Craven, C. | Search bioRxiv

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↗

bioRxiv2022

Partitioned Local Depth analysis of time course transcriptomic data reveals elaborate community structure

Transcriptome studies that provide temporal information are valuable for identifying groups of similarly-behaving transcripts, giving insight into overarching gene regulatory networks. Nevertheless, inferring transcriptional networks from time series data is challenging, in part because it is difficult to holistically consider both local relationships and global structure of these complex and overlapping transcriptional responses. To address this need, we employed the Partitioned Local Depth (PaLD) method to examine four time series transcriptomic datasets generated using the model plant Arabidopsis thaliana. Here, we provide a self-contained description of the method and demonstrate how it can be used to make predictions about gene regulatory networks based on time series data. The analysis provides a global network representation of the data from which graph partitioning methods and neighborhood analysis can reveal smaller, more well-defined groups of like-responding transcripts. These groups of transcripts that change in response to hormone treatment (e.g., auxin or ethylene) or high salinity were demonstrated to be enriched in common biological function and/or binding of transcription factors that were not identified with prior analyses of this data using other clustering and inference methodologies. These results reveal the ability of PaLD to generate predictions about gene regulatory networks using time series transcriptomic data, which can be of value to the systems biology community.

systems biology↗