Biosci. Biotechnol. Biochem., 75 (3), 451–458, 2011

Differences in Hepatic Expression as a Major Distinguishing Factor between Korean Native Pig and Yorkshire

Seung-Soo KIM,1 So-Ra KIM,1 Jung-Rok KIM,1 Jin-Kyoo MOON,1 Bong-Hwan CHOI,2 y Jae-Won LEE,3 Kwan-Suk KIM,4 Tae-Hun KIM,2 Hyun-Jung KIM,5 and Cheol-Koo LEE1;

1College of Life Sciences and Biotechnology, Korea University, Seoul 136-701, Korea 2Division of Animal Genomics and Bioinformatics, National Institute of Animal Science, Rural Development Administration, Omokchun-dong 564, Kwonsun-gu 441-706, Korea 3Department of Statistics, College of Political Science and Economics, Korea University, Seoul 136-701, Korea 4Department of Animal Science, Chungbuk National University, Cheongju 361-763, Korea 5College of Pharmacy, Chung-Ang University, Seoul 156-756, Korea

Received August 26, 2010; Accepted November 25, 2010; Online Publication, March 7, 2011 [doi:10.1271/bbb.100625]

There are phenotypic differences between Korean physiology to humans.7) There are diverse pig breeds native pig (KNP) and Yorkshire (YS) breeds due to worldwide generated by the demarcation of continents different interests in selection. YS has been selected and regions, including Korean pigs (KNPs from Jeju for industrial interests such as a growth rate and lean island and Seonghwan, on the mainland), Chinese pigs meat production, while KNP has been maintained as a (for example Min pig, Xiang pig, and Wuzhishan pig) regional breed with local interests such as disease and European pigs (for example Berkshire, Duroc, resistance and fat content in and between muscle. A Landrace, and YS). These pig breeds have diverse comparison of profiles from liver tissue genetic features and phenotypic traits that provide rich reflected overall long-term effects of artificial selection resources for animal genetics. for these two pig breeds. Based on minimum positive DNA microarrays are used as a tool in genetic false discovery rate (less than 10%) and fold change research for gene expression and genetic variation. The (jFCj > 1:5), 73 differentially expressed (DEGs) porcine genome sequence consortium has completely were identified. Functional analysis of these DEGs sequenced the whole genome, and the first draft is about indicated clear distinctions in signaling capacity related to be released. Currently (August 2010), the NCBI map to epidermal growth factor (EGF), extracellular struc- viewer provides ordered sequence information for nine ture, metabolism, and detoxification. Hepatic of 18 autosomes, including 1, 4, 5, 7, 11, 13, 14, 15, and DEGs demonstrated the importance of hormonal and 17, and the X . The release of the porcine metabolic capabilities to differences between these two genome sequence should accelerate new genetic dis- pig breeds. coveries and the usability of single nucleotide poly- morphisms (SNPs). In particular, for aspects of func- Key words: gene expression profile; Korean native pig; tional genomics, the transcriptome database should be Yorkshire; liver; differentially expressed important and useful in annotating the pig genome.8) gene We have demonstrated the usefulness of transcrip- tome data by searching for DEGs that characterize the The liver is the largest organ in a mammalian system. differences among pig breeds in adipose tissue and It plays an important role in regulating overall body skeletal muscle.9,10) Functional analysis of DEGs in fat growth by secreting insulin-like growth factor 1 (IGF1), tissue from KNP and YS suggested that there was a produced in response to growth hormone secreted from difference in xenobiotic metabolism.10) DEGs in this the pituitary gland. It is also the center for energy functional category were significantly down-regulated in metabolism of major nutrients, including carbohydrates, KNP, and suggests that the difference plays an important lipids, and ,1) and for the elimination of toxic role in overall meat flavor. In addition, we found a chemicals from the body.2) The liver also can regenerate significantly higher content of arachidonic acid and a after hepatic damage by inducing active cell prolifer- lower expression level for cytochrome P450 genes in the ation.3,4) Because of these diverse and active functions, adipose tissue of KNP as compared to YS.11) In the hepatocytes have large number of mitochondria and free skeletal muscle, substantial numbers of DEGs were ribosomes.5) related to proliferation and differentiation. We did not Pigs (Sus scrofa) are used as a research model for find any clear direction of changes in these functions, animal genetics because of their large number of but most proliferation-related DEGs were directly or offspring, relatively short generation interval, and cost- indirectly associated with p53.9) To add to these results, effective population maintenance and disease control.6) we investigated DEGs in the liver tissue. The overall They are also used as a biomedical research model gene expression profiles from KNP and YS showed a because of their similarity in organ size, structure, and clear difference between the two breeds.

