Figures
Abstract
The novel liver protein acetyl-CoA acetyltransferase-2 (ACAT2) is involved in the beta-oxidation and lipid metabolism. Its comprehensive relative expression, in silico non-synonymous single nucleotide polymorphism (nsSNP) analysis, as well as its annotation in terms of metabolic process with another protein from the same family, namely, acetyl-CoA acyltransferase-2 (ACAA2) was performed in Sus scrofa. This investigation was conducted to understand the most important nsSNPs of ACAT2 in terms of their effects on metabolic activities and protein conformation. The two most deleterious mutations at residues 122 (I to V) and 281 (R to H) were found in ACAT2. Validation of expression of genes in the laboratory also supported the idea of differential expression of ACAT2 and ACAA2 conceived through the in silico analysis. Analysis of the relative expression of ACAT2 and ACAA2 in the liver tissue of Jeju native pig showed that the former expressed significantly higher (P<0.05). Overall, the computational prediction supported by wet laboratory analysis suggests that ACAT2 might contribute more to metabolic processes than ACAA2 in swine. Further associations of SNPs in ACAT2 with production traits might guide efforts to improve growth performance in Jeju native pigs.
Citation: Sodhi SS, Ghosh M, Song KD, Sharma N, Kim JH, Kim NE, et al. (2014) An Approach to Identify SNPs in the Gene Encoding Acetyl-CoA Acetyltransferase-2 (ACAT-2) and Their Proposed Role in Metabolic Processes in Pig. PLoS ONE 9(7): e102432. https://doi.org/10.1371/journal.pone.0102432
Editor: Andrew R. Dalby, University of Westminster, United Kingdom
Received: March 17, 2014; Accepted: June 17, 2014; Published: July 22, 2014
Copyright: © 2014 Sodhi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by a grant from the Next-Generation BioGreen 21 Program (No. PJ009032022012 and PJ008196), Rural Development Administration, Republic of Korea. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
The liver protein acetyl-CoA acyltransferase-2 (ACAA2), contributes to metabolism in two ways: it catalyses the last step of beta oxidation of mitochondrial fatty acids and is responsible for lipid catabolism. Apart from ACAA2, the ACAT2 protein is also expressed in the liver. The latter has two isoforms, ACAT1 and ACAT2, with distinct metabolic functions, intracellular localisations and membrane topologies in different mammals [1]–[2]. ACAT2 belongs to the same protein family as ACAA2 and both are present on chromosome 1 in pig.
Liver is one of the most important organs for metabolism and the partitioning of nutrients. It is involved in the transformation of dietary nutrients into fuel and their export via the blood. To meet the changing demands of extra-hepatic tissues for nutrients, liver displays remarkable metabolic flexibility [3]. The ACAT2 has a unique tissue distribution, being predominantly expressed in the liver and intestine [4]. It plays an important role in lipid and cholesterol metabolism in human beings and bovines. The high variability of ACAT2 was observed among the human liver samples and suggested that it is a regulated enzyme that is responsible for more than 50% of the ACAT activity in the majority of human population [4]. Another study, conducted using broiler chickens, investigated the expression of ACAT2 in liver and revealed that this gene has been subjected to selection to either promote or suppress the accumulation of abdominal fat [5]. A correlation between ACAT2 and cholesterol has also been reported in fish [6], and cholesterol has further been associated with increased lean mass in human beings [7]. Finally, ACAT2 controls enzymes involved in lipogenesis or lipolysis in beef cattle [8].
Moreover, analyses of single-nucleotide polymorphisms (SNPs) provide important information about genetic linkages, and are useful in fine-mapping of the regions of candidate genes. SNPs not only have attracted substantial attention in terms of human and vertebrate haplotypes associated with traits of interest, but they have also increased our understanding of the genetic basis of phenotypic diversity within and between populations [9]–[10]. Owing to their abundance in the genome, thousands of potentially informative SNP markers can be identified for the development of high-density SNP maps [11]. SNPs detected in the bovine ACAA2 are reported to be associated with gain in daily weight and loin muscle area (P<0.05) significantly [12]. Further, SNPs in the ovine ACAA2 gene are reported to be associated with milk production traits [13]. Moreover, SNPs associated with production traits were discovered in the ACAT2 gene of Holstein cattle [14].
Given the importance of the SNPs of ACAT2 gene in metabolic processes, our study comprised two aspects. First, we conducted comprehensive in silico non-synonymous single nucleotide polymorphism (nsSNP) analysis and predicted the possible effects of these nsSNPs on protein structure and function. In the second stage, we explored the level of involvement of the ACAT2 gene relative to that of ACAA2 gene in metabolic processes in Sus scrofa.
Materials and Methods
Database retrieval of information on ACAA2 and ACAT2 genes
Data on the ACAA2 and ACAT2 genes from Sus scrofa and Bos taurus were collected from Entrez Gene of the National Centre for Biotechnology Information (NCBI). The SNP ids and types of SNPs related information were obtained from the NCBI dbSNP (http://www.ncbi.nlm.nih.gov/snp/) and SWISSProt databases (http://expasy.org/) for computational analysis. The online tool Treefam (http://www.treefam.org/) was used to analyze the phylogenic relationships between the ACAA2 and ACAT2 genes. A bootstrap threshold of 50% was used for the inclusion of organisms (bovine) in the same clade as Sus scrofa. It was followed by multiple sequence alignment to compare the sequences of the ACAA2 and ACAT2 from both bovine and swine.
Annotation of Gene Ontology (GO) in terms of functional classes
GO analysis has become a commonly used approach for functional studies of large-scale genomic or transcriptomic data. In the current study, GO analysis has been used to analyse the biological processes associated with the functioning of the ACAA2 and ACAT2 genes in S scrofa and B taurus. CateGOrizer (previously known as “GO terms classifications Counter”; www.animalgenome.org/bioinfo/tools/countgo/) [15]; was used as a common language tool to annotate ACAA2 and ACAT2 in S scrofa and B taurus.
Validation of the expression levels of ACAA2 and ACAT2 in Sus scrofa and Bos taurus liver tissues
Ethics Statement and collection of tissue samples.
Pure bred adult animals from Jeju native pigs (JNPs) at an average body weight of 84.7±3.5 kg were used for the study. The study was carried out in strict accordance with the recommendations of the animal bioethics committee of Jeju National University, Jeju-Si, Jeju-Do, Republic of Korea. Bioethics committee specifically approved this study vide permit number: 2013-0009. Animals were reared under same environmental and nutritional conditions. The commercial feed (Seoul Feed, Jeju- Si, South Korea) and water were offered ad- libitum to all the pigs. Pigs were housed in pens with concrete flooring, a nipple bowl drinker and feeder. Bos taurus liver samples were collected from the Jeju joint livestock products market slaughter house, Jeju-Si, Republic of Korea. Five female animals of each species were used for the study and slaughtering method entailed exsanguination following electric stunning. The liver tissues were dissected immediately after slaughter and samples were frozen in liquid nitrogen. The tissue samples were subsequently stored in the laboratory at −80°C until the isolation of RNA and protein.
RNA and protein isolation from liver tissue.
RNA was isolated from 100 mg of the fragmented frozen liver tissue samples of JNPs and bovines. TRIzol reagent (Invitrogen, USA) was used to isolate RNA. Tissue samples were homogenised in 1.5 ml of TRIzol reagent and chloroform, and the RNA extracted was subsequently precipitated using isopropanol (Junsei Chemical Co. Ltd., Japan). Isolated RNA samples were stored at −80°C. Genomic DNA contamination was removed by treating 25 µg of RNA from each sample with the RNase-free DNase set (QIAGEN, Hilden, Germany) and purification with the RNeasy mini kit, in accordance with the user guidelines (QIAGEN, Hilden, Germany). A Bioanalyzer 2100 with RNA 6000 Nano Labchips was used to assess the quality and quantity of RNA by automated capillary gel electrophoresis, in accordance with the user guidelines (Agilent Technologies, Dublin, Ireland). 28S/18S ratios for the RNA samples ranged from 1.8 to 2.0. Superscript III enzyme (Invitrogen, USA) was then used to synthesise first-strand cDNA.
Radio Immuno Precipitation Assay (RIPA) buffer was used to extract protein from the homogenised liver tissue. The manufacturer's guidelines were followed for measurement of the concentration of protein with the Pierce™ BCA protein assay kit (Thermo Scientific, USA) in a Bio-Rad Micro-plate Reader (Model-680). All the RNA and protein samples were stored at −80°C until use.
RT-PCR and quantitative real-time PCR.
Primers used in RT-PCR and quantitative real-time PCR were designed using the online Primer-3 program [16] and the primers that were used to conduct PCR along with their detailed information are listed in Table 1. An Eppendorf Mastercycler gradient instrument was used to conduct RT-PCR under the following conditions: an initial 5 min at 94°C; 35 cycles of 30 seconds at 94°C, 30 seconds at the Ta temperature indicated in Table 1, and 1 min at 72°C; followed by a single extension period of 5 min at 72°C. The results of RT-PCR are presented as the relative expression normalised, using GAPDH transcript as an endogenous reference.
