In order to identify candidate genes and molecular mechanisms that influence meat quality and production in pigs, microarray experiments were carried out to find differences in gene expression levels between two pools of six individuals, constituting the extreme tails of the Gaussian distribution of seven adjusted phenotypes of 100 Landrace and Large White animals. The phenotypes considered in this study were: muscle compactness, marbling, colour uniformity, fat covering, colour, dorsal fat, thickness, ham fat thickness. 437 differentially expressed ESTs (Expressed Sequence Tags) were found, clustering in different pathways according to their ontology. In particular, 73 functional categories were identified and ten of them could have a role in meat quality. Among the ESTs belonging to these pathways, seven of them were selected to be validated in quantitative real-time RT-PCR

Bianca Castiglioni

Gorni, C.

Iacuaniello, S.

Mariani, P.

Pagnacco, G.

Stella, A.

Italian Journal of Animal Science

English

G. Pagnacco

P. Mariani

A. Stella

C. Gorni

I. Iacuaniello

B. Castiglioni

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Microarray analyses to identify differentially expressed genes for assessing meat quality in swine

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Full Terms & Conditions of access and use can be found athttps://www.tandfonline.com/action/journalInformation?journalCode=tjas20Italian Journal of Animal ScienceISSN: (Print) 1828-051X (Online) Journal homepage: https://www.tandfonline.com/loi/tjas20Microarray analyses to identify differentiallyexpressed genes for assessing meat quality inswineS. Iacuaniello, B. Castiglioni, C. Gorni, A. Stella, P. Mariani & G. PagnaccoTo cite this article: S. Iacuaniello, B. Castiglioni, C. Gorni, A. Stella, P. Mariani & G. Pagnacco(2007) Microarray analyses to identify differentially expressed genes for assessing meat quality inswine, Italian Journal of Animal Science, 6:sup1, 144-146, DOI: 10.4081/ijas.2007.1s.144To link to this article:  https://doi.org/10.4081/ijas.2007.1s.144Copyright 2007 Taylor & Francis Group LLCPublished online: 15 Mar 2016.Submit your article to this journal Article views: 53View related articles Citing articles: 2 View citing articles 144 ITAL.J.ANIM.SCI. VOL. 6 (SUPPL. 1), 144-146, 2007Microarray analyses to identify differentially expressed genesfor assessing meat quality in swineS. Iacuaniello1, B. Castiglioni2, C. Gorni3, A. Stella3,P. Mariani3, G. Pagnacco11 Dipartimento di Scienze e Tecnologie Veterinarie per la Sicurezza Alimentare.Università di Milano, Italy2 Istituto di Biologia e Biotecnologia Agraria. Consiglio Nazionale delle Ricerche, Milano, Italy3 Livestock Genomics, Parco Tecnologico Padano. Lodi, ItalyCorresponding author: Stefania Iacuaniello. Dipartimento di Scienze e Tecnologie Veterinarie per laSicurezza Alimentare, Facoltà di Medicina Veterinaria, Università degli Studi di Milano. Via Celoria 10,20133 Milano, Italy - Tel. +39 02 21013508 - Fax: +39 02 26412135 - Email: stefania.iacuaniello@unimi.itABSTRACT: In order to identify candidate genes and molecular mechanisms that influence meat quality andproduction in pigs, microarray experiments were carried out to find differences in gene expression levels between twopools of six individuals, constituting the extreme tails of the Gaussian distribution of seven adjusted phenotypes of100 Landrace and Large White animals. The phenotypes considered in this study were: muscle compactness, marbling,colour uniformity, fat covering, colour, dorsal fat, thickness, ham fat thickness. 437 differentially expressed ESTs(Expressed Sequence Tags) were found, clustering in different pathways according to their ontology. In particular, 73functional categories were identified and ten of them could have a role in meat quality. Among the ESTs belonging tothese pathways, seven of them were selected to be validated in quantitative real-time RT-PCR.Key words: Meat quality, Microarray, Candidate genes.INTRODUCTION - Genes expressed in skeletal muscle could be candidate genes for meat quality. They playa central role in metabolism, development and physiology of this tissue. Gene expression profiling in the skeletalmuscle had already been investigated by different molecular tools, such as microarrays (Bai et al. 2003), theSuppressive Subtractive Hybridisation (SSH) (Gorni et al., 2006) and cDNA libraries (Davoli et al. 2002).Microarray profiling can be