Advances of native and non-native Antarctic species to in vitro conservation: improvement of disinfection protocols

Comparative analysis of different artificial neural networks for predicting and optimizing in vitro seed germination and sterilization of petunia

The process of optimizing in vitro seed sterilization and germination is a complicated task since this process is influenced by interactions of many factors (e.g., genotype, disinfectants, pH of the media, temperature, light, immersion time). This study investigated the role of various types and concentrations of disinfectants (i.e., NaOCl, Ca(ClO)2, HgCl2, H2O2, NWCN-Fe, MWCNT) as well as immersion time in successful in vitro seed sterilization and germination of petunia. Also, the utility of three artificial neural networks (ANNs) (e.g., multilayer perceptron (MLP), radial basis function (RBF), and generalized regression neural network (GRNN)) as modeling tools were evaluated to analyze the effect of disinfectants and immersion time on in vitro seed sterilization and germination. Moreover, non‑dominated sorting genetic algorithm‑II (NSGA‑II) was employed for optimizing the selected prediction model. The GRNN algorithm displayed superior predictive accuracy in comparison to MLP and RBF models. Also, the results showed that NSGA‑II can be considered as a reliable multi-objective optimization algorithm for finding the optimal level of disinfectants and immersion time to simultaneously minimize contamination rate and maximize germination percentage. Generally, GRNN-NSGA-II as an up-to-date and reliable computational tool can be applied in future plant in vitro culture studies.

Machine Learning-Mediated Development and Optimization of Disinfection Protocol and Scarification Method for Improved In Vitro Germination of Cannabis Seeds

In vitro seed germination is a useful tool for developing a variety of biotechnologies, but cannabis has presented some challenges in uniformity and germination time, presumably due to the disinfection procedure. Disinfection and subsequent growth are influenced by many factors, such as media pH, temperature, as well as the types and levels of contaminants and disinfectants, which contribute independently and dynamically to system complexity and nonlinearity. Hence, artificial intelligence models are well suited to model and optimize this dynamic system. The current study was aimed to evaluate the effect of different types and concentrations of disinfectants (sodium hypochlorite, hydrogen peroxide) and immersion times on contamination frequency using the generalized regression neural network (GRNN), a powerful artificial neural network (ANN). The GRNN model had high prediction performance (R² > 0.91) in both training and testing. Moreover, a genetic algorithm (GA) was subjected to the GRNN to find the optimal type and level of disinfectants and immersion time to determine the best methods for contamination reduction. According to the optimization process, 4.6% sodium hypochlorite along with 0.008% hydrogen peroxide for 16.81 min would result in the best outcomes. The results of a validation experiment demonstrated that this protocol resulted in 0% contamination as predicted, but germination rates were low and sporadic. However, using this sterilization protocol in combination with the scarification of in vitro cannabis seed (seed tip removal) resulted in 0% contamination and 100% seed germination within one week.

The Efficient and Easy Micropropagation Protocol of Phyllanthus niruri

Phyllanthus niruri (P. niruri) or Dukung Anak is a herbal plant in the Phyllanthaceae family that has been used traditionally to treat various ailments such as diabetes, jaundice, flu and cough. P. niruri contains numerous medicinal benefits such as anti-tumor and anti-carcinogenic properties and a remedy for hepatitis B viral infection. Due to its beneficial properties, P. niruri is overharvested and wild plants become scarce. This study was conducted to develop an appropriate in vitro culture protocol for the mass production of P. niruri. An aseptic culture of P. niruri was established followed by multiplication of explants using different types of basal medium and its strength and plant growth regulators manipulation. This study also established the induction of in vitro rooting utilizing various types and concentrations of auxin. Treatment of Clorox^® with 30% concentration showed the lowest percentage (%) of contamination, 4.44% in P. niruri culture. Nodal segments of P. niruri were successfully induced in full-strength of Murashige and Skoog (MS) basal media with 2.33 number of shoots, 3.11 cm length of shoot and 27.91 number of leaves. In addition, explants in full-strength MS media without any additional cytokinin were recorded as the optimum results for all parameters including the number of shoots (5.0 shoots), the length of shoots (3.68 cm) and the number of leaves (27.33 leaves). Treatment of 2.5 µM indole-3-butyric acid (IBA) showed the highest number of roots (17.92 roots) and root length (1.29 cm). Rooted explants were transferred for acclimatization, and the plantlet showed over 80% of survival rate. In conclusion, plantlets of P. niruri were successfully induced and multiplied via in vitro culture, which could be a step closer to its commercialization.

