Identification of novel genes potentially involved in rice ( Oryza sativa L.) drought tolerance

Drought is a major constraint affecting rice production and causing yield reduction of up to 60% in the major growing areas of Asia. Developing drought-tolerant cultivars in rice is an appropriate strategy to provide food security and hinder the harmful effects of drought. Therefore, particular attention must be directed toward identifying drought-responsive genes. In the present study, based on the microarray analysis results of two rice genotypes with contrasting response to drought stress, 308 probe sets are uniquely upregulated with equal to or greater than 3 symmetric fold changes in drought-tolerant genotype upon exposure to drought stress. As the next step, mapping of the corresponding genes of these probe sets via the web-based tool “QlicRice” is expected to reveal the genes within the drought stress-associated QTLs (quantitative trait loci). To determine the number of probe sets annotated to the transcription factors in various families, the plant transcription factor database (PlnTFDB) is relatively utilized. Finally, the biclustering analysis using Genevestigator is at hand to unveil the biclusters along with the embedded probe sets annotated to 3 transcription factors in different drought stress studies. The survey is also aimed at determining the possible relationships between up- and co-regulated genes and the transcription factors in the obtained biclusters through plant promoter analysis navigator (PlantPAN). To substantiate how the exploration of transcriptomic changes of the genotypes with contrasting drought tolerance could uncover a number of genes associated with rice drought stress is the ultimate goal of the present study

Deciphering the molecular basis of abiotic stress response in cucumber (Cucumis sativus L.) using RNA-Seq meta-analysis, systems biology, and machine learning approaches

Abstract Abiotic stress in cucumber (Cucumis sativus L.) may trigger distinct transcriptome responses, resulting in significant yield loss. More insight into the molecular underpinnings of the stress response can be gained by combining RNA-Seq meta-analysis with systems biology and machine learning. This can help pinpoint possible targets for engineering abiotic tolerance by revealing functional modules and key genes essential for the stress response. Therefore, to investigate the regulatory mechanism and key genes, a combination of these approaches was utilized in cucumber subjected to various abiotic stresses. Three significant abiotic stress-related modules were identified by gene co-expression network analysis (WGCNA). Three hub genes (RPL18, δ-COP, and EXLA2), ten transcription factors (TFs), one transcription regulator, and 12 protein kinases (PKs) were introduced as key genes. The results suggest that the identified PKs probably govern the coordination of cellular responses to abiotic stress in cucumber. Moreover, the C2H2 TF family may play a significant role in cucumber response to abiotic stress. Several C2H2 TF target stress-related genes were identified through co-expression and promoter analyses. Evaluation of the key identified genes using Random Forest, with an area under the curve of ROC (AUC) of 0.974 and an accuracy rate of 88.5%, demonstrates their prominent contributions in the cucumber response to abiotic stresses. These findings provide novel insights into the regulatory mechanism underlying abiotic stress response in cucumber and pave the way for cucumber genetic engineering toward improving tolerance ability under abiotic stress

N,N-dichloro-4-methylbenzenesulphonimide as a novel and efficient catalyst for acetylation of alcohols under mild conditions

Structurally diverse alcohols were acetylated in a clean and efficient reaction with acetic anhydride based on the use of a catalytic amount of N,N-dichloro-4-methylbenzenesulphonimide in dichloromethane. All reactions were performed at room temperature in good to excellent yields

A comprehensive meta-analysis to identify transcriptional signatures of abiotic stress responses in barley (Hordeum vulgare)

Barley, as one of the major cereals, possesses natural tolerance to major abiotic stresses such as drought and salinity, so that it is an outstanding model in abiotic stresses research. The study focuses on meta-analysis by combining different datasets of barley abiotic stress-related microarray data to identify stress-responsive genes and pathways. In addition to a thorough investigation to determine the up and downregulated gene sets under stress conditions, other analyses including gene ontology (GO) enrichment and protein–protein interaction network analysis were performed for a comprehensive study of differentially expressed genes under abiotic stresses. A total of 256 microarray samples from 14 different experiments were analyzed and 3723 probe sets were identified. The metabolic processes, cellular process, localization, biological regulation and regulation of biological process were the top enriched GO terms. Interestingly, the response to abiotic stress in the functional group contained the highest number of upregulated genes. In addition, the photosynthesis biological category included only downregulated genes. Fourteen genes in this category were related to photosystem II while only six genes belonged to photosystem I. Network analysis of DEGs revealed 52 and 57 genes as critical genes in down and upregulated networks, respectively; module analysis unveiled 28 and 23 clusters for up and downregulated networks. Regarding the GO analysis of modules, one upregulated cluster and two downregulated clusters exhibited a direct response to abiotic stress

In silico identification of transcription factors associated with the biosynthesis of carotenoids in corn ( Zea mays L. )

