Search bioRxiv⌕ Search

bioRxiv · 10.1101/2025.10.08.678256

African Green Monkeys Respond to Synthetic AB Oligomers with Persistent Alzheimers-like Activation

Abstract

Wild African green monkeys (AGMs) provide a promising alternative to congenic rodent models because of their closer evolutionary relationship to humans and natural genetic variation. They share key physiological and biochemical traits with humans, including lifespan, neuroanatomy, vascular structure, and inflammatory responses. Unlike rodents, AGMs naturally develop Alzheimers-like amyloid-{beta} (A{beta}) plaques and tau tangles with age. Immunohistochemical studies further show that AGMs inoculated with synthetic A{beta} oligomers (A{beta}O) exhibit hyperphosphorylated tau and neuroinflammation one year later, in the absence of overt neurodegeneration. The AGM body size permits collection of cerebrospinal fluid (CSF) and CSF derived extracellular vesicles (EV) from living individuals, which are key sources of Alzheimers disease biomarkers that can be monitored during disease progression. Here, we evaluate A{beta}O treated AGMs at the systems level using proteomics of CSF and phosphatidylserine affinity isolated EVs (EVps). We optimized a workflow to obtain paired CSF and EVps proteomics from <1 mL volumes, i.e. comparable to human liquid biopsy. Our measurements reveal robust, persistent AD-like responses at the biochemical level without overt loss of cognitive function. As such, these findings in AGMs suggest potential alternatives for disease tracking or point to protective mechanisms for limiting disease progression in AD. HighlightsO_LIDual proteomics of African green monkeys transiently challenged with synthetic A{beta} oligomers (A{beta}O) C_LIO_LIPhosphotidylserine (TIM4) based workflow enables CSF and EV profiling using clinical volumes C_LIO_LIOne year post-A{beta}O: vascular-inflammatory pathways rise; neuronal-axonal pathways fall C_LIO_LIA{beta}Os drive human-AD-like proteome shifts on time scales shorter than cognitive decline C_LI In briefWild African green monkeys (AGMs) offer a translational model for early Alzheimers biology, with physiology more similar to humans. We transiently exposed AGMs to synthetic A{beta} oligomers and, 12 months later, profiled paired proteomes from whole CSF and a CSF subcompartment enriched for extracellular vesicles. Despite no overt cognitive decline, AGM proteomes showed persistent Alzheimers-like remodeling, particularly in vascular, inflammatory, and neuronal systems. Parallel analysis of the CSF subcompartment revealed proteins and pathways under-represented in bulk CSF, sharpening disease-relevant signals and candidate biomarkers. This systems-level, longitudinal study establishes AGMs as a powerful platform for liquid biopsy discovery and illuminates basic biology of molecular responses to soluble A{beta} oligomers accompany and potentially protect against neurodegeneration.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Brown, B., Li, X., Grasty, M. R., Lopez, I., Dzigurski, S., Geradi, M., Lawrence, M., Elsworth, J., Weed, M., Gursoy, G., Miranker, A. D.. 2025-10-08. African Green Monkeys Respond to Synthetic AB Oligomers with Persistent Alzheimers-like Activation. https://doi.org/10.1101/2025.10.08.678256

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Large language models complement established workflows for mechanistic model construction and refinement in systems biology

Mechanistic models provide a framework for describing, understanding, and predicting biological processes, but their development requires an interdisciplinary team with expertise in biology, mathematics, and computation. Here we present Octomod, a multi-agent workflow based on large language models (LLMs) that integrates biological knowledge retrieval, model construction, parameter estimation, and hypothesis testing. Built on established software tools, including AMICI and pyPESTO, and community standards, including SBML and PEtab, Octomod supports model development and refinement through natural-language interaction with a user. The agents' proposals must pass defined checks and the user's decision before they are used. Every input, model, check, and decision is recorded, so that model building remains transparent and reproducible. We applied Octomod to four problems. Three used published data and differ in model size and in how much the user guided the run: Octomod constructed a model that matched a published model without user guidance, supported the interactive refinement of a gene-regulatory model, and built models of cell-cell communication that explained the responses of five immune cell types to different stimuli. In the fourth application, on newly generated and unpublished lipidomics data, Octomod helped to determine which processes the measurements could inform, identify unresolved label fluxes, and reduce a supplied model to those processes. Together, these results show how LLMs can strengthen and complement established workflows for constructing and refining biologically informed models.

systems biology↗

A Mathematical Model Connecting Appetite-Driven Food Intake to Metabolic Changes across Diet and GLP-1RA Treatments in Mice

Mouse models are widely used to study obesity and the effects of glucagon-like peptide-1 receptor agonists (GLP-1RAs) on appetite-driven meal responses and eating behaviours. However, connecting these behavioural changes to disease prevention remains limited. One reason is that eating behaviour is often summarised as daily or weekly energy intake, obscuring meal-level dynamics. Here we present a mathematical model of mouse appetite-driven food intake across diets and treatment with the GLP-1RAs semaglutide and exenatide. The model jointly described three studies and passed both qualitative assessments and a statistical {chi}2 test (disagreement<threshold, 348<1111). Predictive performance was subsequently validated using four pre-existing studies and one new study. Finally, the model was used to simulate mouse populations under dietary and GLP-1RA interventions, with predictions showing qualitative agreement with the literature. This work aids preclinical drug discovery, supports mouse study design, and improves understanding of how individual meals accumulate into long-term physiological changes.

systems biology↗

Generalized inference of noise-corrected state variability in single cells

Single-cell omics methods operate at their detection limit and are less conducive to technical controls than mini-bulk approaches, which combine cells of the same type before profiling. To integrate mini-bulk samples with paired split-pool controls more holistically, we built stochprofIC, an algorithm that considers data, noise, and covariates in a distributional-regression framework. We vet stochprofIC with synthetic datasets and compare against an adjusted-variance alternative using mini-bulk data in mouse brains and human breast cancers. The noise-batch corrections provided by stochprofIC enable inference of the single-cell populations comprising the mini-bulk, which are validated repeatedly by RNA FISH in different contexts. We use stochprofIC outputs as input to a systems model of nucleocytoplasmic transport and distinguish heterogeneous trafficking states. Pharmacologic inhibition of a MAP3K11-high state inhibits apoptosis in primary breast-cancer organoids, supporting relevance to phenotypes. By deconvolving changes in location and scale, stochprofIC supports mini-bulking when high-sensitivity and reliability profiles are important.

systems biology↗