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Although collagen is widely used as an emulsifier in the food industry, its emulsifying properties are strongly influenced by processing conditions. This research investigated the effects of NaCl on the emulsifying properties of type I collagen after heating. Before heating, the solubility, emulsifying activity index (EAI), emulsifying stability index (ESI), and viscosity of type I collagen initially increased after adding NaCl (0.2 M), after which decreased with increasing NaCl concentration (0.4 M and 0.6 M) due to salt-in effect and the salt-out effects of the protein. While after heating (90℃, 30 min), the collagen became more soluble, with improved EAI and ESI, viscosity, and reduced particle size in response to increasing NaCl concentrations. It was found that NaCl increased the EAI of type I collagen twice after heating, and the EAI reached its maximum at 0.6 M NaCl concentration. We concluded that the improved emulsifying properties may due to thermal denaturation of the protein, resulting in an unfolded and disordered structure with increase of hydrogen bonds with water, rupture of disulfide bonds, and exposure of hydrophobic groups, leading to the increase of adsorption at the oil/water interface. Meanwhile, the Raman spectra analysis and Atomic Force Microscope (AFM) images showed that unfolding of the collagen triple helix was gradually destroyed after NaCl addition and heating, with emulsifying properties improved. The specific outcomes of present study can be adapted towards the requirements to improve the quality of emulsified meat products in the food industry.

Ultrasound-assisted enzymolysis has been applied to improve conventional enzymolysis, while there are rare reports on the application of ultrasound to high-concentration feather protein enzymolysis. Therefore, the feasibility of dual-frequency slit ultrasound (DFSU) for enzymolysis of high-concentration hydrolyzed feather meal (HFM), as well as the biological activities and structural characteristics of hydrolysates were investigated. The single-factor test was used to optimize the ultrasonic processing parameters: substrate concentration, frequency mode, intermittent ratio, power density, and time. The results showed that protein recovery rate and conversion rate increased by 6.08% and 18.63% under the optimal conditions (200 g/L, 28/80 kHz, 5:2 s/s, 600 W/L, and 3 h) compared with conventional enzymolysis, respectively. The macromolecular proteins in hydrolysates were converted into micromolecular peptides (500 Da) when treated by DFSU, and antioxidant activity and angiotensin-I-converting enzyme (ACE) inhibitory activity of hydrolysates were increased. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) images illustrated the microstructure changes of feather protein particles in the ultrasound-assisted enzymatic hydrolysates of HFM (UEH), including more porous, smaller, and more uniform. Additionally, the conformation of protein molecules was significantly affected (P 0.05), including the increase in free sulfhydryl (SH), the decrease in disulfide bond (SS) and surface hydrophobicity (H

Accumulating evidence has shown that casein kinase 2 interacting protein-1 (CKIP-1) is a pivotal regulator of apoptosis and oxidative stress. However, whether CKIP-1 is involved in regulating neuronal injury during the progression of cerebral ischemia/reperfusion injury remains unknown. In the present study, we aimed to investigate the potential role and underlying mechanism of CKIP-1 in regulating neuronal apoptosis and oxidative stress induced by oxygen-glucose deprivation/reoxygenation (OGD/R) treatment in vitro. Herein, we found that OGD/R treatment resulted in a significant increase in CKIP-1 expression in cultured hippocampal neurons. The silencing of CKIP-1 exacerbated OGD/R-induced neuronal apoptosis and production of reactive oxygen species. By contrast, CKIP-1 overexpression reduced the apoptosis and reactive oxygen species production induced by the OGD/R treatment. Mechanistically, CKIP-1 inhibited the expression of Kelch-like ECH-associated protein 1 (Keap1) and promoted the expression of nuclear factor E2-related factor 2 (Nrf2). In addition, CKIP-1 increased the activation of antioxidant response element and the expression of downstream antioxidant genes. However, Keap1 overexpression or Nrf2 knockdown partially reversed the neuroprotective effect of CKIP-1 overexpression. Taken together, our results demonstrate that CKIP-1 overexpression alleviates OGD/R-induced neuronal injury by enhancing the Nrf2-mediated anti-oxidative stress signaling pathway, revealing a neuroprotective role of CKIP-1. Our study suggests CKIP-1 as a potential therapeutic target for neuroprotection.

This study was aimed at elucidating the potential mechanisms of quercetin in the treatment of gastric cancer (GC). A network pharmacology approach was used to analyze the targets and pathways of quercetin in treating GC. The predicted targets of quercetin against GC were obtained through database mining, and the correlation of these targets with GC was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Next, the protein-protein interaction (PPI) network was constructed, and overall survival (OS) analysis of hub targets was performed using the Kaplan–Meier Plotter online tool. Finally, the mechanism was further analyzed via molecular docking of quercetin with the hub targets. Thirty-six quercetin-related genes were identified, 15 of which overlapped with GC-related targets. These targets were further mapped to 319 GO biological process terms and 10 remarkable pathways. In the PPI network analysis, six hub targets were identified, including AKT1, EGFR, SRC, IGF1R, PTK2, and KDR. The high expression of these targets was related to poor OS in GC patients. Molecular docking analysis confirmed that quercetin can bind to these hub targets. In conclusion, this study provided a novel approach to reveal the therapeutic mechanisms of quercetin on GC, which will ease the future clinical application of quercetin in the treatment of GC.

