CSB Seminar: Prof. Ross Sozzani, Department of Plant and Microbial Biology, North Carolina State University.

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.