Expression, purification, characterization, and site-directed mutagenesis of phosphorylase kinase [upsilon] subunit

The overall aim was to elucidate the substrate specificity and regulatory properties of the catalytic subunit of phosphorylase kinase (PhK) to better understand how this enzyme works. I have expressed the PhK [gamma] subunit (full-length and seven truncated forms) in E. coli. One of the truncated forms of [gamma], [gamma][subscript]1-300 has a 2-fold higher specific activity than the full-length [gamma], suggesting that an autoinhibitory domain(s) is located at the C-terminus of [gamma], [gamma][subscript]301-386. The truncated [gamma][subscript]1-300 purified to homogeneity has several properties similar to full-length [gamma], including its substrate specificity and metal ion response. Therefore, [gamma][subscript]1-300 was used as a model enzyme to probe structure-function relationships of PhK [gamma] subunit. Charge to alanine and charge reversal scanning mutations were used to locate substrate and metal ion binding sites of [gamma]. The secondary structures of mutant proteins were evaluated by FT-IR/PAS (Fourier transform infrared/photoacoustic spectroscopy). Those mutant proteins with similar secondary structures compared to wild-type [gamma][subscript]1-300 were further characterized. Two mutant proteins, E111K and E154R, were shown to be involved in substrate, pseudosubstrate, and metal ion binding. Using these two mutants, we demonstrated that E[superscript]111 binds to the P-3 site (K[superscript]11) and E[superscript]154 interacts with the P-2 site (Q[superscript]12) of phosphorylase b. Based on results with these two mutants and others, it is suggested that the second metal ion binding site of [gamma] is between the D[superscript]168FG loop and E[superscript]111--KPE[superscript]154N loop similar to the second metal ion binding site in cAMP-dependent protein kinase. Two synthetic peptides, PhK 13 ([gamma][subscript]302-326) and PhK 5 ([gamma][subscript]342-366), corresponding to two calmodulin binding regions of [gamma] were used as inhibitors. Based on studies of inhibition mechanisms of [gamma][subscript]1-300 and both mutant proteins, we suggest the inhibition mechanism of PhK 13 is through a pseudosubstrate mechanism and PhK 5 is via an inhibitory mechanism. The active site region of [gamma] is proposed to have similarities to calmodulin-binding site based on these data and other speculations. Because PhK and [gamma][subscript]1-300 can phosphorylate seryl and tyrosyl residues dependent on the metal ions, new substrates and pathways might exist for this enzyme beyond the known glycogenolysis cascade. Two proteins, which were phosphorylated on tyrosine in muscle cell extracts either directly or indirectly by PhK, raise this possibility

Identification of the substrate and pseudosubstrate binding sites of phosphorylase kinase gamma-subunit.

Journal ArticleUsing site-directed mutagenesis, we proposed that an autoinhibitory domain(s) is located at the C-terminal region (301-386) of the phosphorylase kinase gamma-subunit (Huang, C.-Y.F., Yuan C.-J., Livanova, N.B., and Graves, D.J. (1993) Mol. Cell. Biochem. 127/128, 7-18). Removal of the putative inhibitory domain(s) by truncation results in the generation of a constitutively active and calmodulin-independent form, gamma 1-300. To probe the structural basis of autoinhibition of gamma-subunit activity, two synthetic peptides, PhK13 (gamma 303-327) and PhK5 (gamma 343-367), corresponding to the two calmodulin-binding regions, were assayed for their ability to inhibit gamma 1-300. Competitive inhibition of gamma 1-300 by PhK13 was found versus phosphorylase b (Ki = 1.8 microM) and noncompetitive inhibition versus ATP. PhK5 showed noncompetitive inhibition with respect to both phosphorylase b and ATP. Calmodulin released the inhibition caused by both peptides. These results indicate that there are two distinct auto-inhibitory domains within the C terminus of the gamma-subunit and that these two domains overlap with the calmodulin-binding regions. Two mutant forms of gamma 1-300, E111K and E154R, were used to probe the enzyme-substrate-binding region using peptide substrate analogs corresponding to residues 9-18 of phosphorylase b (KRK11Q12ISVRGL). The data suggest that Glu111 interacts with the P-3 position of the substrate (Lys11) and Glu154 interacts with the P-2 site (Gln12). Both E111K and E154R were competitively inhibited with respect to phosphorylase b by PhK13, with 14- and 8-fold higher Ki values, respectively, than that observed with the wild-type enzyme. These data are consistent with a model for the regulation of the gamma-subunit of phosphorylase kinase in which PhK13 acts as a competitive pseudosubstrate that directly binds the substrate binding site of the gamma-subunit (Glu111 and Glu154)

