RNA-seq analysis identifies cytoskeletal structural genes and pathways for meat quality in beef

RNA-seq analysis identifies cytoskeletal structural genes and pathways for meat quality in beef | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research article RNA-seq analysis identifies cytoskeletal structural genes and pathways for meat quality in beef Joel David Leal Gutierrez, Mauricio A. Elzo, Raluca G. Mateescu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.2.10331/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background RNA sequencing (RNA-seq) has allowed for transcriptional profiling of biological systems through identification of differentially expressed (DE) genes and pathways. Results A total of 80 steers were selected from the multibreed Angus-Brahman herd of the University of Florida. Sensory panel tenderness, juiciness and connective tissue as well as marbling, WBSF and cooking loss were assessed in longissimus dorsi muscle. Nuclear RNA was extracted from muscle and an RNA-seq library for each sample was constructed, multiplexed, and sequenced based on protocols by Illumina HiSeq 3000 PE100 platform to generate 2 × 101 bp paired-end reads. On average, 34.9 million high-quality paired reads were uniquely mapped to the Btau_4.6.1 reference genome and a total of 8,799 genes were analyzed. Including all 80 animals, gene and exon expression analysis was carried out using a meat quality index as a continuous response variable. The expression of 208 genes and 3,280 exons from 1,565 genes was associated with the meat quality index (p-value ≤ 0.05). Out of the 80 samples sequenced, 40 animals with extreme low and high WBSF, tenderness and marbling values were selected for a differential expression (DE) analysis for gene and isoforms. A total of 676 (adjusted p-value ≤ 0.05), 70 (adjusted p-value ≤ 0.1) and 198 (adjusted p-value ≤ 0.1) genes were DE for WBSF, tenderness and marbling, respectively. A total of 106 isoforms from 98 genes for WBSF, 13 isoforms from 13 genes for tenderness and 43 isoforms from 42 genes for marbling (FDR ≤ 0.1) were DE. Conclusion A number of cytoskeletal and transmembrane anchoring related genes and pathways were identified in the expression, DE and gene enrichment analyses, and these proteins can have a direct effect on meat quality. Cytoskeletal proteins and transmembrane anchoring molecules can influence meat quality by allowing cytoskeletal filament interaction with myocyte and organelle membranes, contributing to cytoskeletal structure, microtubule network stability, and cellular architecture maintenance during the postmortem. Epigenetics & Genomics Cytoskeletal protein gene expression meat quality and transmembrane anchoring protein Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Meat quality phenotypes in beef cattle are economically important traits which are quantitative in nature with usually low to medium genetic control [1,2]. Multiple efforts have been directed to identify genes able to explain part of the phenotypic variability present in meat quality related traits in different populations [3–5]. Large-scale genotyping platforms, high-density panels of molecular markers, and genome-wide association (GWA) analyses are extensively used to identify major genes for improvement of meat quality traits in beef cattle. However, our knowledge about the exact mechanism through which the identified genomic regions contribute to phenotypic variability in quantitative traits is still very limited. This could be partially due to alterations at transcriptional level which are not captured at the DNA level. Recently, RNA sequencing (RNA-seq) has allowed for transcriptional profiling of biological systems through identification of differentially expressed (DE) genes and pathways in order to identify biological mechanisms associated to the phenotypic condition being assessed [6]. Understanding the biological mechanisms associated with complex and economically important traits would help identify genes that could potentially be used as biomarkers in animal selection [7]. Differential expression is most often derived from comparing two or more conditions; however, converting a continuous phenotype such as meat quality into categories leads to loss of phenotypic variability. Seo et al. (2016) [8] demonstrated that expression analysis based on robust regression, which performs an association between a continuous trait and mRNA expression, achieves a lower false discovery rate and higher precision. The objectives of the present research were to perform: 1) a gene and exon expression analysis for a continuous meat quality index defined through a principal component analysis of meat quality related traits; and 2) a gene and isoform differential expression for Warner-Bratzler Shear Force (WBSF), tenderness and marbling as categorical variables. Results Cattle population and phenotypic data Table 1 shows the phenotypic distribution of the meat quality phenotypes for the animals used in this study. For the expression analysis, animals with low meat quality index were tougher, dryer, and had more connective tissue and less marbling than animals with a high index. A clear phenotypic differentiation between high and low performance samples was evident in the DE analysis for WBSF, tenderness and marbling. Paired-end read alignment and paired-end read counting After excluding single reads and filtering out bases and reads with low sequencing quality, the average sequencing depth was 39.8 million paired reads. On average, 34.9 million high-quality paired reads were uniquely mapped to the Btau_4.6.1 reference genome having a mean fragment inner distance of 144±64 bases. Gene expression association analysis for the meat quality index Expression of 208 genes was associated with the meat quality index (Additional File 1 p-value ≤ 0.05). The Rho GTPase Activating Protein 10 ( ARHGAP10 ), Transmembrane Protein 120B ( TMEM120B ), Arrestin Domain Containing 4 ( ARRDC4 ), KIAA2013 , NDRG Family Member 3 ( NDRG3 ), WD Repeat Domain 73 ( WDR73 ) and WD Repeat Domain 77 ( WDR77 ) genes encode cytoskeletal associated proteins and were identified as highly associated (p-value ≤ 1x10 -4 ) with the meat quality index (Figure 1A). Exon expression analysis for the meat quality index A total of 3,280 exons in 1,565 genes were associated with the meat quality index (p-value ≤ 0.05) (Additional File 1 and Figure 1B). The SLMO1 (also named PRELID3A ), TMEM120B , WDR77 , ADP Ribosylation Factor 6 ( ARF6 ), FAM21A , KIAA2013 , DAZ Associated Protein 2 ( DAZAP2 ), Kelch Domain Containing 8B ( KLHDC8B ), and Death Inducer-Obliterator 1 ( DIDO1 ) genes had at least one exon highly associated with meat quality index in the present analysis. Differential expression analysis A total of 676 (Figure 2A; adjusted p-value ≤ 0.05), 70 (Figure 2B; adjusted p-value ≤ 0.1) and 198 (Figure 2C; adjusted p-value ≤ 0.1) genes were DE for WBSF, tenderness and marbling, respectively (Additional File 2). A total of 106 isoforms from 98 genes for WBSF, 13 isoforms from 13 genes for tenderness and 43 isoforms from 42 genes for marbling (Figure 3 and Additional File 3; FDR ≤ 0.1) were DE. Overlapping genes across DE evaluation and genome wide association analysis in the present population A total of 30 genes were simultaneously identified in the expression and DE analysis, and 13 of them encode proteins with structural function; five other genes are transcription factors or co-regulators (Table 2). From the structural proteins, 12 are potential µ-calpain substrates. All 30 genes were initially used to construct a protein-protein interaction network (Figure 4). Out of these 30 genes, 18 genes constitute a network including 150 proteins. From these 150 proteins, 45 were determined as downregulated (red nodes) and 31 others as upregulated (green nodes) in tender samples. Other 78 genes (blue nodes) were not identified in the expression or DE analysis but interconnect with other nodes of this protein-protein interaction network. In this network, NFKB2 (upregulated), ABLIM1 (upregulated), EIF4E2 (upregulated) and ARPC5L (downregulated), and ARF6 (upregulated) were determined as having the highest connectivity. Gene enrichment analysis A gene enrichment analysis was performed using the four gene lists generated from the expression and DE gene analysis for WBSF, marbling and tenderness (Additional File 4). Ten pathways were identified as enriched and they can be classified in two groups, pathways associated to cellular structure and pathways associated to respiration. Discussion Paired-end read alignment and paired-end read counting Highly specialized genes in skeletal muscle such as Titin ( TTN ), Actin Alpha 1 ( ACTA1 ), Myosin Heavy chain 1 ( MYH1 ), Aldolase Fructose-Bisphosphate A ( ALDOA ), Myosin Heavy Chain 7 ( MYH7 ), Nebulin ( NEB ), Filamin C ( FLNC ), ATPase Sarcoplasmic/Endoplasmic Reticulum Ca2+ Transporting 1 ( ATP2A1 ), Tropomyosin 2 ( TPM2 ), and Creatine Kinase, M-type ( CKM ) were the top expressed genes based on number of counts. Since most of these proteins have structural function and are mechanically required for contraction, they are highly expressed in skeletal muscle. TTN and NEB are large sarcomere filament-binding proteins uniformly expressed in skeletal muscle; NEB acts as an actin filament stabilizer, it is involved in myofibrillogenesis, modulates thin filament length and allows proper muscle contraction [10]. NEB knockout mice show muscular weakness, altered calcium homeostasis and glycogen metabolism [10]. Gene expression association analysis for the meat quality index In the following paragraphs we present a short description of the most important genes identified through the gene expression association analysis. The gene showing the most significant association (p-value ≤ 4.2x10 -4 ), ARHGAP10 (Figure 5A), is part of a Rho family of GTPase-activating proteins (RhoGAP). This protein regulates the activity of the small GTPase CDC42 and by doing so, controls the F-Actin and ARP2/3 dynamics at the Golgi complex. The Golgi-associated small GTPase, ARF1 recruits ARHGAP21 and allows interaction between ARHGAP21 and CDC42, inducing GTP hydrolysis and promoting actin filament interaction with Golgi membranes [11]. The ARHGAP10 gene was found to regulate actin cytoskeleton remodeling, cell proliferation, and cell differentiation. The ARHGAP10 interacts with α-tubulin and it is involved in cell-cell adhesion processes and consequently could promote cell migration [12–16]. In our study, overexpression of ARHGAP10 was associated with lower meat quality index. This could be a consequence of a more stable actin cytoskeleton structure which would result in lower meat quality. A higher expression of TMEM120B gene was associated with a reduced meat quality index in the present analysis (Figure 5B). The TMEM120B gene is highly expressed during adipocyte differentiation, and knockdown of this gene alters expression of genes required for adipocyte differentiation such as GATA3 , FASN and GLUT4 [17]. This gene is a cytoskeletal anchoring protein and it can affect tenderness by promoting changes in cytoskeletal structure stability or cellular compartmentalization and size adaptation in adipocytes [18]. The ARRDC4 gene belongs to a plasma membrane associated protein family named α-arrestins, and higher expression of this gene was associated with lower meat quality index (Figure 5C). A better characterized member of this family, ARRDC3, is a breast and prostate cancer suppressor; lower expression of ARRDC3 was significantly associated with high aggressiveness and metastasis in prostate cancer cells [19,20]. The ARRDC3 protein localizes in certain sections of the plasma membrane associated with intracellular vesicles suggesting that ARRDC3 regulates cell-surface proteins such as ITGβ4 in skeletal muscle; this interaction between ARRDC3 and ITGβ4 suggests a possible mechanism through which ARRDC3 could regulate cell motility and migration [20]. The ARRDC3 knockout male mouse is resistant to obesity which was reported to be a result of higher energy expenditure due to increased activity level and thermogenesis in adipose tissues [21]. The association of this gene with meat quality could be explained by variation in adipocyte proliferation or overall cytoskeletal structure and cellular attachment. The KIAA2013 encodes an uncharacterized transmembrane protein [22] and higher expression of this gene was associated with lower meat quality index (Figure 5D). Xu et al. (2015) [23] identified selection signatures on KIAA2013 using Holstein, Angus, Charolais, Brahman, and N’Dama cattle showing that the genomic region harboring KIAA2013 could explain phenotypic differences associated to breed effect in this population. Upregulation of NDRG3 is present in prostate and laryngeal squamous cancerous cells and was also correlated with pathological stage, positive metastatic status and lymph node status [24–26]. High expression of NDRG3 was associated with lower meat quality index (Figure 5E), possibly by generating a more stable cellular attachment [27]. This is supported by the fact that upregulation of a NDRG3 paralogous, NDRG2 , suppresses tumor invasion by inhibiting the matrix metalloproteinases MMP-9 and MMP-2. Higher expression of WDR73 was associated with lower meat quality index (Figure 5F) possibly due to an increment in cytoskeletal structure stability resulting in lower meat quality. Fibroblasts with mutated WDR73 presented abnormal nuclear morphology, low cell viability, and altered microtubule network, suggesting a role in cellular architecture maintenance and cell survival [28]. Downregulation of WDR77 arrests growth and differentiation of lung epithelial cells while its upregulation promoted terminally differentiated cells to undergo a new stage of cell proliferation, triggering lung adenocarcinoma formation [29]. Exon expression analysis for the meat quality index The SLMO1, TMEM120B, ARF6, FAM21A, KIAA2013, DAZAP2, KLHDC8B and DIDO1 genes are discussed below. The SLMO1 gene encodes three different isoforms (Additional File 5) and two of them share exon 9. Higher expression of the exon 9 of SLMO1 was associated with higher meat quality index (Figure 6A). This association could be due to increased lipid deposition given that this protein is part of an intermembrane lipid transfer system located in the mitochondria [30] or it could contribute to cytoskeletal attachment of this organelle membrane. The exon 9 of SLMO1 encodes a total of 30 amino acids (golden region in the Additional File 6) that are part of a PRELI/MSF1 domain. This domain is located between positions 74 and 245 and confers a globular alpha-beta folded structure to SLMO1 [31]. The association of the exon 9 with meat quality index show that the isoforms ENSBTAT00000081878.1 and ENSBTAT00000046981.3 could have a similar phenotypic effect on meat quality in the present population but different from the effect of the isoform ENSBTAT00000084244.1. Expression of multiple exons of the TMEM120B gene and the exon 3 of WDR77 agreed with the overall gene expression analysis (Figure 6B and 6C). All TMEM120B exons were individually associated with the meat quality index.The exon 3 in WDR77 encodes a segment between the amino acids 99 and 148 located inside the WD_REPEATS_REGION which could be important for the formation of the globular structure shown in the Additional File 7 (golden region). Higher expression of the exon 2 of ARF6 was associated with higher meat quality index (Figure 6D) probably due to cell proliferation and cytoskeletal remodeling. This gene encodes a GTP-binding protein involved in plasma membrane trafficking, actin-based cytoskeletal remodeling and cell migration [32]. Knockout ARF6 mice exhibit hypocellularity, midgestational hepatocyte apoptosis with Caspase 3 activation, defective hepatic cord formation and almost completely penetrant embryonic lethality [33]. Higher expression of the exon 25 of FAM21A was associated with higher meat quality index (Figure 6E); FAM21A interacts with a multi-protein complex named WASH (Wiskott-Aldrich Syndrome Protein and SCAR Homolog) involved in endosome-to-plasma membrane trafficking. This complex interacts with tubulin and F-actin, and activates ARP2/3, and endocytosis, sorting and trafficking regulator [34]. The association of FAM21 and meat quality could be due to changes in actin polymerization. The FAM21 protein modulates actin polymerization by preventing actin-capping through a physical interaction with the Capping Actin Protein of Muscle Z-Line (CAPZ). Additionally, FAM21 can interact with phosphatidylserine and some phospholipid species allowing the linkage between the WASH complex and endosomal domains [35,36]. The third and fourth exons of KIAA2013 were associated with meat quality index (Figure 6F) and higher expression of both were associated with lower meat quality index. This gene encodes an uncharacterized transmembrane protein [22] showing that this protein could be a cytoskeletal anchor. Two different transmembrane regions were predicted between the positions 21-40 and 592-614; the latter transmembrane region is encoded by the third KIAA2013 exon (Additional File 5). Higher expression of the first exon of DAZAP2 was associated with higher meat quality index (Figure 6G) and this relationship could be due to cell proliferation given that this gene is a potential tumor suppressor. Patients with multiple myeloma have DAZAP2 downregulation because of promotor methylation [37,38]. Meat quality index was negatively correlated with expression of the third exon of KLHDC8B (Figure 6H) and this gene is associated with some cases of classical Hodgkin lymphoma which is characterized by binucleated cells. The KLHDC8B gene encodes a midbody kelch protein required during mitotic cytokinesis [39,40]. The third KLHDC8B exon is included in both annotated isoforms (Additional File 8) suggesting that additional isoforms involving this exon may be still uncovered. The third KLHDC8B exon could be structurally important for providing a globular conformation (golden region). Expression of the exon number 17 of DIDO1 was associated with higher meat quality index (Figure 6I). Two different DIDO1 isoforms are annotated but only the isoform ENSBTAT00000007879.6 includes the exon 17 (Additional File 5). This isoform has an additional domain located between the amino acids 672 and 792 (TFIIS_CENTRAL) involved in mRNA cleavage [31]. The protein segment encoded by the exon number 17 of DIDO1 (from amino acid number 1088 to 1117) could be structurally crucial for overall molecular activity. The association between exon expression and meat quality index could be related to the pro-apoptotic activity of DIDO1 [41]. DIDO1 is involved in regulating embryonic stem cell maintenance and there exist early differentiation in mouse embryonic stem cells lacking this gene; this protein is also able to positively regulate expression of key pluripotency markers [42]. Differential expression analysis DE genes for WBSF, tenderness and marbling A total of 19 genes were simultaneously identified in at least two analyses and they can be classified in three different groups based on their biological function. The first group of DE genes are related to cell survival, apoptosis and cancer, and include the following genes: Angiopoietin Like 4 ( ANGPTL4 ), Apoptotic Peptidase Activating Factor 1 ( APAF1 ), G0/G1 Switch 2 ( G0S2 ), Hyaluronan Binding Protein 2 ( HABP2 ), Interferon Related Developmental Regulator 1 ( IFRD1 ) and Tribbles Pseudokinase 1 ( TRIB1 ). These genes could promote myocyte and adipocyte proliferation. The second group includes a number of structural proteins associated with cellular membranes or cytoskeletal proteins. The genes Complement C4A ( C4A ), Complement Factor B ( CFB ), Chloride Intracellular Channel 5 ( CLIC5 ), Family With Sequence Similarity 83 Member H ( FAM83H ), Integrin Subunit Beta 6 ( ITGB6 ), Mitochondrial Ribosomal Protein L35 ( MRPL35 ), Phospholamban ( PLN ), Protein Phosphatase, Mg2+/Mn2+ Dependent 1K ( PPM1K ), Transferrin Receptor ( TFRC ), Tripartite Motif Containing 55 ( TRIM55 ) belong to this group. Changes in the amount of these proteins could have a direct effect on cytoskeletal structure and organization, and postmortem proteolysis. Two transcription factors, Early Growth Response 1 ( EGR1 ) and Hes Related Family BHLH Transcription Factor with YRPW Motif-Like ( HEYL ), were also uncovered and they represent the third group. The most important DE genes associated with meat quality in the present analysis are described below. The APAF1 gene was identified as DE in the WBSF and tenderness analyses, and identified as downregulated in tender meat; this protein is a central component of the apoptosome, a mitochondrial caspase activation pathway which mediates apoptosis. After activation of this pathway, the mitochondria release Cytochrome C which in turn binds to APAF1 and promote apoptosis by activating Caspase 9 [43,44]. Long et al. (2013) [44] characterized an APAF1 mutant mouse line which does not promote apoptosis. These mouse embryos presented decreased apoptosis, nervous system development defects and craniofacial deficiencies associated with higher mesenchymal proliferation and delayed ossification resulting in perinatal death. In human, downregulation of APAF1 is evident in colorectal cancer and hepatocellular carcinoma cells given transcriptional regulation by miR-23a and Histone Deacetylases 1–3 [43,46]. The G0S2 gene was upregulated in tender meat in the WBSF and tenderness analyses; G0S2 is highly expressed in adipose tissue and its expression relates to lipid accumulation and adipogenesis in swine. Cell proliferation inhibition is also promoted by this gene giving that there exit G0S2 downregulation in preadipocytes and fetal adipose tissues, and upregulation in adipocytes and adipose tissues from adult pigs [47]. Lipid catabolism is regulated by G0S2 through interaction and inhibition of the Adipose Triglyceride Lipase (ATGL) and upregulation of G0S2 or downregulation of ATGL in non-small cell lung carcinomas stalls triglyceride catabolism and represses cell growth [48]. Female knockout G0S2 mice present lactation defects and knockout mice show lower body weight gain, higher serum glycerol levels, higher acute cold tolerance given upregulation of thermoregulatory and oxidation promoting genes in white adipose tissue [49,50]. High G0S2 methylation is present in squamous lung cancer being this methylation content inversely correlated with G0S2 expression [51]. The IFRD1 gene was downregulated in tender meat in the WBSF analysis; however, this gene was upregulated in high marbling samples. IFRD1 plays a role in muscle differentiation and bone homeostasis. In myoblasts, downregulation of IFRD1 hinders cell cycle exit and differentiation via MyoD downregulation, and promotes acetylation and nuclear localization of p65. In adult muscle, upregulation of IFRD1 stimulate regeneration via myogenesis by negatively regulating NF-κB, which in turn is post-transcriptionally downregulate by MyoD [52]. In bone, IFRD1 is involved in bone homeostasis maintenance; knockout