Clinical significance of VEGF-A, -C and -D expression in esophageal malignancies

Vascular endothelial growth factors ( VEGF)- A, - C and - D are members of the proangiogenic VEGF family of glycoproteins. VEGF-A is known to be the most important angiogenic factor under physiological and pathological conditions, while VEGF-C and VEGF-D are implicated in the development and sprouting of lymphatic vessels, so called lymphangiogenesis. Local tumor progression, lymph node metastases and hematogenous tumor spread are important prognostic factors for esophageal carcinoma ( EC), one of the most lethal malignancies throughout the world. We found solid evidence in the literature that VEGF expression contributes to tumor angiogenesis, tumor progression and lymph node metastasis in esophageal squamous cell carcinoma ( SCC), and many authors could show a prognostic value for VEGF-assessment. In adenocarcinoma (AC) of the esophagus angiogenic properties are acquired in early stages, particularly in precancerous lesions like Barrett's dysplasia. However, VEGF expression fails to give prognostic information in AC of the esophagus. VEGF-C and VEGF-D were detected in SCC and dysplastic lesions, but not in normal mucosa of the esophagus. VEGF-C expression might be associated with lymphatic tumor invasion, lymph node metastases and advanced disease in esophageal SCC and AC. Therapeutic interference with VEGF signaling may prove to be a promising way of anti-angiogenic co-treatment in esophageal carcinoma. However, concrete clinical data are still pending

Cyclic AMP-Dependent Protein Kinase A Regulates the Alternative Splicing of CaMKIIδ

Ca2+/calmodulin-dependent protein kinase (CaMK) IIδ is predominantly expressed in the heart. There are three isoforms of CaMKIIδ resulting from the alternative splicing of exons 14, 15, and 16 of its pre-mRNA, which is regulated by the splicing factor SF2/ASF. Inclusion of exons 15 and 16 or of exon 14 generates δA or δB isoform. The exclusion of all three exons gives rise to δC isoform, which is selectively increased in pressure-overload-induced hypertrophy. Overexpression of either δB or δC induces hypertrophy and heart failure, suggesting their specific role in the pathogenesis of hypertrophy and heart failure. It is well known that the β-adrenergic-cyclic AMP-dependent protein kinase A (PKA) pathway is implicated in heart failure. To determine the role of PKA in the alternative splicing of CaMKIIδ, we constructed mini-CaMKIIδ genes and used these genes to investigate the regulation of the alternative splicing of CaMKIIδ by PKA in cultured cells. We found that PKA promoted the exclusion of exons 14, 15, and 16 of CaMKIIδ, resulting in an increase in δC isoform. PKA interacted with and phosphorylated SF2/ASF, and enhanced SF2/ASF's activity to promote the exclusion of exons 14, 15, and 16 of CaMKIIδ, leading to a further increase in the expression of δC isoform. These findings suggest that abnormality in β-adrenergic-PKA signaling may contribute to cardiomyopathy and heart failure through dysregulation in the alternative splicing of CaMKIIδ exons 14, 15, and 16 and up-regulation of CaMKIIδC

The Congenital Cataract-Linked G61C Mutation Destabilizes γD-Crystallin and Promotes Non-Native Aggregation

γD-crystallin is one of the major structural proteins in human eye lens. The solubility and stability of γD-crystallin play a crucial role in maintaining the optical properties of the lens during the life span of an individual. Previous study has shown that the inherited mutation G61C results in autosomal dominant congenital cataract. In this research, we studied the effects of the G61C mutation on γD-crystallin structure, stability and aggregation via biophysical methods. CD, intrinsic and extrinsic fluorescence spectroscopy indicated that the G61C mutation did not affect the native structure of γD-crystallin. The stability of γD-crystallin against heat- or GdnHCl-induced denaturation was significantly decreased by the mutation, while no influence was observed on the acid-induced unfolding. The mutation mainly affected the transition from the native state to the intermediate but not that from the intermediate to the unfolded or aggregated states. At high temperatures, both proteins were able to form aggregates, and the aggregation of the mutant was much more serious than the wild type protein at the same temperature. At body temperature and acidic conditions, the mutant was more prone to form amyloid-like fibrils. The aggregation-prone property of the mutant was not altered by the addition of reductive reagent. These results suggested that the decrease in protein stability followed by aggregation-prone property might be the major cause in the hereditary cataract induced by the G61C mutation

