The availability and intensity of sunlight are among the major factors of growth, development and metabolism in plants. However, excessive illumination disrupts the electronic balance of photosystems and leads to the accumulation of reactive oxygen species in chloroplasts, further mediating several regulatory mechanisms at the subcellular, genetic, and molecular levels. We carried out a comprehensive bioinformatic analysis that aimed to identify genetic systems and candidate transcription factors involved in the response to high light stress in Arabidopsis thaliana L. using resources GEO NCBI, string-db, ShinyGO, STREME, and Tomtom, as well as programs metaRE, CisCross, and Cytoscape. Through the meta-analysis of five transcriptomic experiments, we selected a set of 1151 differentially expressed genes, including 453 genes that compose the gene network. Ten significantly enriched regulatory motifs for TFs families ZF-HD, HB, C2H2, NAC, BZR, and ARID were found in the promoter regions of differentially expressed genes. In addition, we predicted families of transcription factors associated with the duration of exposure (RAV, HSF), intensity of high light treatment (MYB, REM), and the direction of gene expression change (HSF, S1Fa-like). We predicted genetic components systems involved in a high light response and their expression changes, potential transcriptional regulators, and associated processes.
Reactive oxygen species (ROS) are some of the most damaging factors for living systems. Cells produce ROS during normal metabolism reactions, but ROS production increases under stressful conditions. Improving the antioxidant system in cultivated plants will increase their tolerance to abiotic stresses, such as salinity, drought and cold. However, the biochemical components of the system are redundant, for each reaction is catalyzed by a series of enzymes encoded by different genes. Choosing the most perspective components of this system will help speed up evaluating the optimal breeding strategy for improving abiotic stress tolerance in economically valuable plants.In the present research article, we present the results of an integrative analysis of evolution-and expressionrelated characteristics. The work was carried out on a series of genes that belong to 4 functional groups (APX, GPX, SOD and CAT) of enzymatic components of the antioxidant defense system in six species of C 3 cereal plants and 3 species of C 4 cereal plants. As a result, 25 groups of orthologous genes were evaluated and described. The highest gene expression level and the greatest pressure of purifying selection were found to characterize six groups. These genes were chosen for further verification and use in breeding. Because these genes undergo the most conservative evolution and have the highest level of mRNA expression, we may assume that they contribute a lot to the antioxidant system functioning of the C 3 and C 4 cereal plants studied. We have shown that the integration of evolutionary characteristics and expression data represents a promising approach to predict target genes for plant breeding.Активные формы кислорода (АФК) -один из ключевых повреждающих факторов для живых организмов. АФК производятся в реакциях нормального метаболизма, в стрессовых условиях их выработка повышается. Улучшение характеристик ферментативной системы антиоксидантной защиты культурных растений позволит повысить их устойчивость к абиотическим стрессам, таким как засоленность, засуха и холод. Однако компоненты системы вырождены -каждая реакция катализируется серией ферментов, кодируемых разными генами. Выбор наиболее важных компонентов позволит ускорить нахождение оптимальной селекционной стратегии для улучшения свойств всей системы у хозяйственно ценных видов растений. В настоящей работе впервые проведен системно-биологический анализ особенностей молекулярной эволюции и характеристик экспрессии генов, принадлежащих к четырем функциональным группам ферментов антиоксидантной защиты (APX, GPX, SOD и CAT), у шести представителей C 3 и трех представителей C 4 злаковых растений. Выделены и проанализированы 25 ортологических групп генов. Выявлены шесть ортологических групп с наиболее высоким уровнем экспрессии и наибольшим давлением стабилизирующего отбора для дальнейшей верификации и использования в селекции. Эти шесть ортологических групп, предположительно, вносят больший вклад в функционирование антиоксидантной системы изученных C 3 и C 4 злаковых растений. Показ...
Single-cell technology is a relatively new and promising way to obtain high-resolution transcriptomic data mostly used for animals during the last decade. However, several scientific groups developed and applied the protocols for some plant tissues. Together with deeply-developed cell-resolution imaging techniques, this achievement opens up new horizons for studying the complex mechanisms of plant tissue architecture formation. While the opportunities for integrating data from transcriptomic to morphogenetic levels in a unified system still present several difficulties, plant tissues have some additional peculiarities. One of the plants’ features is that cell-to-cell communication topology through plasmodesmata forms during tissue growth and morphogenesis and results in mutual regulation of expression between neighboring cells affecting internal processes and cell domain development. Undoubtedly, we must take this fact into account when analyzing single-cell transcriptomic data. Cell-based computational modeling approaches successfully used in plant morphogenesis studies promise to be an efficient way to summarize such novel multiscale data. The inverse problem’s solutions for these models computed on the real tissue templates can shed light on the restoration of individual cells’ spatial localization in the initial plant organ—one of the most ambiguous and challenging stages in single-cell transcriptomic data analysis. This review summarizes new opportunities for advanced plant morphogenesis models, which become possible thanks to single-cell transcriptome data. Besides, we show the prospects of microscopy and cell-resolution imaging techniques to solve several spatial problems in single-cell transcriptomic data analysis and enhance the hybrid modeling framework opportunities.
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