Title: Uncovering the mouse olfactory long non-coding transcriptome with a novel machine-learning model
Author: Camargo, Antonio P; Papes, Fabio; Nakahara, Thiago S; Firmino, Luiz E R; Netto, Paulo H M; Do Nascimento, João B P; Donnard, Elisa R; Galante, Pedro A F; Carazzolle, Marcelo F; Malnic, Bettina
Is part of: DNA Research, v. dsz015, p. 1 - 14
Citation: Camargo, Antonio P; Papes, Fabio; Nakahara, Thiago S; Firmino, Luiz E R; Netto, Paulo H M; Do Nascimento, João B P; Donnard, Elisa R; Galante, Pedro A F; Carazzolle, Marcelo F; Malnic, Bettina; Uncovering the mouse olfactory long non-coding transcriptome with a novel machine-learning model. DNA Research, v.dsz015, p. 1-14, 2019
Abstract: Very little is known about long non-coding RNAs (lncRNAs) in the mammalian olfactory sensory epithelia. Deciphering the non-coding transcriptome in olfaction is relevant because these RNAs have been shown to play a role in chromatin modification and nuclear architecture reorganization, processes that accompany olfactory differentiation and olfactory receptor gene choice, one of the most poorly understood gene regulatory processes in mammals. In this study, we used a combination of in silico and ex vivo approaches to uncover a comprehensive catalogue of olfactory lncRNAs and to investigate their expression in the mouse olfactory organs. Initially, we used a novel machine-learning lncRNA classifier to discover hundreds of annotated and unannotated lncRNAs, some of which were predicted to be preferentially expressed in the main olfactory epithelium and the vomeronasal organ, the most important olfactory structures in the mouse. Moreover, we used whole-tissue and single-cell RNA sequencing data to discover lncRNAs expressed in mature sensory neurons of the main epithelium. Candidate lncRNAs were further validated by in situ hybridization and RT-PCR, leading to the identification of lncRNAs found throughout the olfactory epithelia, as well as others exquisitely expressed in subsets of mature olfactory neurons or progenitor cells.
Funding: This work was supported by Sao Paulo Research Foundation (FAPESP) grants to F.P. (#2015/50371-0), B.M. (#2016/24471-0) and M.C. (#2013/08293-7), by FAPESP fellowships to A.C. (#2016/05379-6), T.N. (#2018/11860-4) and J.N. (#2011/22611-6), and fellowships from Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES) to T.N., P.N. and L.F.