Jeggari, A. | bioRxiv Science

bioRxiv2019

A Shh/Gli-driven three-node timer motif controls temporal identity and fate of neural stem cells

How time is measured by neural stem cells during temporal neurogenesis has remained unresolved. By combining experiments and computational modelling, we here define a Shh/Gli-driven three-node timer underlying the sequential generation of motor neurons (MNs) and serotonergic neurons in the brainstem. The timer is founded on temporal decline of Gli-activator and Gli-repressor activities established through downregulation of Gli transcription. The circuitry conforms an incoherent feedforward loop, whereby Gli proteins promote expression of Phox2b and thereby MN-fate, but also account for a delayed activation of a self-promoting Tgf{beta}-node triggering a fate switch by repressing Phox2b. Hysteresis and spatial averaging by diffusion of Tgf{beta} counteracts noise and increases temporal accuracy at the population level. Our study defines how time is reliably encoded during the sequential specification of neurons.

developmental biology↗

bioRxiv2018

Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data

In order to tackle heterogeneity of cancer samples and high data space dimensionality, we propose a method NEAmarker for finding sensitive and robust biomarkers at the pathway level. In this method, scores from network enrichment analysis transform the original space of altered genes into a lower-dimensional space of pathways, which is then correlated with phenotype variables. The analysis was first done on in vitro anti-cancer drug screen datasets and then on clinical data. In parallel, we tested a panel of state-of-the-art enrichment methods. In this comparison, our method proved superior in terms of 1) universal applicability to different data types with a possibility of cross-platform integration, 2) consistency of the discovered correlates between independent drug screens, and 3) ability to explain differential survival of treated patients. Our new in vitro screen validated performance of the discovered multivariate models. Finally, NEAmarker was the only method to discover predictors of both in vitro response and patient survival given administration of the same drug.

cancer biology↗