a-customizable-approach-for-enzymatic-production-purification

A Customizable Approach for the Enzymatic Production and Purification of Diterpenoid Natural Products

Diterpenoids form a diverse class of small molecule natural products that are widely distributed across the kingdoms of life and have critical biological ...

imaging-analysis-for-quantifying-maize-zea-mays-abiotic-stress

Imaging and Analysis for Quantifying Maize (<em>Zea mays</em>) Abiotic Stress Phenotypes

Measuring quantitative plant phenotypes, or traits, is essential for understanding plant responses to environmental factors, including biotic and abiotic ...

University of California Davis

Donald Danforth Plant Science Center

Specialized metabolites constitute key layers of immunity that underlie disease resistance in crops; however, challenges in resolving pathways limit our understanding of the functions and applications of these metabolites. In maize (Zea mays), the inducible accumulation of acidic terpenoids is increasingly considered to be a defence mechanism that contributes to disease resistance. Here, to understand maize antibiotic biosynthesis, we integrated association mapping, pan-genome multi-omic correlations, enzyme structure-function studies and targeted mutagenesis. We define ten genes in three zealexin (Zx) gene clusters that encode four sesquiterpene synthases and six cytochrome P450 proteins that collectively drive the production of diverse antibiotic cocktails. Quadruple mutants in which the ability to produce zealexins (ZXs) is blocked exhibit a broad-spectrum loss of disease resistance. Genetic redundancies ensuring pathway resiliency to single null mutations are combined with enzyme substrate promiscuity, creating a biosynthetic hourglass pathway that uses diverse substrates and in vivo combinatorial chemistry to yield complex antibiotic blends. The elucidated genetic basis of biochemical phenotypes that underlie disease resistance demonstrates a predominant maize defence pathway and informs innovative strategies for transferring chemical immunity between crops.

Genetic elucidation of interconnected antibiotic pathways mediating maize innate immunity.

The cereal scutellum is a hub for diverse specialized defense metabolism and pathway discovery.

Shielding the oil reserves: the scutellum as a source of chemical defenses.

Two major groups of specialized metabolites in maize (Zea mays), termed kauralexins and dolabralexins, serve as known or predicted diterpenoid defenses against pathogens, herbivores, and other environmental stressors. To consider the physiological roles of the recently discovered dolabralexin pathway, we examined dolabralexin structural diversity, tissue-specificity, and stress-elicited production in a defined biosynthetic pathway mutant. Metabolomics analyses support a larger number of dolabralexin pathway products than previously known. We identified dolabradienol as a previously undetected pathway metabolite and characterized its enzymatic production. Transcript and metabolite profiling showed that dolabralexin biosynthesis and accumulation predominantly occur in primary roots and show quantitative variation across genetically diverse inbred lines. Generation and analysis of CRISPR-Cas9-derived loss-of-function Kaurene Synthase-Like 4 (Zmksl4) mutants demonstrated dolabralexin production deficiency, thus supporting ZmKSL4 as the diterpene synthase responsible for the conversion of geranylgeranyl pyrophosphate precursors into dolabradiene and downstream pathway products. Zmksl4 mutants further display altered root-to-shoot ratios and root architecture in response to water deficit. Collectively, these results demonstrate dolabralexin biosynthesis via ZmKSL4 as a committed pathway node biochemically separating kauralexin and dolabralexin metabolism, and suggest an interactive role of maize dolabralexins in plant vigor during abiotic stress.

A dolabralexin-deficient mutant provides insight into specialized diterpenoid metabolism in maize.

A major bottleneck in the crop improvement pipeline is our ability to phenotype crops quickly and efficiently. Image-based, high-throughput phenotyping has a number of advantages because it is nondestructive and reduces human labor, but a new challenge arises in extracting meaningful information from large quantities of image data. Deep learning, a type of artificial intelligence, is an approach used to analyze image data and make predictions on unseen images that ultimately reduces the need for human input in computation. Here, we review the basics of deep learning, assessments of deep learning success, examples of applications of deep learning in plant phenomics, best practices, and open challenges.

