Choosing an appropriate somatic embryogenesis medium of carrot (Daucus carota L.) by data mining technology.

INTRODUCTION: Developing somatic embryogenesis is one of the main steps in successful in vitro propagation and gene transformation in the carrot. However, somatic embryogenesis is influenced by different intrinsic (genetics, genotype, and explant) and extrinsic (e.g., plant growth regulators (PGRs), medium composition, and gelling agent) factors which cause challenges in developing the somatic embryogenesis protocol. Therefore, optimizing somatic embryogenesis is a tedious, time-consuming, and costly process. Novel data mining approaches through a hybrid of artificial neural networks (ANNs) and optimization algorithms can facilitate modeling and optimizing in vitro culture processes and thereby reduce large experimental treatments and combinations. Carrot is a model plant in genetic engineering works and recombinant drugs, and therefore it is an important plant in research works. Also, in this research, for the first time, embryogenesis in carrot (Daucus carota L.) using Genetic algorithm (GA) and data mining technology has been reviewed and analyzed. MATERIALS AND METHODS: In the current study, data mining approach through multilayer perceptron (MLP) and radial basis function (RBF) as two well-known ANNs were employed to model and predict embryogenic callus production in carrot based on eight input variables including carrot cultivars, agar, magnesium sulfate (MgSO4), calcium dichloride (CaCl2), manganese (II) sulfate (MnSO4), 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), and kinetin (KIN). To confirm the reliability and accuracy of the developed model, the result obtained from RBF-GA model were tested in the laboratory. RESULTS: The results showed that RBF had better prediction efficiency than MLP. Then, the developed model was linked to a genetic algorithm (GA) to optimize the system. To confirm the reliability and accuracy of the developed model, the result of RBF-GA was experimentally tested in the lab as a validation experiment. The result showed that there was no significant difference between the predicted optimized result and the experimental result. CONCLUTIONS: Generally, the results of this study suggest that data mining through RBF-GA can be considered as a robust approach, besides experimental methods, to model and optimize in vitro culture systems. According to the RBF-GA result, the highest somatic embryogenesis rate (62.5%) can be obtained from Nantes improved cultivar cultured on medium containing 195.23 mg/l MgSO4, 330.07 mg/l CaCl2, 18.3 mg/l MnSO4, 0.46 mg/l 2,4- D, 0.03 mg/l BAP, and 0.88 mg/l KIN. These results were also confirmed in the laboratory.

Epigenetic factors related to recalcitrance in plant biotechnology.

This review explores the challenges and potential solutions in plant micropropagation and biotechnology. While these techniques have proven successful for many species, certain plants or tissues are recalcitrant and do not respond as desired, limiting the application of these technologies due to unattainable or minimal in vitro regeneration rates. Indeed, traditional in vitro culture techniques may fail to induce organogenesis or somatic embryogenesis in some plants, leading to classification as in vitro recalcitrance. This paper focuses on recalcitrance to somatic embryogenesis due to its promise for regenerating juvenile propagules and applications in biotechnology. Specifically, this paper will focus on epigenetic factors that regulate recalcitrance as understanding them may help overcome these barriers. Transformation recalcitrance is also addressed, with strategies proposed to improve transformation frequency. The paper concludes with a review of CRISPR-mediated genome editing's potential in modifying somatic embryogenesis-related epigenetic status and strategies for addressing transformation recalcitrance.

Transcriptomic Profiling of Embryogenic and Non-Embryogenic Callus Provides New Insight into the Nature of Recalcitrance in Cannabis.

