miRNA-dysregulation associated with tenderness variation induced by acute stress in Angus cattle
© Zhao et al; licensee BioMed Central Ltd. 2012
Received: 29 December 2011
Accepted: 9 May 2012
Published: 1 June 2012
miRNAs are a class of small, single-stranded, non-coding RNAs that perform post-transcriptional repression of target genes by binding to 3’ untranslated regions. Research has found that miRNAs involved in the regulation of many metabolic processes. Here we uncovered that the beef quality of Angus cattle sharply diversified after acute stress. By performing miRNA microarray analysis, 13 miRNAs were significantly differentially expressed in stressed group compared to control group. Using a bioinformatics method, 135 protein-coding genes were predicted as the targets of significant differentially expressed miRNAs. Gene Ontology (GO) term and Ingenuity Pathway Analysis (IPA) mined that these target genes involved in some important pathways, which may have impact on meat quality and beef tenderness.
MicroRNAs are one of the largest gene families and account for ~1% of the genome . They are 21–25 nucleotide small, non-coding RNAs that post-transcriptionally repress the expression of protein-coding genes through binding to the 3’ untranslated regions (UTR) of the target mRNAs [1–5]. Accumulated evidence indicates that miRNAs are important in the regulation of many biological processes, such as developmental timing, cell metabolism, cell differentiation, cell death, cell proliferation, haematopoiesis and patterning of the nervous system, etc[1, 4, 6]. Recent studies have uncovered muscle-specific miRNAs that regulate diverse aspects of muscle function, including myoblast proliferation, differentiation, contractility and stress responsiveness [7–10]. Disruption of miRNA biogenesis causes diverse developmental defects, including abnormal embryogenesis and depletion of stem cells . It has been reported that microRNA-133a regulates cardiomyocyte proliferation and suppresses smooth muscle gene expression in heart . miR-1 and miR-133 have distinct roles in modulating skeletal muscle proliferation and differentiation in cultured myoblast in vitro and in Xenopus laevis embryos in vivo. miR-335 and miR-126 are identified as metastasis suppressors in human breast cancer because their expressions are lost in the majority of primary breast tumors . Additionally, miRNAs have been found involved in viral infections, cancer, cardiovascular disease and neurological and muscular disorders [6, 12–20]. With the progression of research, a large number of miRNAs have been found to play roles in the regulation of metabolic process. Although there are 18226 entries in miRBase, representing hairpin precursor miRNAs and expressing 21643 mature miRNA products in 168 species, only a handful of miRNAs have been studies deeply, and a range of functions extending beyond developmental regulation have been revealed . Especially, 665 miRNAs in bovine are shown in the database, and some of them are studied in bovine cell in vitro, but few have been studied in vivo[21, 22].
Beef tenderness is a complex characteristic influenced by many aspects, such as production, processing factors and cooking aspects, etc. More efforts have been focused on factors influencing meat quality, including breed, sex, feed, handling, environment, finishing weight and age at slaughter, etc[23–28]. So far, no research is performed on whether the variation of beef tenderness is regulated by miRNAs. To test our hypothesis that acute stress may influence beef quality mediated by miRNAs, a miRNA microarray was used to detect differentially expressed miRNAs between stressed and non-stressed groups of cattle. The results from the study demonstrated that acute stress altered both beef quality and miRNA expression, which will help us identify mechanisms underlying the control of beef tenderness.
