Engelman, C. | bioRxiv Science

bioRxiv2025

Gut bacterial metabolite imidazole propionate potentiates Alzheimer's disease pathology

The gut microbiome modulates metabolic and neurovascular processes implicated in Alzheimers disease and related dementias (ADRD), but the underlying mechanisms remain unclear. Here, we identify the bacterial metabolite imidazole propionate (ImP) as a modifier of ADRD pathology. In a cohort of 1,196 cognitively unimpaired adults, higher plasma ImP levels were associated with lower preclinical cognitive scores and biomarkers of ADRD, both cross-sectionally and longitudinally. Fecal metagenomic analysis linked putative ImP producers to ADRD phenotypes. Genome-wide integrative analysis revealed a locus on chromosome 12 associated with both plasma ImP levels and AD risk in humans, supporting a host genetic contribution to ImP regulation and a causal role of this metabolite in AD. In mice, chronic ImP administration exacerbated AD-like pathology. Mechanistically, ImP impaired brain endothelial barrier and promoted tau hyperphosphorylation in primary neurons, an effect blocked by glycogen synthase kinase-3{beta} inhibition. Together, our study links ImP to hallmarks of neurodegeneration and suggest that targeting ImP may represent a potential strategy to modify ADRD risk.

pathology↗

bioRxiv2025

COSIME: Cooperative multi-view integration and Scalable and Interpretable Model Explainer

Single-omics approaches often provide a limited perspective on complex biological systems, whereas multi-omics integration enables a more comprehensive understanding by combining diverse data views. However, integrating heterogeneous data types and interpreting complex relationships between biological features--both within and across views--remains a major challenge. To address these challenges, we introduce COSIME (Cooperative Multi-view Integration with a Scalable and Interpretable Model Explainer). COSIME applies the backpropagation of a learnable optimal transport algorithm to deep neural networks, thus enabling the learning of latent features from several views to predict disease phenotypes. It also incorporates Monte Carlo sampling to enable interpretable assessments of both feature importance and pairwise feature interactions for both within and across views. We applied COSIME to both simulated and real-world datasets--including single-cell transcriptomics, spatial transcriptomics, epigenomics and metabolomics--to predict Alzheimers disease-related phenotypes. Benchmarking of existing methods demonstrated that COSIME improves prediction accuracy and provides interpretability. For example, it reveals that synergistic interactions between astrocyte and microglia genes associated with Alzheimers disease are more likely to localize at the edges of the middle temporal gyrus. Finally, COSIME is also publicly available as an open source tool.

bioinformatics↗