To evaluate the relative expressions of ACAA2 and ACAT2 genes quantitatively in Sus scrofa and B taurus, real-time qRT-PCR was conducted using an Applied Biosystems, Step-One Real Time PCR system. The dye EvaGreen (Biotium, USA) was used to determine the quantity of transcript of target genes present in each sample. Each individual sample was quantified in triplicate under the following amplification conditions: 95°C for 10 min initially, and then 40 cycles of 95°C for 15 sec and 60°C for 1 min. Standard curve methods were used to define the efficiency of real-time PCR. The efficiency of amplification of the target gene was compared with that of the endogenous GAPDH control transcript [17]. Quantification of mRNA levels was performed using the comparative CT method. The results are reported as the relative expression normalised using the level of the transcript of the endogenous reference [18]–[19].
Western blotting analysis.
Sixty micrograms of protein extract was diluted 1∶1 with 2× loading buffer (4% sodium dodecyl sulfate (SDS), 20% glycerol, 0.004% bromophenol blue, 25% 0.5 M Tris, and 5% β-mercaptoethanol). Protein extracts were denatured by boiling for 5 min before loading onto a 12% SDS-PAGE gel. Proteins were transferred to nitrocellulose membrane after electrophoresis. The membrane was blocked for two hours at room temperature and incubated with primary and secondary antibodies specific for ACAA2, ACAT2, and β-actin, as listed in Table 2.
The washed membranes were analysed to observe specific chemiluminescent signals using a Luminescent Image Analyzer (LAS-4000 mini) instrument. The results are depicted as the relative band intensities normalised relative to the band intensities of β-actin bands for each gene using Image J software (National Institute of Health, Bethesda, Maryland, USA). The means were compared between JNPs and bovines.
Functional consequences of the coding of nsSNPs of ACAT2 of Sus scrofa using the sorting intolerant from tolerant (SIFT) tool
Studies on SNPs of ACAT2 (Sus scrofa) were performed to identify amino acid substitutions which effect metabolic activities and protein conformations. SIFT (available at http://www.blocks.fhcrc.org/sift/SIFT.html) uses sequence homology to predict whether an amino acid substitution affects protein function; if it does, it can be prioritised for further study. In this study, by using SIFT, we submitted SNP queries, selecting those with an intolerance score below a certain threshold for further study. This threshold was fixed at 0.005 or less. SIFT scores were classified as indicating that the SNP would be damaging (0.00–0.05), potentially damaging (0.051–0.10), borderline (0.101–0.20), or tolerable (0.201–1.00) [20].
Functional annotation of nsSNPs of ACAT2 (Sus scrofa) using protein variation effect analyzer (PROVEAN)
PROVEAN is an online software tool that predicts the possible impact of amino acid substitutions on the structural and functional properties of proteins. The amino acid sequences of ACAT2 (Sus scrofa) associated with identified SNPs through SIFT were submitted as queries. A delta alignment scoring system was used, where the scores of each supporting sequence were averaged within and across clusters to generate the final PROVEAN score. This computational tool helps to predict the functional effect of variation in amino acids by selecting the common deleterious SNPs through both SIFT and PROVEAN. A protein variant is said to be “deleterious” if the final score is below a certain threshold (default is −2.5) or “neutral” if the score is above this threshold.
Protein stability prediction using I-Mutant
I-Mutant (2.0) is a support vector machine based tool (http://folding.biofold.org/cgi-bin/i-mutant2.0.cgi). In this tool the FASTA format of protein sequence of ACAT2 was used as an input to predict the effect of a mutation on its stability.
Homologous modelling and prediction of root mean square deviation (RMSD)
Protein structures were modelled to compare the structural stability of the native and mutant proteins. In the present study, the three-dimensional structure of ACAT2 from Sus scrofa was generated by subjecting the amino acid sequence in the FASTA format to analysis using the SWISS-MODEL expasy and ESyPred3D tools. The obtained structural model that was identical between these two tools was selected and subjected to structural validation as described below.
Validation of the model
The constructed homologous protein model was further subjected to evaluation of its internal consistency and reliability. A Psi/Phi Ramachandran plot was generated by PROCHECK analysis. The packing quality of the refined structure was investigated using the PROCHECK Quality Control Check. Furthermore, NOMAD–Ref (Normal Mode Analysis, Deformation, and Refinement) was applied for both the native and mutant models to refine the structures to those with lowest energy. The energy-minimised structures were then used for further analysis for the RMSD calculation.
Identification of the stabilising residues using SRide and the effect of mutation on the protein using Project Hope
The stabilising residues (SRide) tool was used to predict stabilising residues within native and mutant ACAT2 proteins of Sus scrofa. A residue is marked as stabilising if it has a high conservation score, high long-range order, high surrounding hydrophobicity, and belongs to a stabilisation centre. The homologous structures of the native and mutant proteins were uploaded as queries. The Project Hope (Have yOur Protein Explained) tool combines the information to give analyses of the effect of a mutation on the protein structure. The protein sequence was used as the input for selection of the mutant variants. The output was given in the form of the structural variation between mutant and wild-type residues.
