Next Article in Journal
Withdrawal-Induced Delirium in Opioid Dependence: A Systematic Review
Previous Article in Journal
Attentional Deficits Following Preterm Birth: A Systematic Review and Meta-Analysis
Previous Article in Special Issue
Contralateral Structure and Molecular Response to Severe Unilateral Brain Injury
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Brain Sciences
Volume 15
Issue 10
10.3390/brainsci15101117
brainsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Aging, Rather than Genotype, Is the Principal Contributor to Differential Gene Expression Within Targeted Replacement APOE2, APOE3, and APOE4 Mouse Brain

by
Amanda Labuza
1,2,
Harshitha Pidikiti
1,
Melissa J. Alldred
1,2,
Kyrillos W. Ibrahim
1,
Katherine Y. Peng
1,2,
Jonathan Pasato
1,
Adriana Heguy
3,4,
Paul M. Mathews
1,2,5 and
Stephen D. Ginsberg
1,2,5,6,*
1
Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY 10962, USA
2
Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA
3
Genome Technology Center, New York University Grossman School of Medicine, New York, NY 10016, USA
4
Department of Pathology, New York University Grossman School of Medicine, New York, NY 10016, USA
5
NYU Neuroscience Institute, New York University Grossman School of Medicine, New York, NY 10016, USA
6
Department of Neuroscience & Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA
*
Author to whom correspondence should be addressed.
Brain Sci. 2025, 15(10), 1117; https://doi.org/10.3390/brainsci15101117
Submission received: 14 September 2025 / Revised: 9 October 2025 / Accepted: 11 October 2025 / Published: 17 October 2025
(This article belongs to the Special Issue Cellular and Molecular Mechanisms Regulating Neuronal Function, Homeostasis, and Disease)
Browse Figures
Review Reports Versions Notes

Abstract

Background: Apolipoprotein E (APOE) is the strongest genetic risk determinant for late-onset Alzheimer’s disease (AD). The APOE3 allele is risk-neutral, the APOE4 allele increases the risk of developing AD, and the APOE2 allele is neuroprotective. Methods: We utilized RNA sequencing of hemi-brains from a mouse model homozygous for each of these humanized APOE alleles to study gene expression profiles between mice aged 12 months of age (MO) and 18 MO, independent of β-amyloid and tau pathology. Results: More than half of the differentially expressed genes (DEGs) within each genotype were shared with at least one other APOE allele, including 1610 DEGs that were shared across the three genotypes. These DEGs represent changes driven by aging rather than APOE genotype. Aging induced DEGs and biological pathways involving metabolism, synaptic function, and protein synthesis, among others. Alterations in these pathways were also identified by DEGs unique to APOE4, suggesting that the APOE4 allele drives the aging phenotype. In contrast, fewer pathways were identified from DEGs unique to APOE2 or APOE3. Conclusions: Transcriptomic results suggest that the most significant impact on brain-level expression changes in humanized APOE mice is aging and that APOE4 exacerbates this process. These in vivo findings within an established model system are consistent with brain aging being the greatest risk factor for AD and suggest that APOE4 expression promotes an aging phenotype in the brain that interacts with, and contributes to, aging-driven AD risk. Results reinforce the impact age and APOE allele contribute to AD and age-related neurodegeneration, and foster greater mechanistic understanding as well as inform therapeutic intervention.

1. Introduction

The apolipoprotein E gene (APOE) has three different polymorphic alleles in humans, termed APOE2, APOE3, and APOE4 [1,2]. These three alleles correspond to variations in the amino acids at residues 112 and 158 of the protein, with APOE3 having a cysteine at position 112 and an arginine at position 158, while APOE2 has cysteine and APOE4 has arginine at both sites, respectively [1,2]. The APOE3 allele is the most common, with an estimated 79% global population, with 8% and 14% having an APOE2 or APOE4 allele, respectively [3,4].
The APOE gene has been identified as the strongest genetic risk factor for late-onset Alzheimer’s disease (AD), including in recent, large-scale, genome-wide association studies (GWAS), GWAS meta-analyses, and population-based meta-analyses [5,6,7,8]. APOE4 increases the risk of AD [5,8,9,10], while APOE2 may be protective against AD [8,11,12], and APOE3 is neutral. A single copy of APOE4 leads to a ~4-fold greater likelihood of developing AD compared to APOE3/APOE3 carriers, while two copies of APOE4 result in a ~12-fold increase [13]. The risk of AD is greater for heterozygous female APOE4 carriers than heterozygous males, while APOE4 homozygosity carries an equal risk for both sexes [10,14,15]. APOE4 also worsens neuropathological changes in AD patients compared to APOE3, including severe cerebral amyloid angiopathy and increased tau pathology encompassing more extensive regions within the brain [16,17]. Several of these neuropathological changes are increased with APOE4 and can occur independently of cognitive decline [18,19,20,21]. A USA cohort of 5000 subjects confirmed that APOE2 homozygosity had both lower odds of developing AD and milder neuropathological changes than APOE2/3 or APOE3/3 carriers [22].
Independent of AD, APOE has been shown to play a role in neurodegeneration [23,24]. APOE4 has been associated with the severity of TDP-43 pathology [25,26], Lewy body disease (LBD) [27,28], tau-mediated neurodegeneration [29], and metabolic syndrome (MetS) [30]. APOE mouse models demonstrate APOE4 expression alters the neuronal endosomal–lysosomal system [31,32]. These APOE4 effects are consistent with APOE4 driving aging-dependent changes in diverse cellular pathways [23,33,34,35]. In contrast to APOE4, APOE2 expression is protective against LBD [27] and correlates with longevity, independent of AD pathology [36,37].
The strong correlation between the APOE allele and neuropathological changes has led to several therapies targeting APOE, summarized by Serrano-Pozo [17,38], though none have been successful to date. It is therefore of interest to study APOE in the context of AD and Alzheimer’s disease-related dementias (ADRDs), along with the upstream and downstream pathways that APOE acts upon.
Multi-’omics have been used to study mechanisms of APOE alleles in neuronal health [31,32,35,39]. Our collaborative lab group reported brain mRNA expression differences driven by APOE genotype are related to the endosomal–lysosomal system in humanized APOE targeted replacement mice [31,32]. RNA sequencing (RNA-seq) of induced pluripotent stem cell (iPSC)-derived neurons, human iPSC (hiPSC)-derived glia, and cerebrospinal fluid (CSF) proteomics in human patients have revealed changes in synaptic function in APOE4 compared to APOE3 [40,41,42,43]. Since human studies intertwine genetic and environmental factors, including diet, lifestyle, aging, and AD pathology, we used a well-characterized in vivo humanized APOE knock-in mouse model comprised of targeted replacement ApoE2, ApoE3, and ApoE4 [32]. We combined brain-level RNA-seq with bioinformatic inquiry to interrogate changes in gene profiles due to aging in the context of APOE genotype. We hypothesize changes in gene expression will identify novel pathways to better understand the underlying mechanistic role(s) that APOE genotype plays in aging and AD dementia risk, which may help direct therapeutic intervention.

2. Materials and Methods

2.1. Animals

Mice used in this study were group-housed under controlled temperature and lighting conditions with same-sex littermates. Mice were given free access to food and water. Mice were originally developed on a C57Bl/6 background to express the human APOE gene in place of the mouse Apoe gene under the endogenous murine promoter [44]. This allowed for expression of human APOE at physiologically regulated levels in the same temporal and spatial pattern as endogenous murine Apoe [44]. APOE2, APOE3, and APOE4 refer to mice homozygous for either human APOE2, APOE3, or APOE4 (RRID:MGI:3695702, RRID:MGI:3695698, and RRID:MGI:4355228), respectively. Murine APOE genotype was confirmed using restriction fragment length polymorphism analysis [45]. The 12 months of age (MO) cohort had 3 male/3 female mice per genotype and the 18 MO cohort had 5 female mice per genotype.

2.2. RNA Sequencing

RNA was purified from the frozen left hemibrains of mice using the miRNeasy mini kit (Qiagen, Germantown, MD, USA). Right hemibrains were used for biochemistry as described previously [32,46]. Validation for quality and quantity was performed utilizing the RNA6000 Nano kit on an Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA, USA). RNA was stored at −80 °C prior to use. All RNA samples had an RNA integrity number (RIN) of 9 or higher. RNA-seq libraries were prepared on a Biomek Fxp liquid handler robot (Beckman, Indianapolis, IN, USA) using the TruSeq Stranded mRNA Library Prep kit (#20020595; Illumina, San Diego, CA, USA), starting with 500 ng of total RNA with 10 cycles of final PCR amplification. Equimolar pooling was performed on all libraries with single-read 50 base pair sequencing performed across three lanes of an Illumina HiSeq4000 flow cell at the New York University Grossman School of Medicine Genome Technology Center (NYUGSOM GTC).

