Microarray-Based Gene Expression Analysis for Veterinary Pathologists: A Review

SFRP2 is a Novel Diagnostic Biomarker and Suppresses the Proliferation of Pituitary Adenoma

Pituitary adenoma (PA) constitutes one of the most common intracranial tumors. The present study was designed to identify potential diagnostic markers for PA. We used gene expression profiles (GEO: GSE26966 and GEO: GSE63357 datasets) derived from human PA and nontumor samples that were made freely accessible by the gene expression omnibus (GEO) datasets. Differentially expressed genes (DEGs) were screened between 14 normal specimens and 34 PA specimens by the use of the limma package of the R. The diagnostic genes were determined using a LASSO regression model and SVM-RFE analysis. SFRP2 expression in PA cells was analyzed using RT-PCR, and the effect of SFRP2 dysregulation on PA cell proliferation was measured using CCK-8 analysis. In this study, 361 DEGs were identified: 309 genes were downregulated and 52 genes were upregulated. The results of KEGG assays revealed that the 361 DEGs were mainly enriched in the PI3K-Akt signaling pathway, MAPK signaling pathway, growth hormone synthesis, secretion and action, and AGE-RAGE signaling pathway in diabetic complications. Results from the LASSO regression model and the SVM-RFE analysis indicated that LOC101060391 and SFRP2 were diagnostic genes. In contrast to normal tissue, the expressions of LOC101060391 and SFRP2 were much lower in PA samples. According to the ROC assays, high LOC101060391 and SFRP2 expression had an AUC value >0.9 for PA. Upregulation of SFRP2 distinctly inhibited the proliferative capacity of PA cells, as shown by CCK-8 analysis. Furthermore, knockdown of SFRP2 had an influence on cell growth in both the AtT-20 and HP75 cell lines. Taken together, our findings indicate that LOC101060391 and SFRP2 have diagnostic potential for PA. Furthermore, SFRP2 may be an antioncogene and a therapeutic target for PA.

Cytokines and Transgenic Matrix in Autoimmune Diseases: Similarities and Differences

Autoimmune diseases are increasingly recognized as disease entities in which dysregulated cytokines contribute to tissue-specific inflammation. In organ-specific and multiorgan autoimmune diseases, the cytokine profiles show some similarities. Despite these similarities, the cytokines have different roles in the pathogenesis of different diseases. Altered levels or action of cytokines can result from changes in cell signaling. This article describes alterations in the JAK-STAT, TGF-β and NF-κB signaling pathways, which are involved in the pathogenesis of multiple sclerosis and systemic lupus erythematosus. There is a special focus on T cells in preclinical models and in patients afflicted with these chronic inflammatory diseases.

Technique of Gene Expression Profiles Extraction Based on the Complex Use of Clustering and Classification Methods

In this paper, we present the results of the research concerning extraction of informative gene expression profiles from high-dimensional array of gene expressions considering the state of patients' health using clustering method, ML-based binary classifiers and fuzzy inference system. Applying of the proposed stepwise procedure can allow us to extract the most informative genes taking into account both the subtypes of disease or state of the patient's health for further reconstruction of gene regulatory networks based on the allocated genes and following simulation of the reconstructed models. We used the publicly available gene expressions data as the experimental ones which were obtained using DNA microarray experiments and contained two types of patients' gene expression profiles-the patients with lung cancer tumor and healthy patients. The stepwise procedure of the data processing assumes the following steps-in the beginning, we reduce the number of genes by removing non-informative genes in terms of statistical criteria and Shannon entropy; then, we perform the stepwise hierarchical clustering of gene expression profiles at hierarchical levels from 1 to 10 using the SOTA (Self-Organizing Tree Algorithm) clustering algorithm with correlation distance metric. The quality of the obtained clustering was evaluated using the complex clustering quality criterion which is considered both the gene expression profiles distribution relative to center of the clusters where these gene expression profiles are allocated and the centers of the clusters distribution. The result of this stage execution was a selection of the optimal cluster at each of the hierarchical levels which corresponded to the minimum value of the quality criterion. At the next step, we have implemented a classification procedure of the examined objects using four well known binary classifiers-logistic regression, support-vector machine, decision trees and random forest classifier. The effectiveness of the appropriate technique was evaluated based on the use of ROC (Receiver Operating Characteristic) analysis using criteria, included as the components, the errors of both the first and the second kinds. The final decision concerning the extraction of the most informative subset of gene expression profiles was taken based on the use of the fuzzy inference system, the inputs of which are the results of the appropriate single classifiers operation and the output is the final solution concerning state of the patient's health. To our mind, the implementation of the proposed stepwise procedure of the informative gene expression profiles extraction create the conditions for the increasing effectiveness of the further procedure of gene regulatory networks reconstruction and the following simulation of the reconstructed models considering the subtypes of the disease and/or state of the patient's health.

Merging microarray studies to identify a common gene expression signature to several structural heart diseases

BACKGROUND

Heart disease is the leading cause of death worldwide. Knowing a gene expression signature in heart disease can lead to the development of more efficient diagnosis and treatments that may prevent premature deaths. A large amount of microarray data is available in public repositories and can be used to identify differentially expressed genes. However, most of the microarray datasets are composed of a reduced number of samples and to obtain more reliable results, several datasets have to be merged, which is a challenging task. The identification of differentially expressed genes is commonly done using statistical methods. Nonetheless, these methods are based on the definition of an arbitrary threshold to select the differentially expressed genes and there is no consensus on the values that should be used.