Paternally expressed gene-10 (PEG10) could participate in several carcinomas and might be regulated by miR-491. To now, miR-491 was found to play an important role in the sensitivity and mechanism of drug usage in the treatment of colorectal cancer, and drug resistance is a key factor to affect the disease healing. In this study, miR-491, PEG10, Wnt1, and β-catenin expression levels and their correlation with colorectal cancer were assessed in cancer tissues and adjacent parts. And the target relationship between PEG10 and miR-491 was verified. Meanwhile, the impaction of Curcumin on miR-491, PEG10, and Wnt/β-catenin signaling pathway were analyzed in HCT-116 cells. The effects of PEG10 and Curcumin on human HCT-116 cells proliferation and apoptosis were investigated by MTT and flow cytometry assay. Results showed that the expression of miR-491 in colon cancer tissues was decreased, but PEG10, Wnt1, and β-catenin were higher than that in adjacent tissues. The PEG10 gene 3' UTR could combine with miR-491 seed sequence and miR-491 overexpression could cause a decrease in PEG10, Wnt1, and β-catenin levels in human HCT-116 cells. Furthermore, PEG10 overexpression increased the expression levels of Wnt1 and β-catenin, thereby promoting cell proliferation and inhibiting apoptosis. In addition, Curcumin could up-regulate miR-491, inhibit PEG10, and Wnt/β-catenin signaling pathway. Consequently, Curcumin reduced HCT-116 cells proliferation and promoted cells apoptosis via the miR-491/PEG10 pathway. In conclusion, PEG10 was a target gene of miR-491, miR-491/PEG10 strengthen the sensitivity of Curcumin in HCT-116 cells proliferation and apoptosis, which might act as an ideal diagnostic biomarker treatment methods.

Acrolein has been reported to have diverse toxic effects on various organs, including the reproductive system. However, little is known regarding the effects of maternal acrolein exposure on testicular steroidogenesis in male offspring. The present study investigated the effects of acrolein on fetal testosterone production and associated genes. Pregnant Sprague‑Dawley rats were intraperitoneally injected with vehicle (normal saline) or 1, 2 or 5 mg/kg acrolein from gestational day (GD) 14‑20, and fetal testes were examined on GD 21. Fetal body and testicular weights were markedly reduced in pups following exposure to high doses of acrolein (5 mg/kg) in late pregnancy. Notably, in utero exposure of 5 mg/kg acrolein significantly decreased the testicular testosterone level and downregulated the expression levels of steroidogenic acute regulatory protein (StAR) and 3β‑hydroxysteroid dehydrogenase (3β‑HSD), whereas the levels of other steroidogenic enzymes, including scavenger receptor class B, cholesterol side‑chain cleavage enzyme and steroid 17 alpha‑hydroxylase/17,20 lyase, were unaffected. Furthermore, the 3β‑HSD immunoreactive area in the interstitial region of the fetal testes was reduced at a 5 mg/kg dose, whereas the protein expression levels of 4‑hydroxynonenalwere dose‑dependently increased following maternal exposure to acrolein. mRNA expression levels of insulin‑like factor 3, a critical gene involved in testicular descent, were unaltered following maternal acrolein exposure. Taken together, the results of the present study suggested that maternal exposure to high doses of acrolein inhibited fetal testosterone synthesis, and abnormal expression of StAR and 3β‑HSD may be associated with impairment of the steroidogenic capacity.

To investigate the effect of bromodomain and extra-terminal (BET) protein inhibitor JQ1 on expression of autoimmune-related genes in CD4+T cells from patients with systemic lupus erythematosus (SLE). Methods: Peripheral CD4+T cells were isolated by positive selection with CD4 microbeads. The percentage of CD4+T cells were detected by flow cytometry. CD4+T cells were treated by JQ1 at 100 nm/L for 6, 24, 48 h. The expression of T cell-related genes was measured by quantitative real-time PCR (qPCR). The secretion levels of cytokines in culture supernatant were measured by ELISA at 48 h. Results: The percentage of CD4+T cells isolated by CD4 microbeads is 97.2%. Compared with the control group, the mRNA expression levels of IFNG, IL-17F, IL-21, CXCR5 and FOXP3 were down-regulated at 6, 24 and 48 h (P0.05), and IL-17A mRNA level was decreased at 6 and 24 h (P0.01); while IL-4 mRNA level was up-regulated at 24, 48 h (P0.01), and TGF-β1 mRNA level was up-regulated at 6 and 48 h (P0.05) in SLE CD4+T cells treated with JQ1. The secretion levels of IFN-γ and IL-21 in JQ1-treated group were decreased significantly (P0.05), while the secretion levels of IL-4 and TGF-β were up-regulated compared with control group (P0.05). Conclusion: JQ1 can reverse the immune dysregulation and improve the immunity homeostasis in CD4+T cells from patients with SLE.目的：探讨溴结构域(bromodomain and extra-terminal，BET)蛋白抑制剂JQ1对系统性红斑狼疮(systemic lupus erythematosus，SLE)外周血CD4+T细胞中自身免疫相关基因表达的作用。方法：利用磁珠分选获得SLE患者CD4+T细胞，采用流式细胞检测CD4+T细胞纯度。用JQ1(100 nm/L)处理CD4+T细胞6，24，48 h后收集细胞。采用实时荧光定量PCR(quantitative real-time PCR，qPCR)检测不同辅助性T细胞亚类特异性基因的mRNA表达。采用ELISA法检测细胞培养上清中细胞因子的蛋白水平。结果：磁珠分选所得CD4+T细胞百分比为97.2%。与对照组相比，JQ1处理组IFNG，IL-17F，IL-21，CXCR5和FOXP3 mRNA表达水平在6，24，48 h均显著降低(P0.05)，IL-17A mRNA表达在6，24 h显著下降(P0.01)，IL-4 mRNA表达在24，48 h显著升高(P0.01)，TGF-β1 mRNA表达在6，48 h显著升高(P0.05)。JQ1处理48 h细胞培养上清中IFN-γ，IL-21和IL-17蛋白水平明显下降(P0.05)，IL-4和TGF-β蛋白水平明显升高(P0.05)。结论：BET蛋白抑制剂JQ1可纠正SLE患者CD4+T细胞中的免疫调节失衡，促进免疫稳态恢复。.

