Multiomic Analysis of Neurons with Divergent Projection Patterns Identifies Novel Regulators of Axon Pathfinding.

Axon pathfinding is a key step in neural circuits formation. However, the transcriptional mechanisms regulating its progression remain poorly understood. The binary decision of crossing or avoiding the midline taken by some neuronal axons during development represents a robust model to investigate the mechanisms that control the selection of axonal trajectories. Here, to identify novel regulators of axon guidance, this work compares the transcriptome and chromatin occupancy profiles of two neuronal subpopulations, ipsilateral (iRGC) and contralateral retinal ganglion cells (cRGC), with similar functions but divergent axon trajectories. These analyses retrieved a number of genes encoding for proteins not previously implicated in axon pathfinding. In vivo functional experiments confirm the implication of some of these candidates in axonal navigation. Among the candidate genes, γ-synuclein is identified as essential for inducing midline crossing. Footprint and luciferase assays demonstrate that this small-sized protein is regulated by the transcription factor (TF) Pou4f1 in cRGCs. It is also shown that Lhx2/9 are specifically expressed in iRGCs and control a program that partially overlaps with that regulated by Zic2, previously described as essential for iRGC specification. Overall, the analyses identify dozens of new molecules potentially involved in axon guidance and reveal the regulatory logic behind the selection of axonal trajectories.

A Zic2-regulated switch in a noncanonical Wnt/ßcatenin pathway is essential for the formation of bilateral circuits.

The Wnt pathway is involved in a wide array of biological processes during development and is deregulated in many pathological scenarios. In neurons, Wnt proteins promote both axon extension and repulsion, but the molecular mechanisms underlying these opposing axonal responses are unknown. Here, we show that Wnt5a is expressed at the optic chiasm midline and promotes the crossing of retinal axons by triggering an alternative Wnt pathway that depends on the accumulation of ßcatenin but does not activate the canonical pathway. In ipsilateral neurons, the transcription factor Zic2 switches this alternative Wnt pathway by regulating the expression of a set of Wnt receptors and intracellular proteins. In combination with this alternative Wnt pathway, the asymmetric activation of EphB1 receptors at the midline phosphorylates ßcatenin and elicits a repulsive response. This alternative Wnt pathway and its Zic2-triggered switch may operate in other contexts that require a two-way response to Wnt ligands.

Sample size calculation to externally validate scoring systems based on logistic regression models.

BACKGROUND: A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence). Scoring systems based on binary logistic regression models are a specific type of predictive model. OBJECTIVE: The aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study. METHODS: The algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index) were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units. RESULTS: In the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature. CONCLUSION: An algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases.