Inferring Gene Regulatory Networks that control maintenance and
identity of the Arabidopsis Root Stem Cells
1
1
1,2
3
Maria Angels de Luis Balaguer , Adam Fisher , Natalie Clark , Cranos Williams , Rosangela Sozzani
1,*
1 Plant and Microbial Biology Department, North Carolina State University, Raleigh, NC, USA.
2 Biomathematics Program, North Carolina State University, Raleigh, NC, USA.
3 Electrical and Computer Engineering Department, North Carolina State University, Raleigh, NC, USA.
* [email protected]
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 (DBNs), which are
integrated into our algorithm to increase the overall prediction capacity of our inference 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.