Deep Learning in Image-Based Plant Phenotyping.

Maize () is one of the world's most important crops, providing food for humans and livestock and serving as a bioenergy source. Climate change and the resulting abiotic stressors in the field reduce crop yields, threatening food security and the global economy. Water deficit (i.e., drought), heat, and insufficient nutrients (e.g., nitrogen and phosphorus) are major environmental stressors that affect maize yields, and impact growth and development at all stages of the plant life cycle. Understanding the biological processes underlying these responses in maize has the potential to increase yields in the face of abiotic stress. Optimizing individual or combined abiotic stress treatments in controlled environments reduces potential noise in data collection that can be present under less controlled growth conditions. Here, we describe methods and conditions for controlled abiotic stress treatments and associated controls during early vegetative growth of maize, conducted in greenhouses or growth chambers. This includes the environmental conditions, equipment, soil preparation, and intensity and duration of heat, drought, nitrogen deficiency, and phosphorous deficiency. Controlled experiments at early growth stages are informative for future in-field studies that require greater labor and inputs, saving researchers time and growing space, and thus research funds, before testing plants across later stages of development. We suggest that stress treatments be severe enough to result in a measurable phenotype, but not so severe that all plants die prior to sample collection. This protocol is designed to set important standards for replicable research in maize.

Maize Abiotic Stress Treatments in Controlled Environments.

Maize (), also known as corn, is an important crop that plays a crucial role in global agriculture. The economic uses of maize are numerous, including for food, feed, fiber, and fuel. It has had a significant historical importance in research as well, with important discoveries made in maize regarding plant domestication, transposons, heterosis, genomics, and epigenetics. Unfortunately, environmental stresses cause substantial yield loss to maize crops each year. Yield losses are predicted to increase in future climate scenarios, posing a threat to food security and other sectors of the global economy. Developing efficient methods to study maize abiotic stress responses is a crucial step toward a more resilient and productive agricultural system. This review describes the importance of and methods for studying the effects of heat, drought, and nutrient deficiency on early developmental stages of maize grown in controlled environments. Studying the early effects of environmental stressors in controlled environments allows researchers to work with a variety of environmental conditions with low environmental variance, which can inform future field-based research. We highlight the current knowledge of physiological responses of maize to heat, drought, and nutrient stress; remaining knowledge gaps and challenges; and information on how standardized protocols can address these issues.

Optimized Methods for Applying and Assessing Heat, Drought, and Nutrient Stress of Maize Seedlings in Controlled Environment Experiments.

High lipid producing (HLP) tobacco (Nicotiana tabacum) is a potential biofuel crop that produces an excess of 30% dry weight as lipid bodies in the form of triacylglycerol. While using HLP tobacco as a sustainable fuel source is promising, it has not yet been tested for its tolerance to warmer environments that are expected in the near future as a result of climate change. We found that HLP tobacco had reduced stomatal conductance, which results in increased leaf temperatures up to 1.5°C higher under control and high temperature (38°C day/28°C night) conditions, reduced transpiration, and reduced CO assimilation. We hypothesize this reduction in stomatal conductance is due to the presence of excessive, large lipid droplets in HLP guard cells imaged using confocal microscopy. High temperatures also significantly reduced total fatty acid levels by 55% in HLP plants; thus, additional engineering may be needed to maintain high titers of leaf oil under future climate conditions. High-throughput image analysis techniques using open-source image analysis platform PlantCV for thermal image analysis (plant temperature), stomata microscopy image analysis (stomatal conductance), and fluorescence image analysis (photosynthetic efficiency) were developed and applied in this study. A corresponding set of PlantCV tutorials are provided to enable similar studies focused on phenotyping future crops under adverse conditions.

Katherine M. Murphy

Department of Plant Biology

University of California Davis

A Customizable Approach for the Enzymatic Production and Purification of Diterpenoid Natural Products

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Katherine M. Murphy

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University of California Davis

A Customizable Approach for the Enzymatic Production and Purification of Diterpenoid Natural Products

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Department of Plant Biology