Naturally Occurring Triploidy in Cannabis.

Polyploidy is a significant evolutionary process in plants that involves the duplication of genomic content and has been recognized as a key mechanism driving plant diversification and adaptation. In natural populations, polyploids frequently arise from unreduced gametes, which subsequently fuse with reduced or unreduced gametes, resulting in triploid or tetraploid offspring, respectively. Cannabis sativa L. is a diploid species, but recent work using artificially induced polyploidy has demonstrated its potential advantages in an agricultural setting. Further, recent work has identified that some elite clonal cultivars, vis. Mac1, are triploid, with no indication that they were artificially produced. The current study was conducted to determine if polyploidy is a naturally occurring phenomenon in cannabis and to estimate the frequency of this phenomenon across populations. To do this, the presence of natural triploid individuals was evaluated in 13 seedling populations of cannabis using a flow cytometry analysis. Among the examined populations, natural triploids were identified in 10 groups with an average frequency of approximately 0.5%. The highest frequency of natural triploids was observed in a self-pollinated population at 2.3%. This research demonstrates that polyploidy is a naturally occurring event in cannabis and triploids are present at an average of approximately 0.5%, or 1 in 200 plants. These data shed light on the natural variation in ploidy within cannabis populations and contribute valuable insights to the understanding of cannabis genetics and breeding practices.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Accumulation of somatic mutations leads to genetic mosaicism in cannabis.

Cannabis (Cannabis sativa L.) is typically propagated using stem cuttings taken from mother plants to produce genetically uniform propagules. However, producers anecdotally report that clonal lines deteriorate over time and eventually produce clones with less vigor and lower cannabinoid levels than the original mother plant. While the cause of this deterioration has not been investigated, one potential contributor is the accumulation of somatic mutations within the plant. To test this, we used deep sequencing of whole genomes (>50×) to compare the variability within an individual cannabis cultivar Honey Banana plant sampled at the bottom, middle, and top. We called over six million sequence variants based on a reference genome and found that the top had the most by a sizable amount. Comparing the variants among the samples uncovered that nearly 600,000 (34%) were unique to the top while the bottom only contained 148,000 (12%), and middle with 77,000 (9%) unique variants. Bioinformatics tools were used to identify mutations in critical cannabinoid-terpene biosynthesis pathways. While none were identified as high impact, four genes contained more than double the average level of nucleotide diversity (π) in or near the gene. Two genes code for essential enzymes required for the cannabinoid pathway while the other two are in the terpene pathways, demonstrating that mutations were accumulating within these pathways and could influence their function. Overall, a measurable number of intraplant genetic diversity was discovered that could impact long-term genetic fidelity of clonal lines and potentially contribute to the observed decline in vigor and cannabinoid content.

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.

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.

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.

Cannabis, the multibillion dollar plant that no genebank wanted.

Although cannabis is legalized and accepted as an agricultural commodity in many places around the world, a significant lack of public germplasm repositories remains an unresolved problem in the cannabis industry. The acquisition, preservation, and evaluation of germplasm, including landraces and ancestral populations, is key to unleashing the full potential of cannabis in the global marketplace. We argue here that accessible germplasm resources are crucial for long-term economic viability, preserving genetic diversity, breeding, innovation, and long-term sustainability of the crop. We believe that cannabis restrictions require a second look to allow genebanks to play a fuller and more effective role in conservation, sustainable use, and exchange of cannabis genetic resources.

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.

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.

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.

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.

Inhibition of phenylpropanoid biosynthesis in Artemisia annua L.: a novel approach to reduce oxidative browning in plant tissue culture.

Oxidative browning is a common and often severe problem in plant tissue culture systems caused by the accumulation and oxidation of phenolic compounds. The current study was conducted to investigate a novel preventative approach to address this problem by inhibiting the activity of the phenylalanine ammonia lyase enzyme (PAL), thereby reducing the biosynthesis of phenolic compounds. This was accomplished by incorporating 2-aminoindane-2-phosphonic acid (AIP), a competitive PAL inhibitor, into culture media of Artemisia annua as a model system. Addition of AIP into culture media resulted in significant reductions in visual tissue browning, a reduction in total phenol content, as well as absorbance and autoflourescence of tissue extracts. Reduced tissue browning was accompanied with a significant increase in growth on cytokinin based medium. Microscopic observations demonstrated that phenolic compounds accumulated in discrete cells and that these cells were more prevalent in brown tissue. These cells were highly plasmolyzed and often ruptured during examination, demonstrating a mechanism in which phenolics are released into media in this system. These data indicate that inhibiting phenylpropanoid biosynthesis with AIP is an effective approach to reduce tissue browning in A. annua. Additional experiments with Ulmus americana and Acer saccharum indicate this approach is effective in many species and it could have a wide application in systems where oxidative browning restricts the development of biotechnologies.