IFRD1 mice develops higher bone mass because of increased bone deposition and decreased bone reabsorption [53]. Downregulation of CLIC5 was identified in tender meat in the WBSF and tenderness assessment. This gene encodes a multiconductance channel for Na+, K+ and Cl–, and is inactivated by F-actin; this channel modulates solute transport at key cellular stages such as apoptosis, and cell division and fusion [54]. A CLIC5 isoform, CLIC5A, is involved in glomerular endothelial cell and podocyte architecture formation and maintenance, and both cell types show high CLIC5A expression. This isoform colocalized with Podocalyxin (PODXL) and Ezrin (EZR) in the apical plasma membrane in podocytes. Knockout CLIC5A mice present lower EZR expression in podocytes altering PODXL and actin filament association [55]. Berryman, Bruno, Price, & Edwards (2004) [56] reported that the de novo assembly of the cytoskeletal complex CLIC5A-EZR requires actin polymerization, being CLIC5A essential for assembly and maintenance of F-actin-based arrangement at the cell cortex. Upregulation of FAM83H was identified in tender meat using the WBSF and tenderness analyses. FAM83H colocalizes with keratin filaments surrounding the nucleus and usually communicates with cell-cell junctions. Downregulation of FAM83H promotes keratin filament formation and its upregulation produces keratin filament disassembly. The filamentous keratin structure is regulated by FAM83H and disorganization of this keratin associated cytoskeleton is caused by upregulation of FAM83H in colorectal cancer cells [57]. Upregulation of FAM83H is mediated by binding of MYC at FAM83H promoter and is characteristic of hepatocellular carcinoma cells. Overexpression of FAM83H drives upregulation of Cyclin D1 , Cyclin E1 , SNAI1 and MMP2 , and repression of P53 and P27 [58]. Downregulation of PLN in tender meat was identified using the WBSF analysis; however, this gene was upregulated in high marbling samples. PLN is a sarcoplasmic reticulum Ca2+-cycling protein and regulatory partner of the ATPase Sarcoplasmic/Endoplasmic Reticulum Ca2+ Transporting 2 (ATP2A2) protein being involved in regulating cardiomyocyte contractility [59,60]. Medin et al. (2007) [62] identified a SNP located in the promoter region of PLN able to decrease its transcriptional activity and associated with apical hypertrophic cardiomyopathy. Some mutations in the cytoplasmic domain of PLN modify its hydrophobic interaction with ATP2A2, and alter PLN regulatory activity. One of these mutations, a deletion in the coding region is associated with left ventricular dilation, contractile dysfunction, episodic ventricular arrhythmias and hereditary heart failure. Transgenic mice overexpressing the PLN -Del allele develop similar symptomatology as well as premature death [59]. This PLN deletion abolishes regulation by phosphorylation, which in turn induces a constitutive PLN inhibitory state [63]. The directionality of expression of most of these genes agreed across analysis. The expression of CFB , G0S2 , C4A, ANGPTL4 and FAM83H was higher in tender meat and expression of MRPL35 , CLIC5 , KLHL34 , HEYL , APAF1 , ITGB6 , PPM1K , TFRC , TRIB1 and EGR1 was lower in tender meat. Isoform DE analysis for WBSF, tenderness and marbling Because isoform annotation for the Btau_4.6.1 reference genome is relatively poor, only gene name in the isoform association analysis was reported and further evaluation was carried out for well annotated isoforms. The Eukaryotic Translation Initiation Factor 4E Family Member 2 ( EIF4E2 ), GNAS Complex Locus (GNAS), Lysosomal Associated Membrane Protein 2 (LAMP2), Mucolipin 1 (MCOLN1) and Reticulon 4 ( RTN4 ) genes were selected for further analysis. The EIF4E2 isoform NM_001075795.2 was identified as DE (Additional File 9). Hypoxic microenvironment is a common feature in tumors and EIF4E2 is preferentially used rather than EIF4E during translation of a number genes [64] such as cytoskeletal related proteins. Cadherin-22 is a cell-surface molecule target of EIF4E2 and it is involved in cell migration, invasion and adhesion during cancer development. Kelly et al. (2018) [65] reported that silencing of EIF4E2 or Cadherin-22 halted breast carcinoma and glioblastoma development during hypoxia. The EIF4E2 isoforms NP_001069263.1 and NP_001193345.1 only differ by a 12-amino acid segment (golden region in Additional File 10). The additional segment present in NP_001069263.1 could confer a differential effect on EIF4E2 translational function during apoptosis affecting the tenderization process. The Additional File 9 shows some structural features of the GNAS isoforms NP_001258700.1 and NP_851364.1, being the latter isoform identified as DE in the present analysis. Both isoforms differ greatly because of alternative promoters. The GNAS locus is paternally, maternally and biallelically imprinted in a tissue-specific manner and code for a number of molecular products by using multiple promoters [66]. The GNAS protein is categorized as a cell membrane associated protein [22], thus it could contribute to cytoskeletal stability. Furukawa et al. (2011) [67] and Wu et al. (2011) [69] reported that somatic mutations in the GNAS locus are frequently identified in Intraductal papillary mucinous neoplasm, a pancreatic cystic neoplasm characterized by being highly invasive and metastatic with poor prognosis. Markers in the GNAS locus are also associated with endocrine tumors, fibrous dysplasia of bone and hereditary osteodystrophy [66]. Isoforms from the LAMP2 and MCOLN1 genes were identified as DE, and their proteins are lysosomal associated proteins. For the LAMP2 gene, the NP_001029742.1 and NP_001106715.1 isoforms were analyzed. The NP_001106715.1 (homologous to the LAMP2A isoform in mice) was determined as DE in the present population. Both isoforms have signal peptide and two transmembrane segments (Additional File 9) nevertheless, homology between them decreases after the amino acid number 363. A monomeric LAMP2A molecule binds to substrate proteins and allows chaperone-mediated autophagy in lysosomes by establishing high-molecular-weight LAMP2A complexes at the lysosomal membrane; the hsc70 and hsp90 chaperones have crucial roles in disassembly and stabilization of the LAMP2A complexes [70]. Cuervo & Dice (2000) [71] found that 25% of total LAMP2 molecules in rat liver lysosomes were LAMP2A and concentration of this isoform was correlated with rates of chaperone-mediated autophagy in liver and fibroblasts in culture; therefore, there exists a substrate protein that binds only to the LAMP2A isoform. The LAMP2A isoform also mediates autophagosome-lysosome fusion in mouse embryonic fibroblasts [72]. The MCOLN1 isoform NP_001159604.1 was identified as DE (Additional File 9). This protein is a Ca2+-releasing cation channel associated to the lysosomal plasma membrane and it is involved in endocytosis. Mutations in this gene cause mislocalization and disrupt Ca2+ flow across the lysosomal membrane and produce Mucolipidosis type IV, a lysosomal storage disorder related to a transport defect in endocytosis [73,74]. Schmiege et al. (2017) [74] reported the conformational assembly of the human MCOLN1 channel which is structurally close to the bovine isoform NP_001159604.1 (Additional File 11); this channel seems to be tightly regulated by aromatic–aromatic and hydrophilic interactions between amino acids and by agonist regulation, allowing adequate selectivity filter dynamics. Cuajungco et al. (2014) [75] reported physical interaction between MCOLN1 and TRPML1, a zinc transporter, and deletion of the MCOLN1’s N-terminus disrupted this interaction. Some other mutations in this gene are able to disrupt inhibition of MCOLN1 by pH and promote channel aggregation [76]. Expression of the DE isoforms of LAMP2 (NM_001113244.1) and MCOLN1 (NM_001166132.1) could promote specific cytoskeletal association with lysosome membranes. This effect on cytoskeletal organization may contribute to overall tenderization postmortem and meat quality. The RTN4 isoform NP_001106692.1 was identified as DE in the present analysis (Additional File 9); this isoform is homologous to the human RTN4 isoform B. RTNs encode a family of membrane associated proteins and RTN4s are involved in shaping and maintaining endoplasmic reticulum tubules. RTN4, Atlastin (ATL) and Lunapark, ER Junction Formation Factor (LNP) proteins are curvature-stabilizing proteins required for the formation of the cellular network of membrane tubules and a RTN4/ATL activity balance is required. Hyperactivity or upregulation of RTN4A induces endoplasmic reticulum fragmentation [77,78]. The RNT4B is expressed in epithelial, fibroblast and neuronal cells and it is localized in curved membranes on endoplasmic reticulum tubules and sheet edges. Upregulation of RNT4B modifies the sheet/tubule balance and induces higher formation of tubules producing membrane deformation; conversely, RNT4B downregulation produces large peripheral endoplasmic reticulum sheets [79]. Overlapping genes across DE evaluation and genome wide association analysis in the present population The key genes identified in the protein-protein interaction network (Figure 4), NFKB2 , ABLIM1 , EIF4E2 , ARPC5L and ARF6 , are involved in multiple cellular functions such as actin polymerization, cytoskeletal structure and transcription factor activity [22]. Table 3 shows a list of genes that were simultaneously identified by Leal-Gutiérrez et al. (2018c) [80] and Leal-Gutiérrez et al. (2019) [81] using genotype-phenotype association in the present population and genes that were identified in the expression or DE analysis. A total of 14 genes were identified using genotype-phenotype and expression-phenotype association approaches simultaneously. These genes could potentially exhibit cis-eQTL regulation suggesting that changes in gene expression could be responsible for the genotype-phenotype association. Based on this theory, a cis-eQTL analysis was performed (unpublished data). Cis-eQTL regulation was identified for the Eukaryotic Translation Initiation Factor 4E Nuclear Import Factor 1 ( EIF4ENIF1 ), Gamma-Glutamyl Carboxylase ( GGCX ), 3-Hydroxyisobutyrate Dehydrogenase ( HIBADH ), SRSF Protein Kinase 1 ( SRPK1 ), and LDL Receptor Related Protein 5 ( LRP5 ). This result suggests that polymorphisms in these genes are able to regulate the expression of harboring genes, and this variation in mRNA expression could have a direct effect on meat quality in the present population. Gene enrichment analysis The ten enriched pathways identified can be classified in two groups. The first group relates to Membrane (GO:0016020) and Membrane part (GO:0044425) which cluster some structural genes. Enrichment of structural protein pathways such as Endoplasmic reticulum membrane (GO:0005789), Golgi apparatus (GO:0005794), and Mitochondrial inner membrane (GO:0005743) were also identified using a gene enrichment analysis based on GWA analysis in the present population [81]. Moreover, enrichment for related pathways such as Cell adhesion and maintenance, Plasma membrane, Integral to plasma membrane, Transmembrane transport, Integral to organelle membrane, Endoplasmic reticulum membrane, and Mitochondrial matrix were identified using copy number variation and selection signatures in Hanwoo, Holstein, Angus, Charolais, Brahman, and N’Dama cattle [23,82]. The second type of pathway, is related to energy metabolism and includes pathways such as Respirasome (GO:0070469), Mitochondrial respiratory chain complex I (GO:0005747) and Respiratory chain complex I (GO:0045271). [23] reported enrichment for ATPase activity and Glucose metabolic process in Holstein, Angus, Charolais, Brahman, and N’Dama. The phenotypes were recorded in longissimus dorsi muscle from a multibreed Angus-Brahman population. * Genes with cis-eQTL effects (unpublished data). This table should appear after the line number 414. Conclusions Expression of a number of cytoskeletal proteins and transmembrane anchoring molecules was identified in the expression and DE analysis in the present population and these proteins can have a direct effect on tenderness and marbling. Cytoskeletal proteins and transmembrane anchoring molecules can influence meat quality by allowing cytoskeletal filament interaction with myocyte and organelle membranes, contributing to cytoskeletal structure, microtubule network stability, and cellular architecture maintenance during the postmortem. Some of these cytoskeletal and transmembrane proteins can modulate cell proliferation. Several pathways related to structural proteins and energy metabolism were identified as enriched showing that these kinds of genes are overrepresented and are crucial for meat quality in the present population. Using genotype-phenotype and expression-phenotype association, a number of genes were revealed as potential genes with cis-eQTL regulation. The existence of these cis-eQTL effects could suggest that polymorphisms in these genes are able to regulate expression of the harboring gene, and this variation in expression modules meat quality in the interrogated population. Methods Cattle population and phenotypic data The research protocol was approved by the University of Florida Institutional Animal Care and Use Committee (201003744). A total of 120 steers born between 2013 and 2014 were included in the analysis. The animals belong to the multibreed Angus-Brahman herd from the University of Florida [83–85]. Cattle were classified into three different groups based on their expected Angus and Brahman breed composition. Based on the Angus composition, the grouping was as follows: 1 = 100 to 65%; 2 = 64% to 40%; 3 = 39 to 0% [86]. Steers were transported to a commercial packing plant when their subcutaneous fat thickness over the ribeye reached 1.27 cm. The average slaughter weight was 573.34±54.79 kg at 12.91±8.69 months. The steers were harvested using established USDA-FSIS procedures, and 5-10 g of the longissimus dorsi muscle were sampled after splitting the carcass. The sample was snapped-frozen in liquid nitrogen and stored at -80 °C for RNA extraction. Marbling was recorded 48 hours postmortem in the ribeye muscle at the 12th/13th rib interface by visual appraisal. Numerical scale used for marbling was as follows: Practically Devoid=100-199, Traces=200-299, Slight=300-399, Small=400-499, Modest=500-599, Moderate=600-699, Slightly Abundant=700-799, Moderately Abundant=800-899, Abundant=900-999. Two 2.54 cm steaks from the longissimus dorsi muscle at the 12th/13th rib interface were sampled from each animal. The first steak was used to measure WBSF and cooking loss, and the second steak was used to measure tenderness, juiciness and connective tissue by a sensory panel. The steaks were transported to the Meat Science Laboratory of the University of Florida, aged for 14 days at 1 to 4°C, and then stored at −20°C. Both frozen steaks from each animal were allowed to thaw at 4°C for 24 hours and cooked to an internal temperature of 71°C on an open-hearth grill. After cooking, the first steak was cooled at 4°C for 18 to 24 hours and used to measure WBSF and cooking loss according to the American Meat Science Association Sensory Guidelines [87]. Six cores with a 1.27-cm diameter and parallel to the muscle fiber were sheared with a Warner-Bratzler head attached to an Instron Universal Testing Machine (model 3343; Instron Corporation, Canton, MA). The Warner-Bratzler head moved at a cross head speed of 200 mm/min. The average peak load (kg) of six cores from the same animal was calculated. The weight lost during cooking was recorded and cooking loss was expressed as a percentage of the cooked weight out of the thaw weight. Tenderness, juiciness and connective tissue were measured by a sensory panel according to the American Meat Science Association Sensory Guidelines [87]. The sensory panel consisted of eight to eleven trained members, and steaks from six animals were assessed per session. Two 1 × 2.54 cm samples from each steak were provided to each panelist. Sensory panel measurements analyzed by the sensory panelists included: tenderness (8=extremely tender, 7=very tender, 6=moderately tender, 5=slightly tender, 4=slightly tough, 3=moderately tough, 2=very tough, 1=extremely tough), juiciness (8=extremely juicy, 7=very juicy, 6=moderately juicy, 5=slightly juicy, 4=slightly dry, 3=moderately dry, 2=very dry, 1=extremely dry), and connective tissue (8=none detected, 7=practically none, 6=traces amount, 5=slight amount, 4=moderate amount, 3=slightly abundant, 2=moderately abundant, 1=abundant amount). For each phenotype, the average score by steak from all members of the panel was analyzed. A principal component analysis using marbling, WBSF, cooking loss, juiciness, tenderness and connective tissue was performed on the 120 steers using PROC FACTOR procedure of SAS software [88], and the first three principal components (PC) were used to construct a meat quality index for each animal. The meat quality index was calculated using the following formula: M e a t q u a l i t y i n d e x i = ∫ j = 1 3 P C S i j * P C W j Where PCS ij is the score of the animal i for the PC j , and PCW j is the weight of the PC j represented by theamount of variability explained by each PC (eigenvalues). The amount of variance explained by PC 1 , PC 2 and PC 3 were 44.26%, 20.04% and 13.29%, respectively. Given that the summation of principal component scores for each PCwas zero, a minimum value was added as a constant in order to have only positive PCS values. The meat quality index was used to rank the animals from low to high performance. Out of the 120 steers, 80 animals with extreme low and high meat quality index were selected and used for RNA sequencing. RNA-seq library preparation and sequencing Nuclear RNA was extracted from muscle using TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s protocol (Invitrogen, catalog no. 15596 – 026). RNA concentration was measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and RNA integrity was verified by formaldehyde gel. RNA-seq library preparation and sequencing procedures were performed by RAPiD Genomics LLC (Gainesville, Florida, United States). Isolation of mRNA was performed using oligo-dT attached magnetic beads prior to its reverse transcription and synthesis of double stranded cDNA. An RNA-seq library for each sample was constructed, multiplexed, and sequenced based on protocols of Illumina HiSeq 3000 PE100 platform (Illumina, San Diego, CA, USA) to generate 2 × 101 bp paired-end reads. Read alignment and counting Read trimming was performed with PRINSEQ 0.20.4 [89] using 3 bp sliding windows and a phred threshold of 20. Reads with more than 2 ambiguous bases were discarded. Cutadapt 1.8.1 [90] was used to remove adapter sequences keeping only reads with a minimum length of 50 bp. FastQC 0.9.6 [91] was used to confirm read quality. Tophat 2.1.0 [92] and Bowtie2 2.3.4 [93] were used to perform paired-end read mapping against the Btau_4.6.1 reference genome [94]. Paired-end read counts for all annotated genes were generated using HTSeq 0.9.1 [95] from paired-end reads uniquely mapped. Cufflinks 2.2.1.1 [96,97] was used to estimate transcript abundance in FPKM (Fragments Per Kilobase of exon per Million fragments mapped). The RNA-seq differential expression analysis pipeline DEXSeq [98,99] was used to determine exon counts per gene. RSeQC 2.6.4 [100] was employed for alignment statistics, gene body coverage, junction annotation, junction saturation and paired-end read inner distance size, while Samtools 1.9 [101] was used for indexing and sorting of the alignment files. Genes and exons with less than 10 counts across all samples were excluded from the analysis. Gene and exon expression association analysis for meat quality index The procedure described by Seo et al. (2016) [8] was utilized to perform the expression analysis by gene and exon for the continuous meat quality index. Gene and exon counts were normalized using trimmed mean of M-values (TMM) normalization method available in the R package edgeR [102–104]. The R packages sfsmisc and MASS [103,105,106] were used to compute the Huber’s M-estimator based robust regression. In the robust regression analysis, the meat quality index was the response variable, and normalized gene or exon counts, the first PC from the “PCA for population structure” work-flow of JMP [107] and year of birth of the animal were explanatory variables. A total of 8,799 genes and 96,645 exons were tested in this analysis. Gene and isoform differential expression analysis Out of the 80 samples selected for sequencing, 40 animals were used in the DE procedure. Analysis for WBSF, tenderness and marbling were carried out to compare 20 high performance versus 20 low performance samples. The R package DESeq2 1.20.0 [108] was used to determine DE genes. Year of birth, breed group and the categorical classification based on phenotype were included as fixed effects in the analysis. The categorical classification was as follows: tender vs tough using WBSF or tenderness and high vs low using marbling. A total of 8,799 genes were analyzed for differential gene expression. Genes with a Benjamini-Hochberg adjusted p-values lower than 0.05 for WBSF and 0.1 for tenderness and marbling were considered to be DE. The DE isoform analysis was performed with MetaDiff [109]. Year of birth, breed group and the categorical classification based on phenotype were included as fixed effects in the model. Only genes with alternative splicing were analyzed and isoforms with less than 10 FPKM across samples were excluded. A total of 957 genes with 4,471 isoforms were included in the DE isoform analysis, and a false discovery rate (FDR) threshold of 0.1 was used to identify DE isoforms. Gene enrichment analysis The R packages GOglm and goseq [103,110,111] were used to identify enriched GO terms. Four gene lists resulting from the expression and DE gene analysis for WBSF, tenderness and marbling were assessed. GO terms with fewer than 30 annotated genes were excluded. Enriched GO terms had p-values lower than 0.05. List of abbreviations RNA sequencing (RNA-seq) Differentially expressed (DE) Warner-Bratzler Shear Force (WBSF) Principal Component Score (PCS) Principal Component (PC) Weight of the Principal Component (PCW) Declarations Ethics approval and consent to participate The research protocol was approved by the University of Florida Institutional Animal Care and Use Committee number 201003744. Consent for publication Not Applicable Availability of data and material RNA-seq data are available at the European Nucleotide Archive, accession number PRJEB31379, https://www.ebi.ac.uk/ena/data/search?query=PRJEB31379 . Competing interests No commercial or financial relationships that could be construed as a potential conflict of interest exist. Funding Financial support provided by Florida Agricultural Experiment Station Hatch FLA-ANS-005548, Florida Beef Council, and Florida Beef Cattle Association – Beef Enhancement Fund Award 022962. The funders were not involved in the study design or collection, analysis, or interpretation of the data. Authors' contributions JL conducted all analyses and drafted the manuscript; ME assisted with the analysis and manuscript; RM conceived and assisted with the analyses and manuscript. Acknowledgements Not Applicable References 1. Bhuiyan MSA, Kim HJ, Lee DH, Lee SH, Cho SH, Yang BS, et al. Genetic parameters of carcass and meat quality traits in different muscles (Longissimus dorsi and semimembranosus) of Hanwoo (Korean cattle). J Anim Sci. 2017;95:3359–69. 