Combining Medicinal Plant In Vitro Culture with Machine Learning Technologies for Maximizing the Production of Phenolic Compounds

Novel assaying method for the accurate and rapid analysis of antioxidant total capacity based on hexachloroiridate(IV)

We introduce a novel method, namely IrRAC, for assessing total antioxidant capacity utilizing the single electron oxidant hexachloroiridate(IV). This method leverages the 488 nm absorption band of [IrCl6]2- largely reducing interferences from antioxidants and their oxidation products. [IrCl6]2- is stable 6 h in phosphate-buffered saline (PBS) ensuring consistent and reproducible absorbance readings and rendering spectrophotometric determinations under physiological neutrality. Individual assessments of 23 antioxidants reveal a linear correlation between decreasing absorbance and increasing antioxidant concentration. When the IrRAC assay was compared with several established water-based methods, strong correlations were found. Importantly, [IrCl6]2- shows a minimal oxidation of non-antioxidative substances. Moreover, IrRAC performs well with synthetic antioxidant mixtures and real samples, highlighting that the nature of antioxidants dominates the assay without much disturbance. Commercial availability of K2[IrCl6] eliminates the need of pretreatment of the oxidant. Undoubtedly, the new method confers a compelling and cost-effective alternative to the existing electron transfer-based methodologies.

The Effect of Combined Elicitation with Light and Temperature on the Chlorogenic Acid Content, Total Phenolic Content and Antioxidant Activity of Berula erecta in Tissue Culture

Chlorogenic acid is one of the most prominent bioactive phenolic acids with great pharmacological, cosmetic and nutritional value. The potential of Berula erecta in tissue culture was investigated for the production of chlorogenic acid and its elicitation combined with light of different wavelengths and low temperature. The content of chlorogenic acid in the samples was determined by HPLC-UV, while the content of total phenolic compounds and the antioxidant activity of their ethanol extracts were evaluated spectrophotometrically. The highest fresh and dry biomasses were obtained in plants grown at 23 °C. This is the first study in which chlorogenic acid has been identified and quantified in Berula erecta. The highest chlorogenic acid content was 4.049 mg/g DW. It was determined in a culture grown for 28 days after the beginning of the experiment at 12 °C and under blue light. The latter also contained the highest content of total phenolic compounds, and its extracts showed the highest antioxidant activity. Berula erecta could, potentially, be suitable for the in vitro production of chlorogenic acid, although many other studies should be conducted before implementation on an industrial scale.

Phenolic compound profiling and antioxidant potential of different types of Schisandra henryi in vitro cultures

Schisandra henryi is an endemic species of medicinal potential known from traditional Chinese medicine. As part of this study, a complex biotechnological and phytochemical assessment was conducted on S. henryi with a focus on phenolic compounds and antioxidant profiling. The following in vitro cultures were tested: microshoot agar and callus, microshoot agitated, and suspension, along with the microshoot culture in PlantForm bioreactors. Qualitative profiling was performed by ultra-high-performance liquid chromatography with a photodiode array detector coupled with ion-trap mass spectrophotometry with electrospray ionization and then quantitative analysis by high-performance liquid chromatography with a diode array detector using standards. In the extracts, mainly the compounds from procyanidins were identified as well as phenolic acids (neochlorogenic acid, caffeic acid, protocatechuic acid) and catechin. The highest content of phenolic compounds was found for in vitro agar microshoot culture (max. total content 229.87 mg/100 g DW) and agitated culture (max. total content 22.82 mg/100 g DW). The max. TPC measured using the Folin-Ciocalteu assay was equal to 1240.51 mg GAE/100 g DW (agar microshoot culture). The extracts were evaluated for their antioxidant potential by the DPPH, FRAP, and chelate iron ion assays. The highest potential was indicated for agar microshoot culture (90% of inhibition and 59.31 nM/L TEAC, respectively). The research conducted on the polyphenol profiling and antioxidant potential of S. henryi in vitro culture extracts indicates the high therapeutic potential of this species. KEY POINTS: • Different types of S. henryi in vitro cultures were compared for the first time. • The S. henryi in vitro culture strong antioxidant potential was determined for the first time. • The polyphenol profiling of different types of S. henryi in vitro cultures was shown.