y To whom correspondence should be addressed. Tel: +82-2-3290-3008; Fax: +82-2-921-1715; E-mail: [email protected] 452 S.-S. KIM et al. Materials and Methods Statistical analysis and identification of DEGs. To obtain statisti- cally significant DEGs between the two breeds, the signals were Animals and the collection of animal tissues. The KNP and YS transformed into a log2 scale to stabilize data variation. To reduce boars used in this experiment were raised under identical conditions of array variation further, the signals were processed by quantile 12) feeding and management in the National Livestock Research Institute, normalization. The signals between the two groups were compared Jeju island, South Korea. When the animals reached market weight by t-test, and the p values were corrected by the minimum positive 13) (89–100 kg for 7 month-old KNPs and 100–110 kg for 6 month-old YS false discovery rate (minimum pFDR ¼ q value). All statistical pigs), eight pigs (four of each breed) were slaughtered on the same day. analyses were done using the Partek Genomics Suite (Partek, USA). To minimize unnecessary variation, we stopped feeding for 24 h before When the transcripts were tested to within less than 10% of the q value slaughter and transported them using the same vehicle. In addition, the error rate and more than 1.5 fold of the difference, they were defined as liver tissue was taken from the end of the middle lobe of all eight pigs. DEGs. In the gene annotation and functional studies, we used the Tissue samples were immediately frozen in liquid nitrogen and stored information from Affymetrix Porcine Annotation Revision 5 (http: 14) at 80 C until preparation of total RNA. All the animals were fed //www4.ncsu.edu/~stsai2/annotation/) for the gene names, and used identically depending on their developmental status. All the diets were GeneCards and PubMed to search for the functions of the genes. purchased from the Nonghyup Feed Inc. (Seoul, Korea). We fed newly born piglets TrueMilk (brand name) for 3 weeks. Then we changed Quantitative real-time polymerase chain reaction (qPCR). To the diet to TrueMill #1 and TrueMill #2 in sequence for 4 weeks. estimate the real differences in DEGs from the microarrays, we After that, we fed the pigs with MyungPum Plus Jutdon for 1.5 months, independently performed real time PCR with a rotary-type real-time then changed to MyungPum Plus Yukseongdon until slaughter. analyzer, Rotor-Gene 6000 (Corbett Research, Australia). The genes TrueMill #1, #2, and MyungPum Plus Jutdon contained antibiotic we tested included all 15 DEGs in a structural category with a mixtures for pathogens. However, the final diet, MyungPum Plus reference gene. All primer sets were designed using sequence Yukseongdon did not contain any antibiotics. The company-made diets information from the NetAffx Analysis Center (http://www.affymetrix. were based on the National Research Council (NRC) standard of the com/analysis/index.affx) using the Primerquest program (http://www. United States. idtdna.com/Scitools/Applications/Primerquest/). Briefly, to obtain the cDNA for qPCR, 25 mg of total RNA was reverse-transcribed. For Ribonucleic acid extraction, target preparation, and DNA micro- cDNA synthesis, total RNA was mixed with 12.5 mg anchored array hybridization. Total RNA was extracted from 1 mg of liver tissue Oligo(dT)20 primer (Invitrogen, USA) and 5 mLof10mM dNTP mix using 2–3 mL of TRIzol Reagent (Invitrogen, USA) by homogeni- (Invitrogen, USA), then incubated at 65 C for 5 min. After the zation with a Power Gen 125 S1 (Fisher Scientific, USA). RNA purity reaction, the tubes were placed on ice. While still on ice, 20 mLof5X M was measured about 1.5 as an A260/A280 ratio by UV spectropho- First-Strand Buffer (Invitrogen, USA) and 10 mL of 0.1 DTT tometer (Eppendorff, Germany). The integrity of the total RNA was (Invitrogen, USA) were added, and the reaction was incubated at checked by electrophoresis on a 1% agarose gel containing form- 42 C for 2 min. Inside the PCR machine, 1,000 units of SuperScript aldehyde. Ten mg of total RNA after DNase I (Invitrogen, USA) II Reverse Transcriptase (Invitrogen, USA) was added, and the mixture treatment was converted into double-stranded cDNAs using a was further incubated at 42 C for 50 min. Then the reaction was GeneChip Expression 30-Amplification One Cycle cDNA Synthesis inactivated at 70 C for 15 min. Using this cDNA solution, qPCR was Kit (Affymetrix, USA), and cDNA was purified with the GeneChip done in a PCR tube at 25 mL total volume containing 5 mM forward and Sample Cleanup Module (Affymetrix, USA). Biotin-labeled antisense reverse primers, and 12.5 mL of 2X SensiMixPlus SYBR (2X mix cRNA was synthesized from the purified cDNA through in vitro containing reaction buffer, heat-activated DNA polymerase, dNTPs, transcription (IVT) using GeneChip Expression 30-Amplification 6mM MgCI2, internal reference dye, stabilizers, and SYBR Green I) Reagents for IVT Labeling Kit (Affymetrix, USA). It was further following the manufacturer’s protocol (Quantace, UK). The qPCR purified and fragmented using the reagents in the GeneChip Sample cycle was set at an initial activation step of 95 C for 10 min, a Cleanup Module, and the fragmented cRNA was hybridized onto the denaturation step at 95 C for 15 s, an annealing step at the average of GeneChip Porcine Genome Array (Affymetrix, USA) for 16 h. To melting temperatures of the primers for 30 s, and an extension step at remove extra cRNA fragments and prevent non-specific hybridization, 72 C for 30 s (66 base pairs/s). After 36 cycles, a final extension was non-stringent and stringent washing steps were done using GeneChip done at 72 C for 1 min. All qPCR reactions for each gene were Fluidics Station 450 (Affymetrix, USA). Finally, the gene chip was technically replicated 3 times. Final PCR products were confirmed to stained with streptavidin-phycoerythrin (Invitrogen, USA). show only one PCR product by 2% agarose gel electrophoresis with ethidium bromide staining. Microarray scanning and image analysis. Microarray images were obtained with the GeneChip Scanner 3000 (Affymetrix, USA) and Results were analyzed with GeneChip Operating Software 1.3 (Affymetrix, USA). Transcripts were detected by probe sets composed of 11 probe Breed-specific clustering of hepatic transcriptomes pairs, and each probe pair consisted of a perfect match (PM) and a and selection of hepatic DEGs mismatch (MM) probe. The PM probe detects the matched RNA fragment from the target mRNA, whereas the MM probe estimates Hepatic transcriptomes were obtained from livers of non-specific signals, because the MM probe has a mismatch nucleotide KNP and YS Breeds. Hierarchical clustering analysis at the middle position of the sequence of the PM probe. Both the PM using entire array signals showed a clear distinction and MM signals were used to compute a discrimination score between breeds in the liver (Fig. 1). To study major ([PMMM]/[PMþMM]) to determine whether the transcript was effects of the difference in gene expression between the detected by the probe set as present (P) or absent (A). The weighted breeds, we selected hepatic DEGs by q value (less than mean value using the signals of the probe pairs in a probe set was 10% of minimum positive false discovery rate), fold determined by Tukey’s one-step bi-weight method. The total signal of the probe sets in each array was normalized by global scaling. To difference (more than 1.5), and detection call (P at all compare the difference in a transcript between two arrays, the GCOS samples in a group). After these rigorous selection matches each probe pair and calculates 11 ratios in log2 scale. Again, methods, 73 hepatic DEGs were collected for further the weighted mean difference value was determined from these 11 functional analysis (Supplemental Table 1, see Biosci. ratios by Tukey’s one-step bi-weight method. To implement this Biotechnol. Biochem. Web site). algorithm to compute the fold difference (KNP/YS ratio) between breeds, 16 pairs-wise comparisons were conducted between four KNP Analysis of hepatic DEGs: overview and YS microarray images. The data discussed here have been deposited in the NCBI Gene Expression Omnibus (GEO, http: To characterize the major functional categories of the //www.ncbi.nlm.nih.gov/geo/) and are accessible through GEO Series 73 hepatic DEGs, we calculated the enrichment score accession no. GSE21408. and the false discovery rate (FDR) of the Hepatic DEGs as between Korean Native Pig and Yorkshire 453

