Open Access

RNA-Sequencing for profiling goat milk transcriptome in colostrum and mature milk

  • Alessandra Crisà1Email author,
  • Fabrizio Ferrè2,
  • Giovanni Chillemi3 and
  • Bianca Moioli1
BMC Veterinary ResearchBMC series – open, inclusive and trusted201612:264

https://doi.org/10.1186/s12917-016-0881-7

Received: 9 July 2016

Accepted: 7 November 2016

Published: 25 November 2016

Abstract

Background

In this work we aimed at sequencing and assembling the goat milk transcriptome corresponding at colostrum and 120 days of lactation. To reconstruct transcripts we used both the genome as reference, and a de novo assembly approach. Additionally, we aimed at identifying the differentially expressed genes (DEGs) between the two lactation stages and at analyzing the expression of genes involved in oligosaccharides metabolism.

Results

A total of 44,635 different transcripts, organized in 33,757 tentative genes, were obtained using the goat genome as reference. A significant sequence similarity match was found for 40,353 transcripts (90%) against the NCBI NT and for 35,701 (80%) against the NR databases. 68% and 69% of the de novo assembled transcripts, in colostrum and 120 days of lactation samples respectively, have a significant match with the merged transcriptome obtained using Cufflinks/Cuffmerge. CSN2, PAEP, CSN1S2, CSN3, LALBA, TPT1, FTH1, M-SAA3, SPP1, GLYCAM1, EEF1A1, CTSD, FASN, RPS29, CSN1S1, KRT19 and CHEK1 were found between the top fifteen highly expressed genes. 418 loci were differentially expressed between lactation stages, among which 207 and 122 were significantly up- and down-regulated in colostrum, respectively. Functional annotation and pathway enrichment analysis showed that in goat colostrum somatic cells predominate biological processes involved in glycolysis, carbohydrate metabolism, defense response, cytokine activity, regulation of cell proliferation and cell death, vasculature development, while in mature milk, biological process associated with positive regulation of lymphocyte activation and anatomical structure morphogenesis are enriched. The analysis of 144 different oligosaccharide metabolism-related genes showed that most of these (64%) were more expressed in colostrum than in mature milk, with eight expressed at very high levels (SLCA3, GMSD, NME2, SLC2A1, B4GALT1, B3GNT2, NANS, HEXB).

Conclusions

To our knowledge, this is the first study comparing goat transcriptome of two lactation stages: colostrum and 120 days. Our findings suggest putative differences of expression between stages and can be envisioned as a base for further research in the topic. Moreover because a higher expression of genes involved in immune defense response, carbohydrate metabolism and related to oligosaccharide metabolism was identified in colostrum we here corroborate the potential of goat milk as a natural source of lactose-derived oligosaccharides and for the development of functional foods.

Keywords

Goat milk Transcriptomic profile RNA-seq Lactation Colostrum Oligosaccharides Milk somatic cells

Background

One of the most important livestock and the oldest domesticated species raised worldwide is the goat (Capra hircus). Goats have long been used for their milk, meat, hair (including cashmere), and skins throughout much of the world and this species is also one of the best model organisms for mammary gland bioreactor studies [1]. Today, there are > 1000 goat breeds, and > 900 million goats are kept around the world according to a report by the UN Food and Agriculture Organization [2, 3]. Despite the agricultural and biological importance of goats, breeding and genetics studies have been delayed due to the lack of a reference genome. In [4] it was reported for the first time the sequencing and automated whole-genome optical mapping of the goat genome. Besides its nature of complete food able to provide nutritional subsistence of infants, healthy growth of children and nourishment of adult humans, milk contains a plethora of components that encode functional health benefits to consumers [5]. Among dairy livestock, goats produce milk containing free milk oligosaccharides (OS) at much higher levels than cattle or sheep [6, 7] and different studies have shown that their concentration is higher in colostrum than in the subsequent lactation stages [810]. A vast number of functional in vitro and in vivo studies gave evidence of the biological functions of OS in human health and nutrition [1115]. Moreover, goat milk contains a significant variability of OS, similar to the one observed in human milk, so to hold high potential as a natural source of lactose-derived OS as supplement for infant formulae and for the development of functional foods, having been successfully isolated by membrane technology [7, 16]. Goat milk contains both neutral and acidic OS. Galactose (Gal), glucose (Glc), fucose (Fuc), Nacetylglucosamine (GlcNAc) and N-acetylgalactosamine (GalNAc) grafted on a lactose core make up neutral OS; these same monomers, plus N-acetylneuraminic acid (NeuAc) and N-glycolylneuraminic acid (NeuGc), comprise acidic OS [17, 18]. Oligosaccharides chains covalently linked to glycoprotein and glycolipids often confer specific biological functions to the molecules carrying them such as signaling, adherence and cellular migration through the body. Glycosylation of proteins and lipids plays a crucial role in numerous biological processes including the regulation of immune and inflammatory responses [19]. To understand the function of glycoconjugated it is important to investigate the regulation of expression of the genes encoding the “glycosylation-related” genes, such as the large families of glycosyltransferase and glycosidase [2022].

Milk component synthesis and secretion by the mammary gland involve expression of a large number of genes. Formerly, many studies of gene expression were confined to the analysis of a limited number of selected genes of interest, e.g. the genes encoding the key lipogenic enzymes in the mammary gland [23, 24]. The recent development of Next-Generation Sequencing technologies allows a much broader and detailed knowledge on the biology of the mammary gland [25]. RNA-seq transcriptomic analysis has been widely used for model and non-model organisms and in particular for organisms for which limited genomic resources are available [2629].

Principal aim of the present study aim was to profile the transcriptome of goat milk somatic cells (GMSCs) by using an RNA-seq approach. Because, currently, there are no official gene annotations for the goat genome, we decided to employ two strategies to reconstruct transcripts as accurate as possible: 1. using the genome as reference to build transcripts from the sequencing reads, and 2. using a de novo, genome-independent assembly approach. Additionally, we aimed to identify the differentially expressed genes (DEGs) between two lactation stages, colostrum (D1) and 120 days (D2); in fact, colostrum composition highly differs from milk composition once the lactation is established, being the colostrum richer in nutrients and characterized by high levels of bioactive components [30]. Finally, we also aimed to compare, in the two different lactation stages, the expression of genes involved in OS metabolism, which, to date, was never analyzed in goats, but calls upon utmost interest, as shown by the studies on the expression of glycosylation-related genes, in mouse [31], humans [20, 32] and cattle [33].

Methods

Sample collection and RNA extraction

RNA used in the present trial was obtained from goat milk somatic cells (GMSCs), in agreement with [34], who showed that GMSCs can be used to accurately reveal the cellular dynamics of mammary gland gene expression, and with other authors [3537] who demonstrated an extensive similarity between the mammary gland and MSC/mammary epithelial cells transcriptome.

Milk was hand collected (both udder halves) from a Maltese goat, first parity, raised in a farm privately owned and traditionally managed. Two lactation stages were sampled: colostrum (D1) and 120 days milk (D2). Fifty mL of milk were transferred to falcon tubes and immediately centrifuged at 2000 g for 10 min at 20 °C in the presence of 50 μl EDTA 0,5M pH 8. The fat layer on the top of the supernatant was removed with a sterile pipette tip and the skim milk was discarded. The milk somatic cell-pellets were resuspended in 1 ml PBS 1× with 300 μl EDTA 0,5M pH 8. After centrifugation the pellets were lysed with 1 mL TRI REAGENT (Sigma-Aldrich, Milan, Italy) and total RNA was extracted following the TRI REAGENT protocol. Samples were digested with RNase-Free DNase Set (Qiagen, Milan, Italy) to remove DNA contamination and finally cleaned with RNeasy Minelute kit (Qiagen, Milan, Italy). RNA quality and quantity was checked with spectrophotometric (NanoPhotometer™ Pearl, Implen GmbH, München, Germany) and microfluidic inspections (RNA 6000 Nano chip, 2100 Bioanalyzer, Agilent Technologies, Milan, Italy).

Illumina sequencing and transcriptome assembly

The RNA was sequenced by using the ARK-Genomic center service at The Roslin Institute, University of Edinburgh [38]. Samples taken on for processing had an RNA integrity number (RIN) value greater than 7. Briefly, the polyadenylated RNA was isolated onto beads to remove it from all of the ribosomal RNAs, was then fragmented to an average size of 180 bases and reverse-transcribed to cDNA using random oligonucleotide primers. Following second-strand cDNA synthesis, end repair, and addition of a single A base, Illumina RNA-seq primers were ligated to the A-tailed double stranded cDNA. The ligated adapters were extended by PCR to add the sample barcode sequences and the resulting PCR products were assessed for quality using the Agilent Bioanalyzer DNA 1000 chip (average insert length of 175 bases) and for quantity using the Kapa Biosciences library quantification kit (Illumina). Following single stranding, dilution, loading onto an Illumina V2 flow cell for the Genome Analyzer IIx instrument, using a cBot following the manufacturer’s recommendations, the samples were finally sequenced for 79 bases paired end in a single lane of an Illumina Genome Analyzer IIx. The data was extracted to FastQ format using the Casava software from Illumina.

The raw read data from this study were deposited to the NCBI Sequence Read Archive (SRA) under accession number SRA261702. Read quality was examined using FastQC (v0.10.1) [39]. After adapter sequence removal, read ends were dynamically trimmed if base quality scores were lower than Q20 using Trimmomatic [40]. At the end of this process, reads were discarded if the total length of the read was shorter than 10 bases or if the fraction of undetermined bases was >2%.

The clean reads were subsequently analyzed with two approaches: a) by using the Tuxedo Suite, comprising of Bowtie (v2.1.0) [41], TopHat (v2.0.9) [42] and Cufflinks (v2.1.1) [43]. In particular, we aligned the sequence reads to the reference goat genome assembly AJPT0000000.1 [4] with a tolerance of two mismatches (Bowtie), we identified splicing junctions (TopHat), and we reconstructed transcripts and measured their expression levels (Cufflinks) reported as Fragments Per Kilobase of transcript per Million mapped reads (FPKM). b) by using the Inchworm RNA-Seq Assembler in the Trinity software [44], we performed a de novo transcriptome assembly, and we mapped the resulting contigs (corresponding to transcripts) to the goat genome using Gmap [45]. Additionally, Cufflinks was run in two different ways: a) using the available goat transcript sequences from the RefSeq database; b) without transcript information. For the latter, the Cufflinks generated transcriptomes obtained independently for the D1 and the D2 sample were merged using Cuffmerge, part of the Cufflinks distribution, to obtain a reference transcriptome. For the former, we started from the 27,946 RefSeq goat transcripts (as of July 2015), of which 27,649 reported as predicted, then we determined their genomic coordinates by using BLAT [46], converted them into General Feature Format (GFF), and feed this GFF file to Cufflinks to compute expression values, in FPKM units, using the Bowtie/TopHat mapped reads for the two samples.

Transcripts generated by Trinity and Cufflinks were compared to each other using BLASTN, requiring a significant match (E-value < = 10E-5) covering at least 50% of the longest transcript sequence.

Venn diagrams were obtained by using eulerAPE software [47].

Functional annotation and pathway enrichment analysis

The Cufflinks/Cuffmerge and the Trinity transcripts were BLASTed against the NCBI GenBank nucleotide (NT) or non-redundant protein (NR) databases to annotate them. A match was accepted if the BLAST top hit E-value was smaller than 10E-5. Transcripts for which no sufficiently good matches were found after this procedure were classified as ‘unannotated’. Gene symbols were associated to each match using bioDBNet [48]. Gene ontology (GO) and KEGG pathways annotations were obtained using Blast2GO [49] against the NR database (E-value cutoff 10E-5). This method has been applied to the full set of transcripts obtained for the two samples.

Lists of highly expressed genes and lists of differentially expressed genes (DEGs) between stages were characterized using the free Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.7 web-service [50, 51]. Enrichment in the GO three ontologies and in pathways was evaluated by DAVID by means of a modified Fisher Exact Test (EASE), using an EASE score threshold of 0.1. The DAVID Functional Annotation Clustering tool was used to group annotations, setting stringency to Medium and using default values for the other parameters. The Enrichment Score was used to rank functional clusters, and a representative annotation was chosen to represent each group as the one having lowest enrichment p-value. Clusters having no term with p-value < 0.05 were discarded.

Differential gene expression profiling

NOISeq was used to evaluate differential gene transcription between stages [52]. Read counts for each gene were compiled using HTSeq [53], normalized using the tmm (trimmed mean of M values) method, and analyzed by NOISeq using parameters nss = 5 (number of simulated replicates), pnr = 0.2 (size of simulated samples), v = 0.02 (simulated samples variability). Differentially expressed genes (DEGs) where selected as those with probability q > = 0.9. The WEB-based GEne SeT AnaLysis Toolkit (WebGestalt) software was used for functional enrichment analysis of the differential expressed genes [54, 55]. This tool integrates functional categories derived from centrally and publicly curated databases as well as computational analyses; it adds functional categories defined by hierarchical protein interaction network modules. The default software options were used with Benjamini-Hochberg multiple test adjustment. The WebGestalt software resulted in the visualization feature DAGs (directly acyclic graph) that not only revealed the hierarchical relationship of enriched GO terms but also extended to define relationship among genes in a network module. For the pathway collection, in addition to the KEGG database, WebGestalt has added data from Pathway Commons, which collects and integrates nine centrally curated biological pathway database, and WikiPathways, that is a primary source for open community-based curation. The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING v9.1) database and web tool [56] was used to provide a critical assessment and integration of protein-protein interactions, including direct (physical) as well indirect (functional) associations. Interactions are derived from multiple sources. The probabilistic confidence score that is assigned to associations, is derived by separately benchmarking groups of associations against the manually curated functional classification scheme of the KEGG database. The various major sources of interaction/association data in STRING are benchmarked independently; a combined score is computed which indicates higher confidence when more than one type of information supports a given association. The “minimum required interaction score” puts a threshold on the confidence score, such that only interactions above this score are included in the predicted network. Lower thresholds lead to more interactions, but also to more false positives. The confidence score is the approximate probability that a predicted link exists between two enzymes in the same metabolic map in the KEGG database. For the present study, we considered Bos Taurus as the reference organism and a minimum required interaction score of 0.7, which corresponds to a high-confidence predicted network.