Predicting and optimizing reactive oxygen species metabolism in Punica granatum L. through machine learning: role of exogenous GABA on antioxidant enzyme activity under drought and salinity stress

BACKGROUND

Drought and salinity stress have been proposed as the main environmental factors threatening food security, as they adversely affect crops' agricultural productivity. As a potential solution, the application of plant growth regulators to enhance drought and salinity tolerance has gained considerable attention. γ-aminobutyric acid (GABA) is a four-carbon non-protein amino acid that accumulates in plants as a response to stressful conditions. This study focused on a comparative assessment of several machine learning (ML) regression models, including radial basis function, generalized regression neural network (GRNN), random forest (RF), and support vector regression (SVR) to develop predictive models for assessing the effect of different concentrations of GABA (0, 10, 20, and 40 mM) on various physio-biochemical traits during periods of drought, salinity, and combined stress conditions. The physio-biochemical traits included antioxidant enzyme activities (superoxide dismutase, SOD; peroxidase, POD; catalase, CAT; and ascorbate peroxidase, APX), protein content, malondialdehyde (MDA) levels, and hydrogen peroxide (H2O2) levels. The non‑dominated sorting genetic algorithm‑II (NSGA‑II) was employed for optimizing the superior prediction model.

RESULTS

The GRNN model outperformed the other ML algorithms and was therefore selected for optimization by NSGA-II. The GRNN-NSGA-II model revealed that treatment with GABA at concentrations of 20.90 mM and 20.54 mM, under combined drought and salinity stress conditions at 20.86 and 20.72 days post-treatment, respectively, could result in the maximum values for protein content (by 0.80 and 0.69), APX activity (by 50.63 and 51.51), SOD activity (by 0.54 and 0.53), POD activity (by 1.53 and 1.72), CAT activity (by 4.42 and 5.66), as well as lower MDA levels (by 0.12 and 0.15) and H2O2 levels (by 0.44 and 0.55), respectively, in the 'Atabaki' and 'Rabab' cultivars.

CONCLUSIONS

This study demonstrates that the GRNN-NSGA-II model, as an advanced ML algorithm with a strong predictive ability for outcomes in combined stressful environmental conditions, provides valuable insights into the significant factors influencing such multifactorial processes.

Bioactive Compounds from Kalanchoe Genus Potentially Useful for the Development of New Drugs

The genus Kalanchoe Adans. (Crassulaceae) is native to Madagascar and comprises 145 species, being naturalized in the tropics and cultivated worldwide. In addition to having ornamental value, several Kalanchoe species are commonly used in popular medicine for the treatment of inflammatory conditions, wounds, gastric ulcers, and other diseases. The great importance of the genus is reflected on its acknowledgment by traditional and alternative health systems and organizations, as well as on the growing number of papers reporting pharmacological properties of extracts and isolated compounds from Kalanchoe. Among these properties, we highlight anti-inflammatory, antitumor, wound healing, antiulcer, and muscle relaxing properties. These activities are attributed mostly to flavonoids and bufadienolides, the main secondary metabolites reported in Kalanchoe extracts. While bufadienolides are generally related to cytotoxic and muscle relaxing activities, flavonoids are commonly reported as anti-inflammatory and wound healing agents. This review provides up to date information and perspectives on bioactive compounds from the Kalanchoe genus that are potentially useful for the development of new drugs. It includes not only a discussion on the advantages of the Kalanchoe species as source of bioactive compounds, but also the gaps, opportunities, and challenges to translate the acquired knowledge into innovation for drug development.

History, Phylogeny, Biodiversity, and New Computer-Based Tools for Efficient Micropropagation and Conservation of Pistachio (Pistacia spp.) Germplasm

The word "pstk" [pistag], used in the ancient Persian language, is the linguistic root from which the current name "pistachio", used worldwide, derives. The word pistachio is generally used to designate the plants and fruits of a single species: Pistacia vera L. Both the plant and its fruits have been used by mankind for thousands of years, specifically the consumption of its fruits by Neanderthals has been dated to about 300,000 years ago. Native to southern Central Asia (including northern Afghanistan and northeastern Iran), its domestication and cultivation occurred about 3000 years ago in this region, spreading to the rest of the Mediterranean basin during the Middle Ages and finally being exported to America and Australia at the end of the 19th century. The edible pistachio is an excellent source of unsaturated fatty acids, carbohydrates, proteins, dietary fiber, vitamins, minerals and bioactive phenolic compounds that help promote human health through their antioxidant capacity and biological activities. The distribution and genetic diversity of wild and domesticated pistachios have been declining due to increasing population pressure and climatic changes, which have destroyed natural pistachio habitats, and the monoculture of selected cultivars. As a result, the current world pistachio industry relies mainly on a very small number of commercial cultivars and rootstocks. In this review we discuss and summarize the current status of: etymology, origin, domestication, taxonomy and phylogeny by molecular analysis (RAPID, RFLP, AFLP, SSR, ISSR, IRAP, eSSR), main characteristics and world production, germplasm biodiversity, main cultivars and rootstocks, current conservation strategies of both conventional propagation (seeds, cutting, and grafting), and non-conventional propagation methods (cryopreservation, slow growth storage, synthetic seed techniques and micropropagation) and the application of computational tools (Design of Experiments (DoE) and Machine Learning: Artificial Neural Networks, Fuzzy logic and Genetic Algorithms) to design efficient micropropagation protocols for the genus Pistacia.

