- Research article
- Open Access
Clustering of transcriptional profiles identifies changes to insulin signaling as an early event in a mouse model of Alzheimer’s disease
© Jackson et al.; licensee BioMed Central Ltd. 2013
- Received: 6 September 2013
- Accepted: 14 November 2013
- Published: 25 November 2013
Alzheimer’s disease affects more than 35 million people worldwide but there is no known cure. Age is the strongest risk factor for Alzheimer’s disease but it is not clear how age-related changes impact the disease. Here, we used a mouse model of Alzheimer’s disease to identify age-specific changes that occur prior to and at the onset of traditional Alzheimer-related phenotypes including amyloid plaque formation. To identify these early events we used transcriptional profiling of mouse brains combined with computational approaches including singular value decomposition and hierarchical clustering.
Our study identifies three key events in early stages of Alzheimer’s disease. First, the most important drivers of Alzheimer’s disease onset in these mice are age-specific changes. These include perturbations of the ribosome and oxidative phosphorylation pathways. Second, the earliest detectable disease-specific changes occur to genes commonly associated with the hypothalamic-adrenal-pituitary (HPA) axis. These include the down-regulation of genes relating to metabolism, depression and appetite. Finally, insulin signaling, in particular the down-regulation of the insulin receptor substrate 4 (Irs4) gene, may be an important event in the transition from age-related changes to Alzheimer’s disease specific-changes.
A combination of transcriptional profiling combined with computational analyses has uncovered novel features relevant to Alzheimer’s disease in a widely used mouse model and offers avenues for further exploration into early stages of AD.
- Singular Value Decomposition
- Amyloid Precursor Protein
- Neurofibrillary Tangle
- Differentially Express
- Transcriptional Profile
Alzheimer’s disease (AD) is an age-related neurodegenerative disease characterized by selective dysfunction and loss of neurons in specific regions of the brain including the cortex and hippocampus [1–3]. The levels of neuronal dysfunction correlate with learning and memory deficits that severely impede a patient’s ability to live independently. Amyloid precursor protein (APP) and its derivatives (forms of β-amyloid, Aβ) are thought to play a central role in AD, affecting both intra- and extra-neuronal processes [4–8]. With an aging population, AD is on the increase and could affect more than 50 million people worldwide by 2050 [9, 10]. Attempts to develop new treatments for AD have proven unsuccessful [11–16] which likely highlights the lack of understanding of the disease, particularly its onset and early progression.
AD is commonly divided into two subtypes – early onset (or familial) and late onset (or sporadic) [17–21]. Early-onset AD generally presents itself before age 65 with late-onset AD developing later in life. Mutations identified in genes such as APP and presenilin 1 and 2 (PSEN1 and PSEN2) contribute to early onset AD [22–25]. Variations in many genes including apolipoprotein E (APOE) have been associated with late-onset AD, but their interactions to cause AD are unclear [26–28]. Key hallmarks of both types of AD are neurofibrillary tangles and Aβ plaques [3, 29, 30]. However, their appearance, particularly neurofibrillary tangles, are likely to represent stages of the disease at which neuronal dysfunction has already begun, and it may be difficult to develop treatments that effectively target events from these stages onwards. Therefore it is essential to better understand the earlier stages of the disease that precede the onset of neurofibrillary tangles and plaques. These earlier stages are likely to extend over many decades in humans and targeting them provides the greatest opportunity for therapeutic intervention.
