• Journal of Life Science
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• v.28 no.9
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• pp.1030-1041
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• 2018

Abiotic stresses limit the growth and productivity of plants. Cellular adaptation to abiotic stresses requires coordinated regulation in gene expression directed by complex mechanisms. This study used the activation tagging system to identify novel salt stress-responsive genes. The study selected 9 activation tagging lines that showed salt stress-tolerant phenotypes during their germination stages. Thermal asymmetric interlaced-PCR (TAIL-PCR) was used to identify the T-DNA tagging sites on the Arabidopsis genome in selected activation tagging lines, including AT7508, AT7512, AT7527, AT7544, AT7548, and AT7556. RT-PCR analysis showed that ClpC2/HSP93-III (At3g48870), plant thionin family (At2g20605), anti-muellerian hormone type-2 receptor (At3g50685), vacuolar iron transporter family protein (At4g27870), and microtubule-associated protein (At5g16730) were activated in AT7508, AT7512, AT7527, AT7544, and AT7556, respectively. Interestingly, in AT7548, both the genes adjacent to the T-DNA insertion site were activated: Arabinogalactan protein 13 (AGP13) (At4g26320) and F-box/RNI-like/FBD-like domains-containing protein (At4g26340). All of the seven genes were newly identified as salt stress-responsive genes from this study. Among them, the expression of ClpC2/HSP93-III, AGP13, F-box/RNI-like/FBD-like domains-containing protein gene, and microtubule-associated protein gene were increased under salt-stress condition. In addition, AT7508, AT7527, and AT7544 were more tolerant to salt stress than wild type at seedling development stage, functionally validating the screening results of the activation tagging lines. Taken together, our results demonstrate that the activation tagging system is useful for identifying novel stress-responsive genes.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.3
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• pp.157-165
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• 2006

The change of external/internal factors of the cell rquires specific biological functions to maintain life. Such functions encourage particular genes to jnteract/regulate each other in multiple ways. Accordingly, we applied a linear decomposition model IFSA, which derives hidden variables, called the 'expression mode' that corresponds to the functions. To interpret gene interaction/regulation, we used a cross-correlation method given an expression mode. Linear decomposition models such as principal component analysis (PCA) and independent component analysis (ICA) were shown to be useful in analyzing high dimensional DNA microarray data, compared to clustering methods. These methods assume that gene expression is controlled by a linear combination of uncorrelated/indepdendent latent variables. However these methods have some difficulty in grouping similar patterns which are slightly time-delayed or asymmetric since only exactly matched Patterns are considered. In order to overcome this, we employ the (IFSA) method of [1] to locate phase- and shut-invariant features. Membership scoring functions play an important role to classify genes since linear decomposition models basically aim at data reduction not but at grouping data. We address a new function essential to the IFSA method. In this paper we stress that IFSA is useful in grouping functionally-related genes in the presence of time-shift and expression phase variance. Ultimately, we propose a new approach to investigate the multiple interaction information of genes.