Decoding RNA Editing Sites Through Transcriptome Analysis in Rice Under Alkaline Stress

Abstract

Ribonucleic acid editing (RE) is a post-transcriptional process that altered the genetics of RNA which provide the extra level of gene expression through insertion, deletions, and substitutions. In animals, it converts nucleotide residues C-U. Similarly in plants, the role of RNA editing sites (RES) in rice under alkaline stress is not fully studied. Rice is a staple food for most of the world population. Alkaline stress cause reduction in yield. Here, we explored the effect of alkaline stress on RES in the whole mRNA from rice chloroplast and mitochondria. Ribonucleic acid editing sites in both genomes (3336 RESs) including chloroplast (345 RESs) and mitochondria (2991 RESs) with average RES efficiency ∼55% were predicted. Our findings showed that majority of editing events found in non-synonymous codon changes and change trend in amino acids was hydrophobic. Four types of RNA editing A-G (A-I), C-T (C-U), G-A, and T-C were identified in treated and untreated samples. Overall, RNA editing efficiency was increased in the treated samples. Analysis of Gene Ontology revealed that mapped genes were engaged in many biological functions and molecular processes. We also checked the expression of pentatricopeptide repeat (PPR), organelle zinc-finger (OZI), and multiple organellar RNA editing factors/RNA editing factor interacting proteins genes in control and treatment, results revealed upregulation of PPR and OZ1 genes in treated samples. This induction showed the role of these genes in RNA editing. The current findings report that RNA editing increased under alkaline stress which may contribute in adaptation for rice by changing amino acids in edited genes (88 genes). These findings will provide basis for identification of RES in other crops and also will be useful in alkaline tolerance development in rice.

Keywords: MORF; OZ1; PPR; RNA editing; RNA-seq; alkaline stress; rice.

FIGURE 1

Summary of RNA-seq data. (A) Mapping rate of RNA-seq data of chloroplast (Chl) and mitochondria (Mito). (B) Codon position of RNA editing sites in two genotypes (CD, Caidao and WD, WD20342) of rice, chloroplast (Chl) genome, and mitochondrion (Mito) genomes. (C) Comparison of single nucleotide conversion in control and treated samples by RNA-seq approach. Values are shown by percentage. Number of total RES and edited genes. (D) Editing efficiency of all the RNA sites in rice chloroplast and mitochondrion genomes. For comparison, t-test was used. ^***p < 0.001; ^**p < 0.01; *p < 0.05 was used for significant while ns, non-significant.

FIGURE 2

Heatmap of RNA editing efficiency of all the RNA sites in two genotypes of rice (CD, Caidao and WD, WD20342), (A) Chloroplast genome; (B) mitochondrion genome. The x-axis shows the genotypes under control (CD and WD) and treated (CDT and WDT) while the y-axis shows the editing sites. Black dotted box representing the RNA editing sites increased efficiency. (C) Reduced RNA editing efficiency in genes of mitochondria. Values insides the boxes were showing RNA editing efficiency. The x-axis shows the genotypes under control (CD and WD) and treatment (CDT and WDT). Gene names with amino acid position and type of changes were shown on the y-axis.

FIGURE 3

Gene Ontology annotation of genes of (A) chloroplast and (B) mitochondria. Red color showing cellular components (CC), blue color representing molecular functions (MF), and green color showing biological functions (BF).

FIGURE 4

Expression of PPR genes (A), OZ1 genes (B), and MORF/RIP genes (C) in two genotypes of rice (CD, Caidao and WD, WD20342) under control and treatment. (D) Expression analysis of PPR, OZ1, and MORF/RIP genes through qRT-PCR in control (C) and treatment (T). Mean of three replicates with ±SE were used. For comparison t-test was used (^**p < 0.01 and *p < 0.05).