APOE, Immune Factors, Sex, and Diet Interact to Shape Brain Networks in Mouse Models of Aging

Abstract

Alzheimer's disease (AD) presents complex challenges due to its multifactorial nature, poorly understood etiology, and late detection. The mechanisms through which genetic, fixed and modifiable risk factors influence susceptibility to AD are under intense investigation, yet the impact of unique risk factors on brain networks is difficult to disentangle, and their interactions remain unclear. To model multiple risk factors including APOE genotype, age, sex, diet, and immunity we leveraged mice expressing the human APOE and NOS2 genes, conferring a reduced immune response compared to mouse Nos2. Employing graph analyses of brain connectomes derived from accelerated diffusion-weighted MRI, we assessed the global and local impact of risk factors in the absence of AD pathology. Aging and a high-fat diet impacted extensive networks comprising AD-vulnerable regions, including the temporal association cortex, amygdala, and the periaqueductal gray, involved in stress responses. Sex impacted networks including sexually dimorphic regions (thalamus, insula, hypothalamus) and key memory-processing areas (fimbria, septum). APOE genotypes modulated connectivity in memory, sensory, and motor regions, while diet and immunity both impacted the insula and hypothalamus. Notably, these risk factors converged on a circuit comprising 63 of 54,946 total connections (0.11% of the connectome), highlighting shared vulnerability amongst multiple AD risk factors in regions essential for sensory integration, emotional regulation, decision making, motor coordination, memory, homeostasis, and interoception. These network-based biomarkers hold translational value for distinguishing high-risk versus low-risk participants at preclinical AD stages, suggest circuits as potential therapeutic targets, and advance our understanding of network fingerprints associated with AD risk.

Significance statement: Current interventions for Alzheimer's disease (AD) do not provide a cure, and are delivered years after neuropathological onset. Addressing the impact of risk factors on brain networks holds promises for early detection, prevention, and revealing putative therapeutic targets at preclinical stages. We utilized six mouse models to investigate the impact of factors, including APOE genotype, age, sex, immunity, and diet, on brain networks. Large structural connectomes were derived from high resolution compressed sensing diffusion MRI. A highly parallelized graph classification identified subnetworks associated with unique risk factors, revealing their network fingerprints, and a common network composed of 63 connections with shared vulnerability to all risk factors. APOE genotype specific immune signatures support the design of interventions tailored to risk profiles.

Keywords: APOE; Alzheimer’s disease; Biological Sciences; MRI; Neuroscience; connectomics; mouse.

Figure 1.. The approach for detecting networks impacted by distinct AD risk factors (APOE genotype, age, immune factors, sex, and diet) comprises a data preprocessing and network analysis pipeline with model validation.

We leveraged mouse models expressing various combinations of risk traits, imaged using compressed sensing diffusion weighted MRI. Brains were parcellated using atlas-based segmentation, which was combined with tractography to construct connnectomes; every entry in the connectome matrix represents the number of streamlines that connect each region pair. The analysis pipeline used the network summary statistics in an initial exploratory analysis, followed by graph class and validation of the resulting networks (A). The candidate networks hypothesized to change when varying the levels of risk traits included: the piriform cortex (Pir), hippocampus (Hc), hypothalamus (Hyp), orbital cortex (OrbitalCx), temporal association cortex (TeA), insula (Ins), amygdala (Amy), frontal association cortex (FrA), motor (M1, M2) and entorhinal cortex (Ent), accumbens (Acb), cingulate cortex (Cg), primary somatosensory cortex (S1), visual cortex (V1/V2), and cerebellum (Cblm).

Figure 2.. Network fingerprints associated with APOE genotype, age, sex, diet and immunity, and their validation ROC curves.

We noted a central role for the entorhinal cortex, parasubiculum, dorsolateral orbital cortex and insula for the APOE2/APOE3 comparison; the frontal association cortex, M1, and a large network including limbic regions, striatum and piriform cortex for APOE3/APOE4; the frontal association cortex, entorhinal cortex, parasubiculum, basal ganglia, insula for APOE2/APOE4. Age also impacted circuits including the entorhinal and piriform cortex, and also giganto cellular reticular nuclei. Sex differences impacted the hypothalamus and amygdala, and also the hippocampus, septum, and fimbria - linking sex vulnerability to memory function. Diet impacted the hippocampus, amygdala and entorhinal cortex, insula, accumbens, and olfactory areas. Immunity (HN presence) showed a role for the frontal association cortex, hypothalamus, piriform cortex and amygdala, and interhemispheric connections though the corpus callosum. Red denotes positive, blue denotes negative edge weights.

Figure 3.. Common edges for vulnerable networks associated with multiple risk factors for LOAD, and their interactions

(A). The immune changes due to HN affected similar networks in different APOE genotypes, including memory networks, with a strong entorhinal circuitry component, but also S2, the auditory cortex, insula and cerebellum. Relative to the neutral APOE3 allele, the connections within the entorhinal cortex (black arrows) in APOE2 mice had negative weights, while in APOE4 mice they had positive weights (Caudal to Dorsal Entorhinal cortex). Common networks with shared vulnerability for APOE genotype, age, sex, diet and immunity (C).