Differential gene expression profiles of various cannabis calli including non-embryogenic and embryogenic (i.e., rooty and embryonic callus) were examined in this study to enhance our understanding of callus development in cannabis and facilitate the development of improved strategies for plant regeneration and biotechnological applications in this economically valuable crop. A total of 6118 genes displayed significant differential expression, with 1850 genes downregulated and 1873 genes upregulated in embryogenic callus compared to non-embryogenic callus. Notably, 196 phytohormone-related genes exhibited distinctly different expression patterns in the calli types, highlighting the crucial role of plant growth regulator (PGRs) signaling in callus development. Furthermore, 42 classes of transcription factors demonstrated differential expressions among the callus types, suggesting their involvement in the regulation of callus development. The evaluation of epigenetic-related genes revealed the differential expression of 247 genes in all callus types. Notably, histone deacetylases, chromatin remodeling factors, and EMBRYONIC FLOWER 2 emerged as key epigenetic-related genes, displaying upregulation in embryogenic calli compared to non-embryogenic calli. Their upregulation correlated with the repression of embryogenesis-related genes, including LEC2, AGL15, and BBM, presumably inhibiting the transition from embryogenic callus to somatic embryogenesis. These findings underscore the significance of epigenetic regulation in determining the developmental fate of cannabis callus. Generally, our results provide comprehensive insights into gene expression dynamics and molecular mechanisms underlying the development of diverse cannabis calli. The observed repression of auxin-dependent pathway-related genes may contribute to the recalcitrant nature of cannabis, shedding light on the challenges associated with efficient cannabis tissue culture and regeneration protocols.

Machine learning: its challenges and opportunities in plant system biology.

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive amounts of data in multiple dimensions (e.g., genomics, epigenomics, transcriptomic, metabolomics, proteomics, and single-cell omics) in plants. To provide comprehensive insights into the complexity of plant biological systems, it is important to integrate different omics datasets. Although recent advances in computational analytical pipelines have enabled efficient and high-quality exploration and exploitation of single omics data, the integration of multidimensional, heterogenous, and large datasets (i.e., multi-omics) remains a challenge. In this regard, machine learning (ML) offers promising approaches to integrate large datasets and to recognize fine-grained patterns and relationships. Nevertheless, they require rigorous optimizations to process multi-omics-derived datasets. In this review, we discuss the main concepts of machine learning as well as the key challenges and solutions related to the big data derived from plant system biology. We also provide in-depth insight into the principles of data integration using ML, as well as challenges and opportunities in different contexts including multi-omics, single-cell omics, protein function, and protein-protein interaction. KEY POINTS: • The key challenges and solutions related to the big data derived from plant system biology have been highlighted. • Different methods of data integration have been discussed. • Challenges and opportunities of the application of machine learning in plant system biology have been highlighted and discussed.

Modeling and optimizing callus growth and development in Cannabis sativa using random forest and support vector machine in combination with a genetic algorithm.

Plant callus is generally considered to be a mass of undifferentiated cells and can be used for secondary metabolite production, physiological studies, and plant transformation/regeneration. However, there are several types of callus with different morphological and developmental characteristics and not all are suitable for all applications. Callogenesis is a multivariable developmental process affected by several intrinsic and extrinsic factors, but the most important driver is plant growth regulator (PGRs) levels and type. Since callogenesis is a non-linear process influenced by many different factors, robust computational methods such as machine learning algorithms have great potential to model, predict, and optimize callus growth and development. The current study was conducted to evaluate the effect of PGRs on callus morphology in drug-type Cannabis sativa to maximize callus growth and promote embryogenic callus production. For this aim, random forest (RF) and support vector machine (SVM) were applied in conjunction with image processing to model and predict callus morphological and physical traits. The results showed that SVM was more accurate than RF. In order to find the optimal level of PGRs for optimizing callus growth and development, the SVM was linked to a genetic algorithm (GA). To confirm the reliability of SVM-GA, the optimized-predicted outcomes were tested in a validation experiment that revealed SVM-GA was able to accurately model and optimize the system. Moreover, our results showed that there is a significant correlation between embryogenic callus production and the true density of callus. KEY POINTS: • The effect of PGRs on callus growth and development of cannabis was studied. • The predictive accuracy of SVM and RF was evaluated and compared. • GA was linked to the SVM for optimizing the callus growth and development.

Advances and Perspectives in Tissue Culture and Genetic Engineering of Cannabis.

For a long time, Cannabis sativa has been used for therapeutic and industrial purposes. Due to its increasing demand in medicine, recreation, and industry, there is a dire need to apply new biotechnological tools to introduce new genotypes with desirable traits and enhanced secondary metabolite production. Micropropagation, conservation, cell suspension culture, hairy root culture, polyploidy manipulation, and Agrobacterium-mediated gene transformation have been studied and used in cannabis. However, some obstacles such as the low rate of transgenic plant regeneration and low efficiency of secondary metabolite production in hairy root culture and cell suspension culture have restricted the application of these approaches in cannabis. In the current review, in vitro culture and genetic engineering methods in cannabis along with other promising techniques such as morphogenic genes, new computational approaches, clustered regularly interspaced short palindromic repeats (CRISPR), CRISPR/Cas9-equipped Agrobacterium-mediated genome editing, and hairy root culture, that can help improve gene transformation and plant regeneration, as well as enhance secondary metabolite production, have been highlighted and discussed.

