Introduction. Growth promoters (GPs) are forbidden at the European Community level. Nevertheless, GPs misuse in cattle still represent a major concern. In the past decade an increasing interest toward the set up and validation of molecular biomarkers to be used side by side with official analytical methods has been recorded (Nebbia, 2010). In preceding pilot studies, a number of tissue-specific responsive genes have been identified (Giantin, 2010; Lopparelli, 2011). In this study, these biomarkers were preliminarily tested under field conditions.
Materials and methods. Ninety-five cattle testis and liver aliquots were collected by chance at slaughterhouses, placed in microtubes with RNAlater\uae and stored at -80\ub0C until use. A robust set of negative controls (44 animals), from earlier pilot studies, were included in the study, too. Total RNA was extracted with TRIzol\uae Reagent and gene expression profiles measured by using a quantitative Real Time RT-PCR approach (qPCR). Seven and eight target genes were chosen for liver and testis, respectively. Results were elaborated (Hierarchical Clustering, HCL, and Principal Component Analysis, PCA) by using the GenEx software (Berkvist, 2010).
Results. In liver, HCL clustered samples into three main groups, supported by PCA: negative controls and most of random samples were clustered together (\u201cnegatives\u201d), while three animals were distinctly grouped in another cluster (\u201csuspects\u201d). Further nine samples, assigned to negatives by GenEx, generated a different cluster; therefore, they were classified as \u201cdoubtful\u201d. In testis, three \u201csuspects\u201d and three \u201cdoubtful\u201d were identified besides \u201cnegatives\u201d. Considering both tissues as a whole, the software identified three \u201csuspects\u201d and two \u201cdoubtful\u201d.
Conclusions. This study aimed to test a set of candidate genes and a popular software for qPCR data processing and analysis upon a large number of random samples. The approach allocated samples into three different clusters, representing different expression profiles. Presented data suggest that transcriptome analysis and bioinformatic tools, coupled with a robust database of negative controls, might be helpful for tracking GPs abuse in cattle. Further studies are needed to confirm these promising results.
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Berkvist A., Rusnakova V., Sindelka R., Garda J. M. A., Sjogreen B., Lindh D., Forootan A., Kubista M., 2010. Gene expression profiling \u2013 Clusters of possibilities. Methods, 50: 323-335.
Acknowledgements. Project supported by a grant from Regione del Veneto (Dgr 2888 07/10/2008) to M.D