ECM related genes

COMMD1 COL4A4 COL4A3 UBE2M CAST PTBP2 C8G

PSMB5 TBN 3 CASP3 BCAR3 FCER1G MAN1A2 RDH16 ETHE1 UBC1

TAF10 DBF4 EGF 2 EGFR SHC1 RPS28 PRKAR2A HBA1 SELP KNP2 KNP1 KNP4 KNP3 YS2 YS1 YS4 YS3

Fig. 1. Dendrogram of Cluster Analysis Using Quantile-Normalized CLDN16 AKAP11 IGF2 ERBB2IP AP2A2 MAPK6 FXR1 ATP11B

Entire Microarray Data Including All Probe Sets in the Liver. Hormonal and signaling genes The results of hierarchical clustering analysis showed clear separation between the KNP and the YS breed. Cluster analysis was Fig. 2. Functional and Physical Interaction of Proteins Encoded in 73 performed using the open source program Gene Cluster and Java DEGs Using STRING 8.3 Program. TreeView, available at http://sourceforge.net/projects/jtreeview/. Black and white indicate up- and downregulation of mRNA The correlation was used as a similarity measurement and the expression respectively in microarray results. Gray indicates no average linkage was used as a clustering method. alteration. The full names of the gray genes are 1Ubiquitin C, 2Epidermal growth factor, and 3TBP-associated factor 8.

Table 1. GO Analysis with 73 DEGs

GO term Enrichment score FDR Gene Biological process No significant terms Cellular component GO:0005587 collagen type IV 65.4 0.020 COL4A3, COL4A4 GO:0030935 sheet-forming collagen 54.5 0.025 COL4A3, COL4A4 GO:0030122 AP-2 adaptor complex 65.4 0.020 EGFR, AP2A2 GO:0030128 clathrin coat of endocytic vesicle 65.4 0.020 EGFR, AP2A2 GO:0030669 clathrin-coated endocytic vesicle membrane 46.7 0.034 EGFR, AP2A2 GO:0045334 clathrin-coated endocytic vesicle 32.7 0.039 EGFR, AP2A2 GO:0030666 endocytic vesicle membrane 25.1 0.048 EGFR, AP2A2 GO:0030132 clathrin coat of coated pit 36.3 0.048 EGFR, AP2A2 GO:0005905 coated pit 12.9 0.043 EGFR, AP2A2, RAMP2 Molecular function No significant terms