2. Mateescu RG, Garrick DJ, Reecy JM. Network analysis reveals putative genes affecting meat quality in Angus cattle. Front Genet. 2017;8. 3. Leal-Gutiérrez JD, Elzo M, Johnson D, Mateescu R. Genome-wide association and gene enrichment analyses of meat tenderness in an Angus-Brahman cattle population. 11th World Congr Genet Appl to Livest Prod. Auckland, New Zealand; 2018. 4. Ramayo-Caldas Y, Renand G, Ballester M, Saintilan R, Rocha D. Multi-breed and multi-trait co-association analysis of meat tenderness and other meat quality traits in three French beef cattle breeds. Genet Sel Evol [Internet]. BioMed Central; 2016;48:37. Available from: http://gsejournal.biomedcentral.com/articles/10.1186/s12711-016-0216-y 5. McClure MC, Ramey HR, Rolf MM, McKay SD, Decker JE, Chapple RH, et al. Genome-wide association analysis for quantitative trait loci influencing Warner-Bratzler shear force in five taurine cattle breeds. Anim Genet. 2012;43:662–73. 6. Chen Y, Lun ATL, Smyth GK. From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline. F1000Research [Internet]. 2016;5:1438. Available from: http://f1000research.com/articles/5-1438/v2 7. Simielli Fonseca LF, Jovino Gimenez DF, dos Santos Silva DB, Barthelson R, Baldi F, Aparecido Ferro J, et al. Differences in global gene expression in muscle tissue of Nellore cattle with divergent meat tenderness. BMC Genomics. BMC Genomics; 2017;18:1–12. 8. Seo M, Kim K, Yoon J, Jeong JY, Lee HJ, Cho S, et al. RNA-seq analysis for detecting quantitative trait-associated genes. Sci Rep [Internet]. Nature Publishing Group; 2016;6:1–12. Available from: http://dx.doi.org/10.1038/srep24375 9. Song J, Tan H, Perry AJ, Akutsu T, Webb GI, Whisstock JC, et al. PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites. PLoS One. 2012;7. 10. Bang ML, Li X, Littlefield R, Bremner S, Thor A, Knowlton KU, et al. Nebulin-deficient mice exhibit shorter thin filament lengths and reduced contractile function in skeletal muscle. J Cell Biol. 2006;173:905–16. 11. Ménétrey J, Perderiset M, Cicolari J, Dubois T, Elkhatib N, Khadali F El, et al. Structural basis for ARF1-mediated recruitment of ARHGAP21 to Golgi membranes. EMBO J. 2007;26:1953–62. 12. Azzato EM, Pharoah PDP, Harrington P, Easton DF, Greenberg D, Caporaso NE, et al. A genome-wide association study of prognosis in breast cancer. Cancer Epidemiol Biomarkers Prev. 2010;19:1140–3. 13. Sanchez Bassères D, Vedelago Tizzei E, Duarte AAS, Ferreira Costa F, Olalla Saad ST. ARHGAP10, a novel human gene coding for a potentially cytoskeletal Rho-GTPase activating protein. Biochem Biophys Res Commun. 2002;294:579–85. 14. Barcellos KSA, Bigarella CL, Wagner M V., Vieira KP, Lazarini M, Langford PR, et al. ARHGAP21 protein, a new partner of α-tubulin involved in cell-cell adhesion formation and essential for epithelial-mesenchymal transition. J Biol Chem. 2013;288:2179–89. 15. Zhang S, Sui L, Zhuang J, He S, Song Y, Ye Y, et al. ARHGAP24 regulates cell ability and apoptosis of colorectal cancer cells via the regulation of P53. Oncol Lett. 2018;16:3517–24. 16. Teng JP, Yang ZY, Zhu YM, Ni D, Zhu ZJ, Li XQ. The roles of ARHGAP10 in the proliferation, migration and invasion of lung cancer cells. Oncol Lett. 2017;14:4613–8. 17. Batrakou DG, De Las Heras JI, Czapiewski R, Mouras R, Schirmer EC. TMEM120A and B: Nuclear envelope transmembrane proteins important for adipocyte differentiation. PLoS One [Internet]. 2015;10:1–21. Available from: http://dx.doi.org/10.1371/journal.pone.0127712 18. Farnier C, Krief S, Blache M, Diot-Dupuy F, Mory G, Ferre P, et al. Adipocyte functions are modulated by cell size change: Potential involvement of an integrin/ERK signalling pathway. Int J Obes. 2003;27:1178–86. 19. Zheng Y, Lin Z-Y, Xie J-J, Jiang F-N, Chen C-J, Li J-X, et al. ARRDC3 Inhibits the Progression of Human Prostate Cancer Through ARRDC3-ITGβ4 Pathway. Curr Mol Med [Internet]. 2017;17:221–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28782483%0Ahttp://www.eurekaselect.com/154738/article 20. Draheim KM. An Integral Role of ARRDC3 in Stem Cell Migration and Breast Cancer Progression : A Dissertation [Internet]. University of Massachusetts; 2010. Available from: https://escholarship.umassmed.edu/gsbs_diss/468 21. Patwari P, Emilsson V, Schadt EE, Chutkow WA, Lee S, Marsili A, et al. The arrestin domain-containing 3 protein regulates body mass and energy expenditure. Cell Metab [Internet]. Elsevier Inc.; 2011;14:671–83. Available from: http://dx.doi.org/10.1016/j.cmet.2011.08.011 22. The UniProt Consortium. UniProt: The universal protein knowledgebase. Nucleic Acids Res. Oxford University Press; 2017;45:D158–69. 23. Xu L, Bickhart DM, Cole JB, Schroeder SG, Song J, Van Tassell CP, et al. Genomic signatures reveal new evidences for selection of important traits in domestic cattle. Mol Biol Evol. 2015;32:711–25. 24. Ma J, Liu S, Zhang W, Zhang F, Wang S, Wu L, et al. High expression of NDRG3 associates with positive lymph node metastasis and unfavourable overall survival in laryngeal squamous cell carcinoma. Pathology. 2016;48:691–6. 25. Ren GF, Tang L, Yang AQ, Jiang WW, Huang YM. Prognostic impact of NDRG2 and NDRG3 in prostate cancer patients undergoing radical prostatectomy. Histol Histopathol. 2014;29:535–42. 26. Wang W, Li Y, Li Y, Hong A, Wang J, Lin B, et al. NDRG3 is an androgen regulated and prostate enriched gene that promotes in vitro and in vivo prostate cancer cell growth. Int J Cancer. 2009;124:521–30. 27. Kim A, Kim MJ, Yang Y, Kim JW, Yeom Y Il, Lim JS. Suppression of NF-κB activity by NDRG2 expression attenuates the invasive potential of highly malignant tumor cells. Carcinogenesis. 2009;30:927–36. 28. Colin E, Huynh Cong E, Mollet G, Guichet A, Gribouval O, Arrondel C, et al. Loss-of-function mutations in WDR73 Are responsible for microcephaly and steroid-resistant nephrotic syndrome: Galloway-mowat syndrome. Am J Hum Genet. 2014;95:637–48. 29. Gu Z, Zhang F, Wang ZQ, Ma W, Davis RE, Wang Z. The p44/wdr77-dependent cellular proliferation process during lung development is reactivated in lung cancer. Oncogene. 2013;32:1888–900. 30. Miliara X, Garnett JA, Tatsuta T, Abid Ali F, Baldie H, Perez-Dorado I, et al. Structural insight into the TRIAP1/PRELI-like domain family of mitochondrial phospholipid transfer complexes. EMBO Rep [Internet]. 2015;16:824–35. Available from: http://embor.embopress.org/cgi/doi/10.15252/embr.201540229 31. www.ebi.ac.uk . www.ebi.ac.uk/gxa/home [Internet]. Available from: www.ebi.ac.uk/gxa/home 32. Fang Z, Miao Y, Ding X, Deng H, Liu S, Wang F, et al. Proteomic Identification and Functional Characterization of a Novel ARF6 GTPase-activating Protein, ACAP4. Mol Cell Proteomics [Internet]. 2006;5:1437–49. Available from: http://www.mcponline.org/lookup/doi/10.1074/mcp.M600050-MCP200 33. Suzuki T, Kanai Y, Hara T, Sasaki J, Sasaki T, Kohara M, et al. Crucial Role of the Small GTPase ARF6 in Hepatic Cord Formation during Liver Development. Mol Cell Biol [Internet]. 2006;26:6149–56. Available from: http://mcb.asm.org/cgi/doi/10.1128/MCB.00298-06 34. Gomez T, Billadeau D. A FAM21-Containing WASH Complex Regulates Retromer- Dependent Sorting. Dev Cell. 2009;17:699–711. 35. Jia D, Gomez TS, Metlagel Z, Umetani J, Otwinowski Z, Rosen MK, et al. WASH and WAVE actin regulators of the Wiskott-Aldrich syndrome protein (WASP) family are controlled by analogous structurally related complexes. Proc Natl Acad Sci [Internet]. 2010;107:10442–7. Available from: http://www.pnas.org/cgi/doi/10.1073/pnas.0913293107 36. Helfer E, Harbour ME, Henriot V, Lakisic G, Sousa-Blin C, Volceanov L, et al. Endosomal recruitment of the WASH complex: Active sequences and mutations impairing interaction with the retromer. Biol Cell. 2013;105:191–207. 37. Hu W-X, Ren W, He L-Q, Sheng R, Shi Y-W. Methylation of CpG islands was involved in the down-regulation of DAZAP2 in multiple meyloma cells. Blood. 2007;110. 38. Hu W-X, Qu Q, Li J, Ren W. Hyper-Methylation DAZAP2 May Suppress Its Expression in Specific Subtypes of Myeloma. Blood. 2009;114. 39. Krem MM, Salipante SJ, Wechsler JD, Horwitz MS. The role of KLHDC8B deficiency in Hodgkin lymphoma pathogenesis. J Clin Oncol. 2010;28. 40. Krem MM, Salipante SJ, Horwitz MS. Mutations in a gene encoding a midbody protein in binucleated Reed-Sternberg cells of Hodgkin lymphoma. Cell Cycle. 2010;9:670–5. 41. Braig S, Bosserhoff AK. Death inducer-obliterator 1 (Dido1) is a BMP target gene and promotes BMP-induced melanoma progression. Oncogene [Internet]. Nature Publishing Group; 2013;32:837–48. Available from: http://dx.doi.org/10.1038/onc.2012.115 42. Liu Y, Kim H, Liang J, Lu W, Ouyang B, Liu D, et al. The death-inducer obliterator 1 (Dido1) gene regulates embryonic stem cell self-renewal. J Biol Chem. 2014;289:4778–86. 43. Buurman R, Sandbothe M, Schlegelberger B, Skawran B. HDAC inhibition activates the apoptosome via Apaf1 upregulation in hepatocellular carcinoma. Eur J Med Res. BioMed Central; 2016;21:1–6. 44. Long AB, Kaiser WJ, Mocarski ES, Caspary T. Apaf1 apoptotic function critically limits Sonic hedgehog signaling during craniofacial development. Cell Death Differ [Internet]. Nature Publishing Group; 2013;20:1510–20. Available from: http://dx.doi.org/10.1038/cdd.2013.97 45. Long AB, Kaiser WJ, Mocarski ES, Caspary T. Apaf1 apoptotic function critically limits Sonic hedgehog signaling during craniofacial development. Cell Death Differ. Nature Publishing Group; 2013;20:1510–20. 46. Yong FL, Wang CW, Roslani AC, Law CW. The involvement of miR-23a/APAF1 regulation axis in colorectal cancer. Int J Mol Sci. 2014;15:11713–29. 47. Ahn J, Oh SA, Suh Y, Moeller SJ, Lee K. Porcine G0/G1 switch gene 2 (G0S2) expression is regulated during adipogenesis and short-term in-vivo nutritional interventions. Lipids. 2013;48:209–18. 48. Zagani R, El-Assaad W, Gamache I, Teodoro JG. Inhibition of adipose triglyceride lipase (ATGL) by the putative tumor suppressor G0S2 or a small molecule inhibitor attenuates the growth of cancer cells. Oncotarget. 2015;6. 49. Ma T, Lopez-Aguiar AGN, Li A, Lu Y, Sekula D, Nattie EE, et al. Mice lacking G0S2 are lean and cold-tolerant. Cancer Biol Ther. 2014;15:643–50. 50. El-Assaad W, El-Kouhen K, Mohammad AH, Yang J, Morita M, Gamache I, et al. Deletion of the gene encoding G0/G1 switch protein 2 (G0s2) alleviates high-fat-diet-induced weight gain and insulin resistance, and promotes browning of white adipose tissue in mice. Diabetologia. 2015;58:149–57. 51. Kusakabe M, Watanabe K, Emoto N, Aki N, Kage H, Nagase T, et al. Impact of DNA demethylation of the G0S2 gene on the transcription of G0S2 in squamous lung cancer cell lines with or without nuclear receptor agonists. Biochem Biophys Res Commun. Elsevier Inc.; 2009;390:1283–7. 52. Micheli L, Leonardi L, Conti F, Maresca G, Colazingari S, Mattei E, et al. PC4/Tis7/IFRD1 Stimulates Skeletal Muscle Regeneration and Is Involved in Myoblast Differentiation as a Regulator of MyoD and NF-κB. J Biol Chem. 2011;286:5691–707. 53. Iezaki T, Fukasawa K, Park G, Horie T, Kanayama T, Ozaki K, et al. Transcriptional Modulator Ifrd1 Regulates Osteoclast Differentiation through Enhancing the NF-κB/NFATc1 Pathway. Mol Cell Biol. 2016;36:2451–63. 54. Singh H, Cousin MA, Ashley RH. Functional reconstitution of mammalian “chloride intracellular channels” CLIC1, CLIC4 and CLIC5 reveals differential regulation by cytoskeletal actin. FEBS J. 2007;274:6306–16. 55. Wegner B, Al-Momany A, Kulak S, Kozlowski K, Obeidat M, Jahroudi N, et al. CLIC5A, a component of the ezrin-podocalyxin complex in glomeruli, is a determinant of podocyte integrity. Am J Physiol Physiol. 2010;298:F1492–503. 56. Berryman M, Bruno J, Price J, Edwards JC. CLIC-5A functions as a chloride channel in vitro and associates with the cortical actin cytoskeleton in vitro and in vivo. J Biol Chem. 2004;279:34794–801. 57. Kuga T, Sasaki M, Mikami T, Miake Y, Adachi Y, Shimizu M, et al. FAM83H and casein kinase I regulate the organization of the keratin cytoskeleton and formation of desmosomes. Sci Rep. 2016;6:1–15. 58. Kim KM, Park SH, Bae JS, Noh SJ, Tao GZ, Kim JR, et al. FAM83H is involved in the progression of hepatocellular carcinoma and is regulated by MYC. Sci Rep. 2017;7:1–13. 59. Haghighi K, Kolokathis F, Gramolini A, Waggoner J, Pater L, Lynch R, et al. A mutation in the human phospholamban gene, deleting arginine 14, results in lethal, hereditary cardiomyopathy. Proc Natl Acad Sci. 2006;103:1388–93. 60. Ceholski DK, Trieber CA, Young HS. Hydrophobic imbalance in the cytoplasmic domain of phospholamban is a determinant for lethal dilated cardiomyopathy. J Biol Chem. 2012;287:16521–9. 61. Medin M, Hermida-Prieto M, Monserrat L, Laredo R, Rodriguez-Rey JC, Fernandez X, et al. Mutational screening of phospholamban gene in hypertrophic and idiopathic dilated cardiomyopathy and functional study of the PLN -42 C>G mutation. Eur J Heart Fail. 2007;9:37–43. 62. Medin M, Hermida M, Monserrat L, Laredo R, Rodriguez J, Fernandez X, et al. Mutational screening of phospholamban gene in hypertrophic and idiopathic dilated cardiomyopathy and functional study of the PLN-42 C>G mutation. Front Cell Dev Biol. 2007;9:37–43. 63. Ceholski DK, Trieber CA, Holmes CFB, Young HS. Lethal, hereditary mutants of phospholamban elude phosphorylation by protein kinase A. J Biol Chem. 2012;287:26596–605. 64. Uniacke J, Perera JK, Lachance G, Francisco CB, Lee S. Cancer cells exploit eIF4E2-directed synthesis of hypoxia response proteins to drive tumor progression. Cancer Res. 2014;74:1379–89. 65. Kelly NJ, Varga JFA, Specker EJ, Romeo CM, Coomber BL, Uniacke J. Hypoxia activates cadherin-22 synthesis via eIF4E2 to drive cancer cell migration, invasion and adhesion. Oncogene [Internet]. Nature Publishing Group; 2018;37:651–62. Available from: http://dx.doi.org/10.1038/onc.2017.372 66. Weinstein LS, Liu J, Sakamoto A, Xie T, Chen M. Minireview: GNAS: Normal and abnormal functions. Endocrinology. 2004;145:5459–64. 67. Furukawa T, Kuboki Y, Tanji E, Yoshida S, Hatori T, Yamamoto M, et al. Whole-exome sequencing uncovers frequent GNAS mutations in intraductal papillary mucinous neoplasms of the pancreas. Sci Rep. 2011;1. 68. Wu J, Matthaei H, Maitra A, Dal Molin M, Wood L, Eshleman J, et al. Recurrent gnas mutations define an unexpected pathway for pancreatic cyst development. Sci Transl Med [Internet]. 2011;3. Available from: http://www.embase.com/search/results?subaction=viewrecord&from=export&id=L70818947 http://dx.doi.org/10.1111/j.1477-2574.2012.00512.x 69. Wu J, Matthaei H, Maitra A, Dal Molin M, Wood L, Eshleman J, et al. Recurrent gnas mutations define an unexpected pathway for pancreatic cyst development. Sci Transl Med. 2011;3. 70. Bandyopadhyay U, Kaushik S, Varticovski L, Cuervo AM. The Chaperone-Mediated Autophagy Receptor Organizes in Dynamic Protein Complexes at the Lysosomal Membrane. Mol Cell Biol [Internet]. 2008;28:5747–63. Available from: http://mcb.asm.org/cgi/doi/10.1128/MCB.02070-07 71. Cuervo A, Dice J. Unique properties of lamp2a compared to other lamp2 isoforms. J Cell Sci [Internet]. 2000;113 Pt 24:4441–50. Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11082038 72. Hubert V, Peschel A, Langer B, Gröger M, Rees A, Kain R. LAMP-2 is required for incorporating syntaxin-17 into autophagosomes and for their fusion with lysosomes. Biol Open [Internet]. 2016;5:1516–29. Available from: http://bio.biologists.org/lookup/doi/10.1242/bio.018648 73. Bassi MT, Manzoni M, Monti E, Pizzo MT, Ballabio A, Borsani G. Cloning of the gene encoding a novel integral membrane protein, mucolipidin - And identification of the two major founder mutations causing mucolipidosis type IV. Am J Hum Genet. 2000;67:1110–20. 74. Schmiege P, Fine M, Blobel G, Li X. Human TRPML1 channel structures in open and closed conformations. Nature. 2017;19:477–91. 75. Cuajungco MP, Basilio LC, Silva J, Hart T, Tringali J, Chen C, et al. Cellular Zinc Levels are Modulated by Trpml1-Tmem163 Interaction. Traffic. 2014;15:1247–65. 76. Raychowdhury MK, González-Perrett S, Montalbetti N, Timpanaro GA, Chasan B, Goldmann WH, et al. Molecular pathophysiology of mucolipidosis type IV: pH dysregulation of the mucolipin-1 cation channel. Hum Mol Genet. 2004;13:617–27. 77. Rämö O, Kumar D, Gucciardo E, Joensuu M, Saarekas M, Vihinen H, et al. NOGO-A/RTN4A and NOGO-B/RTN4B are simultaneously expressed in epithelial, fibroblast and neuronal cells and maintain ER morphology. Sci Rep [Internet]. Nature Publishing Group; 2016;6:1–14. Available from: http://dx.doi.org/10.1038/srep35969 78. Wang S, Tukachinsky H, Romano FB, Rapoport TA. Cooperation of the ER-shaping proteins atlastin, lunapark, and reticulons to generate a tubular membrane network. Elife. 2016;5:1–29. 79. Rämö O, Kumar D, Gucciardo E, Joensuu M, Saarekas M, Vihinen H, et al. NOGO-A/RTN4A and NOGO-B/RTN4B are simultaneously expressed in epithelial, fibroblast and neuronal cells and maintain ER morphology. Sci Rep. Nature Publishing Group; 2016;6:1–14. 80. Leal-Gutiérrez JD, Rezende FM, Elzo MA, Johnson D, Peñagaricano F, Mateescu RG. Structural Equation Modeling and Whole-Genome Scans Uncover Chromosome Regions and Enriched Pathways for Carcass and Meat Quality in Beef. Front Genet [Internet]. 2018;9:1–13. Available from: https://www.frontiersin.org/article/10.3389/fgene.2018.00532/full 81. Leal-Gutiérrez JD, Elzo MA, Johnson DD, Hamblen H, Mateescu RG. Genome wide association and gene enrichment analysis reveal membrane anchoring and structural proteins associated with meat quality in beef. BMC Genomics [Internet]. BMC Genomics; 2019;20:151. Available from: https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-5518-3 82. Shin DH, Lee HJ, Cho S, Kim HJ, Hwang JY, Lee CK, et al. Deleted copy number variation of Hanwoo and Holstein using next generation sequencing at the population level. BMC Genomics [Internet]. BMC Genomics; 2014;15:1–16. Available from: BMC Genomics 83. Elzo MA, Thomas MG, Johnson DD, Martinez CA, Lamb GC, Rae DO, et al. Genomic-polygenic evaluation of multibreed Angus-Brahman cattle for postweaning ultrasound and weight traits with actual and imputed Illumina50k SNP genotypes. Livest Sci [Internet]. Elsevier; 2015;175:18–26. Available from: http://dx.doi.org/10.1016/j.livsci.2015.03.002 84. Elzo MA, Thomas MG, Johnson DD, Martinez CA, Lamb GC, Rae DO, et al. Genetic parameters and predictions for direct and maternal growth traits in a multibreed Angus-Brahman cattle population using genomic-polygenic and polygenic models. Livest Sci [Internet]. Elsevier; 2015;178:43–51. Available from: http://dx.doi.org/10.1016/j.livsci.2015.06.015 85. Elzo MA, Mateescu R, Thomas MG, Johnson DD, Martinez CA, Rae DO, et al. Growth and reproduction genomic-polygenic and polygenic parameters and prediction trends as Brahman fraction increases in an Angus-Brahman multibreed population. Livest Sci [Internet]. Elsevier; 2016;190:104–12. Available from: http://dx.doi.org/10.1016/j.livsci.2016.06.011 86. Leal-Gutiérrez JD, Elzo MA, Johnson DD, Scheffler TL, Scheffler JM, Mateescu RG. Association of μ-Calpain and Calpastatin Polymorphisms with Meat Tenderness in a Brahman–Angus Population. Front Genet [Internet]. 2018;9:1–10. Available from: http://journal.frontiersin.org/article/10.3389/fgene.2018.00056/full 87. Belk KE, Dikeman ME, Calkins CR, Andy King D, Shackelford SD, Hale D, et al. Research Guidelines for Cookery, Sensory Evaluation, and Instrumental Tenderness Measurements of Meat. 2015. 88. SAS Institute, Inc., Cary, NC. United States; 89. Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27:863–4. 90. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal [Internet]. 2011;17:10. Available from: http://journal.embnet.org/index.php/embnetjournal/article/view/200 91. Andrews S, Krueger F, Segonds-Pichon A, Biggins L, Krueger C, Wingett S. FastQC [Internet]. 2018. Available from: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ 92. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg S. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14:0–9. 93. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9. 94. http://emea.support.illumina.com/sequencing/sequencing_software/igenome.html # [Internet]. Available from: http://emea.support.illumina.com/sequencing/sequencing_software/igenome.html # 95. Anders S, Pyl P, Huber W. HTSeq-A Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31:166–9. 96. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, Van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol [Internet]. Nature Publishing Group; 2010;28:511–5. Available from: http://dx.doi.org/10.1038/nbt.1621 97. Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol [Internet]. Nature Publishing Group; 2013;31:46–53. Available from: http://dx.doi.org/10.1038/nbt.2450 98. Anders S, Reyes A, Huber W. Detecting diferential usage of exons from RNA-seq data. Genome Res [Internet]. 2012;22:2008–17. Available from: http://dx.doi.org/10.1101/gr.133744.111 99. Reyes A, Anders S, Weatheritt RJ, Gibson TJ, Steinmetz LM, Huber W. Drift and conservation of differential exon usage across tissues in primate species. Proc Natl Acad Sci [Internet]. 2013;110:15377–82. Available from: http://www.pnas.org/cgi/doi/10.1073/pnas.1307202110 100. Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics. 2015;28:137–46. 101. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9. 102. McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40:4288–97. 103. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing [Internet]. Vienna, Austria; 2018. Available from: http://www.r-project.org . 104. Robinson MD, McCarthy DJ, Smyth GK. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40. 105. Maechler M. sfsmisc: Utilities from “Seminar fuer Statistik” ETH Zurich. R package version 1.1-2. [Internet]. 2018. Available from: https://cran.r-project.org/package=sfsmisc 106. Venables WN, Ripley BD. Modern Applied Statistics with S. Fourth Edi. Springer, New York; 2002. 107. JMP®, Version 13. SAS Institute Inc., Cary, NC, 1989-2007. 108. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21. 109. Jia C, Guan W, Yang A, Xiao R, Tang WHW, Moravec CS, et al. MetaDiff: Differential isoform expression analysis using random-effects meta-regression. BMC Bioinformatics. BMC Bioinformatics; 2015;16:1–12. 110. Mi G, Di Y, Emerson S, Cumbie JS, Chang JH. Length Bias Correction in Gene Ontology Enrichment Analysis Using Logistic Regression. PLoS One. 2012;7. 111. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol [Internet]. 2010;11. Available from: http://genomebiology.com/content/11/2/R14%5Cnfile:///Users/zzhou/Dropbox/Library.papers3/Files/E5/E57EB86C-6036-4CE4-B644-1E522EF11A09.pdf%5Cnpapers3://publication/uuid/F930FBCE-A1CC-45CD-8DD7-CB709BF223BE 112. Orchard S, Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F, et al. The MIntAct project - IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2014;42:358–63. 113. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res [Internet]. 2003;13:2498–504. Available from: http://ci.nii.ac.jp/naid/110001910481/ 114. Möller S, Croning MDR, Apweiler R. Evaluation of methods for the prediction of membrane spanning regions. Bioinformatics. 2001;17:646–53. 115. Sigrist CJ, Cerutti L, Hulo N, Gattiker A, Falquet L, Pagni M, et al. PROSITE: a documented database using patterns and profiles as motif descriptors (available at http://www.expasy.ch/prosite) . Br Bioinforma. 2002;3:265–74. 116. Arnold K, Bordoli L, Schwede T. Structural bioinformatics The SWISS-MODEL workspace : a web-based environment for protein structure homology modelling. 2006;22:195–201. 117. Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, et al. SWISS-MODEL: Modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Res. 2014;42:252–8. 118. Kiefer F, Arnold K, Künzli M, Bordoli L, Schwede T. The SWISS-MODEL Repository and associated resources. Nucleic Acids Res. 2009;37:387–92. 119. Petersen TN, Brunak S, Von Heijne G, Nielsen H. SignalP 4.0: Discriminating signal peptides from transmembrane regions. Nat Methods [Internet]. Nature Publishing Group; 2011;8:785–6. Available from: http://dx.doi.org/10.1038/nmeth.1701 Tables Table 1. Descriptive statistics for the meat quality phenotypes and the constructed meat quality index. Trait Group Mean SD Maximum Minimum N Meat quality index 2.34 0.57 3.35 1.15 80 WBSF (kgs) Tender 2.84 0.23 3.20 2.30 20 Tough 5.61 0.51 6.90 5.02 20 Tenderness Tender 6.24 0.21 6.60 5.90 20 Tough 4.00 0.50 4.50 3.00 20 Marbling Low 321 19.17 360 300 20 High 576 50.93 650 500 20 The phenotypes were recorded in longissimus dorsi muscle from a multibreed Angus-Brahman population. This table should appear after the line number 64. Table 2. Genes that were identified at least three times using an expression and DE analysis approach for meat quality related phenotypes. Gene Expression DE Isoform Function name Gene Exon* WBSF Tenderness Marbling WBSF Marbling ABLIM1+ X X X Cytoskeleton ACTN2+ X X X Cytoskeleton ANKRD12 X X X ANKRD23 X X X ARPC5L+ X X X Cytoskeleton C4A+ X X X X Membrane CFB+ X X X Membrane EIF4E2 X X X RNA binding GEMIN4 X X X HMGXB3 X X X Transcription LOC100852159 X X X LOC101903649 X X X MON1B X X X MPPE1+ X X X Cytoskeleton NFKB2 X X X Transcription PCNXL3 X X X PCOLCE2 X X X Peptidase reg. SBNO2 X X X Transcription ST6GALNAC2+ X X X Membrane STAT5A X X X X Transcription TMEM131+ X X X Membrane TRMT6 X X X UCP2+ X X X Membrane UNC13B X X X VEZT+ X X X Membrane WBP1L X X X WDR34+ X X X Cytoskeleton WDR73+ X X X Cytoskeleton ZNF106 X X X ZNF771 X X X Transcription Meat quality was recorded in longissimus dorsi muscle from a multibreed Angus-Brahman population. * Genes with at least three associated exons were included; + the protease analysis was carried out using the PROSPER server [9]. This table should appear after the line number 105. Table 3. Genes uncovered by the expression and DE analysis, and previously identified as associated with meat quality related phenotypes using a genotype-phenotype association analysis in the present population [80,81]. Analysis SEM analysis GWAS Analysis Expression SRPK1* Expression ZFYVE26 Expression LRP5* DE genes ZNF385A DE genes NCOA5 DE genes BOD1L1 DE genes HIBADH* DE genes GOSR2 DE genes KDR DE genes ATPAF1 DE genes ZBTB39 DE Isoforms EIF4ENIF1* DE Isoforms EFCAB14 DE Isoforms RTN4 The phenotypes were recorded in longissimus dorsi muscle from a multibreed Angus-Brahman population. * Genes with cis-eQTL effects (unpublished data). This table should appear after the line number 414. Supplementary Files AdditionalFile8.png AdditionalFile11.png AdditionalFile7.png AdditionalFile10.png AdditionalFile9.xls AdditionalFile1.xls AdditionalFile5.xls AdditionalFile4.xls AdditionalFile2.xls AdditionalFile3.xls AdditionalFile6.png Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1349","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research article","associatedPublications":[],"authors":[{"id":96784,"identity":"e3087ca5-fff9-4083-b0e4-a412ff6734b6","order_by":1,"name":"Joel David Leal Gutierrez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIie3QPwqDMBTH8RdcA64W+ucKKUIXoWdJcHARXIUWGhB08QAe54GQLh6go9ILeAJpdOuUNxaa75Th9yEhAD7fD8Y0MBwB9iGwCQECEgGUAHynA0EjaxsRSCVBUyHK8srjVwoIZaK082GtkSiHlF82MmQE0uUCVR1YUiCyuqeQYka1PHjc2VvYQiI5oNI9F9FKNIW0RqA0Tx4Nb/t1Joud5NxU0zjfb8ewSZk9JAc3+VpI13ztRBn5fD7fn/cBj7hFoirCx1YAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-2811-5390","institution":"University of Florida","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Joel","middleName":"David Leal","lastName":"Gutierrez","suffix":""},{"id":96785,"identity":"4b2a2101-8be0-4d3c-a11b-4bb3da6d5768","order_by":2,"name":"Mauricio A. Elzo","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mauricio","middleName":"A.","lastName":"Elzo","suffix":""},{"id":96786,"identity":"a08c3781-2c74-465d-ad27-1f19ef7a0d12","order_by":3,"name":"Raluca G. Mateescu","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Raluca","middleName":"G.","lastName":"Mateescu","suffix":""}],"badges":[],"createdAt":"2019-06-13 13:11:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.2.10331/v1","doiUrl":"https://doi.org/10.21203/rs.2.10331/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":608246,"identity":"2fca2159-3d3e-423c-8912-84db8352b649","added_by":"auto","created_at":"2020-03-06 17:45:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3733312,"visible":true,"origin":"","legend":"Results from the association analysis between gene expression (A) or exon expression (B) and meat quality index. \nThe x-axis represents the location of the gene or exon across the bovine genome. The black line shows a p-value threshold of 1x10⁻³.","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Figure 1.png"},{"id":608248,"identity":"5c3ef92f-f052-40c8-9163-57b23f94f9a8","added_by":"auto","created_at":"2020-03-06 17:45:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137626,"visible":true,"origin":"","legend":"Volcano plots relating log fold change and p-value for WBSF (A), tenderness (B) and marbling (C). \nBlue dots represent DE genes. A total of 676 (adjusted p-value ≤ 0.05), 70 (adjusted p-value ≤ 0.1) and 198 (adjusted p-value ≤ 0.1) genes were DE for WBSF, tenderness and marbling, respectively.","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Figure 2.png"},{"id":608250,"identity":"a3ab8d5a-ecaa-4242-b8f6-574d28d1cd9b","added_by":"auto","created_at":"2020-03-06 17:45:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6178391,"visible":true,"origin":"","legend":"Genes whose isoforms were identified as DE for WBSF (A), tenderness (B) and marbling (C). \nThe x-axis represents gene location across the bovine genome. The black line shows the 0.1 FDR threshold. Different colors represent different chromosomes.","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Figure 3.png"},{"id":608252,"identity":"d45c0350-4ac8-4a42-af9a-840c6104f388","added_by":"auto","created_at":"2020-03-06 17:45:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":820112,"visible":true,"origin":"","legend":"Protein-protein interaction network showing upregulated (green) and downregulated (red) genes in tender meat from longissimus dorsi muscle sampled in a multibreed Angus-Brahman population. \nBlue boxes show genes that were not identified in the expression or DE analysis but are part of the network. The genes in Table 2 were used as query in the IntAct database [112]. The network was generated using Cytoscape [113].","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Figure 4.png"},{"id":608254,"identity":"c56d6531-92ad-48f7-942e-e1ca5ed194f1","added_by":"auto","created_at":"2020-03-06 17:45:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":83720,"visible":true,"origin":"","legend":"Scatter plots with regression lines and 95% confidence intervals for gene normalized counts and meat quality index for the top six associated genes. \nThe meat quality index was constructed using observed phenotypes measured in longissimus dorsi muscle from a multibreed Angus-Brahman population.","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Figure 5.png"},{"id":608256,"identity":"c488dd07-2ad2-48f6-acb1-38db1eea23ee","added_by":"auto","created_at":"2020-03-06 17:45:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":115979,"visible":true,"origin":"","legend":"Scatter plots with regression lines and 95% confidence intervals for exon normalized counts and meat quality index for the top nine associated genes.","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Figure 6.png"},{"id":13467100,"identity":"20c8d6d3-be98-48c1-a4eb-2db697882980","added_by":"auto","created_at":"2021-09-16 20:53:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2665865,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/0b1c9d1e-7bab-4ed1-a5d3-62038ad57f76.pdf"},{"id":608261,"identity":"be751f5d-14dd-40e7-bc73-42965c2ab456","added_by":"auto","created_at":"2020-03-06 17:45:45","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1395564,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile8.png","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Additional File 8.png"},{"id":608260,"identity":"bcfebe4e-f50d-49c1-b7ca-a2ba578d80c2","added_by":"auto","created_at":"2020-03-06 17:45:44","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2099197,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile11.png","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Additional File 11.png"},{"id":608259,"identity":"05c49eb7-56bf-4d4a-a5ef-b322bb9cee45","added_by":"auto","created_at":"2020-03-06 17:45:44","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":718595,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile7.png","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Additional File 7.png"},{"id":608258,"identity":"82f90ab0-f87d-4365-9020-31328d0bdcb1","added_by":"auto","created_at":"2020-03-06 17:45:44","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":428743,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile10.png","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Additional File 10.png"},{"id":608257,"identity":"d48afd9c-1243-4367-bbaa-a3d106d16858","added_by":"auto","created_at":"2020-03-06 17:45:44","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":38400,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile9.xls","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Additional File 9.xls"},{"id":608255,"identity":"97924f63-3fd9-4ee2-b79f-729104424153","added_by":"auto","created_at":"2020-03-06 17:45:43","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":370688,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile1.xls","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Additional File 1.xls"},{"id":608253,"identity":"51f9833c-6883-4175-a0cc-0181619aaf37","added_by":"auto","created_at":"2020-03-06 17:45:43","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":37376,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile5.xls","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Additional File 5.xls"},{"id":608251,"identity":"f9308c6d-38a6-4213-8ab1-b2cd1b15e500","added_by":"auto","created_at":"2020-03-06 17:45:43","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":35328,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile4.xls","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Additional File 4.xls"},{"id":608249,"identity":"f06f0703-decc-4fbc-9189-29ce135fadb2","added_by":"auto","created_at":"2020-03-06 17:45:42","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":113664,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile2.xls","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Additional File 2.xls"},{"id":608247,"identity":"42c57817-1d33-4d86-98fb-04a95ecb0178","added_by":"auto","created_at":"2020-03-06 17:45:42","extension":"xls","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":44032,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile3.xls","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Additional File 3.xls"},{"id":608245,"identity":"a9aa0d72-dfc8-4b21-9b59-92ecd9945caf","added_by":"auto","created_at":"2020-03-06 17:45:41","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":134623,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFile6.png","url":"https://assets-eu.researchsquare.com/files/rs-1349/v1/Additional File 6.png"}],"financialInterests":"","formattedTitle":"RNA-seq analysis identifies cytoskeletal structural genes and pathways for meat quality in beef","fulltext":[{"header":"Background","content":"\u003cp\u003eMeat quality phenotypes in beef cattle are economically important traits which are\n quantitative in nature with usually low to medium genetic control [1,2]. Multiple\n efforts have been directed to identify genes able to explain part of the phenotypic\n variability present in meat quality related traits in different populations [3–5].\n Large-scale genotyping platforms, high-density panels of molecular markers, and genome-wide\n association (GWA) analyses are extensively used to identify major genes for improvement\n of meat quality traits in beef cattle. However, our knowledge about the exact mechanism\n through which the identified genomic regions contribute to phenotypic variability\n in quantitative traits is still very limited. This could be partially due to alterations\n at transcriptional level which are not captured at the DNA level. \u003c/p\u003e\n \n\u003cp\u003eRecently, RNA sequencing (RNA-seq) has allowed for transcriptional profiling of biological\n systems through identification of differentially expressed (DE) genes and pathways\n in order to identify biological mechanisms associated to the phenotypic condition\n being assessed [6]. Understanding the biological mechanisms associated with complex\n and economically important traits would help identify genes that could potentially\n be used as biomarkers in animal selection [7]. Differential expression is most often\n derived from comparing two or more conditions; however, converting a continuous phenotype\n such as meat quality into categories leads to loss of phenotypic variability. Seo\n et al. (2016) [8] demonstrated that expression analysis based on robust regression,\n which performs an association between a continuous trait and mRNA expression, achieves\n a lower false discovery rate and higher precision. \u003c/p\u003e\n \n\u003cp\u003eThe objectives of the present research were to perform: 1) a gene and exon expression analysis for a continuous meat quality index defined\n through a principal component analysis of meat quality related traits; and 2) a gene\n and isoform differential expression for Warner-Bratzler Shear Force (WBSF), tenderness\n and marbling as categorical variables.\u003c/p\u003e"},{"header":"Results","content":"\u003cp data-xsweet-list-level=\"1\"\u003eCattle population and phenotypic data\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eTable 1 shows the phenotypic distribution of the meat quality phenotypes for the animals\n used in this study. For the expression analysis, animals with low meat quality index\n were tougher, dryer, and had more connective tissue and less marbling than animals\n with a high index. A clear phenotypic differentiation between high and low performance\n samples was evident in the DE analysis for WBSF, tenderness and marbling.\u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003ePaired-end read alignment and paired-end read counting\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eAfter excluding single reads and filtering out bases and reads with low sequencing\n quality, the average sequencing depth was 39.8 million paired reads. On average, 34.9 million high-quality paired reads were uniquely mapped to the Btau_4.6.1\n reference genome having a mean fragment inner distance of 144±64 bases. \u003c/p\u003e\n\u003cp data-xsweet-list-level=\"1\"\u003eGene expression association analysis for the meat quality index\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eExpression of 208 genes was associated with the meat quality index (Additional File 1 p-value ≤ 0.05). The \u003cem\u003eRho GTPase Activating Protein 10\u003c/em\u003e (\u003cem\u003eARHGAP10\u003c/em\u003e), \u003cem\u003eTransmembrane Protein 120B\u003c/em\u003e (\u003cem\u003eTMEM120B\u003c/em\u003e), \u003cem\u003eArrestin Domain Containing 4\u003c/em\u003e (\u003cem\u003eARRDC4\u003c/em\u003e), \u003cem\u003eKIAA2013\u003c/em\u003e,\u003cem\u003e NDRG Family Member 3\u003c/em\u003e (\u003cem\u003eNDRG3\u003c/em\u003e), \u003cem\u003eWD Repeat Domain 73\u003c/em\u003e (\u003cem\u003eWDR73\u003c/em\u003e) and \u003cem\u003eWD Repeat Domain 77\u003c/em\u003e (\u003cem\u003eWDR77\u003c/em\u003e) genes encode cytoskeletal associated proteins and were identified as highly associated\n (p-value ≤ 1x10\u003csup\u003e-4\u003c/sup\u003e) with the meat quality index (Figure 1A). \u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eExon expression analysis for the meat quality index\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eA total of 3,280 exons in 1,565 genes were associated with the meat quality index\n (p-value ≤ 0.05) (Additional File 1 and Figure 1B). The \u003cem\u003eSLMO1 \u003c/em\u003e(also named \u003cem\u003ePRELID3A\u003c/em\u003e), \u003cem\u003eTMEM120B\u003c/em\u003e, \u003cem\u003eWDR77\u003c/em\u003e, \u003cem\u003eADP Ribosylation Factor 6\u003c/em\u003e (\u003cem\u003eARF6\u003c/em\u003e), \u003cem\u003eFAM21A\u003c/em\u003e, \u003cem\u003eKIAA2013\u003c/em\u003e, \u003cem\u003eDAZ Associated Protein 2\u003c/em\u003e (\u003cem\u003eDAZAP2\u003c/em\u003e), \u003cem\u003eKelch Domain Containing 8B\u003c/em\u003e (\u003cem\u003eKLHDC8B\u003c/em\u003e), and \u003cem\u003eDeath Inducer-Obliterator 1\u003c/em\u003e (\u003cem\u003eDIDO1\u003c/em\u003e) genes had at least one exon highly associated with meat quality index in the present\n analysis. \u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eDifferential expression analysis \u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eA total of 676 (Figure 2A; adjusted p-value ≤ 0.05), 70 (Figure 2B; adjusted p-value\n ≤ 0.1) and 198 (Figure 2C; adjusted p-value ≤ 0.1) genes were DE for WBSF, tenderness\n and marbling, respectively (Additional File 2). A total of 106 isoforms from 98 genes\n for WBSF, 13 isoforms from 13 genes for tenderness and 43 isoforms from 42 genes for\n marbling (Figure 3 and Additional File 3; FDR ≤ 0.1) were DE. \u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eOverlapping genes across DE evaluation and genome wide association analysis in the\n present population\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eA total of 30 genes were simultaneously identified in the expression and DE analysis,\n and 13 of them encode proteins with structural function; five other genes are transcription\n factors or co-regulators (Table 2). From the structural proteins, 12 are potential µ-calpain substrates. All 30 genes\n were initially used to construct a protein-protein interaction network (Figure 4).\n Out of these 30 genes, 18 genes constitute a network including 150 proteins. From\n these 150 proteins, 45 were determined as downregulated (red nodes) and 31 others\n as upregulated (green nodes) in tender samples. Other 78 genes (blue nodes) were not\n identified in the expression or DE analysis but interconnect with other nodes of this\n protein-protein interaction network. In this network, \u003cem\u003eNFKB2\u003c/em\u003e (upregulated), \u003cem\u003eABLIM1\u003c/em\u003e (upregulated), \u003cem\u003eEIF4E2\u003c/em\u003e (upregulated) and \u003cem\u003eARPC5L\u003c/em\u003e (downregulated), and \u003cem\u003eARF6\u003c/em\u003e (upregulated) were determined as having the highest connectivity.\u003c/p\u003e\n\u003cp data-xsweet-list-level=\"1\"\u003eGene enrichment analysis\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eA gene enrichment analysis was performed using the four gene lists generated from the expression and DE gene\n analysis for WBSF, marbling and tenderness (Additional File 4). Ten pathways were identified as enriched and they can be classified in two groups,\n pathways associated to cellular structure and pathways associated to respiration.\n \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp data-xsweet-list-level=\"1\"\u003ePaired-end read alignment and paired-end read counting\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eHighly specialized genes in skeletal muscle such as \u003cem\u003eTitin \u003c/em\u003e(\u003cem\u003eTTN\u003c/em\u003e), \u003cem\u003eActin Alpha 1\u003c/em\u003e (\u003cem\u003eACTA1\u003c/em\u003e), \u003cem\u003eMyosin Heavy chain 1\u003c/em\u003e (\u003cem\u003eMYH1\u003c/em\u003e), \u003cem\u003eAldolase Fructose-Bisphosphate A \u003c/em\u003e(\u003cem\u003eALDOA\u003c/em\u003e), \u003cem\u003eMyosin Heavy Chain 7\u003c/em\u003e (\u003cem\u003eMYH7\u003c/em\u003e), \u003cem\u003eNebulin\u003c/em\u003e (\u003cem\u003eNEB\u003c/em\u003e), \u003cem\u003eFilamin C \u003c/em\u003e(\u003cem\u003eFLNC\u003c/em\u003e), \u003cem\u003eATPase Sarcoplasmic/Endoplasmic Reticulum Ca2+ Transporting 1\u003c/em\u003e (\u003cem\u003eATP2A1\u003c/em\u003e), \u003cem\u003eTropomyosin 2\u003c/em\u003e (\u003cem\u003eTPM2\u003c/em\u003e), and \u003cem\u003eCreatine Kinase, M-type \u003c/em\u003e(\u003cem\u003eCKM\u003c/em\u003e) were the top expressed genes based on number of counts. Since most of these proteins\n have structural function and are mechanically required for contraction, they are highly\n expressed in skeletal muscle. TTN and NEB are large sarcomere filament-binding proteins\n uniformly expressed in skeletal muscle; NEB acts as an actin filament stabilizer,\n it is involved in myofibrillogenesis, modulates thin filament length and allows proper\n muscle contraction [10]. \u003cem\u003eNEB\u003c/em\u003e knockout mice show muscular weakness, altered calcium homeostasis and glycogen metabolism\n [10]. \u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eGene expression association analysis for the meat quality index\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eIn the following paragraphs we present a short description of the most important genes\n identified through the gene expression association analysis. The gene showing the\n most significant association (p-value ≤ 4.2x10\u003csup\u003e-4\u003c/sup\u003e), \u003cem\u003eARHGAP10\u003c/em\u003e (Figure 5A), is part of a Rho family of GTPase-activating proteins (RhoGAP). This\n protein regulates the activity of the small GTPase CDC42 and by doing so, controls\n the F-Actin and ARP2/3 dynamics at the Golgi complex. The Golgi-associated small GTPase,\n ARF1 recruits ARHGAP21 and allows interaction between ARHGAP21 and CDC42, inducing\n GTP hydrolysis and promoting actin filament interaction with Golgi membranes [11].