The effects of novel synthetic cytokinin derivatives and endogenous cytokinins on the in vitro growth responses of hemp (Cannabis sativa L.) explants

Ultra-high levels of CO2, i.e., >10,000 ppm, enhance tissue culture growth and offers a relatively simple and inexpensive method to improve plant productivity in vitro. Growth responses employing ultra-high CO2 levels differ considerably in the literature. Unfortunately, various culture vessels and systems have been employed, making comparisons difficult. In this study, the influence of the vessel container size, medium volume, and various CO2 concentrations (0 to 50,000 ppm) was studied on the growth obtained from lettuce and spearmint cultures. All three of these factors influence growth responses from plants cultured in vitro. Vessel types tested included: culture tubes, Magenta containers, 1-quart jars, 0.5-gallon jars, and 1-gallon jars having culture volumes of 55, 365, 925, 1850, and 3700 ml, respectively. Increasing the size of the culture vessel resulted in an increase growth regardless of the CO2 level tested. For example, fresh weight of spearmint increases of >250% can be obtained in by employing a 1-quart jar compared to using a culture tube. Increasing medium volume using various vessel types, especially using high concentrations of CO2, resulted in dramatic growth increases. For example, a >100% increase in fresh weight could be obtained by increasing the medium volume from 50 ml to 100 ml within a 1-quart jar. These studies suggest that plant growth promoted by supplemental CO2 is limited by the culture vessel size and medium volume. Differences in growth responses obtained in past CO2 studies could be related to vessel type and medium volume as well as the CO2 levels employed. Future in vitro studies should consider these factors in the evaluation of the influence of Ultra-high CO2 levels on plant growth. Peculiar growth responses, especially pertaining to rooting and shooting exhibited by cultures grown in ultra-high CO2 levels will also be discussed.

Download Full-text

Comparative analysis of machine learning and evolutionary optimization algorithms for precision tissue culture of Cannabis sativa: Prediction and validation of in vitro shoot growth and development based on the optimization of light and carbohydrate sources

10.1101/2021.08.09.455719 ◽

2021 ◽

Author(s):

Marco Pepe ◽

Mohsen Hesami ◽

Finlay Small ◽

Andrew Maxwell Phineas Jones

Keyword(s):

Machine Learning ◽

Tissue Culture ◽

Growth And Development ◽

Shoot Growth ◽

Cannabis Sativa ◽

Optimization Algorithms ◽

Growth Responses ◽

Inference System ◽

Validation Experiment

Micropropagation techniques offer opportunity to proliferate, maintain, and study dynamic plant responses in highly controlled environments without confounding external influences, forming the basis for many biotechnological applications. With medicinal and recreational interests for Cannabis sativa L. growing, research related to the optimization of in vitro practices is needed to improve current methods while boosting our understanding of the underlying physiological processes. Unfortunately, due to the exorbitantly large array of factors influencing tissue culture, existing approaches to optimize in vitro methods are tedious and time-consuming. Therefore, there is great potential to use new computational methodologies for analysing data to develop improved protocols more efficiently. Here, we first tested the effects of light qualities using assorted combinations of Red, Blue, Far Red, and White spanning 0-100 umol/m2/s in combination with sucrose concentrations ranging from 1-6 % (w/v), totaling 66 treatments, on in vitro shoot growth, root development, number of nodes , shoot emergence, and canopy surface area. Collected data were then assessed using multilayer perceptron (MLP), generalized regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) to model and predict in vitro Cannabis growth and development. Based on the results, GRNN had better performance than MLP or ANFIS and was consequently selected to link different optimization algorithms (genetic algorithm, biogeography-based optimization, interior search algorithm, and symbiotic organisms search) for prediction of optimal light levels (quality/intensity) and sucrose concentration for various applications. Predictions of in vitro conditions to refine growth responses were subsequently tested in a validation experiment and data showed no significant differences between predicted optimized values and observed data. Thus, this study demonstrates the potential of machine learning and optimization algorithms to predict the most favourable light combinations and sucrose levels to elicit specific developmental responses. Based on these, recommendations of light and carbohydrate levels to promote specific developmental outcomes for in vitro Cannabis are suggested. Ultimately, this work showcases the importance of light quality and carbohydrate supply in directing plant development as well as the power of machine learning approaches to investigate complex interactions in plant tissue culture.

