Identification and Characterization of Soil Acidity Responsive Genes in Rice Landraces of Jharkhand

Abstract

Soil acidification exerts detrimental effects on rice plant leading to severe reduction in its newlineyield. In order to overcome the limitations in growth of rice plant due to soil acidity, it newlinebecomes imperative to study the effect of soil acidity on rice plant. This study aims to newlineidentify acidic pH responsive genes, the results of which can be further applied to newlinedevelop acidity tolerant cultivars of rice. Initially, we investigated the physiological, newlinebiochemical and microstructural changes in the leaves of traditional rice cultivars, newlinenamely, Gora Dhan (GD), Jhilli Dhan (JD), Desi Lalat Dhan (DLD) and Khijur Jhopa newlineDhan (KJD), under varying pH conditions (pH 6.5, 5.5, 4.5 and 3.5). Seedlings were newlinegrown at varying pH levels for 14 days under controlled conditions. Root and shoot newlinegrowth, chlorophyll content and electrolyte leakage were observed at different acidity newlinelevels. Further, biochemical parameters, namely, total soluble sugar (TSS), proline newlinecontent and lipid peroxidation were studied at varying pH levels. Microstructural changes newlinewere observed for rice cultivars through Swept-Source Optical Coherence Tomography newline(SS-OCT). On the basis of physiological, biochemical and microstructural changes newlineobserved in the rice cultivars at varying pH levels we selected JD as the susceptible newlinevariety and KJD as tolerant variety for further study. Further, we performed weighted newlinegene co-expression network analysis (WGCNA) and transcriptomic analysis on JD and newlineKJD varieties to identify acidic responsive genes in rice. newlineIn WGCNA, we utilized publicly available microarray datasets of A. thaliana to identify newlinedifferentially expressed genes (DEGs). A total of 983 DEGs were found as pH responsive newlinegenes responding to acidic stress conditions (at pH 4.5 and 6.0) at different time durations newline(after 1 hour and 8 hour). Thereafter, WGCNA algorithm was applied to construct gene newlineco-expression networks in A. thaliana, which classified the DEGs into six different newlinemodules. Hub genes were identified on the basis of highest intra mod