Respiratory distress is a major cause of mortality in cloned neonatal animals, but its pathogenesis remains poorly understood. Here, we used necropsy and histology procedures to evaluate the lungs of cloned neonatal bovines dying of respiratory distress, finding incomplete lung dilation, alveolar collapse, and thickened alveolar walls. Comparison of the transcriptomes between collapsed lungs of cloned bovines and their normal counterparts revealed 1373 differentially expressed genes in collapsed lungs (p 1.5 or

It has been well recognized that the pace of the development of new drugs and therapeutic interventions lags far behind biological knowledge discovery. Network-based approaches have emerged as a promising alternative to accelerate the discovery of new safe and effective drugs. Based on the integration of several biological resources including two recently published datasets i.e., Drug-target interactions in myocardial infarction (My-DTome) and drug-domain interaction network, this paper reports the association between drugs and protein domains in the context of myocardial infarction (MI). A MI drug-domain interaction network, My-DDome, was firstly constructed, followed by topological analysis and functional characterization of the network. The results show that My-DDome has a very clear modular structure, where drugs interacting with the same domain(s) within each module tend to have similar therapeutic effects. Moreover it has been found that drugs acting on blood and blood forming organs (ATC code B) and sensory organs (ATC code S) are significantly enriched in My-DDome (p0.000001), indicating that by incorporating protein domain information into My-DTome, more detailed insights into the interplay between drugs, their known targets, and seemingly unrelated proteins can be revealed.

Phosphorylation and proteolysis are among the most common post-translational modifications (PTMs), and play critical roles in various biological processes. More recent discoveries imply that the crosstalks between these two PTMs are involved in many diseases. In this work, we construct a post-translational regulatory network (PTRN) consists of phosphorylation and proteolysis processes, which enables us to investigate the regulatory interplays between these two PTMs. With the PTRN, we identify some functional network motifs that are significantly enriched with drug targets, some of which are further found to contain multiple proteins targeted by combinatorial drugs. These findings imply that the network motifs may be used to predict targets when designing new drugs. Inspired by this, we propose a novel computational approach called NetTar for predicting drug targets using the identified network motifs. Benchmarking results on real data indicate that our approach can be used for accurate prediction of novel proteins targeted by known drugs.

Proline-rich tyrosine kinase 2 (Pyk2) is a member of the focal adhesion kinase (FAK) family of non-receptor tyrosine kinases and plays an important role in diverse cellular events downstream of the integrin-family of receptors, including cell migration, proliferation and survival. Here, we have identified a novel role for Src kinase in priming Pyk2 phosphorylation and subsequent activation upon cell attachment on the integrin-ligand fibronectin. By using complementary methods, we show that Src activity is indispensable for the initial Pyk2 phosphorylation on the Y402 site observed in response to cell attachment. In contrast, the initial fibronectin-induced autophosphorylation of FAK in the homologous Y397 site occurs in a Src-independent manner. We demonstrate that the SH2-domain of Src is required for Src binding to Pyk2 and for Pyk2 phosphorylation at sites Y402 and Y579. Moreover, Y402 phosphorylation is a prerequisite for the subsequent Y579 phosphorylation. While this initial phosphorylation of Pyk2 by Src is independent of Pyk2 kinase activity, subsequent autophosphorylation of Pyk2 in trans is required for full Pyk2 phosphorylation and activation. Collectively, our studies reveal a novel function of Src in priming Pyk2 (but not FAK) phosphorylation and subsequent activation downstream of integrins, and shed light on the signaling events that regulate the function of Pyk2.

Aim: Applying nanoscale assembly to the design of a thrombolytic agent. Materials: poly-α,β-DL-aspartyl-L-alanine (molecular weight: 15726 atomic mass units) from the thermal polycondensation of DL-aspartic acid and the amidation of polysuccimide with L-alanine. Methods: The correlation of concentration and pH with nanofeatures, the correlation of concentration with in vitro thrombus lysis activity, the correlation of dose with the in vivo thrombolytic activity and the correlation of the dose with nanofeatures are all explained in this article. Results: Poly-α,β-DL-aspartyl-L-alanine was concentration- (6.4 × 104,2,-4,-6,-8,-10,-12 nM) and pH-dependently (pH 1.2, 7.4 and 7.6) assembled to various nanospecies, exhibited concentration-dependent (4, 8 and 16 µM) lysis action in vitro and dose-dependent (5, 0.5 and 0.05 µmol/kg) thrombolytic action in vivo. Conclusion: Upon entering the stomach, intestinal tract, blood and tissue fluids, poly-α,β-DL-aspartyl-L-alanine assembled to small nanoparticles or nanoblocks to escape the entrapment by macrophages and exhibited desirable thrombolytic action.