Caesalpinia palmeri: First Report on the Phenolic Compounds Profile, Antioxidant and Cytotoxicity Effect

This study aimed to determine the phenolic compounds profile, antioxidant potential and cytotoxicity of extracts and fractions of Caesalpinia palmeri. Methanolic extracts were generated from C. palmeri berries, stems and flowers. The latter was subjected to liquid-liquid partition, obtaining hexane, ethyl acetate and residues fractions. Results showed that the flower extract and ethyl acetate fraction had a larger concentration of phenolic compounds (148.9 and 307.9 mg GAE/g, respectively), being ellagic acid (6233.57 and 19550.08 μg/g, respectively), quercetin-3-β-glycoside (3023.85 and 8952.55 μg/g, respectively) and gallic acid (2212.98 and 8422.34 μg/g, respectively) the most abundant compounds. Flower extract and ethyl acetate fraction also presented the highest antioxidant capacity on all tested methods (DPPH, ABTS, ORAC and FRAP) and low cytotoxicity against ARPE-19 cells (IC₅₀ >170 μg/mL). C. palmeri possessed high antioxidant potential, associated with the presence of phenolic compounds and low cytotoxicity, suggesting that they could represent an option to counter oxidative stress.

Prediction of In vitro organogenesis of Bacopa monnieri using artificial neural networks and regression models

This study was conducted to determine if artificial neural networks (ANN) can be used to accurately predict in vitro organogenesis of Bacopa monnieri compared with statistical regression. Prediction models were developed for shoot and root organogenesis (outputs) on two culture media (Murashige and Skoog and Gamborg B5) affected by two explant types (leaf and node) and two cytokinins (6-Benzylaminopurine and Thidiazuron at 1.0, 5.0, and 10.0 μM levels) with and without the addition of auxin (1-Naphthaleneacetic acid 0.1 μM) (inputs). Categorical data were encoded in numeric form using one-hot encoding technique. Backpropagation (BP) and Kalman filter (KF) learning algorithms were used to develop nonparametric models between inputs and outputs. Correlations between predicted and observed outputs (validation dataset) were similar in both ANN-BP (R values = 0.77, 0.71, 0.68, and 0.48), and ANN-KF (R values = 0.79, 0.68, 0.75, and 0.49), and were higher than regression (R values = 0.13, 0.48, 0.39, and 0.37) models for shoots and roots from leaf and node explants, respectively. Because ANN models have the ability to interpolate from unseen data, they could be used as an effective tool in accurately predicting the in vitro growth kinetics of Bacopa cultures.

Neurofuzzy logic predicts a fine-tuning metabolic reprogramming on elicited Bryophyllum PCSCs guided by salicylic acid

Novel approaches to the characterization of medicinal plants as biofactories have lately increased in the field of biotechnology. In this work, a multifaceted approach based on plant tissue culture, metabolomics, and machine learning was applied to decipher and further characterize the biosynthesis of phenolic compounds by eliciting cell suspension cultures from medicinal plants belonging to the Bryophyllum subgenus. The application of untargeted metabolomics provided a total of 460 phenolic compounds. The biosynthesis of 164 of them was significantly modulated by elicitation. The application of neurofuzzy logic as a machine learning tool allowed for deciphering the critical factors involved in the response to elicitation, predicting their influence and interactions on plant cell growth and the biosynthesis of several polyphenols subfamilies. The results indicate that salicylic acid plays a definitive genotype-dependent role in the elicitation of Bryophyllum cell cultures, while methyl jasmonate was revealed as a secondary factor. The knowledge provided by this approach opens a wide perspective on the research of medicinal plants and facilitates their biotechnological exploitation as biofactories in the food, cosmetic and pharmaceutical fields.

Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants

In this study, a total of 302 molecular structures of phenylnaphthylamine antioxidants based on N-phenyl-1-naphthylamine and N-phenyl-2-naphthylamine skeletons with various substituents were modeled by exhaustive methods. Antioxidant parameters, including the hydrogen dissociation energy, solubility parameter, and binding energy, were calculated through molecular simulations. Then, a group decomposition scheme was determined to decompose 302 antioxidants. The antioxidant parameters and decomposition results constituted machine-learning data sets. Using an artificial neural network model, a correlation coefficient between the predicted and true values above 0.88 and an average relative error within 6% were achieved. Random forest models were used to analyze the factors affecting antioxidant activity from chemical and physical perspectives; the results showed that amino and alkyl groups were conducive to improving antioxidant performance. Moreover, substituent positions 1, 7, and 10 of N-phenyl-1-naphthylamine and 3, 7, and 10 of N-phenyl-2-naphthylamine were found to be the optimal positions for modifications to improve antioxidant activity. Two potentially efficient phenylnaphthylamine antioxidant structures were proposed and their antioxidant parameters were also calculated; the hydrogen dissociation energy and solubility parameter decreased by more than 9% and 7%, respectively, whereas the binding energy increased by more than 16% compared with the benchmark of N-phenyl-1-naphthylamine. These results indicate that molecular simulation and machine learning could provide alternative tools for the molecular design of new antioxidants.

Artificial Neural Networks Elucidated the Essential Role of Mineral Nutrients versus Vitamins and Plant Growth Regulators in Achieving Healthy Micropropagated Plants

The design of an adequate culture medium is an essential step in the micropropagation process of plant species. Adjustment and balance of medium components involve the interaction of several factors, such as mineral nutrients, vitamins, and plant growth regulators (PGRs). This work aimed to shed light on the role of these three components on the plant growth and quality of micropropagated woody plants, using Actinidia arguta as a plant model. Two experiments using a five-dimensional experimental design space were defined using the Design of Experiments (DoE) method, to study the effect of five mineral factors (NH₄NO₃, KNO₃, Mesos, Micros, and Iron) and five vitamins (Myo-inositol, thiamine, nicotinic acid, pyridoxine, and vitamin E). A third experiment, using 20 combinations of two PGRs: BAP (6-benzylaminopurine) and GA₃ (gibberellic acid) was performed. Artificial Neural Networks (ANNs) algorithms were used to build models with the whole database to determine the effect of those components on several growth and quality parameters. Neurofuzzy logic allowed us to decipher and generate new knowledge on the hierarchy of some minerals as essential components of the culture media over vitamins and PRGs, suggesting rules about how MS basal media formulation could be modified to assess the quality of micropropagated woody plants.

Artificial intelligence-based approaches for modeling the effects of spirulina growth mediums on total phenolic compounds

Spirulina is a microalga and its phenolic compound is affected by growth mediums. In this study, Artificial intelligence (AI) based models, namely the Adaptive-Neuro Fuzzy Inference System (ANFIS) and Multilayer perceptron (MLP) models, and Step-Wise-Linear Regression (SWLR) were used to predict total phenolic compounds (TPC) of the spirulina algae. Spirulina productivity (P), extraction yield (EY), total flavonoids (TF), percent of flavonoid (%F) and percent of phenols (%P) are considered as input variables with the corresponding TPC as an output variable. From the result, TPC has a high positive correlation with the input variables with R = 0.99999. Also, the models showed that the ANFIS and SWLR gives superior result in the testing phase and increased its accuracy by 2% compared to MLP model in the prediction of TPC.

The Combination of Untargeted Metabolomics and Machine Learning Predicts the Biosynthesis of Phenolic Compounds in Bryophyllum Medicinal Plants (Genus Kalanchoe)

Phenolic compounds constitute an important family of natural bioactive compounds responsible for the medicinal properties attributed to Bryophyllum plants (genus Kalanchoe, Crassulaceae), but their production by these medicinal plants has not been characterized to date. In this work, a combinatorial approach including plant tissue culture, untargeted metabolomics, and machine learning is proposed to unravel the critical factors behind the biosynthesis of phenolic compounds in these species. The untargeted metabolomics revealed 485 annotated compounds that were produced by three Bryophyllum species cultured in vitro in a genotype and organ-dependent manner. Neurofuzzy logic (NFL) predictive models assessed the significant influence of genotypes and organs and identified the key nutrients from culture media formulations involved in phenolic compound biosynthesis. Sulfate played a critical role in tyrosol and lignan biosynthesis, copper in phenolic acid biosynthesis, calcium in stilbene biosynthesis, and magnesium in flavanol biosynthesis. Flavonol and anthocyanin biosynthesis was not significantly affected by mineral components. As a result, a predictive biosynthetic model for all the Bryophyllum genotypes was proposed. The combination of untargeted metabolomics with machine learning provided a robust approach to achieve the phytochemical characterization of the previously unexplored species belonging to the Bryophyllum subgenus, facilitating their biotechnological exploitation as a promising source of bioactive compounds.