\n The \u003cem\u003eARHGAP10\u003c/em\u003e gene was found to regulate actin cytoskeleton remodeling, cell proliferation, and\n cell differentiation. The ARHGAP10 interacts with α-tubulin and it is involved in cell-cell\n adhesion processes and consequently could promote cell migration [12–16]. In our study,\n overexpression of ARHGAP10 was associated with lower meat quality index. This could\n be a consequence of a more stable actin cytoskeleton structure which would result\n in lower meat quality. \u003c/p\u003e\n \n\u003cp\u003eA higher expression of \u003cem\u003eTMEM120B \u003c/em\u003egene was associated with a reduced meat quality index in the present analysis (Figure\n 5B). The \u003cem\u003eTMEM120B \u003c/em\u003egene is highly expressed during adipocyte differentiation, and knockdown of this gene\n alters expression of genes required for adipocyte differentiation such as \u003cem\u003eGATA3\u003c/em\u003e, \u003cem\u003eFASN\u003c/em\u003e and \u003cem\u003eGLUT4\u003c/em\u003e [17]. This gene is a cytoskeletal anchoring protein and it can affect tenderness\n by promoting changes in cytoskeletal structure stability or cellular compartmentalization\n and size adaptation in adipocytes [18]. \u003c/p\u003e\n \n\u003cp\u003eThe \u003cem\u003eARRDC4 \u003c/em\u003egene belongs to a plasma membrane associated protein family named α-arrestins, and\n higher expression of this gene was associated with lower meat quality index (Figure\n 5C). A better characterized member of this family, \u003cem\u003eARRDC3,\u003c/em\u003e is a breast and prostate cancer suppressor; lower expression of \u003cem\u003eARRDC3\u003c/em\u003e was significantly associated with high aggressiveness and metastasis in prostate\n cancer cells [19,20]. The ARRDC3 protein localizes in certain sections of the plasma\n membrane associated with intracellular vesicles suggesting that ARRDC3 regulates cell-surface\n proteins such as ITGβ4 in skeletal muscle; this interaction between ARRDC3 and ITGβ4\n suggests a possible mechanism through which ARRDC3 could regulate cell motility and\n migration [20]. The \u003cem\u003eARRDC3\u003c/em\u003e knockout male mouse is resistant to obesity which was reported to be a result of\n higher energy expenditure due to increased activity level and thermogenesis in adipose\n tissues [21]. The association of this gene with meat quality could be explained by\n variation in adipocyte proliferation or overall cytoskeletal structure and cellular\n attachment. \u003c/p\u003e\n \n\u003cp\u003eThe \u003cem\u003eKIAA2013 \u003c/em\u003eencodes an uncharacterized transmembrane protein [22] and higher expression of this\n gene was associated with lower meat quality index (Figure 5D). Xu et al. (2015) [23]\n identified selection signatures on \u003cem\u003eKIAA2013 \u003c/em\u003eusing Holstein, Angus, Charolais, Brahman, and N’Dama cattle showing that the genomic\n region harboring \u003cem\u003eKIAA2013 \u003c/em\u003ecould explain phenotypic differences associated to breed effect in this population.\u003c/p\u003e\n \n\u003cp\u003eUpregulation of \u003cem\u003eNDRG3\u003c/em\u003e is present in prostate and laryngeal squamous cancerous cells and was also correlated\n with pathological stage, positive metastatic status and lymph node status [24–26].\n High expression of \u003cem\u003eNDRG3\u003c/em\u003e was associated with lower meat quality index (Figure 5E), possibly by generating\n a more stable cellular attachment [27]. This is supported by the fact that upregulation\n of a \u003cem\u003eNDRG3\u003c/em\u003e paralogous, \u003cem\u003eNDRG2\u003c/em\u003e, suppresses tumor invasion by inhibiting the matrix metalloproteinases MMP-9 and\n MMP-2. \u003c/p\u003e\n \n\u003cp\u003eHigher expression of \u003cem\u003eWDR73\u003c/em\u003e was associated with lower meat quality index (Figure 5F) possibly due to an increment\n in cytoskeletal structure stability resulting in lower meat quality. Fibroblasts with\n mutated \u003cem\u003eWDR73\u003c/em\u003e presented abnormal nuclear morphology, low cell viability, and altered microtubule\n network, suggesting a role in cellular architecture maintenance and cell survival\n [28]. Downregulation of\u003cem\u003e WDR77 \u003c/em\u003earrests growth and differentiation of lung epithelial cells while its upregulation\n promoted terminally differentiated cells to undergo a new stage of cell proliferation,\n triggering lung adenocarcinoma formation [29]. \u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eExon expression analysis for the meat quality index\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eThe \u003cem\u003eSLMO1, TMEM120B, ARF6, FAM21A, KIAA2013, DAZAP2, KLHDC8B \u003c/em\u003eand\u003cem\u003e DIDO1 \u003c/em\u003egenes are discussed below. The \u003cem\u003eSLMO1\u003c/em\u003e gene encodes three different isoforms (Additional File 5) and two of them share exon\n 9. Higher expression of the exon 9 of \u003cem\u003eSLMO1\u003c/em\u003e was associated with higher meat quality index (Figure 6A). This association could\n be due to increased lipid deposition given that this protein is part of an intermembrane\n lipid transfer system located in the mitochondria [30] or it could contribute to cytoskeletal\n attachment of this organelle membrane. The exon 9 of SLMO1 encodes a total of 30 amino\n acids (golden region in the Additional File 6) that are part of a PRELI/MSF1 domain.\n This domain is located between positions 74 and 245 and confers a globular alpha-beta\n folded structure to SLMO1 [31]. The association of the exon 9 with meat quality index\n show that the isoforms ENSBTAT00000081878.1 and ENSBTAT00000046981.3 could have a\n similar phenotypic effect on meat quality in the present population but different\n from the effect of the isoform ENSBTAT00000084244.1.\u003c/p\u003e\n \n\u003cp\u003eExpression of multiple exons of the \u003cem\u003eTMEM120B \u003c/em\u003egene and the exon 3 of \u003cem\u003eWDR77\u003c/em\u003e agreed with the overall gene expression analysis (Figure 6B and 6C). All \u003cem\u003eTMEM120B \u003c/em\u003eexons were individually associated with the meat quality index.The exon 3 in \u003cem\u003eWDR77\u003c/em\u003e encodes a segment between the amino acids 99 and 148 located inside the WD_REPEATS_REGION\n which could be important for the formation of the globular structure shown in the\n Additional File 7 (golden region). \u003c/p\u003e\n \n\u003cp\u003eHigher expression of the exon 2 of \u003cem\u003eARF6 \u003c/em\u003ewas associated with higher meat quality index (Figure 6D) probably due to cell proliferation\n and cytoskeletal remodeling. This gene encodes a GTP-binding protein involved in plasma\n membrane trafficking, actin-based cytoskeletal remodeling and cell migration [32].\n Knockout \u003cem\u003eARF6\u003c/em\u003e mice exhibit hypocellularity, midgestational hepatocyte apoptosis with Caspase 3\n activation, defective hepatic cord formation and almost completely penetrant embryonic\n lethality [33]. \u003c/p\u003e\n \n\u003cp\u003eHigher expression of the exon 25 of \u003cem\u003eFAM21A \u003c/em\u003ewas associated with higher meat quality index (Figure 6E); \u003cem\u003eFAM21A\u003c/em\u003e interacts with a multi-protein complex named WASH (Wiskott-Aldrich Syndrome Protein\n and SCAR Homolog) involved in endosome-to-plasma membrane trafficking. This complex\n interacts with tubulin and F-actin, and activates ARP2/3, and endocytosis, sorting\n and trafficking regulator [34]. The association of FAM21 and meat quality could be\n due to changes in \n actin polymerization. The FAM21\n protein modulates \n actin polymerization by preventing actin-capping through a \n physical interaction with the Capping Actin Protein of Muscle Z-Line (CAPZ). Additionally, \n FAM21 can interact with phosphatidylserine and some phospholipid species allowing\n the linkage between the WASH complex and endosomal domains [35,36].\u003c/p\u003e\n \n\u003cp\u003eThe third and fourth exons of\u003cem\u003e KIAA2013 \u003c/em\u003ewere associated with meat quality index (Figure 6F) and higher expression of both\n were associated with lower meat quality index. This gene encodes an uncharacterized\n transmembrane protein [22] showing that this protein could be a cytoskeletal anchor.\n Two different transmembrane regions were predicted between the positions 21-40 and\n 592-614; the latter transmembrane region is encoded by the third \u003cem\u003eKIAA2013 \u003c/em\u003eexon (Additional File 5). \u003c/p\u003e\n \n\u003cp\u003eHigher expression of the first exon of \u003cem\u003eDAZAP2\u003c/em\u003e was associated with higher meat quality index (Figure 6G) and this relationship could\n be due to cell proliferation given that this gene is a potential tumor suppressor.\n Patients with multiple myeloma have \u003cem\u003eDAZAP2\u003c/em\u003e downregulation because of promotor methylation [37,38]. \u003c/p\u003e\n \n\u003cp\u003eMeat quality index was negatively correlated with expression of the third exon of\n \u003cem\u003eKLHDC8B\u003c/em\u003e (Figure 6H) and this gene is associated with some cases of classical Hodgkin lymphoma\n which is characterized by binucleated cells. The \u003cem\u003eKLHDC8B\u003c/em\u003e gene encodes a midbody kelch protein required during mitotic cytokinesis [39,40].\n The third \u003cem\u003eKLHDC8B\u003c/em\u003e exon is included in both annotated isoforms (Additional File 8) suggesting that additional\n isoforms involving this exon may be still uncovered. The third \u003cem\u003eKLHDC8B\u003c/em\u003e exon could be structurally important for providing a globular conformation (golden\n region). \u003c/p\u003e\n \n\u003cp\u003eExpression of the exon number 17 of \u003cem\u003eDIDO1\u003c/em\u003e was associated with higher meat quality index (Figure 6I). Two different \u003cem\u003eDIDO1\u003c/em\u003e isoforms are annotated but only the isoform ENSBTAT00000007879.6 includes the exon\n 17 (Additional File 5). This isoform has an additional domain located between the\n amino acids 672 and 792 (TFIIS_CENTRAL) involved in mRNA cleavage [31]. The protein\n segment encoded by the exon number 17 of DIDO1 (from amino acid number 1088 to 1117)\n could be structurally crucial for overall molecular activity. The association between\n exon expression and meat quality index could be related to the pro-apoptotic activity\n of \u003cem\u003eDIDO1\u003c/em\u003e [41]. DIDO1 is involved in regulating embryonic stem cell maintenance and there exist\n early differentiation in mouse embryonic stem cells lacking this gene; this protein\n is also able to positively regulate expression of key pluripotency markers [42]. \u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eDifferential expression analysis \u003c/p\u003e\n \n \n \n\u003cp data-xsweet-list-level=\"2\"\u003eDE genes for WBSF, tenderness and marbling\u003c/p\u003e\n \n \n \n \n \n \n \n\u003cp\u003eA total of 19 genes were simultaneously identified in at least two analyses and they\n can be classified in three different groups based on their biological function. The\n first group of DE genes are related to cell survival, apoptosis and cancer, and include\n the following genes: \u003c/p\u003e\n \n\u003cp\u003e\u003cem\u003eAngiopoietin Like 4\u003c/em\u003e (\u003cem\u003eANGPTL4\u003c/em\u003e), \u003cem\u003eApoptotic Peptidase Activating Factor 1\u003c/em\u003e (\u003cem\u003eAPAF1\u003c/em\u003e), \u003cem\u003eG0/G1 Switch 2\u003c/em\u003e (\u003cem\u003eG0S2\u003c/em\u003e), \u003cem\u003eHyaluronan Binding Protein 2\u003c/em\u003e (\u003cem\u003eHABP2\u003c/em\u003e), \u003cem\u003eInterferon Related Developmental Regulator 1\u003c/em\u003e (\u003cem\u003eIFRD1\u003c/em\u003e) and \u003cem\u003eTribbles Pseudokinase 1\u003c/em\u003e (\u003cem\u003eTRIB1\u003c/em\u003e). These genes could promote myocyte and adipocyte proliferation. The second group\n includes a number of structural proteins associated with cellular membranes or cytoskeletal\n proteins. The genes \u003c/p\u003e\n \n\u003cp\u003e\u003cem\u003eComplement C4A\u003c/em\u003e (\u003cem\u003eC4A\u003c/em\u003e), \u003cem\u003eComplement Factor B\u003c/em\u003e (\u003cem\u003eCFB\u003c/em\u003e), \u003cem\u003eChloride Intracellular Channel 5\u003c/em\u003e (\u003cem\u003eCLIC5\u003c/em\u003e), \u003cem\u003eFamily With Sequence Similarity 83 Member H\u003c/em\u003e (\u003cem\u003eFAM83H\u003c/em\u003e), \u003cem\u003eIntegrin Subunit Beta 6\u003c/em\u003e (\u003cem\u003eITGB6\u003c/em\u003e), \u003cem\u003eMitochondrial Ribosomal Protein L35 \u003c/em\u003e(\u003cem\u003eMRPL35\u003c/em\u003e), \u003cem\u003ePhospholamban\u003c/em\u003e (\u003cem\u003ePLN\u003c/em\u003e), \u003cem\u003eProtein Phosphatase, Mg2+/Mn2+ Dependent 1K\u003c/em\u003e (\u003cem\u003ePPM1K\u003c/em\u003e), \u003cem\u003eTransferrin Receptor\u003c/em\u003e (\u003cem\u003eTFRC\u003c/em\u003e), \u003cem\u003eTripartite Motif Containing 55\u003c/em\u003e (\u003cem\u003eTRIM55\u003c/em\u003e) belong to this group. Changes in the amount of these proteins could have a direct\n effect on cytoskeletal structure and organization, and postmortem proteolysis. Two\n transcription factors,\u003cem\u003e Early Growth Response 1\u003c/em\u003e (\u003cem\u003eEGR1\u003c/em\u003e) and \u003cem\u003eHes Related Family BHLH Transcription Factor with YRPW Motif-Like\u003c/em\u003e (\u003cem\u003eHEYL\u003c/em\u003e), were also uncovered and they represent the third group. The most important DE genes\n associated with meat quality in the present analysis are described below.\u003c/p\u003e\n \n\u003cp\u003eThe \u003cem\u003eAPAF1\u003c/em\u003e gene was identified as DE in the WBSF and tenderness analyses, and identified as\n downregulated in tender meat; this protein is a central component of the apoptosome,\n a mitochondrial caspase activation pathway which mediates apoptosis. After activation\n of this pathway, the mitochondria release Cytochrome C which in turn binds to APAF1\n and promote apoptosis by activating Caspase 9 [43,44]. Long et al. (2013) [44] characterized\n an \u003cem\u003eAPAF1\u003c/em\u003e mutant mouse line which does not promote apoptosis. These mouse embryos presented\n decreased apoptosis, nervous system development defects and craniofacial deficiencies\n associated with higher mesenchymal proliferation and delayed ossification resulting\n in perinatal death. In human, downregulation of \u003cem\u003eAPAF1\u003c/em\u003e is evident in colorectal cancer and hepatocellular carcinoma cells given transcriptional\n regulation by miR-23a and Histone Deacetylases 1–3 [43,46].\u003c/p\u003e\n \n\u003cp\u003eThe \u003cem\u003eG0S2\u003c/em\u003e gene was upregulated in tender meat in the WBSF and tenderness analyses; \u003cem\u003eG0S2\u003c/em\u003e is highly expressed in adipose tissue and its expression relates to lipid accumulation\n and adipogenesis in swine. Cell proliferation inhibition is also promoted by this\n gene giving that there exit \u003cem\u003eG0S2\u003c/em\u003e downregulation in preadipocytes and fetal adipose tissues, and upregulation in adipocytes\n and adipose tissues from adult pigs [47]. Lipid catabolism is regulated by G0S2 through\n interaction and inhibition of the Adipose Triglyceride Lipase (ATGL) and upregulation\n of \u003cem\u003eG0S2\u003c/em\u003e or downregulation of \u003cem\u003eATGL\u003c/em\u003e in non-small cell lung carcinomas stalls triglyceride catabolism and represses cell\n growth [48]. Female knockout \u003cem\u003eG0S2\u003c/em\u003e mice present lactation defects and knockout mice show lower body weight gain, higher\n serum glycerol levels, higher acute cold tolerance given upregulation of thermoregulatory\n and oxidation promoting genes in white adipose tissue [49,50]. High \u003cem\u003eG0S2\u003c/em\u003e methylation is present in squamous lung cancer being this methylation content inversely\n correlated with \u003cem\u003eG0S2\u003c/em\u003e expression [51]. \u003c/p\u003e\n \n\u003cp\u003eThe \u003cem\u003eIFRD1\u003c/em\u003e gene was downregulated in tender meat in the WBSF analysis; however, this gene was\n upregulated in high marbling samples. \u003cem\u003eIFRD1 \u003c/em\u003eplays a role in muscle differentiation and bone homeostasis. In myoblasts, downregulation\n of \u003cem\u003eIFRD1\u003c/em\u003e hinders cell cycle exit and differentiation via MyoD downregulation, and promotes\n acetylation and nuclear localization of p65. In adult muscle, upregulation of \u003cem\u003eIFRD1\u003c/em\u003e stimulate regeneration via myogenesis by negatively regulating NF-κB, which in turn\n is post-transcriptionally downregulate by MyoD [52]. In bone, \u003cem\u003eIFRD1\u003c/em\u003e is involved in bone homeostasis maintenance; knockout \u003cem\u003eIFRD1\u003c/em\u003e mice develops higher bone mass because of increased bone deposition and decreased\n bone reabsorption [53]. \u003c/p\u003e\n \n\u003cp\u003eDownregulation of \u003cem\u003eCLIC5\u003c/em\u003e was identified in tender meat in the WBSF and tenderness assessment. This gene encodes\n a multiconductance channel for Na+, K+ and Cl–, and is inactivated by F-actin; this\n channel modulates solute transport at key cellular stages such as apoptosis, and cell\n division and fusion [54]. A CLIC5 isoform, CLIC5A, is involved in glomerular endothelial\n cell and podocyte architecture formation and maintenance, and both cell types show\n high \u003cem\u003eCLIC5A\u003c/em\u003e expression. This isoform colocalized with Podocalyxin (PODXL) and Ezrin (EZR) in\n the apical plasma membrane in podocytes. Knockout \u003cem\u003eCLIC5A\u003c/em\u003e mice present lower \u003cem\u003eEZR\u003c/em\u003e expression in podocytes altering PODXL and actin filament association [55]. Berryman, Bruno,\n Price, \u0026amp; Edwards (2004) [56] reported that the \u003cem\u003ede novo\u003c/em\u003e assembly of the cytoskeletal complex CLIC5A-EZR requires actin polymerization, being\n CLIC5A essential for assembly and maintenance of F-actin-based arrangement at the\n cell cortex.\u003c/p\u003e\n \n\u003cp\u003eUpregulation of \u003cem\u003eFAM83H\u003c/em\u003e was identified in tender meat using the WBSF and tenderness analyses. FAM83H colocalizes\n with keratin filaments surrounding the nucleus and usually communicates with cell-cell\n junctions. Downregulation of \u003cem\u003eFAM83H\u003c/em\u003e promotes keratin filament formation and its upregulation produces keratin filament\n disassembly. The filamentous keratin structure is regulated by FAM83H and disorganization\n of this keratin associated cytoskeleton is caused by upregulation of \u003cem\u003eFAM83H\u003c/em\u003e in colorectal cancer cells [57]. Upregulation of \u003cem\u003eFAM83H\u003c/em\u003e is mediated by binding of MYC at \u003cem\u003eFAM83H\u003c/em\u003e promoter and is characteristic of hepatocellular carcinoma cells. Overexpression\n of \u003cem\u003eFAM83H\u003c/em\u003e drives upregulation of \u003cem\u003eCyclin D1\u003c/em\u003e, \u003cem\u003eCyclin E1\u003c/em\u003e, \u003cem\u003eSNAI1\u003c/em\u003e and \u003cem\u003eMMP2\u003c/em\u003e, and repression of \u003cem\u003eP53\u003c/em\u003e and \u003cem\u003eP27\u003c/em\u003e [58].\u003c/p\u003e\n \n\u003cp\u003eDownregulation of \u003cem\u003ePLN\u003c/em\u003e in tender meat was identified using the WBSF analysis; however, this gene was upregulated\n in high marbling samples. PLN is a sarcoplasmic reticulum Ca2+-cycling protein and\n regulatory partner of the ATPase Sarcoplasmic/Endoplasmic Reticulum Ca2+ Transporting\n 2 (ATP2A2) protein being involved in regulating cardiomyocyte contractility [59,60].\n Medin et al. (2007) [62] identified a SNP located in the promoter region of \u003cem\u003ePLN\u003c/em\u003e able to decrease its transcriptional activity and associated with apical hypertrophic\n cardiomyopathy. Some mutations in the cytoplasmic domain of PLN modify its hydrophobic\n interaction with ATP2A2, and alter PLN regulatory activity. One of these mutations,\n a deletion in the coding region is associated with left ventricular dilation, contractile\n dysfunction, episodic ventricular arrhythmias and hereditary heart failure. Transgenic\n mice overexpressing the \u003cem\u003ePLN\u003c/em\u003e-Del allele develop similar symptomatology as well as premature death [59]. This PLN\n deletion abolishes regulation by phosphorylation, which in turn induces a constitutive\n PLN inhibitory state [63].\u003c/p\u003e\n \n\u003cp\u003eThe directionality of expression of most of these genes agreed across analysis. The\n expression of \u003c/p\u003e\n \n\u003cp\u003e\u003cem\u003eCFB\u003c/em\u003e,\u003cem\u003e G0S2\u003c/em\u003e,\u003cem\u003e C4A, ANGPTL4 \u003c/em\u003eand \u003cem\u003eFAM83H \u003c/em\u003ewas higher in tender meat and expression of \u003cem\u003eMRPL35\u003c/em\u003e, \u003cem\u003eCLIC5\u003c/em\u003e, \u003cem\u003eKLHL34\u003c/em\u003e, \u003cem\u003eHEYL\u003c/em\u003e, \u003cem\u003eAPAF1\u003c/em\u003e, \u003cem\u003eITGB6\u003c/em\u003e, \u003cem\u003ePPM1K\u003c/em\u003e, \u003cem\u003eTFRC\u003c/em\u003e, \u003cem\u003eTRIB1\u003c/em\u003e and \u003cem\u003eEGR1 \u003c/em\u003ewas lower in tender meat. \u003c/p\u003e\n \n \n \n \n \n \n \n\u003cp data-xsweet-list-level=\"2\"\u003e Isoform DE analysis for WBSF, tenderness and marbling\u003c/p\u003e\n \n \n \n \n \n \n \n\u003cp\u003eBecause isoform annotation for the Btau_4.6.1 reference genome is relatively poor,\n only gene name in the isoform association analysis was reported and further evaluation\n was carried out for well annotated isoforms. The \u003cem\u003eEukaryotic Translation Initiation Factor 4E Family Member 2\u003c/em\u003e (\u003cem\u003eEIF4E2\u003c/em\u003e), \u003cem\u003eGNAS Complex Locus \u003c/em\u003e(GNAS), \u003cem\u003eLysosomal Associated Membrane Protein 2\u003c/em\u003e (LAMP2), \u003cem\u003eMucolipin 1\u003c/em\u003e (MCOLN1) and \u003cem\u003eReticulon 4\u003c/em\u003e (\u003cem\u003eRTN4\u003c/em\u003e) genes were selected for further analysis.\u003c/p\u003e\n \n\u003cp\u003eThe \u003cem\u003eEIF4E2\u003c/em\u003e isoform NM_001075795.2 was identified as DE (Additional File 9). Hypoxic microenvironment\n is a common feature in tumors and EIF4E2 is preferentially used rather than EIF4E\n during translation of a number genes [64] such as cytoskeletal related proteins. Cadherin-22\n is a cell-surface molecule target of EIF4E2 and it is involved in cell migration,\n invasion and adhesion during cancer development. Kelly et al. (2018) [65] reported\n that silencing of EIF4E2 or Cadherin-22 halted breast carcinoma and glioblastoma development\n during hypoxia. The EIF4E2 isoforms NP_001069263.1 and NP_001193345.1 only differ\n by a 12-amino acid segment (golden region in Additional File 10). The additional segment\n present in NP_001069263.1 could confer a differential effect on EIF4E2 translational\n function during apoptosis affecting the tenderization process. \u003c/p\u003e\n \n\u003cp\u003eThe Additional File 9 shows some structural features of the GNAS isoforms NP_001258700.1\n and NP_851364.1, being the latter\u0026nbsp;isoform identified as DE in the present analysis.\n Both isoforms differ greatly because of alternative promoters. The GNAS locus is paternally,\n maternally and biallelically imprinted in a tissue-specific manner and code for a\n number of molecular products by using multiple promoters [66]. The GNAS protein is\n categorized as a cell membrane associated protein [22], thus it could contribute to\n cytoskeletal stability. Furukawa et al. (2011) [67] and Wu et al. (2011) [69] reported\n that somatic mutations in the GNAS locus are frequently identified in Intraductal\n papillary mucinous neoplasm, a pancreatic cystic neoplasm characterized by being highly\n invasive and metastatic with poor prognosis. Markers in the GNAS locus are also associated\n with endocrine tumors, fibrous dysplasia of bone and hereditary osteodystrophy [66].