Aggregate Trends of Apolipoprotein E on Cognition in Transgenic Alzheimer's Disease Mice

What Threshold of Amyloid Reduction Is Necessary to Meaningfully Improve Cognitive Function in Transgenic Alzheimer's Disease Mice?

Background

Amyloid-β plaques (Aβ) are associated with Alzheimer's disease (AD). Pooled assessment of amyloid reduction in transgenic AD mice is critical for expediting anti-amyloid AD therapeutic research.

Objective

The mean threshold of Aβ reduction necessary to achieve cognitive improvement was measured via pooled assessment (n = 594 mice) of Morris water maze (MWM) escape latency of transgenic AD mice treated with substances intended to reduce Aβ via reduction of beta-secretase cleaving enzyme (BACE).

Methods

Machine learning and statistical methods identified necessary amyloid reduction levels using mouse data (e.g., APP/PS1, LPS, Tg2576, 3xTg-AD, control, wild type, treated, untreated) curated from 22 published studies.

Results

K-means clustering identified 4 clusters that primarily corresponded with level of Aβ: untreated transgenic AD control mice, wild type mice, and two clusters of transgenic AD mice treated with BACE inhibitors that had either an average 25% "medium reduction" of Aβ or 50% "high reduction" of Aβ compared to untreated control. A 25% Aβ reduction achieved a 28% cognitive improvement, and a 50% Aβ reduction resulted in a significant 32% improvement compared to untreated transgenic mice (p < 0.05). Comparatively, wild type mice had a mean 41% MWM latency improvement over untreated transgenic mice (p < 0.05). BACE reduction had a lesser impact on the ratio of Aβ42 to Aβ40. Supervised learning with an 80% -20% train-test split confirmed Aβ reduction was a key feature for predicting MWM escape latency (R2 = 0.8 to 0.95).

Conclusions

Results suggest a 25% reduction in Aβ as a meaningful treatment threshold for improving transgenic AD mouse cognition.

Multivariate investigation of aging in mouse models expressing the Alzheimer's protective APOE2 allele: integrating cognitive metrics, brain imaging, and blood transcriptomics

APOE allelic variation is critical in brain aging and Alzheimer's disease (AD). The APOE2 allele associated with cognitive resilience and neuroprotection against AD remains understudied. We employed a multipronged approach to characterize the transition from middle to old age in mice with APOE2 allele, using behavioral assessments, image-derived morphometry and diffusion metrics, structural connectomics, and blood transcriptomics. We used sparse multiple canonical correlation analyses (SMCCA) for integrative modeling, and graph neural network predictions. Our results revealed brain sub-networks associated with biological traits, cognitive markers, and gene expression. The cingulate cortex emerged as a critical region, demonstrating age-associated atrophy and diffusion changes, with higher fractional anisotropy in males and middle-aged subjects. Somatosensory and olfactory regions were consistently highlighted, indicating age-related atrophy and sex differences. The hippocampus exhibited significant volumetric changes with age, with differences between males and females in CA3 and CA1 regions. SMCCA underscored changes in the cingulate cortex, somatosensory cortex, olfactory regions, and hippocampus in relation to cognition and blood-based gene expression. Our integrative modeling in aging APOE2 carriers revealed a central role for changes in gene pathways involved in localization and the negative regulation of cellular processes. Our results support an important role of the immune system and response to stress. This integrative approach offers novel insights into the complex interplay among brain connectivity, aging, and sex. Our study provides a foundation for understanding the impact of APOE2 allele on brain aging, the potential for detecting associated changes in blood markers, and revealing novel therapeutic intervention targets.

Machine Learning Selection of Most Predictive Brain Proteins Suggests Role of Sugar Metabolism in Alzheimer's Disease

BACKGROUND

The complex and not yet fully understood etiology of Alzheimer's disease (AD) shows important proteopathic signs which are unlikely to be linked to a single protein. However, protein subsets from deep proteomic datasets can be useful in stratifying patient risk, identifying stage dependent disease markers, and suggesting possible disease mechanisms.

OBJECTIVE

The objective was to identify protein subsets that best classify subjects into control, asymptomatic Alzheimer's disease (AsymAD), and AD.

METHODS

Data comprised 6 cohorts; 620 subjects; 3,334 proteins. Brain tissue-derived predictive protein subsets for classifying AD, AsymAD, or control were identified and validated with label-free quantification and machine learning.

RESULTS

A 29-protein subset accurately classified AD (AUC = 0.94). However, an 88-protein subset best predicted AsymAD (AUC = 0.92) or Control (AUC = 0.92) from AD (AUC = 0.98). AD versus Control: APP, DHX15, NRXN1, PBXIP1, RABEP1, STOM, and VGF. AD versus AsymAD: ALDH1A1, BDH2, C4A, FABP7, GABBR2, GNAI3, PBXIP1, and PRKAR1B. AsymAD versus Control: APP, C4A, DMXL1, EXOC2, PITPNB, RABEP1, and VGF. Additional predictors: DNAJA3, PTBP2, SLC30A9, VAT1L, CROCC, PNP, SNCB, ENPP6, HAPLN2, PSMD4, and CMAS.

CONCLUSION

Biomarkers were dynamically separable across disease stages. Predictive proteins were significantly enriched to sugar metabolism.

Apolipoprotein E knockout may affect cognitive function in D-galactose-induced aging mice through the gut microbiota–brain axis

The gut microbiota plays an important role in central nervous system (CNS) disorders. Apolipoprotein E (ApoE) can affect the composition of the gut microbiota and is closely related to the CNS. However, the mechanism by which ApoE affects cognitive dysfunction through the gut microbiota–brain axis has thus far not been investigated. In this study, we used wild-type mice and ApoE knockout (ApoE^–/–) mice to replicate the aging model and examined the effects of ApoE deletion on cognitive function, hippocampal ultrastructure, synaptophysin (SYP) and postsynaptic density 95 (PSD-95) in aging mice. We also explored whether ApoE deletion affects the gut microbiota and the metabolite profile of the hippocampus in aging mice and finally examined the effect of ApoE deletion on lipids and oxidative stress in aging mice. The results showed that the deletion of ApoE aggravated cognitive dysfunction, hippocampal synaptic ultrastructural damage and dysregulation of SYP and PSD-95 expression in aging mice. Furthermore, ApoE deletion reduced gut microbial makeup in aging mice. Further studies showed that ApoE deletion altered the hippocampal metabolic profile and aggravated dyslipidemia and oxidative stress in aging mice. In brief, our findings suggest that loss of ApoE alters the composition of the gut microbiota, which in turn may affect cognitive function in aging mice through the gut microbiota–brain axis.