Download Full-text

Basal Salt Composition, Cytokinins, and Phenolic Binding Agents Influence In Vitro Growth and Ex Vitro Establishment of Magnolia ‘Ann’

HortScience ◽

10.21273/hortsci.47.11.1625 ◽

2012 ◽

Vol 47 (11) ◽

pp. 1625-1629 ◽

Cited By ~ 7

Author(s):

J. Kevin Parris ◽

Darren H. Touchell ◽

Thomas G. Ranney ◽

Jeffrey Adelberg

Keyword(s):

In Vitro Growth ◽

Growth Responses ◽

Ex Vitro ◽

Salt Composition ◽

Indolebutyric Acid ◽

Binding Agents ◽

Half Strength ◽

Series Of Experiments ◽

Basal Salts

In vitro growth responses of Magnolia ‘Ann’ to basal salt composition, cytokinins, and phenolic binding agents were investigated in a series of experiments to refine micropropagation protocols. Murashige and Skoog (MS), half-strength MS, Woody Plant Medium (WPM), Driver and Kuniyuki (DKW), and Blaydes basal salts in conjunction with 1 g·L−1 activated charcoal (AC) or 1 g·L−1 polyvinylpyrrolidone (PVP) were evaluated as multiplication media. Benzylaminopurine (BAP), meta-topolin (mT), or 6-(γ,γ-dimethylallylamino) purine (2iP) at 2, 4, or 8 μM was investigated to optimize the cytokinin concentration. Murashige and Skoog medium supplemented with 2 μM BAP with no phenolic binding agent was an optimal multiplication medium that yielded 3.2 ± 0.2 shoots with a mean length of 17.2 ± 1.8 mm over an 8-week period. For rooting, microshoots were cultured on half-strength MS media supplemented with 0, 5, 10, or 20 μM indolebutyric acid (IBA) with or without AC. Media containing AC produced elongated microshoots suitable for rooting and ex vitro establishment. Microshoots cultured on medium supplemented with AC also had higher in vitro rooting (16%) and higher ex vitro rooting (75%) compared with those without AC regardless of in vitro IBA concentration.

Download Full-text

Comparative Analysis of Machine Learning and Evolutionary Optimization Algorithms for Precision Micropropagation of Cannabis sativa: Prediction and Validation of in vitro Shoot Growth and Development Based on the Optimization of Light and Carbohydrate Sources

Frontiers in Plant Science ◽

10.3389/fpls.2021.757869 ◽

2021 ◽

Vol 12 ◽

Author(s):

Marco Pepe ◽

Mohsen Hesami ◽

Finlay Small ◽

Andrew Maxwell Phineas Jones

Keyword(s):

Machine Learning ◽

Tissue Culture ◽

Growth And Development ◽

Shoot Growth ◽

Cannabis Sativa ◽

Optimization Algorithms ◽

Growth Responses ◽

Inference System ◽

Validation Experiment

Micropropagation techniques offer opportunity to proliferate, maintain, and study dynamic plant responses in highly controlled environments without confounding external influences, forming the basis for many biotechnological applications. With medicinal and recreational interests for Cannabis sativa L. growing, research related to the optimization of in vitro practices is needed to improve current methods while boosting our understanding of the underlying physiological processes. Unfortunately, due to the exorbitantly large array of factors influencing tissue culture, existing approaches to optimize in vitro methods are tedious and time-consuming. Therefore, there is great potential to use new computational methodologies for analyzing data to develop improved protocols more efficiently. Here, we first tested the effects of light qualities using assorted combinations of Red, Blue, Far Red, and White spanning 0–100 μmol/m2/s in combination with sucrose concentrations ranging from 1 to 6% (w/v), totaling 66 treatments, on in vitro shoot growth, root development, number of nodes, shoot emergence, and canopy surface area. Collected data were then assessed using multilayer perceptron (MLP), generalized regression neural network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS) to model and predict in vitro Cannabis growth and development. Based on the results, GRNN had better performance than MLP or ANFIS and was consequently selected to link different optimization algorithms [genetic algorithm (GA), biogeography-based optimization (BBO), interior search algorithm (ISA), and symbiotic organisms search (SOS)] for prediction of optimal light levels (quality/intensity) and sucrose concentration for various applications. Predictions of in vitro conditions to refine growth responses were subsequently tested in a validation experiment and data showed no significant differences between predicted optimized values and observed data. Thus, this study demonstrates the potential of machine learning and optimization algorithms to predict the most favorable light combinations and sucrose levels to elicit specific developmental responses. Based on these, recommendations of light and carbohydrate levels to promote specific developmental outcomes for in vitro Cannabis are suggested. Ultimately, this work showcases the importance of light quality and carbohydrate supply in directing plant development as well as the power of machine learning approaches to investigate complex interactions in plant tissue culture.

Download Full-text