Results
Functional annotation of ACAA2 and ACAT2 using GO
To investigate the functional similarity in the metabolic processes, genes from same protein family i.e., ACAA2 and ACAT2 in Sus scrofa and Bos taurus respectively were selected for the current study. The phylogenetic trees of ACAA2 and ACAT2 were constructed using Treefam. The Treefam showed that in their respective phylogenetic trees the ACAA2 and ACAT2 of B taurus and S scrofa, respectively are present in same clade.
From the GO annotation, both genes participate in metabolic processes. The CateGOrizer analysis of the ACAA2 gene from the two species showed fractional differences with respect to their GO categories. On the basis of GO categories it was found that 35.7% and 18.18% of GO functions of ACAA2 gene in Bos taurus and Sus scrofa, respectively, are engaged in the regulation of metabolic processes (Table S1). However, the GO function related to lipid metabolism was absent in ACAA2 of S scrofa. Surprisingly, the experimental evidence supported by GO annotations indicated that ACAT2 from Sus scrofa has functional similarity to ACAA2 and ACAT2 from Bos taurus in terms of involvement of both genes in metabolic processes. Furthermore, the CateGOrizer analysis indicated that equal fractions (75%) of ACAT2 are involved in the metabolic processes in the two species (Table S1). In addition to functional similarities, expression profiling was also conducted to compare the level of expression within and between the species.
Validation of mRNA and protein expressions of ACAA2 and ACAT2 in the liver tissue of Sus scrofa and Bos taurus
Both RT-PCR and real-time qRT-PCR were performed to clarify the qualitative and quantitative expressions of the genes under study. Specifically, relative mRNA expression levels of ACAA2 and ACAT2 were determined after their normalisation relative to the transcript levels of an endogenous reference, GAPDH. Here, ACAA2 exhibited significantly (P<0.05) higher expression in bovine liver than was observed in JNP (Figure 1) although opposite was true for ACAT2. It has also been observed that the levels of expression of ACAT2 are not significantly different in the two species.
a) Expression of mRNA after RT-PCR using 1% agarose gel. b) Relative quantitative expression of ACAA2 and ACAT2 genes; bars with different superscripts show significantly different expression in the two species (P<0.05). c) Representative blot expressions of ACAA2, ACAT2, and β-actin proteins obtained by western blotting.
The results of the protein expression for the candidate proteins under study were obtained by western blot and are presented as relative band intensities. These intensities of the candidate proteins have been normalised to the area of intensities of β-actin bands. The results from western blot analysis are in line with the results from the relative quantification of mRNA expression levels. Whereas ACAA2 protein was expressed at a significantly (P<0.05) higher level in bovine liver than in JNP (Figure 1), but vice versa expression was observed for ACAT2.
Retrieval of SNPs and prediction of deleterious SNPs
The present study was carried out to investigate the nsSNPs in the ACAT2 gene of Sus scrofa. Five missense variants, two splice variants, and 14 synonymous SNPs were identified [21]. For further analysis, only the five nsSNPs were selected. The consequences of an amino acid substitution on protein function were estimated using the SIFT tool. It was found that the two nsSNPs, rs321479200 and rs342236888 of ACAT2 were categorised as borderline (0.101–0.20) and tolerable (0.201–1.00) on the basis of their index score i.e. 0.15 and 0.35 respectively (Table 3). In terms of the nucleotide variations, out of nsSNPs, one nsSNP involved a T>C change, another involved a T>G change, and three nsSNPs involved C>T changes.
To predict any deleterious effects, the five nsSNPs were furthermore submitted to PROVEAN. On the basis of their scores (−3.587 and −4.012, respectively), the nsSNPs, rs342236888 and rs321479200 of ACAT2 were predicted to be deleterious (Table 3). Analysis of the structural stability associated with these mutations was carried out using I-Mutant; their destabilising effects were evaluated by subtracting the unfolded Gibbs free energy value of the native protein from that of the mutated one.
Quality analysis of the stability of the homologous model
The 3D structures of the native and mutant proteins were modelled using the SWISS- MODEL expasy and ESyPred3D tools. Both tools gave the same 3D structure using the template PDB ID 1wl 4 of chain A (Figure 2). The validation by PROCHECK verified that 90% of the residues are in the most favoured region in the Ramachandran plot (Figure 3), which indicates the good quality of the model. The QMEAN-Z score (−0.51) for the query model of ACAT2 indicated the high level of accuracy of the constructed model (Figure S1). In the current study, the total free energy and RMSD values of a nsSNPs i.e. rs342236888 of ACAT2 were found to be −17.531 and 0.82 A°, respectively, whereas those of rs321479200 of ACAT2 were reported to be −16.837 and 2.63 A°. This value of 2.63 A° suggests that rs321479200 is a highly deleterious nsSNP.