Differentially expressed miRNAs in LD muscle with differential stress status
Differentially expressed miRNAs between stressed group and control in Angus cattle
qPCR analysis of differentially expressed miRNA
Prediction of targets of differentially expressed miRNA and function annotation
Significant GO terms predicted target genes were involved in
cellular catabolic process
intracellular membrane- bounded organelle
intracellular organelle part
purine nucleotide binding
purine ribonucleotide binding
purine ribonucleoside triphosphate binding
guanyl nucleotide binding
guanyl ribonucleotide binding
Networks and functions that target genes are related with
Drug Metabolism, Endocrine System Development and Function, Lipid Metabolism
Amino Acid Metabolism, Molecular Transport, Small Molecule Biochemistry
Gene Expression, Cellular Movement, Cell Cycle
Cardiovascular System Development and Function, Organismal Development, Cancer
Cell Cycle, Cancer, Gastrointestinal Disease
Cardiovascular Disease, Gene Expression, Hematological Disease
Carbohydrate Metabolism, Lipid Metabolism, Small Molecule Biochemistry
Cellular Growth and Proliferation, Gene Expression, Infectious Disease
RNA Post-Transcriptional Modification, Cellular Compromise, Cellular Development
Cancer, Embryonic Development, Neurological Disease
RNA Damage and Repair, Nutritional Disease, Organismal Injury and Abnormalities
Cellular Growth and Proliferation, Developmental Disorder, Embryonic Development
Developmental Disorder, Genetic Disorder, Metabolic Disease
Cell Signaling, Cellular Assembly and Organization, Cellular Function and Maintenance
Post-Translational Modification, Protein Synthesis, Cell-To-Cell Signaling and Interaction
Cellular Assembly and Organization, Cellular Function and Maintenance, Cellular Movement
Cellular Assembly and Organization, Cellular Function and Maintenance, Cell-To-Cell Signaling and Interaction
Cell Cycle, Cellular Movement, Embryonic Development
Infectious Disease, DNA Replication, Recombination, and Repair, Gene Expression
Abnormal or disease conditions can induce dysregulation of mRNA and protein levels. It has been reported that muscle-specific miRNAs, miR-206 and miR-499, are upregulated and miR-1, miR-133a, and miR-133b are downregulated in extraocular muscles compared to limb muscle, concluding that a miRNA network contributes to the extraocular muscles by regulating posttranscriptional expression of genes involved in structure, signaling, metabolism, angiogenesis, myogenesis, and regeneration in extraocular muscles . In addition, miR-145 is found to be necessary for myocardin-induced reprogramming of adult fibroblasts into smooth muscle cells and can induce differentiation of multipotent neural crest stem cells into vascular smooth muscle . Meanwhile, miR-145 and miR-143 cooperatively target a network of transcription factors to promote differentiation and repress proliferation of smooth muscle cells . Both also act as integral components of the regulatory network whereby serum response factor controls cytoskeletal remodeling and phenotypic switching of smooth muscle cells during vascular disease . In our study, several miRNAs were found to be dysregulated due to different stress status, of which, some have been previously studied. For example, miR-497 has been found to promote ischemic neuronal death by negatively regulating antiapoptotic proteins . Another research found that miR-497 and miR-302b co-regulate ethanol-induced neuronal cell death through BCL2 protein and cyclin D2 . But its function in muscle development has not been reported yet. Therefore, these finding further suggest that miRNAs may play some roles on transcriptional circuits controlling gene expression in skeletal muscle.
Notably, the surgical implantation of rumen canulas imitated a non fatal form of hardware disease. Hardware disease occurs when an animal ingests a sharp piece of metal and the metal pierces the rumen or reticulum wall. As expected, the phenotype in this study indicated that those animals undergoing this stress had significantly higher WBSF. In this research, we identified differently expressed miRNAs associated with divergent stress status in LD muscles samples between stressed and control groups. The annotation of predicted target genes further showed that miRNA may be involved in important pathways regulating target genes, such as lipid metabolism, amino acid metabolism, gene expression, molecular transport, etc. In the future, the predicted miRNA targets need to be validated in vitro and the expression levels of corresponding target genes and proteins should be measured, which will help to elucidate how miRNAs regulate gene transcription and protein expression in the variation of beef quality and tenderness.
Materials and methods
Sample collection and experiment design
Seven purebred Angus steers were obtained from Wye Angus farm (Queenstown, MD). After weaning the steers were acclimated to a pelleted forage diet only to meet maintenance needs. At 10 months of age, 4 steers underwent a surgical procedure that involved anesthetization and placement of a rumen catheter. The surgery was acute stress compared to normal growth condition. Three steers that received no surgery were designated as control group. At the age of 1 year, the steers were harvested. After harvest 10 mg longissimus dorsi (LD) muscle from the 12th to 13th rib of the right side of the carcass were placed in RNAlater solution (Qiagen, Valencia, CA) and stored at −80°C for further analysis. Steaks of the LD from the 12th to 13th rib of the left side of the carcass were obtained, vacuum packed, stored at 4°C for a total of 14 days post harvest, and then frozen at −20°C. Once all steaks were obtained, aged, the steaks were thawed at 4°C, cooked to an internal temperature at 70°C, cooled, cored and then analyzed for WBSF as previously described . After WBSF data were analyzed by student t-test, three extremely tough individuals were chosen to be designed as stressed group and three cattle without stress were designed as control group. Based on these tough and control groups, a total of 6 miRNA microarrays were hybridized and analyzed. All procedures were approved by the University of Maryland Institutional Animal Care and Use Committee (Protocol # R-07-05).
RNA extraction and miRNA microarray hybridization
Total RNAs from the 6 samples were extracted using miRNAeasy Mini Kit (Qiagen) as described in the manufacturer’s instructions. The RNAs were quantified by NanoDrop ND 1000 Spectrophotometer (Thermo-scientific, Wilmington, DE) and RNA integrity determined by 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). Equal aliquots of total RNA from each sample were pooled together as common reference RNA. One μg total RNA from each sample or common reference were labeled with Hy3TM and Hy5TM fluorescent label, respectively, with the help of the miRCURYTM LAN Array power labeling kit (Exiqon, Denmark) following the instructions. The Hy3TM-labeled samples and a Hy5TM-labeled reference RNA sample were mixed pair-wise and hybridized to the miRCURYTM LNA array (Version 9.2; Exiqon, Denmark), which contained capture probes targeting all of the miRNAs for all the species registered in the miRBase (Version 11.0) at the Sanger Institute. One hundred and twenty-six of these probes are bovine-related miRNAs in the miRBase version. Hybridization was performed according to the miRCURYTM LNA array manual with a Tecan HS4800 hybridization station (Tecan, Austria). After hybridization, the microarray slides were scanned and stored in an ozone free environment to prevent potential bleaching of the fluorescent dyes. The miRCURYTM LNA array microarray slides were scanned using the Agilent G2565BA Microarray Scanner System (Agilent) and image analysis was performed with ImaGene 8.0 software (BioDiscovery, Inc., USA).
miRNA microarray data analysis
Microarray data were analyzed in R using the Linear models for microarray data (Limma) package. For each miRNA, quantified signals within arrays were averaged. Normalizations within arrays and between arrays were performed using the global LOWESS (LOcally WEighted Scatterplot Smoothing) regression algorithm. Contrasts were made to compare stressed and control groups. Differentially expressed miRNAs were selected to do further analysis using the stringent statistic criteria of p value less than 0.05 and FDR (false discover rate) less than 0.4.
qRT-PCR analysis of miRNA expression
Total mRNAs including miRNAs were extracted from 6 same samples using miRNeasy Mini Kit (QIAGEN) and RNeasy Mini Kit (QIAGEN) according to the standard protocol. mRNAs were reversely transcribed and quantified with miScript Reverse Transcription Kit (QIAGEN), miScript SYBR Green PCR Kit (QIAGEN), and miScript Primer assays (QIAGEN). In the reverse transcription control, PCR water (Invitrogen) was used to replace miRNA samples. Briefly, 1μg of purified miRNA was used for reverse transcription, and then diluted to 5 volumes. Two μl of diluted RT products were used for real-time PCR quantification. Two types of controls were applied in real-time PCR, including reverse transcription control and blank using PCR water, to ensure that no amplicon was observed in the controls. U6 were used as normalization controls. Data were analyzed using the 2-ΔΔCT method and student T tests were used to compare the miRNAs expression levels (SAS version 9.2).
