Duy Do

By Duy N. Do and Eveline M. Ibeagha-Awemu

The ruminant mammary gland (MG) is an important organ charged with the production of milk for young and human nourishment. Many factors influence MG productivity, including nutrition, genetics, breed, epigenetics (including non-coding RNA [ncRNA]), disease pathogens and other environmental factors. In recent years, increasing research is beginning to determine the role of non-coding RNA in MG functions. Non-coding RNAs (small interfering RNA [siRNA], microRNA [miRNA], PIWI-interacting RNA [piRNA], small nucleolar RNA [snoRNA] and long non-coding RNA [lncRNA]) are a class of untranslated RNA molecules that function to regulate gene expression, associated biochemical pathways and cellular functions and are involved in many biological processes. This chapter presents a review of the current state of knowledge on the role of ncRNAs (particularly miRNAs and lncRNAs) in the MG and lactation processes, lactation signalling pathways, lipid metabolism, MG health of ruminants as well as miRNA roles in milk recipients. Finally, the potential application of new genome editing technology for ncRNA studies in MG development, the lactation process and milk components is presented.

Part of the book: Current Topics in Lactation

By Duy N. Do, Pier-Luc Dudemaine, Bridget Fomenky and Eveline M. Ibeagha-Awemu

The recent remarkable development of transcriptomics technologies, especially next generation sequencing technologies, allows deeper exploration of the hidden landscapes of complex traits and creates great opportunities to improve livestock productivity and welfare. Non-coding RNAs (ncRNAs), RNA molecules that are not translated into proteins, are key transcriptional regulators of health and production traits, thus, transcriptomics analyses of ncRNAs are important for a better understanding of the regulatory architecture of livestock phenotypes. In this chapter, we present an overview of common frameworks for generating and processing RNA sequence data to obtain ncRNA transcripts. Then, we review common approaches for analyzing ncRNA transcriptome data and present current state of the art methods for identification of ncRNAs and functional inference of identified ncRNAs, with emphasis on tools for livestock species. We also discuss future challenges and perspectives for ncRNA transcriptome data analysis in livestock species.

Part of the book: Applications of RNA-Seq and Omics Strategies