Using pseudo-amino acid composition and support vector machine to predict protein structural class

As a result of genome and other sequencing projects, the gap between the number of known protein sequences and the number of known protein structural classes is widening rapidly. In order to narrow this gap, it is vitally important to develop a computational prediction method for fast and accurately determining the protein structural class. In this paper, a novel predictor is developed for predicting protein structural class. It is featured by employing a support vector machine learning system and using a different pseudo-amino acid composition (PseAA), which was introduced to, to some extent, take into account the sequence-order effects to represent protein samples. As a demonstration, the jackknife cross-validation test was performed on a working dataset that contains 204 non-homologous proteins. The predicted results are very encouraging, indicating that the current predictor featured with the PseAA may play an important complementary role to the elegant covariant discriminant predictor and other existing algorithms.     

Identification and characterization of FHL3 as a novel angiogenin-binding partner

Angiogenin (Ang) is known to induce cell proliferation and inhibit apoptosis by cellular signaling pathways and by direct nuclear functions of Ang, but the mechanism of action for Ang is not yet clear. The aim of present study was to identify novel binding partner of Ang and to explore the underlying mechanism. With the use of yeast two-hybrid screening system, Ang was used as the bait to screen human fetal hepatic cDNA library for interacting proteins. Four and a half LIM domains 3 (FHL3) was identified as a novel Ang binding partner. The interaction between Ang and the full length FHL3 was further confirmed by yeast two-hybrid assay, co-immunoprecipitation and GST pull-down assays. Furthermore, FHL3 was required for Ang-mediated HeLa cell proliferation and nuclear translocation of Ang. These findings suggest that the interaction between Ang and FHL3 may provide some clues to the mechanisms of Ang-regulated cell growth and apoptosis.     

L1 cell adhesion molecule overexpression in hepatocellular carcinoma associates with advanced tumor progression and poor patient survival

ABSTRACT: OBJECTIVE: L1 cell adhesion molecule (L1CAM), as a member of the immunoglobulin superfamily, has recently been observed in a variety of human malignancies. However, no data of L1CAM are available for hepatocellular carcinoma (HCC). The aim of this study was to investigate the expression of L1CAM in HCC and determine its correlation with tumor progression and prognosis. METHODS: One-hundred and thirty HCC patients who had undergone curative liver resection were selected and immunohistochemistry, Western blotting, and quantitative real time polymerase chain reaction (Q-PCR) were performed to analyze L1CAM expression in the respective tumors. RESULTS: Immunohistochemistry, Western blotting, and Q-PCR consistently confirmed the overexpression of L1CAM in HCC tissues compared with their adjacent nonneoplastic tissues at both protein and gene level (both P <0.01). Additionally, the high expression of L1CAM was significantly associated with advanced tumor stage (P = 0.02) and advanced tumor grade (P = 0.03), respectively. Moreover, HCC patients with high L1CAM expression were significantly associated with lower 5-year overall survival (P <0.01) and lower 5-year disease-free survival (P <0.01), respectively. The Cox proportional hazards model further showed that L1CAM over-expression was an independent poor prognostic factor for both 5-year disease-free survival (P = 0.02) and 5-year overall survival (P = 0.008) in HCC. CONCLUSION: Our data suggest for the first time that L1CAM expression in HCC was significantly correlated with the advanced tumor progression and was an independent poor prognostic factor for both overall survival and disease-free survival in patients with HCC.Virtual slidesThe virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1970024872761542.     

Comprehensive peptidome analysis of mouse livers by size exclusion chromatography prefractionation and nanoLC-MS/MS identification

Peptidome analysis has received increasing attention in recent years. Cancer diagnosis by serum peptidome has also been reported by peptides' profiling for discovery of peptide biomarkers. Tissue, which may have a higher biomarker concentration than blood, has not been investigated extensively by means of peptidome analysis. Here, a method for the peptidome analysis of mouse liver was developed by the combination of size exclusion chromatography (SEC) prefractionation with nano-liquid chromatography-tamdem mass spectrometry (nanoLC-MS/MS) analysis. The extracted peptides from mouse liver were separated according to their molecular weight using a size exclusion column. MALDI-TOF MS was used to characterize the molecular weight distribution of the peptides in fractions eluted from the SEC column. The low molecular weight (LMW) (MW < 3000 Da) peptides in the collected fractions were directly analyzed by LC-MS/MS which resulted in the identification of 1181 unique peptides (from 371 proteins). The high molecular weight (HMW) (MW > 3000 Da) peptides in the early two fractions from the SEC column were first digested with trypsin, and the resulted digests were then analyzed by LC-MS/MS, which led to the identification of 123 and 127 progenitor proteins of the HMW peptides in fractions 1 and 2, respectively. Analysis of the peptides' cleavage sites showed that the peptides are cleaved in regulation, which may reflect the protease activity and distribution in body, and also represent the biological state of the tissue and provide a fresh source for biomarker discovery.     

Prediction of protein structure class by coupling improved genetic algorithm and support vector machine

Structural class characterizes the overall folding type of a protein or its domain. Most of the existing methods for determining the structural class of a protein are based on a group of features that only possesses a kind of discriminative information for the prediction of protein structure class. However, different types of discriminative information associated with primary sequence have been completely missed, which undoubtedly has reduced the success rate of prediction. We present a novel method for the prediction of protein structure class by coupling the improved genetic algorithm (GA) with the support vector machine (SVM). This improved GA was applied to the selection of an optimized feature subset and the optimization of SVM parameters. Jackknife tests on the working datasets indicated that the prediction accuracies for the different classes were in the range of 97.8–100% with an overall accuracy of 99.5%. The results indicate that the approach has a high potential to become a useful tool in bioinformatics.     