(GO) terms using the GoMiner program.15) In this The differences in this EGF hormonal signaling might analysis, we did not find any significant terms in the be a major factor responsible for the different characters biological process or the molecular function, but in the of KNP and YS. cellular component, there were nine significant terms In addition, we found up regulation of a growth factor (FDR < 5%) (Table 1). The selected terms showed the that is primarily produced by the liver, Insulin-like importance of the coated-pit structure. We also con- growth factor 2 (IGF2) in KNP.17) In addition, there structed a possible protein network map based on were two up regulated genes related to hormone functional and physical interactions of hepatic DEGs transport. One was Solute carrier family 16, member using the STRING 8.3 program.16) In this map, we found 10 (SLC16A10), which encodes a transporter of thyroid three major linked groups, including growth hormone/ hormone that eventually stimulates production of bile receptor signaling, proteins involved in or regulated acid.18) The other was Receptor (G protein-coupled) by ubiquitination, and extracellular matrix (ECM) activity modifying protein 2 (RAMP2), encoding a components (Fig. 2). protein that transports calcitonin-receptor-like receptor (CRLR) to plasma membrane. RAMP2, associated with Up regulation of genes involved in EGF signaling and CRLR, becomes a functional adrenomedullin (AM) other hormonal activities in KNP receptor, and AM expands blood vessels, reduces Hepatocytes secret growth hormones and contain oxidative stress, and inhibits .19) receptors for growth hormones secreted from other endocrine tissues. The DEGs contained several genes Concerted down regulation of genes for ECM and cell related to hormonal activity (Table 2). These DEGs adhesion molecules in KNP were up regulated except for one. We found that six We also observed concerted down regulation of the genes were related to EGF internalization and signaling. DEGs related to ECM and cell adhesion (Table 3). The They included EGF receptor (EGFR), SHC (Src ECM genes encode collagen and enzymes for the homology 2 domain containing) transforming protein 1 synthesis of ECM components. In addition, there was (SHC1), Erbb2 interacting protein (ERBB2IP), RAB4B, a DEG responsible for overall ECM production, Retinol member RAS oncogene family (RAB4B), Breast cancer dehydrogenase 16 (RDH16). This gene encodes an anti-estrogen resistance 3 (BCAR3), and Adaptor- enzyme involved in the rate-limiting step of retinoic acid related protein complex 2, alpha 2 subunit (AP2A2). synthesis from retinol.20) Retinol is known to induce 454 S.-S. KIM et al. Table 2. DEGs for Hormonal Activity

FC Probe set ID Gene title Symbol E value p value Role EGFR related 2.1 Ssc.7633.2.A1 at Erbb2 interacting protein ERBB2IP 0 0.00108 EGFR signaling22) 2.0 Ssc.4725.2.S1 at Adaptor-related protein AP2A2 7:0 10113 0.00184 EGFR endocytosis23) complex 2, alpha 2 subunit 2.0 Ssc.6545.1.S1 a at RAB4B, member RAS RAB4B 0 0.00178 EGFR endocytosis24) oncogene family 1.5 Ssc.55.1.S1 at Epidermal growth factor EGFR 0 0.00092 EGF receptor receptor 1.5 Ssc.5093.1.A1 at SHC (Src homology 2 SHC1 0 0.00037 Adapter protein domain containing) for EGFR transforming protein 1 phosphorylation25) 1:6 Ssc.27464.1.A1 at Breast cancer anti-estrogen BCAR3 2:4 1010 0.00131 EGFR signaling26) resistance 3

Other hormonal activity 2.2 Ssc.26633.1.A1 at Solute carrier family 16, SLC16A1 0.016 0.00040 Thyroid hormone member 10 (aromatic 0 transporter18) amino acid transporter) 2.1 Ssc.15730.1.S1 at Receptor (G protein-coupled) RAMP2 1:6 1094 0.00011 Transporting CRLR to activity modifying protein 2 plasma membrane19) 1.8 Ssc.9365.3.S1 x at Insulin-like growth factor 2 IGF2 4:0 10128 0.00128 Insulin-like growth factor 1.8 Ssc.9365.5.S1 a at Insulin-like growth factor 2 IGF2 4:0 10128 0.00128 Insulin-like growth factor

E value indicates significance of between pig and human. p value was computed by t test between KNP and YS.

Table 3. DEGs for Structure

FC Probe set ID Gene title Symbol E value p value Role ECM 3.4 Ssc.22641.3.S1 at Retinol dehydrogenase 16 RDH16 6:0 10167 0.00033 Retinoic acid biosynthesis20) (all-trans) 2:7 Ssc.21728.1.S1 at Mannosyl-oligosaccharide MAN1A2 0.799 0.00150 N-glycan processing27) 1,2-alpha-mannosidase IB 1:7 Ssc.29202.1.A1 at Collagen, type IV, alpha 4 COL4A4 3:8 1013 0.00020 Collagen protein 1:6 Ssc.16475.1.S1 at Collagen, type IV, alpha 3 COL4A3 0.17 0.00129 Collagen protein (Goodpasture antigen)

Cell adhesion 2:1 Ssc.24952.1.S1 at Claudin 16 CLDN16 5:6 1006 0.00135 Tight junction component 2:0 Ssc.27303.1.A1 at Selectin P (granule SELP 0.733 0.00120 Cell adhesion28) membrane protein 140 kDa, antigen CD62)

ECM synthesis in endothelial cells,20) and a reduction in DEGs, Ubiquitin-conjugating enzyme E2R2 (UBE2R2), retinol due to up regulation of RDH16 in KNP might Ubiquitin-conjugating enzyme E2M (UBC12 homolog, further decrease the production of ECM components. yeast) (UBE2M), and F-box protein 31 (FBXO31) were Cell adhesion-related DEGs included Claudin 16 involved in ubiquitin-dependent proteolysis. There were (CLDN16), encoding a tight junction component, and four genes encoding enzymes for detoxification. All of Selectin P (SELP), encoding a protein that binds to these genes were up regulated in KNP (Table 4). heparin. These genes were all down regulated in KNP. Validation of DEGs by qPCR DEGs responsible for various pathways in protein To validate independently the microarray results, we metabolism and detoxification performed qPCR analysis for some DEGs (Table 5). The We found that several DEGs directly or indirectly 40 S ribosomal protein S3 (RPS3) gene was included as work on the mRNA molecules for protein synthesis a reference. The individual p values for the qPCR results (Table 4). Indirectly related DEGs, up regulated in indicated significant differences between the two breeds. KNP, included Nucleoporin 88 kDa (NUP88), encoding The qPCR results showed a strong positive correlation a nuclear pore component for RNA transport, and with the array results in trend (Fig. 3). Polymerase (RNA) I polypeptide D, 16 kDa (POLR1D), encoding an enzyme for rRNA production. DEGs Discussion directly involved in translation, which encode ribosomal proteins, were down regulated in KNP. We also found The Western pig industry was developed through several genes responsible for protein degradation, which artificial selection among native pig breeds for fast were up regulated in KNP (Table 4). Among these, three growth and lean meat production. Especially in Asian Hepatic DEGs as between Korean Native Pig and Yorkshire 455 Table 4. DEGs for Protein Metabolism and Detoxification

FC Probe set ID Gene title Symbol E value p value Role Protein synthesis 1.7 Ssc.946.1.S1 at Nucleoporin 88 kDa NUP88 6:6 1048 0.00125 Nuclear pore component for RNA transport 1.6 Ssc.2836.1.S1 at Polymerase (RNA) I POLR1D 0 0.00163 Ribosomal RNA synthesis polypeptide D, 16 kDa 1:8 Ssc.947.1.S1 at Similar to 40S ribosomal RPS28 6:9 1061 0.00006 Ribosomal protein small protein S28 40s subunit 28 1:6 Ssc.3747.1.A1 x at TBC1 domain family, TBC1D8 1:0 10144 0.00154 Ribosomal protein large member 8 60s subunit 31 1:6 Ssc.6624.1.S1 a at mRNA turnover 4 homolog MRT4 0 0.00113 Low smilariy to Ribosomal protein large subunit P0

Protein degradation 2.5 Ssc.14281.2.S1 at Ubiquitin-conjugating UBE2R2 1:0 10115 0.00123 E2 ubiquitin conjugating enzyme E2R 2 enzyme29) 2.0 Ssc.235.2.S1 at Calpastatin CAST 0 0.00085 Calpain inhibitor30) 1.6 Ssc.1498.1.S1 at Proteasome (prosome, PSMB5 0 0.00177 Proteasome 20S core beta macropain) subunit, subunit beta type, 5 1.5 Ssc.16974.2.S1 a at Ubiquitin-conjugating UBE2M 0 0.00189 E2 ubiquitin conjugating enzyme E2M enzyme31) (UBC12 homolog, yeast) 1.5 Ssc.18487.3.S1 at F-box protein 31 FBXO31 1:8 1019 0.00043 E3 ubiguitin ligase complex SCF32)

Detoxification 89 2.9 Ssc.12809.5.A1 at Hemoglobin, alpha 1/2 HBA1/ 3:0 10 0.00002 Storing O2 to maintain the HBA2 enzymatic activity of DAO33) 2.4 Ssc.232.1.S1 at D-amino-acid oxidase DAO 0 0.00182 Elimination of D-amino acids34) 1.9 Ssc.3261.1.S1 at Copper metabolism COMMD1 0 0.00121 Sequestration of over- (Murr1) domain abundant copper ions35) containing 1 1.8 Ssc.16162.1.S1 at 4-hydroxyphenylpyruvate HPD 0 0.00006 Prevention of toxic tyrosine dioxygenase accumulation36)

4 ** ** * * * * * 2 *

0 PYHIN1 COL4A3 BCAR3 33 kDa protein ATP8B1 COL4A4 YIPF6 CNBD1 ESRRG DIP ATP11B SELP CLDN16 MAN1A2 KCNK10 HPD BBX DAO RPS3 EGFR RDH16 AP2A2 FUBP1 PCBP2 **

