A carcinogenic trigger to study the function of tumor suppressor genes in Schmidtea mediterranea

Planarians have been long known for their regenerative ability, which hinges on pluripotency. Recently, however, the planarian model has been successfully established for routine toxicological screens aimed to assess overproliferation, mutagenicity and tumorigenesis. In this study, we focused on planarian tumor suppressor genes (TSGs) and their role during chemically induced carcinogenic stress in Schmidtea mediterranea. Combining in silico and proteomic screens with exposure to human carcinogen type 1A agent cadmium (Cd), we showed that many TSGs have a function in stem cells and that, in general, exposure to Cd accelerated the onset and increased the severity of the observed phenotype. This suggested that the interaction between environmental and genetic factors plays an important role in tumor development in S. mediterranea. Therefore, we further focused on the synergistic effects of Cd exposure and p53 knockdown (KD) at the cellular and molecular levels. Cd also produced a specific proteomic landscape in homeostatic animals, with 172 proteins differentially expressed, 43 of which were downregulated. Several of these proteins have tumor suppressor function in human and other animals, namely Wilms Tumor 1 Associated Protein (WT1), Heat Shock Protein 90 (HSP90), Glioma Pathogenesis-Related Protein 1 (GLIPR1) and Matrix Metalloproteinase B (Smed-MMPB). Both Glipr1 and MmpB KD produced large outgrowths, epidermal lesions and epidermal blisters. The epidermal blisters that formed as a consequence of Smed-MmpB KD were populated by smedwi1+ cells, many of which were actively proliferating, while large outgrowths contained ectopically differentiated structures, such as photoreceptors, nervous tissue and a small pharynx. In conclusion, Smed-MmpB is a planarian TSG that prevents stem cell proliferation and differentiation outside the proper milieu

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

Pain assessment in severe demented elderly based on facial expression

Introduction: Pain is an important and underestimated aspect at elderly with dementia, especially when their communication skills deteriorate. Moreover, the risk of under treatment increases with the progression of dementia, despite of the increasing pharmacological possibilities and interest in pain. Facial expression can be considered as a reflection of the real, authentic pain experience. Elderly with cognitive limitations are less socially inhibited to express pain nonverbally. Therefore observation of facial expression seems an interesting pain indicator for nurses, leading to a more accurate pain assessment, which is a must for this group of patients. Methods and Materials: The PAINVISION-project is a pilot study to set up a low-cost vision system that can continually identify pain in real-time by means of facial pattern recognition techniques. This study took place in a specific geriatric centre, and was approved by a medical ethical committee. Nineteen bedridden demented elderly with limited ability to communicate directly, were included. In six assessment sessions images of the patient’s face were recorded by a new bedside two-camera system, linked to pain scores of a digital device (a tablet PC with a touch screen). Results: At the moment, further data collection and processing is carried out to identify the most specific facial pain indicators. All results would be available in May 2010. Conclusion: If indeed specific facial expressions contain sufficient pain information for the observer, a short and thus time efficient observational pain scale can be developed for patients who cannot express their pain verbally anymore. These findings hopefully stimulate nurses to perform more frequent pain measurements on patients with limited ability to communicate to increase the accuracy of the pain evolution. A more adequate treatment can be provided with the knowledge of a more accurate pain level, and thus improving quality of life.status: publishe

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

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

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