Differential gene expression in mammary gland epithelial cells between Holstein individuals with high or low α-lactalbumin milk protein content L.-E. Holm1, N. Poulsen2, B. Thomsen1, F. Panitz1, L. B. Larsen2, T. T. T. Le2, V. R. Gregersen1,3. 1 Dept. of Molecular Biology and Genetics, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark Larserik.Holm@mbg.au.dk (Corresponding author) 2 Dept. of Food Science, Aarhus University, Blichers Allé 20, 8830 Tjele, Denmark 3 Current address: Qiagen Bioinformatics, Prismet, Silkeborgvej 2, 8000 Aarhus C, Denmark α-lactalbumin is one of the most abundant proteins in milk constituting 20-25% of the proteins in human milk. The proportion of α-lactalbumin is considerably lower in bovine milk and it is therefore of interest to increase this level especially when producing infant formula. In this study, we investigated the possibility to look on potentially differential expressed genes in the mammary epithelial cells in an attempt to elucidate potential regulatory mechanisms of α-lactalbumin synthesis. However, the identified differentially expressed genes did not result in identification of genes with a potential impact on α-lactalbumin levels and therefore it is concluded that the regulation of α-lactalbumin is governed by mechanisms in other tissues than the mammary epithelial cells. Keywords: bovine, α-lactalbumin, RNAseq, differential expression Introduction α-lactalbumin is one of the key protein components in milk. It is a small, acidic and Ca2+ binding protein. It has several important functions, primarily as one of the two components in lactose synthase, which catalyzes the final step in lactose synthesis in the mammary gland. α-lactalbumin has a single strong Ca2+ binding site and several zinc binding sites. The binding of these ions can induce conformational changes and greater stability towards denaturing reagents (reviewed in Permyakov & Berliner, 2000). It has also been shown that α-lactalbumin can have apoptotic effects on specific tumors (Ho et al., 2017). α-lactalbumin is also one of the key constituents of infant formula and in human milk it constitutes 20-25% of the protein content, however in bovine milk it only constitutes 2-5% (Lönnerdal & Lien, 2003). This difference makes it necessary to complement bovine milk with additional α-lactalbumin in infant formulae especially as α-lactalbumin is rich in the essential amino acid tryptophan and is the key supplement for this amino acid (Lien, 2003). The purpose of this study was to elucidate gene expression levels in cows with high or low α-lactalbumin content in milk, respectively, in order to identify differentially expressed genes between these two phenotypes. This could aid in the understanding of the processes involved in the production of this protein in the mammary gland and could point towards pathways or genetic variants with an effect on production of α-lactalbumin. Likewise, it could also point to genes and/or pathways affected by changes in levels of α-lactalbumin. Materials and methods Animals sampled for RNAseq analysis consisted of 24 Danish Holstein originating from 4 different herds and all from 1st parity. RNA was extracted from mammary gland epithelial cells utilizing monoclonal anti-cytokeratin peptide 8 (Sigma-Aldrich, St. Louis, Missouri, USA) to capture cells from 0.4 liter fresh whole milk. RNA was precipitated using QIAzol (Qiagen, Hilden, Germany) following the “Purification of total RNA from animal tissue” protocol described in the miRNease Mini Handbook (Qiagen). The RNA was used to prepare strand-specific sequencing libraries (ScriptSeq, Illumina, San Diego) for each sample. The libraries were sequenced on an Illumina HiSeq2000 (100 bp paired-end). Quality assessment was performed using FastQC (v. 0.11.3). SortMeRNA (v. 2.1) (Kopylova et al. 2012) was used to remove rRNA prior to adapter clipping, quality trimming and length filtering with Trimmomatic (v. 0.35) (Bolger et al., 2014). The cleaned paired-end sequences were mapped to the bovine genome UMD3.1 using STAR (v. 2.5.0a) (Dobin et al., 2013). STAR was also used to generate count data for the subsequent differential gene expression analysis. Gene expression differences between the 5 individuals with the highest and 5 with the lowest α-lactalbumin protein content in milk were analyzed using count data. Count data was normalized as suggested by Robinson & Oshlack (2010) prior to performing the likelihood ratio test implemented in EdgeR (Robinson et al., 2010; McCarthy et al., 2012). The list of differentially regulated genes was subjected to functional analysis using the DAVID (v. 6.8) functional annotation tool (Huang et al., 2009a; 2009b). A total of 99 up-regulated and 21 down-regulated genes, respectively, were used for this analysis corresponding to all genes with p<10-4 in the differential expression analysis. Results The identified differentially expressed genes (Table 1) did not reveal specific pathways or genes related to milk protein genes or known genes related to milk production phenotypes. The DAVID functional annotation analysis identified the GO terms “negative regulation of viral genome replication” (GO:0045071), “negative regulation of amyloid-beta formation” (GO: 1902430), “defence response to virus” (GO:0051607) and the KEGG pathway “MicroRNAs in cancer” (bta05206) as the most enriched with p<0.01, however none of these were significantly enriched after Bonferroni adjustment. Discussion The major milk protein genes including caseins and α-lactalbumin were found to be expressed in RNAseq studies on mammary epithelial cells (Lemay et al., 2013; Sigl et al. 2014) showing that the approach of working with this tissue does give a picture of the transcriptome in this cell type. Table 1. List of the 25 most significant Differentially Expressed genes. All are upregulated in high α-lactalbumin milk protein animals except for one gene (underlined). ensembl_gene_id logFC P-Value FDR Gene Gene description ENSBTAG00000042662 3.39 1.5E-13 2.0E-09 SNORD17 Small nucleolar RNA SNORD17 ENSBTAG00000042723 3.12 4.4E-11 2.9E-07 U11 U11 spliceosomal RNA ENSBTAG00000007191 2.52 6.2E-09 2.8E-05 CCL5 C-C motif chemokine ligand 5 ENSBTAG00000007962 2.14 2.0E-08 6.7E-05 ATP9A ATPase phospholipid transporting 9A (putative) ENSBTAG00000043250 2.21 5.3E-08 1.4E-04 7SK 7SK RNA ENSBTAG00000021452 2.62 8.6E-08 1.9E-04 TRANK1 tetratricopeptide repeat and ankyrin repeat containing 1 ENSBTAG00000045966 2.23 1.3E-07 2.5E-04 GPRIN3 GPRIN family member 3 ENSBTAG00000012371 1.74 1.9E-07 2.9E-04 CPD carboxypeptidase D ENSBTAG00000038698 2.45 2.0E-07 2.9E-04 ENSBTAG00000028359 2.09 2.2E-07 3.0E-04 U3 Small nucleolar RNA U3 ENSBTAG00000022799 2.21 3.1E-07 3.4E-04 NOTCH1 notch 1 ENSBTAG00000048062 3.13 3.0E-07 3.4E-04 KDM6B lysine demethylase 6B ENSBTAG00000008154 1.85 4.0E-07 4.1E-04 LIMD2 LIM domain containing 2 ENSBTAG00000018804 2.60 4.9E-07 4.7E-04 CELSR2 cadherin EGF LAG seven-pass G-type receptor 2 precursor ENSBTAG00000003857 2.99 5.3E-07 4.7E-04 SUSD6 sushi domain containing 6 ENSBTAG00000005923 2.22 8.4E-07 6.9E-04 ABTB2 ankyrin repeat and BTB domain containing 2 ENSBTAG00000020713 1.94 1.1E-06 8.6E-04 BACH2 BTB domain and CNC homolog 2 ENSBTAG00000014762 2.10 1.3E-06 9.5E-04 ISG20 interferon stimulated exonuclease gene 20 ENSBTAG00000003840 2.20 1.6E-06 1.2E-03 GUCY1B3 guanylate cyclase 1 soluble subunit beta 1ENSBTAG00000016061 2.22 1.9E-06 1.2E-03 RSAD2 radical S-adenosyl methionine domain containing 2 ENSBTAG00000045661 2.21 2.7E-06 1.6E-03 Metazoa_SRP Metazoan signal recognition particle RNA ENSBTAG00000023026 3.43 2.7E-06 1.6E-03 ENSBTAG00000008040 -1.39 3.0E-06 1.7E-03 SPG20 spastic paraplegia 20 (Troyer syndrome) ENSBTAG00000020053 1.42 3.2E-06 1.7E-03 ZEB1 zinc finger E-box-binding homeobox 1 ENSBTAG00000009785 1.57 3.1E-06 1.7E-03 SAMD4B sterile alpha motif domain containing 4B The list of differentially expressed genes does not give any indication of regulation of α-lactalbumin