Identifying the onset and early progression of any complex age-related disease using humans or non-human primates alone is particularly challenging. Therefore, an increased understanding of the molecular and cellular processes occurring during early disease stages requires the use of animal models. The mouse has been widely used to investigate sub-phenotypes of AD such as amyloidosis [31–33], reactive astrogliosis [34, 35] and neuroinflammation [36–38]. However, the mouse models have not been used to identify the molecular events that occur very early during disease, prior to or at the onset of the traditional hallmarks of AD. To identify these early molecular changes we performed clustering of transcriptional profiling data generated from mouse brains that showed a range in disease severity from no to early signs of AD. We had previously applied transcript clustering to identify early molecular stages in a mouse model of glaucoma [39–41]. In this present study, we used the APP swe Psen1 de9 mouse model of AD, which is reported to develop plaques from 6 months of age [2, 42–44]. Transcript clustering identifies age-specific changes as critical for the onset and early progression of AD in these mice. Moreover, we used clustering of transcriptional profiles to identify changes to insulin signaling as an important early event in AD.
Mouse strains and husbandry
C57BL/6J.APP swe Psen1 de9 mice (JR005864, , herein referred to as B6.APB Tg ) were obtained from The Jackson Laboratory and maintained in 14/10-hour light/dark cycle. All experiments were approved by the Animal Care and Use Committee at The Jackson Laboratory. To generate experimental mice, B6.APB Tg/+ mice were mated to C57BL/6 J (B6) mice to generate AD and wild-type (WT) cohorts. To minimize gene expression variation between mice, all mice in experimental cohorts were bred in the same mouse room, were aged together (to the extent possible) and only females were assessed. Mice from 2–12 months old were used in this study.
Tissue harvesting, RNA isolation and sequencing
A total of 22 mice were selected for transcriptional profiling: 8 at 4 months (4 WT, 4 AD), 7 at 5 months (all AD) and 7 at 6 months (3 WT, 4 AD). At the above ages, mice were sacrificed and the brain dissected free from the skull. The left hemisphere was snap frozen for preservation of RNA for gene expression studies. The right hemisphere was fixed in 4% paraformaldehyde overnight at 4°C and stored in 1× Phosphate-buffered solution (PBS) at 4°C for future use.
RNA isolation and library preparation
RNA was extracted from the left hemisphere of each brain using Trizol (Invitrogen, CA). mRNA was purified from total RNA using biotin-tagged poly dT oligonucleotides and streptavidin-coated magnetic beads followed by QC using an Agilent Technologies 2100 Bioanalyzer. The mRNA was then fragmented, and double-stranded cDNA generated by random priming. The ends of the fragmented DNA were converted into phosphorylated blunt ends. An ‘A’ base was added to the 3’ ends. Illumina®-specific adaptors were ligated to the DNA fragments. Using magnetic bead technology, the ligated fragments were size-selected and then a final PCR was performed to enrich the adapter-modified DNA fragments, since only the DNA fragments with adaptors at both ends will amplify.
To minimize sequencing batch effects, all 22 samples were barcoded and combined, and sequenced across six lanes on an Illumina HiSeq 2000 using standard conditions to generate 100 bp paired end sequences. A minimum of 29 million paired end reads were generated for each sample.
RNA Sequence analysis and clustering
Analysis of sequence was performed in a private instance of the publicly available Galaxy (https://main.g2.bx.psu.edu/).
Quality control of sequence
Sequence quality was assessed using Fastqc QC (v0.5, Babraham). Results showed that the first 16 bases showed minor sequencing bias so these were trimmed from the sequencing reads. After trimming, the average quality score at each base position was greater than 30 (with the majority being closer to 40).
Alignment of Sequence
Tophat/Bowtie (v1.5.0) was used to align sequences to the mouse genome (assembly NCBI37). Flagstat (v1.0.0) was used to show that at least 74.9% of pair end reads aligned to the mouse genome.
For each sample, fragment length per kilobase of exon per million fragments mapped (FPKM) values were generated using Cufflinks (v1.3.0). Quartile normalization (removal of the top 25% of genes from the FPKM denominator) and bias correction (to improve accuracy of transcript abundance estimation) were used. Mouse transcripts were taken from the NCBIM37.59 gene set.
Determining differentially expressed genes
Differentially expressed genes were determined using Cuffdiff (v0.0.5). Again, quartile normalization and bias correction were used.