2.3. Sequence Analysis

FASTQ files from the same biological sample with multiple lanes were merged prior to pre-processing. Since the lanes were technical replicates from the same run, they did not represent different batches [47,48,49]. Quality control of the raw reads was performed using FastQC (v0.11.9) [50]. Trimmomatic (v0.39) [51] was used to trim raw reads with any adapter contamination. This step was skipped if there was no adapter contamination. STAR aligner (v2.7.10a) [52] was used to generate a genome index and align trimmed reads to the indexed murine reference genome (GRCm39) in two-pass basic mode with default parameters [47,48,49].

2.4. Statistical Analysis and Bioinformatics

RSEM (v1.3.3) was used to quantify gene-level expression on aligned reads. Picard (v2.27.1) [53] was used to compute post-alignment and RNA-seq-specific quality metrics. Gene-level counts from RSEM [54] were used to perform differential gene expression analysis. Genes with over 0.1 counts per million in more than 40% of the samples were retained. TMM normalization was implemented by edgeR [55], accounting for any compositional differences between libraries. This step removes lowly expressed genes that provide little evidence of differential expression and increase statistical errors and false discovery rates [56,57].
Gene expression analysis was performed using the DREAM pipeline [58] built on top of the limma-voom pipeline from the variancePartition package [59]. In addition to genotype and age, the following variables were included as covariates: intergenic percentage, intronic percentage, and usable base percentage. Except for genotype and age, other covariates were computed from the RNA-seq reads by Picard. Differentially expressed genes (DEGs) were identified using the topTable function, with statistical significance defined as p-value < 0.05. DEGs were used for subsequent functional enrichment analyses.
Ingenuity Pathway Analysis (IPA; Qiagen, Germantown, MD, USA), Kyoto Encyclopedia of Genes and Genomes (KEGG) [60], and Gene Ontology (GO) bioinformatic pathway analyses were employed using DEGs, with p < 0.05 as the significance cutoff [47,48,49]. For input of the 1610 pan APOE DEGs in GO analysis, p-values from APOE4 DEGs were used as APOE4 DEGs had the highest significance of the three genotypes. Subdivisions of GO categories only included brain-related pathways in the “development” section, excluding pathways involved in peripheral organ development.

3. Results

The two greatest risk determinants for AD are aging and APOE genotype. We sought to examine the interface of these risk factors in the brains of mice homozygous for human APOE2, APOE3, or APOE4. These mice do not develop β-amyloid or tau pathology [3,17,23], but have been previously shown to have AD/ADRD-relevant neuronal pathologies driven by APOE4 expression, often appearing in an age-dependent fashion [31,61]. For each genotype, RNA was isolated from hemi-brains of mice at 12 MO (n = 6 per genotype) and 18 MO (n = 5 per genotype) and utilized for RNA-seq [32,46]. Sequences were interrogated using the DREAM pipeline [58] to identify DEGs between 12 MO and 18 MO mice within each genotype. The majority of DEGs identified were protein-coding (APOE2 92.57%, APOE3 88.92%, and APOE4 94.22%).
APOE2 mice had 5111 DEGs between 12 MO and 18 MO mice (Figure 1A), with 2344 DEGs upregulated by age and 2767 DEGs downregulated by age. APOE3 mice had 3069 DEGs between 12 MO and 18 MO mice (Figure 1B), with 1663 DEGs upregulated by age and 1406 DEGs downregulated by age. APOE4 mice had 6466 DEGs between 12 MO and 18 MO mice (Figure 1C), with 3122 DEGs upregulated by age and 3344 DEGs downregulated by age.
Comparing changes by age, large overlaps were observed between genotypes (Figure 2A), with 1610 DEGs altered from 12 MO to 18 MO mice in all three genotypes. This suggests that these 1610 DEGs, which we termed “pan APOE”, are dependent on aging rather than APOE genotype. Additionally, 1890 DEGs were shared between aging in APOE2 and APOE4. Virtually all of these shared DEGs (1605/1610: 99.7% and 1887/1890: 99.8%) were changed in the same direction with age (Figure 2B,C). The few (seven DEGs) whose aging effect changed direction between genotypes are listed in Supplementary Table S1.
To understand how genes differentiated by age may affect cellular function, IPA was employed to interrogate biological pathways. Interestingly, when looking at aging between APOE genotypes, 12 of the top 20 canonical pathways were shared between each genotype, and all but three pathways were significantly changed in every genotype per age comparison (Figure 3A). Using the 1610 pan APOE DEGs, the top 20 IPA canonical pathways were incorporated in the 27 pathways listed in Figure 3. Only four pathways changed direction between genotypes. For example, “Signaling by ROBO Receptors” was downregulated in APOE2 with aging but upregulated in APOE3 and APOE4 with aging. “Class I MHC mediated antigen processing and presentation” was upregulated in APOE3 but downregulated in APOE2 and APOE4 mice. The majority of the top 20 pathways were relevant to brain function, including the neurodegenerative pathway “Huntington’s Disease Signaling” (Figure 3A). A total of 469 common DEGs were found across aging in APOE2, APOE3, and APOE4 mice. The top 120 common DEGs were mechanistic drivers in at least 12 of the 27 canonical pathways (Figure 3A). These are represented as a circle plot (Figure 3B), depicting overlap between common driver DEGs and top IPA pathways. APOE4 exhibited the highest number of unique DEGs within these pathways (Supplementary Figure S1). APOE4-unique DEGs also contributed to complex protein–protein interaction (PPI) networks (Supplementary Figure S1), including those indicative of oxidative stress and metabolic dysfunction, likely adding to the impact of aging. Aggregating these results, aging is a larger driver of transcriptomic changes in the brain than APOE genotype in the absence of β-amyloid or tau pathology, with the APOE4 allele exacerbating the aging program compared to APOE2 and APOE3.
To study changes in pathways altered by aging specific to each APOE allele, analysis was conducted using DEGs uniquely altered by genotype. A curated list of the top relevant pathways altered by aging was generated for each genotype. APOE2, which is protective against neurodegeneration [11,12,62], had several lipid pathways downregulated during aging, including “Regulation of Lipid Metabolism by PPARalpha” and “Superpathway of Inositol Phosphate Compounds” (Figure 4), although a broad trend for APOE2-specific pathways was not discernible. In APOE3 mice, several signaling pathways, including “RHOGDI Signaling” and “S100 Family Signaling Pathway”, were altered by aging (Figure 5). DEGs unique to APOE4 correlate with increased risk of neurodegeneration [9,11], revealed the largest number of pathways specific to synaptic and neuronal signaling (Figure 6), with most being upregulated, including “Synaptic Long-Term Potentiation”, “Synaptogenesis Signaling Pathway”, and “Serotonin Receptor Signaling”. Notably, APOE4 was the only genotype that had enough unique DEGs altered from 12 MO to 18 MO to independently activate a neurodegenerative pathway (e.g., “Huntington’s Disease Signaling”) as a top pathway, with more than 6-fold significance compared to APOE2- or APOE3-unique DEGs.
KEGG was also used to interrogate top pathways changed by aging between APOE genotypes (Table 1). A total of 58 pathways were altered in APOE2, 19 pathways in APOE3, and 63 pathways in APOE4. As with IPA, the majority of pathways changed by aging in APOE3 were common with APOE2 and APOE4, yielding 18/19 (94.7%) overlapping pathways. A total of 39 pathways were shared between APOE2 and APOE4 age comparisons (9 shown in Table 1), likely being driven by the 1890 aging DEGs common only between APOE2 and APOE4. Among pathways shared between all genotypes, six were related to neurodegeneration, including “Alzheimer’s Disease” and “Huntington’s Disease”. There were not enough DEGs altered by age specifically in APOE2 and APOE3 mice to identify significant KEGG pathways. A total of 18 KEGG pathways were identified through aging DEGs unique to APOE4. Of these, three were shared between pathways that were significant when all DEGs in either APOE2, APOE3, or APOE4 were interrogated (Table 1), including “Pathways of Neurodegeneration-Multiple Disease”. An additional nine pathways from unique APOE4 DEGs overlapped with pathways from all APOE2 DEGs (e.g., not just unique APOE2 DEGs changed by aging). Therefore, six pathways altered by unique APOE4 DEGs were specific to this genotype.
GO analysis was employed to evaluate biological processes altered by aging between genotypes. Similar to IPA and KEGG, pan APOE DEGs accounted for the majority of biological processes enriched for each genotype (Figure 7A). Accordingly, GO processes were identified using DEGs altered in aging unique to each genotype, termed “unique APOE2”, “unique APOE3”, and “unique APOE4” (Figure 7B). Unique APOE4 processes were similar in distribution to GO processes from pan APOE DEGs. Processes categorized in autophagy and synaptic pathways found in unique APOE4 and pan APOE assessments were compared and contrasted to unique APOE2 DEGs and unique APOE3 DEGs. To account for the larger number of DEGs altered by aging unique to APOE4 compared to APOE2 or APOE3, the percentage of each category was assessed (Figure 7C–F). Both in terms of total number of processes and percentage of total processes, pan APOE and unique APOE4 were similar (Figure 7B,C,F). Synaptic processes were parsed into subcategories (Figure 8, Supplementary Table S2). There were few synaptic processes identified by “unique APOE2” or “unique APOE3” aging DEGs. These identified processes were largely associated with synaptic vesicle or synaptic transmission and function, with only one synaptic “other” process in “unique APOE3”. APOE4 aging DEGs were involved in synaptic processes spanning all subcategories (Figure 8), including 61 processes identified by pan APOE DEGs and 93 processes identified in “unique APOE4” DEGs. The largest subcategory was synaptic transmission and function. Therefore, pan APOE and unique APOE4 DEGs were most prominent in this assessment of transcriptomic changes in APOE targeted replacement mice.

