Exploring Genomic Variations in Nematode-Resistant Mutant Rice Lines

Abstract

Rice (Oryza sativa) production is seriously affected by the root-knot nematode Meloidogyne graminicola, which has emerged as a menace in upland and irrigated rice cultivation systems. Previously, activation tagging in rice was utilized to identify candidate gene(s) conferring resistance against M. graminicola. T-DNA insertional mutants were developed in a rice landrace (acc. JBT 36/14), and four mutant lines showed nematode resistance. Whole-genome sequencing of JBT 36/14 was done along with the four nematode resistance mutant lines to identify the structural genetic variations that might be contributing to M. graminicola resistance. Sequencing on Illumina NovaSeq 6000 platform identified 482,234 genetic variations in JBT 36/14 including 448,989 SNPs and 33,245 InDels compared to reference indica genome. In addition, 293,238-553,648 unique SNPs and 32,395-65,572 unique InDels were found in the four mutant lines compared to their JBT 36/14 background, of which 93,224 SNPs and 8,170 InDels were common between all the mutant lines. Functional annotation of genes containing these structural variations showed that the majority of them were involved in metabolism and growth. Trait analysis revealed that most of these genes were involved in morphological traits, physiological traits and stress resistance. Additionally, several families of transcription factors, such as FAR1, bHLH, and NAC, and putative susceptibility (S) genes, showed the presence of SNPs and InDels. Our results indicate that subject to further genetic validations, these structural genetic variations may be involved in conferring nematode resistance to the rice mutant lines.

Keywords: InDels; Meloidogyne graminicola; SNPs; genetic variations; genome; mutants; resistance; rice.

Figure 1

Pipeline followed for the analysis of NGS data in this study.

Figure 2

Genetic variation in JBT 36/14 compared to reference indica genome; (A) Circos diagram of genome wide variations depicting InDel density, SNP density and intrachromosomal translocations; (B) Annotation of SNPs and InDels by location; (C) Type of SNP variants by base substitution in JBT 36/14 genome; (D) Functional annotation of SNP variants; and (E) Size distribution of identified InDels in JBT 36/14 genome.

Figure 3

Circos diagram representing genetic variation in mutant rice lines compared to JBT 36/14 genome depicting InDel density, SNP density and intrachromosomal translocations; (A) line-8, (B) line-9; (C) line-11, and (D) line-15.

Figure 4

Annotation of SNP and InDel variants identified in mutant lines 8, 9, 11, and 15; (A) Annotations of SNP and InDel variants by their location; (B) Size distribution of identified InDels; and (C) Type of SNP variants by base substitution in mutant lines.

Figure 5

Common genes affected by SNPs and InDels in all mutant lines; (A) Venn diagram depicting common genes affected by SNP variants in mutant lines; (B) Common genes affected by InDel variants in mutant lines; (C) Annotation by location of common SNP variants at common genomic locations in all mutant lines; and (D) Annotation by location of common InDel variants at common genomic locations in all mutant lines.

Figure 6

Functional annotation of common genes affected by genetic variations in all mutant lines by KEGG pathway and Gene Ontology analysis (top 10); (A) common genes affected by SNP in all mutant lines and (B) Common genes affected by InDels in all mutant lines.

Figure 7

Pathway, trait and regulatory gene families affected by SNP or InDels; (A) Pathway analysis of common genes affected by genetic variations in all mutant lines; (B) common genes affected by genetic variations involved in major traits in rice; and (C) Common genes affected by SNP or InDel variations belonging to major rice transcription factor families (taken from PlantTFDB).