\u003c/p\u003e\n \n\u003cp\u003eIsoforms from the \u003cem\u003eLAMP2\u003c/em\u003e and \u003cem\u003eMCOLN1\u003c/em\u003e genes were identified as DE, and their proteins are lysosomal associated proteins.\n For the LAMP2 gene, the NP_001029742.1 and NP_001106715.1 isoforms were analyzed.\n The NP_001106715.1 (homologous to the LAMP2A isoform in mice) was determined as DE\n in the present population. Both isoforms have signal peptide and two transmembrane\n segments (Additional File 9) nevertheless, homology between them decreases after the\n amino acid number 363. A monomeric LAMP2A molecule binds to substrate proteins and\n allows chaperone-mediated autophagy in lysosomes by establishing high-molecular-weight\n LAMP2A complexes at the lysosomal membrane; the hsc70 and hsp90 chaperones have crucial\n roles in disassembly and stabilization of the LAMP2A complexes [70]. Cuervo \u0026amp; Dice\n (2000) [71] found that 25% of total LAMP2 molecules in rat liver lysosomes were LAMP2A\n and concentration of this isoform was correlated with rates of chaperone-mediated\n autophagy in liver and fibroblasts in culture; therefore, there exists a substrate\n protein that binds only to the LAMP2A isoform. The LAMP2A isoform also mediates autophagosome-lysosome\n fusion in mouse embryonic fibroblasts [72]. The MCOLN1 isoform NP_001159604.1 was\n identified as DE (Additional File 9). This protein is a Ca2+-releasing cation channel\n associated to the lysosomal plasma membrane and it is involved in endocytosis. Mutations\n in this gene cause mislocalization and disrupt Ca2+ flow across the lysosomal membrane\n and produce Mucolipidosis type IV, a lysosomal storage disorder related to a transport\n defect in endocytosis [73,74]. Schmiege et al. (2017) [74] reported the conformational\n assembly of the human MCOLN1 channel which is structurally close to the bovine isoform\n NP_001159604.1 (Additional File 11); this channel seems to be tightly regulated by\n aromatic–aromatic and hydrophilic interactions between amino acids and by agonist\n regulation, allowing adequate selectivity filter dynamics. Cuajungco et al. (2014)\n [75] reported physical interaction between MCOLN1 and TRPML1, a zinc transporter,\n and deletion of the MCOLN1’s N-terminus disrupted this interaction. Some other mutations\n in this gene are able to disrupt inhibition of MCOLN1 by pH and promote channel aggregation\n [76]. Expression of the DE isoforms of \u003cem\u003eLAMP2\u003c/em\u003e (NM_001113244.1) and \u003cem\u003eMCOLN1\u003c/em\u003e (NM_001166132.1) could promote specific cytoskeletal association with lysosome membranes.\n This effect on cytoskeletal organization may contribute to overall tenderization postmortem\n and meat quality.\u003c/p\u003e\n \n\u003cp\u003eThe RTN4 isoform NP_001106692.1 was identified as DE in the present analysis (Additional\n File 9); this isoform is homologous to the human RTN4 isoform B. RTNs encode a family\n of membrane associated proteins and RTN4s are involved in shaping and maintaining\n endoplasmic reticulum tubules. RTN4, Atlastin (ATL) and Lunapark, ER Junction Formation\n Factor (LNP) proteins are curvature-stabilizing proteins required for the formation\n of the cellular network of membrane tubules and a RTN4/ATL activity balance is required.\n Hyperactivity or upregulation of RTN4A induces endoplasmic reticulum fragmentation\n [77,78]. The RNT4B is expressed in epithelial, fibroblast and neuronal cells and it\n is localized in curved membranes on endoplasmic reticulum tubules and sheet edges.\n Upregulation of RNT4B modifies the sheet/tubule balance and induces higher formation\n of tubules producing membrane deformation; conversely, RNT4B downregulation produces\n large peripheral endoplasmic reticulum sheets [79].\u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eOverlapping genes across DE evaluation and genome wide association analysis in the\n present population\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eThe key genes identified in the protein-protein interaction network (Figure 4), \u003cem\u003eNFKB2\u003c/em\u003e, \u003cem\u003eABLIM1\u003c/em\u003e, \u003cem\u003eEIF4E2\u003c/em\u003e, \u003cem\u003eARPC5L\u003c/em\u003e and \u003cem\u003eARF6\u003c/em\u003e , are involved in multiple cellular functions such as actin polymerization, cytoskeletal\n structure and transcription factor activity [22].\u003c/p\u003e\n \n\u003cp\u003eTable 3 shows a list of genes that were simultaneously identified by Leal-Gutiérrez\n et al. (2018c) [80] and Leal-Gutiérrez et al. (2019) [81] using genotype-phenotype\n association in the present population and genes that were identified in the expression\n or DE analysis. A total of 14 genes were identified using genotype-phenotype and expression-phenotype\n association approaches simultaneously. These genes could potentially exhibit cis-eQTL\n regulation suggesting that changes in gene expression could be responsible for the\n genotype-phenotype association. Based on this theory, a cis-eQTL analysis was performed\n (unpublished data). Cis-eQTL regulation was identified for the \u003cem\u003eEukaryotic Translation Initiation Factor 4E Nuclear Import Factor 1\u003c/em\u003e (\u003cem\u003eEIF4ENIF1\u003c/em\u003e), \u003cem\u003eGamma-Glutamyl Carboxylase\u003c/em\u003e (\u003cem\u003eGGCX\u003c/em\u003e), \u003cem\u003e3-Hydroxyisobutyrate Dehydrogenase\u003c/em\u003e (\u003cem\u003eHIBADH\u003c/em\u003e), \u003cem\u003eSRSF Protein Kinase 1\u003c/em\u003e (\u003cem\u003eSRPK1\u003c/em\u003e), and \u003cem\u003eLDL Receptor Related Protein 5\u003c/em\u003e (\u003cem\u003eLRP5\u003c/em\u003e). This result suggests that polymorphisms in these genes are able to regulate the\n expression of harboring genes, and this variation in mRNA expression could have a\n direct effect on meat quality in the present population. \u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eGene enrichment analysis\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eThe ten enriched pathways identified can be classified in two groups. The first group\n relates to Membrane (GO:0016020) and Membrane part (GO:0044425) which cluster some\n structural genes. Enrichment of structural protein pathways such as Endoplasmic reticulum\n membrane (GO:0005789), Golgi apparatus (GO:0005794), and Mitochondrial inner membrane\n (GO:0005743) were also identified using a gene enrichment analysis based on GWA analysis\n in the present population [81]. Moreover, enrichment for related pathways such as\n Cell adhesion and maintenance, Plasma membrane, Integral to plasma membrane, Transmembrane\n transport, Integral to organelle membrane, Endoplasmic reticulum membrane, and Mitochondrial\n matrix were identified using copy number variation and selection signatures in Hanwoo,\n Holstein, Angus, Charolais, Brahman, and N’Dama cattle [23,82]. The second type of\n pathway, is related to energy metabolism and includes pathways such as Respirasome\n (GO:0070469), Mitochondrial respiratory chain complex I (GO:0005747) and Respiratory\n chain complex I (GO:0045271). [23] reported enrichment for ATPase activity and Glucose\n metabolic process in Holstein, Angus, Charolais, Brahman, and N’Dama. \u003c/p\u003e\n\u003cp\u003eThe phenotypes were recorded in \u003cem\u003elongissimus dorsi\u003c/em\u003e muscle from a multibreed Angus-Brahman population. * Genes with cis-eQTL effects\n (unpublished data). This table should appear after the line number 414.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eExpression of a number of cytoskeletal proteins and transmembrane anchoring molecules\n was identified in the expression and DE analysis in the present population and these\n proteins can have a direct effect on tenderness and marbling. Cytoskeletal proteins\n and transmembrane anchoring molecules can influence meat quality by allowing cytoskeletal\n filament interaction with myocyte and organelle membranes, contributing to cytoskeletal\n structure, microtubule network stability, and cellular architecture maintenance during\n the postmortem. Some of these cytoskeletal and transmembrane proteins can modulate\n cell proliferation. Several pathways related to structural proteins and energy metabolism\n were identified as enriched showing that these kinds of genes are overrepresented\n and are crucial for meat quality in the present population. \u003c/p\u003e\n \n\u003cp\u003eUsing genotype-phenotype and expression-phenotype association, a number of genes were\n revealed as potential genes with cis-eQTL regulation. The existence of these cis-eQTL\n effects could suggest that polymorphisms in these genes are able to regulate expression\n of the harboring gene, and this variation in expression modules meat quality in the\n interrogated population. \u003c/p\u003e"},{"header":"Methods","content":"\u003cp data-xsweet-list-level=\"1\"\u003eCattle population and phenotypic data\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eThe research protocol was approved by the University of Florida Institutional Animal\n Care and Use Committee (201003744). A total of 120 steers born between 2013 and 2014\n were included in the analysis. The animals belong to the multibreed Angus-Brahman\n herd from the University of Florida [83–85]. Cattle were classified into three different\n groups based on their expected Angus and Brahman breed composition. Based on the Angus\n composition, the grouping was as follows: 1 = 100 to 65%; 2 = 64% to 40%; 3 = 39 to\n 0% [86]. \u003c/p\u003e\n \n\u003cp\u003eSteers were transported to a commercial packing plant when their subcutaneous fat\n thickness over the ribeye reached 1.27 cm. The average slaughter weight was 573.34±54.79\n kg at 12.91±8.69 months. The steers were harvested using established USDA-FSIS procedures,\n and 5-10 g of the\u003cem\u003e longissimus dorsi\u003c/em\u003e muscle were sampled after splitting the carcass. The sample was snapped-frozen in\n liquid nitrogen and stored at -80 °C for RNA extraction. Marbling was recorded 48\n hours postmortem in the ribeye muscle at the 12th/13th rib interface by visual appraisal.\n Numerical scale used for marbling was as follows: Practically Devoid=100-199, Traces=200-299,\n Slight=300-399, Small=400-499, Modest=500-599, Moderate=600-699, Slightly Abundant=700-799,\n Moderately Abundant=800-899, Abundant=900-999. \u003c/p\u003e\n \n\u003cp\u003eTwo 2.54 cm steaks from the \u003cem\u003elongissimus dorsi\u003c/em\u003e muscle at the 12th/13th rib interface were sampled from each animal. The first steak\n was used to measure WBSF and cooking loss, and the second steak was used to measure tenderness, juiciness\n and connective tissue by a sensory panel. The steaks were transported to the Meat Science Laboratory of the University of Florida,\n aged for 14 days at 1 to 4°C, and then stored at −20°C. Both frozen steaks from each\n animal were allowed to thaw at 4°C for 24 hours and cooked to an internal temperature\n of 71°C on an open-hearth grill. After cooking, the first steak was cooled at 4°C\n for 18 to 24 hours and used to measure WBSF and cooking loss according to the American\n Meat Science Association Sensory Guidelines [87]. Six cores with a 1.27-cm diameter\n and parallel to the muscle fiber were sheared with a Warner-Bratzler head attached\n to an Instron Universal Testing Machine (model 3343; Instron Corporation, Canton,\n MA). The Warner-Bratzler head moved at a cross head speed of 200 mm/min. The average\n peak load (kg) of six cores from the same animal was calculated. The weight lost during\n cooking was recorded and cooking loss was expressed as a percentage of the cooked\n weight out of the thaw weight.\u003c/p\u003e\n \n\u003cp\u003eTenderness, juiciness and connective tissue were measured by a sensory panel according\n to the American Meat Science Association Sensory Guidelines [87]. The sensory panel\n consisted of eight to eleven trained members, and steaks from six animals were assessed\n per session. Two 1 × 2.54 cm samples from each steak were provided to each panelist.\n Sensory panel measurements analyzed by the sensory panelists included: tenderness\n (8=extremely tender, 7=very tender, 6=moderately tender, 5=slightly tender, 4=slightly\n tough, 3=moderately tough, 2=very tough, 1=extremely tough), juiciness (8=extremely\n juicy, 7=very juicy, 6=moderately juicy, 5=slightly juicy, 4=slightly dry, 3=moderately\n dry, 2=very dry, 1=extremely dry), and connective tissue (8=none detected, 7=practically\n none, 6=traces amount, 5=slight amount, 4=moderate amount, 3=slightly abundant, 2=moderately\n abundant, 1=abundant amount). For each phenotype, the average score by steak from\n all members of the panel was analyzed. \u003c/p\u003e\n \n\u003cp\u003eA principal component analysis using marbling, WBSF, cooking loss, juiciness, tenderness\n and connective tissue was performed on the 120 steers using PROC FACTOR procedure\n of SAS software [88], and the first three principal components (PC) were used to construct\n a meat quality index for each animal. The meat quality index was calculated using the following formula:\u003c/p\u003e\n \n\u003cp\u003e\n \n\u003cp\u003e\n \n \n M\n e\n a\n t\n \n q\n u\n a\n l\n i\n t\n y\n \n i\n n\n d\n e\n x\n \n \n i\n \n \n =\n \n ∫\n \n j\n =\n 1\n \n \n 3\n \n \n \n \n \n \n \n P\n C\n S\n \n \n i\n j\n \n \n *\n \n \n P\n C\n W\n \n \n j\n \n \n \n \n \u003c/p\u003e\n \u003c/p\u003e\n \n\u003cp\u003eWhere PCS\u003csub\u003eij\u003c/sub\u003e is the score of the animal i for the PC\u003csub\u003ej\u003c/sub\u003e, and PCW\u003csub\u003ej\u003c/sub\u003e is the weight of the PC\u003csub\u003ej \u003c/sub\u003erepresented by theamount of variability explained by each PC (eigenvalues). The amount of variance explained\n by PC\u003csub\u003e1\u003c/sub\u003e, PC\u003csub\u003e2\u003c/sub\u003e and PC\u003csub\u003e3\u003c/sub\u003e were 44.26%, 20.04% and 13.29%, respectively. Given that the summation of principal\n component scores for each PCwas zero, a minimum value was added as a constant in order to have only positive PCS\n values. \u003c/p\u003e\n \n\u003cp\u003eThe meat quality index was used to rank the animals from low to high performance.\n Out of the 120 steers, 80 animals with extreme low and high meat quality index were\n selected and used for RNA sequencing.\u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eRNA-seq library preparation and sequencing\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eNuclear RNA was extracted from muscle using TRIzol reagent (Thermo Fisher Scientific, Waltham, MA, USA) according to the\n manufacturer’s protocol (Invitrogen, catalog no. 15596 – 026). RNA concentration was\n measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham,\n MA, USA) and RNA integrity was verified by formaldehyde gel. \u003c/p\u003e\n \n\u003cp\u003eRNA-seq library preparation and sequencing procedures were performed by RAPiD Genomics\n LLC (Gainesville, Florida, United States). Isolation of mRNA was performed using oligo-dT\n attached magnetic beads prior to its reverse transcription and synthesis of double\n stranded cDNA. An RNA-seq library for each sample was constructed, multiplexed, and sequenced based\n on protocols of Illumina HiSeq 3000 PE100 platform (Illumina, San Diego, CA, USA) to generate 2 × 101 bp paired-end reads. \u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eRead alignment and counting\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eRead trimming was performed with PRINSEQ 0.20.4 [89] using 3 bp sliding windows and\n a phred threshold of 20. Reads with more than 2 ambiguous bases were discarded. Cutadapt\n 1.8.1 [90] was used to remove adapter sequences keeping only reads with a minimum\n length of 50 bp. FastQC 0.9.6 [91] was used to confirm read quality. \u003c/p\u003e\n \n\u003cp\u003eTophat 2.1.0 [92] and Bowtie2 2.3.4 [93] were used to perform paired-end read mapping\n against the Btau_4.6.1 reference genome [94]. Paired-end read counts for all annotated\n genes were generated using HTSeq 0.9.1 [95] from paired-end reads uniquely mapped.\n Cufflinks 2.2.1.1 [96,97] was used to estimate transcript abundance in FPKM (Fragments\n Per Kilobase of exon per Million fragments mapped). The RNA-seq differential expression\n analysis pipeline \u003ca href=\"http://bioconductor.org/packages/release/bioc/html/DEXSeq.html\"\u003eDEXSeq\u003c/a\u003e [98,99] was used to determine exon counts per gene. \u003ca href=\"https://help.rc.ufl.edu/doc/RSeQC\"\u003eRSeQC\u003c/a\u003e 2.6.4 [100] was employed for alignment statistics, gene body coverage, junction annotation,\n junction saturation and paired-end read inner distance size, while Samtools 1.9 [101]\n was used for indexing and sorting of the alignment files. Genes and exons with less\n than 10 counts across all samples were excluded from the analysis.\u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eGene and exon expression association analysis for meat quality index\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eThe procedure described by Seo et al. (2016) [8] was utilized to perform the expression analysis by gene and exon for the continuous meat quality index.\n Gene and exon counts were normalized using trimmed mean of M-values (TMM) normalization method available\n in the R package edgeR [102–104]. The R packages sfsmisc and MASS [103,105,106] were used to compute the Huber’s M-estimator based robust regression. In the robust regression analysis, the meat quality index was the response variable,\n and normalized gene or exon counts, the first PC from the “PCA for population structure”\n work-flow of JMP [107] and year of birth of the animal were explanatory variables.\n A total of 8,799 genes and 96,645 exons were tested in this analysis. \u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eGene and isoform differential expression analysis \u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eOut of the 80 samples selected for sequencing, 40 animals were used in the DE procedure. Analysis for WBSF, tenderness and marbling\n were carried out to compare 20 high performance versus 20 low performance samples. \u003c/p\u003e\n \n\u003cp\u003eThe R package DESeq2 1.20.0 [108] was used to determine DE genes. Year of birth, breed\n group and the categorical classification based on phenotype were included as fixed\n effects in the analysis. The categorical classification was as follows: tender vs\n tough using WBSF or tenderness and high vs low using marbling. A total of 8,799 genes were analyzed for differential gene expression. Genes with a Benjamini-Hochberg adjusted p-values lower than 0.05 for WBSF and 0.1\n for tenderness and marbling were considered to be DE. \u003c/p\u003e\n \n\u003cp\u003eThe DE isoform analysis was performed with MetaDiff [109]. Year of birth, breed group and the categorical classification\n based on phenotype were included as fixed effects in the model. Only genes with alternative\n splicing were analyzed and isoforms with less than 10 FPKM across samples were excluded.\n A total of 957 genes with 4,471 isoforms were included in the DE isoform analysis, and a false discovery rate (FDR) threshold of 0.1 was used to identify DE isoforms.\u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eGene enrichment analysis\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eThe R packages GOglm and goseq [103,110,111] were used to identify enriched GO terms.\n Four gene lists resulting from the expression and DE gene analysis for WBSF, tenderness\n and marbling were assessed. GO terms with fewer than 30 annotated genes were excluded.\n Enriched GO terms had p-values lower than 0.05. \u003c/p\u003e"},{"header":"List of abbreviations","content":"\u003cp\u003eRNA sequencing (RNA-seq)\u003c/p\u003e\n \n\u003cp\u003eDifferentially expressed (DE)\u003c/p\u003e\n \n\u003cp\u003eWarner-Bratzler Shear Force (WBSF)\u003c/p\u003e\n \n\u003cp\u003ePrincipal Component Score (PCS)\u003c/p\u003e\n \n\u003cp\u003ePrincipal Component (PC)\u003c/p\u003e\n \n\u003cp\u003eWeight of the Principal Component (PCW)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp data-xsweet-list-level=\"1\"\u003eEthics approval and consent to participate\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003e\n The research protocol was approved by the University of Florida Institutional Animal\n Care and Use Committee number 201003744.\u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eConsent for publication\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eNot Applicable\u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eAvailability of data and material\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003e\n RNA-seq data are available at the European Nucleotide Archive, accession number PRJEB31379,\n \u003ca href=\"https://www.ebi.ac.uk/ena/data/search?query=PRJEB31379\"\u003e\u003ca href=\"https://www.ebi.ac.uk/ena/data/search?query=PRJEB31379\"\u003ehttps://www.ebi.ac.uk/ena/data/search?query=PRJEB31379\u003c/a\u003e\u003c/a\u003e.\u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eCompeting interests\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eNo commercial or financial relationships that could be construed as a potential conflict\n of interest exist.\u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eFunding\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eFinancial support provided by Florida Agricultural Experiment Station Hatch FLA-ANS-005548,\n Florida Beef Council, and Florida Beef Cattle Association – Beef Enhancement Fund\n Award 022962. The funders were not involved in the study design or collection, analysis,\n or interpretation of the data.\u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eAuthors' contributions\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003eJL conducted all analyses and drafted the manuscript; ME assisted with the analysis\n and manuscript; RM conceived and assisted with the analyses and manuscript.\u003c/p\u003e\n \n \n \n \n \n\u003cp data-xsweet-list-level=\"1\"\u003eAcknowledgements\u003c/p\u003e\n \n \n \n \n \n\u003cp\u003e\n Not Applicable\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1. Bhuiyan MSA, Kim HJ, Lee DH, Lee SH, Cho SH, Yang BS, et al. Genetic parameters\n of carcass and meat quality traits in different muscles (Longissimus dorsi and semimembranosus)\n of Hanwoo (Korean cattle). J Anim Sci. 2017;95:3359–69. \u003c/p\u003e\n \n\u003cp\u003e2. Mateescu RG, Garrick DJ, Reecy JM. Network analysis reveals putative genes affecting\n meat quality in Angus cattle. Front Genet. 2017;8. \u003c/p\u003e\n \n\u003cp\u003e3. Leal-Gutiérrez JD, Elzo M, Johnson D, Mateescu R. Genome-wide association and gene\n enrichment analyses of meat tenderness in an Angus-Brahman cattle population. 