Inhibition of Cardiac Sympathetic Afferent Reflex and Sympathetic Activity by Baroreceptor and Vagal Afferent Inputs in Chronic Heart Failure

BACKGROUND: Cardiac sympathetic afferent reflex (CSAR) contributes to sympathetic activation and angiotensin II (Ang II) in paraventricular nucleus (PVN) augments the CSAR in vagotomized (VT) and baroreceptor denervated (BD) rats with chronic heart failure (CHF). This study was designed to determine whether it is true in intact (INT) rats with CHF and to determine the effects of cardiac and baroreceptor afferents on the CSAR and sympathetic activity in CHF. METHODOLOGY/PRINCIPAL FINDINGS: Sham-operated (Sham) or coronary ligation-induced CHF rats were respectively subjected to BD+VT, VT, cardiac sympathetic denervation (CSD) or INT. Under anesthesia, renal sympathetic nerve activity (RSNA) and mean arterial pressure (MAP) were recorded, and the CSAR was evaluated by the RSNA and MAP responses to epicardial application of capsaicin. Either CSAR or the responses of RSNA, MAP and CSAR to Ang II in PVN were enhanced in CHF rats treated with BD+VT, VT or INT. Treatment with VT or BD+VT potentiated the CSAR and the CSAR responses to Ang II in both Sham and CHF rats. Treatment with CSD reversed the capsaicin-induced RSNA and MAP changes and the CSAR responses to Ang II in both Sham and CHF rats, and reduced the RSNA and MAP responses to Ang II only in CHF rats. CONCLUSIONS: The CSAR and the CSAR responses to Ang II in PVN are enhanced in intact CHF rats. Baroreceptor and vagal afferent activities inhibit CSAR and the CSAR responses to Ang II in intact Sham and CHF rats

Predicting Anatomical Therapeutic Chemical (ATC) Classification of Drugs by Integrating Chemical-Chemical Interactions and Similarities

The Anatomical Therapeutic Chemical (ATC) classification system, recommended by the World Health Organization, categories drugs into different classes according to their therapeutic and chemical characteristics. For a set of query compounds, how can we identify which ATC-class (or classes) they belong to? It is an important and challenging problem because the information thus obtained would be quite useful for drug development and utilization. By hybridizing the informations of chemical-chemical interactions and chemical-chemical similarities, a novel method was developed for such purpose. It was observed by the jackknife test on a benchmark dataset of 3,883 drug compounds that the overall success rate achieved by the prediction method was about 73% in identifying the drugs among the following 14 main ATC-classes: (1) alimentary tract and metabolism; (2) blood and blood forming organs; (3) cardiovascular system; (4) dermatologicals; (5) genitourinary system and sex hormones; (6) systemic hormonal preparations, excluding sex hormones and insulins; (7) anti-infectives for systemic use; (8) antineoplastic and immunomodulating agents; (9) musculoskeletal system; (10) nervous system; (11) antiparasitic products, insecticides and repellents; (12) respiratory system; (13) sensory organs; (14) various. Such a success rate is substantially higher than 7% by the random guess. It has not escaped our notice that the current method can be straightforwardly extended to identify the drugs for their 2nd-level, 3rd-level, 4th-level, and 5th-level ATC-classifications once the statistically significant benchmark data are available for these lower levels

A RG-II type polysaccharide purified from Aconitum coreanum and their anti-inflammatory activity

