Biology subjects

Dahmane, N.

Publications and source records attributed to Dahmane, N..

4 recordsLinked to original sources

bioRxiv2025

Machine learning-predicted chromatin organization landscape across pediatric tumors

Structural variants (SVs) are increasingly recognized as important contributors to oncogenesis through their effects on 3D genome folding. Recent advances in whole-genome sequencing have enabled large-scale profiling of SVs across diverse tumors, yet experimental characterization of their individual impact on genome folding remains infeasible. Here, we leveraged a convolutional neural network, Akita, to predict disruptions in genome folding caused by somatic SVs identified in 61 tumor types from the Childrens Brain Tumor Network dataset. Our analysis reveals significant variability in SV-induced disruptions across tumor types, with the most disruptive SVs coming from lymphomas and sarcomas, metastatic tumors, and germline cell tumors. Dimensionality reduction of disruption scores identified five recurrently disrupted regions enriched for high-impact SVs across multiple tumors. Some of these regions are highly disrupted despite not being highly mutated, and harbor tumor-associated genes and transcriptional regulators. To further interpret the functional relevance of high-scoring SVs, we integrated epigenetic data and developed a modified Activity-by-Contact scoring approach to prioritize SVs with disrupted genome contacts at active enhancers. This method highlighted highly disruptive SVs near key oncogenes, as well as novel candidate loci potentially implicated in tumorigenesis. These findings highlight the utility of machine learning for identifying novel SVs, loci, and genetic mechanisms contributing to pediatric cancers. This framework provides a foundation for future studies linking SV-driven regulatory changes to cancer pathogenesis.

genomics↗

bioRxiv2024

Onco-fetal protein Nogo-A restricts human and mouse glioma vascularization and growth via VEGF-Notch-hippo-metabolic signaling

Glioblastoma is one of the most deadly human cancers characterized by high degrees of vascularization, but targeting its vasculature has resulted in very limited success so far. Angiogenesis, the growth of new blood vessels, is highly dynamic during brain development, enters a mostly quiescent state in the adult homeostatic brain, and is reactivated in vascular-dependent CNS diseases including brain tumors. In consequence, a better understanding of the relevance of the onco-fetal axis - describing the reactivation of fetal signaling programs in tumors - in endothelial- and perivascular cells of the human brain tumor vasculature harbors great translational potential, yet remains poorly defined. In development, neurovascular link (NVL) molecules guide both neuronal growth cones as well as capillary endothelial tip cells. Nogo-A is an NVL molecule known to inhibit axonal growth in the developing and adult CNS and to restrict angiogenesis during brain development, but its role in the mouse and human brain tumor vasculature along the onco-fetal axis remains unknown. Here, we characterize Nogo-A as an onco-fetal protein expressed in the neurovascular unit (NVU) in human fetal brains and human gliomas in vivo that negatively regulates sprouting angiogenesis and endothelial tip cells in glioma vascularization. The Nogo-A-specific Delta 20 domain restricts angiogenic sprouting and branching and promotes vascular normalization while inhibiting glioma growth in experimental gliomas. Moreover, Nogo-A expression in tumor cells negatively correlate/s with glioma malignancy in vivo. In vitro, Nogo-A Delta 20 reduced human brain- and brain tumor endothelial cell (HBMVEC, HBTMVEC) and human umbilical vein endothelial cell (HUVEC) spreading, migration, and sprouting, in a dose-dependent manner and inhibited filopodia extension glucose metabolism. Mechanistically, RNA sequencing of Nogo-A Delta 20-treated HBMVECs and HBTMVECs revealed Nogo-A Delta 20-induced positive regulation of the angiogenesis-inhibiting Dll4-Notch-pathway and inhibition of the angiogenesis-promoting VEGF-VEGFR and Hippo-YAP-TAZ pathways, whereas metabolomics and functional metabolic assays revealed Nogo-A Delta 20-induced negative regulation of endothelial glycolysis in HBMVECs and HBTMVECs. These findings characterize Nogo-A as an onco-fetal protein in the human glial brain tumor vasculature and identify Nogo-A Delta 20 signaling as an important negative regulator of human glioma vascularization and growth. Enhancing Nogo-A signaling may be an attractive alternative or combinatorial anti-angiogenic therapy to restrict human glioma/glioblastoma vascularization and growth.

neuroscience↗

bioRxiv2022

The Radiogenomic and Spatiogenomic Landscapes of Glioblastoma, and Their Relationship to Oncogenic Drivers

Glioblastoma (GBM) is well-known for its molecular and spatial heterogeneity, which poses a challenge for precision therapies and clinical trial stratification. Here, in a comprehensive radiogenomics study of 358 GBMs, we investigated the associations between the imaging and spatial characteristics of the tumors with their cancer gene mutation status, as well as with the cross-sectionally inferred likely order of mutational events. We show that cross-validated machine learning analysis of multi-parametric MRI scans results in distinctive in vivo imaging signatures of several mutations, which are relatively more distinctive in homogeneous tumors which harbor only one of these mutations. These imaging signatures offer mechanistic insights into how various mutations influence the phenotype of the tumor and its surrounding infiltrated brain tissue via neovascularization and vascular leakage, increased cell density, invasion and migration, and other characteristics captured by respective imaging features. Furthermore, we found that spatial location and tumor distribution vary, depending on the GBMs molecular characteristics. Finally, distinct imaging and spatial characteristics were associated with cross-sectionally estimated evolutionary trajectories of the tumors. Collectively, our study establishes a panel of in vivo and clinically accessible imaging-AI biomarkers of GBM that reflect their molecular composition and oncogenic drivers.

bioengineering↗

bioRxiv2021

4 SPECIES OF BACTERIA DETERMINISTICALLY FORM A STABLE BIOFILM IN A MILLIFLUIDIC CHANNEL: ASSEMBLY PRINCIPLES

AO_SCPLOWBSTRACTC_SCPLOWMultispecies microbial adherent communities are widespread in nature and organisms but the principles of their assembly and development remain unclear. Yet, the demand to understand and predict the responses of such living communities to environmental changes is increasing, calling for new approaches. Here, we test the possibility to establish a simplified but relevant model of multispecies biofilm in a laboratory setup enabling in situ real-time monitoring of the community development and control of the environmental parameters in order to decipher the mechanisms underlying the formation of the community. Using video-microscopy and species combinatorial approach, we assess the global and individual species spatiotemporal development in millifluidic channels under constant flow of nutrients. Based on quantitative measurements of expansion kinetics, local dynamics and spatial distribution, we demonstrate that the four chosen species (Bacillus thuringiensis, Pseudomonas fluorescens, Kocuria varians and Rhodocyclus sp.) form a dynamical community that deterministically reaches its equilibrium after about 30 hours of growth. We evidence the emergence of complexity in this simplified community as reported by spatial heterogeneity rise and non-monotonic developmental kinetics. We find interspecies interactions consisting in competition for resources -- in particular oxygen -- and both direct and indirect physical interactions but no positive feedback. Thereby, we introduce a model of multispecies adherent community where effective couplings result from individual species quest for fitness optimization in a moving and heterogenous environment. This control and the understanding of this simplified experimental model shall open new avenues to apprehend adherent bacterial communities behavior in a context of rapid global change.

biophysics↗