A 3D structure of the native ACAT2 protein was prepared using SWISS-MODEL expasy and ESyPred3D. It depicts the native structure in 3D ribbon pattern of chain folding in ACAT2.
This plot was used for quality checking and the structural validation of the modelled ACAT2 from Sus scrofa. The red, brown, and yellow regions represent the favoured, allowed and generously allowed regions as defined by Procheck.
The stabilising residues in the native and mutant protein models were identified using the SRide tool; variations in these stabilising residues are shown in Table 4. The 13 stabilising residues, those in the native protein were compared with their counterparts in the mutants. The stability of the native ACAT2 protein was compared with its mutated homologous structures (Figure 4 and 5) showing the causative mutations i.e. I122V and R281H. It was found that nsSNP-rs321479200 (R281H) was associated with more residues having a stabilising effect, which may be important in terms of structure and function.
A 3D ribbon pattern was chosen to explain the mutation. a) The protein is coloured grey; the side chain of the residue is shown as small magenta balls. b) Close-up view of the mutation of isoleucine to valine (I>V) at position 122 in ACAT2 of Sus scrofa. The side chains of both wild-type and mutant residues are shown in green and red, respectively.
A 3D ribbon pattern was chosen to explain the mutation. a) The whole protein is presented in grey, with the side chain of the residue as small magenta balls. b) Close-up view of the mutation of arginine to histidine (R>H) at position 281 in ACAT2 of Sus scrofa. The side chains of both wild-type and mutant residues are shown in green and red, respectively.
Effect of mutation on protein function
The possible effects of mutations at specific residues on protein function were also evaluated. Interestingly, both the native residues, namely, isoleucine and arginine, of ACAT2 protein of Sus scrofa are part of an interpro domain named thiolase (IPR002155). This domain was classified into the following GO categories: 0008152 (metabolic process), 0016747 (transferring acyl groups other than amino-acyl groups), and 0016740 (transferase activity). These same residues, isoleucine and arginine, are also part of an interpro domain called thiolase-like, subgroup (IPR016038), with the GO categories of 0003824 (catalytic activity) and 0008152 (metabolic process). These residues are also part of a third interpro domain named thiolase, N-terminal (IPR020616), which plays a role in metabolic processes (GO: 0008152), transferase activity, and transferring acyl groups other than amino-acyl groups (GO: 0016747). Therefore, changes of these residues can affect functions of the protein.
The wild-type and mutant amino acids of ACAT2 protein of Sus scrofa differ in size. The mutation in a nsSNP-rs342236888 of ACAT2 at position 122, is associated with a change of residue from isoleucine to valine, thus changes the charge (Figure 4). In addition, the mutant residue is smaller than the wild-type one, which leads to empty space in the core of the protein. These differences can disturb the core structure of this important domain and thereby affect the catalytic activity of a nsSNP-rs342236888 of ACAT2.
Similarly, for a nsSNP-rs321479200 of ACAT2 at position 281, mutation of Arg to His alters the charge between the wild-type and mutant amino acids (Figure 5). Specifically, this mutation causes a loss of charge, as well as interactions with molecules different from those that interact with the wild type. The difference in size between the mutant and the wild type would also possibly cause a loss of external interactions. The residue is located on the surface of ACAT2 and is not found to be in contact with other domains with known functions. However, contacts with other molecules or domains are possible and might be affected by this mutation.
Discussion
To our knowledge, this study is the first attempt to characterise nsSNPs that are related to the metabolism and expression of ACAT2 in pig liver. Results of this study show that nsSNPs are considered to be potentially important for the functional and structural analysis of proteins. Such nsSNPs are of particular interest, given that they might provide markers of selection. These 5 nsSNPs were functionally annotated and were subjected to analysis using the SIFT and PROVEAN tools to predict their potential deleterious effects. The tolerance index of selected deleterious nsSNPs is inversely related to the functional impact of the corresponding amino acid residues [22]–[23]. rs342236888 (I122V) and rs321479200 (R281H) were selected as deleterious nsSNPs in this study (low tolerance indexes of −1.89 and −1.31, respectively). The in silico analysis indicated that misfolding and intermolecular interactions of the ACAT2 gene, can have marked effects on the structure and functions of the protein. Thiolase domain has been enlisted as an important in the key enzymatic pathways such as fatty acid, steroid and polypeptide synthesis. It has also been reported to play a role in transferase activity and metabolic process. Therefore, it can be predicted that due to the mutations, I122V and R281H, nsSNPs might have a direct influence on the metabolical functions of ACAT2 in S scarofa [24].