4. Discussion

Mice with homozygous humanized targeted replacement APOE genes were utilized to interrogate DEGs and biological pathways from 12 MO and 18 MO cohorts. A large proportion of DEGs differentiated by age were common among all genotypes. Therefore, we postulate aging has a greater effect on changes in gene expression than APOE genotype. Independent studies support this contention, revealing significant interactions between age, APOE genotype, and sex, with age having the greatest impact [10]. To assess generalizability of the discovered aging ApoE4 genes in the present report, we compared DEGs to a publicly available dataset (GEO: GSE212343) from Lee et al. (2023) [39]. Although utilizing similar targeted replacement APOE4 mice (e.g., Taconic female humanized ApoE4 model), the whole brain bulk RNA-seq dataset from Lee et al. [39] revealed fewer DEGs than the present report, which may have been due to a variety of factors, including the number of samples utilized and other technical and/or methodological considerations. Despite experimental differences between studies, ~32.2% of the DEGs from Lee et al. [39] overlapped with the aging DEGs in APOE4 mice from 12 MO to 18 MO mice, with >81% showing convergent gene expression (e.g., changing in the same direction: either upregulation or downregulation). This initial cross-study comparison provides an interesting validation of a subset of aging APOE4 genes found in the present assessment and provides the impetus to continue comparing and contrasting multi-‘omics study results from humanized APOE mice for future mechanistic studies and potentially translational findings with therapeutic implications.
A total of 1610 DEGs were commonly differentiated between 12 MO versus 18 MO mice among all genotypes, which we termed pan APOE DEGs. This amounts to 52.5% of all APOE3 DEGs being pan APOE DEGs, with 24.0% of DEGs unique to APOE3. Likewise, only 23.5% of APOE2 DEGs and 41.1% of APOE4 DEGs were unique to respective genotypes (Figure 2). While the percentage was larger in APOE4, this was expected due to the greater overall number of APOE4 DEGs. We propose this is due to APOE4 exacerbating the effects of aging (e.g., Figure 3B and Supplementary Figure S1). Overall, the subcategories of biological processes in GO analysis of the pan APOE DEGs more closely resembled unique APOE4 processes than unique APOE2 or unique APOE3 processes, including synaptic pathways (Figure 8). As age is the greatest risk factor for AD [33] and APOE4 is the largest genetic risk factor for late-onset AD [5,6], we posit APOE4 increases aging sequelae, rather than APOE4 directly influencing AD pathology. This is commensurate with the hypothesis that APOE plays a larger role in the context of age-related cognitive impairment, rather than solely serving as a risk factor for AD/ADRD [34,63].
The present results suggest APOE4 contributes to β-amyloid burden, independent of cognitive decline [19]. Our hypothesis is supported by APOE4 increasing the risk of several age-related disorders, including cardiovascular disease, type 2 diabetes mellitus [64], LBD [20,21,27], TDP-43 pathology severity [25,26], and cognitive decline in late Parkinson’s disease [65,66]. Conversely, APOE2 may be protective against aging, while APOE3 remains a neutral allele. APOE2 is protective in the context of AD [11,12,22]. APOE2 is also protective in other age-related diseases, including cerebral amyloid angiopathy [16], atherosclerosis [67], and LBD [27]. APOE2 carriers may develop a dementing illness 10 years later than APOE4 carriers [68] and have higher longevity, independent of AD pathology [62].
Via bioinformatic inquiry, we explored APOE genotype-specific pathways. Although IPA analysis covers a wide range of biologically relevant pathways beyond brain-themed pathways, the top 20 most dysregulated pathways in all genotypes are relevant to brain function (Figure 3). The largest group of dysregulated pathways was in the context of aging, including protein synthesis (e.g., “Eukaryotic Translation Initiation”, “RNA Polymerase II Transcription”, and “Processing of Caped Intron-Containing Pre-mRNA”, among others). KEGG analysis also indicated that “RNA polymerase”, “Ribosome”, and “Protein Processing in Endoplasmic Reticulum” are altered by aging, suggesting aging drives changes through alterations in transcription and translation. Alterations to metabolic pathways were also observed in aging, including “Oxidative Phosphorylation”, “Mitochondrial Dysfunction”, and “Respiratory Electron Transport” via IPA analysis (Figure 3) and “Oxidative Phosphorylation” via KEGG analysis (Table 1). These pathways are also significantly dysregulated across the progression of AD [49,69,70], with the APOE4 allele exacerbating MetS [30].
There were more alterations in gene expression by age specific to APOE4 compared to APOE2 or APOE3. APOE4 had the most DEGs (6466) from 12 MO to 18 MO mice and the greatest percentage of unique DEGs (41.1%) (Figure 2). Aging resulted in dysregulation of synaptic pathways across all analyses, including downregulation of “Synaptogenesis Signaling Pathway” in IPA, “Synaptic Vesicle Cycle” in KEGG, and 61 pathways with GO analysis (Figure 3 and Figure 7B, Table 1). This is expected, as APOE is localized in the synaptic milieu [71,72]. Aging DEGs unique to APOE4 also showed dysregulation of synaptic pathways. IPA displayed dysregulation of synaptic pathways in the top 20 pathways unique to APOE4 (Figure 6), including “Synaptic Long-Term Potentiation” and “Synaptogenesis Signaling Pathway”. Among the six KEGG pathways unique to APOE4, “Dopaminergic Signaling” was dysregulated (Table 1). GO analysis showed 93 synaptic biological processes specific to APOE4, compared to 5 and 8 biological processes in APOE2 and APOE3, respectively (Supplementary Table S2). There were more synaptic processes unique to APOE4 than those common between all genotypes, with the most in the “Transmission and Function” category (Figure 8).
Profound synaptic pathway dysfunction in the context of APOE4 is consistent with literature that indicates APOE4 decreases synaptic plasticity [72], synaptic density [73], and reduces mitochondrial capacity in astrocytes [74,75]. APOE4-targeted replacement mice, similar to those used in this study, have reduced spine density [74] compared to APOE2 or APOE3 mice at multiple timepoints. APOE4 mice also show impaired memory, as measured by the Morris water maze [76,77]. Similar to pathways identified herein, APOE4 mice have decreased mitochondrial function, ATP production, and mitochondrial density [75], as well as disrupted receptor recycling and reduced synaptic plasticity [73]. In addition to mouse models, hiPSC-derived neurons demonstrate APOE4 disrupts synaptic formation and neurotransmission pathways [40], decreases synaptic integrity [78], and alters neuronal excitability [35,79].
Limitations of the present study include a lack of understanding of how APOE drives downstream effects within the brain, especially in the context of hallmark AD/ADRD pathology (e.g., β-amyloid and tau), which is beyond the scope of this investigation. The lack of male mice in the 18 MO cohort is a potential confound and will be targeted in future studies. Importantly, the experimental design of the present study included aged females, which is translationally relevant as the ApoE AD/ADRD risk phenotype is stronger in females [10,13]. Thus, we were able to interrogate a high-risk group in humanized APOE murine models in the context of aging. Notably, no sex differences were identified in the 12 MO cohort or previous studies on these humanized APOE mouse models [32,46]. Further information on the relationship(s) between aging and APOE genotype can be gained from continuing to interrogate in vivo specific cell types (e.g., pyramidal neurons in hippocampus and cortical regions underlying memory and executive function) as well as interneurons and non-neuronal cell types (e.g., astrocytes and microglia) in vulnerable brain regions using single-cell or spatial transcriptomic profiling [39]. Limitations of bulk RNA-seq include loss of resolution for spatial and/or cell-specific drivers. This is a noted caveat of our initial study employing humanized APOE allele murine hemi-brain homogenates as the input sources. Studies have begun to compare profiling of bulk RNA-seq to single-cell populations in a subset of non-neuronal phenotypes [39]. To date, investigations of aging in the context of APOE alleles have limited resolution for neuronal changes or spatially derived network alterations. As such, single-population neuronal and/or spatial-derived validation is key for future studies in these well-established murine models.