Computer-Based Tools Unmask Critical Mineral Nutrient Interactions in Hoagland Solution for Healthy Kiwiberry Plant Acclimatization

The aim of this study was to better understand the response of ex vitro acclimatized plants grown to a set of mineral nutrient combinations based on Hoagland solution. To reach that, two computer-based tools were used: the design of experiments (DOE) and a hybrid artificial intelligence technology that combines artificial neural networks with fuzzy logic. DOE was employed to create a five-dimensional IV-design space by categorizing all macroelements and one microelement (copper) of Hoagland mineral solution, reducing the experimental design space from 243 (35) to 19 treatments. Typical growth parameters included hardening efficiency (Hard), newly formed shoot length (SL), total leaf number (TLN), leaf chlorophyll content (LCC), and leaf area (LA). Moreover, three physiological disorders, namely, leaf necrosis (LN), leaf spot (LS), and curled leaf (CL), were evaluated for each treatment (mineral formulation). All the growth parameters plus LN were successfully modeled using neuro-fuzzy logic with a high train set R 2 between experimental and predicted values (72.67 < R 2 < 98.79). The model deciphered new insights using different sets of "IF-THEN" rules, pinpointing the positive role of Mg2+ and Ca2+ to improve Hard, SL, TLN, and LA and alleviate LN but with opposite influences on LCC. On the contrary, TLN and LCC were negatively affected by the addition of NO3 - into the media, while NH4 + in complex interaction with Cu2+ or Mg2+ positively enhanced SL, TLN, LCC, and LA. In our opinion, the approach and results achieved in this work are extremely fruitful to understand the effect of Hoagland mineral nutrients on the healthy growth of ex vitro acclimatized plants, through identifying key factors, which favor growth and limit physiological abnormalities.

Plant cell cultures as heterologous bio-factories for secondary metabolite production

Synthetic biology has been developing rapidly in the last decade and is attracting increasing attention from many plant biologists. The production of high-value plant-specific secondary metabolites is, however, limited mostly to microbes. This is potentially problematic because of incorrect post-translational modification of proteins and differences in protein micro-compartmentalization, substrate availability, chaperone availability, product toxicity, and cytochrome p450 reductase enzymes. Unlike other heterologous systems, plant cells may be a promising alternative for the production of high-value metabolites. Several commercial plant suspension cell cultures from different plant species have been used successfully to produce valuable metabolites in a safe, low cost, and environmentally friendly manner. However, few metabolites are currently being biosynthesized using plant platforms, with the exception of the natural pigment anthocyanin. Both Arabidopsis thaliana and Nicotiana tabacum cell cultures can be developed by multiple gene transformations and CRISPR-Cas9 genome editing. Given that the introduction of heterologous biosynthetic pathways into Arabidopsis and N. tabacum is not widely used, the biosynthesis of foreign metabolites is currently limited; however, therein lies great potential. Here, we discuss the exemplary use of plant cell cultures and prospects for using A. thaliana and N. tabacum cell cultures to produce valuable plant-specific metabolites.

Benefits and Drawbacks of Ultrasound-Assisted Extraction for the Recovery of Bioactive Compounds from Marine Algae

The increase in life expectancy has led to the appearance of chronic diseases and interest in healthy aging, in turn promoting a growing interest in bioactive compounds (BCs) and functional ingredients. There are certain foods or products rich in functional ingredients, and algae are one of them. Algae consumption has been nominal in Europe until now. However, in recent years, it has grown significantly, partly due to globalization and the adoption of new food trends. With the aim of obtaining BCs from foods, multiple methods have been proposed, ranging from conventional ones, such as maceration or Soxhlet extraction, to more innovative methods, e.g., ultrasound-assisted extraction (UAE). UAE constitutes a novel method, belonging to so-called green chemistry, that enables the extraction of BCs requiring lower amounts of solvent and energy costs, preserving the integrity of such molecules. In recent years, this method has been often used for the extraction of different BCs from a wide range of algae, especially polysaccharides, such as carrageenans and alginate; pigments, including fucoxanthin, chlorophylls, or β-carotene; and phenolic compounds, among others. In this way, the application of UAE to marine algae is an efficient and sustainable strategy to pursue their deep characterization as a new source of BCs, especially suitable for vegetarian and vegan diets.