Carotenoids, a diverse group of colorful pigments, contribute to the development, light harvesting and photoprotection in plants as well as human health. Due to the interesting properties of carotenoids, enhanced carotenoid biosynthesis has been of ongoing interest. Recent advances in computational biology and bioinformatics make it more feasible to understand the transcriptional regulatory network underlying carotenoid biosynthesis. Studies on carotenoid biosynthesis in corn ( Zea mays L. ) have indicated the pivotal role of the phytoene synthase gene PSY1 (accession: GRMZM2G300348) in endosperm color and carotenoid accumulation in corn kernels. Computational approaches such as Genomatix, PlantPAN, PlantCARE, PlantTFDB and IGDE6 have been used for promoter prediction, regulatory features and transcription factor identification, as well as pairwise promoter comparisons. Four transcripts have been identified for the PSY1 gene. Based on Genomatix and PlantPAN, the promoter predicted for GRMZM2G300348_T01 was different from that predicted for the other three transcripts (GRMZM2G300348_T02, GRMZM2G300348_T03 and GRMZM2G300348_T04). The results indiated that the promoter of GRMZM2G300348_T01 has more diverse motifs involved in hormonal/environmental stress responses. The most significant result obtained from this study is the discovery of two transcription factors belonging to the HB family that are co-expressed with all four transcripts of PSY1 under environmental stresses. It is, therefore, likely that these transcription factors may act as critical regulators of PSY1 gene expression in corn. Identification of the proteins acting upstream of PSY1 within corn will shed light on the fine tuning of PSY1 expression regulation. Such an understanding would also contribute to metabolic engineering aimed at enhanced carotenoid biosynthesis

An Efficient Method for the Transthioacetalization of Acylals and Acetals under Mild Conditions

A rapid and efficient method for the transthioacetalization of acylals (1,1-diacetates) and acyclic and cyclic acetals is described. The reaction was carried out using 1-benzyl-4-aza-1-azoniabicyclo[2.2.2]octane tribromide (1 mol%). The yield of the transthioacetalization was high and reaction conditions involve the use of acetonitrile as the solvent at room temperature; isolation is simple and the products are nearly pure

In silico identification of transcription factors associated with the biosynthesis of carotenoids in corn ( Zea mays L. )

Carotenoids, a diverse group of colorful pigments, contribute to the development, light harvesting and photoprotection in plants as well as human health. Due to the interesting properties of carotenoids, enhanced carotenoid biosynthesis has been of ongoing interest. Recent advances in computational biology and bioinformatics make it more feasible to understand the transcriptional regulatory network underlying carotenoid biosynthesis. Studies on carotenoid biosynthesis in corn ( Zea mays L. ) have indicated the pivotal role of the phytoene synthase gene PSY1 (accession: GRMZM2G300348) in endosperm color and carotenoid accumulation in corn kernels. Computational approaches such as Genomatix, PlantPAN, PlantCARE, PlantTFDB and IGDE6 have been used for promoter prediction, regulatory features and transcription factor identification, as well as pairwise promoter comparisons. Four transcripts have been identified for the PSY1 gene. Based on Genomatix and PlantPAN, the promoter predicted for GRMZM2G300348_T01 was different from that predicted for the other three transcripts (GRMZM2G300348_T02, GRMZM2G300348_T03 and GRMZM2G300348_T04). The results indiated that the promoter of GRMZM2G300348_T01 has more diverse motifs involved in hormonal/environmental stress responses. The most significant result obtained from this study is the discovery of two transcription factors belonging to the HB family that are co-expressed with all four transcripts of PSY1 under environmental stresses. It is, therefore, likely that these transcription factors may act as critical regulators of PSY1 gene expression in corn. Identification of the proteins acting upstream of PSY1 within corn will shed light on the fine tuning of PSY1 expression regulation. Such an understanding would also contribute to metabolic engineering aimed at enhanced carotenoid biosynthesis

Biplot graph of mean of grain yield and the stability parameter of first principal component of the genotypes and environments.

(Square and oval shapes show grouping obtained from cluster analysis of genotypes and environments based on the first principal component, respectively. Horizontal and vertical lines pass from the mean yield and first principal component points equal to zero, respectively). e1: Kerman (normal) and fourth crop year, e2: Kerman (normal) and second crop year, e3: Kerman (normal) and third crop year, e4: Sirjan (normal) and fourth crop year, e5: Neyriz (normal) and first crop year, e6: Kerman (normal) and first crop year, and e7: Sirjan (salinity) and fourth crop year. (PDF)</p

Biplot graph of mean of grain yield and second principal component of genotypes and environments.

(Interconnected and non-interconnected lines show grouping obtained from cluster analysis of genotypes and environments based on the second principal component, respectively. Horizontal and vertical lines pass through the mean yield and second principal component points equal to zero, respectively). e1: Kerman (normal) and fourth crop year, e2: Kerman (normal) and second crop year, e3: Kerman (normal) and third crop year, e4: Sirjan (normal) and fourth crop year, e5: Neyriz (normal) and first crop year, e6: Kerman (normal) and first crop year, and e7: Sirjan (salinity) and fourth crop year. (PDF)</p