Light is a key factor that affects phytochemical synthesis and accumulation in plants. Due to limitations of the environment or cultivated land, there is an urgent need to develop indoor cultivation systems to obtain higher yields with increased phytochemical concentrations using convenient light sources. Light-emitting diodes (LEDs) have several advantages, including consumption of lesser power, longer half-life, higher efficacy, and wider variation in the spectral wavelength than traditional light sources; therefore, these devices are preferred for in vitro culture and indoor plant growth. Moreover, LED irradiation of seedlings enhances plant biomass, nutrient and secondary metabolite levels, and antioxidant properties. Specifically, red and blue LED irradiation exerts strong effects on photosynthesis, stomatal functioning, phototropism, photomorphogenesis, and photosynthetic pigment levels. Additionally, ex vitro plantlet development and acclimatization can be enhanced by regulating the spectral properties of LEDs. Applying an appropriate LED spectral wavelength significantly increases antioxidant enzyme activity in plants, thereby enhancing the cell defense system and providing protection from oxidative damage. Since different plant species respond differently to lighting in the cultivation environment, it is necessary to evaluate specific wavebands before large-scale LED application for controlled in vitro plant growth. This review focuses on the most recent advances and applications of LEDs for in vitro culture organogenesis. The mechanisms underlying the production of different phytochemicals, including phenolics, flavonoids, carotenoids, anthocyanins, and antioxidant enzymes, have also been discussed.

Background: Recently, a number of studies have been conducted to investigate how plants respond to stress at the cellular molecular level by measuring gene expression profiles over time. As a result, a set of time-series gene expression data for the stress response are available in databases. With the data, an integrated analysis of multiple stresses is possible, which identifies stress-responsive genes with higher specificity because considering multiple stress can capture the effect of interference between stresses. To analyze such data, a machine learning model needs to be built. Results: In this study, we developed StressGenePred, a neural network-based machine learning method, to integrate time-series transcriptome data of multiple stress types. StressGenePred is designed to detect single stress-specific biomarker genes by using a simple feature embedding method, a twin neural network model, and Confident Multiple Choice Learning (CMCL) loss. The twin neural network model consists of a biomarker gene discovery and a stress type prediction model that share the same logical layer to reduce training complexity. The CMCL loss is used to make the twin model select biomarker genes that respond specifically to a single stress. In experiments using Arabidopsis gene expression data for four major environmental stresses, such as heat, cold, salt, and drought, StressGenePred classified the types of stress more accurately than the limma feature embedding method and the support vector machine and random forest classification methods. In addition, StressGenePred discovered known stress-related genes with higher specificity than the Fisher method. Conclusions: StressGenePred is a machine learning method for identifying stress-related genes and predicting stress types for an integrated analysis of multiple stress time-series transcriptome data. This method can be used to other phenotype-gene associated studies.

Drought tolerance is an important trait related to growth and yield in crop. Until now, drought related research has focused on coding genes. However, non-coding RNAs also respond significantly to environmental stimuli such as drought stress. Unfortunately, characterizing the role of siRNAs under drought stress is difficult since a large number of heterogenous siRNA species are expressed under drought stress and non-coding RNAs have very weak evolutionary conservation. Thus, to characterize the role of siRNAs, we need a well designed biological and bioinformatics strategy. In this paper, to characterize the function of siRNAs we developed and used a bioinformatics pipeline that includes a genomic-location based clustering technique and an evolutionary conservation tool. By comparing the wild type Nipponbare and two drought resistant rice varities, we found that 21 nt and 24 nt siRNAs are significantly expressed in the three rice plants but at different time points under a short-term (0, 1, and 6 hrs) drought treatment. siRNAs were up-regulated in the wild type at an early stage while the up-regulation was delayed in the two drought tolerant plants. Genes targeted by up-regulated siRNAs were related to oxidation reduction and proteolysis, which are well known to be associated with water deficit phenotypes. More interestingly, we found that siRNAs were located in intronic regions as clusters and were of high evolutionary conservation among monocot grass plants. In summary, we show that siRNAs are important respondents to drought stress and regulate genes related to the drought tolerance in water deficit conditions.

