Clustering of transcriptional profiles identifies changes to insulin signaling as an early event in a mouse model of Alzheimer's disease

Abstract

Background: 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.

Results: 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.

Conclusion: 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.

Figure 1

Brain regions of female B6.APB^Tg mice show amyloid plaques at 8–12 months of age. Brains of female B6.APB^Tg mice were assessed for plaque deposition from 8–12 months of age. Plaques were visualized using Thioflavin T (Thio T, blue). Plaques were very obvious in the cortex, hippocampus and other brain regions in all mice assessed. Size and number of plaques increased with increasing age. In general, plaques were associated with reactive astrocytes (as determined by glial fibrillary acidic protein (GFAP) staining, green). DAPI was used to identify nuclei. Scale bars; Upper panels = 100 μm, lower panels = 50 μm.

Figure 2

Activation of microglia (microgliosis) coincides with focal axonal swellings in B6.APB^Tg mice. Little to no microgliosis (as judged by an increase in AIF1, red) was observed in B6.APB^Tg mice at 4 months but significant levels of microgliosis were observed in multiple brain regions at 8 months of age (arrowhead). Images taken from a region of the hippocampus as an example. AIF1 (allograft inducible factor, formerly IBA1) is a marker of microglia. Occasionally, this coincided with localized regions of axonal swellings (arrow, visualized using neurofilament, NFL, green). DAPI was used to identify nuclei. Scale bars = 50 μm.

Figure 3

AD phenotypes first appear in female B6.APB^Tg mice between 4–6 months of age. To determine the ideal ages at which to perform gene expression profiling we assessed plaque formation in brains from female B6.APB^Tg mice between 2–6 months of age. Plaques were visualized using Thioflavin T (Thio T, blue). The earliest detectable plaques occurred in the cortex of 4 months old mice. No plaques were observed in 2 months old mice. As expected, plaques were more apparent in brains from 6 months old mice. Arrows in the upper panels indicate the region shown in the lower panels. Scale bars; upper panels = 100 μm, lower panels = 50 μm.

Figure 4

Transcript clustering implies that age-related changes are of primary importance in the development of AD phenotypes. (A) Singular value decomposition of 4 and 6 months samples shows that age (column 1) is the greatest driver of variation between samples. Column 2 shows that genotype is the second greatest driver of variation. (B) This is confirmed using hierarchical clustering. (C) Comparing the 4 months samples with the 6 months samples using Cuffdiff identified 288 genes that were differentially expressed (184 upregulated, 104 down regulated, see Additional file 1: Table S1). (D) Pathway analysis using DAVID showed that members of the ribosome and oxidative phosphorylation pathways (from KEGG) are significantly overrepresented (P < 0.05) in the DE gene list.

Figure 5

Transcript clustering identifies that genotype is the major driver of variation in the 4 months samples. (A) Singular value decomposition stratifies the 4 months samples into two groups based on genotype. (B). In addition, hierarchical clustering, suggests that two samples (shown in red) are outliers compared to the other samples, and so are not included in comparisons to identify differentially expressed (DE) genes. (C) DE genes were identified by comparing the three 4 months B6.APB^Tg (AD) samples with the three B6 control samples (WT) using Cuffdiff. A total of 151 DE genes were identified (90 upregulated, 61 downregulated, see Additional file 2: Table S2). (D) Pathway analysis identified axon guidance, vascular smooth muscle contraction (VSM cont.) and calcium signaling (Calc sign.) as over-represented pathways (P < 0.05). (E) Analysis of the DE genes with the greatest fold changes identified genes commonly associated with the hypothalamus as down regulated including oxytocin (Oxt), glycoprotein hormones, alpha polypeptide (Cga), pro-opiomelanocortin-alpha (Pomc) and growth hormone (Gh).

Figure 6

Oxytocin (OXT - green) is localized to neurons in the hypothalamus, particularly surrounding the third ventricle (V). OXT was not observed in all other brain regions assessed (data not shown). Nuclei are visualized with DAPI (blue). Scale bars: left panel = 50 μm, right panel = 20 μm.

Figure 7

Transcript clustering of 5 months samples identified insulin signaling as a critical pathway activated in early stages of AD. (A) Hierarchical clustering clustered three of the 5 months AD samples with the 4 months samples (now termed Stage 1), and the remaining four with the 6 months samples (now termed Stage 2). The 5 months AD samples are shown in red. (B) The 5 months samples in Stage 1 were compared with the 5 months samples in Stage 2 using Cuffdiff. A total of 187 genes were differentially expressed (47 upregulated, 187 downregulated, see Additional file 3). (C) Pathway analysis showed that both the ribosome (Rib.) and insulin signaling pathway (Ins. Sign.) were overrepresented in the DE genes (P < 0.05). (D) Comparing Stage 1 with Stage 2, six genes in the insulin signaling pathway were differentially expressed (3 upregulated, 3 downregulated).

Figure 8

Insulin receptor substrate 4 (IRS4) protein is localized to axons in the cortex. Immnofluorescence was used to determine the location of IRS4 protein. The IRS4 protein (green) is readily detectable in multiple brain regions including the cortex. The majority of the IRS4 protein co-localizes with myelinated axons (as indicated by the significant overlap between IRS4 with myelin basic protein (MBP, red). Colocalization is indicated in yellow (e.g. arrowhead). However, IRS4 is also present in non-myelinated axons (longer arrow, lower right panel). Lower panels are a higher magnification of the boxed regions indicated in the upper panels. Scale bars: upper panels = 25 μm, lower panels = 10 μm.