Synergizing Off-Target Predictions for In Silico Insights of CENH3 Knockout in Cannabis through CRISPR/Cas.

The clustered regularly interspaced short palindromic repeats (CRISPR)/Cas-mediated genome editing system has recently been used for haploid production in plants. Haploid induction using the CRISPR/Cas system represents an attractive approach in cannabis, an economically important industrial, recreational, and medicinal plant. However, the CRISPR system requires the design of precise (on-target) single-guide RNA (sgRNA). Therefore, it is essential to predict off-target activity of the designed sgRNAs to avoid unexpected outcomes. The current study is aimed to assess the predictive ability of three machine learning (ML) algorithms (radial basis function (RBF), support vector machine (SVM), and random forest (RF)) alongside the ensemble-bagging (E-B) strategy by synergizing MIT and cutting frequency determination (CFD) scores to predict sgRNA off-target activity through in silico targeting a histone H3-like centromeric protein, HTR12, in cannabis. The RF algorithm exhibited the highest precision, recall, and F-measure compared to all the tested individual algorithms with values of 0.61, 0.64, and 0.62, respectively. We then used the RF algorithm as a meta-classifier for the E-B method, which led to an increased precision with an F-measure of 0.62 and 0.66, respectively. The E-B algorithm had the highest area under the precision recall curves (AUC-PRC; 0.74) and area under the receiver operating characteristic (ROC) curves (AUC-ROC; 0.71), displaying the success of using E-B as one of the common ensemble strategies. This study constitutes a foundational resource of utilizing ML models to predict gRNA off-target activities in cannabis.

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 SO42-, 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.

Investigating the impact of paclobutrazol and tannic acid on floral development of in vitro -grown cannabis plantlets.

Gibberellic acid (GA3) is inhibitory to floral development of in vitro cannabis plants and inhibiting GA3 biosynthesis promotes floral development. As such, paclobutrazol (PBZ), a potent GA3 biosynthesis inhibitor may be useful for increasing floral biomass and expediting development, but due to health concerns, its use is prohibited in cannabis production. The present study was conducted to compare the use of PBZ with tannic acid (TA), a natural compound with potential GA3 inhibiting characteristics. Results confirmed that PBZ significantly affected the number of flowers, percentage of flowering plantlet, and flower appearance time. Treatment using PBZ at a concentration of 10 µM resulted in the greatest number of flowers (7.95) compared to other treatments. Moreover, this compound at concentrations of 5 and 10 µM yielded the highest percentage of flowering plantlets, at 75 % and 70 %, respectively. Flowers also appeared 7-15 days sooner than other treatments. Additionally, the energy transfer efficiency in the photosynthetic system and chlorophyll concentration in plants treated with PBZ were considerably higher than those under other treatments. Under the PBZ treatment, the length of internode was significantly reduced. In contrast, TA generally had the opposite effect of PBZ, suggesting that it does not act as a GA3 inhibitor in this context. Furthermore, positive effects of TA at a concentration of 10 µM were observed on total leaf area (840.08 mm2) and stem length (40.09 mm). The highest number of leaves (12.5) was found in the presence of TA at a concentration of 100 µM. TA at its highest concentration (1000 µM) had an inverse effect on cannabis growth and flowering but was likely due to toxicity rather than any inhibitory effects. Consequently, the obtained results confirm the importance of growth regulators and natural compounds on plant growth and can broaden our understanding for future research and achievement of objectives.

Unveiling the Mysteries of Non-Mendelian Heredity in Plant Breeding.