Singular value decomposition and hierarchical clustering
For each data subset, aligned paired-end reads were filtered to remove any gene that was not expressed in any of the samples. For the 15 samples at 4 and 6 months, filtering yielded 16888 genes. The profile of each gene was centered on the mean of WT samples at 4 months. Hierarchical clustering was performed based on Euclidean Distance between genes across all samples to identify overall similarity. Singular value decomposition (SVD) was used to identify common signals and verify consistency across replicate cohorts. The first SVD component of the 15 samples at 4 and 6 months, which separated the two age cohorts, accounted for 38% of the global variance. The second component, primarily driven by mutant samples at 4 months, represented 10% of the variance. For the subset of 8 samples taken at 4 months, the first SVD component separated mutant from WT samples and accounted for 28% of the variance.
Pathway analyses of differentially expressed genes
Annotations were generated and overrepresented pathways were identified using DAVID v6.7 (http://david.abcc.ncifcrf.gov/). P values reported in this study were corrected for multiple tests. Pathways were colored using Kyoto Encyclopedia of Genes and Genomes (KEGG - http://www.genome.jp/kegg/tool/map_pathway2.html).
Immunofluorescence, immunohistochemistry and imaging
Mice were transcardially perfused with 4% PFA in 1× PBS. Brains were dissected and postfixed in 4% PFA overnight, cryoprotected in 10% and 30% sucrose, and embedded in OCT. Frozen sections (25 μm) were incubated for 1–2 nights in the following primary antibodies: mouse anti-phosphorylated neurofilament (2 F11; 1:1,000; Dako), rabbit anti-IBA1 (1:500, Wako), rabbit anti-MBP (1:1000; Abcam), rabbit polyclonal GFAP (1:300, Dako); rabbit polyclonal OXT (1:200, Abcam); and goat polyclonal IRS4 (1:200; Everest Biotechnology). With the exception of anti-OXT, the rabbit polyclonal antibodies were diluted in PBTB (1× PBS with 1% TritonX-100 and 1% BSA) containing 10% of normal goat serum and the goat polyclonal antibody was diluted in PBT (1× PBS with 1% TritonX-100) containing 10% donkey serum. For anti-OXT, the BSA was not included in the dilution buffer. After incubation with the primary antibodies, the brain sections were washed with PBT and incubated in the respective secondary antibodies (goat anti-mouse Alexa Fluor 647 and goat anti-rabbit Alexa Fluor 488, 1:1000 dilution, Invitrogen) for 2 h, washed in PBT, counterstained with DAPI, and mounted with Aqua-PolyMount. For Thio-T staining, brain sections processed for GFAP immunohistochemistry were incubated 10 min in 1% Thio-T (diluted in dH2O), transferred to 0.5% Acetic acid for 10 min, rinsed in dH2O for 5 min and mounted with Aqua-PolyMount. Thio-T is a dye that binds to beta sheet-rich structures such as amyloid plaques and produces similar results to those obtained with antibodies to Aβ such as 4G10 and 6E10 (data not shown).
For each antibody, at least 4 AD and 4 WT sections from multiple brain regions were assessed. Particular attention was given to the cortex, hippocampus and hypothalamus. Imaging and photography was performed on both a Zeiss Axio Imager microscope and a Leica SP5 confocal microscope. Post image processing was performed in Fiji (http://fiji.sc/Fiji).
AD phenotypes first occur in B6.APBTgmice from 4 months of age
Previous reports using the B6.APB Tg strain show that females develop plaques and other AD-like phenotypes from around 6–8 months of age [2, 42–44]. Given the potential for environmental differences to influence onset and progression of AD in different colonies of mice, we first assessed AD phenotypes in our colony from 8–12 months of age. As expected, mice of all ages within this span showed plaques in multiple brain regions and there was an observable increase in plaque numbers and size with increasing age (Figure 1). Activation of microglia both surrounding plaques and associated with axonal swellings was also observed at these time points (Figure 2). As previously reported, we saw no significant loss of neurons in any brain regions including the cortex and hippocampus at these ages (data not shown). These results indicate that onset and early stages of AD are occurring in B6.APB Tg female mice younger than 8 months old.