Screening of Bioactive Properties in Brown Algae from the Northwest Iberian Peninsula

Algae are an underexploited source of natural bioactive compounds in Western countries, so an increasing interest in the valorization of these marine organisms has emerged in recent years. In this work, the effect of extracting solvent on the extraction yield, phenolic content, antioxidant capacity, and antimicrobial activity of nine brown macroalgae species (Ascophyllum nodosum, Himanthalia elongata, Undaria pinnatifida, Pelvetia canaliculata, Saccharina latissima, Bifurcaria bifurcata, Laminaria ochroleuca, Sargassum muticum, and Fucus spiralis) was assessed. Total phenolic content (TPC) and the antioxidant properties of extracts by different assays: radical scavenging activity (DPPH-RSA) and ferric reducing antioxidant power (FRAP) were performed. The antimicrobial activity of extracts was studied against six different foodborne microorganisms: Staphylococcus aureus, Staphylococcus epidermidis, Bacillus cereus, Escherichia coli, Salmonella enteritidis, and Pseudomonas aeruginosa. The highest extraction yield was achieved in ethanolic extracts. However, the highest TPC and FRAP values were obtained on the ethyl acetate extracts, especially from A. nodosum. Concerning algal species, the highest TPC and FRAP values were found in A. nodosum, while the highest DPPH-RSA values were achieved in the hexane extracts of B. bifurcata. The antimicrobial activity of algal extracts varied according to the solvent and alga selected, suggesting the species- and solvent-dependent behavior of this property, with B. bifurcata extracts showing the highest results for a wide range of bacteria. Our results provide insight on the characterization of widespread brown algae in the coasts of the North-Western region of the Iberian Peninsula, reflecting multiple health-enhancing properties which may lead to their exploitation in food, pharmacological, and cosmetic industries.

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.

Next Generation Cereal Crop Yield Enhancement: From Knowledge of Inflorescence Development to Practical Engineering by Genome Editing

Artificial domestication and improvement of the majority of crops began approximately 10,000 years ago, in different parts of the world, to achieve high productivity, good quality, and widespread adaptability. It was initiated from a phenotype-based selection by local farmers and developed to current biotechnology-based breeding to feed over 7 billion people. For most cereal crops, yield relates to grain production, which could be enhanced by increasing grain number and weight. Grain number is typically determined during inflorescence development. Many mutants and genes for inflorescence development have already been characterized in cereal crops. Therefore, optimization of such genes could fine-tune yield-related traits, such as grain number. With the rapidly advancing genome-editing technologies and understanding of yield-related traits, knowledge-driven breeding by design is becoming a reality. This review introduces knowledge about inflorescence yield-related traits in cereal crops, focusing on rice, maize, and wheat. Next, emerging genome-editing technologies and recent studies that apply this technology to engineer crop yield improvement by targeting inflorescence development are reviewed. These approaches promise to usher in a new era of breeding practice.

From Ethnomedicine to Plant Biotechnology and Machine Learning: The Valorization of the Medicinal Plant Bryophyllum sp

The subgenus Bryophyllum includes about 25 plant species native to Madagascar, and is widely used in traditional medicine worldwide. Different formulations from Bryophyllum have been employed for the treatment of several ailments, including infections, gynecological disorders, and chronic diseases, such as diabetes, neurological and neoplastic diseases. Two major families of secondary metabolites have been reported as responsible for these bioactivities: phenolic compounds and bufadienolides. These compounds are found in limited amounts in plants because they are biosynthesized in response to different biotic and abiotic stresses. Therefore, novel approaches should be undertaken with the aim of achieving the phytochemical valorization of Bryophyllum sp., allowing a sustainable production that prevents from a massive exploitation of wild plant resources. This review focuses on the study of phytoconstituents reported on Bryophyllum sp.; the application of plant tissue culture methodology as a reliable tool for the valorization of bioactive compounds; and the application of machine learning technology to model and optimize the full phytochemical potential of Bryophyllum sp. As a result, Bryophyllum species can be considered as a promising source of plant bioactive compounds, with enormous antioxidant and anticancer potential, which could be used for their large-scale biotechnological exploitation in cosmetic, food, and pharmaceutical industries.

Advancements in Low-Chill Blueberry Vaccinium corymbosum L. Tissue Culture Practices

The demand for blueberry Vaccinium corymbosum L. (and hybrids) plants has significantly increased in the last 30 years due to its market expansion. In vitro propagation of sterile plants are required for commercial purposes but also for research applications such as plant transformation. Thus far, tissue culture characteristics of the tropical-adapted blueberry have been scarcely studied. In this study we present the following findings: (i) zeatin, a hormone used to promote plant growth, should be used in the 1-2 mg/L range to promote plant architecture optimal for transformation experiments; (ii) red-blue LED lights induce more production of meristems and biomass than white LED or fluorescent lights; (iii) levels as high as 1000 mg/L of decontamination agents (the antibiotics timentin and cefotaxime) can be used to eliminate Agrobacterium overgrowth without inhibiting plant growth during plant transformation experiments; (iv) kanamycin, paromomycin, and geneticin, which are widely used antibiotics to select transgene-carrying transformants, cannot be efficiently used in this system; (v) glufosinate, a widely used herbicide, shows potential to be used as an effective selectable marker for transformed plants.