Mendelian heredity is the cornerstone of plant breeding and has been used to develop new varieties of plants since the 19th century. However, there are several breeding cases, such as cytoplasmic inheritance, methylation, epigenetics, hybrid vigor, and loss of heterozygosity (LOH), where Mendelian heredity is not applicable, known as non-Mendelian heredity. This type of inheritance can be influenced by several factors besides the genetic architecture of the plant and its breeding potential. Therefore, exploring various non-Mendelian heredity mechanisms, their prevalence in plants, and the implications for plant breeding is of paramount importance to accelerate the pace of crop improvement. In this review, we examine the current understanding of non-Mendelian heredity in plants, including the mechanisms, inheritance patterns, and applications in plant breeding, provide an overview of the various forms of non-Mendelian inheritance (including epigenetic inheritance, cytoplasmic inheritance, hybrid vigor, and LOH), explore insight into the implications of non-Mendelian heredity in plant breeding, and the potential it holds for future research.

Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs.

In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvement and develop new varieties with higher yield performance and greater resilience to climate changes, pests, and diseases. With the use of these new advanced technologies, large amounts of data have been generated on the genetic architecture of plants, which can be exploited for manipulating the key characteristics of plants that are important for crop improvement. Therefore, plant breeders have relied on high-performance computing, bioinformatics tools, and artificial intelligence (AI), such as machine-learning (ML) methods, to efficiently analyze this vast amount of complex data. The use of bigdata coupled with ML in plant breeding has the potential to revolutionize the field and increase food security. In this review, some of the challenges of this method along with some of the opportunities it can create will be discussed. In particular, we provide information about the basis of bigdata, AI, ML, and their related sub-groups. In addition, the bases and functions of some learning algorithms that are commonly used in plant breeding, three common data integration strategies for the better integration of different breeding datasets using appropriate learning algorithms, and future prospects for the application of novel algorithms in plant breeding will be discussed. The use of ML algorithms in plant breeding will equip breeders with efficient and effective tools to accelerate the development of new plant varieties and improve the efficiency of the breeding process, which are important for tackling some of the challenges facing agriculture in the era of climate change.

Morphological Characterization of Cannabis sativa L. Throughout Its Complete Life Cycle.

This study extensively characterizes the morphological characteristics, including the leaf morphology, plant structure, flower development, and trichome features throughout the entire life cycle of Cannabis sativa L. cv. White Widow. The developmental responses to photoperiodic variations were investigated from germination to mature plant senescence. The leaf morphology showed a progression of complexity, beginning with serrations in the 1st true leaves, until the emergence of nine leaflets in the 6th true leaves, followed by a distinct shift to eight, then seven leaflets with the 14th and 15th true leaves, respectively. Thereafter, the leaf complexity decreased, culminating in the emergence of a single leaflet from the 25th node. The leaf area peaked with the 12th leaves, which coincided with a change from opposite to alternate phyllotaxy. The stipule development at nodes 5 and 6 signified the vegetative phase, followed by bract and solitary flower development emerging in nodes 7-12, signifying the reproductive phase. The subsequent induction of short-day photoperiod triggered the formation of apical inflorescence. Mature flowers displayed abundant glandular trichomes on perigonal bracts, with stigma color changing from whitish-yellow to reddish-brown. A pronounced increase in trichome density was evident, particularly on the abaxial bract surface, following the onset of flowering. The trichomes exhibited simultaneous growth in stalk length and glandular head diameter and pronounced shifts in color. Hermaphroditism occurred well after the general harvest date. This comprehensive study documents the intricate photoperiod-driven morphological changes throughout the complete lifecycle of Cannabis sativa L. cv. White Widow. The developmental responses characterized provide valuable insights for industrial and research applications.

Current status and future prospects in cannabinoid production through in vitro culture and synthetic biology.

For centuries, cannabis has been a rich source of fibrous, pharmaceutical, and recreational ingredients. Phytocannabinoids are the most important and well-known class of cannabis-derived secondary metabolites and display a broad range of health-promoting and psychoactive effects. The unique characteristics of phytocannabinoids (e.g., metabolite likeness, multi-target spectrum, and safety profile) have resulted in the development and approval of several cannabis-derived drugs. While most work has focused on the two main cannabinoids produced in the plant, over 150 unique cannabinoids have been identified. To meet the rapidly growing phytocannabinoid demand, particularly many of the minor cannabinoids found in low amounts in planta, biotechnology offers promising alternatives for biosynthesis through in vitro culture and heterologous systems. In recent years, the engineered production of phytocannabinoids has been obtained through synthetic biology both in vitro (cell suspension culture and hairy root culture) and heterologous systems. However, there are still several bottlenecks (e.g., the complexity of the cannabinoid biosynthetic pathway and optimizing the bioprocess), hampering biosynthesis and scaling up the biotechnological process. The current study reviews recent advances related to in vitro culture-mediated cannabinoid production. Additionally, an integrated overview of promising conventional approaches to cannabinoid production is presented. Progress toward cannabinoid production in heterologous systems and possible avenues for avoiding autotoxicity are also reviewed and highlighted. Machine learning is then introduced as a powerful tool to model, and optimize bioprocesses related to cannabinoid production. Finally, regulation and manipulation of the cannabinoid biosynthetic pathway using CRISPR- mediated metabolic engineering is discussed.

