Modeling plant development: from signals to gene networks

Genome-Wide Identification and Expression Analysis of Homeodomain Leucine Zipper Subfamily IV (HD-ZIP IV) Gene Family in Cannabis sativa L

The plant-specific homeodomain zipper family (HD-ZIP) of transcription factors plays central roles in regulating plant development and environmental resistance. HD-ZIP transcription factors IV (HDZ IV) have been involved primarily in the regulation of epidermal structure development, such as stomata and trichomes. In our study, we identified nine HDZ IV-encoding genes in Cannabis sativa L. by conducting a computational analysis of cannabis genome resources. Our analysis suggests that these genes putatively encode proteins that have all the conserved domains of HDZ IV transcription factors. The phylogenetic analysis of HDZ IV gene family members of cannabis, rice (Oryza sativa), and Arabidopsis further implies that they might have followed distinct evolutionary paths after divergence from a common ancestor. All the identified cannabis HDZ IV gene promoter sequences have multiple regulation motifs, such as light- and hormone-responsive elements. Furthermore, experimental evidence shows that different HDZ IV genes have different expression patterns in root, stem, leaf, and flower tissues. Four genes were primarily expressed in flowers, and the expression of CsHDG5 (XP_030501222.1) was also correlated with flower maturity. Fifty-nine genes were predicted as targets of HDZ IV transcription factors. Some of these genes play central roles in pathogen response, flower development, and brassinosteroid signaling. A subcellular localization assay indicated that one gene of this family is localized in the Arabidopsis protoplast nucleus. Taken together, our work lays fundamental groundwork to illuminate the function of cannabis HDZ IV genes and their possible future uses in increasing cannabis trichome morphogenesis and secondary metabolite production.

Restoring circadian gene profiles in clock networks using synthetic feedback control

The circadian system-an organism's built-in biological clock-is responsible for orchestrating biological processes to adapt to diurnal and seasonal variations. Perturbations to the circadian system (e.g., pathogen attack, sudden environmental change) often result in pathophysiological responses (e.g., jetlag in humans, stunted growth in plants, etc.) In view of this, synthetic biologists are progressively adapting the idea of employing synthetic feedback control circuits to alleviate the effects of perturbations on circadian systems. To facilitate the design of such controllers, suitable models are required. Here, we extend our recently developed model for the plant circadian clock-termed the extended S-System model-to model circadian systems across different kingdoms of life. We then use this modeling strategy to develop a design framework, based on an antithetic integral feedback (AIF) controller, to restore a gene's circadian profile when it is subject to loss-of-function due to external perturbations. The use of the AIF controller is motivated by its recent successful experimental implementation. Our findings provide circadian biologists with a systematic and general modeling and design approach for implementing synthetic feedback control of circadian systems.

The Spring of Systems Biology-Driven Breeding

Genetics and molecular biology have contributed to the development of rationalized plant breeding programs. Recent developments in both high-throughput experimental analyses of biological systems and in silico data processing offer the possibility to address the whole gene regulatory network (GRN) controlling a given trait. GRN models can be applied to identify topological features helping to shortlist potential candidate genes for breeding purposes. Time-series data sets can be used to support dynamic modelling of the network. This will enable a deeper comprehension of network behaviour and the identification of the few elements to be genetically rewired to push the system towards a modified phenotype of interest. This paves the way to design more efficient, systems biology-based breeding strategies.

Quantitatively Understanding Plant Signaling: Novel Theoretical-Experimental Approaches

With the need to respond to and integrate a multitude of external and internal stimuli, plant signaling is highly complex, exhibiting signaling component redundancy and high interconnectedness between individual pathways. We review here novel theoretical-experimental approaches in manipulating plant signaling towards the goal of a comprehensive understanding and targeted quantitative control of plant processes. We highlight approaches taken in the field of synthetic biology used in other systems and discuss their applicability in plants. Finally, we introduce existing tools for the quantitative analysis and monitoring of plant signaling and the integration of experimentally obtained quantitative data into mathematical models. Incorporating principles of synthetic biology into plant sciences more widely will lead this field forward in both fundamental and applied research.

From Genes to Networks: Characterizing Gene-Regulatory Interactions in Plants

Plants, like other eukaryotes, have evolved complex mechanisms to coordinate gene expression during development, environmental response, and cellular homeostasis. Transcription factors (TFs), accompanied by basic cofactors and posttranscriptional regulators, are key players in gene-regulatory networks (GRNs). The coordinated control of gene activity is achieved by the interplay of these factors and by physical interactions between TFs and DNA. Here, we will briefly outline recent technological progress made to elucidate GRNs in plants. We will focus on techniques that allow us to characterize physical interactions in GRNs in plants and to analyze their regulatory consequences. Targeted manipulation allows us to test the relevance of specific gene-regulatory interactions. The combination of genome-wide experimental approaches with mathematical modeling allows us to get deeper insights into key-regulatory interactions and combinatorial control of important processes in plants.