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Biology subjects

Baselli, G.

Publications and source records attributed to Baselli, G..

3 recordsLinked to original sources

SOX21 suppresses GBM growth by repressing AP-1 regulated target genes

BackgroundTreatment-resistant glioblastoma stem cells (GSCs) drive glioblastoma (GBM) growth and recurrence. Thus, targeting the molecular machinery that sustains GSCs in an undifferentiated and self-renewing state is a promising therapeutic strategy. The transcription factor SOX21 effectively suppresses the tumorigenic capacity of GSCs. However, the mechanism by which SOX21 impedes GSC features is unknown. MethodsPatient-derived GSCs were engineered with a transgenic TetOn system to enable inducible expression of SOX21 or appropriate controls. The capacity of SOX21 to incapacitate GSCs was assessed using in vitro cell culture models and orthotopic mouse models. Cellular and genome-wide techniques, including RNA-seq, ChIP-seq, and ATAC-seq, were employed to examine the mechanisms by which SOX21 regulates GSCs. ResultsWe show that SOX21 expression in primary GSCs induces an anti-tumorigenic transcriptional program, aligning with clinical data showing a positive correlation between SOX21 levels and improved GBM patient survival. Induced SOX21 expression in GSCs within pre-established GBM reduces their capacity to sustain tumor growth and significantly extends the survival of the transplanted mice. Mechanistically, SOX21 functions as a tumor suppressor by binding a large set of AP-1-targeted chromatin regions, leading to epigenetic repression of AP-1-activated genes that support GSC survival and proliferation. Consistently, the anti-tumorigenic activities of SOX21 are replicated by AP-1 inhibitors, while overexpression of the AP-1 family member, c-JUN, counteracts these effects. ConclusionOur findings identify SOX21 as a key regulator that prevents GSC malignancy by targeting and repressing an AP-1-driven, tumor-promoting gene expression program. These results highlight SOX21-regulated pathways as promising therapeutic targets for GBM. Key PointsO_LIInduced SOX21 expression suppresses GSCs and inhibits the growth of established GBM C_LIO_LISOX21 acts as a tumor suppressor in GSCs by directly repressing AP-1-driven genes C_LIO_LIPharmacological inhibition of AP-1 mimics SOX21 activity in GSCs C_LI Importance of the StudyGBM is the most common and aggressive malignant brain tumor in adults. Recurrence following treatment often stems from the failure of therapeutic interventions to effectively target GSCs, which serve as the primary reservoir for tumor regrowth. The resilience of GSCs to treatment is partly due to the inactivation of intrinsic tumor suppressor programs that would otherwise direct GSCs to cellular senescence and death. This study demonstrates that increased expression levels of the tumor suppressor SOX21 in pre-established GBM disrupt tumor progression by disabling self-renewing GSCs. We show that SOX21 exerts its tumor-suppressive function by targeting and repressing an AP-1-driven gene network, which is a key regulator of GSC maintenance and proliferation. By uncovering the molecular mechanisms through which SOX21 controls GSC biology, our findings provide valuable insights for basic cancer research and may inform the development of novel therapeutic strategies for GBM.

cancer biology↗

Integrating large-scale neuroimaging research datasets: harmonisation of white matter hyperintensity measurements across Whitehall and UK Biobank datasets

Large scale neuroimaging datasets present the possibility of providing normative distributions for a wide variety of neuroimaging markers, which would vastly improve the clinical utility of these measures. However, a major challenge is our current poor ability to integrate measures across different large-scale datasets, due to inconsistencies in imaging and non-imaging measures across the different protocols and populations. Here we explore the harmonisation of white matter hyperintensity (WMH) measures across two major studies of healthy elderly populations, the Whitehall II imaging sub-study and the UK Biobank. We identify pre-processing strategies that maximise the consistency across datasets and utilise multivariate regression to characterise sample differences contributing to study-level differences in WMH variations. We also present a parser to harmonise WMH-relevant non-imaging variables across the two datasets. We show that we can provide highly calibrated WMH measures from these datasets with: (1) the inclusion of a number of specific standardised processing steps; and (2) appropriate modelling of sample differences through the alignment of demographic, cognitive and physiological variables. These results open up a wide range of applications for the study of WMHs and other neuroimaging markers across extensive databases of clinical data. HIGHLIGHTSO_LIWe harmonised measures of WMHs across two studies on healthy ageing C_LIO_LISpecific pre-processing strategies can increase comparability across studies C_LIO_LIModelling of biological differences is crucial to provide calibrated measures C_LI

neuroscience↗

Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase

Circulatory shock is a life-threatening disease that accounts for around one-third of all admissions to intensive care units (ICU). It requires immediate treatment, which is why the development of tools for planning therapeutic interventions is required to deal with shock in the critical care environment. In this study, the ShockOmics European project original database is used to extract attributes capable of predicting mortality due to shock in the ICU. Missing data imputation techniques and machine learning models were used, followed by feature selection from different data subsets. Selected features were later used to build Bayesian Networks, revealing causal relationships between features and ICU outcome. The main result is a subset of predictive features that includes well-known indicators such as the SOFA and APACHE II scores, but also less commonly considered ones related to cardiovascular function assessed through echocardiograpy or shock treatment with pressors. Importantly, certain selected features are shown to be most predictive at certain time-steps. This means that, as shock progresses, different attributes could be prioritized. Clinical traits obtained at 24h. from ICU admission are shown to accurately predict cardiogenic and septic shock mortality, suggesting that relevant life-saving decisions could be made shortly after ICU admission.

bioinformatics↗