Korean mondshood root polysaccharides (KMPS) isolated from the root of Aconitum coreanum (Lévl.) Rapaics have shown anti-inflammatory activity, which is strongly influenced by their chemical structures and chain conformations. However, the mechanisms of the anti-inflammatory effect by these polysaccharides have yet to be elucidated. A RG-II polysaccharide (KMPS-2E, Mw 84.8 kDa) was isolated from KMPS and its chemical structure was characterized by FT-IR and NMR spectroscopy, gas chromatography–mass spectrometry and high-performance liquid chromatography. The backbone of KMPS-2E consisted of units of [→6) -β-D-Galp (1→3)-β-L-Rhap-(1→4)-β-D-GalpA-(1→3)-β-D-Galp-(1→] with the side chain →5)-β-D-Arap (1→3, 5)-β-D-Arap (1→ attached to the backbone through O-4 of (1→3,4)-L-Rhap. T-β-D-Galp is attached to the backbone through O-6 of (1→3,6)-β-D-Galp residues and T-β-D-Ara is connected to the end group of each chain. The anti-inflammatory effects of KMPS-2E and the underlying mechanisms using lipopolysaccharide (LPS) - stimulated RAW 264.7 macrophages and carrageenan-induced hind paw edema were investigated. KMPS-2E (50, 100 and 200 µg/mL) inhibits iNOS, TLR4, phospho-NF-κB–p65 expression, phosphor-IKK, phosphor-IκB-α expression as well as the degradation of IκB-α and the gene expression of inflammatory cytokines (TNF-α, IL-1β, iNOS and IL-6) mediated by the NF-κB signal pathways in macrophages. KMPS-2E also inhibited LPS-induced activation of NF-κB as assayed by electrophorectic mobility shift assay (EMSA) in a dose-dependent manner and it reduced NF-κB DNA binding affinity by 62.1% at 200µg/mL. In rats, KMPS-2E (200 mg/kg) can significantly inhibit carrageenan-induced paw edema as ibuprofen (200 mg/kg) within 3 h after a single oral dose. The results indicate that KMPS-2E is a promising herb-derived drug against acute inflammation

Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases

Imbalanced Multi-Modal Multi-Label Learning for Subcellular Localization Prediction of Human Proteins with Both Single and Multiple Sites

It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using Gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches

A Multi-Label Predictor for Identifying the Subcellular Locations of Singleplex and Multiplex Eukaryotic Proteins

Subcellular locations of proteins are important functional attributes. An effective and efficient subcellular localization predictor is necessary for rapidly and reliably annotating subcellular locations of proteins. Most of existing subcellular localization methods are only used to deal with single-location proteins. Actually, proteins may simultaneously exist at, or move between, two or more different subcellular locations. To better reflect characteristics of multiplex proteins, it is highly desired to develop new methods for dealing with them. In this paper, a new predictor, called Euk-ECC-mPLoc, by introducing a powerful multi-label learning approach which exploits correlations between subcellular locations and hybridizing gene ontology with dipeptide composition information, has been developed that can be used to deal with systems containing both singleplex and multiplex eukaryotic proteins. It can be utilized to identify eukaryotic proteins among the following 22 locations: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centrosome, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome, (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole. Experimental results on a stringent benchmark dataset of eukaryotic proteins by jackknife cross validation test show that the average success rate and overall success rate obtained by Euk-ECC-mPLoc were 69.70% and 81.54%, respectively, indicating that our approach is quite promising. Particularly, the success rates achieved by Euk-ECC-mPLoc for small subsets were remarkably improved, indicating that it holds a high potential for simulating the development of the area. As a user-friendly web-server, Euk-ECC-mPLoc is freely accessible to the public at the website http://levis.tongji.edu.cn:8080/bioinfo/Euk-ECC-mPLoc/. We believe that Euk-ECC-mPLoc may become a useful high-throughput tool, or at least play a complementary role to the existing predictors in identifying subcellular locations of eukaryotic proteins