1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Deep neural network prediction of interplay between lncRNAs and salinity response in rice
Paper ID : 1480-ICB10
Authors:
Raheleh Mirdar Mansuri1, Amir-Hossein Azizi2, Zahra-Sadat Shobbar *3
1Agricultural Biotechnology Research Institute of Iran (ABRII), Department of Systems Biology, Agricultural Research, Education and Extension Organization (AREEO), PO Box 31535-1897, Karaj, Iran
2Agricultural Biotechnology Research Institute of Iran (ABRII), Department of Systems Biology, Agricultural Research, Education and Extension Organization (AREEO), PO Box 31535-1897, Karaj, Iran.
3Agricultural Biotechnology Research Institute of Iran (ABRII), Department of Systems Biology, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.
Abstract:
Salt stress seriously constrains growth and fertility of rice worldwide. Long noncoding RNAs (lncRNAs) play crucial roles in plant abiotic stress response, however, no systematic screening of lncRNAs under salt stress in rice has been reported. Herein, a transcriptional study using RNA-seq was conducted which resulted in the identification of differentially expressed lncRNAs (DE-lncRNAs) in FL478 as salt tolerant rice genotypes compared to its susceptible parent (IR29). Initially, a total of 15131 and 16256 potential noncoding RNAs were respectively identified in FL478 and IR29, among which 8724 (FL478) and 9235 (IR29) transcripts with length of > 200 bp were nominated as lncRNAs. Applying a strict pipeline, four and nine DE-lncRNAs were respectively detected in FL478 (2 up- and 2 down-regulated) and IR29 (6 up- and 3 down-regulated) under salt conditions, while only 2 DE-lncRNAs were in common among both genotypes. Using ATAC-seq data, we showed that the genomic regions of all four lncRNAs in FL478 and 6/9 in IR29 are significantly accessible for transcription. To identify the potential functions of salt-responsive DE-lncRNAs in cis-regulation, protein-coding genes were initially searched 100 kb upstream and downstream of these lncRNAs; then DE-lncRNAs and their neighbors mRNA were subjected to co-expression network analysis. We further explored potential functions of salt-responsive DE-lncRNAs to identify trans-regulatory networks of lncRNAs in each genotype. The crosstalk between DE-lncRNAs and miRNAs were subsequently investigated using exploring the DE-lncRNAs acting as target mimic of known miRNAs in Oryza sativa. WGCNA analyses identified 49 modules in the two genotypes. Interestingly, lncRNA.2-FL in FL478 was highly correlated with 173 mRNAs in the transcriptional module M39, whereas this module was not significant in IR29. Furthermore, we used a deep neural network trained on the rice genome to predict the effect of nearby mutations on the expression of mRNAs. We identified many important regulatory elements and showed the causal effect of lncRNA.2-FL on the expression level of a differentially expressed gene in the tolerant genotype. The identified DE-lncRNAs might be involved in rice response to salt stress, and lncRNA.2-FL may play a role as a regulatory hub in the salt stress tolerance of FL478.
Keywords:
Oryza Sativa; Salinity; LncRNA; co-expression; weighted correlation network analysis
Status : Paper Accepted (Poster Presentation)