11th\n World Congr Genet Appl to Livest Prod. Auckland, New Zealand; 2018. \u003c/p\u003e\n \n\u003cp\u003e4. Ramayo-Caldas Y, Renand G, Ballester M, Saintilan R, Rocha D. Multi-breed and multi-trait\n co-association analysis of meat tenderness and other meat quality traits in three\n French beef cattle breeds. Genet Sel Evol [Internet]. BioMed Central; 2016;48:37.\n Available from: \u003ca href=\"http://gsejournal.biomedcentral.com/articles/10.1186/s12711-016-0216-y\"\u003ehttp://gsejournal.biomedcentral.com/articles/10.1186/s12711-016-0216-y\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e5. McClure MC, Ramey HR, Rolf MM, McKay SD, Decker JE, Chapple RH, et al. Genome-wide\n association analysis for quantitative trait loci influencing Warner-Bratzler shear\n force in five taurine cattle breeds. Anim Genet. 2012;43:662–73. \u003c/p\u003e\n \n\u003cp\u003e6. Chen Y, Lun ATL, Smyth GK. From reads to genes to pathways: differential expression\n analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline.\n F1000Research [Internet]. 2016;5:1438. Available from: \u003ca href=\"http://f1000research.com/articles/5-1438/v2\"\u003ehttp://f1000research.com/articles/5-1438/v2\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e7. Simielli Fonseca LF, Jovino Gimenez DF, dos Santos Silva DB, Barthelson R, Baldi\n F, Aparecido Ferro J, et al. Differences in global gene expression in muscle tissue\n of Nellore cattle with divergent meat tenderness. BMC Genomics. BMC Genomics; 2017;18:1–12.\n \u003c/p\u003e\n \n\u003cp\u003e8. Seo M, Kim K, Yoon J, Jeong JY, Lee HJ, Cho S, et al. RNA-seq analysis for detecting\n quantitative trait-associated genes. Sci Rep [Internet]. Nature Publishing Group;\n 2016;6:1–12. Available from: \u003ca href=\"http://dx.doi.org/10.1038/srep24375\"\u003ehttp://dx.doi.org/10.1038/srep24375\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e9. Song J, Tan H, Perry AJ, Akutsu T, Webb GI, Whisstock JC, et al. PROSPER: An Integrated\n Feature-Based Tool for Predicting Protease Substrate Cleavage Sites. PLoS One. 2012;7.\n \u003c/p\u003e\n \n\u003cp\u003e10. Bang ML, Li X, Littlefield R, Bremner S, Thor A, Knowlton KU, et al. Nebulin-deficient\n mice exhibit shorter thin filament lengths and reduced contractile function in skeletal\n muscle. J Cell Biol. 2006;173:905–16. \u003c/p\u003e\n \n\u003cp\u003e11. Ménétrey J, Perderiset M, Cicolari J, Dubois T, Elkhatib N, Khadali F El, et al.\n Structural basis for ARF1-mediated recruitment of ARHGAP21 to Golgi membranes. EMBO\n J. 2007;26:1953–62. \u003c/p\u003e\n \n\u003cp\u003e12. Azzato EM, Pharoah PDP, Harrington P, Easton DF, Greenberg D, Caporaso NE, et\n al. A genome-wide association study of prognosis in breast cancer. Cancer Epidemiol\n Biomarkers Prev. 2010;19:1140–3. \u003c/p\u003e\n \n\u003cp\u003e13. Sanchez Bassères D, Vedelago Tizzei E, Duarte AAS, Ferreira Costa F, Olalla Saad\n ST. ARHGAP10, a novel human gene coding for a potentially cytoskeletal Rho-GTPase\n activating protein. Biochem Biophys Res Commun. 2002;294:579–85. \u003c/p\u003e\n \n\u003cp\u003e14. Barcellos KSA, Bigarella CL, Wagner M V., Vieira KP, Lazarini M, Langford PR,\n et al. ARHGAP21 protein, a new partner of α-tubulin involved in cell-cell adhesion\n formation and essential for epithelial-mesenchymal transition. J Biol Chem. 2013;288:2179–89.\n \u003c/p\u003e\n \n\u003cp\u003e15. Zhang S, Sui L, Zhuang J, He S, Song Y, Ye Y, et al. ARHGAP24 regulates cell ability\n and apoptosis of colorectal cancer cells via the regulation of P53. Oncol Lett. 2018;16:3517–24.\n \u003c/p\u003e\n \n\u003cp\u003e16. Teng JP, Yang ZY, Zhu YM, Ni D, Zhu ZJ, Li XQ. The roles of ARHGAP10 in the proliferation,\n migration and invasion of lung cancer cells. Oncol Lett. 2017;14:4613–8. \u003c/p\u003e\n \n\u003cp\u003e17. Batrakou DG, De Las Heras JI, Czapiewski R, Mouras R, Schirmer EC. TMEM120A and\n B: Nuclear envelope transmembrane proteins important for adipocyte differentiation.\n PLoS One [Internet]. 2015;10:1–21. Available from: \u003ca href=\"http://dx.doi.org/10.1371/journal.pone.0127712\"\u003ehttp://dx.doi.org/10.1371/journal.pone.0127712\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e18. Farnier C, Krief S, Blache M, Diot-Dupuy F, Mory G, Ferre P, et al. Adipocyte\n functions are modulated by cell size change: Potential involvement of an integrin/ERK\n signalling pathway. Int J Obes. 2003;27:1178–86. \u003c/p\u003e\n \n\u003cp\u003e19. Zheng Y, Lin Z-Y, Xie J-J, Jiang F-N, Chen C-J, Li J-X, et al. ARRDC3 Inhibits\n the Progression of Human Prostate Cancer Through ARRDC3-ITGβ4 Pathway. Curr Mol Med\n [Internet]. 2017;17:221–9. Available from: \u003ca href=\"http://www.ncbi.nlm.nih.gov/pubmed/28782483%0Ahttp://www.eurekaselect.com/154738/article\"\u003ehttp://www.ncbi.nlm.nih.gov/pubmed/28782483%0Ahttp://www.eurekaselect.com/154738/article\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e20. Draheim KM. An Integral Role of ARRDC3 in Stem Cell Migration and Breast Cancer\n Progression : A Dissertation [Internet]. University of Massachusetts; 2010. Available\n from: \u003ca href=\"https://escholarship.umassmed.edu/gsbs_diss/468\"\u003ehttps://escholarship.umassmed.edu/gsbs_diss/468\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e21. Patwari P, Emilsson V, Schadt EE, Chutkow WA, Lee S, Marsili A, et al. The arrestin\n domain-containing 3 protein regulates body mass and energy expenditure. Cell Metab\n [Internet]. Elsevier Inc.; 2011;14:671–83. Available from: \u003ca href=\"http://dx.doi.org/10.1016/j.cmet.2011.08.011\"\u003ehttp://dx.doi.org/10.1016/j.cmet.2011.08.011\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e22. The UniProt Consortium. UniProt: The universal protein knowledgebase. Nucleic\n Acids Res. Oxford University Press; 2017;45:D158–69. \u003c/p\u003e\n \n\u003cp\u003e23. Xu L, Bickhart DM, Cole JB, Schroeder SG, Song J, Van Tassell CP, et al. Genomic\n signatures reveal new evidences for selection of important traits in domestic cattle.\n Mol Biol Evol. 2015;32:711–25. \u003c/p\u003e\n \n\u003cp\u003e24. Ma J, Liu S, Zhang W, Zhang F, Wang S, Wu L, et al. High expression of NDRG3 associates\n with positive lymph node metastasis and unfavourable overall survival in laryngeal\n squamous cell carcinoma. Pathology. 2016;48:691–6. \u003c/p\u003e\n \n\u003cp\u003e25. Ren GF, Tang L, Yang AQ, Jiang WW, Huang YM. Prognostic impact of NDRG2 and NDRG3\n in prostate cancer patients undergoing radical prostatectomy. Histol Histopathol.\n 2014;29:535–42. \u003c/p\u003e\n \n\u003cp\u003e26. Wang W, Li Y, Li Y, Hong A, Wang J, Lin B, et al. NDRG3 is an androgen regulated\n and prostate enriched gene that promotes in vitro and in vivo prostate cancer cell\n growth. Int J Cancer. 2009;124:521–30. \u003c/p\u003e\n \n\u003cp\u003e27. Kim A, Kim MJ, Yang Y, Kim JW, Yeom Y Il, Lim JS. Suppression of NF-κB activity\n by NDRG2 expression attenuates the invasive potential of highly malignant tumor cells.\n Carcinogenesis. 2009;30:927–36. \u003c/p\u003e\n \n\u003cp\u003e28. Colin E, Huynh Cong E, Mollet G, Guichet A, Gribouval O, Arrondel C, et al. Loss-of-function\n mutations in WDR73 Are responsible for microcephaly and steroid-resistant nephrotic\n syndrome: Galloway-mowat syndrome. Am J Hum Genet. 2014;95:637–48. \u003c/p\u003e\n \n\u003cp\u003e29. Gu Z, Zhang F, Wang ZQ, Ma W, Davis RE, Wang Z. The p44/wdr77-dependent cellular\n proliferation process during lung development is reactivated in lung cancer. Oncogene.\n 2013;32:1888–900. \u003c/p\u003e\n \n\u003cp\u003e30. Miliara X, Garnett JA, Tatsuta T, Abid Ali F, Baldie H, Perez-Dorado I, et al.\n Structural insight into the TRIAP1/PRELI-like domain family of mitochondrial phospholipid\n transfer complexes. EMBO Rep [Internet]. 2015;16:824–35. Available from: \u003ca href=\"http://embor.embopress.org/cgi/doi/10.15252/embr.201540229\"\u003ehttp://embor.embopress.org/cgi/doi/10.15252/embr.201540229\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e31. \u003ca href=\"http://www.ebi.ac.uk\"\u003ewww.ebi.ac.uk\u003c/a\u003e. \u003ca href=\"http://www.ebi.ac.uk/gxa/home\"\u003ewww.ebi.ac.uk/gxa/home\u003c/a\u003e [Internet]. Available from: \u003ca href=\"http://www.ebi.ac.uk/gxa/home\"\u003ewww.ebi.ac.uk/gxa/home\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e32. Fang Z, Miao Y, Ding X, Deng H, Liu S, Wang F, et al. Proteomic Identification\n and Functional Characterization of a Novel ARF6 GTPase-activating Protein, ACAP4.\n Mol Cell Proteomics [Internet]. 2006;5:1437–49. Available from: \u003ca href=\"http://www.mcponline.org/lookup/doi/10.1074/mcp.M600050-MCP200\"\u003ehttp://www.mcponline.org/lookup/doi/10.1074/mcp.M600050-MCP200\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e33. Suzuki T, Kanai Y, Hara T, Sasaki J, Sasaki T, Kohara M, et al. Crucial Role of\n the Small GTPase ARF6 in Hepatic Cord Formation during Liver Development. Mol Cell\n Biol [Internet]. 2006;26:6149–56. Available from: \u003ca href=\"http://mcb.asm.org/cgi/doi/10.1128/MCB.00298-06\"\u003ehttp://mcb.asm.org/cgi/doi/10.1128/MCB.00298-06\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e34. Gomez T, Billadeau D. A FAM21-Containing WASH Complex Regulates Retromer- Dependent\n Sorting. Dev Cell. 2009;17:699–711. \u003c/p\u003e\n \n\u003cp\u003e35. Jia D, Gomez TS, Metlagel Z, Umetani J, Otwinowski Z, Rosen MK, et al. WASH and\n WAVE actin regulators of the Wiskott-Aldrich syndrome protein (WASP) family are controlled\n by analogous structurally related complexes. Proc Natl Acad Sci [Internet]. 2010;107:10442–7.\n Available from: \u003ca href=\"http://www.pnas.org/cgi/doi/10.1073/pnas.0913293107\"\u003ehttp://www.pnas.org/cgi/doi/10.1073/pnas.0913293107\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e36. Helfer E, Harbour ME, Henriot V, Lakisic G, Sousa-Blin C, Volceanov L, et al.\n Endosomal recruitment of the WASH complex: Active sequences and mutations impairing\n interaction with the retromer. Biol Cell. 2013;105:191–207. \u003c/p\u003e\n \n\u003cp\u003e37. Hu W-X, Ren W, He L-Q, Sheng R, Shi Y-W. Methylation of CpG islands was involved\n in the down-regulation of DAZAP2 in multiple meyloma cells. Blood. 2007;110. \u003c/p\u003e\n \n\u003cp\u003e38. Hu W-X, Qu Q, Li J, Ren W. Hyper-Methylation DAZAP2 May Suppress Its Expression\n in Specific Subtypes of Myeloma. Blood. 2009;114. \u003c/p\u003e\n \n\u003cp\u003e39. Krem MM, Salipante SJ, Wechsler JD, Horwitz MS. The role of KLHDC8B deficiency\n in Hodgkin lymphoma pathogenesis. J Clin Oncol. 2010;28. \u003c/p\u003e\n \n\u003cp\u003e40. Krem MM, Salipante SJ, Horwitz MS. Mutations in a gene encoding a midbody protein\n in binucleated Reed-Sternberg cells of Hodgkin lymphoma. Cell Cycle. 2010;9:670–5.\n \u003c/p\u003e\n \n\u003cp\u003e41. Braig S, Bosserhoff AK. Death inducer-obliterator 1 (Dido1) is a BMP target gene\n and promotes BMP-induced melanoma progression. Oncogene [Internet]. Nature Publishing\n Group; 2013;32:837–48. Available from: \u003ca href=\"http://dx.doi.org/10.1038/onc.2012.115\"\u003ehttp://dx.doi.org/10.1038/onc.2012.115\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e42. Liu Y, Kim H, Liang J, Lu W, Ouyang B, Liu D, et al. The death-inducer obliterator\n 1 (Dido1) gene regulates embryonic stem cell self-renewal. J Biol Chem. 2014;289:4778–86.\n \u003c/p\u003e\n \n\u003cp\u003e43. Buurman R, Sandbothe M, Schlegelberger B, Skawran B. HDAC inhibition activates\n the apoptosome via Apaf1 upregulation in hepatocellular carcinoma. Eur J Med Res.\n BioMed Central; 2016;21:1–6. \u003c/p\u003e\n \n\u003cp\u003e44. Long AB, Kaiser WJ, Mocarski ES, Caspary T. Apaf1 apoptotic function critically\n limits Sonic hedgehog signaling during craniofacial development. Cell Death Differ\n [Internet]. Nature Publishing Group; 2013;20:1510–20. Available from: \u003ca href=\"http://dx.doi.org/10.1038/cdd.2013.97\"\u003ehttp://dx.doi.org/10.1038/cdd.2013.97\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e45. Long AB, Kaiser WJ, Mocarski ES, Caspary T. Apaf1 apoptotic function critically\n limits Sonic hedgehog signaling during craniofacial development. Cell Death Differ.\n Nature Publishing Group; 2013;20:1510–20. \u003c/p\u003e\n \n\u003cp\u003e46. Yong FL, Wang CW, Roslani AC, Law CW. The involvement of miR-23a/APAF1 regulation\n axis in colorectal cancer. Int J Mol Sci. 2014;15:11713–29. \u003c/p\u003e\n \n\u003cp\u003e47. Ahn J, Oh SA, Suh Y, Moeller SJ, Lee K. Porcine G0/G1 switch gene 2 (G0S2) expression\n is regulated during adipogenesis and short-term in-vivo nutritional interventions.\n Lipids. 2013;48:209–18. \u003c/p\u003e\n \n\u003cp\u003e48. Zagani R, El-Assaad W, Gamache I, Teodoro JG. Inhibition of adipose triglyceride\n lipase (ATGL) by the putative tumor suppressor G0S2 or a small molecule inhibitor\n attenuates the growth of cancer cells. Oncotarget. 2015;6. \u003c/p\u003e\n \n\u003cp\u003e49. Ma T, Lopez-Aguiar AGN, Li A, Lu Y, Sekula D, Nattie EE, et al. Mice lacking G0S2\n are lean and cold-tolerant. Cancer Biol Ther. 2014;15:643–50. \u003c/p\u003e\n \n\u003cp\u003e50. El-Assaad W, El-Kouhen K, Mohammad AH, Yang J, Morita M, Gamache I, et al. Deletion\n of the gene encoding G0/G1 switch protein 2 (G0s2) alleviates high-fat-diet-induced\n weight gain and insulin resistance, and promotes browning of white adipose tissue\n in mice. Diabetologia. 2015;58:149–57. \u003c/p\u003e\n \n\u003cp\u003e51. Kusakabe M, Watanabe K, Emoto N, Aki N, Kage H, Nagase T, et al. Impact of DNA\n demethylation of the G0S2 gene on the transcription of G0S2 in squamous lung cancer\n cell lines with or without nuclear receptor agonists. Biochem Biophys Res Commun.\n Elsevier Inc.; 2009;390:1283–7. \u003c/p\u003e\n \n\u003cp\u003e52. Micheli L, Leonardi L, Conti F, Maresca G, Colazingari S, Mattei E, et al. PC4/Tis7/IFRD1\n Stimulates Skeletal Muscle Regeneration and Is Involved in Myoblast Differentiation\n as a Regulator of MyoD and NF-κB. J Biol Chem. 2011;286:5691–707. \u003c/p\u003e\n \n\u003cp\u003e53. Iezaki T, Fukasawa K, Park G, Horie T, Kanayama T, Ozaki K, et al. Transcriptional\n Modulator Ifrd1 Regulates Osteoclast Differentiation through Enhancing the NF-κB/NFATc1\n Pathway. Mol Cell Biol. 2016;36:2451–63. \u003c/p\u003e\n \n\u003cp\u003e54. Singh H, Cousin MA, Ashley RH. Functional reconstitution of mammalian “chloride\n intracellular channels” CLIC1, CLIC4 and CLIC5 reveals differential regulation by\n cytoskeletal actin. FEBS J. 2007;274:6306–16. \u003c/p\u003e\n \n\u003cp\u003e55. Wegner B, Al-Momany A, Kulak S, Kozlowski K, Obeidat M, Jahroudi N, et al. CLIC5A,\n a component of the ezrin-podocalyxin complex in glomeruli, is a determinant of podocyte\n integrity. Am J Physiol Physiol. 2010;298:F1492–503. \u003c/p\u003e\n \n\u003cp\u003e56. Berryman M, Bruno J, Price J, Edwards JC. CLIC-5A functions as a chloride channel\n in vitro and associates with the cortical actin cytoskeleton in vitro and in vivo.\n J Biol Chem. 2004;279:34794–801. \u003c/p\u003e\n \n\u003cp\u003e57. Kuga T, Sasaki M, Mikami T, Miake Y, Adachi Y, Shimizu M, et al. FAM83H and casein\n kinase I regulate the organization of the keratin cytoskeleton and formation of desmosomes.\n Sci Rep. 2016;6:1–15. \u003c/p\u003e\n \n\u003cp\u003e58. Kim KM, Park SH, Bae JS, Noh SJ, Tao GZ, Kim JR, et al. FAM83H is involved in\n the progression of hepatocellular carcinoma and is regulated by MYC. Sci Rep. 2017;7:1–13.\n \u003c/p\u003e\n \n\u003cp\u003e59. Haghighi K, Kolokathis F, Gramolini A, Waggoner J, Pater L, Lynch R, et al. A\n mutation in the human phospholamban gene, deleting arginine 14, results in lethal,\n hereditary cardiomyopathy. Proc Natl Acad Sci. 2006;103:1388–93. \u003c/p\u003e\n \n\u003cp\u003e60. Ceholski DK, Trieber CA, Young HS. Hydrophobic imbalance in the cytoplasmic domain\n of phospholamban is a determinant for lethal dilated cardiomyopathy. J Biol Chem.\n 2012;287:16521–9. \u003c/p\u003e\n \n\u003cp\u003e61. Medin M, Hermida-Prieto M, Monserrat L, Laredo R, Rodriguez-Rey JC, Fernandez\n X, et al. Mutational screening of phospholamban gene in hypertrophic and idiopathic\n dilated cardiomyopathy and functional study of the PLN -42 C\u0026gt;G mutation. Eur J Heart\n Fail. 2007;9:37–43. \u003c/p\u003e\n \n\u003cp\u003e62. Medin M, Hermida M, Monserrat L, Laredo R, Rodriguez J, Fernandez X, et al. Mutational\n screening of phospholamban gene in hypertrophic and idiopathic dilated cardiomyopathy\n and functional study of the PLN-42 C\u0026gt;G mutation. Front Cell Dev Biol. 2007;9:37–43.\n \u003c/p\u003e\n \n\u003cp\u003e63. Ceholski DK, Trieber CA, Holmes CFB, Young HS. Lethal, hereditary mutants of phospholamban\n elude phosphorylation by protein kinase A. J Biol Chem. 2012;287:26596–605. \u003c/p\u003e\n \n\u003cp\u003e64. Uniacke J, Perera JK, Lachance G, Francisco CB, Lee S. Cancer cells exploit eIF4E2-directed\n synthesis of hypoxia response proteins to drive tumor progression. Cancer Res. 2014;74:1379–89.\n \u003c/p\u003e\n \n\u003cp\u003e65. Kelly NJ, Varga JFA, Specker EJ, Romeo CM, Coomber BL, Uniacke J. Hypoxia activates\n cadherin-22 synthesis via eIF4E2 to drive cancer cell migration, invasion and adhesion.\n Oncogene [Internet]. Nature Publishing Group; 2018;37:651–62. Available from: \u003ca href=\"http://dx.doi.org/10.1038/onc.2017.372\"\u003ehttp://dx.doi.org/10.1038/onc.2017.372\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e66. Weinstein LS, Liu J, Sakamoto A, Xie T, Chen M. Minireview: GNAS: Normal and abnormal\n functions. Endocrinology. 2004;145:5459–64. \u003c/p\u003e\n \n\u003cp\u003e67. Furukawa T, Kuboki Y, Tanji E, Yoshida S, Hatori T, Yamamoto M, et al. Whole-exome\n sequencing uncovers frequent GNAS mutations in intraductal papillary mucinous neoplasms\n of the pancreas. Sci Rep. 2011;1. \u003c/p\u003e\n \n\u003cp\u003e68. Wu J, Matthaei H, Maitra A, Dal Molin M, Wood L, Eshleman J, et al. Recurrent\n gnas mutations define an unexpected pathway for pancreatic cyst development. Sci Transl\n Med [Internet]. 2011;3. Available from: \u003ca href=\"http://www.embase.com/search/results?subaction=viewrecord\u0026amp;from=export\u0026amp;id=L70818947\"\u003ehttp://www.embase.com/search/results?subaction=viewrecord\u0026amp;from=export\u0026amp;id=L70818947\u003c/a\u003e \u003ca href=\"http://dx.doi.org/10.1111/j.1477-2574.2012.00512.x\"\u003ehttp://dx.doi.org/10.1111/j.1477-2574.2012.00512.x\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e69. Wu J, Matthaei H, Maitra A, Dal Molin M, Wood L, Eshleman J, et al. Recurrent\n gnas mutations define an unexpected pathway for pancreatic cyst development. Sci Transl\n Med. 2011;3. \u003c/p\u003e\n \n\u003cp\u003e70. Bandyopadhyay U, Kaushik S, Varticovski L, Cuervo AM. The Chaperone-Mediated Autophagy\n Receptor Organizes in Dynamic Protein Complexes at the Lysosomal Membrane. Mol Cell\n Biol [Internet]. 2008;28:5747–63. Available from: \u003ca href=\"http://mcb.asm.org/cgi/doi/10.1128/MCB.02070-07\"\u003ehttp://mcb.asm.org/cgi/doi/10.1128/MCB.02070-07\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e71. Cuervo A, Dice J. Unique properties of lamp2a compared to other lamp2 isoforms.\n J Cell Sci [Internet]. 2000;113 Pt 24:4441–50. Available from: \u003ca href=\"http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\u0026amp;db=PubMed\u0026amp;dopt=Citation\u0026amp;list_uids=11082038\"\u003ehttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\u0026amp;db=PubMed\u0026amp;dopt=Citation\u0026amp;list_uids=11082038\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e72. Hubert V, Peschel A, Langer B, Gröger M, Rees A, Kain R. LAMP-2 is required for\n incorporating syntaxin-17 into autophagosomes and for their fusion with lysosomes.\n Biol Open [Internet]. 2016;5:1516–29. Available from: \u003ca href=\"http://bio.biologists.org/lookup/doi/10.1242/bio.018648\"\u003ehttp://bio.biologists.org/lookup/doi/10.1242/bio.018648\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e73. Bassi MT, Manzoni M, Monti E, Pizzo MT, Ballabio A, Borsani G. Cloning of the\n gene encoding a novel integral membrane protein, mucolipidin - And identification\n of the two major founder mutations causing mucolipidosis type IV. Am J Hum Genet.\n 2000;67:1110–20. \u003c/p\u003e\n \n\u003cp\u003e74. Schmiege P, Fine M, Blobel G, Li X. Human TRPML1 channel structures in open and\n closed conformations. Nature. 2017;19:477–91. \u003c/p\u003e\n \n\u003cp\u003e75. Cuajungco MP, Basilio LC, Silva J, Hart T, Tringali J, Chen C, et al. Cellular\n Zinc Levels are Modulated by Trpml1-Tmem163 Interaction. Traffic. 2014;15:1247–65.\n \u003c/p\u003e\n \n\u003cp\u003e76. Raychowdhury MK, González-Perrett S, Montalbetti N, Timpanaro GA, Chasan B, Goldmann\n WH, et al. Molecular pathophysiology of mucolipidosis type IV: pH dysregulation of\n the mucolipin-1 cation channel. Hum Mol Genet. 2004;13:617–27. \u003c/p\u003e\n \n\u003cp\u003e77. Rämö O, Kumar D, Gucciardo E, Joensuu M, Saarekas M, Vihinen H, et al. NOGO-A/RTN4A\n and NOGO-B/RTN4B are simultaneously expressed in epithelial, fibroblast and neuronal\n cells and maintain ER morphology. Sci Rep [Internet]. Nature Publishing Group; 2016;6:1–14.\n Available from: \u003ca href=\"http://dx.doi.org/10.1038/srep35969\"\u003ehttp://dx.doi.org/10.1038/srep35969\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e78. Wang S, Tukachinsky H, Romano FB, Rapoport TA. Cooperation of the ER-shaping proteins\n atlastin, lunapark, and reticulons to generate a tubular membrane network. Elife.\n 2016;5:1–29. \u003c/p\u003e\n \n\u003cp\u003e79. Rämö O, Kumar D, Gucciardo E, Joensuu M, Saarekas M, Vihinen H, et al. NOGO-A/RTN4A\n and NOGO-B/RTN4B are simultaneously expressed in epithelial, fibroblast and neuronal\n cells and maintain ER morphology. Sci Rep. Nature Publishing Group; 2016;6:1–14. \u003c/p\u003e\n \n\u003cp\u003e80. Leal-Gutiérrez JD, Rezende FM, Elzo MA, Johnson D, Peñagaricano F, Mateescu RG.\n Structural Equation Modeling and Whole-Genome Scans Uncover Chromosome Regions and\n Enriched Pathways for Carcass and Meat Quality in Beef. Front Genet [Internet]. 2018;9:1–13.\n Available from: \u003ca href=\"https://www.frontiersin.org/article/10.3389/fgene.2018.00532/full\"\u003ehttps://www.frontiersin.org/article/10.3389/fgene.2018.00532/full\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e81. Leal-Gutiérrez JD, Elzo MA, Johnson DD, Hamblen H, Mateescu RG. Genome wide association\n and gene enrichment analysis reveal membrane anchoring and structural proteins associated\n with meat quality in beef. BMC Genomics [Internet]. BMC Genomics; 2019;20:151. Available\n from: \u003ca href=\"https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-5518-3\"\u003ehttps://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-019-5518-3\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e82. Shin DH, Lee HJ, Cho S, Kim HJ, Hwang JY, Lee CK, et al. Deleted copy number variation\n of Hanwoo and Holstein using next generation sequencing at the population level. BMC\n Genomics [Internet]. BMC Genomics; 2014;15:1–16. Available from: BMC Genomics\u003c/p\u003e\n \n\u003cp\u003e83. Elzo MA, Thomas MG, Johnson DD, Martinez CA, Lamb GC, Rae DO, et al. Genomic-polygenic\n evaluation of multibreed Angus-Brahman cattle for postweaning ultrasound and weight\n traits with actual and imputed Illumina50k SNP genotypes. Livest Sci [Internet]. Elsevier;\n 2015;175:18–26. Available from: \u003ca href=\"http://dx.doi.org/10.1016/j.livsci.2015.03.002\"\u003ehttp://dx.doi.org/10.1016/j.livsci.2015.03.002\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e84. Elzo MA, Thomas MG, Johnson DD, Martinez CA, Lamb GC, Rae DO, et al. Genetic parameters\n and predictions for direct and maternal growth traits in a multibreed Angus-Brahman\n cattle population using genomic-polygenic and polygenic models. Livest Sci [Internet].