Martineau, J. | bioRxiv Science

bioRxiv2026

Microbiota-gut-brain axis modulation drives glioblastoma progression and therapy resistance

Glioblastoma is a highly aggressive brain tumour with poor prognosis, whose aetiology, progression, and therapeutic resistance remain incompletely understood. While the microbiota-gut-brain axis has emerged as a key regulator of neurological disorders, its role in glioblastoma biology and treatment response is still largely unexplored. Using a clinically relevant immunocompetent murine model combining glioblastoma stem cell implantation, dextran sodium sulfate-induced gut inflammation, and a full Stupp-like therapeutic protocol, we investigated bidirectional gut-brain communication in glioblastoma. Tumour growth and recurrence were monitored by bioluminescence imaging, tumour transcriptomic profiles were analysed by RNA sequencing, and brain and colon tissues were subjected to histological and molecular analyses. Gut microbiota composition was assessed by 16S rRNA sequencing, while systemic metabolites and cytokines were quantified in plasma. Cross-compartment association bioinformatic analyses were performed to correlate multi-organ readouts. Gut inflammation enhanced glioblastoma growth and promoted tumour recurrence following therapy. Tumour progression was associated with increased infiltration of immunosuppressive macrophages, whereas recurrence correlated with elevated oxidative DNA damage. Remarkably, glioblastoma exerted systemic immunomodulatory effects, attenuating intestinal and systemic inflammatory responses, and induced profound remodelling of gut microbiota composition and predicted metabolic function, including enrichment of Akkermansia and depletion of Lactobacillus. Systemic metabolic profiling was investigated as a route of communication within the gut-brain axis and revealed adaptations in DSS-treated mice associated with tumour burden and therapeutic response. Multi-compartment correlation and multivariable association analyses identified specific bacterial genera and circulating metabolites associated with tumour volume, intestinal inflammation, and genomic instability. These findings uncover a dynamic, bidirectional microbiota-gut-brain axis in glioblastoma and identify intestinal inflammation as a critical determinant of tumour progression and therapeutic outcome. Targeting gut disturbances and microbiota-associated metabolic pathways may represent novel strategies to modulate glioblastoma aggressiveness and treatment response.

cancer biology↗

bioRxiv2024

Deep Kernel Inversion: Rapid and Accurate Molecular Interaction Prediction for Drug Design

Computational drug design offers the opportunity to dramatically accelerate novel therapeutics for untreated diseases. Designing compounds with optimal efficacy and specificity, however, requires understanding and optimizing immense numbers of molecular interactions. While advances in predicting one-to-one molecular interactions continue, there has been limited progress in scaling one-to-many or many-to-many molecular interaction models. In this paper, we introduce a deep learning framework that embeds molecules into a high-dimensional vector space, which we have named Deep Kernel Inversion. In this framework, the dot product between vectors accurately predicts molecular interactions. This approach reduces the complexity of predicting an entire molecular interaction network from O(n2) to O(n), enabling new molecular design tasks previously inaccessible to computational approaches. In the case of human protein-protein interactions (PPI), we demonstrate a 100,000 fold decrease in the computation required to map the full human PPI network. We also demonstrate best-in-class performance across multiple molecular interaction tasks with this approach. This work offers a new way forward in scaling accurate molecular interaction predictions with applications in mapping biological pathways, target discovery, drug design, and therapeutic development.

systems biology↗