Protein domains are functional and structural units of proteins. Therefore, identification of domain-domain interactions (DDIs) can provide insight into the biological functions of proteins. In this article, we propose a novel discriminative approach for predicting DDIs based on both protein-protein interactions (PPIs) and the derived information of non-PPIs. We make a threefold contribution to the work in this area. First, we take into account non-PPIs explicitly and treat the domain combinations that can discriminate PPIs from non-PPIs as putative DDIs. Second, DDI identification is formalized as a feature selection problem, in which it tries to find out a minimum set of informative features (i.e., putative DDIs) that discriminate PPIs from non-PPIs, which is plausible in biology and is able to predict DDIs in a systematic and accurate manner. Third, multidomain combinations including two-domain combinations are taken into account in the proposed method, where multidomain cooperations may help proteins to interact with each other. Numerical results on several DDI prediction benchmark data sets show that the proposed discriminative method performs comparably well with other top algorithms with respect to overall performance, and outperforms other methods in terms of precision. The PPI data sets used for prediction of DDIs and prediction results can be found at http://csb.shu.edu.cn/dipd.

Generally, protein classification is a multi-class classification problem and can be reduced to a set of binary classification problems, where one classifier is designed for each class. The proteins in one class are seen as positive examples while those outside the class are seen as negative examples. However, the imbalanced problem will arise in this case because the number of proteins in one class is usually much smaller than that of the proteins outside the class. As a result, the imbalanced data cause classifiers to tend to overfit and to perform poorly in particular on the minority class. This article presents a new technique for protein classification with imbalanced data. First, we propose a new algorithm to overcome the imbalanced problem in protein classification with a new sampling technique and a committee of classifiers. Then, classifiers trained in different feature spaces are combined together to further improve the accuracy of protein classification. The numerical experiments on benchmark datasets show promising results, which confirms the effectiveness of the proposed method in terms of accuracy. The Matlab code and supplementary materials are available at http://eserver2.sat.iis.u-tokyo.ac.jp/ approximately xmzhao/proteins.html.

It is well established that most of the human genome and those of other mammals and plants are transcribed into RNA without protein-coding capacity, which we define as non-coding RNA. From siRNA to microRNA, whose functions and features have been well characterized, non-coding RNAs have been a popular topic in life science research over the last decade. Long non-coding RNAs (lncRNAs), however, as a novel class of transcripts, are distinguished from these other small RNAs. Recent studies have revealed a diverse population of lncRNAs with different sizes and functions across different species. These populations are expressed dynamically and act as important regulators in a variety of biological processes, especially in gene expression. Nevertheless, the functions and mechanisms of most lncRNAs remain unclear. In this review, we present recent progress in the identification of lncRNAs, their functions and molecular mechanisms, their roles in human diseases, their potential diagnostic and therapeutic applications as well as newer technologies for identifying deregulated lncRNAs in disease tissues.

This paper presents a novel approach to extracting features from motif content and protein composition for protein sequence classification. First, we formulate a protein sequence as a fixed-dimensional vector using the motif content and protein composition. Then, we further project the vectors into a low-dimensional space by the Principal Component Analysis (PCA) so that they can be represented by a combination of the eigenvectors of the covariance matrix of these vectors. Subsequently, the Genetic Algorithm (GA) is used to extract a subset of biological and functional sequence features from the eigen-space and to optimize the regularization parameter of the Support Vector Machine (SVM) simultaneously. Finally, we utilize the SVM classifiers to classify protein sequences into corresponding families based on the selected feature subsets. In comparison with the existing PSI-BLAST and SVM-pairwise methods, the experiments show the promising results of our approach.

A weak cation-exchanger (XIDACE-WCX) has been synthesized by the indirect method. The chromatographic characteristics of the synthesized packing was studied in detail. The standard protein mixture and lysozyme from egg white were separated with the prepared chromatographic column. The chromatographic thermodynamics of proteins was studied in a wide temperature range. Thermodynamic parameters standard enthalpy change (deltaH0) and standard entropy change (deltaS0) and compensation temperature (beta) at protein denaturation were determined in the chromatographic system. By using obtained deltaS0, the conformational change of proteins was judged in the chromatographic process. The linear relationship between deltaH0 and deltaS0 can be used to identify the identity of the protein retention mechanism in the weak cation-exchange chromatography. The interaction between weak cation-exchanger and metal ions was investigated. Several metal chelate columns were prepared. The effects of introducing metal ion into the naked column on protein retention and the retention mechanism of proteins in the metal chalet affinity chromatography were discussed.