Introducing a hybrid artificial intelligence method for high-throughput modeling and optimizing plant tissue culture processes: the establishment of a new embryogenesis medium for chrysanthemum, as a case study

Data-driven models in a combination of optimization algorithms could be beneficial methods for predicting and optimizing in vitro culture processes. This study was aimed at modeling and optimizing a new embryogenesis medium for chrysanthemum. Three individual data-driven models, including multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR), were developed for callogenesis rate (CR), embryogenesis rate (ER), and somatic embryo number (SEN). Consequently, the best obtained results were used in the fusion process by a bagging method. For medium reformulation, effects of eight ionic macronutrients on CR, ER, and SEN and effects of four vitamins on SEN were evaluated using data fusion (DF)-non-dominated sorting genetic algorithm-II (NSGA-II) and DF-genetic algorithm (GA), respectively. Results showed that DF models with the highest R² had superb performance in comparison with all other individual models. According to DF-NSGAII, the highest ER and SEN can be obtained from the medium containing 14.27 mM NH₄⁺, 38.92 mM NO₃^-, 22.79 mM K⁺, 5.08 mM Cl^-, 3.34 mM Ca²⁺, 1.67 mM Mg²⁺, 2.17 mM SO₄^2-, and 1.44 mM H₂PO₄^-. Based on the DF-GA model, the maximum SEN can be obtained from a medium containing 0.61 μM thiamine, 5.93 μM nicotinic acid, 0.25 μM biotin, and 0.26 μM riboflavin. The efficiency of the established-optimized medium was experimentally compared to Murashige and Skoog medium (MS) for embryogenesis of five chrysanthemum cultivars, and results indicated the efficiency of optimized medium over MS medium.Key points• MLP, SVR, and ANFIS were fused by a bagging method to develop a data fusion model.• NSGA-II and GA were linked to the data fusion model for establishing and optimizing a new embryogenesis medium.• The new culture medium (HNT) had better efficiency than MS medium.

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.

Natural Variation in Plant Pluripotency and Regeneration

Plant regeneration is essential for survival upon wounding and is, hence, considered to be a strong natural selective trait. The capacity of plant tissues to regenerate in vitro, however, varies substantially between and within species and depends on the applied incubation conditions. Insight into the genetic factors underlying this variation may help to improve numerous biotechnological applications that exploit in vitro regeneration. Here, we review the state of the art on the molecular framework of de novo shoot organogenesis from root explants in Arabidopsis, which is a complex process controlled by multiple quantitative trait loci of various effect sizes. Two types of factors are distinguished that contribute to natural regenerative variation: master regulators that are conserved in all experimental systems (e.g., WUSCHEL and related homeobox genes) and conditional regulators whose relative role depends on the explant and the incubation settings. We further elaborate on epigenetic variation and protocol variables that likely contribute to differential explant responsivity within species and conclude that in vitro shoot organogenesis occurs at the intersection between (epi) genetics, endogenous hormone levels, and environmental influences.

Exploring the Use of Bryophyllum as Natural Source of Bioactive Compounds with Antioxidant Activity to Prevent Lipid Oxidation of Fish Oil-In-Water Emulsions

The current industrial requirements for food naturalness are forcing the development of new strategies to achieve the production of healthier foods by replacing the use of synthetic additives with bioactive compounds from natural sources. Here, we investigate the use of plant tissue culture as a biotechnological solution to produce plant-derived bioactive compounds with antioxidant activity and their application to protect fish oil-in-water emulsions against lipid peroxidation. The total phenolic content of Bryophyllum plant extracts ranges from 3.4 to 5.9 mM, expressed as gallic acid equivalents (GAE). The addition of Bryophyllum extracts to 4:6 fish oil-in-water emulsions results in a sharp (eight-fold) increase in the antioxidant efficiency due to the incorporation of polyphenols to the interfacial region. In the emulsions, the antioxidant efficiency of extracts increased linearly with concentration and levelled off at 500 μM GAE, reaching a plateau region. The antioxidant efficiency increases modestly (12%) upon increasing the pH from 3.0 to 5.0, while an increase in temperature from 10 to 30 °C causes a six-fold decrease in the antioxidant efficiency. Overall, results show that Bryophyllum plant-derived extracts are promising sources of bioactive compounds with antioxidant activity that can be eventually be used to control lipid oxidation in food emulsions containing (poly)unsaturated fatty acids.