-2 * * * * FC * * ** -4 ** ** ** ** * -6

Microarray ** -8 Real-time PCR

r = 0.85 **

-10

Fig. 3. Comparison of Fold Differences between qPCR and Microarray Results. Pearson correlation analysis of fold changes between the two methods showed a strong correlation (r = Pearson correlation coefficient). Twenty-three genes on qPCR analysis showed significant changes between two breeds (p value, < 0:05 and < 0:01). The RPS3 gene was used as a reference, because it is consistently expressed in both breeds. countries, the industry has focused not only on fast selected to have genetic characteristics for fast growth growth, but also on fat accumulation in meat, due to and lean meat, while the KNP breed, isolated on Jeju different taste preferences. Therefore, comparing West- Island, has been artificially selected to maintain proper ern pig breeds with Asian innate breeds is good way to fat content in lieu of growth rate. Therefore, the DEGs understand the mechanisms underlying lean meat pro- should provide information about these artificially fixed duction and fat accumulation. The YS breed was genetic characteristics. 456 S.-S. KIM et al. Table 5. Primers for qPCR and Results

Primer Product size Average Ct SD Symbol FC p value Forward/Reverse Tm (C) (bp) KNP YS Cell adhesion CLDN16 ATGGCAGTTGTAGCTAAACCAGGG/ 58.9/58.3 108 22:2 0:75 20:4 0:45 3:7 0.0077 GAGAGAGCACCTGAACTGGTGAAT SELP AAGTCACGTCATCGCCTACATCCT/ 59.6/59.4 103 20:3 0:67 19:0 0:20 2:5 0.0261 TGAAACCACACTGGAGTCCCTGTA Detoxification DAO AGAGGTCTTGATTGACCTGTGCCA/ 59.9/59.2 90 14:5 0:81 16:1 0:55 2.9 0.0230 GCAGATGCCTTTATGAAGAGCCCA HPD ATGAGGAGTCCATCAAGATGCCCA/ 59.7/60.1 179 17:9 0:56 19:1 0:43 2.3 0.0161 AACAGCCAAGAACTCCACACCTCT ECM RDH16 ATGGAGACGCATTCGTGGCTAACT/ 60.2/58.6 80 22:4 0:36 24:1 0:63 3.1 0.0068 CACCAAAGACAGATTCCCAGACCA COL4A3 TGTATACAGTTGCAAGTCTGGGC/ 56.9/60.5 113 25:9 0:24 25:3 0:25 1:6 0.0099 TTGCCAAATGCACAGATGCACAGG COL4A4 TTTGTGTTCCTTTGTGACGGGCTG/ 59.9/60.1 121 19:4 0:67 17:1 0:57 5:0 0.0019 AAATGCAGGTGAGCCATACAACGC MAN1A2 TCTCTAACCCACTGAGCAAAGCCA/ 59.9/59.6 96 15:8 0:67 13:5 0:51 5:1 0.0018 TGTAGGGAGTTCCTGTTGTGGTGT EGFR related EGFR CAGCATGCATACCAGCAGCCTTTA/ 59.6/60.0 149 13:9 0:56 15:1 0:31 2.3 0.0159 TTTCAAGAGCAGCTTCCGTTTCGC AP2A2 AATTGGACTGAAGTCCGAGTTTCGGC/ 60.8/60.4 81 16:3 0:49 17:3 0:67 2.1 0.0451 AGGAACTGCGTGGAGGTCTTGTTT BCAR3 AGCCAGCTGACTTTGGGATAGGAT/ 59.7/60.2 175 23:6 0:75 20:6 0:77 8:5 0.0012 ATGTGTCACGTGGCTCCATCTTCA Miscellaneous PYHIN1 ACTGAATGAGGCCAGGGATGGAAT/ 60.0/59.9 116 25:9 0:66 24:8 0:33 2:1 0.0397 GCCACCACAGCACTGAATTTCCTT KCNK10 TCTCCCAAAGAGTGCTTGGAGGTT/ 59.9/58.1 189 21:8 0:78 20:3 0:43 2:7 0.0243 CACTGGCACTCAAAGTTAGCATGG ATP8B1 AGAGGGTGATGAACCAGTCTTCGT/ 59.4/59.9 92 22:0 0:63 20:0 0:43 4:0 0.0029 CATGCGACTAGGCAGGCAATGTTT ATP11B TGCTTTCCAGTGCTCCAGAGA/ 58.2/61.7 127 27:0 0:79 24:2 0:71 6:9 0.0020 TCAACCCGCTAGGCCATCAGAGAA ESRRG TCAGGTGTCCAGCAAAGTGGTTCA/ 60.4/58.3 122 20:0 0:81 18:2 0:49 3:5 0.0127 AGGACCTACTGGTTAGCATAGGGA DIP GGATGTGGTACCAAGTTCAGCCC/ 59.6/63.7 81 22:8 1:06 20:4 1:22 5:5 0.0239 TTGGGAGTGGCCAGAGCTGGATGAAA PCBP2 TTACCATCACTGGATCTGCTGCCA/ 60.0/59.9 154 14:9 0:83 16:4 0:47 2.9 0.0259 AGATGGATCATGGGTGGTGGTGAA FUBP1 TGGCCTCCCAGAAAGGTCTTGTAT/ 59.6/60.1 173 17:0 0:24 18:6 0:53 3.0 0.0051 ATCCTGCCTTGCTAGCTGGAATCA Unknown BBX AAGCCCTTCTACCCGGACTTGAAA/ 59.9/59.4 142 12:1 0:53 13:2 0:52 2.1 0.0262 AAGAGTCACACGAGTTGAGAGGCT 33 kDa ACAGCAGGCAGCGATTTCTACAAG/ 59.3/59.7 91 18:4 0:62 17:5 0:44 1:9 0.0493 protein GGCATGCCAAGGTTCCCAGTTAAT CNBD1 ACGTGAAGACATCAGTTCCCGTAG/ 58.1/55.8 154 22:7 1:13 20:9 0:29 3:5 0.0460 GAGTATTCTGTAAGACTAGGGAATGAAACA YIPF6 GACAGAGTAGGGAAGGCCATTGAA/ 58.2/59.7 143 28:0 0:69 26:1 0:54 3:6 0.0061 CTGCATGCCGAAGGAATAGCCAAA Reference RPS3 TGTCTGGGAAACTTCGAGGACAGA/ 59.3/59.5 103 11:4 0:57 11:4 0:54 1.0 0.9070 ACGGCGGTGTCAACGTAGTAGTTA

