The Research on the Osmotic Stress Gene Mining Model Based on the Arabidopsis Genome

The Research on the Osmotic Stress Gene Mining Model Based on the Arabidopsis Genome

Xiao Yu, Xiang Li, Huihui Deng, Yuchen Tang, Zhepeng Hou, Qingming Kong
Copyright: © 2019 |Pages: 16
DOI: 10.4018/JITR.2019010109
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In the field of the bioinformatics, during osmotic stress response genes mining processing, it is also very crucial to verify experimental data obtained in the course of complex experiments by using the computer. Aim of this paper is taking Arabidopsis thaliana as the experimental crop, designing technology roadmap, taking advantage of the skills of function and programming, then designing algorithms. After using the program to predict the transcription start point, the promoter sequence is extracted and simplified. In addition, different alignment methods are classified. Then, comparing the promoter sequence with the cis-element and using the formula for further processing. Finally, get the probability P value, which provide further help to experts and scholars on the basis of probability values to determine the correlation between the osmotic stress. The experimental data source of chromosomal sequences is received from Genbank database files, and cis-element sequence that associated with osmotic stress is collected from TRANSFAC and TRRD database. From this, the authors not only used the Arabidopsis promoter as the experimental data, but also use a variety of eukaryotic promoters include promoters GhNHX1 rice, cotton OsNHX1 promoter, as a comparison. Wherein the data obtained in the biological laboratory, which in the course of running the program, 70% have been verified. P value close to 0.8, this article will be treated as the promoter contains osmotic stress cis-elements, the expression of gene induced by osmotic stress. For thaliana, cotton and rice, programs running average time was 51s, 72s and 114s. Through the use of some commonly used bioinformatics gene mining algorithms, MEME algorithm and BioProspector algorithm for the same data have been processed, the average running time of the system is increasing with the increase of data. Running time of MEME algorithm increases from 60s to reach 198s, BioProspector algorithm increases from 45s to 150s model process used herein were 50s, 75s, 110s, 135s. At the same time, the authors can see in the three algorithms, the model algorithm used herein with respect to the first two more optimized. To ensure the accuracy rate, meanwhile has high speed and stabilization of higher.
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Osmotic stress is affecting crop growth, development and production of one of the most serious abiotic stress. For a long time, improve crops for osmotic stress resistance has always been the efforts goal of breeding scientists. However, due to osmotic stress response is a very complicated process, through conventional breeding success is very limited. In recent years, due to the development of molecular biology and osmotic stress related genes found ceaselessly, molecular mechanism of plant osmotic stress resistance has a deeper understanding, which people laid a foundation for genetic engineering of plant osmotic stress resistance (Jie Li, Lihua & Chen, Yanming, 2005).

Some studies have reported that osmotic stress inhibits the growth of crops (Deguang Yang, Yongxi Liu, & Qian Zhang, 2015; Munns R, 2002). As found in wheat research, osmotic stress decreased the relative water content of wheat seedlings, inhibited the growth of seedlings (Lei, Yin & Ren, 2007). In the research of Phaseolus found that osmotic stress can inhibit the accumulation of dry matter and quality, resulting in destruction of chlorophyll components, leaves changed yellow, and the photosynthetic efficiency decreased. It was also found that the inhibition to the stem being more intense than the root suffered (Aydi, Aydi & Gonzalez, 2008). In addition, osmotic stress destroyed the balance of active oxygen metabolism, which resulted in oxidative stress. In the experiment of tomato, it was found that the excessive reactive oxygen species could cause oxidative damage to tomato leaves (Nasibi & Kalantari, 2009). Under osmotic stress, the increase of superoxide radical can induce ethylene biosynthesis in mung bean (Ke & Sun, 2004). The results showed that superoxide radicals could be used as catalysts for the conversion aminocyclopropane carboxylic acid to ethylene, which resulted in the increase of ethylene content and finally resulted the aging of pea seeds.

Abiotic stresses seriously affect plant growth and development, reduce crop yields. Plant have a variety of ways to resist or tolerate abiotic stress, mainly is the expression of a variety of abiotic stress resistance gene. Gene expression is regulated by its promoter and transcription factors, the current study of abiotic stress-inducible promoter cis-acting elements and transcription factors has become a hot issue (Guo, Zhan, & Yang, 2015). Many plant gene expressions are subject to the stress induced by stress, stress resistance gene expression is to rely on its upstream promoter regulation to achieve. Figure 1 shows the specific process of induction of genes by abiotic stress.

Higher plants promoter belongs to the type II promoter, different type of promoter has a relatively conserved sequence block, between closely related species, the promoter has some versatility, so using Arabidopsis as experimental model crop to with the phylogenetic relationships of plants of the same genome mining is feasible (Stockinger & Gilmour, 1997).

Many biology experts through the biological experiment of osmotic stress response genes in higher plants to forecast and digging, part shown in the Table 1.

In addition, the drought can induce hypothermia in Arabidopsis CBF1 (JASMONATE,2008)and DREB1 / 2 gene (Guo, He & Liu, 2005), JAZ family genes (Guo, Jiao & Di, 2009), JERF gene (Xie, Zhang & Zou, 2005), WRKY gene (Eulgem & Somssich, 2007; Zheng, Guo & Zhang, 2011) and MYB gene (Xu, Zhang & Wangang, 2006).

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