RESULTS

Nine publicly available microarray datasets from studies of different heart diseases were merged to form a dataset composed of 689 samples and 8354 features. Subsequently, the adjusted p-value and fold change were determined and by combining a set of adjusted p-values cutoffs with a list of different fold change thresholds, 12 sets of differentially expressed genes were obtained. To select the set of differentially expressed genes that has the best accuracy in classifying samples from patients with heart diseases and samples from patients with no heart condition, the random forest algorithm was used. A set of 62 differentially expressed genes having a classification accuracy of approximately 95% was identified.

CONCLUSIONS

We identified a gene expression signature common to different cardiac diseases and supported our findings by showing their involvement in the pathophysiology of the heart. The approach used in this study is suitable for the identification of gene expression signatures, and can be extended to different diseases.

Reactive Oxygen Species Are Key Mediators of Demyelination in Canine Distemper Leukoencephalitis but not in Theiler's Murine Encephalomyelitis

(1) Background: Canine distemper virus (CDV)-induced demyelinating leukoencephalitis (CDV-DL) in dogs and Theiler’s murine encephalomyelitis (TME) virus (TMEV)-induced demyelinating leukomyelitis (TMEV-DL) are virus-induced demyelinating conditions mimicking Multiple Sclerosis (MS). Reactive oxygen species (ROS) can induce the degradation of lipids and nucleic acids to characteristic metabolites such as oxidized lipids, malondialdehyde, and 8-hydroxyguanosine. The hypothesis of this study is that ROS are key effector molecules in the pathogenesis of myelin membrane breakdown in CDV-DL and TMEV-DL. (2) Methods: ROS metabolites and antioxidative enzymes were assessed using immunofluorescence in cerebellar lesions of naturally CDV-infected dogs and spinal cord tissue of TMEV-infected mice. The transcription of selected genes involved in ROS generation and detoxification was analyzed using gene-expression microarrays in CDV-DL and TMEV-DL. (3) Results: Immunofluorescence revealed increased amounts of oxidized lipids, malondialdehyde, and 8-hydroxyguanosine in CDV-DL while TMEV-infected mice did not reveal marked changes. In contrast, microarray-analysis showed an upregulated gene expression associated with ROS generation in both diseases. (4) Conclusion: In summary, the present study demonstrates a similar upregulation of gene-expression of ROS generation in CDV-DL and TMEV-DL. However, immunofluorescence revealed increased accumulation of ROS metabolites exclusively in CDV-DL. These results suggest differences in the pathogenesis of demyelination in these two animal models.

Cytokine expression and lymphocyte proliferative capacity in diseased harbor porpoises (Phocoena phocoena) - Biomarkers for health assessment in wildlife cetaceans

Harbor porpoises (Phocoena phocoena) in the North and Baltic Seas are exposed to anthropogenic influences including acoustic stress and environmental contaminants. In order to evaluate immune responses in healthy and diseased harbor porpoise cells, cytokine expression analyses and lymphocyte proliferation assays, together with toxicological analyses were performed in stranded and bycaught animals as well as in animals kept in permanent human care. Severely diseased harbor porpoises showed a reduced proliferative capacity of peripheral blood lymphocytes together with diminished transcription of transforming growth factor-β and tumor necrosis factor-α compared to healthy controls. Toxicological analyses revealed accumulation of polychlorinated biphenyls (PCBs), dichlorodiphenyldichloroethylene (DDE), and dichlorodiphenyltrichloroethane (DDT) in harbor porpoise blood samples. Correlation analyses between blood organochlorine levels and immune parameters revealed no direct effects of xenobiotics upon lymphocyte proliferation or cytokine transcription, respectively. Results reveal an impaired function of peripheral blood leukocytes in severely diseased harbor porpoises, indicating immune exhaustion and increased disease susceptibility.

Scientific and Regulatory Policy Committee Points to Consider: Data Visualization for Clinical and Anatomic Pathologists

Assessment and communication of toxicology data are fundamental components of the work performed by veterinary anatomic and clinical pathologists involved in toxicology research. In recent years, there has been an evolution in the number and variety of software tools designed to facilitate the evaluation and presentation of toxicity study data. A working group of the Society of Toxicologic Pathology Scientific and Regulatory Policy Committee reviewed existing and emerging visualization technologies. This Points to Consider article reviews some of the currently available data visualization options, describes the utility of different types of graphical displays, and explores potential areas of controversy and ambiguity encountered with the use of these tools.

Integration of protein phosphorylation, acetylation, and methylation data sets to outline lung cancer signaling networks

Protein posttranslational modifications (PTMs) have typically been studied independently, yet many proteins are modified by more than one PTM type, and cell signaling pathways somehow integrate this information. We coupled immunoprecipitation using PTM-specific antibodies with tandem mass tag (TMT) mass spectrometry to simultaneously examine phosphorylation, methylation, and acetylation in 45 lung cancer cell lines compared to normal lung tissue and to cell lines treated with anticancer drugs. This simultaneous, large-scale, integrative analysis of these PTMs using a cluster-filtered network (CFN) approach revealed that cell signaling pathways were outlined by clustering patterns in PTMs. We used the t-distributed stochastic neighbor embedding (t-SNE) method to identify PTM clusters and then integrated each with known protein-protein interactions (PPIs) to elucidate functional cell signaling pathways. The CFN identified known and previously unknown cell signaling pathways in lung cancer cells that were not present in normal lung epithelial tissue. In various proteins modified by more than one type of PTM, the incidence of those PTMs exhibited inverse relationships, suggesting that molecular exclusive "OR" gates determine a large number of signal transduction events. We also showed that the acetyltransferase EP300 appears to be a hub in the network of pathways involving different PTMs. In addition, the data shed light on the mechanism of action of geldanamycin, an HSP90 inhibitor. Together, the findings reveal that cell signaling pathways mediated by acetylation, methylation, and phosphorylation regulate the cytoskeleton, membrane traffic, and RNA binding protein-mediated control of gene expression.