We have found that the ubiquitin-proteasome pathway exerts exquisite control of osteoblast differentiation and bone formation in vitro and in vivo in rodents. Structurally different inhibitors that bind to specific catalytic beta subunits of the 20S proteasome stimulated bone formation in bone organ cultures in concentrations as low as 10 nM. When administered systemically to mice, the proteasome inhibitors epoxomicin and proteasome inhibitor-1 increased bone volume and bone formation rates over 70% after only 5 days of treatment. Since the ubiquitin-proteasome pathway has been shown to modulate expression of the Drosophila homologue of the bone morphogenetic protein-2 and -4 (BMP-2 and BMP-4) genes, we examined the effects of noggin, an endogenous inhibitor of BMP-2 and BMP-4 on bone formation stimulated by these compounds and found that it was abrogated. These compounds increased BMP-2 but not BMP-4 or BMP-6 mRNA expression in osteoblastic cells, suggesting that BMP-2 was responsible for the observed bone formation that was inhibited by noggin. We show proteasome inhibitors regulate BMP-2 gene expression at least in part through inhibiting the proteolytic processing of Gli3 protein. Our results suggest that the ubiquitin-proteasome machinery regulates osteoblast differentiation and bone formation and that inhibition of specific components of this system may be useful therapeutically in common diseases of bone loss.

The tumor necrosis factor-alpha death domain pathway contributes to cellular degeneration in a variety of conditions. This study investigates the hypothesis that this death domain pathway is progressively induced in the brain during the progression of Alzheimer's disease (AD). AD cases had increased levels of proapoptotic markers including tumor necrosis factor-alpha (TNFalpha), TNF receptor type 1 (TNF-R1), TNF receptor-associated death domain (TRADD), and caspase-3, 2- to 10-fold higher (P.01) than age-matched controls and 1 to 3 times higher than transitional cases. In striking contrast, potentially neuroprotective TNF receptor type 2 (TNF-R2), and Fas-associated death domain-like interleukin-1beta-converting enzyme (FLICE) inhibitor protein (FLIP) were decreased in AD as compared with age-matched control cases (P.01). Overall, there was an elevation in proapoptotic elements, including a 5-fold increase in TNF-R1 and a 12-fold decrease in FLIP in AD brains. These changes may translate to increased degenerative potential because the downstream effector caspase-3 and product of the TNF pathway was also increased in parallel with enhanced TNF proapoptotic conditions. Our findings suggest that the TNF death receptor pathway and caspases are activated in the early stages of neuronal degeneration in AD.

As an approach for improving the outcome and predictability of periodontal regenerative therapies, we have focused on determining the responses of cells within the local environment to putative regenerative factors. This study examined the effects of bone morphogenetic protein-2 (BMP-2) on murine cementoblasts in vitro. Northern blot analysis indicated that BMP-2 decreased mRNA levels of bone sialoprotein and type I collagen dose-dependently (10–300 ng/mL). At low doses, up to 100 ng/mL, BMP-2 had no effect on transcripts for osteocalcin and osteopontin, whereas at 300 ng/mL, BMP-2 greatly increased expression of these two genes. BMP-2 also inhibited cementoblast-mediated mineral nodule formation in a dose-dependent manner (inhibition was noted at 10 ng/mL). Noggin reversed the effects of BMP-2 on gene expression and on mineralization. These findings reflect the diverse responses of periodontal cells to BMP-2 and highlight the need to consider the complexity of factors involved in designing predictable regenerative therapies.

Background Ibutilide, a class III antiarrhythmic agent has been shown to be cardioprotective in treating atrial fibrillation, promoting cardioconversion and recently this agent has been shown to protect against ER stress induced apoptosis in cardiomyocytes. In this study we begin to identify the mechanism by which ibutilide exerts its cardioprotection in tunicamycin treated cardiomyocytes. We examined ER stress markers including calumenin; a calcium binding ER chaperone protein that has recently been linked to ER stress in cardiomyocytes, in our treated cells. Methods To assess the effect of ibutilide we used the well characterized in vitro model of ER stress induced apoptosis in rat neonatal cardiomyocytes (RNC). RNC were treated with tunicamycin and the degree of ER stress was assessed by quantifying mRNA and protein levels of GRP78, GRP94 and calumenin, and examined the extent of apoptosis by assessing the protein levels of caspase-3/9/12, CHOP, ATF6, p-PERK, spliced XBP-1, the ratio of Bax/Bcl-2 and the percentage of deoxynucleotidyl-transferase- mediated dUTP nick end labeling (TUNEL) positive cells. Results We demonstrate ibutilide attenuated the up-regulation of ER stress markers GRP78 and GRP94 and rescued the decline in calumenin mRNA and protein levels in tunicamycin treated cardiomyocytes. The up-regulation of apoptotic markers caspase-3, CHOP, ATF6, p-PERK, spliced XBP-1, the ratio of Bax/Bcl-2 and the percentage of TUNEL positive cells were also attenuated after ibutilide treatment while the protein levels of Caspase-9 and Caspase-12 were unaffected. Conclusions This study suggests another cardioprotective effect of the antiarrhythmic agent ibutilide whereby pretreatment leads to the attenuation of ER stress induced apoptosis by regulating calumenin expression. This study provides further evidence for the role of calumenin in the cardiomyocyte ER stress response.