5. Conclusions

The present findings demonstrate age has a greater effect on gene dysregulation than APOE genotype. Moreover, we show differential gene expression and mechanistic pathways underlying alterations caused by aging in the context of the APOE allele. Few changes are seen specific to APOE3, indicating that this common allele is neutral. APOE4 exacerbates aging effects, notably in synaptic pathways. We also identified underlying gene expression changes directly impacting synaptic dysregulation in the context of the APOE4 allele in this in vivo model system. Taken together, these data provide mechanistic and therapeutic insight into both aging and AD.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/brainsci15101117/s1: Supplementary Figure S1. STRING analysis in Cytoscape (v3.10.3) was performed on APOE4-unique DEGs derived from the top 20 canonical pathways. Of the 446 DEGs, 441 proteins were queried for PPIs at a stringency of a 0.6 cutoff. A total of 368 DEGs displayed 1 or more PPIs. An additional 73 DEGs were not associated with PPIs. Several PPI clusters were evident with close interactions, including oxidative phosphorylation dysregulation (insert), commensurate with findings of oxidative phosphorylation pathway defects in AD/ADRD models and postmortem human brain. Supplementary Table S1. Aging DEGs common between genotypes that changed direction. DEGs common in aging between APOE2, APOE3, and APOE4 were identified. DEGs that did not upregulate or downregulate uniformly across genotypes are listed. Both logFC and p-value of each DEG in each genotype comparison are listed. Supplementary Table S2. Biological processes per GO category and subcategory. GO analysis yielded several biological processes for each comparison, which were categorized into several groups: autophagy, axon, -cytosis (exocytosis, endocytosis, and phagocytosis), dendritic, developmental, neurotransmitter, protein, receptor, RNA, signal transduction, and synaptic. All other biological processes were categorized as “Other”. The number of biological processes in each category and the total for each comparison are listed. Select categories (developmental, protein, signal, and synaptic) were further broken down into subcategories. Values for APOE4 were used for the pan APOE analysis.

Author Contributions

Conceptualization: A.L., P.M.M. and S.D.G.; Data curation: A.L., H.P., M.J.A. and K.W.I.; Formal analysis: A.L., H.P., M.J.A. and K.W.I.; Funding acquisition: P.M.M. and S.D.G., Investigation: A.L., H.P., M.J.A., K.W.I., K.Y.P., J.P., P.M.M. and S.D.G.; Methodology: A.L., H.P., K.Y.P., P.M.M. and S.D.G.; Project administration: P.M.M. and S.D.G.; Software: H.P. and K.W.I.; Supervision: P.M.M. and S.D.G.; Validation A.L. and H.P.; Visualization: A.L., H.P., M.J.A. and K.W.I.; Writing—original draft: A.L., H.P., M.J.A., P.M.M. and S.D.G.; Writing—review and editing: A.L., H.P., M.J.A., K.W.I., K.Y.P., J.P., A.H., P.M.M. and S.D.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by grants AG014449, AG017617, AG072599, AG074004, AG077103, and AG085572 from the National Institutes of Health.

Institutional Review Board Statement

Animal protocols were originally approved 9/2020 and were renewed after review every three years, with the last review being 9/2023 by the Nathan Kline Institute/NYU Grossman School of Medicine IACUC (approval code: AP2020-663, approval date: 3 September 2020) in accordance with NIH guidelines.

Informed Consent Statement

Not applicable, no human component.

Data Availability Statement

The datasets supporting the conclusions of this article are available as follows. RNA-seq data analyzed within this study are available from GEO with 12 MO data accessible through GEO Series accession number GSE188267 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE188267, accessed on 10 October 2025) and 18 MO data accessible through GEO Series accession number GSE234711 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE234711, accessed on 10 October 2025) or from the corresponding author upon request.

Acknowledgments

We thank Arthur Saltzman, for technical assistance.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ADAlzheimer’s disease
ADRDsAlzheimer’s disease-related dementias
ApoEApolipoprotein E
CSFCerebrospinal fluid
DEGsDifferentially expressed genes
GOGene Ontology
GTCGenome Technology Center
GWASGenome-wide association studies
hiPSCHuman-induced pluripotent stem cell
IPAIngenuity Pathway Analysis
iPSCInduced pluripotent stem cell
KEGGKyoto Encyclopedia of Genes and Genomes
LBDLewy body disease
logFCLog-fold change
MOMonths of age
NYUGSOMNew York University Grossman School of Medicine
PPIsProtein–protein interactions
RNA-seqRNA sequencing
RINRNA integrity number