or processes regulated by α-lactalbumin levels, although there are a number of small RNA genes which might be involved in regulation of other genes. The DAVID annotation indicates a relationship towards viral defence or cancer-related issues although not significant. α-lactalbumin in complex with fatty acids (HAMLET) are known to have an anti-tumor effect caused by specific folding properties (Mossberg et al., 2010) but no association with miRNA regulation has been described. Association of specific folding variants of α-lactalbumin with bactericidal properties has also been described (Håkansson et al., 2000) but there are no studies showing anti-viral properties of α-lactalbumin as otherwise indicated by the DAVID annotation. However, the most likely explanation for the observed differentially expressed genes is that effects on α-lactalbumin expression levels are not caused by genes expressed in the mammary epithelial cells, and that α-lactalbumin levels do not affect the expression of other genes in the mammary epithelial cells. This leads to the conclusion that the genetic pathways affecting or being affected by α-lactalbumin levels are controlled in tissues other than the mammary epithelial cells. List of References Bolger, A.M., M. Lohse & B. Usadel, 2014. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics, 30, 2114-2120. Dobin, A., C.A. Davis, F. Schlesinger, J. Drenkow, C. Zaleksi, S. Jha, P. Batut, M. Chaisson & T.R. Gingeras, 2013. STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29, 15-21. Ho, J.C., A. Nadeem & C. Svanborg, 2017. HAMLET – a protein-lipid complex with broad tumoricidal activity. Biochem. Biophys. Res. Commun., 482, 454-458. Huang, D.W., B.T. Sherman & R.A. Lempicki, 2009a. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 4, 44-57. Huang, D.W., B.T. Sherman & R.A. Lempicki, 2009b. Bioinformatics enrichment tools: path toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res., 37, 1-13. Håkansson, A., M. Svensson, A.-K. Mossberg, H. Sabharwal, S. Linse, I. Lazou, B. Lönnerdal & C. Svanborg, 2000. A folding variant of α-lactalbumin with bactericidal activity against Streptococcus pneumoniae. Mol. Microbiol., 35, 589-600. Kopylova, E., L. Noé & H. Touzet, 2012. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics, 28, 3211-3217. Lemay, D.G., O.A. Ballard, M.A. Hughes, A.L. Morrow, N.D. Horseman & L.A. Nommsen-Rivers, 2013. RNA sequencing of the human milk fat layer transcriptome reveals distinct gene expression profiles at three stages of lactation. PLOSone, 8, e67531. Lien, E.L., 2003. Infant formulas with increased concentrations of alpha-lactalbumin. Am. J. Clin. Nutr., 77, 1555S-1558S. Lönnerdal, B. & E.L. Lien, 2003. Nutritional and physiological significance of alpha-lactalbumin in infants. Nutr. Rev., 61, 295-305. McCarthy, J.D., Y. Chen & K.G. Smyth, 2012. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res., 40, 4288-4297. Mossberg, A.K., K. Hun Mok, L.A. Morozova-Roche & C. Svanborg, 2010. Structure and function of human α-lactalbumin made lethal to tumor cells (HAMLET)-type complexes. FEBS J. 277, 4614-4625. Permyakov, E.A. & J.L. Berliner, 2000. α-lactalbumin: structure and function. FEBS Letters, 473, 267-274. Robinson, M.D., D.J. McCarthy & G.K. Smyth, 2010. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26, 139-140. Robinson, M.D. & A. Oshlack, 2010. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol., 11, R25. Sigl, T., H.H.D. Meyer & S. Wiedemann, 2014. Gene expression analysis of protein synthesis pathways in bovine mammary epithelial cells purified from milk during lactation and short-term restricted feeding. J. Anim. Physiol. Anim. Nutr., 98, 84-95.

Lars-Erik Holm, Nina Poulsen, Bo Thomsen, Frank Panitz, Lotte Bach Larsen, Thao Thi Thu Le, Vivi Raundal Gregersen

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume Electronic Poster Session - Biology & Species - Bovine (dairy) 2, , 820, 2018
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