Identification of Protein Networks Involved in the Disease Course of Experimental Autoimmune Encephalomyelitis, an Animal Model of Multiple Sclerosis

A more detailed insight into disease mechanisms of multiple sclerosis (MS) is crucial for the development of new and more effective therapies. MS is a chronic inflammatory autoimmune disease of the central nervous system. The aim of this study is to identify novel disease associated proteins involved in the development of inflammatory brain lesions, to help unravel underlying disease processes. Brainstem proteins were obtained from rats with MBP induced acute experimental autoimmune encephalomyelitis (EAE), a well characterized disease model of MS. Samples were collected at different time points: just before onset of symptoms, at the top of the disease and following recovery. To analyze changes in the brainstem proteome during the disease course, a quantitative proteomics study was performed using two-dimensional difference in-gel electrophoresis (2D-DIGE) followed by mass spectrometry. We identified 75 unique proteins in 92 spots with a significant abundance difference between the experimental groups. To find disease-related networks, these regulated proteins were mapped to existing biological networks by Ingenuity Pathway Analysis (IPA). The analysis revealed that 70% of these proteins have been described to take part in neurological disease. Furthermore, some focus networks were created by IPA. These networks suggest an integrated regulation of the identified proteins with the addition of some putative regulators. Post-synaptic density protein 95 (DLG4), a key player in neuronal signalling and calcium-activated potassium channel alpha 1 (KCNMA1), involved in neurotransmitter release, are 2 putative regulators connecting 64% of the identified proteins. Functional blocking of the KCNMA1 in macrophages was able to alter myelin phagocytosis, a disease mechanism highly involved in EAE and MS pathology. Quantitative analysis of differentially expressed brainstem proteins in an animal model of MS is a first step to identify disease-associated proteins and networks that warrant further research to study their actual contribution to disease pathology

Effects of tail suspension on serum testosterone and molecular targets regulating muscle mass

Introduction: The contribution of reduced testosterone levels to tail suspension (TS)-induced muscle atrophy remains equivocal. The molecular mechanism by which testosterone regulates muscle mass during TS has not been investigated. Methods: Effects of TS on serum testosterone levels, muscle mass, and expression of muscle atrophy- and hypertrophy-inducing targets were measured in soleus (SOL) and extensor digitorum longus (EDL) muscles after testosterone administration during 1, 5, and 14 days of TS in male mice. Results: TS produced an increase followed by a transient drop in testosterone levels. Muscle atrophy was associated with downregulation of Igf1 and upregulation of Mstn, Redd1, Atrogin-1, and MuRF1 mRNA with clear differences in Igf1, Mstn, and MAFbx/Atrogin-1 gene expression between SOL and EDL. Testosterone supplementation did not affect muscle mass or protein expression levels during TS. Conclusions The known anabolic effects of testosterone are not sufficient to ameliorate loss of muscle mass during T

GO-Compartments.

<p>The 75 unique proteins (ANOVA ≤ 0.05) were categorized according to the subcellular compartment (extracellular space, plasma membrane, cytoplasm, nucleus, and unknown). Information was collected from Gene ontology by IPA. Percentages are presented.</p

Validation of the 2D-DIGE results.

<p>Immunohistochemistry was performed to demonstrate the presence of macrophages and CNP. Macrophage (ED-1) and CNP immunostaining of rat spinal cords (same animals as for 2D-DIGE) from control, and EAE rats before disease onset, top and recovery are shown in panel A. These IHC stainings were quantified (Panel B and C), and expression levels compared by Dunn's multiple comparison test (GraphPad Prism4). The error bars indicate standard deviations of measurements performed at least in triplicate. *: significant difference, p<0.01 and **: significant difference, p<0.001. In Panel D, a quantitative 1D CNP immunoblot of EAE brainstem homogenate from control and disease top is shown. An overview of the fluorescent total protein staining, anti-CNP immunostaining, the fluorescent overlay of both (red and green overlay), and finally a representation of the fluorescent signals as processed with ImageQuant TL software (GE Healthcare). The red curve corresponds with the total protein content and the green curve with the CNP fluorescence. Both a representative control animal (c) and one at the disease top (t) are presented.</p

Western blot analysis of DLG4 and KCNMA1.

<p>A quantitative fluorescent western blot was performed to analyze the presence and expression levels of KCNMA1 (Panel A) and DLG4 (Panel B). By means of peak detection, the normalized peak volumes were used for quantification. No significant difference was found in expression levels, but both proteins were detected in the samples of the 2D-DIGE experiment. All animals were included in the WB analysis; control (C), onset (O), top (T) and recovery (R).</p

Ingenuity pathway analysis networks build with focus proteins.

<p>The DLG4-KCNMA1 network (Panel A), APP-ACTB network (Panel B) and AGT-TP53 network (Panel C) are represented. These networks were obtained using the IPA-KB by linking proteins from the data-set (75 unique proteins) to the focus proteins. Nodes containing proteins identified in the dataset have a grey fill.</p

2D-DIGE gel image.

<p>The 92 spots presented have a shift in abundance over the four experimental conditions (control, disease onset, top, and recovery) (ANOVA ≤ 0.05). Spots were picked from preparative 2D-gels and proteins identified by nano-LC-ESI-mass spectrometry. The proteins were identified with significant MASCOT and SEQUEST scores. Spots are numbered as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035544#pone.0035544.s001" target="_blank">Table S1</a>.</p

Unsupervised multivariate analysis discriminating between early and late groups.

<p>PCA reduces the dimensionality of a multidimensional analysis and displays the two principle components that can distinguish between the two largest sources of variation within the dataset (92 spots, ANOVA ≤ 0.05). Principle component analysis clustering the 12 individual spotmaps into the four conditions by two principle components: PC1, which distinguishes 90% of the variance, and PC2 distinguishes an additional 3.8% of the variance.</p

Clinical scores and weight changes of EAE and control animals.

<p>EAE was induced by injection of MBP in CFA (bars and squares). Control animals were CFA injected (dots). The controls showed no clinical symptoms. Each value represents the mean ± standard deviation of n animals: control day1-15, n = 3; EAE day1–9, n = 9; day10–14, n = 6 and day15–18, n = 3.</p