References

  1. Mahley, R.W. Apolipoprotein E: From cardiovascular disease to neurodegenerative disorders. J. Mol. Med. 2016, 94, 739–746. [Google Scholar] [CrossRef]
  2. Koutsodendris, N.; Nelson, M.R.; Rao, A.; Huang, Y. Apolipoprotein E and Alzheimer’s Disease: Findings, Hypotheses, and Potential Mechanisms. Annu. Rev. Pathol. 2022, 17, 73–99. [Google Scholar] [CrossRef] [PubMed]
  3. Liu, C.C.; Kanekiyo, T.; Xu, H.; Bu, G. Apolipoprotein E and Alzheimer disease: Risk, mechanisms and therapy. Nat. Rev. Neurol. 2013, 9, 106–118. [Google Scholar] [CrossRef]
  4. Bertram, L.; McQueen, M.B.; Mullin, K.; Blacker, D.; Tanzi, R.E. Systematic meta-analyses of Alzheimer disease genetic association studies: The AlzGene database. Nat. Genet. 2007, 39, 17–23. [Google Scholar] [CrossRef]
  5. Corder, E.H.; Saunders, A.M.; Strittmatter, W.J.; Schmechel, D.E.; Gaskell, P.C.; Small, G.W.; Roses, A.D.; Haines, J.L.; Pericak-Vance, M.A. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 1993, 261, 921–923. [Google Scholar] [CrossRef] [PubMed]
  6. Kunkle, B.W.; Grenier-Boley, B.; Sims, R.; Bis, J.C.; Damotte, V.; Naj, A.C.; Boland, A.; Vronskaya, M.; van der Lee, S.J.; Amlie-Wolf, A.; et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 2019, 51, 414–430. [Google Scholar] [CrossRef]
  7. Andrews, S.J.; Fulton-Howard, B.; Goate, A. Interpretation of risk loci from genome-wide association studies of Alzheimer’s disease. Lancet Neurol. 2020, 19, 326–335. [Google Scholar] [CrossRef]
  8. Abrego-Guandique, D.M.; Saraceno, G.F.; Cannataro, R.; Manzzo de Burnside, M.; Caroleo, M.C.; Cione, E. Apolipoprotein E and Alzheimer’s Disease in Italian Population: Systematic Review and Meta-Analysis. Brain Sci. 2024, 14, 908. [Google Scholar] [CrossRef]
  9. Chartier-Harlin, M.C.; Parfitt, M.; Legrain, S.; Pérez-Tur, J.; Brousseau, T.; Evans, A.; Berr, C.; Vidal, O.; Roques, P.; Gourlet, V.; et al. Apolipoprotein E, epsilon 4 allele as a major risk factor for sporadic early and late-onset forms of Alzheimer’s disease: Analysis of the 19q13.2 chromosomal region. Hum. Mol. Genet. 1994, 3, 569–574. [Google Scholar] [CrossRef] [PubMed]
  10. Zhao, N.; Ren, Y.; Yamazaki, Y.; Qiao, W.; Li, F.; Felton, L.M.; Mahmoudiandehkordi, S.; Kueider-Paisley, A.; Sonoustoun, B.; Arnold, M.; et al. Alzheimer’s Risk Factors Age, APOE Genotype, and Sex Drive Distinct Molecular Pathways. Neuron 2020, 106, 727–742.e6. [Google Scholar] [CrossRef]
  11. Corder, E.H.; Saunders, A.M.; Risch, N.J.; Strittmatter, W.J.; Schmechel, D.E.; Gaskell, P.C., Jr.; Rimmler, J.B.; Locke, P.A.; Conneally, P.M.; Schmader, K.E.; et al. Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease. Nat. Genet. 1994, 7, 180–184. [Google Scholar] [CrossRef]
  12. Panza, F.; Solfrizzi, V.; Torres, F.; Mastroianni, F.; Colacicco, A.M.; Basile, A.M.; Capurso, C.; D’Introno, A.; Del Parigi, A.; Capurso, A. Apolipoprotein E in Southern Italy: Protective effect of epsilon 2 allele in early- and late-onset sporadic Alzheimer’s disease. Neurosci. Lett. 2000, 292, 79–82. [Google Scholar] [CrossRef] [PubMed]
  13. Farrer, L.A.; Cupples, L.A.; Haines, J.L.; Hyman, B.; Kukull, W.A.; Mayeux, R.; Myers, R.H.; Pericak-Vance, M.A.; Risch, N.; van Duijn, C.M. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. JAMA 1997, 278, 1349–1356. [Google Scholar] [CrossRef] [PubMed]
  14. Belloy, M.E.; Napolioni, V.; Greicius, M.D. A Quarter Century of APOE and Alzheimer’s Disease: Progress to Date and the Path Forward. Neuron 2019, 101, 820–838. [Google Scholar] [CrossRef]
  15. Riedel, B.C.; Thompson, P.M.; Brinton, R.D. Age, APOE and sex: Triad of risk of Alzheimer’s disease. J. Steroid Biochem. Mol. Biol. 2016, 160, 134–147. [Google Scholar] [CrossRef]
  16. Serrano-Pozo, A.; Qian, J.; Monsell, S.E.; Betensky, R.A.; Hyman, B.T. APOEε2 is associated with milder clinical and pathological Alzheimer disease. Ann. Neurol. 2015, 77, 917–929. [Google Scholar] [CrossRef]
  17. Serrano-Pozo, A.; Das, S.; Hyman, B.T. APOE and Alzheimer’s disease: Advances in genetics, pathophysiology, and therapeutic approaches. Lancet Neurol. 2021, 20, 68–80. [Google Scholar] [CrossRef]
  18. Blanchard, J.W.; Bula, M.; Davila-Velderrain, J.; Akay, L.A.; Zhu, L.; Frank, A.; Victor, M.B.; Bonner, J.M.; Mathys, H.; Lin, Y.T.; et al. Reconstruction of the human blood-brain barrier in vitro reveals a pathogenic mechanism of APOE4 in pericytes. Nat. Med. 2020, 26, 952–963. [Google Scholar] [CrossRef]
  19. Salvadó, G.; Grothe, M.J.; Groot, C.; Moscoso, A.; Schöll, M.; Gispert, J.D.; Ossenkoppele, R. Differential associations of APOE-ε2 and APOE-ε4 alleles with PET-measured amyloid-β and tau deposition in older individuals without dementia. Eur. J. Nucl. Med. Mol. Imaging 2021, 48, 2212–2224. [Google Scholar] [CrossRef] [PubMed]
  20. Tsuang, D.; Leverenz, J.B.; Lopez, O.L.; Hamilton, R.L.; Bennett, D.A.; Schneider, J.A.; Buchman, A.S.; Larson, E.B.; Crane, P.K.; Kaye, J.A.; et al. APOE ε4 increases risk for dementia in pure synucleinopathies. JAMA Neurol. 2013, 70, 223–228. [Google Scholar] [CrossRef]
  21. Dickson, D.W.; Heckman, M.G.; Murray, M.E.; Soto, A.I.; Walton, R.L.; Diehl, N.N.; van Gerpen, J.A.; Uitti, R.J.; Wszolek, Z.K.; Ertekin-Taner, N.; et al. APOE ε4 is associated with severity of Lewy body pathology independent of Alzheimer pathology. Neurology 2018, 91, e1182–e1195. [Google Scholar] [CrossRef]
  22. Reiman, E.M.; Arboleda-Velasquez, J.F.; Quiroz, Y.T.; Huentelman, M.J.; Beach, T.G.; Caselli, R.J.; Chen, Y.; Su, Y.; Myers, A.J.; Hardy, J.; et al. Exceptionally low likelihood of Alzheimer’s dementia in APOE2 homozygotes from a 5000-person neuropathological study. Nat. Commun. 2020, 11, 667. [Google Scholar] [CrossRef]
  23. Martens, Y.A.; Zhao, N.; Liu, C.C.; Kanekiyo, T.; Yang, A.J.; Goate, A.M.; Holtzman, D.M.; Bu, G. ApoE Cascade Hypothesis in the pathogenesis of Alzheimer’s disease and related dementias. Neuron 2022, 110, 1304–1317. [Google Scholar] [CrossRef]
  24. Chen, Y.; Strickland, M.R.; Soranno, A.; Holtzman, D.M. Apolipoprotein E: Structural Insights and Links to Alzheimer Disease Pathogenesis. Neuron 2021, 109, 205–221. [Google Scholar] [CrossRef] [PubMed]
  25. Wennberg, A.M.; Tosakulwong, N.; Lesnick, T.G.; Murray, M.E.; Whitwell, J.L.; Liesinger, A.M.; Petrucelli, L.; Boeve, B.F.; Parisi, J.E.; Knopman, D.S.; et al. Association of Apolipoprotein E ε4 With Transactive Response DNA-Binding Protein 43. JAMA Neurol. 2018, 75, 1347–1354. [Google Scholar] [CrossRef] [PubMed]
  26. Yang, H.S.; Yu, L.; White, C.C.; Chibnik, L.B.; Chhatwal, J.P.; Sperling, R.A.; Bennett, D.A.; Schneider, J.A.; De Jager, P.L. Evaluation of TDP-43 proteinopathy and hippocampal sclerosis in relation to APOE ε4 haplotype status: A community-based cohort study. Lancet Neurol. 2018, 17, 773–781. [Google Scholar] [CrossRef]
  27. Berge, G.; Sando, S.B.; Rongve, A.; Aarsland, D.; White, L.R. Apolipoprotein E ε2 genotype delays onset of dementia with Lewy bodies in a Norwegian cohort. J. Neurol. Neurosurg. Psychiatry 2014, 85, 1227–1231. [Google Scholar] [CrossRef] [PubMed]
  28. Sabir, M.S.; Blauwendraat, C.; Ahmed, S.; Serrano, G.E.; Beach, T.G.; Perkins, M.; Rice, A.C.; Masliah, E.; Morris, C.M.; Pihlstrom, L.; et al. Assessment of APOE in atypical parkinsonism syndromes. Neurobiol. Dis. 2019, 127, 142–146. [Google Scholar] [CrossRef]
  29. Therriault, J.; Pascoal, T.A.; Benedet, A.L.; Tissot, C.; Savard, M.; Chamoun, M.; Lussier, F.; Kang, M.S.; Berzgin, G.; Wang, T.; et al. Frequency of Biologically Defined Alzheimer Disease in Relation to Age, Sex, APOE ε4, and Cognitive Impairment. Neurology 2021, 96, e975–e985. [Google Scholar] [CrossRef]
  30. Torres-Perez, E.; Ledesma, M.; Garcia-Sobreviela, M.P.; Leon-Latre, M.; Arbones-Mainar, J.M. Apolipoprotein E4 association with metabolic syndrome depends on body fatness. Atherosclerosis 2016, 245, 35–42. [Google Scholar] [CrossRef]
  31. Nuriel, T.; Peng, K.Y.; Ashok, A.; Dillman, A.A.; Figueroa, H.Y.; Apuzzo, J.; Ambat, J.; Levy, E.; Cookson, M.R.; Mathews, P.M.; et al. The Endosomal-Lysosomal Pathway is Dysregulated by APOE4 Expression in Vivo. Front. Neurosci. 2017, 11, 702. [Google Scholar] [CrossRef]
  32. Peng, K.Y.; Liemisa, B.; Pasato, J.; D’Acunzo, P.; Pawlik, M.; Heguy, A.; Penikalapati, S.C.; Labuza, A.; Pidikiti, H.; Alldred, M.J.; et al. Apolipoprotein E2 Expression Alters Endosomal Pathways in a Mouse Model With Increased Brain Exosome Levels During Aging. Traffic 2024, 25, e12937. [Google Scholar] [CrossRef] [PubMed]
  33. Hebert, L.E.; Bienias, J.L.; Aggarwal, N.T.; Wilson, R.S.; Bennett, D.A.; Shah, R.C.; Evans, D.A. Change in risk of Alzheimer disease over time. Neurology 2010, 75, 786–791. [Google Scholar] [CrossRef] [PubMed]
  34. Palmer, J.M.; Huentelman, M.; Ryan, L. More than just risk for Alzheimer’s disease: APOE ε4’s impact on the aging brain. Trends Neurosci. 2023, 46, 750–763. [Google Scholar] [CrossRef] [PubMed]
  35. Verduzco Espinoza, A.P.; Na, N.; Campanati, L.; Ngo, P.; Baldwin, K.K.; Cline, H.T. Microglia-to-neuron signaling links APOE4 and inflammation to enhanced neuronal lipid metabolism and network activity. Proc. Natl. Acad. Sci. USA 2025, 122, e2516103122. [Google Scholar] [CrossRef]
  36. Feng, J.; Xiang, L.; Wan, G.; Qi, K.; Sun, L.; Huang, Z.; Zheng, C.; Lv, Z.; Hu, C.; Yang, Z. Is APOEε3 a favourable factor for the longevity: An association study in Chinese population. J. Genet. 2011, 90, 343–347. [Google Scholar] [CrossRef]
  37. Shinohara, M.; Kanekiyo, T.; Tachibana, M.; Kurti, A.; Shinohara, M.; Fu, Y.; Zhao, J.; Han, X.; Sullivan, P.M.; Rebeck, G.W.; et al. APOE2 is associated with longevity independent of Alzheimer’s disease. Elife 2020, 9, e62199. [Google Scholar] [CrossRef]
  38. Raulin, A.C.; Doss, S.V.; Trottier, Z.A.; Ikezu, T.C.; Bu, G.; Liu, C.C. ApoE in Alzheimer’s disease: Pathophysiology and therapeutic strategies. Mol. Neurodegener. 2022, 17, 72. [Google Scholar] [CrossRef] [PubMed]
  39. Lee, S.; Devanney, N.A.; Golden, L.R.; Smith, C.T.; Schwartz, J.L.; Walsh, A.E.; Clarke, H.A.; Goulding, D.S.; Allenger, E.J.; Morillo-Segovia, G.; et al. APOE modulates microglial immunometabolism in response to age, amyloid pathology, and inflammatory challenge. Cell Rep. 2023, 42, 112196. [Google Scholar] [CrossRef]
  40. Lin, Y.T.; Seo, J.; Gao, F.; Feldman, H.M.; Wen, H.L.; Penney, J.; Cam, H.P.; Gjoneska, E.; Raja, W.K.; Cheng, J.; et al. APOE4 Causes Widespread Molecular and Cellular Alterations Associated With Alzheimer’s Disease Phenotypes in Human iPSC-Derived Brain Cell Types. Neuron 2018, 98, 1141–1154.e7. [Google Scholar] [CrossRef]