Enrichment of sugar metabolism genes

We edited a list of genes implicated in milk OS metabolism and of genes relative to the lactose synthesis pathway following [33] and [57] respectively, with integrations. A list of 144 genes belonging to different functional OS metabolism categories was examined in the milk goat transcriptome. Goat orthologs were identified by using BLASTX against the NCBI NR (E-value < 10E-5), and expression values of the orthologs were computed by Cufflinks, as described above.

Results

RNA-seq mapping statistics and de novo assembly

RNA sequencing produced a total of ~ 100 million reads per sample; the number of reads obtained before and after the quality filtering are reported in Table 1. The raw read data were deposited to the NCBI Sequence Read Archive (SRA) under accession number SRP057582.
Table 1

Summary of read counts and alignments

Samples

Total reads

Clean reads

Mapped reads

Mapped/clean

D1

115,900,892

115,402,457

106,631,870

92.4%

D2

109,955,930

109,511,963

100,312,958

91.6%

Two strategies were employed to profile the goat milk transcriptome to overcome the limits of the currently available goat genome sequence and transcriptome annotations. We employed the putative transcripts provided by RefSeq through the Bowtie/TopHat/Cufflinks pipeline, but we also let the software reconstruct genes and transcripts directly from the read mapping. These pipelines were applied independently to both lactation samples, and the resulting transcripts were then merged and annotated. We also reconstructed the transcriptome using a de novo approach (i.e. without using a reference genome) by means of the software Trinity, with the aim of detecting transcripts encoded in missing or mis-assembled loci of the current genome assembly. Results of both approaches were compared and finally integrated.

The Cufflinks procedure when the RefSeq annotations were not provided generated 37,969 transcripts for the D1 sample, having an average length of 1,947 bp, organized in 31,311 putative genes, while the D2 sample generated 38,533 transcripts from 31,817 putative genes, 1,896 bp long on average. A total of 44,635 different transcripts, having average length of 2,296 bp (the longest transcript is 33,250 bp long) was obtained by merging the two datasets. The merging procedure, using Cuffmerge, also automatically discarded potential artifacts. These transcripts are organized in 33,757 clusters (i.e. tentative genes), which encode for 1.3 splicing variants on average (Additional file 1). All these transcripts were BLASTed against the NCBI nucleotide (NT) and non-redundant protein (NR) databases; a significant match was found for 40,353 of them (90%) against the NT and for 35,701 (80%) against the NR databases (Table 2).
Table 2

Statistics of the Cufflinks transcripts

Samples

Transcripts (Cufflinks)

Putative genes (Cufflinks)

Averaged fragments length (Cufflinks)

Total merged transcripts (Cuff merge)

D1

37,969

31,311

1,947

44,635 of which

40,353 (90% match against NT)

35,701 (80% match against NR)

D2

38,533

31,817

1,896

The de novo transcriptome assembly for the D1 sample produced 136,420 assembled fragments from 75,147 clusters (i.e. putative genes), with 1,311 bp of fragment average length, of which 112,001 (82%) map to the goat genome assembly with at least 90% identity and 90% sequence coverage. 58,495 fragments (43%) found a significant match with the NCBI NR and only 5404 assembled fragments (0.03%) do not show any significant match with the assembly. A total of 142,344 fragments from 73,823 putative genes (average fragment length 1,363 bp) were assembled from the D2 sample reads; 115,686 fragments (82%) map well on the goat genome (only 0.05% do not have any significant genomic match), while 66,919 (47%) have a significant hit in the NCBI NR (Table 3).
Table 3

Statistics of the de novo transcripts

Samples

Transcripts de novo

Averaged fragments length de novo

Transcripts mapped to goat genome (90% identity)

Transcripts matched with BLAST NR (E-value 10E-5)

D1

136,420

1,311

112,001 (82%)

58,495 (43%)

D2

142,344

1,363

115,686 (82%)

66,919 (47%)

The de novo assembled transcripts in the two lactation stages show a relevant correspondence with the merged transcriptome obtained using Cufflinks/Cuffmerge (44,635), as detected by BLASTing the former against the latter: 68% of the D1 sample de novo transcripts, and 69% of the D2 sample transcripts, have a significant match (E-value < 10E-5), and for 55% of both the D1 and the D2 sample transcripts, the match also covers at least 50% of the transcript sequence. While the assembled transcripts align remarkably well with the goat genome assembly, they must be taken cautiously since a large fraction of them is likely incomplete (i.e. not full length transcripts). To further validate them, we compared the de novo transcripts with those reconstructed by the Cufflinks/Cuffmerge pipeline, discarded those that find a good correspondence, and selected for further analysis those that, while not having a match with the genome, found a significant match in the NCBI NR database. The rationale is that this subset of de novo transcripts are more likely encoded by regions not present or mis-assembled in the current goat genome assembly and therefore missed by Cufflinks/Cuffmerge. Figure 1a reports as a Venn diagram a classification of the D1 de novo transcripts, that shows how many have a significant match with those reconstructed using the goat genome as reference by the Cufflinks pipeline, how many have a significant match with the NR database, and how many align well on the genome assembly, and the overlap between these groups. For example, a relevant portion of all transcripts (39,738, 29% of the total) shows significant matches with the Cufflinks transcripts, the NR and the genome, and corresponds to the region of overlap of the three groups at the center of the Venn diagram; another large group (35,939, 26%) aligns well on the genome and with the Cufflinks transcripts, but not with the NR, and corresponds to the overlap region between these two groups at the bottom of the Venn diagram. Figure 1b reports a similar Venn diagram for the D2 sample de novo transcripts. In this case, transcripts that have significant alignment with the Cufflinks transcripts, the NR and the genome are 45,641 (32% of the total), while those having a good match with the Cufflinks and the genome but not with the NR are 34,351 (24%). These might represent goat-specific transcripts, even if a fraction might not find a significant match in the NR because their sequence was not fully reconstructed, or reconstructed only or mainly in a non-translated region, or by the lack of an homologous protein in the database. In general, there is a good agreement between transcripts reconstructed using the genome as reference and those reconstructed using a de novo approach, and many of them can be annotated using a match with the NR dataset; this holds true for both the D1 and the D2 sample, and the relative size of these groups of transcripts is similar in the two samples. Yet, a large fraction of the de novo transcripts cannot be annotated.
Fig. 1

Matches between the de novo transcripts with goat genome, Cufflinks/Cuffmerge transcripts, NR database. a De novo D1 transcripts match with goat genome, Cufflinks/Cuffmerge transcripts and NR database. b De novo D2 transcripts match with goat genome, Cufflinks/Cuffmerge transcripts and NR database

A small fraction of transcripts (2699 for the D1 sample and 4098 for the D2 sample) find a significant match in the NR while not having correspondence either with the goat genome or with the Cufflinks transcripts. We suggest that those transcripts are encoded by loci not present or not well assembled in the current goat genome assembly. On the other hand, many de novo transcripts do not show any significant match with the Cufflinks/Cuffmerge transcripts, even though they can be mapped reliably to the goat genome assembly. Specifically, these are 36,324 for the D1 sample (the 34,119 that match only with the genome assembly plus the 2,205 that match with both the genome and the NR) and 35,794 for the D2 sample (33,902 plus 1,892), as shown in Fig. 1. Nevertheless, in the majority of cases (30,556 for the D1 and 30,672 for the D2 sample), even if the sequence of these transcript cannot be aligned significantly with any Cufflinks transcript, the mapped genomic coordinates of these de novo transcripts overlap with one of the loci reconstructed by Cufflinks with at least 200bp, suggesting that these transcripts are transcribed by the same loci. The lack of significant similarity as detected by BLASTing can be ascribed to errors in the transcript assembly, to the ability of Trinity and Cufflinks in reconstructing different portions of the transcript, or to incorrect reconstruction of alternative splicing events. The 3553 remaining de novo D1 and 3997 D2 sample transcripts need further investigation. A relevant fraction of the highly expressed transcripts (182 out of 798, 23% in the D1 sample; 180 out of 644, 28% in the D2 sample) did not show any significant match with the NCBI NR dataset. These un-annotated transcripts tend to be short, and nucleotide BLAST versus the NCBI NT collection showed that most of them find significant matches with ribosomal RNAs, suggesting that they might result from rRNA sample contamination.

Milk gene expression statistics

We first analyzed expression data, considering the transcripts obtained with the Cufflink procedure, setting four thresholds for the FPKM values (0.1, 1, 5, 15). Thereafter divided them into high (> = 500 FPKM), medium (> = 10 to 500 FPKM), and low expression (<10 FPKM) (Table 4). We observed a general agreement between the two samples (Table 4), both when using and when not using RefSeq predicted transcript annotations. In particular, there were 798 (3%) and 644 (2%) highly expressed genes in the D1 and in the D2 sample respectively when the Cufflinks procedure was employed without providing the RefSeq transcript coordinates, of which 496 genes are common between them. In general, gene expression resulted similar in the two considered samples, regardless of the analysis method.
Table 4

Gene expressed in D1 and D2 or co-expressed (i.e. found expressed in both samples) as percentage of the total number of transcripts and following the categorization

 

Without annotations

With RefSeq annotations

Category

D1

D2

Co-expressed

D1

D2

Co-expressed

FPKM value > =0.1

31,040 (95%)

31,136 (95%)

29,392

16,646 (60%)

16,200 (58%)

15,242

FPKM value > =1

17,188 (52%)

17,751 (54%)

15,768

11,327 (41%)

10,634 (38%)

10,203

FPKM value > =5

8,800 (27%)

8,472 (26%)

7,801

6,028 (22%)

4,983 (18%)

4,826

FPKM value > =15

4,465 (14%)

3,876 (12%)

3,643

3,017 (11%)

2,153 (8%)

2,095

Highly expressed genes (> = 500 FPKM)

798 (3%)

644 (2%)

594

164 (0.06%)

104 (0.03%)

103

Medium expressed genes (> = 10 FPKM to 500 FPKM)

7,733 (24%)

7,771 (24%)

6,856

3,782 (14%)

2,888 (11%)

2,750

Lowly expressed genes (<10FPKM)

24,453 (74%)

24,512 (75%)

23,663

23,460 (86%)

24,414 (89%)

23,383

In order to further categorize the genes with different level of expression, a multiphasic graph was obtained by plotting the log2 transformed FPKM value versus the expressed genes (Fig. 2). Two peaks, at 0.2 and 1 FPKM, respectively, are visible.
Fig. 2

Plot of the log2 (FPKM) value distribution in the D1 and D2 samples. Vertical red lines show the gene expression groups

Global milk transcriptome functional annotation and pathway analysis

To look at the different pattern of biological functions in the two studied lactation stages, transcripts reconstructed by Cufflinks in the D1 (31,311) and D2 sample (31,817) were annotated independently using the Blast2GO pipeline, starting from the BLASTX against the NR results. A total of 14,179 transcripts from the D1 and 14,620 from the D2 sample can be associated with at least one GO term. Distribution of GO terms and KEGG pathways were investigated, resulting quite similar. Both D1 and D2 sample GO terms lists, ranked by the Blast2GO score show at the top rather generic GO terms related to transcription regulation, signal transduction, transport, metabolic processes, cell division, phosphorylation, and others. It is to be expected that these two samples, that share the expression of the large majority of genes, also show similar GO terms distributions.

We also assessed the enrichment in the number of genes associated to a specific GO term or pathway comparing one sample to the other (Fisher exact test, p-value < = 0.05, FDR 5%). In the D1 sample, there was enrichment in 67 terms associated to cellular metabolic processes, cellular organization, biosynthesis, and response to stimuli (Additional file 2 and Fig. 3a). In the D2 sample, instead, the 20 enriched GO terms included several terms related to apoptosis (positive regulation of cell death, positive regulation of apoptotic process, induction of programmed cell death, developmental programmed cell death) (Additional file 2 and Fig. 3b). The examination of KEGG pathway categories and subcategories indicated that a large number of pathways (124) results expressed in both samples, and the most represented ones are related to purine and pyrimidine metabolism, aminoacyl-tRNA biosynthesis, inositol and phosphatidylinositol metabolism and signaling, glycerolipid, glycerophospholipid and sphingolipid metabolism, lysine degradation, oxidative phosphorylation, amino- and nucleotide-sugar metabolism, and T cell receptor signaling. Three pathways (amino sugar and nucleotide sugar metabolism, lysine degradation, steroid hormone biosynthesis) are enriched in the D1 with respect to the D2 sample in terms of number of expressed pathway members (Fisher exact test, p-value < = 0.05, FDR 5%). Two pathways, the naphthalene degradation and the lipoic acid metabolism pathways, are specific for the D1 sample, even though represented by very few expressed genes, while no pathway appears specific for the D2 sample (Additional file 3).
Fig. 3

Pie charts of the GO terms associated with the expressed genes in the two analyzed samples, D1 (a) and D2 (b). Each GO term detected by blast2go on the results of the BLAST against the NCBI NR database was mapped to its corresponding GOSlim (a selection of representative GO terms maintained by the Gene Ontology Consortium)

Top genes expressed in colostrum and mid lactation

In general the dynamic range of gene expression intensities covered several orders of magnitude and gene expression values in the two samples were highly correlated (Pearson correlation coefficient 0.93). The fifteen genes with the highest FPKM values in D1 and/or D2 sample were β-casein (CSN2), progestagen-associated endometrial protein (PAEP), α-S2 casein (CSN1S2), κ-casein (CSN3), α-lactalbumin (LALBA), tumor protein translationally-controlled 1 (TPT1), ferritin heavy chain 1 (FTH1), serum amyloid A3 (M-SAA3, goat locus LOC100860781), secreted phosphoprotein 1 (SPP1), translationally-controlled tumor protein-like (LOC102179679), glycosylation dependent cell adhesion molecule-1 (GLYCAM1), eukaryotic translation elongation factor 1 alpha 1 (EEF1A1), cathepsin D (CTSD), fatty acid synthase (FASN), ribosomal protein S29 (RPS29), α-S1 casein (CSN1S1), keratin 19 (KRT19) and checkpoint kinase 1 (CHEK1 or CHK1) (Table 5).
Table 5

List of the fifteen highly expressed genes (in bold) in the D1 and D2 samples, with their respective FPKM values

Gene symbol

D1 (FPKM)

D2 (FPKM)

CSN2

121511

320950

PAEP or BLG

65569

45561

CSN1S2

48645

103105

CSN3

47454

111404

LALBA

39379

60454

TPT1

12008

7603

FTH1

11827

6563

M-SAA3

10640

3141

SPP1

10047

7286

LOC102179679

7751

3769

GLYCAM1

7034

18498

EEF1A1

6294

3463

CTSD

5532

2974

FASN

4995

3580

RPS29

4775

1949

CSN1S1

2113

5089

KRT19

4103

4806

CHEK1

2325

3923

Ten of the fifteen highly expressed genes in D1 (PAEP, TPT1, FTH1, M-SAA3, SPP1, LOC102179679, EEF1A1, CTSD, FASN, RPS29) showed a decrease in expression in the D2 sample, while five (CSN2, CSN1S2, CSN3, LALBA, GLYCAM1) showed a gene expression increase from D1 to D2 lactation stages. In D2 sample three genes, CSN1S1, KRT19, CHEK1, showed a higher expression relative to D1 sample. Only three genes out of fifteen (M-SAA3, CTSD, RPS29) are highly expressed in one condition but not in the other.

Functional annotation analysis of highly expressed genes during lactation

To better characterize only the most abundantly transcribed genes, the groups of highly expressed genes described before (FPKM ≥ 500) in the two lactation stages were subjected to gene ontology classification and functional analysis using DAVID (Additional file 4). Annotations results showed more significant terms in the three GO categories and in the annotation cluster for D1 sample than for D2. Overall, the data indicated that in both samples highly expressed genes are involved in mRNA translation, protein localization, transport and modification, ATP biosynthesis, regulation of apoptosis and cell proliferation. In the GO apoptosis relative terms, some gene were ubiquitously expressed both with positive and negative regulation action (HMOX1, GRX1, UBB, UBA52, RPS27A, CIDEA, NOTCH1, CDKN1A, SQSTM1), some were implicated in the positive regulation (LALBA, TXNIP, PRDX1, RPS3, CD44, JUN, RPL11) and some in the negative regulation (CCL2, NFKBIA, TPT1, HSPA5, GSTP1).

Both in colostrum and in late lactation we found statistically significant (P < 0.001) enrichment of genes associated with GO terms related to nervous system (long-term strengthening of neuromuscular junction, axonogenesis, regulation of transmission of nerve impulse).

In the D1 sample most genes were specifically involved in carbohydrates metabolism (e.g. glucose and hexose catabolism), oxidative phosphorylation, regulation of unfolded protein, defense response, cellular homeostasis. We identified the solute carrier family 2 (facilitated glucose transporter), member 3, gene (SLC2A3) highly expressed in both lactation stages.

Heat shock protein genes (HSPA5, HSPA6, HSPA8, HSP90AA1, HSP 70.1) were found between the highly expressed genes with HSPA1A and HSPB1 identified between the differentially expressed genes up-regulated in colostrum. GO-BP classification for HSP genes showed association with the following terms: response to unfolded protein, negative regulation of programmed cell death, negative regulation of apoptosis, response to extracellular stimulus, chaperone mediated protein folding requiring cofactor, response to endoplasmic reticulum stress, ‘de novo’ posttranslational protein folding, cellular response to stress, negative regulation of peptidase activity.

The highly expressed genes in D2 sample included genes involved in positive regulation of apoptosis, in hydrogen peroxide catabolic process, in regulation of protein transport, modification process and localization, in negative regulation of response to stimulus and of cytokine secretion.

Differentially expressed genes between lactation stages

Differentially expressed loci (DEGs) between the D1 and the D2 samples were identified using expression levels of the 33,757 transcripts reconstructed by the Cufflinks/Cuffmerge procedure and the goat genome assembly as reference. A total of 418 transcripts, of which 401 were annotated to 329 distinct gene symbols, have NOISeq differential expression probability q > = 0.9. Of these, 275 reconstructed transcripts associated to 207 different gene symbols were up-regulated in D1, and 143 transcripts associated to 122 different genes were down-regulated (Additional file 5). The DAVID and WebGestalt tools were used for the functional enrichment analysis of these two gene lists. These web-services do not support all the organisms and gene identifiers, so we set the Homo sapiens species as reference. 169 out of 207 and 111 out of 122 were the up and down regulated genes in D1 sample included in the final analyses. Most GO categories in which DEGs were annotated correspond to those already described for the highly expressed genes.

Functional enrichment analysis of DEGs in D1 sample

The functional characterization of the annotated up-regulated genes in D1 showed enriched genes associated with GO-BP categories involved in glycolysis, carbohydrate metabolism and catabolism, defense response, cytokine activity, regulation of growth, cell death, vasculature development. The most important GO-MF terms were associated with protein binding and chemokine activity and the most important GO-CC terms were associated with cytoplasm and extracellular space (Additional files 6, 7).

Between the DEGs up-regulated in D1 sample we found the carbohydrate transporter encoding genes SLC2A1 and SLC2A10 and the nucleotide sugar transporter gene SLC35E1.

Higher expression of genes annotated in immune and defense terms (IL1R2, CCL3, CCL2, OLR1, C3, CLU, CCL5, CCL4, FCRL4, CCR9, GPI, BPI, PPBP, FCAR, CCL20, SEMA3C, IL12B, LBP, CFI, NFIL3, PTX3, DMBT1) was also here identified.

Many DEGs up-regulated in D1 were found in GO terms relative to positive (CCL2, PRTN3, CSF1, CLU, IGF1, ADM, F3, JUN, TGIF1, IL12B, FGF1, IL13RA1, SCG2) and negative (INHBA, ACVR1B, CDA, ING1, ENO1) regulation of cell proliferation and cell growth. Otherwise other genes were identified in terms relative to apoptosis and programmed cell death (FASTKD3, DLC1, EGLN3, DDIT4, MAGED1, ECE1, JUN, SIAH2, PPP1R15A, FGF2, PHLDA1, SOX9, PPIF, INHBA, ACVR1B, F3, IL12B, IGFBP3) with some as negative regulators (NOL3, CCL2, BAG3, F3, CLU, SERPINB2, TNFRSF18, HSPB1, IGF1, HSPA1A, SCG2). CCL2, CSF1, JUN, DDIT4 and PPP1R15a were also found between the highly expressed genes during lactation.

In D1 sample many DEGs were found in the statistically significant (P < 0.01) enriched GO terms associated with blood vessel development and morphogenesis (GPI, PLXDCI, JUN, APOLD1, SEMAC3C, FGF1, FGF2, SCG2); GPI and JUN were present also in the highly expressed gene list.

The WebGestalt KEGG/DAVID pathway enrichment analyses showed the glycolysis/gluconeogenesis and the metabolism of carbohydrates as specific for the D1 up-regulated genes (GPI, TPI1, LDHA, SLC2A1, PGAM1, HK2, HK1, PGK1, GAPDH, ENO1) while the cytokine-cytokine receptor interaction as the common pathway between the up (IL1R2, CCL3, CCL2, CSF1, CCL5, CCL4, CCR9, ACVR1B, INHBA, CCL20, PPBP, TNFRSF18, IL12B, IL13RA1) and down (IL20RB, LIFR, CNTFR, IL7R, CD27, TGFB2) regulated gene analysis (Additional file 6). Even the WebGestalt Pathway Commons analysis showed some pathway in common between D1 (FN1, CCL2, NDRG1, SLC2A1, KAT5, RCAN1, IL13RA1, BHLHE40, CCL5, HSPA1A, DDIT4, JUN, FOSB, IGF1, TNFRSF18, EGLN3, ENO1, NT5E, SAP30, IGFBP3, TGIF1, MT2A, GAPDH, IGFBP1, LDHA, MMP1, ADM, MDK, FGF1, PGK1, CSF1, PPP1R15A, HK2, HK1, LBP, CLU, HSPB1) and D2 (PLCG1, PAOX, LEF1, CTLA4, CD4, GJA1, RET, NDRG2, MAPK13, COL4A1, TGFB2, ESR1, ETV1, FABP4, MYB) up-regulated genes: integrin family cell surface interactions, glypican1 network and nectin.

Given the several biological functions and pathways identified through the analysis of gene expression in colostrum, we looked at the list of up regulated genes to search for predicted protein-protein interactions by using the STRING web resource. In the network view nodes are proteins while edges represent the predicted functional associations; the disconnected nodes were hidden (Additional file 8). Out of the 207 genes in the list, for 180 of them STRING found 97 protein interaction with a predicted confidence score > 0.7. The interaction enrichment analysis results showed that the network have significantly more interactions than expected.

Functional enrichment analysis of DEGs in D2 sample

The down-regulated DEGs in colostrum and consequently up-regulated in mid lactation stage showed enrichment of the GO-BP terms associated with positive regulation of lymphocyte activation and anatomical structure morphogenesis. The most important GO-MF terms were associated with actin binding and molecular transducer activity; the most important GO-CC terms were associated with plasma membrane and cell surface (Additional files 6, 9).

Between DEGs many transporters were identified: solute carrier family 15 (H+/peptide transporter), member 2 (SLC15A2), solute carrier family 28 (concentrative nucleoside transporter), member 1 (SLC28A1), solute carrier family 5 (sodium/sugar co-transporter), member 9 (SLC5A9), solute carrier family 5 (sodium/glucose co-transporter), member 1 (SLC5A1).

The WebGestalt KEGG/DAVID pathway enrichment analyses showed the T cell receptor signaling pathway (MAPK13, PLCG1, CTLA4, CD4, ICOS, CD28), cell adhesion molecules (CAMs) (ICOS, CTLA4, CD4, CD28), Jak-STAT signaling pathway (IL20RB, LIFR, CNTFR, IL7R), VEGF signaling pathway (PLCG1, MAPK13, PLA2G2C) specific for the D2 up-regulated genes. WebGestalt KEGG identified two more terms in Fc epsilon RI signaling pathway and hematopoietic cell lineage. The WebGestalt Pathway Commons analysis showed statistically significant gene enrichment (PLCG1, LEF1, CTLA4, CD4, GJA1, RET, NDRG2, MAPK13, TGFB2, ESR1, ETV1, FABP4, MYB) of the class I PI3K signaling events mediated by Akt, signaling events mediated by Hepatocyte Growth factor Receptor (c-Met), EGFR-dependent Endothelial signaling events, IL5-mediated signaling events, IFN-gamma pathway (Additional file 6).

Enrichment of oligosaccharides and lactose metabolism related genes

To identify the OS metabolism genes in goat milk we looked at the expression of 144 genes belonging to different functional glycosyltransferases commonly found in mammals, namely fucosyltransferases, galatosaminyl transferases, galactosyltransferases, sialyltransferases, mannosyltransferases, N-acetylglucosaminyl transferases. We included the glycosidases, fucose synthesis genes, sialic acid (SIA) synthesis genes, sugar transporters, and the genes for the lactose pathway in consideration that lactose L (Galb1-4Glc) forms the reducing end of milk oligosaccharides.

One hundred-ten genes out of 144 were expressed in the two milk samples with a minimum of 0.02 FPKM (ST6GALNAC3) in the D1 samples and a maximum of 60,453 FPKM (LALBA) in the D2 sample (Additional file 10). All genes of lactose synthesis, SIA synthesis genes, and fucose synthesis, were expressed in goat milk, whereas 34 genes belonging to the other categories were lacking, indicating a selective expression of some of these. Half of the genes showed a low expression (FPKM < 10) and half had medium expression value (>10 and <100 FPKM) while nine genes were expressed at higher levels, namely LALBA, SLCA3, GDP-mannose-4, 6-dehydratase (GMSD), NME/NM23 nucleoside diphosphate kinase 2 (NME2), SLC2A1, B4GALT1, UDP-GlcNAc:betaGal beta-1,3-N-acetylglucosaminyltransferase 2 (B3GNT2), N-acetylneuraminic acid synthase (sialic acid synthase) (NANS), hexosaminidase subunit beta (HEXB).

In Fig. 4 the log2 FPKM values for the expressed genes in D1 and D2 samples were graphically plotted. Most of the genes (64%) showed higher expression value in colostrum than in the later lactation stage, which can be better seen by plotting the log2 of the expression fold change (FC) (Additional file 11). The highest FC was for the ST6GALNAC3 gene with a value of 69 in D2 sample even with a small FPKM expression value (1.38).
Fig. 4

FPKM values of genes involved in goat oligosaccharides metabolism belonging to eleven functional categories

In general, almost all the genes coding for enzymes included in the lactose pathway showed an up-regulation in D1 stage; in particular, and increased LALBA and B4GALT1 gene expression was revealed between D1 and D2.

Considering the other galactosyltransferase genes, the B3GALT family was expressed at lower level respect to B4GALT as found in bovine [33].

Among sialyltransferases genes, both α2,3-sialyltransferases and α2,6- sialyltransferases showed higher levels of expression in D1 than D2 sample except for ST3GAL2, ST3GAL6, ST6GALNAC2, ST6GALNAC3, ST8SIA1. ST3GALT1, ST3GALT4, ST3GALT6, ST6GALNAC2, ST6GALNAC4 genes showed the highest expression and ST3GAL1 showed a 4-fold change FPKM value with respect to ST6GAL1.

Among the ST8SIA family genes, only ST8SIA1 was expressed in D1 and D2 samples.

Considering fucose synthesis, GMSD and TSTA3 gene were highly expressed relative to FPGT and FUK both in D1 and D2 samples. Among fucosyltransferases, FUT1, 2, 4, 7, 8, 10, 11 and POFUT1 were found expressed even if at low levels, while POFUT2 at medium level.