Exposure to hypobaric hypoxia causes neuron cell damage, resulting in impaired cognitive function. Effective interventions to antagonize hypobaric hypoxia-induced memory impairment are in urgent need. Ketogenic diet (KD) has been successfully used to treat drug-resistant epilepsy and improves cognitive behaviors in epilepsy patients and other pathophysiological animal models. In the present study, we aimed to explore the potential beneficial effects of a KD on memory impairment caused by hypobaric hypoxia and the underlying possible mechanisms. We showed that the KD recipe used was ketogenic and increased plasma levels of ketone bodies, especially β-hydroxybutyrate. The results of the behavior tests showed that the KD did not affect general locomotor activity but obviously promoted spatial learning. Moreover, the KD significantly improved the spatial memory impairment caused by hypobaric hypoxia (simulated altitude of 6000 m, 24 h). In addition, the improving-effect of KD was mimicked by intraperitoneal injection of BHB. The western blot and immunohistochemistry results showed that KD treatment not only increased the acetylated levels of histone H3 and histone H4 compared to that of the control group but also antagonized the decrease in the acetylated histone H3 and H4 when exposed to hypobaric hypoxia. Furthermore, KD-hypoxia treatment also promoted PKA/CREB activation and BDNF protein expression compared to the effects of hypoxia alone. These results demonstrated that KD is a promising strategy to improve spatial memory impairment caused by hypobaric hypoxia, in which increased modification of histone acetylation plays an important role.

Celastrol, a tripterine derived from the traditional Chinese medicine plant Tripterygium wilfordii Hook F. (“Thunder of God Vine”), has been reported to have multiple effects, such as anti-inflammation, suppression of tumor angiogenesis, inhibition of tumor growth, induction of apoptosis and protection of cells against human neurodegenerative diseases. However, the mechanisms that underlie these functions are not well defined. In this study, we reported for the first time that Celastrol could induce HIF-1α protein accumulation in multiple cancer cell lines in an oxygen-independent manner and that the enhanced HIF-1α protein entered the nucleus and promoted the transcription of the HIF-1 target genes VEGF and Glut-1. Celastrol did not influence HIF-1α transcription. Instead, Celastrol induced the accumulation of the HIF-1α protein by inducing ROS and activating Akt/p70S6K signaling to promote HIF-1α translation. In addition, we found that the activation of Akt by Celastrol was transient. With increased exposure time, inhibition of Hsp90 chaperone function by Celastrol led to the subsequent depletion of the Akt protein and thus to the suppression of Akt activity. Moreover, in HepG2 cells, the accumulation of HIF-1α increased the expression of BNIP3, which induced autophagy. However, HIF-1α and BNIP3 did not influence the cytotoxicity of Celastrol because the main mechanism by which Celastrol kills cancer cells is through stimulating ROS-mediated JNK activation and inducing apoptosis. Furthermore, our data showed that the dose required for Celastrol to induce HIF-1α protein accumulation and enhance HIF-1α transcriptional activation was below its cytotoxic threshold. A cytotoxic dose of Celastrol for cancer cells did not display cytotoxicity in LO2 normal human liver cells, which indicated that the novel functions of Celastrol in regulating HIF-1 signaling and inducing autophagy might be used in new applications, such as in anti-inflammation and protection of cells against human neurodegenerative diseases. Future studies regarding these applications are required.

Autophagy is a dynamic and highly regulated process of self-digestion responsible for cell survival and reaction to oxidative stress. As oxidative stress is increased in uremia and is associated with vascular calcification, we studied the role of autophagy in vascular calcification induced by phosphate. In an in vitro phosphate-induced calcification model of vascular smooth muscle cells (VSMCs) and in an in vivo model of chronic renal failure, autophagy was inhibited by the superoxide dismutase mimic MnTMPyP, superoxide dismutase-2 overexpression, and by knockdown of the sodium-dependent phosphate cotransporter Pit1. Although phosphate-induced VSMC apoptosis was reduced by an inhibitor of autophagy (3-methyladenine) and knockdown of autophagy protein 5, calcium deposition in VSMCs was increased during inhibition of autophagy, even with the apoptosis inhibitor Z-VAD-FMK. An inducer of autophagy, valproic acid, decreased calcification. Furthermore, 3-methyladenine significantly promoted phosphate-induced matrix vesicle release with increased alkaline phosphatase activity. Thus, autophagy may be an endogenous protective mechanism counteracting phosphate-induced vascular calcification by reducing matrix vesicle release. Therapeutic agents influencing the autophagic response may be of benefit to treat aging or disease-related vascular calcification and osteoporosis.

The identification of interactions between drugs and proteins plays key roles in understanding mechanisms underlying drug actions and can lead to new drug design strategies. Here, we present a novel statistical approach, namely PDTD (Predicting Drug Targets with Domains), to predict potential target proteins of new drugs based on derived interactions between drugs and protein domains. The known target proteins of those drugs that have similar therapeutic effects allow us to infer interactions between drugs and protein domains which in turn leads to identification of potential drug-protein interactions. Benchmarking with known drug-protein interactions shows that our proposed methodology outperforms previous methods that exploit either protein sequences or compound structures to predict drug targets, which demonstrates the predictive power of our proposed PDTD method.