  41. He, L.; Davila-Velderrain, J.; Sumida, T.S.; Hafler, D.A.; Kellis, M.; Kulminski, A.M. NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data. Commun. Biol. 2021, 4, 629. [Google Scholar] [CrossRef]
  42. Krasemann, S.; Madore, C.; Cialic, R.; Baufeld, C.; Calcagno, N.; El Fatimy, R.; Beckers, L.; O’Loughlin, E.; Xu, Y.; Fanek, Z.; et al. The TREM2-APOE Pathway Drives the Transcriptional Phenotype of Dysfunctional Microglia in Neurodegenerative Diseases. Immunity 2017, 47, 566–581.e9. [Google Scholar] [CrossRef]
  43. Konijnenberg, E.; Tijms, B.M.; Gobom, J.; Dobricic, V.; Bos, I.; Vos, S.; Tsolaki, M.; Verhey, F.; Popp, J.; Martinez-Lage, P.; et al. APOE ε4 genotype-dependent cerebrospinal fluid proteomic signatures in Alzheimer’s disease. Alzheimers Res. Ther. 2020, 12, 65. [Google Scholar] [CrossRef]
  44. Sullivan, P.M.; Mezdour, H.; Aratani, Y.; Knouff, C.; Najib, J.; Reddick, R.L.; Quarfordt, S.H.; Maeda, N. Targeted replacement of the mouse apolipoprotein E gene with the common human APOE3 allele enhances diet-induced hypercholesterolemia and atherosclerosis. J. Biol. Chem. 1997, 272, 17972–17980. [Google Scholar] [CrossRef] [PubMed]
  45. Hixson, J.E.; Vernier, D.T. Restriction isotyping of human apolipoprotein E by gene amplification and cleavage with HhaI. J. Lipid Res. 1990, 31, 545–548. [Google Scholar] [CrossRef]
  46. Peng, K.Y.; Pérez-González, R.; Alldred, M.J.; Goulbourne, C.N.; Morales-Corraliza, J.; Saito, M.; Saito, M.; Ginsberg, S.D.; Mathews, P.M.; Levy, E. Apolipoprotein E4 genotype compromises brain exosome production. Brain 2019, 142, 163–175. [Google Scholar] [CrossRef] [PubMed]
  47. Alldred, M.J.; Ibrahim, K.W.; Pidikiti, H.; Chiosis, G.; Mufson, E.J.; Stutzmann, G.E.; Ginsberg, S.D. Down syndrome frontal cortex layer III and layer V pyramidal neurons exhibit lamina specific degeneration in aged individuals. Acta Neuropathol. Commun. 2024, 12, 182. [Google Scholar] [CrossRef]
  48. Alldred, M.J.; Ibrahim, K.W.; Pidikiti, H.; Lee, S.H.; Heguy, A.; Chiosis, G.; Mufson, E.J.; Stutzmann, G.E.; Ginsberg, S.D. Profiling hippocampal neuronal populations reveals unique gene expression mosaics reflective of connectivity-based degeneration in the Ts65Dn mouse model of Down syndrome and Alzheimer’s disease. Front. Mol. Neurosci. 2025, 18, 1546375. [Google Scholar] [CrossRef]
  49. Labuza, A.; Alldred, M.J.; Pidikiti, H.; Malek-Ahmadi, M.H.; Lee, S.H.; Heguy, A.; Coleman, P.D.; Chakrabarty, S.; Chiosis, G.; Mufson, E.J.; et al. Frontal cortex pyramidal neuron expression profiles differentiate the prodromal stage from progressive degeneration across the Alzheimer’s disease spectrum. Alzheimers Dement. 2025, 21, e70395. [Google Scholar] [CrossRef]
  50. Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. 2010. Available online: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 10 October 2025).
  51. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [PubMed]
  52. Dobin, A.; Davis, C.A.; Schlesinger, F.; Drenkow, J.; Zaleski, C.; Jha, S.; Batut, P.; Chaisson, M.; Gingeras, T.R. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 2013, 29, 15–21. [Google Scholar] [CrossRef] [PubMed]
  53. Picard Toolkit. Available online: https://broadinstitute.github.io/picard/ (accessed on 10 October 2025).
  54. Li, B.; Dewey, C.N. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011, 12, 323. [Google Scholar] [CrossRef] [PubMed]
  55. Robinson, M.D.; McCarthy, D.J.; Smyth, G.K. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010, 26, 139–140. [Google Scholar] [CrossRef]
  56. Zehetmayer, S.; Posch, M.; Graf, A. Impact of adaptive filtering on power and false discovery rate in RNA-seq experiments. BMC Bioinform. 2022, 23, 388. [Google Scholar] [CrossRef] [PubMed]
  57. Rau, A.; Gallopin, M.; Celeux, G.; Jaffrézic, F. Data-based filtering for replicated high-throughput transcriptome sequencing experiments. Bioinformatics 2013, 29, 2146–2152. [Google Scholar] [CrossRef]
  58. Hoffman, G.E.; Roussos, P. Dream: Powerful differential expression analysis for repeated measures designs. Bioinformatics 2021, 37, 192–201. [Google Scholar] [CrossRef]
  59. Hoffman, G.E.; Schadt, E.E. variancePartition: Interpreting drivers of variation in complex gene expression studies. BMC Bioinform. 2016, 17, 483. [Google Scholar] [CrossRef]
  60. Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef]
  61. Li, G.; Bien-Ly, N.; Andrews-Zwilling, Y.; Xu, Q.; Bernardo, A.; Ring, K.; Halabisky, B.; Deng, C.; Mahley, R.W.; Huang, Y. GABAergic interneuron dysfunction impairs hippocampal neurogenesis in adult apolipoprotein E4 knockin mice. Cell Stem Cell 2009, 5, 634–645. [Google Scholar] [CrossRef]
  62. Li, Z.; Shue, F.; Zhao, N.; Shinohara, M.; Bu, G. APOE2: Protective mechanism and therapeutic implications for Alzheimer’s disease. Mol. Neurodegener. 2020, 15, 63. [Google Scholar] [CrossRef]
  63. Belaidi, A.A.; Bush, A.I.; Ayton, S. Apolipoprotein E in Alzheimer’s disease: Molecular insights and therapeutic opportunities. Mol. Neurodegener. 2025, 20, 47. [Google Scholar] [CrossRef]
  64. Liu, S.; Liu, J.; Weng, R.; Gu, X.; Zhong, Z. Apolipoprotein E gene polymorphism and the risk of cardiovascular disease and type 2 diabetes. BMC Cardiovasc. Disord. 2019, 19, 213. [Google Scholar] [CrossRef]
  65. Federoff, M.; Jimenez-Rolando, B.; Nalls, M.A.; Singleton, A.B. A large study reveals no association between APOE and Parkinson’s disease. Neurobiol. Dis. 2012, 46, 389–392. [Google Scholar] [CrossRef]
  66. Harhangi, B.S.; de Rijk, M.C.; van Duijn, C.M.; Van Broeckhoven, C.; Hofman, A.; Breteler, M.M. APOE and the risk of PD with or without dementia in a population-based study. Neurology 2000, 54, 1272–1276. [Google Scholar] [CrossRef]
  67. Arboleda-Velasquez, J.F.; Lopera, F.; O’Hare, M.; Delgado-Tirado, S.; Marino, C.; Chmielewska, N.; Saez-Torres, K.L.; Amarnani, D.; Schultz, A.P.; Sperling, R.A.; et al. Resistance to autosomal dominant Alzheimer’s disease in an APOE3 Christchurch homozygote: A case report. Nat. Med. 2019, 25, 1680–1683. [Google Scholar] [CrossRef]
  68. Slooter, A.J.; Cruts, M.; Kalmijn, S.; Hofman, A.; Breteler, M.M.; Van Broeckhoven, C.; van Duijn, C.M. Risk estimates of dementia by apolipoprotein E genotypes from a population-based incidence study: The Rotterdam Study. Arch. Neurol. 1998, 55, 964–968. [Google Scholar] [CrossRef] [PubMed]
  69. Alldred, M.J.; Lee, S.H.; Stutzmann, G.E.; Ginsberg, S.D. Oxidative Phosphorylation Is Dysregulated Within the Basocortical Circuit in a 6-month old Mouse Model of Down Syndrome and Alzheimer’s Disease. Front. Aging Neurosci. 2021, 13, 707950. [Google Scholar] [CrossRef] [PubMed]
  70. Alldred, M.J.; Penikalapati, S.C.; Lee, S.H.; Heguy, A.; Roussos, P.; Ginsberg, S.D. Profiling Basal Forebrain Cholinergic Neurons Reveals a Molecular Basis for Vulnerability Within the Ts65Dn Model of Down Syndrome and Alzheimer’s Disease. Mol. Neurobiol. 2021, 58, 5141–5162. [Google Scholar] [CrossRef] [PubMed]
  71. Koffie, R.M.; Hashimoto, T.; Tai, H.C.; Kay, K.R.; Serrano-Pozo, A.; Joyner, D.; Hou, S.; Kopeikina, K.J.; Frosch, M.P.; Lee, V.M.; et al. Apolipoprotein E4 effects in Alzheimer’s disease are mediated by synaptotoxic oligomeric amyloid-beta. Brain 2012, 135, 2155–2168. [Google Scholar] [CrossRef]
  72. Konings, S.C.; Torres-Garcia, L.; Martinsson, I.; Gouras, G.K. Astrocytic and Neuronal Apolipoprotein E Isoforms Differentially Affect Neuronal Excitability. Front. Neurosci. 2021, 15, 734001. [Google Scholar] [CrossRef]
  73. Chen, Y.; Durakoglugil, M.S.; Xian, X.; Herz, J. ApoE4 reduces glutamate receptor function and synaptic plasticity by selectively impairing ApoE receptor recycling. Proc. Natl. Acad. Sci. USA 2010, 107, 12011–12016. [Google Scholar] [CrossRef]
  74. Dumanis, S.B.; Tesoriero, J.A.; Babus, L.W.; Nguyen, M.T.; Trotter, J.H.; Ladu, M.J.; Weeber, E.J.; Turner, R.S.; Xu, B.; Rebeck, G.W.; et al. ApoE4 decreases spine density and dendritic complexity in cortical neurons in vivo. J. Neurosci. 2009, 29, 15317–15322. [Google Scholar] [CrossRef]
  75. Schmukler, E.; Solomon, S.; Simonovitch, S.; Goldshmit, Y.; Wolfson, E.; Michaelson, D.M.; Pinkas-Kramarski, R. Altered mitochondrial dynamics and function in APOE4-expressing astrocytes. Cell Death Dis. 2020, 11, 578. [Google Scholar] [CrossRef]
  76. Andrews-Zwilling, Y.; Bien-Ly, N.; Xu, Q.; Li, G.; Bernardo, A.; Yoon, S.Y.; Zwilling, D.; Yan, T.X.; Chen, L.; Huang, Y. Apolipoprotein E4 causes age- and Tau-dependent impairment of GABAergic interneurons, leading to learning and memory deficits in mice. J. Neurosci. 2010, 30, 13707–13717. [Google Scholar] [CrossRef] [PubMed]
  77. Knoferle, J.; Yoon, S.Y.; Walker, D.; Leung, L.; Gillespie, A.K.; Tong, L.M.; Bien-Ly, N.; Huang, Y. Apolipoprotein E4 produced in GABAergic interneurons causes learning and memory deficits in mice. J. Neurosci. 2014, 34, 14069–14078. [Google Scholar] [CrossRef] [PubMed]
  78. Zhao, J.; Fu, Y.; Yamazaki, Y.; Ren, Y.; Davis, M.D.; Liu, C.C.; Lu, W.; Wang, X.; Chen, K.; Cherukuri, Y.; et al. APOE4 exacerbates synapse loss and neurodegeneration in Alzheimer’s disease patient iPSC-derived cerebral organoids. Nat. Commun. 2020, 11, 5540. [Google Scholar] [CrossRef]
  79. Blumenfeld, J.; Yip, O.; Kim, M.J.; Huang, Y. Cell type-specific roles of APOE4 in Alzheimer disease. Nat. Rev. Neurosci. 2024, 25, 91–110. [Google Scholar] [CrossRef] [PubMed]
Brainsci 15 01117 g001
Figure 1. Distribution of DEGs comparing 12 MO to 18 MO mice. DEGs were identified between 12 MO and 18 MO mice homozygous for APOE2 (A), APOE3 (B), and APOE4 (C). Volcano plots show the distribution by log-fold change (logFC). Blue represents DEGs downregulated with age (light blue: p-value < 0.05, dark blue: p-value < 0.01) and red represents DEGs upregulated with age (red: p-value < 0.05, dark red: p-value < 0.01).
Figure 1. Distribution of DEGs comparing 12 MO to 18 MO mice. DEGs were identified between 12 MO and 18 MO mice homozygous for APOE2 (A), APOE3 (B), and APOE4 (C). Volcano plots show the distribution by log-fold change (logFC). Blue represents DEGs downregulated with age (light blue: p-value < 0.05, dark blue: p-value < 0.01) and red represents DEGs upregulated with age (red: p-value < 0.05, dark red: p-value < 0.01).
Brainsci 15 01117 g001
Brainsci 15 01117 g002