Therefore, we assessed AD phenotypes in mice 2–6 months old. At 2 months of age, B6.APB Tg female mice were indistinguishable from WT controls. However, by 4 months of age, very small plaques were observed in discrete regions of the brain, particularly in the cortex (Figure 3). Plaques were readily observed in 6 months mice in the cortex and hippocampus. Together these results indicated that profiling B6.APB Tg female mice at 4–6 months of age would identify very early molecular changes that may be key drivers and/or important biomarkers of early stages of AD.
Clustering of transcriptional profiles identifies age is a major driver of disease onset
To identify these age-specific changes responsible for the variation between the 4 and 6 months samples, we compared transcript profiles from all 4 months samples (4 AD and 4 WT, 8 in total) to profiles from all 6 months samples (4 AD and 3 WT, 7 in total) using Cuffdiff (see Methods). A total of 288 genes were differentially expressed (DE) between the ‘all 4 months’ and ‘all 6 months’ groups (Figure 4C and Additional file 1). Similar results were obtained when comparing either the 4 months AD samples to the 6 months AD samples, or the 4 months WT to the 6 months WT samples (data not shown) indicating that these truly are age-specific changes. Pathway analysis revealed that the ribosome and oxidative phosphorylation pathways were over-represented in the 288 DE genes (Figure 4D). These pathways have previously been linked to aging including down-regulation of protein synthesis/mRNA processing and mitochondrial dysfunction [47–54]. Our data imply that perturbations in these pathways are necessary for the onset or early progression of AD in B6.APB Tg mice. Understanding these age-specific changes is likely important for all forms of Alzheimer’s disease, not just the familial form modeled in B6.APB Tg mice.
Disruption to genes normally associated with the hypothalamic-pituitary-adrenal (HPA) axis is an early event in B6.APBTgmice
Clustering of 5 months samples identifies perturbation in the insulin signaling pathway as an important event in AD
To help answer these questions and to determine the gene expression differences driving the variation between the two groups of 5 months samples (termed stage 1 and stage 2) we used Cuffdiff. A total of 187 genes were differentially expressed comparing the early to late group (Figure 7B and Additional file 3). Pathway analysis of the DE genes identified the ribosome pathway and the insulin signaling pathway as overrepresented in stage 2 compared to stage 1 (Figure 7C). The ribosome pathway was also overrepresented comparing the 4 to the 6 months data suggesting that differences in physiological aging are driving some of the variation between the chronologically-matched 5 months old samples. Insulin signaling is a novel finding that was only significant in clustering of the 5 months samples. Therefore, it is not clear whether the variation between the 5 months old samples is due in part to the mice physiologically aging at different rates, or disease-specific changes.
Here we report the use of transcript clustering to identify molecular mechanisms contributing to early stages of AD in mice. This is the first time this approach has been used to assess early events in AD. We show that plaques first appear in the cortex of female B6.APB Tg mice around 4 months, and more significantly at 6 months of age. Transcript clustering identified age as the greatest driver of variation in the 4 and 6 months samples, with genotype being the second major driver. Pathway analysis of the genes differentially expressed between the 4 and 6 months samples suggests at least two biological processes are driving these changes namely mRNA processing/protein translation and oxidative phosphorylation. Both of these processes have previously been implicated in age-related changes [47–54]. Although it is unexpected that these aging-linked changes occur so early, it may suggest that subtle aging changes occur in mice as early as 4–6 months. At this age, mice are considered to be equivalent in age to early adulthood in humans (e.g. between 20–40 years of age) an age when mild cognitive impairment (MCI) can occur in some individuals .
