Graser, C. | Search bioRxiv

bioRxiv2026

Histone neutralization protects the ischemic brain against stroke-associated pneumonia

Bacterial pneumonia aggravates ischemic stroke via mechanisms that still remain to be determined. In ischemic stroke patients and mice exposed to middle cerebral artery occlusion, we show that stroke-associated pneumonia markedly worsens clinical stroke outcome. In mice, pneumonia induced 3 days after stroke impaired neurological recovery and increased brain neutrophil infiltrates, blood-brain barrier breakdown, cerebral microvascular thrombosis, and progressive brain atrophy. The antibiotic amoxicillin only partially ameliorated pneumonia-associated neurological deficits and neutrophil infiltrates. Neutrophils were critical mediators of pneumonia-induced blood-brain barrier breakdown and microvascular thrombosis. Notably, administration of a neutralizing anti-histone antibody during pneumonia--unlike degradation or blockade of neutrophil extracellular trap formation or myeloperoxidase inhibition--restored long-term neurological recovery and prevented brain atrophy in stroke-associated pneumonia mice. This study identifies extracellular histones as key drivers of secondary inflammatory brain injury and establishes histone neutralization as a therapeutic strategy with an extended treatment window in the post-acute stroke phase. One Sentence SummaryNeutralizing extracellular histones reverses pneumonia-driven secondary brain injury and restores long-term recovery after ischemic stroke.

neuroscience↗

bioRxiv2025

Mechanistic modeling and machine learning identifies optimum radiotherapy schedules to prevent treatment-induced metastasis

Lung cancer patients often experience increased metastasis formation after radiotherapy. However, it is incompletely understood whether radiation affects the migratory behavior of tumor cells and how altered radiotherapy schedules might mitigate this risk. To address these questions, we performed live-cell microscopy experiments to profile changes in cell migration during radiation across 12 cancer cell lines and developed a predictive computational modeling platform describing tumor volume and dissemination during radiotherapy. Using this platform, we identified optimal fractionation schedules and then performed extensive in silico clinical trials, establishing that our optimized schedules substantially reduce metastatic seeding relative to the standard of care schedule. Training transformer models on the in silico clinical trial data enabled us to recover mechanistic parameters with high accuracy, demonstrating that the features determining optimal radiotherapy can be inferred from longitudinal tumor data. Our integrative predictive approach enables the rational design of optimum clinical trials across indications.

bioinformatics↗