Among the N-acetylglucosaminyl transferase category, the B3GNT2 gene was one of the higher expressed in D1 sample.

In the galactosaminyl transferase category, the B3GALNT1 gene was expressed at higher level in D1 relative to D2 with a 4-fold change value.

A number of genes encoding proteins involved in complex N-glycan degradation were identified in goat milk, lysosomal sialidase (NEU1, NEU3), β-galactosidase (GLB1), β-hexosaminidase subunit α/β (HEXA/HEXB), α-L-fucosidase (FUCA1, FUCA2) able to catalyze the removal of Neu/NeuNAc, β-D-Gal, GlcNAc and Fuc, respectively, from the non-core structure of N-glycans. The HEXB was one of the most expressed genes overall and had high FPKM value with a two fold change ratio in D1 relative to D2 sample.

Discussion

RNA-seq mapping and gene expression statistics

In our experiment we choose to sequence the colostrum and 120 days samples at a resolution never used before in similar studies during lactation: i.e. more than 100 million mapped reads. Actually, forty millions have been considered sufficient to provide reliable measurement of a single transcript per cell and for the detection of major splice isoforms for abundant and moderately abundant transcripts [58]. However, greater sequencing depth allows more accurate assessment of the relative abundance of low-expression transcripts as well as the detection, by at least one read mapping, of a greater number of genes [59, 60]. In this work we wanted to explore as much as in detail the expressed genes in very different physiological stages, therefore we privileged having high sequencing depth and we chose to not pool samples and do not include biological replicates.

Considering the de novo assembly approach we compared our results with a similar study on goat mammary gland in which 98,864 transcripts and 51, 361 unigenes with a mean length of 1,219 bp were obtained [61]. Numbers are comparable, even if smaller, to those obtained by us. Moreover the authors used RNA pools of tissue at four life times and constructed a reference dataset for gene annotation without using a reference genome.

We compared our results with similar RNA-seq profiling in bovine milk somatic cells. In all these studies different experimental design, sampling, and sequencing depth were described but from a global point of view a media of 15,000-24,000 expressed genes have been identified [27, 28, 36]. In a previous work a threshold value of 0.2 RPKM was found for detectable gene expression in milk somatic cells [58]. If we consider the category of a more conservative FPKM value > 1, we get a number of co-expressed genes comparable to that found in bovine MSCs, here defined as those genes expressed at the FPKM threshold in both samples. In an analogous work in sheep a minor number of expressed genes was found, i.e. about 10,000 [29].

Global milk transcriptome functional annotation and pathway analysis

Through the analysis of global milk transcriptome we noted that pathways involving nucleosides and ribonucleosides were enriched in colostrum. These compounds belong to the non-protein-nitrogen fraction, with biological role which is not limited to act as metabolites but to work as bioactive substances in the regulation of body functions, contributing to cell renewal or restoration [62]. Moreover nucleotide sugars act as donors for sugar residues in the glycosylation reactions that produce polysaccharides They are the glycosyl donors for glycosyltransferases and the precursors of glycoproteins, glycolipids and OS [63]. On the other hand, also the lysine degradation pathway was highly represented, in agreement with the findings of [64] suggesting that when Lys is in excess of requirements, the mammary gland appears to dispose of the extra supply via the oxidative mechanism. An increase in the catabolism of several amino acids was indicated in different studies on bovine mammary gland [65, 66], because amino acids could be an important pool of energy during lactation. The results of the BLAST2GO pipeline obtained in the present study confirm therefore the ability of the mammary gland to adapt different molecular functions according to the biological need of the animal.

Top genes expressed in colostrum and mid lactation

Among the top expressed genes in colostrum and mid lactation, we detected CSN2, LALBA, CSN1S1, CSN3 FTH1, SPP1, FASN and EEF1A1, also identified by [57] among the top 20 expressed genes in the milk fat layer of mature human milk. The high expression of casein (CN) genes, all increasing in expression from D1 to D2 lactation stages, was not surprising because caseins, together with the whey proteins, represent the 90% of milk proteins fraction [67]. In this work, we found that CSN2 and the whey protein α-lactalbumin (α-LA) coding gene (LALBA) have the highest expression values, as already detected by [61] in goat, [33] in cow and [507] in human.

The PAEP gene, also known as BLG, encodes the beta-lactoglobulin, the major whey protein in bovine, buffalo, goat, and horse milk [68], while EEF1A1 high expression is justified by the considerable request of milk protein synthesis during lactation, being EEF1A1 one of the most abundant protein synthesis factors [69, 70].

As for the CTSD (cathepsin D) gene, studies on rat mammary gland demonstrated that CTSD dependent activity is required to effect the cleavage of prolactin, the fundamental hormone for the function of the mammary gland, influencing also the expression of a number of genes as well the secretion of lipids and milk proteins [7173].

The FTH1 gene encodes for ferritin, and has been identified as an anti-apoptotic factor [74]. Ferritin is present in blood and extracellular fluids, including milk. In cows it was found that ferritin concentrations were higher in colostrum than in serum [75], in agreement with [57] who reported a higher ferritin genes expression in human colostrum with respect to mature milk. The expression of M-SAA3.2 gene in the bovine mammary gland was reported by [76] who confirmed the role of this gene in host defense, likely in the neonate gastrointestinal tract. Moreover it has been suggested that M-SAA3 represent one of the most promising candidates affecting milk protein and fat percentage of dairy cattle [27].

The SPP1 gene, encoding for ospeopontin protein, is essential for mammary gland development, milk production, local mammary gland immunity and it has a significant role in the modulation of milk protein gene expression [77, 78]. The FASN gene is directly involved in de novo synthesis of most of the short and medium-chain fatty acids in milk and is essential for the development, functional competence, and maintenance of the lactating mammary gland [61, 79, 80]. KRT19 gene expression has been associated with an intact cytoskeleton required for autophagic cell death in goat mammary gland [81]. The GLYCAM1 gene encodes for a milk fat globule glycoprotein member of the mucin family. GLYCAM1 mRNA was reported to be highly expressed during lactation in goat mammary gland [82], so suggesting a protective role of this gene.

High expression of the RPS29 gene in goat mammary gland was already reported by [83] and in human mammary gland epithelium by [84].

Functional annotation analysis of highly expressed genes during lactation

The finding of most statistically significant GO terms associated with translation, ribosomial biogenesis, rRNA processing is in agreement with the 25% of the highly expressed ribosomal protein genes. This is biologically meaningful because an increased translational requirement is needed to support caseins and other milk proteins synthesis. The gene encoding for the EEF2 translational elongation factor has been found expressed during goat lactation as previously reported in bovine [66]. However in ruminants mammary gland only a minor portion of the synthetic machinery appears to be used for production of milk proteins but for the biological needs of mammary tissue during lactation [85].

Many highly expressed genes during lactation were also associated to GO terms implicated in regulation of programmed cell death and cell proliferation relative terms. Mammary apoptosis was first demonstrated during tissue involution after lactation, but it has been detected also during lactation, in mammary tissue of lactating mice, goats and cattle [86, 87]. In bovine mammary tissue a high level of apoptosis was found in early lactation [8890]. These authors speculated that their results were an effect of tissue edema and increased apoptotic leucocytes or that may be due to death of nonfunctional or senescent cells or to removal of excess capacity of newly synthesized and yet undifferentiated cells. In colostrum samples many different somatic cells type (leukocytes and macrophages) are present beyond milk epithelial cells, so we can suppose that both hypothesis could be corroborated by our results; on the other hand, we did not find, in the present trial, any of the most apoptotic markers (i.e. Bax, Bcl-2, TGF-β1, CPP-32, Fas, CASP genes) [9092].

UBB, UBA 52 and RPS27A genes represent three of the four ubiquitin genes found in mammals, expressing the main non-lysosomal route for intracellular protein degradation in eukaryotes. Both [93] and [28] found genes belonging to the ubiquitin-proteasome pathway expressed during early involution of mouse mammary gland and at peak lactation in bovine MSC, respectively. Recent findings also substantiate a pivotal role of the ubiquitin/proteasome pathway in the regulation of apoptosis [94, 95]. Also the NFκBIA gene, here found as highly expressed both in D1 and D2 samples, could have a role in the regulation of cell proliferation and programmed cell death because involved in activation of stressful situations requiring rapid reprogramming of gene expression [96]. It was reported to be expressed in bovine mammary gland during lactation by [90].

Heat shock protein genes (HSPA5, HSPA6, HSPA8, HSP90AA1, HSP 70.1), found between the highly expressed genes in the lactation, have a protective function, allowing cells to survive lethal conditions, and also interact with various components of the programmed cell death machinery; on the other hand, membrane bound HSPs mediate immunological functions [97, 98]. Our finding confirms what reported in literature about HSP functions and demonstrates how MSCs, in goat colostrum, can support a high level of protein synthesis act to operate in both immunity and apoptosis.

Another highly expressed GO category of genes included the nervous system and the regulation of transmission of nerve impulse, already reported by [65] in cow lactation given its role in milk ejection and control of blood flow.

Most of the highly expressed identified genes were specifically involved in carbohydrates metabolism. A previous study on goat mammary gland showed how glucose is oxidized about twice as rapidly as acetate and is essential for milk secretion and that the glucose uptake by the lactating gland was much greater than that required for milk lactose synthesis, suggesting that substantial amounts are oxidized under normal conditions, as in the isolated gland [99]. On the other hand the glucose transporter SLC2A3 was highly expressed in both lactation stages, in agreement with the findings of [66] on cow lactation; these authors suggested that the high expression of genes involved in energy metabolism was likely related to the high-energy demand for milk synthesis and secretion [65].

Differentially expressed genes between lactation stages

One of the major focuses of the present trial was to evaluate the differentially expressed genes between lactation stages, to assess and confirm the high nutritional value of goat colostrum and milk. Colostrum is a source of immunomodulatory molecules that positively influence the immune status of the neonate, including polymorphonuclear leukocytes and macrophages, T-lymphocytes, B-lymphocytes, plasma cells and epithelial cells [100, 101]. In the colostrum stage, the present study identified as up-regulated genes encoding carbohydrate transporter (SLC2A1 and SLC2A10) and the nucleotide sugar transporter gene SLC35E1. Glucose transport across the plasma membranes of mammalian cells is carried out by two distinct processes employing the passive, facilitative, energy-independent glucose transporters GLUT, encoded by SLC2A genes, and the active sodium-dependent and energy-dependent glucose transporters SGLT, encoded by SLC5A genes. The member of the solute carrier family SLC35 encode antiporters transporting nucleotide sugars pooled in the cytosol into the lumen of the Golgi and/or the ER in exchange for the corresponding nucleoside monophosphates. The transported nucleotide sugars are utilized as sugar donors by glycosyltransferases for the synthesis of sugar chains of glycoproteins, glycolipids and polysaccharides [102]. Currently, the SLC35 human family comprises 31 hydrophobic and homologous proteins, which are divided into 7 subfamilies, from SLC35A to SLC35G. Among the 31 SLC35 family members, only about one-third of them have been studied in detail. Little is known about the remaining members of the family, particularly the members of the SLC35E, SLC35F and SLC35G subfamilies and to date there are no known reports on the biological functions and the subcellular localizations of these proteins [103]. SLC35E1 gene expression was found to be ubiquitous in various human adult and fetal tissues and cell lines with the highest value in mammary gland [104]. So our results confirm the expression of this gene in GMSC. Moreover we found the polysaccharides and glycosaminoglycan binding molecular function enriched in colostrum sample. This suggests a possible support to the oligosaccharides metabolism whose concentration has been reported to decrease from colostrum to late lactation stage in goat milk [105].

The finding of genes annotated in immune and defense terms in D1 sample is consistent with the general conception that milk after parturition provides passive protection for the neonate and has immunostimulatory capabilities. The chemokinesis and chemotaxis properties of goat milk toward different leukocyte populations were studied during lactation; mononuclear cells (MNCs) were the major cell found in colostrum and polymorphonuclear leukocytes (PMNs) the predominant cell type found during the late lactation stages (>weak 19) [106].

Many DEGs up-regulated in D1 were found in GO terms relative to either positive or negative regulation of cell proliferation and cell growth, as well as to apoptosis and programmed cell death. In the colostrum sample the protein encoded by chemokine (C-C Motif) Ligand 2 (CCL2) gene seems to have a positive effects on cell proliferation and a negative effects on apoptosis; this cytokine displays chemotactic activity for monocytes and basophils but not for neutrophils or eosinophils [107]. Among the genes involved in the positive regulation of cell proliferation, the fundamental role of the Colony Stimulating Factor 1 (CSF1) gene has to be underlined. Being the protein encoded by this gene a cytokine controlling production, differentiation, and function of macrophages, it promotes the release of proinflammatory chemokines, so playing an important role in innate immunity and in inflammatory processes [108]. Hence, we can argue that in colostrum cells the expression of both CCL2 and CSF1 genes could be involved in the recruitment of monocyte/macrophage to participate in immune responses, development, and tissue homeostasis and in monocyte recruitment in mammary gland tissue where collateral vessels are actively [109]. Furthermore the finding of DEGs in enriched GO terms associated with blood vessel development and morphogenesis agree with a study in bovine lactation [58] and confirm an important role for angiogenesis at the onset of goat lactation, while increasing blood flow across the mammary gland [110].

Of the common pathway of the up-regulated genes identified in the two lactation stages, integrins occupy an important role. Through integrins, the extra-cellular matrix influences growth, differentiation, and survival of mammary epithelial cells. Both in mouse [93] and bovine mammary gland [111] these proteins has been found expressed at different level during lactation and involution. In D1 sample enrichment of IGF-1, IGFBP1 and IGFBP3 genes was here revealed. The IGF system plays a pivotal role in mammary tissue homeostasis, regulating cell proliferation and differentiation during lactogenesis [112]; furthermore. IGF-1 and IGFBP3 were found expressed in bovine mammary gland at higher level during early lactation [89], while in goat mammary gland they have been found expressed during involution [113].

