Gene expression analysis of drought tolerance and cuticular wax biosynthesis in diploid and tetraploid induced wallflowers.

Whole-genome doubling leads to cell reprogramming, upregulation of stress genes, and establishment of new pathways of drought stress responses in plants. This study investigated the molecular mechanisms of drought tolerance and cuticular wax characteristics in diploid and tetraploid-induced Erysimum cheiri. According to real-time PCR analysis, tetraploid induced wallflowers exhibited increased expression of several genes encoding transcription factors (TFs), including AREB1 and AREB3; the stress response genes RD29A and ERD1 under drought stress conditions. Furthermore, two cuticular wax biosynthetic pathway genes, CER1 and SHN1, were upregulated in tetraploid plants under drought conditions. Leaf morphological studies revealed that tetraploid leaves were covered with unique cuticular wax crystalloids, which produced a white fluffy appearance, while the diploid leaves were green and smooth. The greater content of epicuticular wax in tetraploid leaves than in diploid leaves can explain the decrease in cuticle permeability as well as the decrease in water loss and improvement in drought tolerance in wallflowers. GC‒MS analysis revealed that the wax components included alkanes, alcohols, aldehydes, and fatty acids. The most abundant wax compound in this plant was alkanes (50%), the most predominant of which was C29. The relative abundance of these compounds increased significantly in tetraploid plants under drought stress conditions. These findings revealed that tetraploid-induced wallflowers presented upregulation of multiple drought-related and wax biosynthesis genes; therefore, polyploidization has proved useful for improving plant drought tolerance.

Mathematical modeling and optimizing the in vitro shoot proliferation of wallflower using multilayer perceptron non-dominated sorting genetic algorithm-II (MLP-NSGAII).

Novel computational methods such as artificial neural networks (ANNs) can facilitate modeling and predicting results of tissue culture experiments and thereby decrease the number of experimental treatments and combinations. The objective of the current study is modeling and predicting in vitro shoot proliferation of Erysimum cheiri (L.) Crantz, which is an important bedding flower and medicinal plant. Its micropropagation has not been investigated before and as a case study multilayer perceptron- non-dominated sorting genetic algorithm-II (MLP-NSGAII) can be applied. MLP was used for modeling three outputs including shoots number (SN), shoots length (SL), and callus weight (CW) based on four variables including 6-benzylaminopurine (BAP), kinetin (Kin), 1-naphthalene acetic acid (NAA) and gibberellic acid (GA3). The R2 correlation values of 0.84, 0.99 and 0.93 between experimental and predicted data were obtained for SN, SL, and CW, respectively. These results proved the high accuracy of MLP model. Afterwards the model connected to Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was used to optimize input variables for obtaining the best predicted outputs. The results of sensitivity analysis indicated that SN and CW were more sensitive to BA, followed by Kin, NAA and GA. For SL, more sensitivity was obtained for GA3 than NAA. The validation experiment indicated that the difference between the validation data and MLP-NSGAII predicted data were negligible. Generally, MLP-NSGAII can be considered as a powerful method for modeling and optimizing in vitro studies.