Biology subjects

Bonner, S.

Publications and source records attributed to Bonner, S..

4 recordsLinked to original sources

bioRxiv2026

Ependymomas are cancers of the pre-neural crest/roof plate lineage

Distinct molecular variants of the brain cancer ependymoma are distributed along the rostral-caudal extent of the central nervous system (CNS). Historically proposed to arise from ventricular ependyma, recent studies have suggested conflicting cellular origins, including the neural radial glia and the roof plate lineages. Using single-cell transcriptomics, immunohistochemistry, and lineage tracing, we demonstrate that ependymomas across all CNS compartments transcriptionally mirror MSX1+ve pre-neural crest/roof plate (Pre-NC/RP) lineage derivatives. Ependymoma subgroups recapitulate the spatial and molecular diversity of regional Pre-NC/RP populations, while retaining conserved MSX1 expression. Expression of the oncogenic fusion ZFTA-RELA within the murine Pre-NC/RP lineage generated tumors that faithfully resembled human ependymoma. These findings identify a common embryonic cellular origin for ependymomas and reconcile previously conflicting models of tumorigenesis.

developmental biology↗

bioRxiv2026

Biological Network Organization, Not Generic Graph Topology, Drives Graph-Based Gene Essentiality Prediction

Predicting gene essentiality across cellular contexts is a central challenge in computational biology, with implications for identifying cancer vulnerabilities. Graph neural networks (GNNs) integrate molecular interaction networks with gene-level features, but it remains unclear whether their performance gains arise from biologically meaningful connectivity or generic graph structure. Here, we systematically evaluate the role of network information in gene essentiality prediction using 2,741 genes across three tissues. We compare GNNs to feature-only baselines, including multilayer perceptron (MLP) and random forest (RF) methods, under a strict gene-level 5-fold cross-validation scheme to prevent information leakage. To isolate the role of network information, we assess models on the STRING protein-protein interaction network, a degree-preserving shuffled network, and a fully randomized network, with and without network-derived features. GNNs outperform feature-only models, reducing mean squared error and improving Matthews correlation coefficient across all tissues. However, these gains depend critically on biologically structured connectivity: performance degrades substantially under randomized topology and is not preserved by degree-constrained rewiring. Network features are largely redundant when using biologically meaningful graphs, as their information is recovered through message passing, but become important when topology is uninformative. Per-gene analyses reveal uniformly low correlations across models, highlighting intrinsic limits imposed by data variability. Graph Transformer models incorporating global attention do not outperform standard GNNs, indicating that predictive signals are predominantly local. Together, these results show that predictive gains arise from biologically structured connectivity rather than generic graph topology.

systems biology↗

bioRxiv2026

Testing hypotheses about correlations between brain activation patterns

Many functional magnetic resonance imaging (fMRI) studies conclude that two conditions engage "overlapping, yet partly distinct" patterns of activation. Yet, there is currently no commonly accepted method for determining the extent of this overlap. While correlations between activation patterns can serve as a measure of their correspondence, empirical correlations are strongly biased towards zero due to measurement noise, preventing their use in testing hypotheses about the actual degree of pattern correspondence. In this paper, we derive the maximum-likelihood estimate for the correlation of the true (noise-less) activation patterns and examine its behavior in the low signal-to-noise regime that is typical for fMRI studies. We show that although the maximum-likelihood estimate corrects for much of the influence of measurement noise, it is ultimately biased. We examine different ways of drawing inferences about the size of the underlying true correlations. We find that a subject-wise bootstrap on the maximum-likelihood group estimate performs best over the tested conditions. We extend the proposed method to test more general hypotheses about the representational geometry of activation patterns for more conditions, and highlight best practices, as well as common pitfalls and problems, in testing such hypotheses.

neuroscience↗

bioRxiv2025

Childhood brain tumours instruct cranial haematopoiesis and immunotolerance

Recent research has revealed a remarkable role for immunosurveillance in healthy and diseased brains, dispelling the notion that this organ is a passive immune-privileged site1-3. Better understanding of how this immunosurveillance operates could improve the treatment of neurological diseases. Here, using a novel genetically engineered mouse model of ZFTA-RELA ependymoma4-a childhood brain tumour-we characterised an immune circuit between the tumour and antigen presenting, haematopoietic stem/progenitor cells (HSPCs) in the skull bone marrow. The presentation of antigens in the cerebrospinal fluid (CSF) by HSPCs to CD4+ T cells, biased HSPC lineages toward myelopoiesis and polarised CD4+ T-cells to regulatory T cells (T- regs), culminating in tumour immunotolerance. Remarkably, a single infusion of antibodies directed against cytokines enriched in the CSF of mice bearing ZFTA-RELA ependymomas, choroid plexus carcinomas or Group-3 medulloblastoma-all aggressive childhood brain tumours-disrupted this process and caused profound tumour regression. These data unmask a mechanism by which skull bone marrow-derived HSPCs and CD4+ T cells cooperate to promote the immunotolerance of childhood brain tumours. Antibodies that disrupt this immunosurveillance could prove an effective therapy for these cancers that are less toxic than current treatments.

immunology↗