In the predicted protein interaction network the up-regulated genes in D1, obtained with STRING, the central role of the Jun Proto-Oncogene (JUN) is outlined by its multiple connections with other proteins. JUN, which was also one of the highly expressed genes, encodes a member of the activator protein-1 (AP-1) transcription factor family, a key component of many signal transduction pathways. AP-1 is composed of dimers of Jun (cJun, JunB, and JunD), Fos (cFos, FosB, Fra-1, and Fra-2), or other closely related factors such as ATF proteins. The Jun members homodimerize or heterodimerize with different Fos or ATF members, while Fos members only heterodimerize with different Jun members [114]. So the predicted JUN interaction with FOSB in the network has experimental biological relevance. Differential expression and activation of Jun and Fos members allow AP-1 complexes to control a wide variety of cellular functions. The AP-1 factor was indicated as a regulator of postnatal proliferation of mouse mammary epithelial cells [114] and of the early and late phases of mouse mammary gland involution [115]. The genes that are activated by FosB are unknown, but one excellent candidate is the oxytocin receptor gene which contains within its promoter an AP-1 site. A requirement for FosB in the nurturing response in mice was demonstrated [116], raising the hypothesis that FosB might be induced by oxytocin binding to receptors on preoptic area of the hypothalamus (POA) neurons, this leading to increased expression of the oxytocin receptor gene. Thus, by a feed-forward mechanism, POA neurons would express more oxytocin receptors and would therefore have enhanced sensitivity to oxytocin.

Some of JUN interactions could be relevant in cells physiology, as the interaction with the various CCL proteins and IL12B, which plays a role in cell proliferation/cell growth and immunity. The interaction with FGF2/FGF1 could be explained by their inclusion in the same GO terms functional enrichment, the blood vessel development and morphogenesis. JUN is predict to link to HSPB1, which is in turn strongly associated to HSPA1A/DNAJB1/BAG3. All these genes are involved in cell maintenance and cellular assembly and organization. Small heat shock proteins (sHsp), of which HSPB1 is a member, comprise the most widespread but also the most poorly conserved family of molecular chaperones and are involved in protecting cells from various unfavorable conditions, therefore playing a fundamental role in cell survival. At a molecular level, sHsp are involved in preventing aggregation of partially misfolded proteins and have been shown to intervene in modulating cell death pathways by interacting with various components of the cell death machinery upstream and downstream of the mitochondrial apoptotic events so preventing apoptosis in different lethal stress situations [117, 118]. At the bottom side of the network graph, the nine genes (GAPDH, PGK1, TPI1, ENO1, LDHA, PGAM1, GPI, HK1, HK2) linked together with many interaction edges belong to the glycolysis/gluconeogenesis KEGG pathway and carbohydrate metabolism biological process, which are two of the functional enrichment terms that significantly differentiate D1 from D2 lactation stage. The link between JUN and GAPDH derives from text-mining, because co-mentioned in a PubMed abstracts (GAPDH was used as gene reference gene).

Considering the up-regulated DEGs in D2 samples, some of these belong to the solute carrier family (SLC), coding for the SGLT family proteins. SGLT is a family of solute-linked carriers that contain sodium-coupled transporters for several nutrients. The Na +−electrochemical gradient provided by the Na +−K+ ATPase pump is utilized to transport substrates into cells against its concentration gradient. The co-transported substrates are sugars, inositol, proline, pantothenate, iodide, urea and other undetermined solutes [119]. In cattle, the glucose transporter SGLT1, encoded by SLC5A1 gene, is responsible for the high-affinity, conservative uptake of most monosaccharides (glucose and galactose), except fructose; in human, SGLT4, encoded by SLC5A9 gene, is an essential transporter for mannose, 1,5-anhydro-D-glucitol, and fructose [120]. In mammary gland epithelia, the protein encoded by the SLC15A2 gene (PEPT2) is reported to be involved in the reuptake of small peptides accumulated from the hydrolysis of milk proteins [121]. The SLC28A1 gene encodes for sodium-dependent, concentrative nucleoside transporter, pyrimidine- nucleoside preferring, epithelia localized, CNT1. We might conclude therefore that, both in D1 and D2 stages, sugar and nucleotide sugar transporters were differentially expressed to take account of the different physiological need of the mammary gland during lactation.

Within the more specific pathways of the up-regulated genes in D2 stage, the T cell receptor signaling, cell adhesion molecules, Jak-STAT signaling and VEGF signaling pathways must be mentioned.

In particular, the JAK-STAT signaling, which regulates many cellular processes, including innate and adaptive immune function, development, cell proliferation, differentiation, and apoptosis, has been found to be crucial in the mammary gland with STAT5 factor been important for controlling expression of milk protein genes in rodent [122]. However, in cattle and sheep mammary gland, the role of JaK2-STAT5 signaling pathway seemed to support a minor role for milk protein synthesis [29, 66]. In the present work we did not find STAT5 between the highly expressed genes either in D1 or D2 samples but we found the transcription factor E74-like factor 5 (ELF5) expressed. This factor has been postulated to work as regulator of milk protein expression, encompassing STAT5 as a key player and its expression has been found increased during cow and sheep lactation [29, 66]. Therefore our findings allow to hypothesize a similar physiological role of this gene during lactation in goats.

In conclusion, the results obtained for the genes up-regulated in mature milk depict a scenario in which cells are ready to respond at external and immunological stimuli, to transduce signals inside cells and to orchestrate appropriate actions (e.g. cytokine gene transcription, JAK activation responsible for apoptosis execution) [123126].

Enrichment of oligosaccharides and lactose metabolism related genes

The synthesis of free OS and OS chain on glycoconjugates is mediated by the concerted action of many glycosyltransferases (GTs), glycosidases, sulfotransferase and accessory molecules; GTs typically catalyze the transfer of a monosaccharide from a nucleotide-sugar to a growing acceptor-OS attached to a carrier (in some cases directly to the carrier molecule). The OS product of one reaction is the substrate in the next reaction, and the array of glycosylation-related genes expressed in a particular cell is an important factor in the control of OS synthesis [20].

In this work we identified many expressed genes encoding for protein involved at different steps in the OS metabolism.

α-lactalbumin (LALBA) has important biological and functional properties and, in the mammary gland, interacts with β1,4-Galactosyltransferase (B4GALT1) to form the lactose synthase complex that produce lactose, the core unit from which all the other OS are synthesized [127, 128]. The increased LALBA expression that we found between D1 and D2 is consistent both with an increase of milk protein output and with lactose synthesis. The B4GALT1 gene showed a high expression during goat lactation suggesting that the protein essential for the lactose synthase complex formation is ensured. Almost all the other genes coding for enzymes included in the lactose pathway showed an up-regulation in D1 stage similarly to what reported for human milk [57].

The B4GALT1 encoded protein is one of the best-studied galactosyltransferase enzymes and has been identified on the plasma membrane, so that roles in cell-cell or cell-substrate recognition were hypothesized [129].

The genes implicated genes in sialylated OS metabolism were found expressed at medium values in our experiment. Considering the SIA synthesis genes [130], the NANS and the cytidine monophosphate N-acetylneuraminic acid synthetase (CMAS) were the most expressed, similarly to what found in bovine [33]. Goat milk has been reported to contain a high concentration of sialylated OS, specifically 3′sialyllactose (3′-SL), 6′sialyllactose (6′-SL); their biosynthesis starts from Gal in lactose with the addition of N-acetylneuraminic acid (NeuAc) or N-glycolylneuraminic acid (NeuGc), in α2-3 aor α2-6 linkages respectively. A detailed qualitative and quantitative characterization of goat colostrum oligosaccharides has been carried out by [131]. The authors identified 78 oligosaccharides, of which 40 (51.3%) were neutral non-fucosylated, 3 (3.8%) neutral fucosylated and 35 (44.9%) corresponded to sialylated (Neu5Ac/Neu5Gc) oligosaccharides. The predominant OS were sialyl-lactoses followed by Hex-HexNAc-Neu5Ac and Hex-HexNAc-Neu5Gc residues. The most abundant OS were galactosyl-lactoses.

The sialyltransferase enzymes ST6GAL1 and ST3GAL4 have been shown to be responsible for the production of 6′-SL and 3′SL in mouse milk respectively [132]. So we can suggest that both in goat ST6GAL1 could act in the biosynthesis of 6′SL and ST3GAL4 plus ST3GAL1 and ST3GAL6 in the biosynthesis of 3′SL. Our results agree with the literature both because higher OS concentrations have been reported in colostrum than in late lactation in different animal species and given that some oligosaccharide profile analyses indicated how 3′-sialylactose is more abundant than the 6′-sialylactose in colostrum and late lactation goat milk [105, 133].

ST3GAL5 encodes the GM3 synthase responsible for the biosynthesis of ganglio-series gangliosides that are sialic acid-bearing glycosphingolipids, the major glycans in nerve cells. It was recently demonstrated that ST3Gal5 catalyzes GM4 synthesis in vivo [134136] suggesting that the sialic acid moieties of gangliosides and glycoproteins in the brain frontal cortex probably participate in a variety of cellular events including the formation of memory and learning. It was shown that it may be feasible to use sialyllactose, separated from cheese whey or colostrum, as a functional food for brain activation [137].

The protein encoded by one of the higher expressed gene in D1 sample (B3GNT2) synthesizes a unique structure known as poly-N-acetyllactosamine (polyLacNAc) a linear carbohydrate polymer composed of alternating N-acetylglucosamine and galactose residues. Recently, results obtained with knockout B3gnt2−/− mice strains showed that hyperactivation of lymphocytes occurred due to a lack of polylactosamine on receptor molecules, indicating that polylactosamine has an important role in immunological biofunctions [138, 139]. The analysis of gene expression profiles in human monocytes, dendritic cells and macrophages found expressed B3GNT2 suggesting that monocytes synthesize N-glycans carrying di, tri, and tetra-antennary structures, possibly elongated by poly-N-acetyllactosamine chains. This agrees with our results of genes annotated in immune and defense terms in D1 somatic cell and with the finding that mononuclear cells (MNCs) are the major cells found in colostrum [106].

The expressed B3GALNT1 gene encodes for the globoside synthase an enzyme that catalyze the addition of N-acetylgalactosamine to globotriaosylceramide (Gb3) to form globoside (Gb4), a globo-series glycosphingolipid (GSL) member [140]. Because of the relationship to many human pathogens and diseases the interest in glycolipid-based blood group antigens remains high. Moreover milk glycosphingolipids have been reported to participate in the newborn’s defense against pathogens. Ovine milk has been found to have a high content of complex neutral GSL, such as Gb3 and Gb4, which are very abundant in human milk but not in bovine milk and which are able to bind to some E. coli strains to prevent infection [141].

In our samples many genes both for fucose synthesis and fucosyltransferase were found expressed. In contrast to human milk, the content of fucosylated oligosaccharides is rather low in the milk of domestic animal species [18]. In goat colostrum 2′FL was found by [142], while in [131] 2′-FL plus fucosyl-lactosamine and lacto-N- fuco-pentaose were detected even if at low abundance. During fucosylation, activated fucose is transferred to various lactosamine acceptors by specific fucosyltransferases (Fuc-T) encoded by the fucosyltransferase gene (FUT). The biosynthesis of GDP-L-fucose consists of two cytosolic pathways. The constitutively active de novo pathway involves two enzymes (GMD, encoded by GMSD gene and FX, encoded by TSTA3 gene). In the alternative salvage metabolism, that utilizes L-Fuc obtained from extracellular sources or from intracellular degradation of glycoproteins and glycolipids, L-fucokinase (FUK gene) and GDP-L-fucose pyrophosphorylase (FPGT gene) are involved. After synthesis in the cytosol, GDP-L-Fuc is translocated into the Golgi apparatus via a specific GDP-fucose transporter encoded by gene FUCT1/SLC35C1 [143, 144]. Because in D1 and D2 samples GMSD and TSTA3 gene were highly expressed relative to FPGT and FUK we suggesting that in GMSC the de novo pathway is preferentially activated and that a high amount of fucose is synthesized in cytoplasm ready to be imported in Golgi apparatus for fucosylation. Among the fucosyltransferases, POFUT2 gene was the highest expressed in D1 and D2 samples. The encoded soluble enzymes is localized in the endoplasmic reticulum (ER) and its ability to distinguish between folded and unfolded structures has led to the proposal that POFUT2 function as chaperones or in the folding and secretion of their target proteins [145].

Among glycosidase genes, the HEXB gene has been found highly expressed in both samples with a two fold change ratio in D1 relative to D2 sample. Two major lysosomal isoenzymes exist in human tissues, which are the products of the assembly of two subunits, α and β, encoded by the homologous genes HEXA and HEXB. The two isoenzymes Hex A (αβ) and Hex B (ββ) differ in their substrate specificity. The active site of the β-subunit hydrolyzes uncharged substrates, whereas the α-subunit, in addition, cleaves negatively charged substrates [146]. The greater expression of HEXB could be associated with the presence of higher percentage of neutral OS in goat milk. In bovine MSCs higher expression values for HEXA gene were found [33]. Moreover because lysosomal hexosaminidases release terminal β-glycosidically linked N-acetylglucosamine and N-acetylgalactosamineresidues from a number of glycoconjugates [147] we can speculate that the higher oligosaccharides concentration in colostrum could be due both to glycan degradation activities and glycosyltransferase activities.

Considering neuraminidase genes, NEU2 and NEU4 were not found expressed, as in bovine [33].

All the investigated sugar transporter genes were found expressed in goat milk, except SLC2A8 and SLC35A4. These last two genes, contrary to goat, were present in bovine mammary gland and somatic cells [33, 66]. We already discussed SLC2A1, SLC2A10 and SLC2A3 as DEG and highly expressed genes respectively. Among the nucleotide sugars transporters, SLC35A2, SLC35A5, SLC35B1 and SLC35C1 were expressed at higher levels. Both the CMP-Sialic Acid Transporter (SLC35A1) [148] and GDP-Fucose Transporter 1 (SLC35C1) [149] were found expressed at low and medium levels, respectively. SLC17A5 (also known as AST), encode a protein (sialin) with a sialic acid transport function whose mutations are the primary cause of lysosomal sialic acid storage diseases [150].