very helpful to discover molecular processes that govern the phenotypic characteristicsof porcine skeletal muscles and expression of specific fibre types. This information is important for enhancing theefficiency of livestock production (Bai et al. 2003). Microarray technologies for transcriptional profiling allow toextract genome-wide expression data, and numerous methods have been developed to analyse this type of data,including clustering procedures that divide genes into disjoint or overlapping sets representing all or part of thegenes being analysed.In this study microarrays were used to identify any difference in gene expression levels between two pools of sixindividuals, constituting the extreme tails of the Gaussian distribution of seven adjusted phenotypes of 100Landrace and Large White animals. The aim of the study is to identify candidate genes and molecular mechanismsthat influence pork meat quality and production.MATERIAL AND METHODS - Tissue samples were collected from skeletal muscles of 100 individuals ofLandrace and Large White animals. Samples were collected immediately after animals slaughtering and stored inRNA later (Sigma-Aldrich). Total RNA was isolated from the skeletal muscle (longissimus dorsi) of 12 of these ani-mals (6 of each extreme tail of the Gaussian distribution of seven adjusted phenotypes) using TRIzol® Reagent(Invitrogen) according to the manufacturer’s instructions. RNA purity, integrity and concentrations were evaluat-ed on the Agilent 2100 Bioanalyzer.Total RNA samples from the six animals of each extreme tail were pooled to give a total amount of 5 µg. RNAlabelled probes were obtained using Amino Allyl MessageAmp-kit  (Ambion) and were hybridised onto microarray PROC. 17th NAT. CONGR. ASPA, ALGHERO, ITALYITAL.J.ANIM.SCI. VOL. 6 (SUPPL. 1), 144-146, 2007 145slides spotted in single replicate with the 70mer Pig Genome Oligo Set Version 1.0 representing the 10,665 Susscrofa gene sequences (Qiagen) using the Sigma hybridization chambers at 42°C for 48 hours. Six independenthybridization experiments were carried out with three replicates for each microarray experiment, including dye-swaps. Fluorescent signals were measured with a ScanArray Lite laser scanning system (PerkinElmer). Spot analy-sis of 10-µm-resolution TIFF images was carried out by using the Scan-Array Express Microarray Analysis System2.1 software supplied with the scanner.Lowess normalization is performed by the Scan-Array Express Microarray Analysis System 2.1 software. The suc-cess of the normalization was tested by visualization of the M-A plots.For identifying differentially expressed ESTs, a statistical analysis based on the paired t-test between the upperand lower tail pools was performed. The dependent variables were the log 2 transformed intensity values. To eval-uate significance, these t-values were compared against a critical value of 0.05, considering the Bonferroni correc-tion for multiple tests (N = 10,665).437 differentially expressed ESTs were found, ordered for ascending p-value and corrected for false discovery rate.To classify the differentially expressed ESTs into functional pathways, a computerized analysis approach usingGene Ontology (GO) (http://www.geneontology.org) was performed. Data mining was performed using the softwareTable 1. Description of the 13 differentially expressed ESTs found in the functional cat-egories. The seven sequences underlined were selected to be validated inreal-time RT-PCR.Human_Ref Seq Gene Simbol Description Cytoband PathwayNM_001517 GTF2H4 general transcription 6p21.3 Transcription, Basal factor IIH transcription factorsNM_007055 POLR3A polymerase (rna) III 10q22-q23 TranscriptionNM_005643 TAF11 taf11 rna polymerase  6p21.31 Transcription,BasalII, tata box binding transcription factorsprotein (tbp)NM_003194 TBP tata box binding 6q27 Transcription,Basalprotein transcription factorsNM_002194 INPP1 inositol 2q32 Phosphatidylinositol  polyphosphate-1- signaling system, Signalphosphatase TransductionNM_005027 PIK3R2 phosphoinositide-3- 19q13.2- Phosphatidylinositol  kinase, regulatory q13.4 signaling system, Signalsubunit 2 TransductionNM_130393 PTPRD protein tyrosine 19p13.3, Phosphatidylinositol  phosphatase 9p23-p24.3 signaling system, SignalTransductionNM_182811 PLCG1 phospholipase c, - Phosphatidylinositol siggamma 1 naling system, Signal TransductionNM_003805 CRADD casp2 and ripk1  12q21.33- Apoptosis, Cell Growth domain containing q23.1 and DeathNM_001605 AARS alanyl-trna synthetase 16q22 TranslationNM_000076 CDKN1C cyclin-dependent  11p15.5 Cell cycle, Cell Growth kinase inhibitor 1c and DeathNM_170692 RASAL2 ras protein activator 1q24 Signal Transduction,like 2 MAPKsignaling pathNM_001749 CAPNS1 calpain, small subunit 1 19q13.12 Cell CommunicationPROC. 