Innovative 3D-Image Analysis of Cerebellar Vascularization Highlights Angiogenic Gene Dysregulations in a Murine Model of Apnea of Prematurity - A. Rodriguez-Duboc; C. Racine; M. Basille-Dugay; D. Vaudry; B. Gonzalez; D. Burel (The Cerebellum, 2026)
Apnea of prematurity (AOP) affects 50% of preterm infants causing intermittent hypoxia (IH), which can lead to long-term neurodevelopmental deficits. Cerebellar abnormalities have been observed in AOP but the relationship between vascular alterations and neural development remains unclear. This study investigates how IH affects cerebellar angiogenesis using a murine model of AOP. We developed an innovative 3D imaging workflow combining Imaris and VesselVio software to quantitatively analyze cerebellar vascularization at different postnatal stages (P4, P8, P12, P21, and P70). We correlated these results with a transcriptomic analysis of 23 angiogenesis-related genes in the same stages to uncover the associated molecular pathways. We found that IH induced significant vascular changes, particularly at P4, with a global increase in vascular-network dimensions. By P8, the vascular network normalized, but genes were downregulated in all pathways studied. After P12, at the end of the IH protocol, transcriptional regulations had varied but persisted long-term. Moreover, differential analysis showed distinct effects on superficial versus deep vascular networks, allowing for a more precise understanding of remodeling patterns throughout development. Overall, transcriptomic changes were associated with morphological alterations in a time-dependent manner, suggesting a multiphasic IH response through development with lasting effects. Key regulations included VEGF, angiopoietin, and matrix metalloprotease signaling. These findings demonstrate that IH disrupts cerebellar angiogenesis in parallel with neurogenesis, potentially contributing to the neurodevelopmental deficits observed in AOP. Thus, the interconnected nature of angio- and neurogenesis during cerebellar development makes it crucial to take vascular aspects into account in therapeutic approaches to neurodevelopmental disorders.

P13-31 Characterization of human isogenic epithelial cell lines as a relevant tool to study colon carcinogenesis and interaction between genes and environment - A. Tête; L. Arnaud; H. Le Mentec; N. Poupin; I. Gallais; N. Tournade; C. Duarte Hospital; Y. Lippi; F. Mathevet; C. Pilati; G. Lefort; C. Lavau; et al. (Toxicology Letters, 2024)

P12-03 Hepatotoxicity induced by a mixture of PAHs: role of extracellular vesicles - F. Barathon; M. Bescher; A. Burel; N. Podechard; D. Lagadic-Gossmann; O. Sergent (Toxicology Letters, 2024)

The interplay of landscape composition and configuration: new pathways to manage functional biodiversity and agroecosystem services across Europe - Emily A. Martin; Matteo Dainese; Yann Clough; András Báldi; Riccardo Bommarco; Vesna Gagic; Michael P.D. Garratt; Andrea Holzschuh; David Kleijn; Anikó Kovács‐Hostyánszki; Lorenzo Marini; Simon G. Potts; et al. (Ecology Letters, 2019)
Managing agricultural landscapes to support biodiversity and ecosystem services is a key aim of a sustainable agriculture. However, how the spatial arrangement of crop fields and other habitats in landscapes impacts arthropods and their functions is poorly known. Synthesising data from 49 studies (1515 landscapes) across Europe, we examined effects of landscape composition (% habitats) and configuration (edge density) on arthropods in fields and their margins, pest control, pollination and yields. Configuration effects interacted with the proportions of crop and non‐crop habitats, and species’ dietary, dispersal and overwintering traits led to contrasting responses to landscape variables. Overall, however, in landscapes with high edge density, 70% of pollinator and 44% of natural enemy species reached highest abundances and pollination and pest control improved 1.7‐ and 1.4‐fold respectively. Arable‐dominated landscapes with high edge densities achieved high yields. This suggests that enhancing edge density in European agroecosystems can promote functional biodiversity and yield‐enhancing ecosystem services.

Early Disruption of Extracellular Pleiotrophin Distribution Alters Cerebellar Neuronal Circuit Development and Function - M. M. Hamza; S. A. Rey; P. Hilber; A. Arabo; T. Collin; D. Vaudry; D. Burel (Molecular Neurobiology, 2016)

Interactions of PACAP and Ceramides in the Control of Granule Cell Apoptosis During Cerebellar Development - A. Falluel-Morel; N. Aubert; D. Vaudry; A. Desfeux; A. Allais; D. Burel; M. Basille; H. Vaudry; V. Laudenbach; B. J. Gonzalez (Journal of Molecular Neuroscience, 2008)

Prediction uncertainty of environmental change effects on temperate European biodiversity - Carsten F. Dormann; Oliver Schweiger; P. Arens; I. Augenstein; St. Aviron; Debra Bailey; J. Baudry; R. Billeter; R. Bugter; R. Bukácek; F. Burel; M. Cerny; et al. (Ecology Letters, 2008)
Observed patterns of species richness at landscape scale (gamma diversity) cannot always be attributed to a specific set of explanatory variables, but rather different alternative explanatory statistical models of similar quality may exist. Therefore predictions of the effects of environmental change (such as in climate or land cover) on biodiversity may differ considerably, depending on the chosen set of explanatory variables. Here we use multimodel prediction to evaluate effects of climate, land‐use intensity and landscape structure on species richness in each of seven groups of organisms (plants, birds, spiders, wild bees, ground beetles, true bugs and hoverflies) in temperate Europe. We contrast this approach with traditional best‐model predictions, which we show, using cross‐validation, to have inferior prediction accuracy. Multimodel inference changed the importance of some environmental variables in comparison with the best model, and accordingly gave deviating predictions for environmental change effects. Overall, prediction uncertainty for the multimodel approach was only slightly higher than that of the best model, and absolute changes in predicted species richness were also comparable. Richness predictions varied generally more for the impact of climate change than for land‐use change at the coarse scale of our study. Overall, our study indicates that the uncertainty introduced to environmental change predictions through uncertainty in model selection both qualitatively and quantitatively affects species richness projections.