Browsing publications of the research group Microbial Proteomics (MPRO) by Subjects
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Development and evaluation of a milk protein transcript depletion method for differential transcriptome analysis in mammary gland tissue.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 challengeassociated 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.