Gravel, M. | bioRxiv Science

bioRxiv2025

Cellular Reprogramming of H3K27M Pediatric High-Grade Glioma to Neuron-like State

This study explores the cell fate reprogrammability of H3K27M-mutant pediatric high-grade gliomas (pHGG) using neuronal transdifferentiation as a potential targeted therapy. We treated the BT245 patient-derived glioma cell line with pharmacological combinations targeting neuronal differentiation pathways and performed bulk RNA sequencing to characterize gene expression patterns driving cell fate transitions. Our findings reveal that the drug combinations induce transcriptomic changes consistent with differentiation towards neuronal phenotypes, including the upregulation of synaptic and dendritic signaling genes and the downregulation of malignant signatures. In comparison, astrocytic differentiation media (DM) and H3K27M knockout (KO) promote residual astrocytic phenotypes, suggesting neuronal transdifferentiation as a more effective strategy for mitigating tumor aggressiveness and progression. Differentially expressed genes such as GRIK1, GRIN1, NRXN3, NRXN1, CALB2, SCGN, SLC32A1, SLC1A2, KCNC3, and neurodevelopmental regulators including WNT7A, DLX6, ERBB4, ARX, BCL11B, SEMA3C, and FGFBP3 were identified as key markers regulating the neuron-like lineage transition. This study demonstrates that pHGGs can be phenotypically redirected toward neuronal-like identities through modulating cell fate differentiation programs. These findings advance the concept of differentiation therapy as a promising intervention to reduce phenotypic plasticity and malignancy in pHGG ecosystems. While these are early in vitro findings, the ability to steer and control glioma cells toward stable, less malignant fates offers promising translational potential for patient-centered targeted therapies.

cancer biology↗

bioRxiv2017

Genome-wide characterization of copy number variants in epilepsy patients

Epilepsy will affect nearly 3% of people at some point during their lifetime. Previous copy number variants (CNVs) studies of epilepsy have used array-based technology and were restricted to the detection of large or exonic events. In contrast, whole-genome sequencing (WGS) has the potential to more comprehensively profile CNVs but existing analytic methods suffer from limited accuracy. We show that this is in part due to the non-uniformity of read coverage, even after intra-sample normalization. To improve on this, we developed PopSV, an algorithm that uses multiple samples to control for technical variation and enables the robust detection of CNVs. Using WGS and PopSV, we performed a comprehensive characterization of CNVs in 198 individuals affected with epilepsy and 301 controls. For both large and small variants, we found an enrichment of rare exonic events in epilepsy patients, especially in genes with predicted loss-of-function intolerance. Notably, this genome-wide survey also revealed an enrichment of rare non-coding CNVs near previously known epilepsy genes. This enrichment was strongest for non-coding CNVs located within 100 Kbp of an epilepsy gene and in regions associated with changes in the gene expression, such as expression QTLs or DNase I hypersensitive sites. Finally, we report on 21 potentially damaging events that could be associated with known or new candidate epilepsy genes. Our results suggest that comprehensive sequence-based profiling of CNVs could help explain a larger fraction of epilepsy cases.\n\nAuthor summaryEpilepsy is a common neurological disorder affecting around 3% of the population. In some cases, epilepsy is caused by brain trauma or other brain anomalies but there are often no clear causes. Genetic factors have been associated with epilepsy in the past such as rare genetic variations found by linkage studies as well as common genetic variations found by genome-wide association studies and large copy-number variants. We sequenced the genome of[~] 200 epilepsy patients and[~] 300 healthy controls and compared the distribution of deletion (loss of a copy) and duplication (additional copy) of genomic regions. Thanks to the sequencing technology and a new method that takes advantage of the large sample size, we could compare the distribution of small copy- number variants between epilepsy patients and controls. Overall, we found that small variants are also associated with epilepsy. Indeed, the genome of epilepsy patients had more exonic copy- number variants, especially when rare or affecting genes with predicted loss-of-function intolerance. Focusing on regions around genes that have been previously associated with epilepsy, we also found more non-coding variants in epilepsy patients, especially deletions or variants in regulatory regions. Finally, we provide a list of 21 regions in which we found likely pathogenic variants.

genomics↗