17th NAT. CONGR. ASPA, ALGHERO, ITALY146 ITAL.J.ANIM.SCI. VOL. 6 (SUPPL. 1), 144-146, 2007EASE (Expression Analysis Systematic Explorer), available in http://www.DAVID.niaid.nih.gov, (Glynn et al., 2003).To validate microarray results, quantitative real-time RT-PCR was performed using an ABI 5700 instrument, usingthe AppliedBiosystems SYBR-Green® fluorophore on seven selected porcine genes: INPP1, PIK3R2, PTPRD,PLCG1, CRADD, CDKN1C, CAPNS1 (Table 1). The protocol, based on the use of the relative standard curve, wasperformed as previously described by Gorni et al., 2006. Three different housekeeping genes (b-actin, rRNA 18S andribosomial protein L35) were chosen for the normalization of the samples. The Relative Standard Method (Pfaffl,2001) was applied to investigate the differential expression of the ESTs. Data from the RT-PCR were analyzedusing the t-test. Results were considered to be significant for p-value < 0.05.To evaluate the differences in gene expression levels, microarray experiments were performed using pooled RNAextracted from porcine skeletal muscles of 12 animals, belonging to the extreme tails of the Gaussian distributionof seven adjusted phenotypes. The phenotypes considered were: muscle compactness, marbling, colour uniformity,fat covering, colour, dorsal fat, thickness, ham fat thickness. Three replicates of each microarray experiment werecarried out, each one with dye-swap.RESULTS AND CONCLUSIONS - After the normalization, statistical analysis was performed as describedin Matherials and Method. 437 differentially expressed ESTs were found, ordered for ascending p-value, cor-rected for false discovery rate and classified into functional pathways according to GO classification derived fromEASE analysis. The interpretation of our microarray results was not trivial because of a lack of informationabout the swine genes. So, the swine differentially expressed ESTs were analyzed based on homology compari-son with human genes. 73 categories in the biochemical pathway were identified. Ten of them supported theother 63 pathways and they could have a role in meat quality. Table 1 describes all the 13 differentially expressedESTs belonging to these functional categories. Among these sequences, we selected seven of them to be validat-ed in quantitative real-time RT-PCR.Our results demonstrate the power of microarray analysis in identifying candidate genes that influence swine meatquality. The challenge will be confirming these associations and demonstrating how these genes are involved in therelevant muscle phenotypes.The research was supported by the Ministry of Education, University and Scientific Research (FIRB MIURRBNE01MMHS). Samples and phenotypic data were collected at Levoni slaughterhouse, Mantua, Italy.REFERENCES - Bai, Q., McGillivray, C., da Costa, N., Dornan, S., Evans, G., Stear, M.J., Chang, K.C. 2003.Development of a porcine skeletal muscle cDNA microarray: analysis of differential transcript expression in phe-notypically distinct muscles. BMC Genomics 4: 8. Davoli, R., Fontanesi, L., Zambonelli, P., Bigi, D., Gellin, J., Yerle,M., Milc, J., Braglia, S., Cenci, V., Cagnazzo, M., Russo, V. 2002. Isolation of porcine expressed sequence tags for theconstruction of a first genomic transcript map of the skeletal muscle in pig. Anim. Genet. 33 : 3-18. Glynn, D.J.,Sherman, B.T., Hosack D.A., Yang J., Gao W., Lane H.C., Lempicki R.A. 2003. DAVID: Database for Annotation,Visualization, and Integrated Discovery. Genome Biology 4: R60. Gorni, C., Garino, C., Iacuaniello S., CastiglioniB., Restelli G.L., Yerle M., Stella A., Pagnacco G., Mariani P. 2006. Identification of candidate genes affecting meatquality in swine. Communication 22-27, Pag. 203, 8th World Congress On Genetics Applied To Livestock Production,Belo Horizonte, Mg, Brasil. Pfaffl, M.W. 2001. A new mathematical model for relative quantification in real-timeRT-PCR. Nucleic Acids Res. 29: E45.

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Microarray analyses to identify differentially expressed genes for assessing meat quality in swine

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