MazF cleaves cellular mRNAs specifically at ACA to block protein synthesis in Escherichia coli

Escherichia coli contains operons called "addiction modules," encoding toxin and antitoxin, which are responsible for growth arrest and cell death. Here, we demonstrate that MazF toxin encoded by "mazEF addiction module" is a sequence-specific (ACA) endoribonuclease functional only for single-stranded RNA. MazF works as a ribonuclease independent of ribosomes, and is, therefore, functionally distinct from RelE, another E. coli toxin, which assists mRNA cleavage at the A site on ribosomes. Upon induction, MazF cleaves whole cellular mRNAs to efficiently block protein synthesis. Purified MazF inhibited protein synthesis in both prokaryotic and eukaryotic cell-free systems. This inhibition was released by MazE, the labile antitoxin against MazF. Thus, MazF functions as a toxic endoribonuclease to interfere with the function of cellular mRNAs by cleaving them at specific sequences leading to rapid cell growth arrest and cell death. The role of such endoribonucleases may have broad implication in cell physiology under various growth conditions.     

黄芪对镉致大鼠睾丸支持细胞超微结构及波形蛋白、E-钙粘蛋白表达改变的保护作用

目的 探讨黄芪对镉致大鼠睾丸支持细胞损伤的保护作用.方法 21只成年雄性SD大鼠随机分成镉组(0.1％氯化镉腹腔内注射,1mg/Kg体重/天,5天/周,处理后1、2、3、4周取材)、镉加黄芪组(注射氯化镉的同时注射黄芪,10g/Kg体重/天,5天/周,处理后2、4周取材)和对照组(腹腔内注射等量生理盐水).睾丸取材作光镜、免疫组织化学染色和图像分析及超微结构观察.结果 光镜H.E染色对照组支持细胞核不规则,染色浅,核仁明显,镉处理后胞浆内有空泡形成,镉加黄芪组支持细胞未见明显改变.对照组波形蛋白阳性产物在支持细胞靠近基室腔的胞浆中表达,E-钙粘蛋白阳性产物则主要定位于生精上皮近腔室的支持细胞和部分生精细胞胞浆中.镉处理后支持细胞胞浆中波形蛋白和E-钙粘蛋白阳性产物表达的平均光密度值均明显降低(P＜0.05),镉加黄芪组阳性产物表达虽较对照组减弱但明显高于相应镉组(P＜0.05).镉处理组支持细胞胞质特化区和紧密连接破坏,镉加黄芪组支持细胞超微病变较相应镉组为轻.结论 镉降低大鼠睾丸支持细胞波形蛋白和E-钙粘蛋白的表达并造成支持细胞的超微结构损伤,黄芪具有较好的保护效果.     

‘Green tides’ are overwhelming the coastline of our blue planet: taking the world’s largest example

A broad spectrum of events that come under the category of green tide are recognized world-wide as a response to elevated levels of seawater nutrients in coastal areas. Green tides involve a wide diversity of sites, macroalgal species, consequences, and possible causes. Here we review the effect of natural and man-induced environmental fluctuations on the frequency and apparent spread of green tides. This article highlights the need for interdisciplinary research aimed at shedding light on the basic mechanisms governing the occurrence and succession of green algae in coastal seas. This will result in more effective management and mitigation of the effects of green tides, thus safeguarding the intrinsic and commercial value of coastal marine ecosystems.     

MicroRNA-30a sensitizes tumor cells to cis-platinum via suppressing beclin 1-mediated autophagy

Autophagy is activated in cancer cells during chemotherapy and often contributes to tumor chemotherapy resistance. In this study, we characterized the role of microRNA-30a (miR-30a) in the coordination of cancer cell apoptosis and autophagy, which determines the sensitivity of cancer cells to chemotherapy. First, the autophagy activity in cancer cells increased after cis-dichloro-diamine platinum (cis-DDP) or Taxol treatment, as indicated by the enhanced expression of beclin 1, a key regulator of autophagy, and increased number of LC3-positive autophagosomes. Second, miRNA screening using a TaqMan probe-based quantitative RT-PCR assay identified that miR-30a, a miRNA that targets beclin 1, was significantly reduced in tumor cells by cis-DDP treatment. Forced expression of miR-30a significantly reduced beclin 1 and the autophagy activity of tumor cells induced by cis-DDP. Third, the blockade of tumor cell autophagy activity by miR-30a expression or 3-methyladenine significantly increased tumor cell apoptosis induced by cis-DDP treatment. Finally, an in vivo tumor implantation mouse model clearly showed that elevation of miR-30a in implanted tumor cells by administration of the recombinant lentivirus expressing miR-30a strongly enhanced cis-DDP-induced apoptosis of tumor cells. In conclusion, our results demonstrate for the first time that miR-30a can sensitize tumor cells to cis-DDP via reducing beclin 1-mediated autophagy and that increasing miR-30a level in tumor cells represents a novel approach to enhance the efficacy of chemotherapy during cancer treatment.