Summary of the appendix survey results.

<p>Summary of the appendix survey results.</p

Tissues identified as containing abnormal PrP and/or infectivity in clinical and subclinical vCJD patients.

<p>Tissues identified as containing abnormal PrP and/or infectivity in clinical and subclinical vCJD patients.</p

Over-expression of APP isoforms in HEK cells does not alter endogenous PrP^C.

<p>(A) Representative western blot of APP and PrP^C (antibody 3F4) in HEK cells stably transfected with either the vector alone (Hyg) or one of the APP isoforms (APP₆₉₅, APP₇₅₁, APP₇₇₀), and subsequent β-actin staining to allow adjustments for equal protein loading. Approximate molecular weights (kDa) are indicated. (B) Quantification of APP and PrP^C protein levels expressed relative to Hyg control cells (dashed line). Data from 4 independent experiments. Statistical analysis by one way ANOVA with Dunnett's post test comparison to the Hyg cells, ***p<0.001, **p<0.01, n.s. not significant.</p

Unaltered PrP^C protein levels in transgenic mice over-expressing human wild type or familial AD mutant APP.

<p>(A) Western blot of APP and PrP^C (antibody 6D11) in I5 (n = 3) and J20 (n = 2) transgenic, and age-matched non-transgenic control, mouse brain homogenates, with membrane re-probing for β-actin. Approximate molecular weights (kDa) are indicated. (B) Quantification of APP and PrP^C protein levels expressed relative to the control mice (dashed line). Error bars represent ± SD. Statistical analysis by unpaired t-test, **p<0.01, n.s. not significant.</p

Changes in neuronal markers demonstrate specific neuronal populations targeted to certain brain regions.

<p>(a) Tyrosine hydroxylase staining of midbrain neurons. No visible change in staining pattern could be observed in any GSS/101LL animal tested at 240 dpi (<i>n</i> = 6) compared to age- and region-matched NBH/101LL controls (<i>n</i> = 6). A marked loss of staining pattern is observed in the midbrain neurons upon clinical onset (291.1 ± 5.3 dpi), indicative of a loss of tyrosine hydroxylase neurons upon clinical onset of disease. Scale bar = 200 μm. (b) MAP2 staining in brain stem has marked loss of MAP2 cell–associated staining compared to NBH/101LL brain stem age-matched control. Overall, levels of MAP2 staining are visibly lost in the ventral-medial parts of the thalamus compared to region- and age-matched NBH/101LL controls. No change could be observed in the staining pattern of MAP2 in any part of the cortex or cerebellum compared to region- and age-matched NBH/101LL controls. These findings are observed consistently across all animals tested; GSS/101LL (<i>n</i> = 9), NBH/101LL (<i>n</i> = 4). Scale bars = 100 μm. (c) Higher magnification examples of MAP2 neurons lost in the gigantoreticular nuclei of the brain stem but no loss of neurons evidenced in the cortex. (d) Parvalbumin staining of Purkinje cells of the cerebellum at clinical stages of disease in GSS/101LL animals (291.1 ± 5.3 dpi; <i>n</i> = 3) compared to age-matched NBH/101LL controls (<i>n</i> = 3). Scale bars = 100 μm. (e) Neuronal cell counts of substantia nigra (SN) neurons of the midbrain, gigantocellular reticular nuclei (Gi) of the brain stem, and retrosplenial granular region (RSGc) of the cortex from three representative animals. Cells counted based upon the number of cells showing positive staining for either tyrosine hydroxylase (TyHy+) in the SN or MAP2 in the Gi and RSGc. (f) Quantification of MAP2+ staining intensity from three representative animals showing a loss of MAP2 staining in brain stem and the thalamus but no change in the cortex or cerebellum. Quantification of staining was performed using colour deconvolution plug-in of Image-J software.</p

Microglial-neuron communication may define the relative resilience or susceptibility of neurons to degenerate.

<p>Microglia are known to survey the neuronal parenchyma and interact intermittently with all parts of the neuron. The accumulation of misfolded PrP, and potentially the different types of aggregates, will have an impact on the communication and interaction between neurons and microglia. As a result, we observe different microglial responses during disease as well as selective vulnerability of neurons to degenerate in specific brain regions. It remains unclear whether the physiological differences of neuronal signalling, the known gene expression differences of microglia between brain regions [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002579#pbio.1002579.ref037" target="_blank">37</a>], or the different types of misfolded PrP aggregate are responsible for the different microglial activation states. Based on the associations between the different microglial activation states, the restricted neurodegeneration observed, and current knowledge of the importance of innate immune activation in defining severity of neurodegeneration, we speculate that the different microglial activation states could be defining neurodegeneration between different brain regions. This could either occur as a protective microglial response in regions showing resilience to neurodegeneration or a contributor to neurodegeneration in susceptible regions, or both. Our study highlights the need to further understand the basis for different microglial activation states, which could allow future studies to manipulate microglial responses from a primed activated state to one that regulates homeostasis and, thus, could represent a vital therapeutic target for intervention during disease.</p

All GSS/101LL brain regions show disease-associated gene expression changes.