Figure 2. Comparison of shared DEGs between genotypes. (A) Venn diagram depicts overlap of aging DEGs between APOE2 (pink), APOE3 (green), and APOE4 (purple). The number of shared and unique DEGs are listed. (B) Heatmap shows the logFC of the 1610 DEGs shared between all 3 genotypes. All but 7 DEGs changed in the same direction. (C) Heatmap of 1896 DEGs shared between APOE2 and APOE4 illustrating DEGs changing in the same direction. Even though more DEGs were shared between APOE2 and APOE4 than all 3 genotypes, the range of logFC was smaller.
Figure 2. Comparison of shared DEGs between genotypes. (A) Venn diagram depicts overlap of aging DEGs between APOE2 (pink), APOE3 (green), and APOE4 (purple). The number of shared and unique DEGs are listed. (B) Heatmap shows the logFC of the 1610 DEGs shared between all 3 genotypes. All but 7 DEGs changed in the same direction. (C) Heatmap of 1896 DEGs shared between APOE2 and APOE4 illustrating DEGs changing in the same direction. Even though more DEGs were shared between APOE2 and APOE4 than all 3 genotypes, the range of logFC was smaller.
Brainsci 15 01117 g002
Brainsci 15 01117 g003
Figure 3. (A) Top 20 canonical pathways via IPA across genotypes. IPA generated a list of all canonical pathways significantly differentiated between 12 MO and 18 MO timepoints. The −log(p-values) of the top 20 pathways for each genotype were combined into one list. Orange depicts pathways upregulated (positive z-score) and blue depicts pathways downregulated, with shading indicating the top 20. Purple indicates that the z-score was 0 and grey indicates that a z-score could not be calculated. (B) Circle plot illustrating the top DEGs driving canonical pathway dysregulation, shown in light green, with the corresponding pathways in the center (pink) and grey arrows drawn between individual DEGs and pathways of interest.
Figure 3. (A) Top 20 canonical pathways via IPA across genotypes. IPA generated a list of all canonical pathways significantly differentiated between 12 MO and 18 MO timepoints. The −log(p-values) of the top 20 pathways for each genotype were combined into one list. Orange depicts pathways upregulated (positive z-score) and blue depicts pathways downregulated, with shading indicating the top 20. Purple indicates that the z-score was 0 and grey indicates that a z-score could not be calculated. (B) Circle plot illustrating the top DEGs driving canonical pathway dysregulation, shown in light green, with the corresponding pathways in the center (pink) and grey arrows drawn between individual DEGs and pathways of interest.
Brainsci 15 01117 g003
Brainsci 15 01117 g004
Figure 4. Curated top 20 list of canonical pathways via IPA unique to APOE2. A curated list of the top 20 pathways from DEGs unique to APOE2 was created and compared to pathways altered by aging in APOE3 and APOE4. Orange depicts pathways upregulated (positive z-score) and blue depicts pathways downregulated, with shading indicating the top 20. Purple indicates that the z-score was 0 and grey indicates that a z-score could not be calculated. Shading indicates only 1 top 20 pathway shared with APOE2.
Figure 4. Curated top 20 list of canonical pathways via IPA unique to APOE2. A curated list of the top 20 pathways from DEGs unique to APOE2 was created and compared to pathways altered by aging in APOE3 and APOE4. Orange depicts pathways upregulated (positive z-score) and blue depicts pathways downregulated, with shading indicating the top 20. Purple indicates that the z-score was 0 and grey indicates that a z-score could not be calculated. Shading indicates only 1 top 20 pathway shared with APOE2.
Brainsci 15 01117 g004
Brainsci 15 01117 g005
Figure 5. Curated top 20 list of canonical pathways via IPA unique to APOE3. A curated list of the top 20 pathways from DEGs unique to APOE3 was generated and compared to the pathways altered by aging in APOE2 and APOE4. Orange depicts pathways upregulated (positive z-score) and blue depicts pathways downregulated, with shading indicating the top 20. Purple indicates that the z-score was 0 and grey indicates that a z-score could not be calculated. Shading indicates only 3 top 20 pathways shared with APOE3.
Figure 5. Curated top 20 list of canonical pathways via IPA unique to APOE3. A curated list of the top 20 pathways from DEGs unique to APOE3 was generated and compared to the pathways altered by aging in APOE2 and APOE4. Orange depicts pathways upregulated (positive z-score) and blue depicts pathways downregulated, with shading indicating the top 20. Purple indicates that the z-score was 0 and grey indicates that a z-score could not be calculated. Shading indicates only 3 top 20 pathways shared with APOE3.
Brainsci 15 01117 g005
Brainsci 15 01117 g006
Figure 6. Curated top 20 list of canonical pathways via IPA unique to APOE4. A curated list of the top 20 pathways from DEGs unique to APOE4 was created and compared to the pathways altered by aging in APOE2 and APOE3. Orange depicts pathways upregulated (positive z-score) and blue depicts pathways downregulated, with shading indicating the top 20. Purple indicates that the z-score was 0 and grey indicates that a z-score could not be calculated. Shading indicates only 2 top 20 pathways shared with APOE4.
Figure 6. Curated top 20 list of canonical pathways via IPA unique to APOE4. A curated list of the top 20 pathways from DEGs unique to APOE4 was created and compared to the pathways altered by aging in APOE2 and APOE3. Orange depicts pathways upregulated (positive z-score) and blue depicts pathways downregulated, with shading indicating the top 20. Purple indicates that the z-score was 0 and grey indicates that a z-score could not be calculated. Shading indicates only 2 top 20 pathways shared with APOE4.
Brainsci 15 01117 g006
Brainsci 15 01117 g007
Figure 7. Curated biological pathways via GO analysis. Biological processes identified via GO analysis were categorized into autophagy, axon, -cytosis (exocytosis, endocytosis, and phagocytosis), dendritic, developmental (only those present in the CNS), neurotransmitter, protein, receptors, RNA, signal transduction, and synaptic. Bar graph in (A) shows the analysis for all the DEGs in APOE2, APOE3, APOE4, and pan APOE (with logFC values from APOE4). Bar graph (B) shows the analysis for pan APOE and aging DEGs unique to APOE2, APOE3, and APOE4. Pie graphs show the percentage of each category shown in B (C–F). Values are shown for aging DEGs in pan APOE (C), unique APOE2 (D), unique APOE3 (E), and unique APOE4 (F).
Figure 7. Curated biological pathways via GO analysis. Biological processes identified via GO analysis were categorized into autophagy, axon, -cytosis (exocytosis, endocytosis, and phagocytosis), dendritic, developmental (only those present in the CNS), neurotransmitter, protein, receptors, RNA, signal transduction, and synaptic. Bar graph in (A) shows the analysis for all the DEGs in APOE2, APOE3, APOE4, and pan APOE (with logFC values from APOE4). Bar graph (B) shows the analysis for pan APOE and aging DEGs unique to APOE2, APOE3, and APOE4. Pie graphs show the percentage of each category shown in B (C–F). Values are shown for aging DEGs in pan APOE (C), unique APOE2 (D), unique APOE3 (E), and unique APOE4 (F).
Brainsci 15 01117 g007
Brainsci 15 01117 g008
Figure 8. Synaptic subcategories within GO analysis. The synaptic category from the GO analysis was parsed into subcategories. Bar graph shows the total number of biological processes in each category for comparisons of unique DEGs in APOE2, APOE3, and APOE4. Additionally, processes were compared to pan APOE DEGs with logFC values from APOE4.
Figure 8. Synaptic subcategories within GO analysis. The synaptic category from the GO analysis was parsed into subcategories. Bar graph shows the total number of biological processes in each category for comparisons of unique DEGs in APOE2, APOE3, and APOE4. Additionally, processes were compared to pan APOE DEGs with logFC values from APOE4.
Brainsci 15 01117 g008
Table 1. List of pathways via KEGG analysis. KEGG generated a list of canonical pathways significantly differentiated between 12 MO and 18 MO. The −log(p-value) of pathways altered with all DEGs in APOE2, APOE3, and APOE4 are listed. DEGs unique to APOE4 generated additional KEGG pathways. There were not enough aging DEGs unique to APOE2 or APOE3 to significantly alter any pathways in KEGG. An additional 30 pathways were changed with all aging DEGs in APOE2 and APOE4, but not in APOE3, which are not shown.
Table 1. List of pathways via KEGG analysis. KEGG generated a list of canonical pathways significantly differentiated between 12 MO and 18 MO. The −log(p-value) of pathways altered with all DEGs in APOE2, APOE3, and APOE4 are listed. DEGs unique to APOE4 generated additional KEGG pathways. There were not enough aging DEGs unique to APOE2 or APOE3 to significantly alter any pathways in KEGG. An additional 30 pathways were changed with all aging DEGs in APOE2 and APOE4, but not in APOE3, which are not shown.
DescriptionAPOEZ
−log(p-Value)		APOE3
−log(p-Value)		APOE4
−log(p-Value)		APOE4
Unique
−log(p-Value)
Parkinson’s disease13.8917.6723.63
Huntington’s disease18.4517.9523.08
Alzheimer’s disease12.9716.0421.58
Amyotrophic lateral sclerosis15.7416.4218.28
Prion disease10.3616.1917.68
Retrograde endocannabinoid signaling8.505.0314.14
Spinocerebellar ataxia8.927.3913.63
Thermogenesis11.0711.9313.44
Autophagy–animal8.303.4211.16
Oxidative phosphorylation11.9715.4510.93
Ubiquitin-mediated proteolysis7.504.588.08
Ribosome8.6922.915.69
Proteasome3.223.544.05
Synaptic vesicle cycle2.742.583.75
RNA polymerase3.232.643.08
Pathways of neurodegeneration–multiple diseases14.3216.3621.972.87
Mitophagy–animal5.302.7612.083.05
Protein processing in endoplasmic reticulum6.542.456.242.87
Axon guidance7.35 12.316.10
Endocytosis5.42 10.934.57
Gap junction4.77 10.612.84
Long-term potentiation3.93 8.362.51
Glutamatergic synapse3.37 8.723.71
Inositol phosphate metabolism2.55 2.952.57
Choline metabolism in cancer2.42 5.802.49
Rap1 signaling pathway2.17 5.624.13
Adherens junction2.05 5.093.67
Dopaminergic synapse 8.073.91
Phospholipase D signaling pathway 5.713.51
EGFR tyrosine kinase inhibitor resistance 5.382.45
Amphetamine addiction 5.094.75
Cocaine addiction 3.374.29
MAPK signaling pathway 3.123.90
Nucleotide excision repair3.883.35
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Labuza, A.; Pidikiti, H.; Alldred, M.J.; Ibrahim, K.W.; Peng, K.Y.; Pasato, J.; Heguy, A.; Mathews, P.M.; Ginsberg, S.D. Aging, Rather than Genotype, Is the Principal Contributor to Differential Gene Expression Within Targeted Replacement APOE2, APOE3, and APOE4 Mouse Brain. Brain Sci. 2025, 15, 1117. https://doi.org/10.3390/brainsci15101117