The earliest AD-relevant changes detected in 4 months AD mice compared to WT mice were hormone-related and may indicate changes to the HPA axis. Our study is the first to propose that these may be among the earliest changes in AD and that they may be important indicators of susceptibility to and the onset of AD. Genes expressed by cells in the HPA axis control behaviors such as mood, sleep, and eating and these behaviors are commonly disrupted in AD patients. For instance, systemic metabolic changes as well as mood changes have been observed in very early phases of AD and suggest early dysfunction in the hypothalamus [55–57]. Another change seen in AD patients is a behavioral one, affecting mood and depression [63–66]. Oxytocin, often prescribed as a medication to treat depression, is almost exclusively expressed in the hypothalamus  and our data shows a downregulation early in B6.APB Tg mice.
It is not clear why the hypothalamus may be more susceptible than other brain regions to early dysfunction in AD but it may be due to the absence of a traditional blood brain barrier in this region. Instead, vessels surrounding the hypothalamus, particularly the median basal region of the hypothalamus, are fenestrated to allow the easy passage of hormones into the circulation [68–70]. This structure is constitutively leaky and may be particularly susceptible to increases in AD-relevant phenotypes such as amyloid accumulation. Furthermore, vascular dysfunction (such as cerebral hemorrhages or micro-bleeds) may precede the molecular changes identified in our study. Age-related vascular dysfunction has been reported but more detailed studies relating to the role of age-related vasculature dysfunction in AD are required.
Transcript clustering of the 5 months data identified changes to the insulin signaling pathway including Irs4 as an early event in AD. Insulin signaling is strongly associated with aging and age-related diseases including obesity and diabetes, conditions that have recently been identified as possible risk factors for AD [71–75]. Irs4 is expressed strongly in the hypothalamus but at lower levels in other brain regions such as the cortex. Therefore, downregulation of Irs4 likely impacts both the hypothalamus and the cortex. IRS4 is a good candidate for further studies as it is known to function in a number of processes relevant to AD including interacting with endosomes  to control Aβ levels in neurons [8, 77, 78], or through interactions with Irs2 in the phosphorylation of Tau during the formation of neurofibrillary tangles [58, 59]. Although further work is required to elucidate the precise role of IRS4 in AD, our data suggests that understanding the role of IRS4 in AD may lead to potential new therapies for AD.
Our study, using a genetic model relevant to early onset AD, shows that clustering of transcriptional profiling data is a powerful method to identify important early molecular changes at the onset of traditional hallmarks of AD. However, it is widely accepted that these models do not recapitulate all aspects of AD including hyperphosphorylation of Tau leading to neurofibrillary tangles and neuronal cell loss. This is intriguing given that mutations that disrupt APP processing do cause neurofibrillary tangles and associated neuronal changes in human AD. Given this, our work highlights the need for further investigation of B6.APB Tg mice at the onset of plaque formation to better understand the role of insulin signaling and other early molecular events that follow APP misprocessing. Furthermore, our study focuses on a mouse model relevant to early onset AD, which accounts for <5% of all cases human of AD. It is not clear whether mechanisms that lead to plaque formation and neurofibrillary tangles in early onset AD are similar or distinct to those that lead to AD in late-onset AD. Nevertheless, understanding and targeting these early events is likely to provide the greatest potential for developing new treatments for Alzheimer’s disease.
Availability of supporting data
Raw data is being made available through Geo datasets.
This work was supported in part by the Jane B. Cook 1983 Charitable Trust. G.W.C. was supported in part by grant K25 GM079404 from NIGMS. We thank Katherine MacNicoll and Sam Groh for mouse colony maintenance and tissue harvesting and acknowledge the Scientific Services at The Jackson Laboratory including Genome Technologies, Computational Sciences and imaging Sciences. We are grateful to Stephen Sampson for careful reading of the manuscript.
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