SD, standard deviation FC, fold change on microarray p values on qPCR.

The hepatic transcriptional differences between these of cell adhesion among hepatocytes, though it is difficult two breeds showed differences in hormonal and meta- to judge whether the loosening of these connections bolic capabilities. For example, EGF-related DEGs accelerates or impedes signaling. Also, the balance suggest that available EGF is more efficiently signal- between IGF1 and IGF2 produced by the liver can transferred and internalized in KNP than in YS. systematically influence the overall rate and quality of Furthermore, the efficiency of signaling might be body growth between the breeds. Additional evidence of affected by changes in extracellular matrix and degree effects can be found in transcriptional differences in the Hepatic DEGs as between Korean Native Pig and Yorkshire 457

Liver

Response to growth factors -EGF-related signaling and processing Better stress resistance Translation capacity Lower capacity for protein synthesis Detoxification

RAB4B

EGFR Blood vessel

EGF IGF2

Adipose tissue Skeletal muscle

Muscular structure AA content Stronger Various metabolism Lower boar taint (Glucose, lipid, protein and RNA) CYP-related metabolism lean growth p53 related process

Fig. 4. Summary of Transcriptome Differences in KNP as Compared to YS. Bold black indicates changes based on DEG analysis. Arrows indicate up ("), down (#), or a mix ($) of up and downregulation. Gray indicates prediction based on indirect evidence from DEGs. White indicates possible overall effects. genes involved in protein synthesis. Protein synthesis References can be evaluated by both efficiency and capacity. Acute alteration of protein synthesis is determined by trans- 1) Burwen SJ, Schmucker DL, and Jones AL, Int. Rev. Cytol., 135, lation initiation factors, which increase the efficiency of 269–313 (1992). 21) 2) Mueting D, Am. J. Dig. Dis., 10, 790–795 (1965). protein synthesis. In contrast, long-term alteration of 3) Michalopoulos GK and DeFrances MC, Science, 276, 60–66 total protein is determined by the number of ribosomes, (1997). which increases capacity for protein synthesis. Based on 4) Desbois-Mouthon C, Wendum D, Cadoret A, Rey C, Leneuve the hepatic DEGs in the protein synthesis category, YS P, Blaise A, Housset C, Tronche F, Le Bouc Y, and have a greater capacity for protein synthesis based on Holzenberger M, FASEB J., 20, 773–775 (2006). transcriptional up regulation of the genes encoding 5) Florendo NT, J. Cell Biol., 41, 335–339 (1969). for ribosome components as compared to KNP. The 6) Schook L, Beattie C, Beever J, Donovan S, Jamison R, Zuckermann F, Niemi S, Rothschild M, Rutherford M, and difference in gene expression related to detoxification Smith D, Anim. Biotechnol., 16, 183–190 (2005). indicates how these pig breeds have been selected in 7) Lunney JK, Int. J. Biol. Sci., 3, 179–184 (2007). different locations for different purposes. 8) Tuggle CK, Wang Y, and Couture O, Int. J. Biol. Sci., 3, 132– Altogether, global gene expression analysis in three 152 (2007). tissues, fat, skeletal muscle, and liver, was useful for 9) Kim SS, Kim JR, Moon JK, Choi BH, Kim TH, Kim KS, Kim understanding how economically valuable characters JJ, and Lee CK, Mol. Cells, 28, 565–573 (2009). can be maintained in different regions (Fig. 4). Over 10) Moon JK, Kim KS, Kim JJ, Choi BH, Cho BW, Kim TH, and Lee CK, Anim. Genet., 40, 115–118 (2009). time, differences in gene expression in different tissues 11) Choi KM, Moon JK, Choi SH, Kim KS, Choi YI, Kim JJ, and might be a reason for differences of important economic Lee CK, Asian-Aust. J. Anim. Sci., 21, 