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Background In the mammary gland transcriptome of lactating dairy cows genes encoding milk proteins are highly abundant, which can impair the detection of lowly expressed transcripts and can bias the outcome in global transcriptome analyses. Therefore, the aim of this study was to develop and evaluate a method to deplete extremely highly expressed transcripts in mRNA from lactating mammary gland tissue. Results Selective RNA depletion was performed by hybridization of antisense oligonucleotides targeting genes encoding the caseins (CSN1S1, CSN1S2, CSN2 and CSN3) and whey proteins (LALBA and PAEP) within total RNA followed by RNase H-mediated elimination of the respective transcripts. The effect of the RNA depletion procedure was monitored by RNA sequencing analysis comparing depleted and non-depleted RNA samples from Escherichia coli (E. coli) challenged and non-challenged udder tissue of lactating cows in a proof of principle experiment. Using RNase H-mediated RNA depletion, the ratio of highly abundant milk protein gene transcripts was reduced in all depleted samples by an average of more than 50% compared to the non-depleted samples. Furthermore, the sensitivity for discovering transcripts with marginal expression levels and transcripts not yet annotated was improved. Finally, the sensitivity to detect significantly differentially expressed transcripts between non-challenged and challenged udder tissue was increased without leading to an inadvertent bias in the pathogen challenge-associated biological signaling pathway patterns. Conclusions The implementation of selective RNase H-mediated RNA depletion of milk protein gene transcripts from the mammary gland transcriptome of lactating cows will be highly beneficial to establish comprehensive transcript catalogues of the tissue that better reflects its transcriptome complexity. Electronic supplementary material The online version of this article (10.1186/s12864-019-5781-3) contains supplementary material, which is available to authorized users.

Lists of IPA canonical pathways significantly enriched for differentially expressed genes in non-depleted (0) and depleted RNA samples (protocol variants A and B) from pathogen-challenged and non-challenged udder quarters. Pathways are classified for being observed across all protocols (0, A or B) or shared between two or all three protocols. (PDF 214 kb)

Distribution of gene expression level (FPKM) for the depleted and non-depleted RNA samples from non-challenged udder samples. 0, A, and B represent the non-depleted or the depleted RNA samples (protocol variant A and B), respectively. (PDF 283 kb)

IGV screen shot of the read distribution across the PAEP gene for the E. coli challenged udder sample from cow 2. O, A and B represent the non-depleted or the depleted RNA samples (protocol variant A and B), respectively. Exon 6 is zoomed in for better demonstration of the position of the antisense capture oligonucleotide (see red line). (PDF 235 kb)

Commercial and customized microarrays are valuable tools for the analysis of holistic expression patterns, but require the integration of the latest genomic information. This study provides a comprehensive workflow implemented in an R package (rePROBE) to assign the entire probes and to annotate the probe sets based on up-to-date genomic and transcriptomic information. The rePROBE R package is freely available at https://github.com/friederhadlich/rePROBE. It can be applied to available gene expression microarray platforms and addresses both public and custom databases. The revised probe assignment and updated probe-set annotation were applied to commercial microarrays available for different livestock species, i.e. ChiGene-1_0-st (Gallus gallus, 443,579 probes; 18,530 probe sets), PorGene-1_1-st (Sus scrofa, 592,005; 25,779) and BovGene-1_0-st (Bos taurus, 530,717; 24,759) as well as human (Homo sapiens, HuGene-1_0-st) and mouse (Mus musculus, HT_MG-430_PM) microarrays. Using current specie-specific transcriptomic information (RefSeq, Ensembl and partially non-redundant nucleotide sequences) and genomic information, the applied workflow revealed 297,574 probes for chickens (pig: 384,715; cattle: 363,077; human: 481,168; mouse: 324,942) assigned to 15,689 probe sets (pig: 21,673; cattle: 21,238; human: 23,495; mouse: 32,494). These are representative of 12,641 unique genes that were both annotated and positioned (pig: 15,758; cattle: 18,046; human: 20,167; mouse: 16,335). Additionally, the workflow collects information on the number of single nucleotide polymorphisms (SNPs) within respective targeted genomic regions and thus provides a detailed basis for comprehensive analyses such as quantitative trait locus (eQTL) expression studies to identify quantitative and functional traits.

Regulation of microRNA (miRNA) expression contributes to a wide range of target gene expression and phenotypes. The miRNA expression in the liver, the central metabolic organ, was examined in 209 pigs, and integrated with haematological and clinical biomarkers of metabolic and overall health, mRNA-target expression levels and single-nucleotide polymorphism (SNP) genotypes. The expression levels of 426 miRNA species correlated with plasma haematological or biochemical traits (r² = |0.19–0.45|, false discovery rate < 5%). Pairs of these miRNAs and their predicted target mRNAs showing expressing levels associated with the identical traits were examined to understand how immune and metabolic traits are affected by miRNA-mediated regulatory networks derived by mapping miRNA abundance as an expression quantitative trait. In total, 221 miRNA-expression-QTL correspond to 164 SNPs and 108 miRNAs, including miR-34a, miR-30e, miR-148-3p, miR-204, miR-181-5p, miR-143-5p and let-7 g that also correlate with the biomarkers. Sixty-one SNPs were simultaneously associated with 29 miRNA and 41 mRNA species. The expression levels of 13 out of 29 miRNA were correlated with one of the biochemical or haematological traits. For example, the expression levels of miR-34a were correlated with serum phosphorus and cholesterin levels; miR-204, miR-15a and miR-16b were correlated with triglyceride. For haematological traits, the expression levels of miR-652 and miR-204 were correlated with the mean corpuscular haemoglobin concentration, and the expression of miR-143 was correlated with plateletcrit. Pleiotropic association analyses revealed genetic links between mRNA and miRNA on SSC6 for miR-34a, SSC9 for miR-708 and SSC14 for miR-652. Our analysis of miRNA and mRNA transcript profiles, their correlation with clinically important plasma parameters of hepatic functions as well as information on their genetic regulation provide novel regulatory networks and potential new biomarkers for immune and metabolic traits.