Effect of AAV9-synuclein treatment on the LC.

<p>Quantification of TH immunohistochemistry in the locus coeruleus using unbiased stereological techniques. Graph shows that 4 months following α-synuclein gene transfer there is a significant loss of TH positive cells in the LC (2-way ANOVA did not show a significant interaction, but did reveal main effects of diet and treatment, and bonferonni post-hoc revealed a difference between the α-synuclein control and diet treated groups, p<0.01). Treatment with spirulina was able to prevent the loss of TH positive cells in the locus coeruleus. Asterisk denotes significance (**p<0.01 vs control GFP; *p<0.05 vs control α-synuclein).</p

Gene transfer efficiency is not affected by spirulina.

<p>Quantification of GFP positive cells in the SN. Stereological estimates of the number of GFP positive cells in the SN one month after gene transfer. There was no significant effect of the spirulina diet on numbers of GFP transduced cells (Student’s two-talied t-test).</p

Effect of spirulina on TH immunoreactive cells in the SNpc.

<p>TH positive cells in the SNpc after 1 or 4 months of α-synuclein expression. Cells were counted using unbiased stereological counting techniques. (A) One month after α-synuclein gene transfer, there was a significant decrease in TH positive cells as compared to GFP control (N = 12–18/group). The spirulina diet group lesioned with α-synuclein (N = 12) had greater numbers of TH positive cells compared to the control diet group lesioned with α-synuclein (N = 18). There was a diet by vector group interaction in the two way ANOVA analysis [F = 5.569, p<0.01]. (B) Results at four months were similar. There was a similar loss of TH positive cells and protective effect of spirulina. Two-way ANOVA yielded a main effect of diet (F = 81.3), and a main effect of vector group (F = 45.5; p<0.01), although without a significant interaction. Bonferonni post-hoc tests comparing NIH 31 GFP (N = 10) vs NIH 31 synuclein (N = 8) and NIH31 synuclein vs spirulina synuclein (N = 8) groups were significant). Asterisk denotes significance (*P<0.05; **p<0.01).</p

Effect of spirulina on NeuN immunoreactive cells in the SNpc.

<p>NeuN positive cells in the SNpc after 1 or 4 months of α-synuclein expression. At 1 month, there was a decrease in NeuN positive cells in the SN, mirroring the loss of TH positive cells in Fig. 1. This confirms cell loss rather than loss of TH expression. The effect was similar at 4 months of expression (B). There was neuroprotection in the groups that received a diet enhanced with spirulina at both intervals. Two-way ANOVA yielded a significant interaction of diet and vector treatment at both time points [1 month F = 6.931, df = 1; 4 months F = 8.899 df = 1]. (*p<0.05; **p<0.01) after Bonferonni's post-hoc.</p

Effects of spirulina on CX3CR1.

<p>Spirulina diet increased expression of CX3CR1; inset of Western blot in upper right. When the data are analyzed across groups there is a significant increase in expression of CX3CR1 in the spirulina treatment groups. Asterisk denotes significance (p<0.05; Bonferroni post-hoc). Two-way ANOVA shows a main effect of diet (F = 19.90; df = 1) and no interaction or main effect of vector treatment.</p

Additional file 1: Figure S1. of A novel antagonist of p75NTR reduces peripheral expansion and CNS trafficking of pro-inflammatory monocytes and spares function after traumatic brain injury

Complete PCA solution. To understand the relationship of behavioral outcome and innate immune cell responses, multivariate PC analysis was used. (A) PCA applies eigenvalue decomposition to the entire matrix cross correlations of all outcomes and clusters variables that move together as an integrated, optimally weighted linear composite (PC) to maximally explain the variance in cross-correlation. Once the first eigenvalue decomposition (PC1) is extracted, a second orthogonal (uncorrelated) decomposition calculated accounting for the second highest cluster of variance (PC2) and so on until all of the variance in the data set is partitioned into sequentially numbered PCs. The PC measures are rank ordered by the proportion of the total variance in the dataset that they explain. Standardized statistical PC retention rules are then applied to retain ‘significant’ PCs with eigenvalues > 1 and (B) PCs above the ‘elbow of the scree plot. (C) Interpretation of PC content based on PC loadings, which are equivalent to a pearson correlation between each variable and the PC composites. The PC loading pattern shows the contribution every single variable (outcome measures) on the extracted PCs. This analysis yielded 5 dimensional PC loading patterns (total, 80.55 % of variance). The PC1 module represented the relationship between neurological outcome and pro-inflammatory monocyte responses (27.13 % of variance). The individual PC score (see Fig. 9b) for each subject is the sum of the multiplied loading by the individual raw value of single outcome measures. The individual PC scores are used to define the impact of experimental manipulations on the relationship between outcome measures. (PDF 91 kb