Conclusion

Transcriptional profiling is a powerful approach for identification of genes globally and functionally expressed in various tissue including mammary gland and milk. In this work we characterized the goat milk somatic cells transcriptome at two lactation stages by using RNA-seq and by integrating two bioinformatic methodologies. We looked at the gene expression, using software applications that integrate functional enrichment analysis and statistical analysis of large set of genes. We identified distinct genes and pathway underlying the differences between colostrum and 120 days milk. Finally, for the first time, a study of glycosylation-related genes was carried out in goat milk to get a picture of genes possibly implicated in oligosaccharides biosynthesis.

Abbreviations

DEG: 

Differentially expressed gene

GO-BP: 

Gene ontology biological processes

GO-CC: 

Gene ontology cellular component

GO-MF: 

Gene ontology molecular function

Declarations

Acknowledgments

We thanks Carmela Tripaldi that kindly provided milk samples.

Funding

This study was supported by the research programme “Improvement of Italian livestock through the use of innovative biotechnologies: functional genomics, transcriptomics and proteomics (GENZOOT)” funded by the Italian Ministry of Agriculture.

Availability of data and materials

The datasets supporting the conclusions of this article are deposited to the NCBI Sequence Read Archive (SRA) under accession number SRP057582 and included within the article and its additional files.

Authors’ contributions

AC conceived the study, carried out the experiments and drafted the manuscript. FF analyzed sequencing data and helped in writing the manuscript. GC helped with the preparation/submission of the additional manuscript materials/sequencing data. BM was a contributor in the manuscript final version. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Milk samples were collected by the owner of a private farm as part of routine milking; colostrum was also collected by the owner. Collection protocol and animal care were in accordance with Directive 2010/63/EU of the European Parliament and the Council of 22 September 2010, on the protection of animals used for scientific purposes [European Commission 2010 Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes. Official Journal of the European Union L 276: 33–79].

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) - Animal production research centre
(2)
Department of Pharmacy and Biotechnology (FaBiT), University of Bologna Alma Mater
(3)
Applications and Innovation Department, CINECA, SCAI SuperComputing

