Meta-expression analysis of unannotated genes in rice and approaches for network construction to suggest the probable roles

Abstract

This work suggests 2020 potential candidates in rice for the functional annotation of unannotated genes using meta-analysis of anatomical samples derived from microarray and RNA-seq technologies and this information will be useful to identify novel morphological agronomic traits. Although the genome of rice (Oryza sativa) has been sequenced, 14,365 genes are considered unannotated because they lack putative annotation information. According to the Rice Genome Annotation Project Database ( http://rice.plantbiology.msu.edu/ ), the proportion of functionally characterized unannotated genes (0.35%) is quite limited when compared with the approximately 3.9% of annotated genes with assigned putative functions. Researchers require additional information to help them investigate the molecular mechanisms associated with those unannotated genes. To determine which of them might regulate morphological or physiological traits in the rice genome, we conducted a meta-analysis of expression data that covered a wide range of tissue/organ samples. Overall, 2020 genes showed cultivar-, tissue-, or organ-preferential patterns of expression. Representative candidates from featured groups were validated by RT-PCR, and the GUS reporter system was used to validate the expression of genes that were clustered according to their leaf or root preference. Taking a molecular and genetics approach, we examined meta-expression data and found that 127 genes were differentially expressed between japonica and indica rice cultivars. This is potentially significant for future agronomic applications. We also used a T-DNA insertional mutant and performed a co-expression network analysis of Sword shape dwarf1 (SSD1), a gene that regulates cell division. This network was refined via RT-PCR analysis. Our results suggested that SSD1 represses the expression of four genes related to the processes of DNA replication or cell division and provides insight into possible molecular mechanisms. Together, these strategies present a valuable tool for in-depth characterization of currently unannotated genes.

Keywords: Co-expression network; Meta-analysis of gene expression data; Mutant; Protein–protein interaction; Rice; Unannotated genes.