PHYLOGENETIC ANALYSES, GENE DOSAGE EFFECT AND SELECTION IN MEMBERS OF THE LATERAL BOUNDARY DOMAIN (LBD) GENES TRANSCRIPTION FACTOR FAMILY

Wood formation is an economically and environmentally important process and has played a significant role in the evolution of terrestrial plants. Despite its significance, the molecular underpinnings of the process are still poorly understood. We have previously shown that four Lateral Boundary Domain (LBD) transcription factors have important roles in the regulation of wood formation with two (LBD1 and LBD4) involved in secondary phloem and ray cell development and two (LBD15 and LBD18) in secondary xylem formation. Here, we used comparative phylogenetic analyses to test potential roles of the four LBD genes in the evolution of woodiness. We studied the copy number and variation in DNA and amino acid sequences of the four LBDs in a wide range of woody and herbaceous plant taxa with fully sequenced and annotated genomes. LBD1 showed the highest gene copy number across the studied species, and LBD1 gene copy number was strongly and significantly correlated with the level of ray seriation. The lianas, cucumber and grape, with multiseriate ray cells showed the highest gene copy number (12 and 11, respectively). Because lianas’ growth habit requires significant twisting and bending, the less lignified ray parenchyma cells likely facilitate stem flexibility and maintenance of xylem conductivity. We further demonstrate conservation of amino acids in the LBD18 protein sequences that are specific to woody taxa. Neutrality tests showed evidence for strong purifying selection on these gene regions across various orders, indicating adaptive convergent evolution of LBD18. Structural modeling demonstrates that the conserved amino acids have a significant impact on the tertiary protein structure and thus are likely of significant functional importance.

DISSECTION OF STRESS RESPONSE NETWORKS REGULATING MULTIPLE STRESSES IN RICE

Important food crops like rice are constantly exposed to various stresses that can have devastating effect on their survival and productivity. Being sessile, these highly evolved organisms have developed elaborate molecular machineries to sense a mixture of stress signals and elicit a precise response to minimize the damage. However, recent discoveries revealed that the interplay of these stress regulatory and signaling molecules is highly complex and remains largely unknown. In this work, we conducted large scale analysis of differential gene expression using advanced computational methods to dissect regulation of stress response which is at the heart of all molecular changes leading to the observed phenotypic susceptibility. One of the most important stress conditions in terms of loss of productivity is drought. We performed genomic and proteomic analysis of epigenetic and miRNA mechanisms in regulation of drought responsive genes in rice and found subsets of genes with striking properties. Overexpressed genesets included higher number of epigenetic marks, miRNA targets and transcription factors which regulate drought tolerance. On the other hand, underexpressed genesets were poor in above features but were rich in number of metabolic genes with multiple co-expression partners contributing majorly towards drought resistance. Identification and characterization of the patterns exhibited by differentially expressed genes hold key to uncover the synergistic and antagonistic components of the cross talk between stress response mechanisms. We performed meta-analysis on drought and bacterial stresses in rice and Arabidopsis, and identified hundreds of shared genes. We found high level of conservation of gene expression between these stresses. Weighted co-expression network analysis detected two tight clusters of genes made up of master transcription factors and signaling genes showing strikingly opposite expression status. To comprehensively identify the shared stress responsive genes between multiple abiotic and biotic stresses in rice, we performed meta-analyses of microarray studies from seven different abiotic and six biotic stresses separately and found more than thirteen hundred shared stress responsive genes. Various machine learning techniques utilizing these genes classified the stresses into two major classes' namely abiotic and biotic stresses and multiple classes of individual stresses with high accuracy and identified the top genes showing distinct patterns of expression. Functional enrichment and co-expression network analysis revealed the different roles of plant hormones, transcription factors in conserved and non-conserved genesets in regulation of stress response.