Background It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required. Results In this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Based on the selected features, nine individual-feature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS (A combined model based on Protrusion Index and Solvent accessibility), is developed to further improve the prediction accuracy. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. In addition, we also demonstrate the predictive power of our proposed method by modelling two protein complexes: the calmodulin/myosin light chain kinase complex and the heat shock locus gene products U and V complex, which indicate that our method can identify more hot spots in these two complexes compared with other state-of-the-art methods. Conclusion We have developed an accurate prediction model for hot spot residues, given the structure of a protein complex. A major contribution of this study is to propose several new features based on the protrusion index of amino acid residues, which has been shown to significantly improve the prediction performance of hot spots. Moreover, we identify a compact and useful feature subset that has an important implication for identifying hot spot residues. Our results indicate that these features are more effective than the conventional evolutionary conservation, pairwise residue potentials and other traditional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spot residues. The data and source code are available on web site http://home.ustc.edu.cn/~jfxia/hotspot.html.

In this study, we propose a new method to predict hairpins in proteins and its evaluation based on the support vector machine. Different from previous methods, new feature representation scheme based on auto covariance is adopted. We also investigate two structure properties of proteins (protein secondary structure and residue conformation propensity), and examine their effects on prediction. Moreover, we employ an ensemble classifier approach based on the majority voting to improve prediction accuracy on hairpins. Experimental results on a dataset of 1926 protein chains show that our approach outperforms those previously published in the literature, which demonstrates the effectiveness of the proposed method.

Phi-turns are irregular secondary structure elements consisting of short backbone fragments (six-amino-acid residues) where the backbone reverses its overall direction. They play an important role in proteins from both the structural and functional points of view. Recently, some methods have been proposed to predict phi-turns. In this study, a new method of phi-turn prediction that uses a two-stage classification scheme is proposed based on support vector machine. In addition, different from previous methods, new coding schemes based on the physicochemical properties and the structural properties of proteins are adopted. Seven-fold cross validation based on a dataset of 640 non-homologue protein chains is used to evaluate the performance of our method. The experiment results show our method can yield a promising performance, which confirms the effectiveness of the proposed approach.

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.

Annotating proteins with biological functions is one of the main goals in post genomic era. Various high-throughout technologies, e.g., yeast two-hybrid systems and microarray, have provided an alternative way to protein function prediction. Despite the success obtained by high-throughout data, the errors in the data have not been handled well. In this work, a new technique for protein function prediction is presented, where a weighted functional linkage graph is generated by exploiting the existing protein-protein interaction data, complex data and gene expression data. By finding the shortest path in the functional linkage graph, the functional links among proteins can be captured. With the functional links available, the functions of unknown proteins can be predicted utilising support vector machines and the functions of those proteins that have functional links to the unknown proteins. In addition, the boosting algorithm is employed to further improve the prediction accuracy. The experiments on yeast genes show promising results and prove the efficiency of the proposed methods.

Protein function prediction is one of the main challenges in post-genomic era. The availability of large amounts of high-throughput data provides an alternative approach to handling this problem from the computational viewpoint. In this review, we provide a comprehensive description of the computational methods that are currently applicable to protein function prediction, especially from the perspective of machine learning. Machine learning techniques can generally be classified as supervised learning, semi-supervised learning and unsupervised learning. By classifying the existing computational methods for protein annotation into these three groups, we are able to present a comprehensive framework on protein annotation based on machine learning techniques. In addition to describing recently developed theoretical methodologies, we also cover representative databases and software tools that are widely utilized in the prediction of protein function.

Domains are structural and functional units of proteins and play an important role in functional genomics. Theoretically, the functions of a protein can be directly inferred if the biological functions of its component domains are determined. Despite the important role that domains play, only a small number of domains have been annotated so far, and few works have been performed to predict the functions of domains. Hence, it is necessary to develop automatic methods for predicting domain functions based on various available data. In this article, two new methods, that is, the threshold-based classification method and the support vector machines method, are proposed for protein domain function prediction by integrating heterogeneous information sources, including protein-domain mapping features, domain-domain interactions, and domain coexisting features. We show that the integration of heterogeneous information sources improves not only prediction accuracy but also annotation reliability when compared with the methods using only individual information sources.

Common-path differential phase-contrast interferometry measures the spatial gradient of surface dipole density on a bio-optical compact disk (BioCD) and is sensitive to small changes in dipole density following molecular binding of target molecules out of solution. The recognition molecules are antibody IgG proteins that are deposited in periodic patterns on the BioCD using soft lithography or photolithography on the silanized silica surfaces of dielectric mirrors. Spatial carrier-wave sideband demodulation extracts the slowly varying protein envelope that modulates the protein carrier frequency. The experimental interferometric profilometry has surface height sensitivity down to 20 pm averaged over a lateral scale of 70 microm with a corresponding scaling mass sensitivity limit of 1.5 pg/mm. Under the conditions of an IgG immunoassay with background changes caused during incubation, the scaling mass sensitivity is approximately 7 pg/mm. A saturated reverse immunoassay performed with IgG at 100 ng/ml showed false positive and false negative rates of 0.2%.