Effect of Explant Source on Phenotypic Changes of In Vitro Grown Cannabis Plantlets over Multiple Subcultures.

Drug-type cannabis is often multiplied using micropropagation methods to produce genetically uniform and disease/insect-free crops. However, micropropagated plantlets often exhibit phenotypic variation, leading to culture decline over time. In cannabis, the source of these changes remains unknown, though several factors (e.g., explant's sources and prolonged in vitro culture) can result in such phenotypical variations. The study presented herein evaluates the effects of explant sources (i.e., nodal segments derived from the basal, near-basal, middle, and apical parts of the greenhouse-grown mother plant) over multiple subcultures (4 subcultures during 235 days) on multiplication parameters and leaf morphological traits of in vitro cannabis plantlets. While initial in vitro responses were similar among explants sourced from different regions of the plant, there were significant differences in performance over the course of multiple subcultures. Specifically, explant source and/or the number of subcultures significantly impacted plantlet height, number of nodes, and canopy surface area. The explants derived from the basal and near-basal parts of the plant resulted in the tallest shoots with the greatest number of nodes, while the explants derived from the middle and apical regions led to shorter shoots with fewer nodes. Moreover, the basal-derived explants produced cannabis plantlets with shorter but wider leaves which demonstrated the potential of such explants for in vitro rejuvenation practices with minimal culture decline. This study provides new evidence into the long-term impacts of explant source in cannabis micropropagation.

A journey with psychedelic mushrooms: From historical relevance to biology, cultivation, medicinal uses, biotechnology, and beyond.

Psychedelic mushrooms containing psilocybin and related tryptamines have long been used for ethnomycological purposes, but emerging evidence points to the potential therapeutic value of these mushrooms to address modern neurological, psychiatric health, and related disorders. As a result, psilocybin containing mushrooms represent a re-emerging frontier for mycological, biochemical, neuroscience, and pharmacology research. This work presents crucial information related to traditional use of psychedelic mushrooms, as well as research trends and knowledge gaps related to their diversity and distribution, technologies for quantification of tryptamines and other tryptophan-derived metabolites, as well as biosynthetic mechanisms for their production within mushrooms. In addition, we explore the current state of knowledge for how psilocybin and related tryptamines are metabolized in humans and their pharmacological effects, including beneficial and hazardous human health implications. Finally, we describe opportunities and challenges for investigating the production of psychedelic mushrooms and metabolic engineering approaches to alter secondary metabolite profiles using biotechnology integrated with machine learning. Ultimately, this critical review of all aspects related to psychedelic mushrooms represents a roadmap for future research efforts that will pave the way to new applications and refined protocols.

New Insight into Ornamental Applications of Cannabis: Perspectives and Challenges.

The characteristic growth habit, abundant green foliage, and aromatic inflorescences of cannabis provide the plant with an ideal profile as an ornamental plant. However, due to legal barriers, the horticulture industry has yet to consider the ornamental relevance of cannabis. To evaluate its suitability for introduction as a new ornamental species, multifaceted commercial criteria were analyzed. Results indicate that ornamental cannabis would be of high value as a potted-plant or in landscaping. However, the readiness timescale for ornamental cannabis completely depends on its legal status. Then, the potential of cannabis chemotype Ⅴ, which is nearly devoid of phytocannabinoids and psychoactive properties, as the foundation for breeding ornamental traits through mutagenesis, somaclonal variation, and genome editing approaches has been highlighted. Ultimately, legalization and breeding for ornamental utility offers boundless opportunities related to economics and executive business branding.