An efficient in vitro direct shoot regeneration system has been described for Lycium chinense Miller using leaf explants. Influence of various parameters such as growth regulator concentration, explant type, effect of basal salt type, Murashige and Skoog (1962) medium (MS), Schenk and Hildebrandt (1972) medium (SH), Gamborg et al. (1968) medium (B5), and carbon sources (sucrose, maltose, and fructose) on the regenerating shoots has been studied. Micromorphological studies and genetic fidelity of regenerated shoots were assessed and compared with those of the donor plants. Among the different concentrations of plant growth regulator (PGRs) tested, MS supplemented with lower concentration of 6-benzylaminopurine (BAP) (0.5 mgL⁻¹) and thidiazuron (TDZ) (0.5 mgL⁻¹) increased the frequency of shoot. Comparatively, indole-3-butyric acid (IBA) was more effective in the regeneration and growth of the root system. A higher number of root formation (6.67 ± 1.25) was observed when the rooting medium comprised half-strength MS salts supplemented with 3% sucrose. The surviving plantlets were gradually transferred to the greenhouse and natural soil. More than 90% of the plantlets survived and matured within 85 days. Similarity in the band patterns produced by inter simple sequence repeat (ISSR) primers confirmed the genetic stability and uniformity between the regenerated and donor plants. The present optimized direct shoot regeneration system may be useful for mass propagation and improving the genetic traits in L. chinense.

Ginseng is a popular traditional herbal medicine in north-eastern Asia. It has been used for human health for over thousands of years. With the rise in global temperature, the production of Korean ginseng (Panax ginseng C.A.Meyer) in Korea have migrated from mid to northern parts of the Korean peninsula to escape from the various higher temperature related stresses. Under the high ambient temperature, vegetative growth was accelerated, which resulted in early flowering. This precocious phase change led to yield loss. Despite of its importance as a traditional medicine, biological mechanisms of ginseng has not been well studied and even the genome sequence of ginseng is yet to be determined due to its complex genome structure. Thus, it is challenging to investigate the molecular biology mechanisms at the transcript level. To investigate how ginseng responds to the high ambient temperature environment, we performed high throughput RNA sequencing and implemented a bioinformatics pipeline for the integrated analysis of small-RNA and mRNA-seq data without a reference genome. By performing reverse transcriptase (RT) PCR and sanger sequencing of transcripts that were assembled using our pipeline, we validated that their sequences were expressed in our samples. Furthermore, to investigate the interaction between genes and non-coding small RNAs and their regulation status under the high ambient temperature, we identified potential gene regulatory miRNAs. As a result, 100,672 contigs with significant expression level were identified and 6 known, 214 conserved and 60 potential novel miRNAs were predicted to be expressed under the high ambient temperature. Collectively, we have found that development, flowering and temperature responsive genes were induced under high ambient temperature, whereas photosynthesis related genes were repressed. Functional miRNAs were down-regulated under the high ambient temperature. Among them are miR156 and miR396 that target flowering (SPL6/9) and growth regulating genes (GRF) respectively.

Transcription factor (TF) has a significant influence on the state of a cell by regulating multiple down-stream genes. Thus, experimental and computational biologists have made great efforts to construct TF gene networks for regulatory interactions between TFs and their target genes. Now, an important research question is how to utilize TF networks to investigate the response of a plant to stress at the transcription control level using time-series transcriptome data. In this article, we present a new computational network, PropaNet, to investigate dynamics of TF networks from time-series transcriptome data using two state-of-the-art network analysis techniques, influence maximization and network propagation. PropaNet uses the influence maximization technique to produce a ranked list of TFs, in the order of TF that explains differentially expressed genes (DEGs) better at each time point. Then, a network propagation technique is used to select a group of TFs that explains DEGs best as a whole. For the analysis of Arabidopsis time series datasets from AtGenExpress, we used PlantRegMap as a template TF network and performed PropaNet analysis to investigate transcriptional dynamics of Arabidopsis under cold and heat stress. The time varying TF networks showed that Arabidopsis responded to cold and heat stress quite differently. For cold stress, bHLH and bZIP type TFs were the first responding TFs and the cold signal influenced histone variants, various genes involved in cell architecture, osmosis and restructuring of cells. However, the consequences of plants under heat stress were up-regulation of genes related to accelerating differentiation and starting re-differentiation. In terms of energy metabolism, plants under heat stress show elevated metabolic process and resulting in an exhausted status. We believe that PropaNet will be useful for the construction of condition-specific time-varying TF network for time-series data analysis in response to stress. PropaNet is available at http://biohealth.snu.ac.kr/software/PropaNet.