RT Conference Proceedings
T1 Gene Regulatory Networks controlling Arabidopsis Root Stem Cells
A1 De Luis Balaguer, María Angels
K1 Transcripción genética - Regulación
AB Gene Regulatory Networks controlling Arabidopsis Root Stem CellsIdentifying the transcription factors (TFs) and associated regulatory processes involved in stemcell regulation is key for understanding the initiation and growth of tissues and organs. Althoughmany TFs have been described in the Arabidopsis root stem cells, a comprehensive view of thetranscriptional signature of the stem cells is lacking. We used a systems biology approach topredict interactions among the genes involved in stem cell identity and maintenance.We first transcriptionally profiled four stem cell populations and developed a gene regulatorynetwork (GRN) inference algorithm, GENIST, which combines spatial and temporal transcriptomicdatasets to identify important TFs and infer gene-to-gene interactions. Our approach resulted in amap of gene interactions that orchestrates the transcriptional regulation of stem cells. In additionto linking known stem cell factors, our resulting GRNs predicted additional TFs involved in stemcell identity and maintenance. We mathematically modeled and experimentally validated some ofour predicted transcription factors, which confirmed the robustness of our algorithm and ourresulting networks. Our approach resulted in the finding of a factor, PERIANTHIA (PAN), whichmay play an important role in stem cell maintenance and QC function.We then developed an imaging system to perform in vivo, long-term imaging experiments that willbe used to understand the dynamics of the regulatory interactions between PAN and itsdownstream TFs in a cell-specific manner. For this, we designed and 3-D printed a Multi-sampleArabidopsis Growth and Imaging Chamber (MAGIC) that provides near-physiological imagingconditions and allows high-throughput time-course imaging experiments in the ZEISS LightsheetZ.1. We showed MAGIC’s imaging capabilities by following cell divisions, as an indicator of plantgrowth and development, over prolonged time periods, and demonstrated that plants imaged withour chamber undergo cell divisions for >16 times longer than those with the glass capillarysystem supplied by the ZEISS Z1. Future in vivo observations of the expression of PAN and itspredicted downstream factors will be key to refine our model predictions and obtain informationabout the dynamics of the regulatory processes.Our systems biology approach illustrates the strength of integrating computational andtechnological tools into the experimental approaches to solve key biological questions. Weanticipate that our algorithm and our approach can be applied to solve similar problems in adiverse number of systems, which can result in unsupervised predictions of gene functions andgene candidates.
YR 2016
FD 2016-03-17
LK http://hdl.handle.net/10630/11082
UL http://hdl.handle.net/10630/11082
LA eng
NO Conferencia sobre aproximaciones sistémicas al conocimiento biológico
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 24 ene 2026