AMA Style

Labuza A, Pidikiti H, Alldred MJ, Ibrahim KW, Peng KY, Pasato J, Heguy A, Mathews PM, Ginsberg SD. Aging, Rather than Genotype, Is the Principal Contributor to Differential Gene Expression Within Targeted Replacement APOE2, APOE3, and APOE4 Mouse Brain. Brain Sciences. 2025; 15(10):1117. https://doi.org/10.3390/brainsci15101117

Chicago/Turabian Style

Labuza, Amanda, Harshitha Pidikiti, Melissa J. Alldred, Kyrillos W. Ibrahim, Katherine Y. Peng, Jonathan Pasato, Adriana Heguy, Paul M. Mathews, and Stephen D. Ginsberg. 2025. "Aging, Rather than Genotype, Is the Principal Contributor to Differential Gene Expression Within Targeted Replacement APOE2, APOE3, and APOE4 Mouse Brain" Brain Sciences 15, no. 10: 1117. https://doi.org/10.3390/brainsci15101117

APA Style

Labuza, A., Pidikiti, H., Alldred, M. J., Ibrahim, K. W., Peng, K. Y., Pasato, J., Heguy, A., Mathews, P. M., & Ginsberg, S. D. (2025). Aging, Rather than Genotype, Is the Principal Contributor to Differential Gene Expression Within Targeted Replacement APOE2, APOE3, and APOE4 Mouse Brain. Brain Sciences, 15(10), 1117. https://doi.org/10.3390/brainsci15101117

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop