Transcriptomic Profiling Reveals Neuroinflammation in the Corpus Callosum of a Transgenic Mouse Model of Alzheimer’s Disease

Abstract

Background:

Alzheimer’s disease (AD) is a widespread neurodegenerative disorder characterized by progressive cognitive decline, affecting a significant portion of the aging population. While the cerebral cortex and hippocampus have been the primary focus of AD research, accumulating evidence suggests that white matter lesions in the brain, particularly in the corpus callosum, play an important role in the pathogenesis of the disease.

Objective:

This study aims to investigate the gene expression changes in the corpus callosum of 5xFAD transgenic mice, a widely used AD mouse model.

Methods:

We conducted behavioral tests for spatial learning and memory in 5xFAD transgenic mice and performed RNA sequencing analyses on the corpus callosum to examine transcriptomic changes.

Results:

Our results show cognitive decline and demyelination in the corpus callosum of 5xFAD transgenic mice. Transcriptomic analysis reveals a predominance of upregulated genes in AD mice, particularly those associated with immune cells, including microglia. Conversely, downregulation of genes related to chaperone function and clock genes such as Per1, Per2, and Cry1 is also observed.

Conclusions:

This study suggests that activation of neuroinflammation, disruption of chaperone function, and circadian dysfunction are involved in the pathogenesis of white matter lesions in AD. The findings provide insights into potential therapeutic targets and highlight the importance of addressing white matter pathology and circadian dysfunction in AD treatment strategies.

Keywords

Alzheimer’s disease circadian rhythm corpus callosum microglia molecular chaperones neuroinflammation RNA-seq

INTRODUCTION

Alzheimer’s disease (AD) is the most common neurodegenerative disease occurring especially in the elderly population. AD is characterized by a progressive decline in neurological functions, including memory and non-memory cognition, and accounts for an estimated 60–70% of dementia cases [1, 2]. While past AD research has clarified its genetic, molecular, and cellular aspects, much remains to be understood in the pursuit of a definitive cure [2]. Highlighting AD’s impact, it presents not only financial problems, but also substantial human costs to countries, societies, families, and individuals [1]. With the global population over 65 years set to triple in the next 30 years, understanding AD pathology more deeply is urgently required to discover effective therapeutic targets to address its socioeconomic burden [3].

Gray matter, specifically the cerebral cortex and hippocampus, has been considered the major lesions of AD pathology [4]. However, growing evidence suggests that white matter, including changes such as atrophy of the corpus callosum [5 –8], is also vulnerable in AD patients and thus a potential novel therapeutic target [9 –12]. Myelin damage was clinically indicated and (micro-) structural and metabolic changes in the white matter were shown in AD patients via developing imaging technique [13 –17]. In preclinical studies, an AD-causing gene mutation in oligodendrocytes (OLG) demonstrated adverse effects associated with glutamate and amyloid-β (Aβ), a contributor protein to senile plaques in AD brain [18]. Moreover, recent research demonstrated crucial evidence that tau, a protein that causes neurofibrillary tangles in human AD, could be also toxic for myelin and OLG lineage cells, and therefore might play negative roles in white matter integrity [19]. In clinical studies, AD patients often exhibit atrophy and functional dysconnectivity in the corpus callosum [20, 21]. Given the corpus callosum’s pivotal role in neurocognitive functions as a white matter structure, it emerges as a potential therapeutic target. Although several insights are demonstrated, at present, there are no therapeutic approaches to overcome AD via white matter treatment, partly because of a lack of understanding of how AD-specific condition changes gene expression in white matter lesion.

Because rodents do not naturally develop the defining neuropathology linked to AD, a variety of approaches have been attempted to examine whether expressing the offending proteins also causes AD-like phenotypes [22]. To date, genetic engineering enabled to mimic AD characteristics in mice brains, and some models are developed as AD mice. Among them, a 5xFAD (harboring five familial AD mutations) transgenic mouse, which was developed in 2006, overexpresses human amyloid precursor protein with three FAD mutations and human presenilin 1 with two FAD mutations specifically in brain neurons and thus is commonly used to understand the changes in AD brain and to develop pharmacological interventions [23, 24]. Therefore, we examined the gene expression changes in the corpus callosum, one of the main structures of white matter for neurocognitive functions, in 5xFAD transgenic mice through RNA sequencing (RNA-seq) analyses.

METHODS

Animals

All experimental procedures followed NIH guidelines and were approved by the Massachusetts General Hospital Institutional Animal Care and Use Committee. Male 5xFAD transgenic mice and their wild-type mice, both with a B6SJL background, were purchased from The Jackson Laboratory and were housed in a specific pathogen-free conditioned 12-h light/dark cycle room with free access to food and water throughout the experiment. In this study, four 12-month-old male mice were used per group. At 12 months of age, as expected, 5xFAD transgenic mice exhibited cognitive impairments.

Corpus callosum sampling

Mice were transcardially perfused with ice-cold 0.9% physiological saline followed by decapitation. Brains were removed and cooled in pre-chilled Hanks’ Balanced Salt Solution for 1 min. After the removal of the meninges and the choroid plexus, the cerebrum was sliced into 5 coronal sections using a brain matrix slicer. To minimize the inclusion of tissue outside of the corpus callosum, the thicker parts of the corpus callosum from the 2nd and 3rd slices were isolated with direct visualization using a light microscope. We quickly froze corpus callosum samples in an RNAase-free tube using liquid nitrogen. Half of the corpus callosum was used for western blotting and the other half for RNA-seq experiments. Our tissue sampling was performed in Zeitgeber Time (ZT) 3–5.

Novel object recognition test (NORT)

Mice were tested for short-term recognition memory by NORT between 8–10 a.m., as previously described with slight modifications [25]. Briefly, mice were placed in a clean empty cage for 10 min. Mice were then exposed to two identical objects in the same cage for 5 min (acquisition period). After an interval of 30 min, mice were then presented with two different objects (one original and one novel object, which were placed in the same position as the objects in the acquisition period) in the same cage for 5 min (retention period). We videotaped the object recognition and scored it based on the total investigation time spent either sniffing or touching the object. The performance of short-term recognition memory was described by the ratio of the time spent on the new object to the total time spent on both objects minus 0.5 (e.g. Discrimination index: ranged from –0.5 to 0.5). Experiments and analyses were conducted by an investigator who was blinded to the group allocation.

Western blot

Corpus callosum samples were dissected in NP40 cell lysis buffer (MyBiosource, San Diego, CA, USA). Following centrifugation, the supernatant was combined with an equal volume of LDS sample buffer containing a reducing agent and then heated at 70°C for 10 min. Seven μg of each sample were then loaded onto 4–12% Bis-Tris gels (NuPAGE; ThermoFisher Scientific, Waltham, MA, USA). After electrophoresis and transfer to nitrocellulose membranes, the membranes were blocked in 5% skim milk with Tris-buffered saline containing 0.1% Tween-20. Afterward, they were incubated with a primary antibody against MBP (MBP101; 1:1,000, MA1-10837, ThermoFisher Scientific) and β-actin (1:10,000, A5441, Sigma-Aldrich, Burlington, MA, USA). Then, membranes were processed with HRP-conjugated secondary antibodies (1:2,000, Jackson ImmunoResearch Laboratories, West Grove, PA, USA) for 1 h at room temperature and visualized by enhanced chemiluminescence (SuperSignal^TM West Femto Maximum Sensitivity Substrate, ThermoFisher Scientific). Visualized bands were semi-quantified using ImageJ by an operator who was blinded to the group allocation.

RNA extraction

RNA extraction from the corpus callosum samples was performed using QIAzol^® (QIAGEN, Venlo, Netherlands) following the manufacturer’s instructions. Briefly, sonicated tissue was resuspended in 1 mL of pre-chilled QIAzol, and 0.2 mL of chloroform was added to the lysate. After mixing with a vortex mixer, the tube was centrifuged for 15 min at 12,000 g. The supernatant was then transferred to a new tube, and the same amount of propanol was added. After centrifugation for 10 min at 12,000 g, the supernatant was aspirated, and 1 mL of 75% ethanol was added for washing. Finally, after centrifuging the tube for 5 min at 7,500 g, we suspended the pellet in RNase-free water. The amount and purity of purified RNA were measured by NanoDrop Spectrophotometers. The RNA sample was stored at –80°C until use.

RNA-seq analysis

We processed RNA samples from each group for RNA-seq. Genewiz, Inc. (South Plainfield, NJ, USA) carried out both the library preparation using the rRNA depletion approach (single index) and sequencing on the Illumina HiSeq4000 platform (pair-end; 2×150 bp). The raw FASTQ data were aligned to the mm10 reference genome using STAR software (version: 2.7.10a) without any trimming or preprocessing, as quality checks using fastp (version 0.23.4) confirmed that the data were of high quality (Supplementary Table 1). Quantification of transcripts was achieved with RSEM (version: 1.3.3), which generated count data and transcripts per kilobase million (TPM) values. While most of our analytical steps employed normalized count data, TPM values were used for graphical representations. We conducted our bioinformatics analysis in R (version: 4.3.0), primarily utilizing the DESeq2 package (version: 1.40.1; Wald test) for differential expression analysis. We set a significance threshold at an adjusted p-value (padj) of less than 0.05. Our sequence data, presented as FASTQ files, have been stored under accession number PRJNA1012900. For the gene ontology (GO) evaluations, we used the Metascape platform [26]. We acquired the processed dataset of microglia derived from the brains of 6-month-old 5xFAD transgenic mice (GSE178296) [27], and conducted differential expression analysis using DESeq2.

Statistical methods

Statistical analysis was conducted by unpaired t-test for the NORT data and two-way repeated-measures analysis of variance followed by post-hoc multiple comparisons test for the body weight data. Differences were considered statistically significant at p < 0.05, with data presented as mean±SD. For specific datasets, such as NORT, we used standard deviation and clearly indicated this in the figure legends.

RESULTS

Cognitive impairment and demyelination in 5xFAD mice

During the novel object recognition test (NORT), unlike the wild-type mice which showed an inclination towards a novel object, the 5xFAD transgenic mice failed to do so (Fig. 1A). Yet, there was not a significant difference in the total investigation time of the objects (Fig. 1B). Biochemical analyses via western blotting also confirmed that 5xFAD transgenic mice had reduced levels of myelin basic protein (MBP) (Fig. 1C), indicating myelin and oligodendrocyte damage.

Fig. 1

Evaluation of AD Model Mice. A) Discrimination index (3 min) in NORT. B) Investigation time (3 min) in NORT. Error bars indicate SD. C) Western blot of MBP and β-Actin, and quantitative data. *p < 0.05 (unpaired t-test). Error bars indicate SD.

Evaluation of marker gene expression profiling in the corpus callosum from AD model mice using RNA-seq

We isolated the corpus callosum from 12-month-old wild-type (WT) mice with a B6SJL background, as well as from 5x FAD transgenic (AD model) mice (Fig. 2A). We ascertained the purity of the corpus callosum samples by assessing gene expression markers for oligodendrocytes (Myelin Basic Protein (Mbp) and Myelin-Associated Oligodendrocyte Basic Protein (Mobp)) and neurons across different layers Reelin (Reln) in layer I, Ras Protein-Specific Guanine Nucleotide-Releasing Factor 2 (Rasgrf2) in layers II/III, POU Class 3 Homeobox 2 (Pou3f2) in layers II-V, Forkhead Box P2 (Foxp2) in layer IV), ensuring no contamination from other areas (Fig. 2B). Additionally, a PCA plot revealed a clear distinction between the WT and AD model mice groups (Fig. 2C).

Fig. 2

Evaluation of Corpus Callosum Samples from AD Model Mice. A) Schematic representation of corpus callosum (CC) sampling from wild-type (WT) and 5x FAD transgenic (AD model) mice. Four samples were collected from each group. B) Violin plots illustrating the expression of oligodendrocyte markers (Mbp and Mobp) and cortical neuron markers (Reln in layer I, Rasgrf2 in layers II/III, Pou3f2 in layers II-V, Foxp2 in layer IV). Data from both WT and AD model mice are integrated. C) PCA plots of WT and AD model mice.

Transcriptome profiling in the corpus callosum of AD model mice

To identify AD-affected genes, we filtered the differentially expressed genes (DEGs) with the criteria: |Fold change (FC)| > 1.5, padj < 0.05, and mean Base > 50. Notably, 575 genes exhibited increased expression in the corpus callosum of the AD model mice, while 75 genes showed reduced expression in AD model mice (Fig. 3A). Upon examining the top 10 genes with upregulated and downregulated expression (Fig. 3B), the list of the most upregulated genes included Cystatin F (Cst7) (log2FC = 7.839, padj = 1.918e-70), Integrin Alpha X (Itgax) (log2FC = 7.112, padj = 1.347e-69), and C-Type Lectin Domain Family 7 Member A (Clec7a) (log2FC = 5.081, padj = 1.017e-55), all of which are associated with disease-associated microglia [28]. Additionally, genes relevant to neurologic disorders, including AD, were identified. Specifically, Chemokine Ligand 3 (Ccl3) (log2FC = 6.256, padj = 1.247e-31) [29], Cathepsin E (Ctse) (log2FC = 5.085, padj = 1.271e-48) [30], Leukocyte Immunoglobulin Like Receptor B4 (Lilrb4a) (log2FC = 4.948, padj = 7.637e-38) [31], Glycoprotein Non-Metastatic Melanoma Protein B (Gpnmb) (log2FC = 4.863, padj = 1.121e-08) [32], Interleukin 4 Induced 1 (Il4i1) (log2FC = 4.647, padj = 3.452e-18) [33], and Hydroxycarboxylic Acid Receptor 2 (Hcar2) (also known as Gpr109; log2FC = 4.500, padj = 1.206e-23) [34] were identified, and these genes have been reported to play roles in immune responses. Among the top 10 downregulated genes were those involved in chaperone function, including the heat shock protein 70kDa (HSP70) family members Heat Shock Protein Family A (Hsp70) Member 1A (Hspa1a) (log2FC = –2.048, padj = 2.440e-06) and Hsp70 Member 1B (Hspa1b) (log2FC = –2.322, padj = 1.485e-06), as well as neural activity-dependent genes like Activity-Regulated Cytoskeleton-Associated Protein (Arc) (log2FC = –1.458, padj = 0.045). Interestingly, the circadian rhythm-associated gene Period Circadian Regulator 2 (Per2) was also among the downregulated genes (log2FC = –1.243, padj = 8.111e-10) (Fig. 3B).

Fig. 3

Transcriptome Profiling in the Corpus Callosum of AD Model Mice. A) Volcano plot of DEGs between WT and AD model mice. The DEGs cutoff was set at padj < 0.05 and | Fold Change | > 1.5. B) Bar graph of the top 10 genes upregulated and downregulated in AD model mice compared to WT.

Microglial gene expression alterations in the corpus callosum of AD model mice

GO analysis of the DEGs revealed that the top 10 upregulated genes predominantly fell under immune response-associated GO categories. Notably, these categories included the Tyrobp causal network in microglia (WP3625: logP = –37.201), cell activation (GO:0001775: logP = –35.822), leukocyte activation (GO:0045321: logP = –34.817), Neutrophil degranulation (R-MMU-6798695: logP = –34.481), positive regulation of immune response (GO:0050778: logP = –33.895), immune effector process (GO:0002252: logP = –33.124), innate immune response (GO:0045087: logP = –31.874), regulation of immune effector process (GO:0002697: logP = –30.299), and the inflammatory response (GO:0006954: logP = –29.498) (Fig. 4A).

Fig. 4

Elevated Microglial Gene Profiling in the Corpus Callosum of AD Model Mice. A) Top 10 GO list with DEGs. B) Classification of DEGs using cellular markers from previously published RNA-seq data [35, 36]. C) Violin plots of microglial markers in WT and AD model mice. D) MA plots in WT and AD model mice; genes associated with GWAS are shown in blue, DEGs in red, and DEGs highly expressed in microglia and associated with GWAS are shown in green. E) Bar plot (log2FC) of DEGs highly expressed in microglia and associated with GWAS. F) Classification of gene lists using public single-cell RNA-seq data [37]. The abbreviations used in this figure are as follows: In the oligodendrocyte lineage, OPC stands for oligodendrocyte precursor cells, OLG for oligodendrocytes, and OEG for olfactory ensheathing glia; in the astrocyte lineage, NSC represents neural stem cells, ARP for astrocyte-restricted precursors, and ASC for astrocytes; the neuronal lineage includes NRP as neuronal-restricted precursors, NEUR_immature for immature neurons, NEUR_mature for mature neurons, and NendC for neuroendocrine cells; ependymal cells have EPC as ependymocytes, HypEPC for hypendymal cells, TNC for tanycytes, and CPC for choroid plexus epithelial cells; vasculature cells comprise EC for endothelial cells, PC for pericytes, Hb-VC for hemoglobin-expressing vascular cells, VSMC for vascular smooth muscle cells, VLMC for vascular and leptomeningeal cells, and ABC for arachnoid barrier cells; and for immune cells, MG indicates microglia, MNC for monocytes, MAC for macrophages, DC for dendritic cells, and NEUT for neutrophils. G) Bar plots (with dot plots) depict the altered expression of selected genes identified in (E) and (F), which were associated with AD GWAS and highly expressed in microglia. The dataset used for revalidation is derived from the bulk RNA-seq of microglia isolated from 6-month-old 5xFAD transgenic mice, sourced from a public dataset (GSE178296; |log2FC| > 0.58, padj < 0.05) [27]. Error bars represent SD.

When analyzing genes with notable expression in central nervous system cells based on public RNA-seq datasets [35, 36], the majority were derived from microglia (Fig. 4B). A direct examination using microglia markers further revealed that most of them had increased expression (Fig. 4C). By cross-referencing our DEGs with genes implicated in AD from GWAS studies (MONDO_0004975; representing 1,125 genes in mice), we identified multiple genes that showed enhanced expression in the corpus callosum of the AD model mice. These genes included Itgax (log2FC = 7.112, padj = 1.347e-69), Triggering Receptor Expressed on Myeloid Cells 2 (Trem2) (log2FC = 2.918, padj = 4.742e-86), B-Cell CLL/Lymphoma 3 (Bcl3) (log2FC = 2.417, padj = 3.107e-10), Fragment of IgE Receptor Ig (Fcer1 g) (log2FC = 2.322, padj = 4.488e-33), Thromboxane A Synthase 1 (Tbxas1) (log2FC = 2.081, padj = 1.448e-21), Hepatitis A Virus Cellular Receptor 2 (Havcr2) (log2FC = 1.968, padj = 6.244e-31), CD33 Molecule (Cd33) (log2FC = 1.640, padj = 5.157e-18), Granulin Precursor(Grn) (log2FC = 1.437, padj = 8.186e-53), Inositol Polyphosphate-5-Phosphatase D (Inpp5d) (log2FC = 1.248, padj = 4.597e-30), Phospholipase C Gamma 2 (Plcg2) (log2FC = 1.210, padj = 6.237e-14), and ABI Family Member 3 (Abi3) (log2FC = 1.058, padj = 1.817e-15) (Fig. 4D, E; Supplementary Figure 1). In further detailed analysis using publicly available single-cell RNA-seq data of the adult mouse brain [37], the majority of these genes were enriched in microglia and other immune cells such as monocytes, macrophages, and neutrophils (Fig. 4F). In addition, re-analysis of the bulk RNA-seq of microglia derived from the brains of 6-month-old 5xFAD transgenic mice (GSE178296) [27] revealed that among the DEGs obtained in the corpus callosum in this study (a total of 650 genes), 134 genes, including Itgax (log2FC = 4.498, padj = 1.282e-230), Trem2 (log2FC = 1.881, padj = 5.416e-24), Fcer1 g (log2FC = 1.311, padj = 7.989e-11), and Grn (log2FC = 1.342, padj = 2.307e-15), were similarly altered DEGs in microglia (|log2FC | > 0.58, padj < 0.05) (Fig. 4G).

Potential decrease in chaperone function in the corpus callosum of AD model mice

In the corpus callosum of AD, downregulated genes included members of the HSP70 family, such as Hspa1a and Hspa1b (Figs. 3B and 5). In the GO analysis, top-ranked categories predominantly pertained to chaperone function, such as Regulation of HSF1-mediated heat shock response (R-MMU-3371453: logP = –5.768), chaperone-mediated protein folding (GO:0061077: logP = –5.698), chaperone cofactor-dependent protein refolding (GO:0051085: logP = –5.446), Cellular response to heat stress (R-MMU-3371556: logP = –5.210), ‘de novo’ post-translational protein folding (GO:0051084: logP = –5.187), ‘de novo’ protein folding (GO:0006458: logP = –5.140), protein refolding (GO:0042026: logP = –4.734), and HSF1-dependent transactivation (R-MMU-3371571: logP = –4.301) (Fig. 4A). Within these GO categories, apart from Hspa1a and Hspa1b, genes such as Hsp70 Member 5 (Hspa5) (log2FC = –0.608, padj = 5.542e-09), Heat Shock Protein Family B (Small) Member 1 (Hspb1) (log2FC = –0.593, padj = 0.005), DnaJ Heat Shock Protein Family (Hsp40) Member B1 (Dnajb1) (log2FC = –0.747, padj = 1.055e-06), and Nucleoporin 43 (Nup43) (log2FC = –0.736, padj = 0.020) also demonstrated decreased expression in AD model mice (Fig. 5).

Fig. 5

Downregulation of Genes Related to Chaperone Function in AD Model Mice. Bar plots (with dot plots) comparing gene expression between WT and AD model mice. Error bars indicate SD.

Potential disruption of circadian rhythm functions in the corpus callosum of AD model mice

Notably, circadian rhythm-involved genes like Per2 (log2FC = –1.243, padj = 8.111e-10) were among the top downregulated genes (Fig. 3B). Some of these downregulated DEGs (Cryptochrome Circadian Regulator 1 (Cry1), Period Circadian Regulator 1 (Per1), Per2, Von Willebrand Factor (Vwf)) are regulated by genes such as Circadian Locomotor Output Cycles Kaput (Clock) and Aryl Hydrocarbon Receptor Nuclear Translocator Like (Arntl; also known as Bmal1) (Fig. 6A). GO analysis revealed top-ranked categories associated with the entrainment of the circadian clock, such as by photoperiod (GO:0043153: logP = –4.130) (Fig. 4A). Other categories included entrainment of the circadian clock (GO:0009649: logP = –4.028) and Circadian rhythm – Mus musculus (house mouse) (mmu04710: logP = –3.724) in GSEA plots in AD (Fig. 6B). Upon examining genes associated with circadian rhythms in the GO, we found genes such as Cry1 (log2FC = –0.697, padj = 5.564e-04), Per1 (log2FC = –0.728, padj = 3.698e-04), Per2 (log2FC = –1.243, padj = 8.111e-10), and Vwf (log2FC = –0.692, padj = 4.964e-08) included (Fig. 6C). However, genes such as D-Box Binding PAR BZIP Transcription Factor (Dbp) (log2FC = –0.530, padj = 0.039), Cry2 (log2FC = –0.697, padj = 5.564e-04), Per3 (log2FC = –0.418, padj = 0.007), and Nuclear Receptor Subfamily 1 Group D Member 1 (Nr1d1) (log2FC = –0.369, padj = 0.085) showed a reduced or declining expression pattern, and other circadian rhythm-associated genes such as basic helix-loop-helix ARNT like 1 (Arntl) (log2FC = 0.184, padj = 0.466), Clock (log2FC = 0.106, padj = 0.467), and RAR-Related Orphan Receptor B (Rorb) (log2FC = –0.093, padj = 0.703) displayed minimal changes or only slight reductions in expression (Supplementary Figure 2).

Fig. 6

Altered Expression of Circadian Rhythm Genes in AD Model Mice. A) Target Regulatory Relationship (TRR) plot using the reduced DEGs. Clock and Arntl play a central role in circadian rhythms. B) GSEA plot of gene sets related to circadian rhythm. C) Bar graph of genes related to circadian rhythm in DEGs in the brain of AD model mice. Error bars indicate SD.

DISCUSSION

In this study, we analyzed the transcriptomic changes in the corpus callosum of the AD model mice using RNA-seq. Our findings revealed that (i) pathway analyses demonstrated an upregulation of genes associated with inflammatory response and regulation of cytokine production, especially those derived from microglia and other immune cells, in AD model mice (Fig. 4), and (ii) genes related to chaperone function were downregulated (Fig. 5), and (iii) notably, clock genes exhibited a marked downregulation (Fig. 6). The upregulation of genes linked to microglia-mediated inflammatory responses, as observed in our study, aligns with previous research that emphasizes the central role of demyelination and neuronal damage driven by microglia-mediated neuroinflammation in white matter injury [38, 39]. Furthermore, our data also support the idea that disruption of chaperone function and circadian dysfunction may contribute to AD pathologies.

One of the most significant findings of this study is the notable transcriptomic changes related to microglial- or other immune-cell-associated genes in the corpus callosum of the AD brain. Microglia, the principal immune cells constituting 10% of all brain cells, play pivotal roles in both healthy and pathological states [39, 40]. Accumulating evidence suggests that in the aging brain and in AD, microglia-mediated chronic neuroinflammation might be the central mechanism [41]. In AD patients, activated microglia have been observed surrounding neurotic plaques composed of Aβ peptide, highlighting an association between microglia and the accumulation of Aβ in the pathology [42]. Recent studies have elucidated that myelin dysfunction and demyelination injuries in the corpus callosum are strongly implicated as potent promoting factors of amyloid deposition in AD model mice [27], and re-analysis of the bulk RNA-seq of microglia derived from the brains of 6-month-old 5xFAD transgenic mice (GSE178296) [27] revealed that among the DEGs obtained in the corpus callosum in this study (a total of 650 genes), 134 genes, including Itgax, Trem2, Fcer1g, and Grn, were similarly altered DEGs in microglia (Fig. 4G). This suggests a potential role of microglial- or other immune-cell-associated genes in the corpus callosum of the 5xFAD transgenic mice are deeply involved in their pathology. An in-vitro study shows that microglia can phagocytize Aβ, leading to the significant accumulation of surface molecules associated with type I and II major histocompatibility complex [43 –45]. Moreover, activated microglia can stimulate neurons to overproduce Aβ, causing synaptic damage and other neurodegenerative changes in AD [46]. Genome-wide association studies have highlighted genes related to the immune response (Complement C3b/C4b Receptor 1 [CR1], CD33, Membrane-spanning 4A [MS4A], Clusterin [CLU], ATP Binding Cassette Subfamily A Member 7 [ABCA7], EPH Receptor A1 [EPHA1], and Major Histocompatibility Complex, Class II, DR Beta 5 [HLA- DRB5] HLA- DR Beta 1 [-HLA-DRB1]) that are highly expressed in microglia and correlate with an increased risk of sporadic and late-onset AD [47]. These findings, spanning preclinical and clinical studies as well as in vitro and in vivo studies, emphasize the multifaceted role of microglia in AD pathology. Although the exact role of microglia and neuroinflammation in the corpus callosum of AD brain remains unclear, our results underscore the significance of microglia-mediated neuroinflammation in its pathology.

On the other hand, downregulated genes highlighted a suppression of chaperone function, indicating potential disruptions in protein folding in the corpus callosum of AD model mice. Given that protein misfolding is a key player in AD pathogenesis, this decrease suggests compromised cellular machinery to manage misfolded proteins in the corpus callosum. Members of the HSP70 family, such as Hspa1a and Hspa1b, which were found to be downregulated in our study of AD mouse models, are believed to co-localize with Aβ plaques and play a role in neuroprotective responses that inhibit Aβ aggregation [48 –50]. Since Aβ plaques are observed in the corpus callosum of 12-month-old 5xFAD transgenic mice used in this study [24], disruption of chaperone function might contribute to the formation of these Aβ plaques. We also showed that the expression levels of several genes regulated by circadian rhythms (Cry1, Per1, Per2, and Vwf) were decreased in a mouse model of AD (Fig. 5C). The changes in genes regulated by circadian rhythms that we found in this study may be involved in AD pathogenesis. Circadian rhythms are modulated by various pathways, such as the master oscillator in the suprachiasmatic nuclei located in the hypothalamus and the other oscillators in all organs throughout the body, for robust timekeeping. Recent research suggests that circadian rhythms play critical roles in various mechanisms associated with CNS diseases, including AD and stroke [51 –55]. Pathologically, a study using transgenic AD mice showed a significantly reduced number of the neuropeptides, arginine vasopressin, and vasoactive intestinal polypeptide, secreted from neurons in suprachiasmatic nuclei [56]. In the clinical setting, circadian rhythm disruption is prevalent in the early stage of AD patients, and circadian alignment therapy is beneficial for treating sundowning syndrome and other cognitive symptoms in advanced AD patients [57 –61]. Preclinical studies have also reported circadian dysfunction in mouse models of AD, although the results are somewhat variable and inconsistent across models, ages, and conditions [62]. These findings underscore that while the cerebral cortex and hippocampus remain central to AD pathology, circadian dysfunction and non-typical lesions of AD in other regions might also be involved in the disease process. Our finding exhibiting a downregulation of clock genes in the white matter of the AD brain also supports these findings and suggests that white matter can be a novel therapeutic target of circadian dysfunction in AD patients.

However, our current study has several caveats and limitations. First, our study examined only 5xFAD transgenic mice. While this transgenic AD mouse model has contributed to our understanding of AD mechanisms, it is essential to note that high Aβ expression alone does not induce tau pathology, which is another neuropathological hallmark of AD [63]. Consequently, the full spectrum of AD effects has not been entirely recapitulated in this mouse model. As the 5xFAD mice used in this study do not exhibit the tau pathology characteristic of AD, evaluations using other AD mouse models that do display tau pathology, such as PS19 or human tau knock-in 5xFAD, will be essential [64, 65]. Additionally, further studies using other AD models, such as EFAD mice (5xFAD mice expressing a human apoE isoform), may also be required to confirm our current findings [66]. Second, the use of bulk samples of corpus callosum in this study contains the possibility that significant changes in gene expression in some cell types may have been canceled. It will be useful for future studies to examine gene expression profiles with single-cell RNA sequencing to further our understanding of transcriptomic profiles of the corpus callosum in the AD brain. Third, while we focused on the changes in gene expression in the AD corpus callosum, we did not examine the cerebral cortex and hippocampus, which are central to AD pathology. Future studies should investigate whether these changes in gene expression are region-specific or not. Fourth, our tissue sampling was performed in only a one-time point, ZT3-5, which is the inactive phase for rodents. Since emerging finding suggests that the influence of circadian rhythm must be considered for translational study in the central nervous system, further study with multiple time points may be needed to confirm our findings [51]. Fifth, our data indicate that the mRNA levels of Arntl (known as Bmal1) and Clock, which play a central role in circadian rhythms, remained unchanged (Supplementary Figure 2). One possible interpretation is that the mRNA levels of Arntl and Clock may vary between WT and AD mice at different time points other than ZT3-5. Another possibility is that changes may occur in their protein levels, post-translational modifications, or protein-protein interactions without corresponding changes in mRNA levels. Since our current study lacks validation experiments with western blotting to assess protein expression levels of the DEGs, future studies are warranted to investigate which proteins are altered in the corpus callosum of AD mice for a deeper understanding of AD pathology in cerebral white matter. And lastly, it is important to consider potential male animal bias since we exclusively used only male subjects in the present study.

In conclusion, our study sheds light on the previously understudied white matter pathology in AD, particularly in the corpus callosum. We have identified key transcriptomic changes, including microglia-mediated inflammation, chaperone dysfunction, and circadian disruption, in this critical brain region. These findings broaden our understanding of AD beyond the cortex and hippocampus, offering new avenues for therapeutic interventions.

CREDIT AUTHOR STATEMENT

Hajime Takase (Conceptualization, Methodology, Investigation, Resource, Writing - Original Draft); Gen Hamanaka (Conceptualization, Methodology, Investigation, Resource, Writing – Review & Editing); Tomonori Hoshino (Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data Curation, Writing – Original Draft, Writing – Review & Editing, Visualization); Ryo Ohtomo (Methodology, Investigation); Shuzhen Guo (Methodology, Investigation); Emiri T. Mandeville (Methodology, Investigation); Eng H. Lo (Conceptualization, Writing - Review & Editing, Supervision, Funding acquisition); Ken Arai (Conceptualization, Methodology, Software, Formal analysis, Data Curation, Writing - Original Draft, Writing - Review & Editing, Supervision, Project administration, Funding acquisition).

Footnotes

ACKNOWLEDGMENTS

We thank our colleagues at the Neuroprotection Research Laboratories, Departments of Radiology and Neurology, Massachusetts General Hospital, and Harvard Medical School.

FUNDING

This work was supported by a fellowship from the Uehara Memorial Foundation (FY 2023 to T.H.) and funding from the NIH.

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

DATA AVAILABILITY

The RNA-seq data have been deposited in the public repository under the accession code listed in the Methods.

The supplementary material is available in the electronic version of this article: .

References

World Health Organization (2017) Global action plan on the public health response to dementia 2017 - 2025. World Health Organization, Geneva.

Scheltens

, De Strooper

, Kivipelto

, Holstege

, Chételat

, Teunissen

, Cummings

, van der Flier

(2021) Alzheimer’s disease. Lancet 397, 1577–1590.

Patterson

(2018) World Alzheimer Report 2018. The state of the art of dementia research: New frontiers. Alzheimer’s Disease International, London, UK.

Villain

, Desgranges

, Viader

, De La Sayette

, Mézenge

, Landeau

, Baron

, Eustache

, Chételat

(2008) Relationships between hippocampal atrophy, white matter disruption, and gray matter hypometabolism in Alzheimer’s disease. J Neurosci 28, 6174–6181.

Hampel

, Teipel

, Alexander

, Horwitz

, Teichberg

, Schapiro

, Rapoport

(1998) Corpus callosum atrophy is a possible indicator of region- and cell type-specific neuronal degeneration in Alzheimer disease: A magnetic resonance imaging analysis. Arch Neurol 55, 193–198.

Walterfang

, Luders

, Looi

JCL

, Rajagopalan

, Velakoulis

, Thompson

, Lindberg

, Östberg

, Nordin

, Svensson

, Wahlund

(2014) Shape analysis of the corpus callosum in Alzheimer’s disease and frontotemporal lobar degeneration subtypes. J Alzheimers Dis 40, 897–906.

Kamal

, Park

, Kim

, Lee

(2021) Alteration of the corpus callosum in patients with Alzheimer’s disease: Deep learning-based assessment. PLoS One 16, e0259051.

Khasawneh

, Abu-El-Rub

, Alzu’bi

, Abdelhady

, Al-Soudi

(2022) Corpus callosum anatomical changes in Alzheimer patients and the effect of acetylcholinesterase inhibitors on corpus callosum morphometry. PLoS One 17, e0269082.

Gold

, Johnson

, Powell

, Smith

(2012) White matter integrity and vulnerability to Alzheimer’s disease: Preliminary findings and future directions. Biochim Biophys Acta 1822, 416–422.

10.

Soldan

, Pettigrew

, Zhu

, Wang

M-C

, Moghekar

, Gottesman

, Singh

, Martinez

, Fletcher

, DeCarli

, Albert

(2020) White matter hyperintensities and CSF Alzheimer disease biomarkers in preclinical Alzheimer disease. Neurology 94, e950–e960.

11.

Nasrabady

, Rizvi

, Goldman

, Brickman

(2018) White matter changes in Alzheimer’s disease: A focus on myelin and oligodendrocytes. Acta Neuropathol Commun 6, 22.

12.

Rizvi

, Lao

, Chesebro

, Dworkin

, Amarante

, Beato

, Gutierrez

, Zahodne

, Schupf

, Manly

, Mayeux

, Brickman

(2021) Association of regional white matter hyperintensities with longitudinal alzheimer-like pattern of neurodegeneration in older adults. JAMA Netw Open 1–13.

13.

Reisberg

, Franssen

, Hasan

, Monteiro

, Boksay

, Souren

, Kenowsky

, Auer

, Elahi

, Kluger

(1999) Retrogenesis: Clinical, physiologic, and pathologic mechanisms in brain aging, Alzheimer’s and other dementing processes. Eur Arch Psychiatry Clin Neurosci 249(Suppl), 28–36.

14.

Lee

, Viqar

, Zimmerman

, Narkhede

, Tosto

, Benzinger

TLS

, Marcus

, Fagan

, Goate

, Fox

, Cairns

, Holtzman

, Buckles

, Ghetti

, McDade

, Martins

, Saykin

, Masters

, Ringman

, Ryan

, Förster

, Laske

, Schofield

, Sperling

, Salloway

, Correia

, Jack

, Weiner

, Bateman

, Morris

, Mayeux

, Brickman

(2016) White matter hyperintensities are a core feature of Alzheimer’s disease: Evidence from the dominantly inherited Alzheimer network. Ann Neurol 79, 929–939.

15.

Jeong

, Yoon

, Kang

(2017) Assessment of change in glucose metabolism in white matter of amyloid-positive patients with Alzheimer disease using F-18 FDG PET. Medicine (Baltimore) 96, e9042.

16.

Huang

, Friedland

, Auchus

(2007) Diffusion tensor imaging of normal-appearing white matter in mild cognitive impairment and early Alzheimer disease: Preliminary evidence of axonal degeneration in the temporal lobe. Am J Neuroradiol 28, 1943–1948.

17.

Gao

, Cheung

RT-F

, Lee

TMC

, Chu

L-W

, Chan

Y-S

, Mak

HK-F

, Zhang

, Qiu

, Fung

, Cheung

(2011) Possible retrogenesis observed with fiber tracking: An anteroposterior pattern of white matter disintegrity in normal aging and Alzheimer’s disease. J Alzheimers Dis 26, 47–58.

18.

Pak

, Chan

, Mattson

(2003) Presenilin-1 mutation sensitizes oligodendrocytes to glutamate and amyloid toxicities, and exacerbates white matter damage and memory impairment in mice. Neuromolecular Med 3, 53–64.

19.

Alexander

(2017) An emerging role for imaging white matter in the preclinical risk for Alzheimer disease: Linking β-amyloid to myelin. JAMA Neurol 74, 17–19.

20.

Teipel

, Bayer

, Alexander

, Zebuhr

, Teichberg

, Kulic

, Schapiro

, Möller

H-J

, Rapoport

, Hampel

(2002) Progression of corpus callosum atrophy in Alzheimer disease. Arch Neurol 59, 243–248.

21.

Qiu

, Liu

, Hilal

, Loke

, Ikram

, Xu

, Yeow Tan

, Venketasubramanian

, Chen

CLH

, Zhou

(2016) Inter-hemispheric functional dysconnectivity mediates the association of corpus callosum degeneration with memory impairment in AD and amnestic MCI. Sci Rep 6, 32573.

22.

Vitek

, Araujo

, Fossel

, Greenberg

, Howell

, Rizzo

SJS

, Seyfried

, Tenner

, Territo

, Windisch

, Bain

, Ross

, Carrillo

, Lamb

, Edelmayer

(2020) Translational animal models for Alzheimer’s disease: An Alzheimer’s Association Business Consortium Think Tank. Alzheimers Dement 6, e12114.

23.

Chen

, Mobley

(2019) Alzheimer disease pathogenesis: Insights from molecular and cellular biology studies of oligomeric Aβ and tau species. Front Neurosci 13, 659.

24.

Oblak

, Lin

, Kotredes

, Pandey

, Garceau

, Williams

, Uyar

, O’Rourke

, Ingraham

, Bednarczyk

, Belanger

, Cope

, Little

, Williams

SPG

, Ash

, Bleckert

, Ragan

, Logsdon

, Mangravite

, Sukoff Rizzo

, Territo

, Carter

, Howell

, Sasner

, Lamb

(2021) Comprehensive evaluation of the 5XFAD mouse model for preclinical testing applications: A MODEL-AD study. Front Aging Neurosci 13, 713726.

25.

Takase

, Hamanaka

, Ohtomo

, Ishikawa

, Chung

, Mandeville

, Lok

, Fornage

, Herrup

, Tse

K-H

, Lo

, Arai

(2021) Transcriptome profiling of mouse corpus callosum after cerebral hypoperfusion. Front Cell Dev Biol 9, 685261.

26.

Zhou

, Zhou

, Pache

, Chang

, Khodabakhshi

, Tanaseichuk

, Benner

, Chanda

(2019) Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 10, 1523.

27.

Depp

, Sun

, Sasmita

, Spieth

, Berghoff

, Nazarenko

, Overhoff

, Steixner-Kumar

, Subramanian

, Arinrad

, Ruhwedel

, Möbius

, Göbbels

, Saher

, Werner

, Damkou

, Zampar

, Wirths

, Thalmann

, Simons

, Saito

, Saido

, Krueger-Burg

, Kawaguchi

, Willem

, Haass

, Geschwind

, Ehrenreich

, Stassart

, Nave

(2023) Myelin dysfunction drives amyloid-β deposition in models of Alzheimer’s disease. Nature 618, 349–357.

28.

Deczkowska

, Keren-shaul

, Weiner

, Colonna

, Schwartz

, Amit

(2018) Perspective disease-associated microglia: A universal immune sensor of neurodegeneration. Cell 173, 1073–1081.

29.

Xia

, Qin

, Wu

, Mackay

, Hyman

(1998) Immunohistochemical study of the beta-chemokine receptors CCR3 and CCR5 and their ligands in normal and Alzheimer’s disease brains. Am J Pathol 153, 31–37.

30.

Xie

, Meng

, Kong

, Wu

, Lan

, Narengaowa , Hayashi

, Yang

, Bai

, Nakanishi

, Qing

, Ni

(2022) Microglial cathepsin E plays a role in neuroinflammation and amyloid β production in Alzheimer’s disease. Aging Cell 21, e13565.

31.

Zhou

, Song

, Andhey

, Swain

, Levy

, Miller

, Poliani

, Cominelli

, Grover

, Gilfillan

, Cella

, Ulland

, Zaitsev

, Miyashita

, Ikeuchi

, Sainouchi

, Kakita

, Bennett

, Schneider

, Nichols

, Beausoleil

, Ulrich

, Holtzman

, Artyomov

, Colonna

(2020) Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease. Nat Med 26, 131–142.

32.

Hüttenrauch

, Ogorek

, Klafki

, Otto

, Stadelmann

, Weggen

, Wiltfang

, Wirths

(2018) Glycoprotein NMB: A novel Alzheimer’s disease associated marker expressed in a subset of activated microglia. Acta Neuropathol Commun 6, 108.

33.

Psachoulia

, Chamberlain

, Heo

, Davis

, Paskus

, Nanescu

, Dupree

, Wynn

, Huang

(2016) IL4I1 augments CNS remyelination and axonal protection by modulating T cell driven inflammation. Brain 139, 3121–3136.

34.

Moutinho

, Puntambekar

, Tsai

, Coronel

, Lin

, Casali

, Martinez

, Oblak

, Lasagna-Reeves

, Lamb

, Landreth

(2022) The niacin receptor HCAR2 modulates microglial response and limits disease progression in a mouse model of Alzheimer’s disease. Sci Transl Med 14, eabl7634.

35.

Zhang

, Chen

, Sloan

, Bennett

, Scholze

, O’Keeffe

, Phatnani

, Guarnieri

, Caneda

, Ruderisch

, Deng

, Liddelow

, Zhang

, Daneman

, Maniatis

, Barres

, Wu

(2014) An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J Neurosci 34, 11929–11947.

36.

Voskuhl

, Itoh

, Tassoni

, Matsukawa

, Ren

, Tse

, Jang

, Suen

, Itoh

(2019) Gene expression in oligodendrocytes during remyelination reveals cholesterol homeostasis as a therapeutic target in multiple sclerosis. Proc Natl Acad Sci U S A 116, 10130–10139.

37.

Ximerakis

, Lipnick

, Innes

, Simmons

, Adiconis

, Dionne

, Mayweather

, Nguyen

, Niziolek

, Ozek

, Butty

, Isserlin

, Buchanan

, Levine

, Regev

, Bader

, Levin

, Rubin

(2019) Single-cell transcriptomic profiling of the aging mouse brain. Nat Neurosci 22, 1696–1708.

38.

Salminen

, Ojala

, Kauppinen

, Kaarniranta

, Suuronen

(2009) Inflammation in Alzheimer’s disease: Amyloid-beta oligomers trigger innate immunity defence via pattern recognition receptors. Prog Neurobiol 87, 181–194.

39.

