Modelling temporal transcriptional regulatory programs activation over reconstructed gene regulatory networks describing cell fate transitions
Creators: name: Marco-Antonio Mendoza-Parra
name: Moehlin, Julien
name: Pierre-Etienne Cholley
name: Gronemeyer, Hinrich
Publisher: name: F1000Research
Year: 2016

description: Studying living organisms as an ensemble of components in which the whole is the consequence of the complexity of their interactions represents the biggest challenge of the current “big-data omics” era. Specifically since the release of the first draft of the human genome in 2001, followed by the rapid development of the massive parallel sequencing technologies, the avenue towards the analysis of genome functions from a holistic point of view has been opened. Importantly, the combination of a multiplicity of genomic readouts will provide means to <i>describe living systems through the reconstitution of their genomic-regulatory functions which are at the basis of their defined state. </i>Moreover, understanding the reorganization of their regulatory wires – as a consequence of external/internal cues – represents a new approach to interpret the acquisition of novel physiological or aberrant system states. In a cellular context, the <i>detailed comprehension of</i> these reorganizations, known as <i>cell fate transitions</i>, <i>is a major component of the novel therapeutic developments in regenerative medicine.</i><br><br>In our laboratory, we take advantage of retinoic acid (RA)-driven cell differentiation model systems as a systematic approach for enhancing our understanding of cell fate transitions. Specifically it is based on (i) the integration of temporal functional genomic readouts for the reconstruction of gene regulatory networks (GRNs) describing the regulatory principles taken place during the cell fate transition process; (ii) the use of computational approaches for modelling signal transduction propagation over the reconstructed GRN; and finally (iii) the validation of the prediction readouts by taken advantage of the current genome editing approaches (CRISPR).<br><br>Here we present our implemented signal transduction model able to verify the coherence of the reconstructed GRN with the temporal transcriptional information describing the cell fate transition; but also predict the capacity of any nodes composing the GRN to drive the expected cell-fate transition behavior. This methodology mimics signalling propagation over multiple temporal transcriptional response layers by (i) verifying a change in the temporal transcriptional state between interconnected nodes; (ii) a coherent temporal directionality in the context of TF-TG interactions; and (iii) a signal propagation interconnections derived only from the initial signalling cue. The presented approach has been challenged over large number of nodes, and complex interconnected GRNs describing processes like cell differentiation, tumorigenesis and cell fate reprogramming.<br><br>Following the strong prediction power – verified by the use of CRISPR-dCas9 activation assays – observed in our RA-driven cell differentiation studies, we are currently working in a second version able to incorporate <i>in-silico</i> knock-out modelling, but also combinatorial nodes activation assays in order to predict cooperative situations able to enhance signal transduction performance. Taken in consideration the interest of the scientific community to access to this type of signal transduction modelling instruments, we are currently preparing a Cytoscape app, such that users could benefit of the multiple options available in such environment.