Here we only validated the most significant miRNA, namely bta-miR-497, which sequence is shown as CAGCAGCACACUGUGGUUUGUA. The mimic miRNA of bta-miR-497 was synthesized by Qiagen.
Prediction of miRNA targets
The target genes for miRNAs were predicted by TargetScanHuman (http://www.targetscan.org/vert_50/). In the menu of “Select a species”, cow was chosen and the names of significantly differentially expressed miRNAs were inputted and then submitted. From the output only the genes with the conserved sites were reserved as predicted target genes of this miRNA.
Data mining and network analysis of significantly differentially expressed miRNAs and predicted target genes
Hierarchical clustering of significantly differentially expressed miRNAs was performed using Cluster 3.0 . The expression data were further filtered, adjusted and normalized. Average linkage clustering was performed and visualized using Treeview. The initial information on Gene Ontology  functions and functional relevance of predicted target genes was obtained from Gene Ontology Enrichment Analysis Software Toolkit (GOEAST) . The GO analysis included biological process, molecular function and cellular component. Ingenuity Pathway Analysis (IPA, Ingenuity System, Redwood City, CA) was used to generate networks and assess statistically relevant biofunctions and canonical pathways that predicted target genes are involved in. These genes were mapped to corresponding genes in the Ingenuity knowledge database. The biofunctional analysis identified the molecular and cellular function, physiological system development and function. Canonical Pathway Analysis identified the most significant pathways in the dataset.
The work was supported by Maryland Agricultural Experiment Station and Jorgensen Endowment Funds. We cannot thank the Dr. Brian J. Bequette and Dr. Ransom L. Baldwin enough for their generous support and donation of their time.
- Bartel DP: MicroRNAs: Genomics, Biogenesis, Mechanism, and Function. Cell. 2004, 116: 281-297. 10.1016/S0092-8674(04)00045-5.View ArticlePubMedGoogle Scholar
- Ambros V: The functions of animal microRNAs. Nature. 2004, 431: 350-355. 10.1038/nature02871.View ArticlePubMedGoogle Scholar
- Grimson A, Farh KKH, Johnston WK, Garrett-Engele P, Lim LP, Bartel DP: MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Mol Cell. 2007, 27: 91-105. 10.1016/j.molcel.2007.06.017.PubMed CentralView ArticlePubMedGoogle Scholar
- He L, Hannon GJ: MicroRNAs: small RNAs with a big role in gene regulation. Nat Rev Genet. 2004, 5: 522-531. 10.1038/nrg1379.View ArticlePubMedGoogle Scholar
- Kim VN: MicroRNA biogenesis: coordinated cropping and dicing. Nat Rev Mol Cell Biol. 2005, 6: 376-385.View ArticlePubMedGoogle Scholar
- Esquela-Kerscher A, Slack FJ: Oncomirs - microRNAs with a role in cancer. Nat Rev Cancer. 2006, 6: 259-269. 10.1038/nrc1840.View ArticlePubMedGoogle Scholar
- Zeiger U, Khurana TS: Distinctive patterns of microRNA expression in extraocular muscles. Physiol Genomics. 2010, 41: 289-296. 10.1152/physiolgenomics.00169.2009.PubMed CentralView ArticlePubMedGoogle Scholar
- Liu N, Bezprozvannaya S, Williams AH, Qi X, Richardson JA, Bassel-Duby R, Olson EN: microRNA-133a regulates cardiomyocyte proliferation and suppresses smooth muscle gene expression in the heart. Genes Dev. 2008, 22: 3242-10.1101/gad.1738708.PubMed CentralView ArticlePubMedGoogle Scholar
- Chen JF, Mandel EM, Thomson JM, Wu Q, Callis TE, Hammond SM, Conlon FL, Wang DZ: The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation. Nat Genet. 2005, 38: 228-233.PubMed CentralView ArticlePubMedGoogle Scholar
- Cordes KR, Sheehy NT, White MP, Berry EC, Morton SU, Muth AN, Lee TH, Miano JM, Ivey KN, Srivastava D: miR-145 and miR-143 regulate smooth muscle cell fate and plasticity. Nature. 2009, 460: 705-710.PubMed CentralPubMedGoogle Scholar
- Tavazoie SF, Alarcón C, Oskarsson T, Padua D, Wang Q, Bos PD, Gerald WL, Massagué J: Endogenous human microRNAs that suppress breast cancer metastasis. Nature. 2008, 451: 147-152. 10.1038/nature06487.PubMed CentralView ArticlePubMedGoogle Scholar
- Abelson JF, Kwan KY, O'Roak BJ, Baek DY, Stillman AA, Morgan TM, Mathews CA, Pauls DL, Rasin MR, Gunel M: Sequence variants in SLITRK1 are associated with Tourette's syndrome. Science. 2005, 310: 317-10.1126/science.1116502.View ArticlePubMedGoogle Scholar
- Calin GA, Croce CM: MicroRNA signatures in human cancers. Nat Rev Cancer. 2006, 6: 857-866. 10.1038/nrc1997.View ArticlePubMedGoogle Scholar
- Eisenberg I, Eran A, Nishino I, Moggio M, Lamperti C, Amato AA, Lidov HG, Kang PB, North KN, Mitrani-Rosenbaum S: Distinctive patterns of microRNA expression in primary muscular disorders. Proc Natl Acad Sci. 2007, 104: 17016-10.1073/pnas.0708115104.PubMed CentralView ArticlePubMedGoogle Scholar
- He L, Thomson JM, Hemann MT, Hernando-Monge E, Mu D, Goodson S, Powers S, Cordon-Cardo C, Lowe SW, Hannon GJ: A microRNA polycistron as a potential human oncogene. Nature. 2005, 435: 828-833. 10.1038/nature03552.PubMed CentralView ArticlePubMedGoogle Scholar
- Jopling CL, Yi MK, Lancaster AM, Lemon SM, Sarnow P: Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science. 2005, 309: 1577-10.1126/science.1113329.View ArticlePubMedGoogle Scholar
- Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA: MicroRNA expression profiles classify human cancers. Nature. 2005, 435: 834-838. 10.1038/nature03702.View ArticlePubMedGoogle Scholar
- Pedersen IM, Cheng G, Wieland S, Volinia S, Croce CM, Chisari FV, David M: Interferon modulation of cellular microRNAs as an antiviral mechanism. Nature. 2007, 449: 919-922. 10.1038/nature06205.PubMed CentralView ArticlePubMedGoogle Scholar
- Triboulet R, Mari B, Lin YL, Chable-Bessia C, Bennasser Y, Lebrigand K, Cardinaud B, Maurin T, Barbry P, Baillat V: Suppression of microRNA-silencing pathway by HIV-1 during virus replication. Science. 2007, 315: 1579-10.1126/science.1136319.View ArticlePubMedGoogle Scholar
- Tian F, Luo J, Zhang H, Song J: MiRNA expression signatures induced by Marek's disease virus infection in chickens. Genomics. 2012, 99: 152-159. 10.1016/j.ygeno.2011.11.004.View ArticlePubMedGoogle Scholar
- Ma T, Jiang H, Gao Y, Zhao Y, Dai L, Xiong Q, Xu Y, Zhao Z, Zhang J: Microarray analysis of differentially expressed microRNAs in non-regressed and regressed bovine corpus luteum tissue; microRNA-378 may suppress luteal cell apoptosis by targeting the interferon gamma receptor 1 gene. J Appl Genet. 2011, 52: 481-486. 10.1007/s13353-011-0055-z.View ArticlePubMedGoogle Scholar