Katsumoto

, Takeuchi

, Takahashi

, Tanaka

(2018) Microglia in Alzheimer’s disease: Risk factors and inflammation. Front Neurol 9, 978.

40.

Wake

, Moorhouse

, Miyamoto

, Nabekura

(2013) Microglia: Actively surveying and shaping neuronal circuit structure and function. Trends Neurosci 36, 209–217.

41.

Heneka

, Kummer

, Latz

(2014) Innate immune activation in neurodegenerative disease. Nat Rev Immunol 14, 463–477.

42.

Glenner

, Wong

, Quaranta

, Eanes

(1984) The amyloid deposits in Alzheimer’s disease: Their nature and pathogenesis. Appl Pathol 2, 357–369.

43.

Hamanaka

, Kubo

, Ohtomo

, Takase

, Reyes-Bricio

, Oribe

, Osumi

, Lok

, Lo

, Arai

(2020) Microglial responses after phagocytosis: Escherichia coli bioparticles, but not cell debris or amyloid beta, induce matrix metalloproteinase-9 secretion in cultured rat primary microglial cells. Glia 68, 1435–1444.

44.

McGeer

, Itagaki

, McGeer

(1988) Expression of the histocompatibility glycoprotein HLA-DR in neurological disease. Acta Neuropathol 76, 550–557.

45.

Koenigsknecht

, Landreth

(2004) Microglial phagocytosis of fibrillar beta-amyloid through a beta1 integrin-dependent mechanism. J Neurosci 24, 9838–9846.

46.

Meraz-Ríos

, Toral-Rios

, Franco-Bocanegra

, Villeda-Hernández

, Campos-Peña

(2013) Inflammatory process in Alzheimer’s disease. Front Integr Neurosci 7, 59.

47.

Karch

, Goate

(2015) Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol Psychiatry 77, 43–51.

48.

Magrané

, Smith

, Walsh

, Querfurth

(2004) Heat shock protein 70 participates in the neuroprotective response to intracellularly expressed β-amyloid in neurons. J Neurosci 24, 1700–1706.

49.

Kakimura

J-I

, Kitamura

, Takata

, Umeki

, Suzuki

, Shibagaki

, Taniguchi

, Nomura

, Gebicke-Haerter

, Smith

, Perry

, Shimohama

(2002) Microglial activation and amyloid-beta clearance induced by exogenous heat-shock proteins. FASEB J 16, 601–603.

50.

Hoshino

, Murao

, Namba

, Takehara

, Adachi

, Katsuno

, Sobue

, Matsushima

, Suzuki

, Mizushima

(2011) Suppression of Alzheimer’s disease-related phenotypes by expression of heat shock protein 70 in mice. J Neurosci 31, 5225–5234.

51.

Esposito

, Li

, T Mandeville

, Park

J-H

, Şencan

, Guo

, Shi

, Lan

, Lee

, Hayakawa

, Sakadžić

, Ji

, Lo

(2020) Potential circadian effects on translational failure for neuroprotection. Nature 582, 395–398.

52.

, Albers

, Dichgans

, Donnan

, Esposito

, Foster

, Howells

, Huang

Y-G

, Ji

, Klerman

, Lee

, Li

, Liebeskind

, Lizasoain

, Mandeville

, Moro

, Ning

, Ray

, Sakadžić

, Saver

, Scheer

FAJL

, Selim

, Tiedt

, Zhang

, Buchan

(2021) Circadian biology and stroke. Stroke 52, 2180–2190.

53.

Musiek

, Holtzman

(2016) Mechanisms linking circadian clocks, sleep, and neurodegeneration. Science 354, 1004–1008.

54.

Musiek

(2015) Circadian clock disruption in neurodegenerative diseases: Cause and effect? Front Pharmacol 6, 29.

55.

Lee

, Kim

, Griffin

, Sheehan

, Kim

, Musiek

, Yoon

(2020) Inhibition of REV-ERBs stimulates microglial amyloid-beta clearance and reduces amyloid plaque deposition in the 5XFAD mouse model of Alzheimer’s disease. Aging Cell 19, e13078.

56.

Sterniczuk

, Dyck

, Laferla

, Antle

(2010) Characterization of the 3xTg-AD mouse model of Alzheimer’s disease: Part 1. Circadian changes. Brain Res 1348, 139–148.

57.

Mishima

, Okawa

, Hishikawa

, Hozumi

, Hori

, Takahashi

(1994) Morning bright light therapy for sleep and behavior disorders in elderly patients with dementia. Acta Psychiatr Scand 89, 1–7.

58.

Pollak

, Perlick

(1991) Sleep problems and institutionalization of the elderly. J Geriatr Psychiatry Neurol 4, 204–210.

59.

Harper

, Volicer

, Stopa

, McKee

, Nitta

, Satlin

(2005) Disturbance of endogenous circadian rhythm in aging and Alzheimer disease. Am J Geriatr Psychiatry 13, 359–368.

60.

Satlin

, Volicer

, Stopa

, Harper

(1995) Circadian locomotor activity and core-body temperature rhythms in Alzheimer’s disease. Neurobiol Aging 16, 765–771.

61.

Musiek

, Ju

Y-ES

(2022) Targeting sleep and circadian function in the prevention of Alzheimer disease. JAMA Neurol 79, 835–836.

62.

Sheehan

, Musiek

(2020) Evaluating circadian dysfunction in mouse models of Alzheimer’s disease: Where do we stand? Front Neurosci 14, 703.

63.

Yetman

, Fowler

, Jankowsky

(2016) Humanized tau mice with regionalized amyloid exhibit behavioral deficits but no pathological interaction. PLoS One 11, e0153724.

64.

Jankowsky

, Zheng

(2017) Practical considerations for choosing a mouse model of Alzheimer’s disease. Mol Neurodegener 12, 89.

65.

Barendrecht

, Schreurs

, Geissler

, Sabanov

, Ilse

, Rieckmann

, Eichentopf

, Künemund

, Hietel

, Wussow

, Hoffmann

, Körber-Ferl

, Pandey

, Carter

, Demuth

, Holzer

, Roßner

, Schilling

, Preuss

, Balschun

, Cynis

(2023) A novel human tau knock-in mouse model reveals interaction of Abeta and human tau under progressing cerebral amyloidosis in 5xFAD mice. Alzheimers Res Ther 15, 16.

66.

Lewandowski

, Maldonado Weng

, LaDu

(2020) Alzheimer’s disease pathology in APOE transgenic mouse models: The who, what, when, where, why, and how. Neurobiol Dis 139, 104811.