\n Elsevier; 2015;178:43–51. Available from: \u003ca href=\"http://dx.doi.org/10.1016/j.livsci.2015.06.015\"\u003ehttp://dx.doi.org/10.1016/j.livsci.2015.06.015\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e85. Elzo MA, Mateescu R, Thomas MG, Johnson DD, Martinez CA, Rae DO, et al. Growth\n and reproduction genomic-polygenic and polygenic parameters and prediction trends\n as Brahman fraction increases in an Angus-Brahman multibreed population. Livest Sci\n [Internet]. Elsevier; 2016;190:104–12. Available from: \u003ca href=\"http://dx.doi.org/10.1016/j.livsci.2016.06.011\"\u003ehttp://dx.doi.org/10.1016/j.livsci.2016.06.011\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e86. Leal-Gutiérrez JD, Elzo MA, Johnson DD, Scheffler TL, Scheffler JM, Mateescu RG.\n Association of μ-Calpain and Calpastatin Polymorphisms with Meat Tenderness in a Brahman–Angus\n Population. Front Genet [Internet]. 2018;9:1–10. Available from: \u003ca href=\"http://journal.frontiersin.org/article/10.3389/fgene.2018.00056/full\"\u003ehttp://journal.frontiersin.org/article/10.3389/fgene.2018.00056/full\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e87. Belk KE, Dikeman ME, Calkins CR, Andy King D, Shackelford SD, Hale D, et al. Research\n Guidelines for Cookery, Sensory Evaluation, and Instrumental Tenderness Measurements\n of Meat. 2015. \u003c/p\u003e\n \n\u003cp\u003e88. SAS Institute, Inc., Cary, NC. United States; \u003c/p\u003e\n \n\u003cp\u003e89. Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets.\n Bioinformatics. 2011;27:863–4. \u003c/p\u003e\n \n\u003cp\u003e90. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads.\n EMBnet.journal [Internet]. 2011;17:10. Available from: \u003ca href=\"http://journal.embnet.org/index.php/embnetjournal/article/view/200\"\u003ehttp://journal.embnet.org/index.php/embnetjournal/article/view/200\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e91. Andrews S, Krueger F, Segonds-Pichon A, Biggins L, Krueger C, Wingett S. FastQC\n [Internet]. 2018. Available from: \u003ca href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e92. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg S. TopHat2: accurate\n alignment of transcriptomes in the presence of insertions, deletions and gene fusions.\n Genome Biol. 2013;14:0–9. \u003c/p\u003e\n \n\u003cp\u003e93. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods.\n 2012;9:357–9. \u003c/p\u003e\n \n\u003cp\u003e94. \u003ca href=\"http://emea.support.illumina.com/sequencing/sequencing_software/igenome.html\"\u003ehttp://emea.support.illumina.com/sequencing/sequencing_software/igenome.html\u003c/a\u003e# [Internet]. Available from: \u003ca href=\"http://emea.support.illumina.com/sequencing/sequencing_software/igenome.html\"\u003ehttp://emea.support.illumina.com/sequencing/sequencing_software/igenome.html\u003c/a\u003e#\u003c/p\u003e\n \n\u003cp\u003e95. Anders S, Pyl P, Huber W. HTSeq-A Python framework to work with high-throughput\n sequencing data. Bioinformatics. 2015;31:166–9. \u003c/p\u003e\n \n\u003cp\u003e96. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, Van Baren MJ, et al. Transcript\n assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform\n switching during cell differentiation. Nat Biotechnol [Internet]. Nature Publishing\n Group; 2010;28:511–5. Available from: \u003ca href=\"http://dx.doi.org/10.1038/nbt.1621\"\u003ehttp://dx.doi.org/10.1038/nbt.1621\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e97. Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. Differential\n analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol\n [Internet]. Nature Publishing Group; 2013;31:46–53. Available from: \u003ca href=\"http://dx.doi.org/10.1038/nbt.2450\"\u003ehttp://dx.doi.org/10.1038/nbt.2450\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e98. Anders S, Reyes A, Huber W. Detecting diferential usage of exons from RNA-seq\n data. Genome Res [Internet]. 2012;22:2008–17. Available from: \u003ca href=\"http://dx.doi.org/10.1101/gr.133744.111\"\u003ehttp://dx.doi.org/10.1101/gr.133744.111\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e99. Reyes A, Anders S, Weatheritt RJ, Gibson TJ, Steinmetz LM, Huber W. Drift and\n conservation of differential exon usage across tissues in primate species. Proc Natl\n Acad Sci [Internet]. 2013;110:15377–82. Available from: \u003ca href=\"http://www.pnas.org/cgi/doi/10.1073/pnas.1307202110\"\u003ehttp://www.pnas.org/cgi/doi/10.1073/pnas.1307202110\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e100. Wang L, Wang S, Li W. RSeQC: quality control of RNA-seq experiments. Bioinformatics.\n 2015;28:137–46. \u003c/p\u003e\n \n\u003cp\u003e101. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence\n Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9. \u003c/p\u003e\n \n\u003cp\u003e102. McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor\n RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 2012;40:4288–97.\n \u003c/p\u003e\n \n\u003cp\u003e103. R Core Team. R: A language and environment for statistical computing. R Foundation\n for Statistical Computing [Internet]. Vienna, Austria; 2018. Available from: \u003ca href=\"http://www.r-project.org\"\u003ehttp://www.r-project.org\u003c/a\u003e.\u003c/p\u003e\n \n\u003cp\u003e104. Robinson MD, McCarthy DJ, Smyth GK. edgeR: A Bioconductor package for differential\n expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.\n \u003c/p\u003e\n \n\u003cp\u003e105. Maechler M. sfsmisc: Utilities from “Seminar fuer Statistik” ETH Zurich. R package\n version 1.1-2. [Internet]. 2018. Available from: \u003ca href=\"https://cran.r-project.org/package=sfsmisc\"\u003ehttps://cran.r-project.org/package=sfsmisc\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e106. Venables WN, Ripley BD. Modern Applied Statistics with S. Fourth Edi. Springer,\n New York; 2002. \u003c/p\u003e\n \n\u003cp\u003e107. JMP®, Version 13. SAS Institute Inc., Cary, NC, 1989-2007. \u003c/p\u003e\n \n\u003cp\u003e108. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion\n for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–21. \u003c/p\u003e\n \n\u003cp\u003e109. Jia C, Guan W, Yang A, Xiao R, Tang WHW, Moravec CS, et al. MetaDiff: Differential\n isoform expression analysis using random-effects meta-regression. BMC Bioinformatics.\n BMC Bioinformatics; 2015;16:1–12. \u003c/p\u003e\n \n\u003cp\u003e110. Mi G, Di Y, Emerson S, Cumbie JS, Chang JH. Length Bias Correction in Gene Ontology\n Enrichment Analysis Using Logistic Regression. PLoS One. 2012;7. \u003c/p\u003e\n \n\u003cp\u003e111. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq:\n accounting for selection bias. Genome Biol [Internet]. 2010;11. Available from: http://genomebiology.com/content/11/2/R14%5Cnfile:///Users/zzhou/Dropbox/Library.papers3/Files/E5/E57EB86C-6036-4CE4-B644-1E522EF11A09.pdf%5Cnpapers3://publication/uuid/F930FBCE-A1CC-45CD-8DD7-CB709BF223BE\u003c/p\u003e\n \n\u003cp\u003e112. Orchard S, Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F, et al.\n The MIntAct project - IntAct as a common curation platform for 11 molecular interaction\n databases. Nucleic Acids Res. 2014;42:358–63. \u003c/p\u003e\n \n\u003cp\u003e113. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape:\n A Software Environment for Integrated Models of Biomolecular Interaction Networks.\n Genome Res [Internet]. 2003;13:2498–504. Available from: \u003ca href=\"http://ci.nii.ac.jp/naid/110001910481/\"\u003ehttp://ci.nii.ac.jp/naid/110001910481/\u003c/a\u003e\u003c/p\u003e\n \n\u003cp\u003e114. Möller S, Croning MDR, Apweiler R. Evaluation of methods for the prediction of\n membrane spanning regions. Bioinformatics. 2001;17:646–53. \u003c/p\u003e\n \n\u003cp\u003e115. Sigrist CJ, Cerutti L, Hulo N, Gattiker A, Falquet L, Pagni M, et al. PROSITE:\n a documented database using patterns and profiles as motif descriptors (available\n at \u003ca href=\"http://www.expasy.ch/prosite)\"\u003ehttp://www.expasy.ch/prosite)\u003c/a\u003e. Br Bioinforma. 2002;3:265–74. \u003c/p\u003e\n \n\u003cp\u003e116. Arnold K, Bordoli L, Schwede T. Structural bioinformatics The SWISS-MODEL workspace :\n a web-based environment for protein structure homology modelling. 2006;22:195–201.\n \u003c/p\u003e\n \n\u003cp\u003e117. Biasini M, Bienert S, Waterhouse A, Arnold K, Studer G, Schmidt T, et al. SWISS-MODEL:\n Modelling protein tertiary and quaternary structure using evolutionary information.\n Nucleic Acids Res. 2014;42:252–8. \u003c/p\u003e\n \n\u003cp\u003e118. Kiefer F, Arnold K, Künzli M, Bordoli L, Schwede T. The SWISS-MODEL Repository\n and associated resources. Nucleic Acids Res. 2009;37:387–92. \u003c/p\u003e\n \n\u003cp\u003e119. Petersen TN, Brunak S, Von Heijne G, Nielsen H. SignalP 4.0: Discriminating signal\n peptides from transmembrane regions. Nat Methods [Internet]. Nature Publishing Group;\n 2011;8:785–6. Available from: \u003ca href=\"http://dx.doi.org/10.1038/nmeth.1701\"\u003ehttp://dx.doi.org/10.1038/nmeth.1701\u003c/a\u003e\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cb\u003eTable 1.\u003c/b\u003e Descriptive statistics for the meat quality phenotypes and the constructed meat quality index.\u003c/p\u003e\n\u003ctable class=table\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eTrait \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eGroup\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eMean\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eSD\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eMaximum\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eMinimum\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eMeat quality index\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003c/td\u003e\u003ctd\u003e\u003cp\u003e2.34\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.57\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e3.35\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e1.15\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e80\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eWBSF (kgs)\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eTender\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e2.84\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.23\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e3.20\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e2.30\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eTough\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e5.61\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e6.90\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e5.02\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eTenderness\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eTender\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e6.24\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.21\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e6.60\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e5.90\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eTough\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e4.00\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e0.50\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e4.50\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e3.00\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eMarbling\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eLow\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e321\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e19.17\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e360\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e300\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eHigh\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e576\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e50.93\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e650\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e500\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eThe phenotypes were recorded in \u003ci\u003elongissimus dorsi\u003c/i\u003e muscle from a multibreed Angus-Brahman population. This table should appear after the line number 64.\u003c/p\u003e\n\n\n\n\u003cp\u003e\u003cb\u003eTable 2.\u003c/b\u003e Genes that were identified at least three times using an expression and DE analysis approach for meat quality related phenotypes. \u003c/p\u003e\n\u003ctable class=table\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eGene\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eExpression\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003c/td\u003e\u003ctd\u003e\u003c/td\u003e\u003ctd\u003e\u003cp\u003eDE\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003c/td\u003e\u003ctd\u003e\u003cp\u003eIsoform\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003c/td\u003e\u003ctd\u003e\u003cp\u003e Function\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003ename\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eGene\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eExon*\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eWBSF\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eTenderness\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eMarbling\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eWBSF\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eMarbling\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eABLIM1+\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eCytoskeleton\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eACTN2+\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eCytoskeleton\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eANKRD12\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eANKRD23\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eARPC5L+\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eCytoskeleton\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eC4A+\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eMembrane\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eCFB+\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eMembrane\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eEIF4E2\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eRNA binding\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eGEMIN4\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eHMGXB3\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eTranscription\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eLOC100852159\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eLOC101903649\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eMON1B\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eMPPE1+\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eCytoskeleton\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eNFKB2\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eTranscription\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003ePCNXL3\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003ePCOLCE2\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003ePeptidase reg.\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eSBNO2\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eTranscription\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eST6GALNAC2+\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eMembrane\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eSTAT5A\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eTranscription\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eTMEM131+\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eMembrane\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eTRMT6\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eUCP2+\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eMembrane\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eUNC13B\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eVEZT+\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eMembrane\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eWBP1L\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eWDR34+\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eCytoskeleton\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eWDR73+\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eCytoskeleton\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eZNF106\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eZNF771\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eX\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e \u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eTranscription\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eMeat quality was recorded in \u003ci\u003elongissimus dorsi\u003c/i\u003e muscle from a multibreed Angus-Brahman population. * Genes with at least three associated exons were included; + the protease analysis was carried out using the PROSPER server [9]. This table should appear after the line number 105.\u003c/p\u003e\n\n\n\n\u003cp\u003e\u003cb\u003eTable 3.\u003c/b\u003e Genes uncovered by the expression and DE analysis, and previously identified as associated with meat quality related phenotypes using a genotype-phenotype association analysis in the present population [80,81]. \u003c/p\u003e\n\u003ctable class=table\u003e\u003ctbody\u003e\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eAnalysis\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eSEM analysis\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003eGWAS Analysis\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eExpression\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eSRPK1*\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eExpression\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eZFYVE26\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eExpression\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eLRP5*\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eDE genes\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eZNF385A\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eDE genes\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eNCOA5\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eDE genes\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eBOD1L1\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eDE genes\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eHIBADH*\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eDE genes\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eGOSR2\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eDE genes\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eKDR\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eDE genes\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eATPAF1\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eDE genes\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eZBTB39\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eDE Isoforms\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eEIF4ENIF1*\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eDE Isoforms\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eEFCAB14\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp/\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003ctr\u003e\t\u003ctd\u003e\u003cp\u003eDE Isoforms\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003eRTN4\u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\t\u003ctd\u003e\u003cp\u003e\u003ci\u003e \u003c/i\u003e\u003c/p\u003e\n\u003c/td\u003e\u003c/tr\u003e\n\u003c/tbody\u003e\u003c/table\u003e\n\u003cp\u003eThe phenotypes were recorded in \u003ci\u003elongissimus dorsi\u003c/i\u003e muscle from a multibreed Angus-Brahman population. * Genes with cis-eQTL effects (unpublished data). This table should appear after the line number 414.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cytoskeletal protein, gene expression, meat quality and transmembrane anchoring protein","lastPublishedDoi":"10.21203/rs.2.10331/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.2.10331/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Background\n\nRNA sequencing (RNA-seq) has allowed for transcriptional profiling of biological systems through identification of differentially expressed (DE) genes and pathways.\n\nResults\n\nA total of 80 steers were selected from the multibreed Angus-Brahman herd of the University of Florida. Sensory panel tenderness, juiciness and connective tissue as well as marbling, WBSF and cooking loss were assessed in longissimus dorsi muscle. Nuclear RNA was extracted from muscle and an RNA-seq library for each sample was constructed, multiplexed, and sequenced based on protocols by Illumina HiSeq 3000 PE100 platform to generate 2 × 101 bp paired-end reads. On average, 34.9 million high-quality paired reads were uniquely mapped to the Btau_4.6.1 reference genome and a total of 8,799 genes were analyzed. Including all 80 animals, gene and exon expression analysis was carried out using a meat quality index as a continuous response variable. The expression of 208 genes and 3,280 exons from 1,565 genes was associated with the meat quality index (p-value ≤ 0.05). Out of the 80 samples sequenced, 40 animals with extreme low and high WBSF, tenderness and marbling values were selected for a differential expression (DE) analysis for gene and isoforms. A total of 676 (adjusted p-value ≤ 0.05), 70 (adjusted p-value ≤ 0.1) and 198 (adjusted p-value ≤ 0.1) genes were DE for WBSF, tenderness and marbling, respectively. A total of 106 isoforms from 98 genes for WBSF, 13 isoforms from 13 genes for tenderness and 43 isoforms from 42 genes for marbling (FDR ≤ 0.1) were DE.\n\nConclusion\n\nA number of cytoskeletal and transmembrane anchoring related genes and pathways were identified in the expression, DE and gene enrichment analyses, and these proteins can have a direct effect on meat quality. Cytoskeletal proteins and transmembrane anchoring molecules can influence meat quality by allowing cytoskeletal filament interaction with myocyte and organelle membranes, contributing to cytoskeletal structure, microtubule network stability, and cellular architecture maintenance during the postmortem.","manuscriptTitle":"RNA-seq analysis identifies cytoskeletal structural genes and pathways for meat quality in beef","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2019-06-17 22:30:19","doi":"10.21203/rs.2.10331/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"42473ccc-caec-4fe4-acb1-ca4e2e4f1556","owner":[],"postedDate":"June 17th, 2019","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":13755,"name":"Epigenetics \u0026 Genomics"}],"tags":[],"updatedAt":"","versionOfRecord":[],"versionCreatedAt":"2019-06-17 22:30:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1349","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"identity":"rs-1349","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}