<p>(A) Biolayout <i>Express</i>^3D graph showing a spatial representation of genes orientated according to their correlation to one another. Three major and separate components of highly correlated genes were formed using this software, which we term components A–C. This structure was used by the Markov clustering algorithm to divide the graph into clusters of co-expressed transcripts, which are shown in the graph as different colours. Representative clusters, shown as numbers 1–6, are shown on the graph, which highlights the expression differences found between each of the three major components identified. These can be viewed as average gene expression as bar graphs (B). (C) Filtered gene lists from the GSS/101LL cerebellum, thalamus, and brain stem are overlaid onto the original graph. Shown here are the filtered genes highlighted in yellow (cerebellum), purple (thalamus), or turquoise (brain stem) as part of component B of the main graph shown in (A). The highlighted genes in each brain region were observed predominantly within the same part of the graph rather than segregating into distinct groups, demonstrating that these differentially expressed genes between brain regions were highly correlated.</p

Morphological glial cell responses are restricted to specific brain regions.

<p>(a) Severe astrogliosis is observed in the brain stem and thalamus of GSS/101LL but is not detected in NBH/101LL age-matched controls or in the cortex or cerebellum of GSS/101LL mice. (b) High magnification image demonstrating the change in astrocyte expression of GFAP in GSS/101LL mice compared to equivalent NBH/101LL brain regions. (c) A distinct change in cell morphology to that of a hypertrophied cell body and short thick processes could be observed in Iba1+ cells, indicative of activated microglia, in GSS/101LL brain stem and thalamus. No change in cell morphology was observed in either NBH/101LL age- and region-matched control samples or in GSS/101LL cortex and cerebellum samples. (d) High magnification image to highlight the shortening and thickening of microglial processes, a characteristic common to morphologically activated microglia. These findings are observed consistently across all animals tested; GSS/101LL (<i>n</i> = 9), NBH/101LL (<i>n</i> = 4). Scale bars = 100 μm.</p

Detection of misfolded PrP using IHC at different time-points in different brain regions.

<p>(a) At 150 dpi, small quantities of fine-punctate misfolded PrP deposits can be detected in the midbrain. This positive staining could be observed in five of twelve GSS/101LL mice tested, but no staining was observed in any NBH/101LL animal in any brain region (<i>n</i> = 12). At 220 dpi, fine-punctate misfolded PrP deposits were detectable in both the midbrain and brain stem, which was observed in four of six GSS/101LL mice tested, but no staining was observed in any NBH/101LL animal in any brain region (<i>n</i> = 6). At clinical onset of disease (291.1 ± 5.3 dpi), misfolded PrP staining could be observed in midbrain, brain stem, and the thalamus but not in cortex or cerebellum in GSS/101LL mice. This staining pattern was observed in all mice tested at this stage (<i>n</i> = 9), whereas no staining was observed in any NBH/101LL animal in any brain region tested (<i>n</i> = 4). Scale bars: midbrain = 100 μm, brain stem, thalamus, cortex, and cerebellum = 200 μm. (b) Quantification of PrP+ staining intensity. The levels of PrP+ staining are originally high in the midbrain, but at later time-points in other brain regions, such as brain stem and thalamus, the levels of PrP+ staining increase to comparable levels to that of the midbrain. In cortex and cerebellum, no change in PrP+ staining was observed. Quantitation was performed using colour deconvolution plug-in to Image-J software.</p

Disease-associated gene expression changes can be predominantly attributed to microglia in all GSS/101LL brain regions tested.

<p>(a) Spider graph representation of component B genes after filtering. Up-regulation of genes in all GSS/101LL brain regions, but particularly increased in GSS/101LL brain stem and thalamus compared to GSS/101LL cerebellum and cortex. N = number of genes present after data are filtered that constitute the average intensity value. The number of genes represented is highest in GSS/101LL brain stem but lowest in GSS/101LL cortex. (b) Gene expression can be attributed to specific cell types when overlaid onto previous microarray datasets. These data show a simplified version to demonstrate how different genes that are known to have selective expression in specific cell types in vivo can be attributed to their expected cell type. For example, Cd11b is a gene generally regarded as a pan-macrophage marker, and hence we show the increased expression of this gene in macrophage/microglial cell populations compared to other cell types. Colony-stimulating factor 1 (Csf1) is a gene that is up-regulated during immune cell activation, shown here by its increased expression in lipopolysaccharide-activated macrophages. Gfap is a gene expressed highly in astrocytes, and, indeed, we show the high and specific expression of Gfap in astrocytes in this dataset. Finally, synapsin I is a synaptic-specific protein and therefore will most commonly be expressed in neurons, as is shown here. (c) Attribution of genes that are identified in component b to their respective cell type shows that a majority of genes that are identified in component b can be attributed to macrophage/microglia. (d) Representation of the macrophage/microglia gene list overlap of different brain regions tested.</p