Moreover, the food industry has been criticized in particular for the decline in the quality of pork from lean breeds [25]. Further, it has been observed that the properties of lean meat are greatly influenced by its chemical composition. The amino acid contents of semimembranosus muscle (threonine, isoleucine, lysine, aspartic acid, serine, proline) increase with an increase in the proportion of lean meat [26]. Moreover, in our study where isoleucine mutates to valine will result in the decrease in the number of residues of isoleucine in the native form of ACAT2 of Sus scrofa. Therefore, it can be one of the contributing factors for decrease in the amount of lean meat.
GO categories have been used previously to characterise protein function and to elucidate trends in protein datasets [27]. GO terms here helped to identify biological processes that could be affected by the mutations. Our finding that ACAT2 is mainly responsible for metabolic processes involving lipid metabolism (Table S1) is supported by previous study [28]. It has been well established that both genes are involved in metabolic pathways [29] and belong to the same protein family. It has also been observed that ACAT2 is mostly involved in lipid metabolism; in the present investigation, it has shown more metabolic activity than ACAA2. Validation in the laboratory reported empirical evidence of actual biological similarities between ACAT2 and ACAA2. Later, this concept was supported by the similarities during GO annotation. Significantly higher expression of ACAT2 in the JNP liver supports the metabolic role of ACAT2 and its importance in physiological processes. In an earlier study, higher expression of ACAT2 was also linked to cholesterol levels, which are in turn reported to be linked to increased lean mass [8].
ACAT2 plays an important role in efficient cholesterol absorption, lipoprotein metabolism, and atherosclerosis [28], [30]–[31]. Cholesterol plays a direct role in inflammation. It is also believed that inflammation plays a significant role in muscle building [8]. Moreover, an earlier study has also reported significant role of cholesterol in the muscle building under the dose dependent manner [32]. Therefore, it has been concluded that cholesterol levels are dose-dependently associated with increased muscle size and strength.
ACAT2 can esterify cholesterol and oxysterols in humans and other animals [33]–[34]. Cholesterol is also essential for the formation of lipid rafts, which function in the assembly of the components of signalling pathways through sorting of proteins and construction of signalling complexes [35]. Earlier studies have reported that these signalling pathways contribute to the development of skeletal muscle [36]–[39]. Therefore, significantly high expression of ACAT2 in S scrofa during the current study indicates that it can also be involved in the high muscle growth.
In conclusion, this study demonstrates the significant application of bioinformatic tools to clarify the changes in functions associated with the metabolic processes in which ACAT2 participates. In silico analysis of the preferred nsSNPs of the ACAT2 gene that are most strongly related to metabolic activity could explain the alteration in the metabolic activity caused by the mutations. Annotation of the effects of “I to V” substitution at residue 122 and “R to H” at residue 281 of ACAT2 might be useful as selection marker for molecular diagnosis. In terms of the importance of this loss of arginine (R), previous researchers suggested its role for improving meat quality [40]. Further evidence also indicates that arginine regulates the partitioning of dietary energy in favour of muscle protein accretion and fat reduction in animals [41]. Moreover, it was found that activation of endogenous arginine synthesis increased the average daily weight gain in piglets, compared with that in a control group [40], [42].
Furthermore, our GO analysis and evaluation of mRNA and protein expression in the laboratory indicates that ACAT2 plays more important role in lipid and cholesterol metabolism in pig than bovine. Therefore, this study can be used as a stepping stone for planning future studies on the role of nsSNPs in metabolic processes, especially in pigs. The associations of SNPs in ACAT2 with fertility and production traits can be used to plan studies for the improvement of growth performance and fertility traits in Jeju native pigs.
Supporting Information
Figure S1.
QMEAN-Z score of the model for ACAT2 of Sus scrofa. The red legend indicates the QMEAN-Z score.
https://doi.org/10.1371/journal.pone.0102432.s001
(TIF)
Table S1.
Gene ontology analysis suggests the biological processes associated with functioning of the ACAA2 and ACAT2 genes in Sus scrofa and Bos taurus.
https://doi.org/10.1371/journal.pone.0102432.s002
(DOCX)
Author Contributions
Conceived and designed the experiments: SSS DKJ KDS NS JHK NEK. Performed the experiments: SSS MG. Analyzed the data: SSS MG DKJ. Contributed reagents/materials/analysis tools: DKJ SJL CWK SJO. Wrote the paper: SSS MG DKJ.
References
- 1. Anderson RA, Joyce C, Davis M, Reagan JW, Clark M, et al. (1998) Identification of a form of acyl- CoA:cholesterol acyltransferase specific to liver and intestine in nonhuman primates. J Biol Chem 273: 26747–26754.