967–971 (2008). traits, such as lean muscle production and boar taint in 12) Durbin BP, Hardin JS, Hawkins DM, and Rocke DM, these two breeds. Conventionally, markers for traits Bioinformatics, 18 (Suppl 1), S105–S110 (2002). were found after a massive breeding experiment. Recent 13) Storey JD and Tibshirani R, Proceedings of the National development of porcine microarray makes it possible to Academy of Sciences of the United States of America, 100, 9440–9445 (2003). screen for these markers directly, and provides imme- 14) Tsai S, Cassady JP, Freking BA, Nonneman DJ, Rohrer GA, and diate chances to understand possible molecular mecha- Piedrahita JA, Anim. Genet., 37, 423–424 (2006). nisms after functional studies of those markers. Because 15) Zeeberg BR, Feng W, Wang G, Wang MD, Fojo AT, Sunshine the pig has several interesting properties related to M, Narasimhan S, Kane DW, Reinhold WC, Lababidi S, Bussey growth and metabolism, the development of pig-related KJ, Riss J, Barrett JC, and Weinstein JN, Genome Biol., 4, R28 resources, including specific antibodies for porcine (2003). proteins, should promote pig as a powerful model in 16) Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, Doerks T, Julien P, Roth A, Simonovic M, Bork P, and von molecular biology. Mering C, Nucleic Acids Res., 37, D412–D416 (2009). 17) O’Dell SD and Day IN, Int. J. Biochem. Cell Biol., 30, 767–771 Acknowledgments (1998). 18) Smith TJ, Drummond GS, Kourides IA, and Kappas A, Proc. This work was supported by a grant from the Natl. Acad. Sci. USA, 79, 7537–7541 (1982). BioGreen 21 Program (no. PJ007028201005), Rural 19) Kato J, Tsuruda T, Kita T, Kitamura K, and Eto T, Arterioscler. Development Administration, Republic of Korea to Thromb. Vasc. Biol., 25, 2480–2487 (2005). 20) Paige K, Palomares M, D’Amore PA, and Braunhut SJ, In Vitro CKL, and by Mid-career Research Program through Cell. Dev. Biol., 27A, 151–157 (1991). NRF grant funded by the MEST (no. 20090079095) to 21) Kimball SR, Mellor H, Flowers KM, and Jefferson LS, Prog. JWL. Nucleic Acid Res. Mol. Biol., 54, 165–196 (1996). 458 S.-S. KIM et al. 22) Favre B, Fontao L, Koster J, Shafaatian R, Jaunin F, Saurat JH, 29) Semplici F, Meggio F, Pinna LA, and Oliviero S, Oncogene, 21, Sonnenberg A, and Borradori L, J. Biol. Chem., 276, 32427– 3978–3987 (2002). 32436 (2001). 30) Nakayama J, Yoshizawa T, Yamamoto N, and Arinami T, 23) Kelly BT, McCoy AJ, Spate K, Miller SE, Evans PR, Honing S, Neurosci. Lett., 320, 77–80 (2002). and Owen DJ, Nature, 456, 976–979 (2008). 31) Huang DT, Paydar A, Zhuang M, Waddell MB, Holton JM, and 24) Krawczyk M, Leimgruber E, Seguin-Estevez Q, Dunand-Sauthier Schulman BA, Mol. Cell, 17, 341–350 (2005). I, Barras E, and Reith W, Nucleic Acids Res., 35, 595–605 32) Kumar R, Neilsen PM, Crawford J, McKirdy R, Lee J, (2007). Powell JA, Saif Z, Martin JM, Lombaerts M, Cornelisse CJ, 25) Pelicci G, Lanfrancone L, Grignani F, McGlade J, Cavallo F, Cleton-Jansen AM, and Callen DF, Cancer Res., 65, 11304– Forni G, Nicoletti I, Pawson T, and Pelicci PG, Cell, 70, 93–104 11313 (2005). (1992). 33) Chien LJ, Wu JM, Kuan IC, and Lee CK, Biotechnol. Prog., 20, 26) Oh MJ, van Agthoven T, Choi JE, Jeong YJ, Chung YH, Kim 1359–1365 (2004). CM, and Jhun BH, Biochem. Biophys. Res. Commun., 375, 430– 34) Pilone MS, Cell. Mol. Life Sci., 57, 1732–1747 (2000). 434 (2008). 35) Stuehler B, Reichert J, Stremmel W, and Schaefer M, J. Mol. 27) Tremblay LO, Campbell Dyke N, and Herscovics A, Glycobi- Med., 82, 629–634 (2004). ology, 8, 585–595 (1998). 36) Endo F, Awata H, Katoh H, and Matsuda I, Genomics, 25, 164– 28) Wang JG and Geng JG, Thromb. Haemost., 90, 309–316 (2003). 169 (1995).