In vitro plant tissue culture as the fifth generation of bioenergy.

Developing and applying a novel and sustainable energy crop is essential to reach an efficient and economically feasible technology for bioenergy production. In this study, plant tissue culture, also referred to as in vitro culture, is introduced as one of the most promising and environmentally friendly methods for the sustainable supply of biofuels. The current study investigates the potential of in vitro -grown industrial hemp calli obtained from leaf, root, and stem explants as a new generation of energy crop. For this purpose, the in vitro grown explants were first fully characterized in terms of elemental and chemical composition. Secondly, HTL experiments were designed by Design Expert 11 with a particular focus on biocrude. Finally, the chemical components, functional groups, and petroleum-like hydrocarbons present in the biocrude were identified by PY-GCMS. A 22.61 wt.% biocrude was produced for the sample grown through callogenesis of the leaf (CL). The obtained biocrude for CL consisted of 19.55% acids, 0.42% N compounds, 15.44% ketones, 16.03% aldehydes, 2.21% furans, 20.01% aromatics, 5.2% alcohols, and 19.88% hydrocarbons. To the best of the authors' knowledge, this is the first report that in vitro -grown biomass is hydrothermally liquefied toward biocrude production; the current work paves the way for integrating plant tissue culture and thermochemical processes for the generation of biofuels and value-added chemicals.

Tissue culture coupled with a gas exchange system offers new perspectives on phenotyping the developmental biology of Solanum lycopersicum L. cv. 'MicroTom'.

Solanum lycopersicum L. cv. 'Microtom' (MicroTom) is a model organism with a relatively rapid life cycle, and wide library of genetic mutants available to study different aspects of plant development. Despite its small stature, conventional MicroTom research often requires expensive growth cabinets and/or expansive greenhouse space, limiting the number of experimental and control replications needed for experiments, and can render plants susceptible to pests and disease. Thus, alternative experimental approaches must be devised to reduce the footprint of experimental units and limit the occurrence problematic confounding variables. Here, tissue culture is presented as a powerful option for MicroTom research that can quell the complications associated with conventional MicroTom research methods. A previously established, non-invasive, analytical tissue culture system is used to compare in vitro and conventionally produced MicroTom by assessing photosynthesis, respiration, diurnal carbon gain, and fruit pigments. To our knowledge, this is the first publication that measures in vitro MicroTom fruit pigments and compares diurnal photosynthetic/respiration responses to abiotic factors between in vitro and ex vitro MicroTom. Comparable trends would validate tissue culture as a new benchmark method in MicroTom research, as it is like Arabidopsis, allowing replicable, statistically valid, high throughput genotyping and selective phenotyping experiments. Combining the model plant MicroTom with advanced tissue culture methods makes it possible to study bonsai-style MicroTom responses to light, temperature, and atmospheric stimuli in the absence of confounding abiotic stress factors that would otherwise be unachievable using conventional methods.

A Noninvasive Gas Exchange Method to Test and Model Photosynthetic Proficiency and Growth Rates of In Vitro Plant Cultures: Preliminary Implication for Cannabis sativa L.

Supplemental sugar additives for plant tissue culture cause mixotrophic growth, complicating carbohydrate metabolism and photosynthetic relationships. A unique platform to test and model the photosynthetic proficiency and biomass accumulation of micropropagated plantlets was introduced and applied to Cannabis sativa L. (cannabis), an emerging crop with high economic interest. Conventional in vitro systems can hinder the photoautotrophic ability of plantlets due to low light intensity, low vapor pressure deficit, and limited CO2 availability. Though exogenous sucrose is routinely added to improve in vitro growth despite reduced photosynthetic capacity, reliance on sugar as a carbon source can also trigger negative responses that are species-dependent. By increasing photosynthetic activity in vitro, these negative consequences can likely be mitigated, facilitating the production of superior specimens with enhanced survivability. The presented methods use an open-flow/force-ventilated gas exchange system and infrared gas analysis to measure the impact of [CO2], light, and additional factors on in vitro photosynthesis. This system can be used to answer previously overlooked questions regarding the nature of in vitro plant physiology to enhance plant tissue culture and the overall understanding of in vitro processes, facilitating new research methods and idealized protocols for commercial tissue culture.