (2009).
pubmed: 19729616
pmcid: 2879337
doi: 10.1126/science.1179050
Haley, C. & De Koning, D. J. Genetical genomics in livestock: potentials and pitfalls. Animal Genet. 37(10–12), 395 (2006).
Zou, F. et al. Brain expression genome-wide association study (eGWAS) identifies human disease-associated variants. PLoS Genet. 8, e1002707 (2012).
pubmed: 22685416
pmcid: 3369937
doi: 10.1371/journal.pgen.1002707
Sabbagh, U., Mullegama, S. & Wyckoff, G. J. Identification and evolutionary analysis of potential candidate genes in a human eating disorder. BioMed Res. Int. 2016, 1–11 (2016).
doi: 10.1155/2016/7281732
Grigoryev, D. N. et al. Identification of new biomarkers for Acute Respiratory Distress Syndrome by expressionbased genome-wide association study. BMC Pulm. Medicine 15, 95 (2015).
The GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).
Lopdell, T. J. et al. DNA and RNA-sequence based GWAS highlights membrane-transport genes as key modulators of milk lactose content. BMC Genomics 18, 968 (2017).
pubmed: 29246110
pmcid: 5731188
doi: 10.1186/s12864-017-4320-3
Castel, S. E. et al. Tools and best practices for data processing in allelic expression analysis. Genome Biol. 16, 195 (2015).
pubmed: 26381377
pmcid: 4574606
doi: 10.1186/s13059-015-0762-6
Murani, E., Ponsuksili, S., Srikanchai, T., Maak, S. & Wimmers, K. Expression of the porcine adrenergic receptor beta 2 gene in Longissimus dorsi muscle is affected by cis-regulatory DNA variation. Animal Genet. 40, 80–89 (2009).
pubmed: 19016678
doi: 10.1111/j.1365-2052.2008.01811.x
Chen, J. et al. A uniform survey of allele-specific binding and expression over 1000-Genomes-Project individuals. Nat. Commun. 7, 11101 (2016).
pubmed: 27089393
pmcid: 4837449
doi: 10.1038/ncomms11101
Lagarrigue, S. et al. Analysis of allele-specific expression in mouse liver by RNA-Seq: A comparison with cis-eQTL identified using genetic linkage. Genetics 195, 1157–1166 (2013).
pubmed: 24026101
pmcid: 3813844
doi: 10.1534/genetics.113.153882
Fear, J. M. et al. Buffering of genetic regulatory networks in Drosophila melanogaster. Genetics 203, 1177–1190 (2016).
pubmed: 27194752
pmcid: 4937466
doi: 10.1534/genetics.116.188797
Maroilley, T. et al. Deciphering the genetic regulation of peripheral blood transcriptome in pigs through expression genome-wide association study and allele-specific expression analysis. BMC Genomics 18, 967 (2017).
pubmed: 29237423
pmcid: 5729405
doi: 10.1186/s12864-017-4354-6
Zhuo, Z., Lamont, S. J. & Abasht, B. RNA-Seq analyses identify frequent allele specific expression and no evidence of genomic imprinting in specific embryonic tissues of chicken. Sci. Reports 7, 11944 (2017).
Ghazanfar, S. et al. Gene expression allelic imbalance in ovine brown adipose tissue impacts energy homeostasis. PLoS ONE 12, e0180378 (2017).
pubmed: 28665992
pmcid: 5493397
doi: 10.1371/journal.pone.0180378
Esteve-Codina, A. et al. Exploring the gonad transcriptome of two extreme male pigs with RNA-seq. BMC Genomics 12, 552 (2011).
pubmed: 22067327
pmcid: 3221674
doi: 10.1186/1471-2164-12-552
Chitwood, J. L., Rincon, G., Kaiser, G. G., Medrano, J. F. & Ross, P. J. RNA-seq analysis of single bovine blastocysts. BMC Genomics 14, 350 (2013).
pubmed: 23705625
pmcid: 3668197
doi: 10.1186/1471-2164-14-350
Chamberlain, A. J. et al. Extensive variation between tissues in allele specific expression in an outbred mammal. BMC Genomics 16, 993 (2015).
pubmed: 26596891
pmcid: 4657355
doi: 10.1186/s12864-015-2174-0
Allais, S. et al. The two mutations, Q204X andnt821, of the myostatin gene affect carcass and meat quality in young heterozygous bulls of French beef breeds. J. Animal Sci. 88, 446–54 (2009).
doi: 10.2527/jas.2009-2385
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
pubmed: 19451168
pmcid: 2705234
doi: 10.1093/bioinformatics/btp324
Zimin, A. V. et al. A whole-genome assembly of the domestic cow, Bos taurus. Genome Biol. 10, R42 (2009).
pubmed: 19393038
pmcid: 2688933
doi: 10.1186/gb-2009-10-4-r42
Picard tools by broad institute. http://broadinstitute.github.io/picard/.
Djari, A. et al. Gene-based single nucleotide polymorphism discovery in bovine muscle using next-generation transcriptomic sequencing. BMC Genomics 14, 307 (2013).
pubmed: 23651547
pmcid: 3751807
doi: 10.1186/1471-2164-14-307
Billerey, C. et al. Identification of large intergenic non-coding RNAs in bovine muscle using next-generation transcriptomic sequencing. BMC Genomics 15, 499 (2014).
pubmed: 24948191
pmcid: 4073507
doi: 10.1186/1471-2164-15-499
Meersseman, C. et al. Genetic variability of the activity of bidirectional promoters: a pilot study in bovine muscle. DNA Res. 24, 221–33 (2017).
pubmed: 28338730
pmcid: 5499805
doi: 10.1093/dnares/dsx004
Okonechnikov, K., Conesa, A. & Garcia-Alcalde, F. Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data. Bioinformatics 32, 292–294 (2016).
pubmed: 26428292
Dobin, A. et al. STAR: Ultrafast universal RNA-Seq aligner. Bioinformatics 29, (15–21 (2013).
McKenna, A. et al. The genome analysis toolkit: A mapreduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
pubmed: 20644199
pmcid: 2928508
doi: 10.1101/gr.107524.110
Auwera, G. A. et al. From fastQ data to high-confidence variant calls: The genome analysis toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.10.33 (2013).
McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).
pubmed: 27268795
pmcid: 4893825
doi: 10.1186/s13059-016-0974-4
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience 4, 1–16 (2015).
doi: 10.1186/s13742-015-0047-8
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–69 (2015).
doi: 10.1093/bioinformatics/btu638
pubmed: 25260700
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
pubmed: 25516281
pmcid: 4302049
doi: 10.1186/s13059-014-0550-8
Smit, A., Hubley, R. & Green, P. RepeatMasker Open-4.0. http://www.repeatmasker.org (2013–2015).
Enright, A. J. et al. MicroRNA targets in Drosophila. Genome Biol. 5, R1 (2004).
doi: 10.1186/gb-2003-5-1-r1
McLoughlin, K. E. et al. RNA-seq transcriptional profiling of peripheral blood leukocytes from cattle infected with Mycobacterium bovis. Front. Immunol. 5, 396 (2014).
pubmed: 25206354
pmcid: 4143615
doi: 10.3389/fimmu.2014.00396
Choi, J.-W. et al. Whole-genome resequencing analysis of Hanwoo and Yanbian cattle to identify genome-wide SNPs and signatures of selection. Mol. Cells 38, 466–473 (2015).
pubmed: 26018558
pmcid: 4443289
doi: 10.14348/molcells.2015.0019
Xu, Y. et al. Whole-genome sequencing reveals mutational landscape underlying phenotypic differences between two widespread Chinese cattle breeds. PLoS ONE 12, e0183921 (2017).
pubmed: 28841720
pmcid: 5571935
doi: 10.1371/journal.pone.0183921
Letaief, R. et al. Identification of Copy Number Variation in French dairy and beef breeds using next-generation sequencing. Genet. Sel. Evol. 49, 77 (2017).
pubmed: 29065859
pmcid: 5655909
doi: 10.1186/s12711-017-0352-z
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
pubmed: 23618408
pmcid: 4053844
doi: 10.1186/gb-2013-14-4-r36
Hu, Z.-L., Park, C. A. & Reecy, J. M. Developmental progress and current status of the Animal QTLdb. Nucleic Acids Res. 44, D827–D833 (2015).
pubmed: 26602686
pmcid: 4702873
doi: 10.1093/nar/gkv1233
Yates, A. et al. Ensembl 2016. Nucleic Acids Res. 44, D710–D716 (2016).
pubmed: 26687719
doi: 10.1093/nar/gkv1157
Eden, E., Navon, R., Steinfeld, I., Lipson, D. & Yakhini, Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10, 48 (2009).
pubmed: 19192299
pmcid: 2644678
doi: 10.1186/1471-2105-10-48
Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).
pubmed: 22955828
pmcid: 3771521
doi: 10.1126/science.1222794
Kel, A. et al. MATCH: a tool for searching transcription factor binding sites in DNA sequences. Nucleic Acids Res. 31, 3576–3579 (2003).
pubmed: 12824369
pmcid: 169193
doi: 10.1093/nar/gkg585
Vymetalkova, V. et al. Polymorphisms in microRNA binding sites of mucin genes as predictors of clinical outcome in colorectal cancer patients. Carcinogenesis 38, 28–39 (2017).
pubmed: 27803053
doi: 10.1093/carcin/bgw114
Muroya, S. et al. Profiling of differentially expressed microRNA and the bioinformatic target gene analyses in bovine fast- and slow-type muscles by massively parallel sequencing. J. Animal Sci. 91, 90–103 (2013).
doi: 10.2527/jas.2012-5371
Miretti, S., Volpe, M. G., Martignani, E., Accornero, P. & Baratta, M. Temporal correlation between differentiation factor expression and microRNAs in Holstein bovine skeletal muscle. Animal 11, 227–235 (2017).
pubmed: 27406318
doi: 10.1017/S1751731116001488
Zhang, W. W. et al. Effect of differentiation on microRNA expression in bovine skeletal muscle satellite cells by deep sequencing. Cell. Mol. Biol. Lett. 21, 8 (2016).
pubmed: 28536611
pmcid: 5415838
doi: 10.1186/s11658-016-0009-x
Sadkowski, T., Ciecierska, A., Oprzadek, J. & Balcerek, E. Breed-dependent microRNA expression in the primary culture of skeletal muscle cells subjected to myogenic differentiation. BMC Genomics 19, 109 (2018).
pubmed: 29390965
pmcid: 5793348
doi: 10.1186/s12864-018-4492-5
Jin, W., Grant, J. R., Stothard, P., Moore, S. S. & Guan, L. L. Characterization of bovine miRNAs by sequencing and bioinformatics analysis. BMC Mol. Biol. 10, 90 (2009).
pubmed: 19758457
pmcid: 2761914
doi: 10.1186/1471-2199-10-90
Sun, J. et al. Identification and profiling of conserved and novel microRNAs from Chinese Qinchuan bovine longissimus thoracis. BMC Genomics 14, 42 (2013).
pubmed: 23332031
pmcid: 3563516
doi: 10.1186/1471-2164-14-42
Huang, Y. et al. Genome-wide DNA methylation profiles and their relationships with mRNA and the microRNA transcriptome in bovine muscle tissue (Bos taurine). Sci. Reports 4, 6546 (2014).
Sun, J. et al. Comparative transcriptome analysis reveals significant differences in microRNA expression and their target genes between adipose and muscular tissues in cattle. PLoS ONE 9, 1–9 (2014).
Sun, J. et al. Altered microRNA expression in bovine skeletal muscle with age. Animal Genet. 46(227–238), 495 (2015).
Moisá, S. J., Shike, D. W., Shoup, L. & Loor, J. J. Maternal plane of nutrition during late-gestation and weaning age alter steer calf Longissimus muscle adipogenic microRNA and target gene expression. Lipids 51, 123–138 (2016).
pubmed: 26597919
doi: 10.1007/s11745-015-4092-y
Oliveira, G. B. et al. Integrative analysis of microRNAs and mRNAs revealed regulation of composition and metabolism in Nelore cattle. BMC Genomics 19, 126 (2018).
pubmed: 29415651
pmcid: 5804041
doi: 10.1186/s12864-018-4514-3
Kamli, M. R. et al. Expressional studies of the aldehyde oxidase (AOX1) gene during myogenic differentiation in C2C12 cells. Biochem. Biophys. Res. Commun. 450, 1291–1296 (2014).
pubmed: 24996175
doi: 10.1016/j.bbrc.2014.06.126
Cannon, A. R. et al. Palladin expression is a conserved characteristic of the desmoplastic tumor microenvironment and contributes to altered gene expression. Cytoskelet. 72, 402–411 (2015).
doi: 10.1002/cm.21239
Jin, L. The actin associated protein palladin in smooth muscle and in the development of diseases of the cardiovasculature and in cancer. J. Muscle Res. Cell Motil. 32, 7–17 (2011).
pubmed: 21455759
pmcid: 3143271
doi: 10.1007/s10974-011-9246-9
Nguyen, N. & Wang, H. Dual roles of palladin protein in in vitro myogenesis: Inhibition of early induction but promotion of myotube maturation. PLoS ONE 10, e0124762 (2015).
pubmed: 25875253
pmcid: 4396843
doi: 10.1371/journal.pone.0124762
Saatchi, M. et al. QTLs associated with dry matter intake, metabolic mid-test weight, growth and feed efficiency have little overlap across 4 beef cattle studies. BMC Genomics 15, 1004 (2014).
pubmed: 25410110
pmcid: 4253998
doi: 10.1186/1471-2164-15-1004
Barendse, W. J. DNA markers for meat tenderness. Int. patent publication WO 02/064820 A1 (2002).