References

  1. MacHugh DE, Bradley DG. Livestock genetic origins: goat buck the trend. Proc Natl Acad Sci. 2001;98:5382–4.PubMedPubMed CentralView ArticleGoogle Scholar
  2. Food and Agriculture Organization of the United nations. http://www.fao.org/corp/statistics/en. Accessed 11 Oct 2016.
  3. Devendra C. Dairy goats in Asia: multifunctional relevance and contribution to food and nutrition security. In: E-Proceeding of the First Asia Dairy Goat Conference. Serdang: University Putra Malaysia and the Food and Agricultural Organization of the United Nation Publ; 2012. p. 1–6.Google Scholar
  4. Dong Y, Xie M, Jiang Y, Xiao Y, Du N, Zhang X, Tosser-Klopp W, Wang G, Yang J, Liang S, Chen J, Chen W, Zeng J, Hou P, Bian Y, Pan C, Li S, Liu Y, Wang X, Servin W, Sayre B, Zhu B, Sweeney B, Moore D, Nie R, Shen W, Zhao Y, Zhang R, Li G, Faraut J, Womack T, Zhang J, Kijas Y, Cockett J, Xu N, Zhao X, Wang S, Wang J, Wang W. Sequencing and automated whole-genome optical mapping of the genome of a domestic goat (Capra hircus). Nat Biotechnol. 2013;31:135–41.PubMedView ArticleGoogle Scholar
  5. Mills S, Ross RP, Hill C, Fitzgerald GF, Stanton C. Milk intelligence: Mining milk for bioactive substances associated with human health. Int Dairy J. 2011;21:377–401.View ArticleGoogle Scholar
  6. Nakamura T, Urashima T. The milk oligosaccharides of domestic farm animals. Trends Glycosci Glyc. 2004;16:135–42.View ArticleGoogle Scholar
  7. Martinez-Ferez A, Rudloff S, Guadix A, Henkel CA, Pohlentz G, Boza JJ, Guadix EM, Kunz C. Goats’ milk as a natural source of lactose-derived oligosaccharidess: Isolation by membrane technology. Int Dairy J. 2006;16:173–81.View ArticleGoogle Scholar
  8. Urashima T, Fukuda K, Kitaoka M, Ohnishi M, Terabayashi T and Kobata. Milk Oligosaccharides. Nutrition and Diet Research Progress. New York: Nova Biomedical Books; 2011.Google Scholar
  9. Barile D, Marotta M, Chu C, Mehra R, Grimm R, Lebrilla CB, German JB. Neutral and acidic oligosaccharides in Holstein-Friesian D1 during the first 3 days of lactation measured by high performance liquid chromatography on a microfluidic chip and time-of-flight mass spectrometry. J Dairy Sci. 2010;93:3940–9.PubMedPubMed CentralView ArticleGoogle Scholar
  10. Nakamura T, Kawase H, Kimura K, Watanabe Y, Ohtani M, Arai I, Urashima T. Concentrations of Sialyloligosaccharides in Bovine D1 and Milk during the Prepartum and Early Lactation. J Dairy Sci. 2003;86:1315–2.PubMedView ArticleGoogle Scholar
  11. Lars B. Recent advances on structure, metabolism, and function of human milk oligosaccharides. J Nut. 2006;136:2127–30.Google Scholar
  12. Chichlowski M, German JB, Lebrilla CB, Mills DA. The influence of milk oligosaccharides on microbiota of infants: opportunities for formulas. Ann rev food sci technol. 2011;2:331–51.View ArticleGoogle Scholar
  13. Hickey RM. The role of oligosaccharides from human milk and other sources in prevention of pathogen adhesion. Internat Dairy J. 2012;22:141–6.View ArticleGoogle Scholar
  14. Urashima T, Taufik E, Fukuda K, Asakuma S. Recent advances in studies on milk oligosaccharides of cows and other domestic farm animals. Biosci Biotech Bioch. 2013;77:455–66.View ArticleGoogle Scholar
  15. Crisà A. Milk carbohydrates and oligosacchharides. In: Park YW, Haenlein GFW, editors. Milk dairy products in human nutrition. Chichester: John Wiley & Sons, Ltd; 2013. p. 129–47.View ArticleGoogle Scholar
  16. Martinez-Ferez A, Guadix A, Zapata-Montoya JE, Guadix EM. Influence of transmembrane pressure on the separation of caprine milk oligosaccharides from protein by cross-flow ultrafiltration. Int J Dairy Technol. 2008;61:333–9.View ArticleGoogle Scholar
  17. Kiskini A, Defilippo E. Oligosaccharides in goat milk: structure, health effects and isolation. Cell Mol Biol. 2013;1:25–30.Google Scholar
  18. Albrecht S, Lane JA, Mariño K, Al Busadah KA, Carrington SD, Rita M, Hickey RM, Rudd PM. A comparative study of free oligosaccharides in the milk of domestic animals. Br J Nutr. 2014;111:1313–28.PubMedView ArticleGoogle Scholar
  19. Marth JD, Grewal PK. Mammalian glycosylation in immunity. Nat Rev Immunol. 2008;8:874–87.PubMedPubMed CentralView ArticleGoogle Scholar
  20. GarcÍa-Vallejo JJ, Gringhuis SI, Dijk W, Die I. Gene Expression Analysis of Glycosylation-Related Genes by Real-Time Polymerase Chain Reaction. Glycobiology protocols. New Jersey: Human Press Inc; 2007. p. 187–209.Google Scholar
  21. Hashimoto K, Tokimatsu T, Kawano S, Yoshizawa AC, Okuda S, Goto S, Kanehisa M. Comprehensive analysis of glycosyltransferases in eukaryotic genomes for structural and functional characterization of glycans. Carbohydrate Res. 2009;344:881–7.View ArticleGoogle Scholar
  22. Harduin-Leper A. Comprehensive Analysis of Sialyltransferases in Vertebrate Genomes. Glycobiology Insights. 2010;2:29–61.View ArticleGoogle Scholar
  23. Bionaz M, Loor JJ. Gene networks driving bovine milk fat synthesis during the lactation cycle. BMC Genomics. 2008;9:366.PubMedPubMed CentralView ArticleGoogle Scholar
  24. Harvatine KJ, Boisclair YR, Bauman DE. Recent advances in the regulation of milk fat synthesis. Animal. 2009;3:40–54.PubMedView ArticleGoogle Scholar
  25. Ferreira AM, Bislev SL, Bendixen E, Almeida AM. The Mammary Gland in Domestic Ruminants: A Systems Biology Perspective. J Proteom. 2013;94:110–23.View ArticleGoogle Scholar
  26. Roy NC, Altermann E, Park Z, McNabb WC. A comparison of analog and Next-Generation transcriptomic tools for mammalian studies. Brief funct genomics. 2011;10:135–50.PubMedView ArticleGoogle Scholar
  27. Cui X, Hou Y, Yang S, Xie Y, Zhang S, Zhang Y, Zhang Q, Lu X, Liu GE, Sun D. Transcriptional profiling of mammary gland in Holstein cows with extremely different milk protein and fat percentage using RNA sequencing. BMC Genomics. 2014;15:226.PubMedPubMed CentralView ArticleGoogle Scholar
  28. Wickramasinghe S, Gonzalo Rincon G, Alma I-TA, Medrano JF. Transcriptional profiling of bovine milk using RNA sequencing. BMC Genomics. 2012;13:45.PubMedPubMed CentralView ArticleGoogle Scholar
  29. Paten AM, Duncan EJ, Pain SJ, Peterson SW, Kenyon PR, Blair HT, Dearden P. Functional Development of the Adult Ovine Mammary Gland—insights from Gene Expression Profiling. BMC Genomics. 2015;16:748.PubMedPubMed CentralView ArticleGoogle Scholar
  30. Elfstrand L, Lindmark-Månsson H, Paulsson M, Nyberg L, Àkesson B. Immunoglobulins, Growth Factors and Growth Hormone in Bovine D1 and the Effects of Processing. Int Dairy J. 2002;12:879–87.View ArticleGoogle Scholar
  31. Nairn AV, York WS, Harris K, Hall EM, Pierce JM, Moremen KW. Regulation of glycan structures in animal tissues: transcript profiling of glycan-related genes. J Biol Chem. 2008;283:17298–313.PubMedPubMed CentralView ArticleGoogle Scholar
  32. Clark KJ-R, Griffiths J, Bailey KM, Harcum SW. Gene-expression profiles for five key glycosylation genes for galactose-fed CHO cells expressing recombinant IL-4/13 cytokine trap. Biotechnol Bioeng. 2005;90:568–77.PubMedView ArticleGoogle Scholar
  33. Wickramasinghe S, Hua S, Rincon G, Islas-Trejo A, German JB, Lebrilla CB, Medrano JF. Transcriptome Profiling of Bovine Milk Oligosaccharide Metabolism Genes Using RNA-Sequencing. PlosOne. 2011;11, e18895.View ArticleGoogle Scholar
  34. Boutinaud M, Jammes H. Potential uses of milk epithelial cells: a review. Reprod Nutr Dev. 2002;42:133–47.PubMedView ArticleGoogle Scholar
  35. Medrano JF, Rincon G. Comparative Analysis Of Bovine Milk And Mammary Gland Transcriptome Using RNA-Seq. Leipzig: 9th World Congress on Genetics applied to livestock production; 2009. p. 852.Google Scholar
  36. Canovas A, Rincòn G, Bevilacqua C, Islas-Trejo A, Brenaut P, Hovey RC, Boutinaud M, Morgenthaler C, VanKlompenberg MK, Martin P, Medrano JF. Comparison of five different RNA sources to examine the lactating bovine mammary gland transcriptome using RNA-Sequencing. Scientific Reports. 2014;4:5297.PubMedView ArticleGoogle Scholar
  37. Boutinaud M, Herve L, Lollivier V. Mammary epithelial cells isolated from milk are a valuable, non-invasive source of mammary transcripts. Front Genet. 2015;6:323.PubMedPubMed CentralView ArticleGoogle Scholar
  38. The ArkDB. http://www.thearkdb.org/arkdb/). Accessed 11 Oct 2016.
  39. The Babraham Bioinformatics group. http://www.bioinformatics.babraham.ac.uk/projects/fastqc. Accessed 11 Oct 2016.
  40. Bolger AM, Lohse M, Usadel B. Genome Analysis Trimmomatic : A Flexible Trimmer for Illumina Sequence Data. Bioinformatics. 2014;1–7.Google Scholar
  41. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.PubMedPubMed CentralView ArticleGoogle Scholar
  42. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25:1105–11.PubMedPubMed CentralView ArticleGoogle Scholar
  43. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, Van Baren MJ, Salzberg SL, Wold BJ, Pachter L. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511–5.PubMedPubMed CentralView ArticleGoogle Scholar
  44. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, Di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnol. 2011;29:644–52.View ArticleGoogle Scholar
  45. Wu TD, Watanabe CK. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics. 2005;21:1859–75.PubMedView ArticleGoogle Scholar
  46. Kent WJ. BLAT - The BLAST-like alignment tool. Genome Res. 2002;12:656–64.PubMedPubMed CentralView ArticleGoogle Scholar
  47. Micallef L, Rodgers P. eulerAPE : Drawing Area-Proportional 3-Venn Diagrams Using Ellipses. PLoS ONE. 2014;9:e101717.PubMedPubMed CentralView ArticleGoogle Scholar
  48. Mudunuri U, Che A, Yi M, Stephens RM. bioDBnet: the biological database network. Bioinformatics. 2009;25:555–6.PubMedPubMed CentralView ArticleGoogle Scholar
  49. Götz S, García-Gómez JM, Terol J, Williams TD, Nagaraj SH, Nueda MJ, Robles M, Talón M, Dopazo J, Ana Conesa A. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res. 2008;36:3420–35.PubMedPubMed CentralView ArticleGoogle Scholar
  50. The DAVID Bioinformatic database. http://david.abcc.ncifcrf.gov. Accessed 11 Oct 2016.
  51. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37:1–13.View ArticleGoogle Scholar
  52. Tarazona S, García-Alcalde F, Dopazo J, Ferrer A, Conesa A. Differential expression in RNA-seq: a matter of depth. Genome Res. 2011;21:2213–23.PubMedPubMed CentralView ArticleGoogle Scholar
  53. HTSeq: Analysing high-throughput sequencing data with Python. http://www-huber.embl.de/users/anders/HTSeq/doc/overview.html. Accessed 11 Oct 2016.
  54. The WEB-based GEne SeT AnaLysis Toolkit. http://www.webgestalt.org. Accessed 11 Oct 2016.
  55. Wang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res. 2013;41:W77–83.PubMedPubMed CentralView ArticleGoogle Scholar
  56. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, Kuhn M, Bork P, Jensen IJ, Von Mering C. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43:D447–52.PubMedView ArticleGoogle Scholar
  57. Lemay DG, Ballard O, Hughes M, Morrow AL, Horseman ND, Nommsen-Rivers L. RNA Sequencing of the Human Milk Fat Layer Transcriptome Reveals Distinct Gene Expression Profiles at Three Stages of Lactation. PLoS ONE. 2013;8, e67531.PubMedPubMed CentralView ArticleGoogle Scholar
  58. Mortazavi A, Williams BA, Mccue K, Schaeffer L, Wold B. Mapping and Quantifying Mammalian Transcriptomes by RNA-Seq. Nat Methods. 2008;5:1–8.View ArticleGoogle Scholar
  59. Łabaj PP, Leparc GG, Linggi BE, Markillie LM, Wiley HS, Kreil DP. Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics. 2011;27:i383–91.PubMedPubMed CentralView ArticleGoogle Scholar
  60. Toung JM, Morley M, Li M, Cheung VG. RNA-sequence analysis of human B-cells. Genome Res. 2011;21:991–8.PubMedPubMed CentralView ArticleGoogle Scholar
  61. Shi H, Jiangjiang Zhu J, Jun Luo J, Wenting Cao W, Huaiping Shi H, Dawei Yao D, Jun Li J, Sun Y, Xu H, Yu K, Loor JJ. Genes Regulating Lipid and Protein Metabolism Are Highly Expressed in Mammary Gland of Lactating Dairy Goats. Funct Integr Genomics. 2014;15:309–21.PubMedView ArticleGoogle Scholar
  62. Schlimme E, Martin D, Meisel H. Nucleosides and nucleotides: natural bioactive substances in milk and D1. British J Nut. 2000;84:S59–68.View ArticleGoogle Scholar
  63. Park YW. Rheological characteristics of goat and sheep milk. Small Rumin Res. 2007;68:73–87.View ArticleGoogle Scholar
  64. Mabjeesh SJ, Kyle CE, Macrae JC, Bequette BJ. Lysine Metabolism by the Mammary Gland of Lactating Goats at Two Stages of Lactation. J Dairy Sci. 2000;83:996–1003.PubMedView ArticleGoogle Scholar
  65. Bionaz M, Periasamy K, Rodriguez-Zas SL, Everts RR, Lewin H, Hurley WL, Loor JJ. Old and new stories: revelations from functional analysis of the bovine mammary transcriptome during the lactation cycle. PLoS ONE. 2012;7:e33268.PubMedPubMed CentralView ArticleGoogle Scholar
  66. Bionaz M, Loor JJ. Gene Networks Driving Bovine Mammary Protein Synthesis during the Lactation Cycle. Bioinformatics and Biology Insights. 2011;5:83–98.PubMedPubMed CentralGoogle Scholar
  67. Farrell HM, Jimenez-Flores R, Bleck GT, Brown EM, Butler JE, Creamer LK, Hicks CL, Hollar CM, Ng-Kwai-Hang KF, Swaisgood HE. Nomenclature of the Proteins of Cows’ Milk—Sixth Revision. J Dairy Sci. 2004;87:1641–74.PubMedView ArticleGoogle Scholar
  68. Hinz K, O’Connor PM, Huppertz T, Ross RP, Kelly AL. Comparison of the principal proteins in bovine, caprine, buffalo, equine and camel milk. J Dairy Res. 2012;79:185–91.PubMedView ArticleGoogle Scholar
  69. Aggarwal J, Sharma A, Kishore A, Mishra BP, Yadav A, Mohanty A, Sodhi M, Kataria RS, Malakar D, Mukesh M. Identification of Suitable Housekeeping Genes for Normalization of Quantitative Real-Time PCR Data during Different Physiological Stages of Mammary Gland in Riverine Buffaloes (Bubalus Bubalis). J Anim Physiol An N. 2013;97:1132–41.View ArticleGoogle Scholar
  70. Pradeep J, Monika S, Ankita S, Umesh KS, Amit K, Ashok M, Mishra BP, Sandeep M, Kataria RS, Kaushik J, Mukesh M. Expression Analysis of Solute Carrier (SLC2A) Genes in Milk Derived Mammary Epithelial Cells during Different Stages of Lactation in Sahiwal (Bos indicus) Cows. Research Article. J Adv Dairy Res. 2014;2:117.Google Scholar
  71. Lkhider M, Castino R, Bouguyon E, Isidoro C, Nd Ollivier-Bousquet M, Cathepsin D. Released by Lactating Rat Mammary Epithelial Cells Is Involved in Prolactin Cleavage under Physiological Conditions. J Cell Sci. 2004;117:5155–64.PubMedView ArticleGoogle Scholar
  72. Ishida M, Maehara M, Watanabe T, Yanagisawa Y, Takata Y, Nakajima R, Suzuki M, Harigaya T. Vasoinhibins, N-Terminal Mouse Prolactin Fragments, Participate in Mammary Gland Involution. J Mol Endocrinol. 2014;52:279–87.PubMedView ArticleGoogle Scholar
  73. CastinoR DS, Bouguyon E, Demoz M, Isidoro C, Ollivier-Bousquet M. Prolactin Promotes the Secretion of Active Cathepsin D at the Basal Side of Rat Mammary Acini. Endocrinology. 2008;149:4095–105.View ArticleGoogle Scholar
  74. Berberat PO, Katori M, Kaczmarek E, Anselmo D, Lassman C, Ke B, Shen X, Busuttil RW, Yamashita K, Csizmadia E, Tyagi S, Ottobein LE, Brouard S, Tobiash E, Bach FH, Kupiec-Weglinski JW, Soares MP. Heavy Chain Ferritin Acts as an Antiapoptotic Gene That Protects Livers from Ischemia Reperfusion Injury. FASEB J. 2003;17:1724–26.PubMedGoogle Scholar
  75. Kanno Y, Ohtsuka H, Yoshikawa Y, Watanabe K, Orino K. Measurement of ferritin and anti-ferritin autoantibodies in serum and colostrum of Holstein and Japanese Black cows. Animal Sci J. 2013;84:556–61.View ArticleGoogle Scholar
  76. Molenaar AJ, Harris DP, Rajan GH, Pearson ML, Callaghan MR, Sommer L, Farr VC, Oden KE, Miles MC, Petrova RS, Good LL, Singh K, McLaren RD, Prosser CG, Kim KS, Wieliczko RJ, Dines MH, Johannessen KM, Grigor MR, Davis SR, Stelwagen K. The acute-phase protein serum amyloid A3 is expressed in the bovine mammary gland and plays a role in host defence. Biomarkers. 2009;14:26–37.PubMedView ArticleGoogle Scholar