Calcium Oxalate Crystals in Leaves of the Extremophile Plant Colobanthus quitensis (Kunth) Bartl. (Caryophyllaceae)

The presence of calcium oxalate (CaOx) crystals has been widely reported in the plant kingdom. These structures play a central role in various physiological functions, including calcium regulation, metal detoxification, and photosynthesis. However, precise knowledge about their possible roles and functions in plants is still limited. Therefore, the present work aims to study the ecotypic variability of Colobanthus quitensis, an extremophile species, concerning CaOx crystal accumulation. The CaOx crystals were studied in leaves of C. quitensis collected from different provenances within a latitudinal gradient (From Andes mountains in central Chile to Antarctica) and grown under common garden conditions. Polarized light microscopy, digital image analysis, and electron microscopy were used to characterize CaOx crystals. The presence of CaOx crystals was confirmed in the four provenances of C. quitensis, with significant differences in the accumulation among them. The Andean populations presented the highest accumulation of crystals and the Antarctic population the lowest. Electron microscopy showed that CaOx crystals in C. quitensis are classified as druses based on their morphology. The differences found could be linked to processes of ecotypic differentiation and plant adaptation to harsh environments.

Treasuring the computational approach in medicinal plant research

Medicinal plants serve as a valuable source of secondary metabolites since time immemorial. Computational Research in 21st century is giving more attention to medicinal plants for new drug design as pharmacological screening of bioactive compound was time consuming and expensive. Computational methods such as Molecular Docking, Molecular Dynamic Simulation and Artificial intelligence are significant Insilico tools in medicinal plant research. Molecular docking approach exploits the mechanism of potential phytochemicals into the target active site to elucidate its interactions and biological therapeutic properties. MD simulation illuminates the dynamic behavior of biomolecules at atomic level with fine quality representation of biomolecules. Dramatical advancement in computer science is illustrating the biological mechanism via these tools in different diseases treatment. The advancement comprises speed, the system configuration, and other software upgradation to insights into the structural explanation and optimization of biomolecules. A probable shift from simulation to artificial intelligence has in fact accelerated the art of scientific study to a sky high. The most upgraded algorithm in artificial intelligence such as Artificial Neural Networks, Deep Neural Networks, Neuro-fuzzy Logic has provided a wide opportunity in easing the time required in classical experimental strategy. The notable progress in computer science technology has paved a pathway for understanding the pharmacological functions and creating a roadmap for drug design and development and other achievement in the field of medicinal plants research. This review focus on the development and overview in computational research moving from static molecular docking method to a range of dynamic simulation and an advanced artificial intelligence such as machine learning.

THE CONTENT OF PHENOLIC COMPOUNDS AND FLAVONOIDS IN Deschampsia antarctica TISSUE CULTURE

Aim. The aim of the study was to determine the quantitative and qualitative content of phenolic compounds and flavonoids in Deschampsia antarctica E. Desv. tissue cultures obtained from plants originating from different islands of the maritime Antarctic. Methods. In vitro tissue culture, Folin-Ciocalteu method, spectrophotometry, HPLC analysis. Results. The quantitative content of phenolic compounds and flavonoids in D. antarctica tissue cultures obtained from plants of six genotypes (DAR12, DAR13, G/D12-2a, Y66, R30 and L57) was determined. The highest content of phenolic compounds (4.46 and 3.75 mg/g) was found in tissue cultures obtained from root and leaf explants of plant genotype L57. The highest amount of flavonoids (7.17 mg/g) was accumulated in G/D12-2a tissue culture of root origin. The content of the studied biologically active compounds (BACs) did not change with increasing number of subculture generations (from passage 10 to 19). HPLC analysis showed that in D. antarctica tissue cultures, a shift in the biosynthesis of BACs occurred towards the synthesis of more polar metabolites compared to explant donor plants. Conclusions. It was found that the transition of cells to undifferentiated growth affected the content of BACs, the amount of which decreased 2–5 times simultaneously with a significant change in their profile. This provided a basis for further biochemical studies, as well as for careful selection of tissue culture of D. antarctica to use it as a potential source of BACs.

Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture

Artificial intelligence (AI) models and optimization algorithms (OA) are broadly employed in different fields of technology and science and have recently been applied to improve different stages of plant tissue culture. The usefulness of the application of AI-OA has been demonstrated in the prediction and optimization of length and number of microshoots or roots, biomass in plant cell cultures or hairy root culture, and optimization of environmental conditions to achieve maximum productivity and efficiency, as well as classification of microshoots and somatic embryos. Despite its potential, the use of AI and OA in this field has been limited due to complex definition terms and computational algorithms. Therefore, a systematic review to unravel modeling and optimizing methods is important for plant researchers and has been acknowledged in this study. First, the main steps for AI-OA development (from data selection to evaluation of prediction and classification models), as well as several AI models such as artificial neural networks (ANNs), neurofuzzy logic, support vector machines (SVMs), decision trees, random forest (FR), and genetic algorithms (GA), have been represented. Then, the application of AI-OA models in different steps of plant tissue culture has been discussed and highlighted. This review also points out limitations in the application of AI-OA in different plant tissue culture processes and provides a new view for future study objectives. KEY POINTS: • Artificial intelligence models and optimization algorithms can be considered a novel and reliable computational method in plant tissue culture. • This review provides the main steps and concepts for model development. • The application of machine learning algorithms in different steps of plant tissue culture has been discussed and highlighted.