Tait, R. G. et al. CAPN1, CAST, and DGAT1 genetic effects on preweaning performance, carcass quality traits, and residual variance of tenderness in a beef cattle population selected for haplotype and allele equalization. J. Animal Sci. 92, 5382–5393 (2014).
doi: 10.2527/jas.2014-8211
Coelho, C. et al. The first mammalian aldehyde oxidase crystal structure: insights into substrate specificity. J. Biol. Chem. 287, 40690–40702 (2012).
pubmed: 23019336
pmcid: 3504782
doi: 10.1074/jbc.M112.390419
Terao, M. et al. Structure and function of mammalian aldehyde oxidases. Arch. Toxicol. 90, 753–780 (2016).
pubmed: 26920149
doi: 10.1007/s00204-016-1683-1
Adachi, M., Itoh, K., Masubuchi, A., Watanabe, N. & Tanaka, Y. Construction and expression of mutant cDNAs responsible for genetic polymorphism in aldehyde oxidase in Donryu strain rats. J. Biochem. Mol. Biol. 40, 1021–1027 (2007).
pubmed: 18047800
Hartmann, T. et al. The impact of single nucleotide polymorphisms on human aldehyde oxidase. Drug Metab. Dispos. 40, 856–864 (2012).
pubmed: 22279051
pmcid: 4738704
doi: 10.1124/dmd.111.043828
Foti, A., Dorendorf, F. & Leimkühler, S. A single nucleotide polymorphism causes enhanced radical oxygen species production by human aldehyde oxidase. PLoS One 12, e0182061 (2017).
pubmed: 28750088
pmcid: 5531472
doi: 10.1371/journal.pone.0182061
Foti, A. et al. Optimization of the Expression of Human Aldehyde Oxidase for Investigations of Single-Nucleotide Polymorphisms. Drug Metab. Dispos. 44, 1277–1285 (2016).
pubmed: 26842593
doi: 10.1124/dmd.115.068395
Hunt, R. C., Simhadri, V. L., Iandoli, M., Sauna, Z. E. & Kimchi-Sarfaty, C. Exposing synonymous mutations. Trends Genet. 30, 308–321 (2014).
pubmed: 24954581
doi: 10.1016/j.tig.2014.04.006
Joyce, P. I. et al. Deficiency of the zinc finger protein ZFP106 causes motor and sensory neurodegeneration. Hum. Mol. Genet. 25, 291–307 (2016).
pubmed: 26604141
doi: 10.1093/hmg/ddv471
Anderson, D. M. et al. Severe muscle wasting and denervation in mice lacking the RNA-binding protein ZFP106. Proc. Natl. Acad. Sci. 113, E4494–E4503 (2016).
Celona, B. et al. Suppression of C9orf72 RNA repeat-induced neurotoxicity by the ALS-associated RNA520 binding protein Zfp106. eLife 6, e19032 (2017).
pubmed: 28072389
pmcid: 5283830
doi: 10.7554/eLife.19032
Casey, L. M., Lyon, H. D. & Olmsted, J. B. Muscle-specific microtubule-associated protein 4 is expressed early in myogenesis and is not sufficient to induce microtubule reorganization. Cell Motil. 54, 317–336 (2003).
doi: 10.1002/cm.10105
Mogessie, B., Roth, D., Rahil, Z. & Straube, A. A novel isoform of MAP4 organises the paraxial microtubule array required for muscle cell differentiation. eLife 4, e05697 (2015).
pubmed: 25898002
pmcid: 4423121
doi: 10.7554/eLife.05697
Venuti, J. M., Morris, J. H., Vivian, J. L., Olson, E. N. & Klein, W. H. Myogenin is required for late but not early aspects of myogenesis during mouse development. J. Cell Biol. 128, 563–576 (1995).
pubmed: 7532173
doi: 10.1083/jcb.128.4.563
Hasty, P. et al. Muscle deficiency and neonatal death in mice with a targeted mutation in the myogenin gene. Nature 364, 501–506 (1993).
pubmed: 8393145
doi: 10.1038/364501a0