Embryonic stem (ES) cells can maintain self-renewal and differentiate into all three embryonic germ layer derivatives. The regulatory network of key transcription factors including Oct4, Nanog, and Sox2 plays a crucial role in maintaining the pluripotency of ES cells. Dissection of these transcriptional regulators has provided critical insights into mechanisms underlying the self-renewal and early differentiation of ES cells. Here, we identified a highly differentially expressed EST between human ES (hES) cells and 7-day embryoid bodies (EBs) by microarray analysis. By EST assembly, 5'-RACE and Northern blot, a novel hES cells related gene (named as HESRG) with two different transcripts in size of 1.2 and 3.1 kb was successfully cloned. Further RT-PCR and real-time RT-PCR (qPCR) results showed that HESRG was highly expressed in undifferentiated hES cells, but not expressed in all kinds of normal human abortive fetal tissues or human adult testis. The expression of HESRG was repressed upon differentiation of hES cells, in vitro and in vivo. Moreover, the transcription factor Oct4 binding site was found in the putative promoter region of HESRG. The above suggested an important role for the novel HESRG gene in supporting the undifferentiated state or self-renewal of hES cells.

The Smith-Waterman (SW) algorithm is a typical technique for local sequence alignment in computational biology. However, the SW algorithm does not consider the local behaviours of the amino acids, which may result in loss of some useful information. Inspired by the success of Markov Edit Distance (MED) method, this paper therefore proposes a novel Markov pairwise protein sequence alignment (MPPSA) method that takes the local context dependencies into consideration. The numerical results have shown its superiority to the SW for pairwise protein sequence comparison.

Background: We describe a direct-detection immunoassay that uses high-speed optical interferometry on a biological compact disc (BioCD). Methods: We fabricated phase-contrast BioCDs from 100-mm diameter 1.1-mm thick borosilicate glass disks coated with a 10-layer dielectric stack of Ta2O5/SiO2 that serves as a mirror with a center wavelength at 635 nm. The final layer is a lambda/4 layer of SiO2 onto which protein patterns are immobilized through several different chemical approaches. Protein on the disc is scanned by a focused laser spot as the disc spins. Interaction of the light with the protein provides both a phase-modulated signal and a local reference that are combined interferometrically to convert phase into intensity. A periodic pattern of protein on the spinning disc produces an intensity modulation as a function of time that is proportional to the surface-bound mass. The binding of antigen or antibodies is detected directly, without labels, by a change in the interferometric intensity. The technique is demonstrated with a reverse assay of immobilized rabbit and mouse IgG antigen incubated against anti-IgG antibody in a casein buffer. Results: The signal increased with increased concentration of analyte. The current embodiment detected a concentration of 100 ng/L when averaged over similar to 3000 100-micron-diameter protein spots. Conclusions: High-speed interferometric detection of label-free protein assays on a rapidly spinning BioCD is a high-sensitivity approach that is amenable to scaling up to many analytes. (c) 2006 American Association for Clinical Chemistry.

Bone morphogenetic proteins (BMPs) are known to promote periodontal tissue regeneration, while noggin inhibits the biological activities of BMP-2, -4, and -7. To investigate the effect of BMPs and noggin gene transfer on cementogenesis, we used cloned murine cementoblasts (OCCM). Cells were transduced using adenoviruses encoding BMP-7 (Ad-BMP-7), noggin devoid of the heparin binding site (Ad-NOGDeltaB2), or a control adenovirus encoding green fluorescent protein (Ad-GFP). Cells were seeded into 3D polymer scaffolds and implanted into SCID mice to determine the in vivo mineral-inducing ability of the cells. Cells transduced with Ad-NOGDeltaB2 at 3 and 6 weeks postimplantation exhibited reduced mineral formation compared with all other groups. Although gene expression of osteocalcin and bone sialoprotein increased after Ad-BMP-7 transduction in vitro, following BMP-7 gene transfer in vivo, transcripts for OCN and BSP were not significantly different from controls, and mineral density was not significantly increased compared with Ad-GFP and NT groups. These results indicate that in mature cementoblast populations, gene transfer of noggin inhibits biomineralization induced by cementoblasts, whereas exogenous BMP has minimal effects on mineralization.

Silk glands are specialized in the synthesis of several secretory proteins. Expression of genes encoding the silk proteins in Bombyx mori silk glands with strict territorial and developmental specificities is regulated by many transcription factors. In this study, we have characterized B. mori sage, which is closely related to sage in the fruitfly Drosophila melanogaster. It is termed Bmsage; it encodes transcription factor Bmsage, which belongs to the Mesp subfamily, containing a basic helix-loop-helix motif. Bmsage transcripts were detected specifically in the silk glands of B. mori larvae through RT-PCR analysis. Immunoblotting analysis confirmed the Bmsage protein existed exclusively in B. mori middle and posterior silk gland cells. Bmsage has a low level of expression in the 4th instar molting stages, which increases gradually in the 5th instar feeding stages and then declines from the wandering to the pupation stages. Quantitative PCR analysis suggested the expression level of Bmsage in a high silk strain was higher compared to a lower silk strain on day 3 of the larval 5th instar. Furthermore, far western blotting and co-immunoprecipitation assays showed the Bmsage protein interacted with the fork head transcription factor silk gland factor 1 (SGF1). An electrophoretic mobility shift assay showed the complex of Bmsage and SGF1 proteins bound to the A and B elements in the promoter of fibroin H-chain gene(fib-H), respectively. Luciferase reporter gene assays confirmed the complex of Bmsage and SGF1 proteins increased the expression of fib-H. Together, these results suggest Bmsage is involved in the regulation of the expression of fib-H by being together with SGF1 in B. mori PSG cells.