Posters from the Symposium on Milk Genomics and Human Health

Deep RNAseq reveals miRNome Differences in Mammary Tissue of Holstein and Montbéliarde Lactating Cows

Christine Leroux, Department of Food Science & Technology, University of California, Davis USA and INRA, Saint-Genès-Champanelle, France

Billa P.A1, Faulconnier Y.¹, Bes S.¹, Ye T.², Chervet M.³, Pires J.A.A.¹, Leroux C1, 3
1. INRA, Saint-Genès-Champanelle, France
2. Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), Illkirch-Graffenstaden, France Centre National de la Recherche Scientifique, Paris, France, Institut National de la Santé et de la Recherche Médicale, Université de Strasbourg, Strasbourg, France
3. Department of Food Science & Technology, University of California, Davis, USA

Genetic polymorphism is known to influence milk production and composition. However, genomic mechanisms involved in the regulation of milk component synthesis and their secretion are not completely understood. MicroRNAs (miRNAs) are small non-coding RNAs that take part in the regulation of gene expression by base-pairing mRNA that induce their degradation or inhibiting their translation. Recently, 54 differentially expressed miRNAs were detected in mammary tissue from dairy (Holstein-Friesian) compared to beef (Limousin) postpubertal heifers, two breeds with different mammogenic potential. The results of this study suggest that the high developmental potential of dairy cattle MG, leading to high milk productivity, may depend also on a specific miRNA expression pattern (Wicik et al., 2016). The objective of the present study was to identify the genetic influence on mammary gland miRNOme by comparing miRNA profiles of two dairy cow breeds (Holstein and Montbéliarde) with different milk performances by RNAseq analyzes.

Milk production and composition of 19 multiparous mid-lactation (165 ± 21 DIM) cows (9 Holstein and 10 Montbéliarde) were analyzed. Total RNAs were extracted from MG biopsies (n=5 Holstein and n=6 Montbéliarde) with miRVana kit. MiRNAs were sequenced by RNAseq using Illumina HiSeq 4000. The sequence reads were mapped and annotated using miRDeep2 after trimming of adaptor sequences allowing the identification of known and predicted miRNAs. Statistical analyses were performed using DESeq2 package of R. Significance was considered at padj≤0.05 and tendency at 0.05<padj≤0.10. Target genes of studied miRNA and corresponding putative pathways were investigated using miRWalk and MetacoreTM softwares.

Milk, protein and fat yields were lower in Montbéliarde than in Holstein cows as previously reported (Pomiès et al., 2007). Analyses of miRNA profiles (miRNomes) revealed 623 distinct expressed miRNAs, of which 596 known and 27 predicted miRNAs. Among them, 19 miRNAS were significantly differentially expressed and 15 tended to differ between Holstein and Montbéliarde cow. Among the most abundant 20 miRNA in MG tissue, one miRNA was differentially expressed (miR-186) and one in tended to differ (miR-143) between Holstein and Montbéliarde. Among the 19 differential miRNAs, 7 presented a fold change ≥2. The most abundant among the differentially expressed miRNA was miR-186 known to inhibit cell proliferation (Su et al., 2018) and epithelial-to-mesenchymal transition (Li et al., 2018). Data mining showed that the 19 differentially miRNAs may be involved in 19 KEGG pathways (Padj<0.05) including mTOR signaling pathway and Sphingolipid signaling pathway. Interestingly, cytoskeleton remodeling was the first pathway map identified using MetacoreTM analysis, which could be related to differences in the tissue structure of MG between the two studied breeds which could be related to milk production as shown for dairy versus beef breeds (Wicik et al., 2016).

In conclusion, we showed that MG miRNome differs between Holstein and Montbéliarde cows during
established lactation. That opens a way of investigation in genetic regulation of miRNA and their role.

Funding: This research was funded in part by the GISA program of INRA (LongHealth project).
Li et al. 2018. Arch. Biochem. Biophys. 640:53-60 Pomiès et al. 2007. Animal. 1(10)1497-1505 Su et al. 2018. J. Cancer Res. Ther. 14(Supplement):S60-S64 Wicik et al. 2016. J. Anim. Breed Genet. 133(1): 31-42

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