Identification of processed pseudogenes involved in resistance mechanism against biotic stress in Oryza sativa using RNA-seq data

Oryza sativa (rice), belonging to Poaceae, is the third most cultivated crop due to being rich in nutrients and energy sources. It is constantly exposed to biotic and abiotic stress factors. Biotic stress caused by living organisms such as fungi, nematodes, insects, bacteria, and viruses cause significant physiological and molecular changes, decreasing crop quality and yield. Plants have various defense mechanisms against these biotic stress factors. In particular, resistance (R) genes play an essential role in plant resistance mechanisms to biotic stress. R proteins interact with effector proteins that are products of avirulence (Avr) genes synthesized by pathogens. Pseudogenes are disabled genes, occurring with accumulated mutations as copies of protein-coding genes over time. These are typically classified into three groups processed (retrotransposed), duplicated (unprocessed), and unitary pseudogenes. Although it is debated whether these genes are functional or not, pseudogenes are known to play an important role in the regulation of various genes. RNA-seq is an approach that enables the identification of genes expressed under specific conditions using sequencing methods. In this thesis, the role of R genes in the resistance of commercially important rice to biotic stress and its relation with pseudogenes be sought using RNA-seq data from the literature. As a result, two pseudogenes related to nematode stress and a pseudogene related to fungus stress were associated with biotic stress. 10, 34, and 15 R genes were found as differentially expressed genes (DEGs) in the nematode, fungus ZB13, and fungus ZH10 datasets. Using artificial neural networks (ANN), training and test data consisting of known pseudogenes and genes were correctly classified at 87 percent and 90 percent rates. The outcomes of this study need to be validated further in other cereals or even other plant species to find traits that are generally traceable for disease prevention.