- 2. Joyce CW, Shelness GS, Davis MA, Lee RG, Skinner K, et al. (2000) ACAT1 and ACAT2 membrane topology segregates a serine residue essential for activity to opposite sides of the endoplasmic reticulum membrane. Mol Biol Cell 11: 3675–3687.
- 3. Baik M, Etchebarne BE, Bong J, VandeHaar MJ (2009) gene expression profiling of liver and mammary tissues of lactating dairy cows. Asian-Aust J Anim Sci 22: 871–884.
- 4. Parini P, Davis M, Lada AT, Erickson SK, Wright TL, et al. (2004) ACAT2 is localized to hepatocytes and is the major cholesterol-esterifying enzyme in human liver. Circulation 110: 2017–2023.
- 5.
Wang X, Carré W, Rejto L, Cogburn LA (2005) Transcriptional profiling in liver of hormonally-manipulated chickens. In: Dawson A, Sharp PJ, editors. Functional avian endocrinology. Narosa Publishing House, New Delhi, India.
- 6. Ushio H, Nagasaka R (2013) Utilization of biological responses of fish and shellfish for improving seafood qualities. Aqua Bio Sci Mono 6: 91–98.
- 7. Riechman SE, Andrews RD, Maclean DA, Sheather S (2007) Statins and dietary and serum cholesterol are associated with increased lean mass following resistance training. J Gerontol A Biol Sci Med Sci 62: 1164–1171.
- 8. Jiang Z, Michal JJ, Chen J, Daniels TF, Kunej T, et al. (2009) Discovery of novel genetic networks associated with 19 economically important traits in beef cattle. Int J Biol Sci 5: 528–542.
- 9. Guryev V, Eugene B, Malik R, Ronald H, Plasterk A, et al. (2004) Single nucleotide polymorphisms associated with rat expressed sequences. Genome Res 14: 1438–1443.
- 10. Wong GK, Liu B, Wang J, Zhang Y, Yang X, et al. (2004) International chicken polymorphism map consortium. A genetic variation map for chicken with 2.8 million single-nucleotide polymorphisms. Nature 432: 717–722.
- 11. Zimdahl H, Nyakatura G, Brandt P, Schulz H, Ummel O, et al. (2004) A SNP map of the rat genome generated from cDNA sequences. Science 303: 807.
- 12.
Li HD (2008) I: Segregation, Cloning, SNP Detection and Its Association with Economic Traits of 5 Bovine Genes II: Quantitative Trait Loci Analysis of Meat Quality Traits in Pigs. Ph.D. thesis. Dissertation ID: 1647914. Available: http://www.dissertationtopic.net/doc/1647914.
- 13. Orford M, Hadjipavlou G, Tzamaloukas O, Chatziplis D, Koumas A, et al. (2012) A single nucleotide polymorphism in the acetyl-coenzyme A acyltransferase 2 (ACAA2) gene is associated with milk yield in Chios sheep. J Dairy Sci 95: 3419–3427.
- 14. Cochran SD, Cole JB, Null DJ, Peter JH (2013) Discovery of single nucleotide polymorphisms in candidate genes associated with fertility and production traits in Holstein cattle. BMC Genet 14: 49.
- 15. Hu Zhi-Liang BJ, Reecy James M (2008) CateGOrizer: A Web-Based Program to Batch Analyze Gene Ontology Classification Categories. Online J Bioinform© 9: 108–112.
- 16. Rozen S, Skaletsky H (2000) Primer3 on the www for general users and for biologist programmers. Methods Mol Biol 132: 365–386.
- 17. Wang MC, Bohmann D, Jasper H (2003) JNK signalling confers tolerance to oxidative stress and extends lifespan in Drosophila. Developmental Cell 5: 811–816.
- 18. Erkens T, Van Poucke M, Vandesompele J, Goossens K, Van Zeveren A, et al. (2006) Development of a new set of reference genes for normalization of real-time RT-PCR data of porcine backfat and longissimus dorsi muscle, and evaluation with PPARGC1A. BMC Biotechnol 6: 41.
- 19. Van Poucke M, Yerle M, Tuggle C, Piumi F, Genet C, et al. (2001) Integration of porcine chromosome 13 maps. Cytogenet Cell Genet 93: 297–303.
- 20. Hussain MRM, Shaik NA, Yousuf Al-Aama J, Asfour HZ, Khan FS, et al. (2012) In silico analysis of single nucleotide polymorphisms (SNPs) in human BRAF gene. Gene 508: 188–196.
- 21.
