No CrossRef data available.
Article contents
Gelsolin expression in sheep milk somatic cells during lactation
Published online by Cambridge University Press: 06 March 2019
Abstract
The identification of genes involved in phenotypes related to milk quality is important for both economic and health aspects in livestock production. The aim of this study was to assess the level of gelsolin gene expression in two breeds of dairy sheep – Sarda and Gentile – with pronounced differences in quantitative and qualitative milk traits. Gelsolin, a type of actin-modulating proteins is involved in the processes of actin remodeling during cell growth and apoptosis; therefore a role of this protein in mammary changes during lactation was here hypothesized. Individual milk samples were collected three times during lactation from 26 ewes of the two breeds. The differential gene expression of gelsolin in the two breeds and the three lactation times was estimated by quantitative PCR on RNA extracted from milk somatic cells. Correlations of gelsolin gene expression with milk yield and quality and days of lactation were also estimated. The results showed that gelsolin gene expression was significantly higher in the Sarda compared to the Gentile at each lactation stage, in agreement with the longer lactation duration and the higher daily milk yield of the first breed. Significant correlations of gelsolin gene expression were found with milk fat content in Sarda breed (−0.46, P<0.05). Gelsolin expression analysis confirmed the link between gelsolin gene function and milk fat content of sheep.
- Type
- Research Article
- Information
- Copyright
- © The Animal Consortium 2019
References
Addis, MF, Pisanu, S, Ghisaura, S, Pagnozzi, D, Marogna, G, Tanca, A, Biosa, G, Cacciotto, C, Alberti, A, Pittau, M, Roggio, T and Uzzau, S 2011. Proteomics and pathway analyses of the milk fat globule in sheep naturally infected by mycoplasma agalactiae provide indications of the in vivo response of the mammary epithelium to bacterial infection. Infection and Immunity 79, 3833–3845.CrossRefGoogle ScholarPubMed
Altschul, SF, Madden, TL, Schaffer, AA, Zhang, J, Zhang, Z, Miller, W and Lipman, DJ 1997. Gapped BLAST and PSI‐BLAST: a new generation of protein database search programs. Nucleic Acids Research 25, 3389–3402.CrossRefGoogle ScholarPubMed
Amato, PA and Loizzi, RF 1981. The identification and localization of actin and actin-like filaments in lactating guinea pig mammary gland alveolar cells. Cell Motility 1, 329–347.CrossRefGoogle ScholarPubMed
Association of Official Analytical Chemists 1990. Official methods of analysis, 15th edition. AOAC, Arlington, VA, USA.Google Scholar
Bauman, DE, Mather, IH, Wall, RJ and Lock, AL 2006. Major advances associated with the biosynthesis of milk. Journal of Dairy Science 89, 1235–1243.CrossRefGoogle ScholarPubMed
Berglund, I, Pettersson, G, Östensson, K and Svennersten-Sjaunja, K 2007. Quarter milking for improved detection of increased SCC. Reproduction in Domestic Animal 42, 427–432.CrossRefGoogle ScholarPubMed
Bianchi, L, Bolla, A, Budelli, E, Caroli, A, Casoli, C, Pauselli, M and Duranti, E 2004. Effect of udder health status and lactation phase on the characteristics of Sardinian ewe milk. Journal of Dairy Science 87, 2401–2408.CrossRefGoogle ScholarPubMed
Bobbo, T, Ruegg, PL, Stocco, G, Fiore, E, Gianesella, M, Morgante, M, Pasotto, D, Bittante, G and Cecchinato, A 2017. Associations between pathogen-specific cases of subclinical mastitis and milk yield, quality, protein composition, and cheese-making traits in dairy cows. Journal of Dairy Science 100, 4868–4883.CrossRefGoogle ScholarPubMed
Crisà, A, De Matteis, G, Scatà, MC and Moioli, B 2013. Analysis of SLC11A1 gene expression in healthy water buffalo (Bubalus bubalis) blood cells using qPCR. Genetics and Molecular Research 12, 6957–6967.CrossRefGoogle ScholarPubMed
Crisà, A, Ferrè, F, Chillemi, G and Moioli, B 2016. RNA-sequencing for profiling goat milk transcriptome in colostrum and mature milk. BMC Veterinary Research 12, 264.CrossRefGoogle ScholarPubMed
De Olives, AM, Díaz, JR, Molina, MP and Peris, C 2013. Quantification of milk yield and composition changes as affected by subclinical mastitis during the current lactation in sheep. Journal of Dairy Science 96, 7698–7708.CrossRefGoogle Scholar
Dos Remedios, CG, Chhabra, D, Kekic, M, Dedova, IV, Tsubakihara, M, Berry, DA and Nosworthy, NJ 2003. Actin binding proteins: regulation of cytoskeletal microfilaments. Physiological Reviews 83, 433–473.CrossRefGoogle ScholarPubMed
Franke, WW, Schmidt, E, Freudenstein, C, Appelhans, B, Osborn, M, Weber, K and Keenan, TW 1980. Intermediate-sized filaments of the prekeratin type in myoepithelial cells. Journal of Cell Biology 84, 633–654.CrossRefGoogle ScholarPubMed
Haile-Mariam, M and Pryce, JE 2017. Genetic parameters for lactose and its correlation with other milk production traits and fitness traits in pasture-based production systems. Journal of Dairy Science 100, 3754–3766.CrossRefGoogle ScholarPubMed
Heid, H and Keenan, TW 2005. Intracellular origin and secretion of milk fat globules. European Journal of Cell Biology 84, 245–258.CrossRefGoogle ScholarPubMed
Hellemans, J, Mortier, G, De Paepe, A, Speleman, F and Vandesompele, J 2007. qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biology 8, R19.CrossRefGoogle ScholarPubMed
Hruz, T, Wyss, M, Docquier, M, Pfaffl, MW, Masanetz, S, Borghi, L, Verbrugghe, P, Kalaydjieva, L, Bleuler, S, Laule, O, Descombes, P, Gruissem, W and Zimmermann, P 2011. RefGenes: identification of reliable and condition specific reference genes for RT-qPCR data normalization. BMC Genomics 12, 156.CrossRefGoogle ScholarPubMed
Li, GH, Arora, PD, Chen, Y, McCulloch, CA and Liu, P 2012. Multifunctional roles of gelsolin in health and diseases. Medicinal Research Reviews 32, 999–1025.CrossRefGoogle ScholarPubMed
Nag, S, Larsson, M, Robinson, RC and Burtnick, LD 2013. Gelsolin: the tail of a molecular gymnast. Cytoskeleton 70, 360–384.CrossRefGoogle ScholarPubMed
Napolitano, F, Annicchiarico, G, Catillo, G, Crisà, A, Grandoni, F, Marchitelli, C and Moioli, B 2014. Identification of Ovis aries gelsolin isoform b, a candidate gene for milk quality. Small Ruminant Research 116, 21–27.CrossRefGoogle Scholar
Pottiez, G, Haverland, N and Ciborowski, P 2010. Mass spectrometry characterization of gelsolin isoforms. Rapid Communications in Mass Spectrometry 24, 2620–2624.CrossRefGoogle ScholarPubMed
Pyörälä, S 2003. Indicators of inflammation in the diagnosis of mastitis. Veterinary Research 34, 565–578.CrossRefGoogle Scholar
Romao, JM, He, ML, McAllister, TA and Guan, LL 2014. Effect of age on bovine subcutaneous fat proteome: molecular mechanisms of physiological variations during beef cattle growth. Journal of Animal Science 92, 3316–3327.CrossRefGoogle ScholarPubMed
SAS Institute 2010. SAS/STAT ® 9.3 user’s guide. SAS Institute Incorporation, Cary, NC, USA.Google Scholar
Signorelli, F, Cifuni, GF, Napolitano, F and Miarelli, M 2010. Comparative proteomic analysis of mammary gland in dairy sheep of different breeds. In Proceedings of 9th World Congress on Genetics Applied to Livestock Production, 1–6 August 2010, Leipzing, Germany, p. ID327.Google Scholar
Spinardi, L and Witke, W 2007. Gelsolin and diseases. Subcellular Biochemistry 45, 55–69.CrossRefGoogle ScholarPubMed
Sun, HQ, Yamamoto, M, Mejillano, M and Yin, HL 1999. Gelsolin, a multifunctional actin regulatory protein. Journal of Biological Chemistry 274, 33179–33182.CrossRefGoogle ScholarPubMed
Toral, PG, Hervás, G, Suárez-Vega, A, Arranz, JJ and Frutos, P 2016. Isolation of RNA from milk somatic cells as an alternative to biopsies of mammary tissue for nutrigenomic studies in dairy ewes. Journal of Dairy Science 99, 8461–8471.CrossRefGoogle ScholarPubMed
Van Soest PV, Robertson JB and Lewis BA 1991. Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. Journal of Dairy Science 74, 3583–3597.CrossRefGoogle Scholar
Vandesompele, J, De Preter, K, Pattyn, F, Poppe, B, Van Roy, N, De Paepe, A and Speleman, F 2002. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology 3, 1–12.CrossRefGoogle ScholarPubMed
Napolitano et al. supplementary material
Tables S1-S3
File
25.9 KB