Plant Stem Cells, Plant Systems Biology, North Carolina State University
Our growing society faces new and dynamic challenges such as global climate change, the scarcity of arable land and the need for sustainable energy. Maximizing the utility of plants in each of these areas is key to meeting these challenges. Overall growth rate and biomass is largely regulated by the temporal and spatial control of stem cell self-renewal and differentiation of their progeny. When a stem cell divides it produces a copy of itself, and it produces a daughter cell that can develop into different types of cells. The means and mechanisms by which this occurs are poorly understood.
The Sozzani Lab research focuses on understanding how stem cells are organized and maintained in the root of the model plant Arabidopsis thaliana. Our goal is to gain a coherent qualitative and quantitative understanding of stem cell maintenance at the systems-level. Our research leverages techniques derived from molecular, developmental and cell biology, mathematics, physics, chemistry, computer science and engineering. In plant systems, stem cell regulation has clear implications for increasing the production of crops used for food, fiber and fuel. Our research will reveal a specific molecular pathway of plant stem cells, and provide broader insights into the fundamental properties of stem cells across the plant and animal kingdoms.
Abstract:The stem cells in the tip of the Arabidopsis root form all the root tissues by undergoing rounds of coordinated cell division while maintaining their undifferentiated state. While a number of transcription factors involved in root stem cell maintenance have been described, a comprehensive view of the transcriptional signature of the stem cells is lacking. A better understanding of the transcription factors that maintain the stem cells and control each stem cell’s identity would give us more insight into how the growth and development of the root is initiated. In this work, we generated a model of the transcriptional mechanisms underlying the identity and maintenance of the Arabidopsis root stem cells that links known and newly predicted factors involved in these processes. For this, we first sorted and transcriptionally profiled four stem cell populations. We then developed GENIST, an algorithm for GEne regulatory Network Inference from Spatio Temporal Data. These datasets are processed by two computational strategies of clustering and Dynamic Bayesian Networks, which are integrated into our algorithm to increase the overall prediction capacity of our method. We inferred GRNs in the Arabidopsis root stem cell niche by applying GENIST to a combination of our stem cell dataset and a public time-series dataset. Our approach led to a map of genetic interactions that orchestrate the transcriptional regulation of stem cells. In addition to linking known stem cell factors, our resulting GRNs predicted novel implications of TFs in stem cell molecular events. We experimentally validated some of our key predicted transcription factors, which confirmed the robustness of our algorithm and our resulting networks.
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