Xuelian HE (2007) Studies of common variations in two candidate genes of dyslipidemia and coronary artery disease. Ph.D. thesis, National University of Singapore.
- 22. Ng PC, Henikoff S (2001) Predicting deleterious amino acid substitutions. Genome Res 11: 863–874.
- 23. Xi T, Jones IM, Mohrenweiser HW (2004) Many amino acid substitution variants identified in DNA repair genes during human population screenings are predicted to impact protein function. Genomics 83: 970–979.
- 24. Panitz F, Stengaard H, Hornshoj H, Gorodkin J, Hedegaard J, et al. (2007) SNP mining porcine ESTs with MAVIANT, a novel tool for SNP evaluation and annotation. Bioinformatics 23: 387–391.
- 25.
Wood JD (2001) Meat quality and the designer pig. In: Lyons TP, Coles DJA, editors. Concepts of meat science. Nottingham Nutrition International, Nottingham. pp. 17–31.
- 26. Okrouhla M, Stupka R, Citek J, Sprysl M, Trnka M, et al. (2008) Effect of lean meat proportion on the chemical composition of pork. Czech J Food Sci 26: 464–469.
- 27. Agarwal SM, Raghav D, Singh H, Raghava GPS (2010) CCDB: a curated database of genes involved in cervix cancer. Nucleic Acids Res 39: D975–979.
- 28. Nguyen TM, Sawyer JK, Kelley KL, Davis MA, Kent CR, et al. (2012) ACAT2 and ABCG5/G8 are both required for efficient cholesterol absorption in mice: evidence from thoracic lymph duct cannulation. J Lipid Res 53: 1598–1609.
- 29. Cao W, Liu N, Tang S, Bao L, Shen L, et al. (2008) Acetyl-Coenzyme A acyltransferase 2 attenuates the apoptotic effects of BNIP3 in two human cell lines. Biochimica et Biophysica Acta 1780: 873–880.
- 30. Kushwaha RS, Rosillo A, Rodriguez R, McGill HC Jr (2005) Expression levels of ACAT1 and ACAT2 genes in the liver and intestine of baboons with high and low lipemic responses to dietary lipids. J Nutr Biochem 16: 714–721.
- 31. Alger HM, Brown JM, Sawyer JK, Kelley KL, Shah R, et al. (2010) Inhibition of acyl-Coenzyme A: Cholesterol acyltransferase 2 (ACAT2) prevents dietary cholesterol-associated steatosis by enhancing hepatic triglyceride mobilization. J Biol Chem 285: 14267–14274.
- 32. Riechman SE, Andrews RD, MacLean DA, Sheather S (2007) Statins and dietary and serum cholesterol are associated with increased lean mass following resistance training. J Gerontolo A Biol Sci Med Sci 62: 1164–1171.
- 33. Osada K, Sasaki E, Sugano M (1994) Lymphatic absorption of oxidized cholesterol in rats. Lipids 29: 555–559.
- 34. Cases S, Novak S, Zheng YW, Myers HM, Lear SR, et al. (1998) ACAT-2, a second mammalian acyl-CoA:cholesterol acyltransferase. J Biol Chem 273: 26755–26764.
- 35. Simons K, Toomre D (2000) Lipid rafts and signal transduction. Nat Rev Mol Cell Biol 1: 31–39.
- 36. Smythe GM, Eby JC, Disatnik MH, Rando TA (2003) A caveolin-3 mutant that causes limb girdle muscular dystrophy type 1C disrupts Src localization and activity and induces apoptosis in skeletal myotubes. J Cell Sci 116: 4739–4749.
- 37. Lucero HA, Robbins PW (2004) Lipid rafts-protein association and the regulation of protein activity. Arch Biochem Biophys 426: 208–224.
- 38. Freeman MR, Solomon KR (2004) Cholesterol and prostate cancer. J Cell Biochem 91: 54–69.
- 39. Bellot AC, Patel KC, Burkholder TJ (2005) Reduction of caveolin-3 expression does not inhibit stretch induced phosphorylation of ERK2 in skeletal muscle myotubes. J Appl Physiol 98: 1554–1561.
- 40. Ma X, Lin Y, Jiang Z, Zheng C, Zhou G, et al. (2010) Dietary arginine supplementation enhances antioxidative capacity and improves meat quality of finishing pigs. Amino Acids 38: 95–102.
- 41. Wu G, Bazer FW, Davis TA, Kim SW, Li P, et al. (2008) Arginine metabolism and nutrition in growth, health and disease. Amino Acids 37: 153–168.
- 42. Frank JW, Escobar J, Nguyen HV, Jobgen SC, Jobgen WS, et al. (2007) Oral N carbamylglutamate supplementation increase protein synthesis in skeletal muscle of piglets. J Nutr 137: 315–319.