  77. Dudemaine PL, Thibault C, Alain K, Bissonnette N. Genetic Variations in the SPP1 Promoter Affect Gene Expression and the Level of Osteopontin Secretion into Bovine Milk. Anim Genet. 2014;45:629–40.PubMedView ArticleGoogle Scholar
  78. Sheehy PA, Riley LG LG, HRaadsma HW, Williamson P, Wynn PC. A Functional Genomics Approach to Evaluate Candidate Genes Located in a QTL Interval for Milk Production Traits on BTA6. Anim Genet. 2009;40:492–98.PubMedView ArticleGoogle Scholar
  79. Crisà A, Marchitelli C, Pariset L, Contarini G, Signorelli F, Napolitano F, Catillo G, Valentini A, Moioli B. Exploring Polymorphisms and Effects of Candidate Genes on Milk Fat Quality in Dairy Sheep. J Dairy Sci. 2010;93:834–45.View ArticleGoogle Scholar
  80. Suburu J, Shi L, Wu JJ, Wang S, Samuel M, Thomas MJ, Kock ND, Yang G, Kridel S, Chen YO. Fatty acid synthase is required for mammary gland development and milk production during lactation. Am Physiol - Endoc M. 2014;306:E1132–43.Google Scholar
  81. Ollier S, Robert-Granié C, Bernard L, Chilliard Y, Leroux C. Mammary transcriptome analysis of food-deprived lactating goats highlights genes involved in milk secretion and programmed cell death. J Nut. 2007;137:560–7.Google Scholar
  82. Le Provost F, Cassy S, Hayes H, Martin P. Structure and Expression of Goat GLYCAM1 Gene: Lactogenic-Dependent Expression in Ruminant Mammary Gland and Interspecies Conservation of the Proximal Promoter. Gene. 2003;313:83–9.PubMedView ArticleGoogle Scholar
  83. Faucon F, Rebours E, Bevilacqua C, Helbling JC, Aubert J, Makhzami S, Dhorne-Pollet S, Robin S, Martin P. Terminal Differentiation of Goat Mammary Tissue during Pregnancy Requires the Expression of Genes Involved in Immune Functions. Physiol Genomics. 2009;40:61–82.PubMedView ArticleGoogle Scholar
  84. Maningat D, Sen P, Rijnkels M, Sunehag AL, Hadsell DL, Bray M, Haymond MW. Gene Expression in the Human Mammary Epithelium during Lactation: The Milk Fat Globule Transcriptome. Physiol Genomics. 2009;37(1):12–22.PubMedView ArticleGoogle Scholar
  85. Hanigan MD, France J, Mabjeesh SJ, McNabb WC, Bequette BJ. High Rates of Mammary Tissue Protein Turnover in Lactating Goats Are Energetically Costly. J Nut. 2009;139:1118–27.View ArticleGoogle Scholar
  86. Knight CH, Peaker M. Mammary Development and Regression during Lactation in Goats in Relation to Milk Secretion. Quarterly J Exp Physiol. 1984;69:331–38.View ArticleGoogle Scholar
  87. Stefanon B, Colitti M, Gabai G, Knight CH, Wilde CJ. Mammary Apoptosis and Lactation Persistency in Dairy Animals. J Dairy Res. 2002;69:37–52.PubMedView ArticleGoogle Scholar
  88. Capuco AV, Wood DL, Baldwin R, McLeod K, Papee MJ. Mammary Cell Number, Proliferation, and Apoptosis During a Bovine Lactation: Relation to Milk Production and Effect of bST. J Dairy Sci. 2001;84:2177–87.PubMedView ArticleGoogle Scholar
  89. Sorensen MT, JNørgaard JV, Theil PK, Vestergaard M, Sejrsen K. Cell Turnover and Activity in Mammary Tissue during Lactation and the Dry Period in Dairy Cows. J Dairy Sci. 2006;89:4632–39.PubMedView ArticleGoogle Scholar
  90. Nørgaard JV, Thaareil PK, Sørensen MT, Sejrsen K. Cellular Mechanisms in Regulating Mammary Cell Turnover during Lactation and Dry Period in Dairy Cows. J Dairy Sci. 2008;91:2319–27.PubMedView ArticleGoogle Scholar
  91. Wareski P, Motyl T, Ryniewicz Z, Orzechowski A, Gajkowska B, Wojewodzka U, Ploszaj T. Expression of Apoptosis-Related Proteins in Mammary Gland of Goat. Small Ruminant Res. 2001;40:279–89.View ArticleGoogle Scholar
  92. Zarzynska J, Motyl T. Apoptosis and autophagy in involuting bovine mammary gland. J Physiol Pharmacol. 2008;59:275–88.PubMedGoogle Scholar
  93. Lemay DG, Neville MC, Rudolph MC, Pollard KS, German JB. Gene Regulatory Networks in Lactation: Identification of Global Principles Using Bioinformatics. BMC Syst Biol. 2007;1:56.PubMedPubMed CentralView ArticleGoogle Scholar
  94. Jesenberger V, Jenstch S. Deadly encounter: ubiquitin meets apoptosis. Nature Reviews Mol Cell Biol. 2002;3:112–21.View ArticleGoogle Scholar
  95. Yang Y, Yu X. Regulation of Apoptosis: The Ubiquitous Way. FASEB J. 2003;17:790–99.PubMedView ArticleGoogle Scholar
  96. Karin M, Ben-Neriah Y. Phosphorylation Meets Ubiquitination: The Control of NF-κB Activity. Annual Rev Immunol. 2000;18:621–63.View ArticleGoogle Scholar
  97. Dubey A, Prajapati KS, Swamy M, Pachauri V. Heat Shock Proteins : A Therapeutic Target Worth to Consider. Veterinary World. 2015;8:46–51.PubMedPubMed CentralView ArticleGoogle Scholar
  98. Schmitt E, Gehrmann M, Brunet M, Multhoff G, Garrido C. Intracellular and Extracellular Functions of Heat Shock Proteins: Repercussions in Cancer Therapy. J Leukocyte Biol. 2006;81:15–27.PubMedView ArticleGoogle Scholar
  99. Annison EF, Linzell JL. The oxidation and utilization of glucose and acetate by the mammary gland of the goat in relation to their over-all metabolism and to milk formation. J Physiol. 1964;175:372–85.PubMedPubMed CentralView ArticleGoogle Scholar
  100. Hagiwara K, Kataoka S, Yamanaka H, Kirisawa R, Iwai H. Detection of Cytokines in Bovine Colostrum. Vet Immunol Immunopat. 2000;76:183–90.View ArticleGoogle Scholar
  101. Uruakpa FO, Ismond MAH, Akobundu ENT. Oligosaccharide and Its Benefits : A Review. Breast. 2002;22:755–67.Google Scholar
  102. Ishida N, Kawakita M. Molecular physiology and pathology of the nucleotide sugar transporter family (SLC35). Eur J Physiol. 2004;447:768–75.View ArticleGoogle Scholar
  103. Song Z. Roles of the nucleotide sugar transporters (SLC35 family) in health and disease. Mol Aspects Med. 2013;34:590–600.PubMedView ArticleGoogle Scholar
  104. Nishimura M, Suzuki S, Satoh T, Naito S. Tissue-Specific mRNA Expression Profiles of Human Solute Carrier 35 Transporters. Drug Metab Pharmacokinet. 2009;24:91–9.PubMedView ArticleGoogle Scholar
  105. Claps S, Di Napoli MA, Sepe L, Caputo AR, Rufrano D, Di Trana A, Annicchiarico G, Fedele V. Sialyloligosaccharides content in colostrum and milk of twogoat breeds. Small Rumin Res. 2014;121:116–9.View ArticleGoogle Scholar
  106. Manlongat N, Yang TJ, Hinckley LS, Bendel RB, Krider HM. Physiologic-Chemoattractant-Induced Migration of Polymorphonuclear Leukocytes in Milk. Clini Diag L Immun. 1998;5:375–81.Google Scholar
  107. Deshmane SL, Kremlev S, Amini S, Sawaya BE. Monocyte Chemoattractant Protein-1 (MCP-1): An Overview. J Interf Cyt Res. 2009;29:313–26.View ArticleGoogle Scholar
  108. Sapi E. The role of CSF-1 in normal physiology of mammary gland and breast cancer: an update. Exp Biol Med. 2004;229:1–11.Google Scholar
  109. Schwertfeger KL, Rosen JM, Cohen DA. Mammary Gland Macrophages: Pleiotropic Functions in Mammary Development. J Mammary Gland Biol Neoplasia. 2006;11:229–38.PubMedView ArticleGoogle Scholar
  110. Mepham TB. Amino Acid Utilization by Lactating Mammary Gland. J Dairy Scie. 1982;65:287–98.View ArticleGoogle Scholar
  111. Singh K, Dobson J, Phyn CVC, Davis SR, Farr VC, Molenaar AJ, Stelwagen K. Milk Accumulation Decreases Expression of Genes Involved in Cell–extracellular Matrix Communication and Is Associated with Induction of Apoptosis in the Bovine Mammary Gland. Livest Prod Sci. 2005;98:67–78.View ArticleGoogle Scholar
  112. Fleming JM, Brandimarto JA, Cohick WS. The Mitogen-Activated Protein Kinase Pathway Tonically Inhibits Both Basal and IGF-I-Stimulated IGF-Binding Protein-5 Production in Mammary Epithelial Cells. J Endocrinol. 2007;194:349–59.PubMedView ArticleGoogle Scholar
  113. Lin J, Bao ZK, Zhang Q, Hu WW, Yu QH, Yang Q. Transcriptome Analysis of the Mammary Gland from GH Transgenic Goats during Involution. Gene. 2015;565:228–34.PubMedView ArticleGoogle Scholar
  114. Shen O, Zhang Y, Uray IP, Hill JH, Kim HT, Lu C, Young MR, Gunther EJ, Hilsenbeck SG, Chodosh LA, Colburn NH, Brown PH. The AP-1 transcription factor regulates postnatal mammary gland development. Dev Biol. 2006;295:589–603.PubMedView ArticleGoogle Scholar
  115. Marti A, Lazar H, Ritter P, Jaggi R. Transcription Factor Activities and Gene Expression During Mouse Mammary Gland Involution. J Mammary Gland Biol. 1999;4:154–2.View ArticleGoogle Scholar
  116. Brown JR, Ye H, Bronson RT, Dikkes P, Greenberg ME. A Defect in Nurturing in Mice Lacking the Immediate Early Gene fosB. Cell. 1996;86:297–309.PubMedView ArticleGoogle Scholar
  117. Acunzo J, Katsogiannou M, Rocchi P. Small heat shock proteins HSP27 (HspB1), αB-crystallin (HspB5) and HSP22 (HspB8) as regulators of cell death. Int J Biochem Cell B. 2012;44:1622–31.View ArticleGoogle Scholar
  118. Vos MJ, Hageman J, Carra S, Kampinga HH. Structural and Functional Diversities between Members of the Human HSPB, HSPH, HSPA, and DNAJ Chaperone Families. Biochemistry. 2008;47:7001–11.PubMedView ArticleGoogle Scholar
  119. Ostrowska M, Jarczak J, Zwierzchowski L. Glucose transporters in cattle - a review. Anim Sci P. 2015;33:191–212.Google Scholar
  120. Tazawa S, Yamato T, Fujikura H, Hiratochi M, Itoh F, Tomae M, Takemura Y, Maruyama H, Sugiyama T, Wakamatsu A, Isogai T, Isaji M. SLC5A9/SGLT4, a new Na + −dependent glucose transporter, is an essential transporter for mannose, 1,5-anhydro-D-glucitol, and fructose. Life Sci. 2005;76:1039–50.PubMedView ArticleGoogle Scholar
  121. Groneberg D, Döring F, Theis S, Nickolaus M, Fischer A, Daniel H. Peptide Transport in the Mammary Gland: Expression and Distribution of PEPT2 mRNA and Protein. Am J Physiol Endocrinol Metab. 2002;282:E1172–79.PubMedView ArticleGoogle Scholar
  122. Mertens C, Darnell JE. SnapShot: JAK-STAT Signaling. Cell. 2007;131:612–612.e1.PubMedView ArticleGoogle Scholar
  123. Rawlings JS, Rosler KM, Harrison D. The JAK/STAT Signaling Pathway. J Cell Sci. 2004;117:1281–83.PubMedView ArticleGoogle Scholar
  124. Jung ID, Lee HS, Lee HY, Choi OH. FcεRI-Mediated Mast Cell Migration: Signaling Pathways and Dependence on Cytosolic Free Ca2+ Concentration. Cell Signal. 2009;21:1698–705.PubMedPubMed CentralView ArticleGoogle Scholar
  125. Hemmings BA, Restuccia DF. PI3K-PKB/Akt Pathway. In: Cantley L, Hunter T, Sever R, Thorner J, editors. Cold Spring Harb Perspect Biol, vol. 4. 2012. p. a011.Google Scholar
  126. Organ SL, Tsao MS. An Overview of the c-MET Signaling Pathway. Therapeutic Advances in Medical Oncology. 2011;3:S7–19.PubMedPubMed CentralView ArticleGoogle Scholar
  127. Stinnakre MG, Vilotte JL, Soulier S, Mercier JC. Creation and phenotypic analysis of a-lactalbumin-deficient mice. Proc Natl Acad Sci U S A. 1994;91:6544–8.PubMedPubMed CentralView ArticleGoogle Scholar
  128. Asano M, Furukawa K, Kido M, Matsumoto S, Umesaki Y, Kochibe N, Iwakura Y. Growth retardation and early death of β-1, 4-galactosyltransferase knockout mice with augmented proliferation and abnormal differentiation of epithelial cells. EMBO J. 1997;16:1850–7.PubMedPubMed CentralView ArticleGoogle Scholar
  129. Qasba PK, Ramakrishnan B, Boeggeman E. Structure and function of −1,4-galactosyltransferase. Curr Drug Targets. 2008;9:292.PubMedPubMed CentralView ArticleGoogle Scholar
  130. Tanner ME. The enzymes of sialic acid biosynthesis. Bioorg Chem. 2005;33:216–28.PubMedView ArticleGoogle Scholar
  131. Martín-Ortiz A, Salcedo J, Barile D, Bunyatratchata A, Moreno FJ, Martin-García I, Clemente A, Sanz ML, Ruiz-Matute AI. Characterization of goat colostrum oligosaccharides by nano-liquid chromatography on chip quadrupole time-of-flight mass spectrometry and hydrophilic interaction liquid chromatography-quadrupole mass spectrometry. J Chromatogr A. 2016;1428:143–53.PubMedView ArticleGoogle Scholar
  132. Fuhrer A, Sprenger N, Kurakevich E, Borsig L, Chassard C, Hennet T. Milk sialyllactose influences colitis in mice through selective intestinal bacterial colonization. J Exp Med. 2010;13:2843–54.View ArticleGoogle Scholar
  133. Monti L, Cattaneo TMP, Orlandic M, Curadic MC. Capillary electrophoresis of sialylated oligosaccharides in milk from different species. J Chromatogr A. 2015;1409:288–91.PubMedView ArticleGoogle Scholar
  134. Schnaar RL. Brain gangliosides in axon-myelin stability and axon regeneration. FEBS Lett. 2010;584:1741–7.PubMedView ArticleGoogle Scholar
  135. Inokuchi J, Uemura S. ST3 Beta-Galactoside Alpha-2,3-Sialyltransferase 5 (ST3GAL5). In: Taniguchi N, Honke K, Fukuda M, Narimatsu H, Yamaguchi Y, Angata T, editors. Handbook of Glycosyltransferases and Related Genes, Springer Japan. 2014. p. 675–86.View ArticleGoogle Scholar
  136. Wang B, Brand-Miller J. The role and potential of sialic acid in human nutrition. Eur J Clin Nutr. 2003;57:1351–69.PubMedView ArticleGoogle Scholar
  137. Sakai F, Ikeuchi Y, Urashima T, Fujihara M. Effects of Feeding Sialyllactose and Galactosylated N-Acetylneuraminic Acid on Swimming Learning Ability and Brain Lipid Composition in Adult Rats. Components. 2006;254:249.Google Scholar
  138. Togayachi A, Kozono Y, Kuno A, Ohkura T, Sato T, Hirabayashi J, Ikehara Y, Narimatsu H. β3GnT2 (B3GNT2), a Major Polylactosamine Synthase: Analysis of B3gnt2-Deficient Mice. Method Enzymol. 2010;479:185–204.View ArticleGoogle Scholar
  139. Trottein F, Schaffer L, Ivanov S, Paget C, Vendeville C, Cazet A, Groux-Degroote S, Lee S, Krzewinski-Recchi MA, Faveeuw C, Head SR, Gosset P, Delannoy P. Glycosyltransferase and sulfotransferase gene expression profiles in human monocytes, dendritic cells and macrophages. Glycoconj J. 2009;26:1259–74.PubMedPubMed CentralView ArticleGoogle Scholar
  140. Honke K. Beta1,3-N-Acetylgalactosaminyltransferase 1 (B3GALNT1). In: Taniguchi N, Honke K, Fukuda M, Narimatsu H, Yamaguchi Y, Angata T, editors. Handbook of Glycosyltransferases and Related Genes, Springer Japan. 2014. p. 447–53.View ArticleGoogle Scholar
  141. Zancada L, Sánchez-Juanes F, Alonso JM, Hueso P. Neutral glycosphingolipid content of ovine milk. J Dairy Sci. 2010;93:19–26.PubMedView ArticleGoogle Scholar
  142. Urashima T, Bubb WA, Messer M, Tsuji Y, Taneda Y. Studies of the neutral trisaccharides of goat (Capra hircus) colostrum and of the one- and two-dimensional 1H and 13C NMR spectra of 6′-N acetylglucosaminyllactose. Carbohydr Res. 1994;262:173–84.PubMedView ArticleGoogle Scholar
  143. Niittymäki J, Mattila P, Renkonen R. Differential gene expression of GDP-L-fucose synthesizing enzymes, GDP-fucose transporter and fucosyltransferase VII. APMIS. 2006;114:539–48.PubMedView ArticleGoogle Scholar
  144. Becker DJ, Lowe JB. Fucose: biosynthesis and biological function in mammals. Glycobiology. 2003;13:41R–53R.PubMedView ArticleGoogle Scholar
  145. Chen CI, Keusch JJ, Klein D, Hess D, Hofsteenge J, Gut H. Structure of human POFUT2: insights into thrombospondin type 1 repeat fold and O-fucosylation. EMBO J. 2012;31:3183–97.PubMedPubMed CentralView ArticleGoogle Scholar
  146. Kytzia HJ, Sandhoff K. Evidence for two different active sites on human beta-hexosaminidase A. Interaction of GM2 activator protein with beta-hexosaminidase A. J Biol Chem. 1985;260:7568–72.PubMedGoogle Scholar
  147. Gravel RA, Kaback MM, Proia RL, Sandhoff K, Suzuki K, Suzuki K. The GM2 gangliosidoses. In: Scriver CR, Beaudet AL, Valle D, Sly WS, editors. The Metabolic and Molecular Bases of Inherited Diseas. 8th ed. 76: McGraw-Hill; 2001. p. 3827–76.Google Scholar
  148. Nishihara S. Solute Carrier Family 35 (CMP-Sialic Acid Transporter), Member A1 (SLC35A1). In: Taniguchi N, Honke K, Fukuda M, Narimatsu H, Yamaguchi Y, Angata T, editors. Handbook of Glycosyltransferases and Related Genes, Springer Japan. 2014. p. 1369–77.View ArticleGoogle Scholar
  149. Bakker A, Ashikov A, Routier FH, Gerardy-Schahn R. GDP-Fucose Transporter 1 (2014) (SLC35C1). In: Taniguchi N, Honke K, Fukuda M, Narimatsu H, Yamaguchi Y, Angata T, editors. Handbook of Glycosyltransferases and Related Genes, Springer Japan. 2014. p. 1403–11.View ArticleGoogle Scholar
  150. Verheijen FW, Verbeek E, Aula N, Beerens CEMT, Havelaar AC, Joosse M, Peltonen L, Aula P, Galjaard H, Van der Spek PJ, Mancini GMS. A new gene, encoding an anion transporter, is mutated in sialic acid storage diseases. Nat Genet. 1999;23:462–5.PubMedView ArticleGoogle Scholar

Copyright

© The Author(s). 2016

Advertisement