- Glazov EA, Kongsuwan K, Assavalapsakul W, Horwood PF, Mitter N, Mahony TJ: Repertoire of bovine miRNA and miRNA-like small regulatory RNAs expressed upon viral infection. PLoS One. 2009, 4: e6349-10.1371/journal.pone.0006349.PubMed CentralView ArticlePubMedGoogle Scholar
- Brady DE: A study of the factors influencing tenderness and texture of beef. J Anim Sci. 1937, 1937: 246-250.Google Scholar
- Hertzman C, Olsson U, Tornberg E: The influence of high temperature, type of muscle and electrical stimulation on the course of rigor, ageing and tenderness of beef muscles. Meat Sci. 1993, 35: 119-141. 10.1016/0309-1740(93)90074-R.View ArticlePubMedGoogle Scholar
- Minks D, Stringer WC: The influence of aging beef in vacuum. J Food Sci. 1972, 37: 736-738. 10.1111/j.1365-2621.1972.tb02739.x.View ArticleGoogle Scholar
- Monsón F, Sañudo C, Sierra I: Influence of breed and ageing time on the sensory meat quality and consumer acceptability in intensively reared beef. Meat Sci. 2005, 71: 471-479. 10.1016/j.meatsci.2005.04.026.View ArticlePubMedGoogle Scholar
- Neely TR, Lorenzen CL, Miller RK, Tatum JD, Wise JW, Taylor JF, Buyck MJ, Reagan JO, Savell JW: Beef customer satisfaction: Cooking method and degree of doneness effects on the top round steak. J Anim Sci. 1999, 77: 653-660.PubMedGoogle Scholar
- O'Connor SF, Tatum JD, Wulf DM, Green RD, Smith GC: Genetic effects on beef tenderness in Bos indicus composite and Bos taurus cattle. J Anim Sci. 1997, 75: 1822-1830.PubMedGoogle Scholar
- Xin M, Small EM, Sutherland LB, Qi X, McAnally J, Plato CF, Richardson JA, Bassel-Duby R, Olson EN: MicroRNAs miR-143 and miR-145 modulate cytoskeletal dynamics and responsiveness of smooth muscle cells to injury. Genes Dev. 2009, 23: 2166-2178. 10.1101/gad.1842409.PubMed CentralView ArticlePubMedGoogle Scholar
- Yin KJ, Deng Z, Huang H, Hamblin M, Xie C, Zhang J, Chen YE: miR-497 regulates neuronal death in mouse brain after transient focal cerebral ischemia. Neurobiol Dis. 2010, 38: 17-26. 10.1016/j.nbd.2009.12.021.PubMed CentralView ArticlePubMedGoogle Scholar
- Yadav S, Pandey A, Shukla A, Talwelkar SS, Kumar A, Pant AB, Parmar D: miR-497 and miR-302b Regulate Ethanol-induced Neuronal Cell Death through BCL2 Protein and Cyclin D2. J Biol Chem. 2011, 286: 37347-37357. 10.1074/jbc.M111.235531.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhao C, Tian F, Yu Y, Luo J, Hu Q, Bequette BJ, Baldwin Vi RL, Liu G, Zan L, Scott Updike M: Muscle transcriptomic analyses in Angus cattle with divergent tenderness. Mol Biol Rep. 2012, 39: 4185-4193. 10.1007/s11033-011-1203-6.View ArticlePubMedGoogle Scholar
- Eisen MB, Spellman PT, Brown PO, Botstein D: Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci. 1998, 95: 14863-10.1073/pnas.95.25.14863.PubMed CentralView ArticlePubMedGoogle Scholar
- Zheng Q, Wang XJ: GOEAST: a web-based software toolkit for Gene Ontology enrichment analysis. Nucleic Acids Res. 2008, 36: W358-W363. 10.1093/nar/gkn276.PubMed CentralView ArticlePubMedGoogle Scholar
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