Application of Artificial Neural Network for Modeling and Studying In Vitro Genotype-Independent Shoot Regeneration in Wheat

Optimizing in vitro shoot regeneration conditions in wheat is one of the important steps in successful micropropagation and gene transformation. Various factors such as genotypes, explants, and phytohormones affect in vitro regeneration of wheat, hindering the ability to tailor genotype-independent protocols. Novel computational approaches such as artificial neural networks (ANNs) can facilitate modeling and predicting outcomes of tissue culture experiments and thereby reduce large experimental treatments and combinations. In this study, generalized regression neural network (GRNN) were used to model and forecast in vitro shoot regeneration outcomes of wheat on the basis of 10 factors including genotypes, explants, and different concentrations of 6-benzylaminopurine (BAP), kinetin (Kin), 2,4-dichlorophenoxyacetic acid (2,4-D), indole-3-acetic acid (IAA), indole-3-butyric acid (IBA), 1-naphthaleneacetic acid (NAA), zeatin, and CuSO4. In addition, GRNN was linked to a genetic algorithm (GA) to identify an optimized solution for maximum shoot regeneration. Results indicated that GRNN could accurately predict the shoot regeneration frequency in the validation set with a coefficient determination of 0.78. Sensitivity analysis demonstrated that shoot regeneration frequency was more sensitive to variables in the order of 2,4-D > explant > genotype < zeatin < NAA. Results of this study suggest that GRNN-GA can be used as a tool, besides experimental approaches, to develop and optimize in vitro genotype-independent regeneration protocols.

Machine Learning Technology Reveals the Concealed Interactions of Phytohormones on Medicinal Plant In Vitro Organogenesis

Organogenesis constitutes the biological feature driving plant in vitro regeneration, in which the role of plant hormones is crucial. The use of machine learning (ML) technology stands out as a novel approach to characterize the combined role of two phytohormones, the auxin indoleacetic acid (IAA) and the cytokinin 6-benzylaminopurine (BAP), on the in vitro organogenesis of unexploited medicinal plants from the Bryophyllum subgenus. The predictive model generated by neurofuzzy logic, a combination of artificial neural networks (ANNs) and fuzzy logic algorithms, was able to reveal the critical factors affecting such multifactorial process over the experimental dataset collected. The rules obtained along with the model allowed to decipher that BAP had a pleiotropic effect on the Bryophyllum spp., as it caused different organogenetic responses depending on its concentration and the genotype, including direct and indirect shoot organogenesis and callus formation. On the contrary, IAA showed an inhibiting role, restricted to indirect shoot regeneration. In this work, neurofuzzy logic emerged as a cutting-edge method to characterize the mechanism of action of two phytohormones, leading to the optimization of plant tissue culture protocols with high large-scale biotechnological applicability.

Machine Learning Unmasked Nutritional Imbalances on the Medicinal Plant Bryophyllum sp. Cultured in vitro

Plant nutrition is a crucial factor that is usually underestimated when designing plant in vitro culture protocols of unexploited plants. As a complex multifactorial process, the study of nutritional imbalances requires the use of time-consuming experimental designs and appropriate statistical and multiple regression analysis for the determination of critical parameters, whose results may be difficult to interpret when the number of variables is large. The use of machine learning (ML) supposes a cutting-edge approach to investigate multifactorial processes, with the aim of detecting non-linear relationships and critical factors affecting a determined response and their concealed interactions. Thus, in this work we applied artificial neural networks coupled to fuzzy logic, known as neurofuzzy logic, to determine the critical factors affecting the mineral nutrition of medicinal plants belonging to Bryophyllum subgenus cultured in vitro. The application of neurofuzzy logic algorithms facilitate the interpretation of the results, as the technology is able to generate useful and understandable "IF-THEN" rules, that provide information about the factor(s) involved in a certain response. In this sense, ammonium, sulfate, molybdenum, copper and sodium were the most important nutrients that explain the variation in the in vitro culture establishment of the medicinal plants in a species-dependent manner. Thus, our results indicate that Bryophyllum spp. display a fine-tuning regulation of mineral nutrition, that was reported for the first time under in vitro conditions. Overall, neurofuzzy model was able to predict and identify masked interactions among such factors, providing a source of knowledge (helpful information) from the experimental data (non-informative per se), in order to make the exploitation and valorization of medicinal plants with high phytochemical potential easier.