POINeT: protein interactome with sub-network analysis and hub prioritization

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools.</p> <p>Results</p> <p>We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3) to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles.</p> <p>Conclusion</p> <p>The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to selected tissues can be revealed. The straightforward interface of POINeT makes PPI search and analysis just a few clicks away. The modular design permits further functional enhancement without hampering the simplicity. POINeT is available at <url>http://poinet.bioinformatics.tw/</url>.</p

Selection of DDX5 as a novel internal control for Q-RT-PCR from microarray data using a block bootstrap re-sampling scheme

<p>Abstract</p> <p>Background</p> <p>The development of microarrays permits us to monitor transcriptomes on a genome-wide scale. To validate microarray measurements, quantitative-real time-reverse transcription PCR (Q-RT-PCR) is one of the most robust and commonly used approaches. The new challenge in gene quantification analysis is how to explicitly incorporate statistical estimation in such studies. In the realm of statistical analysis, the various available methods of the probe level normalization for microarray analysis may result in distinctly different target selections and variation in the scores for the correlation between microarray and Q-RT-PCR. Moreover, it remains a major challenge to identify a proper internal control for Q-RT-PCR when confirming microarray measurements.</p> <p>Results</p> <p>Sixty-six Affymetrix microarray slides using lung adenocarcinoma tissue RNAs were analyzed by a statistical re-sampling method in order to detect genes with minimal variation in gene expression. By this approach, we identified <it>DDX5 </it>as a novel internal control for Q-RT-PCR. Twenty-three genes, which were differentially expressed between adjacent normal and tumor samples, were selected and analyzed using 24 paired lung adenocarcinoma samples by Q-RT-PCR using two internal controls, <it>DDX5 </it>and <it>GAPDH</it>. The percentage correlation between Q-RT-PCR and microarray were 70% and 48% by using <it>DDX5 </it>and <it>GAPDH </it>as internal controls, respectively.</p> <p>Conclusion</p> <p>Together, these quantification strategies for Q-RT-PCR data processing procedure, which focused on minimal variation, ought to significantly facilitate internal control evaluation and selection for Q-RT-PCR when corroborating microarray data.</p

Co-Overexpression of Cyclooxygenase-2 and Microsomal Prostaglandin E Synthase-1 Adversely Affects the Postoperative Survival in Non-small Cell Lung Cancer

IntroductionCyclooxygenase (COX)-2 and microsomal prostaglandin E synthase (mPGES)-1 have been found to be overexpressed in non-small cell lung cancer (NSCLC). The aim of this study was to investigate the expression profiles of COX-2 and mPGES-1 and their correlation with the clinical characteristics and survival outcomes in patients with resected NSCLC.Methods/ResultsSeventy-nine paired adjacent normal-tumor matched samples were prospectively procured from patients undergoing surgery for NSCLC. The protein levels of COX-2 and mPGES-1 were assessed by Western blot analysis. Overexpression in the tumor sample was defined as more than twofold increase in protein expression compared with the corresponding adjacent normal tissue. Co-overexpression of COX-2 and mPGES-1 were further confirmed by immunohistochemistry. COX-2 was overexpressed in 58% and mPGES-1 in 70% of the tumor samples (p < 0.0001). Co-overexpression of mPGES-1 and COX-2 was noted in 43%, and they were unrelated to each other (p = 0.232). Co-overexpression of both proteins was significantly associated with less tumor differentiation (p = 0.046), tumor size larger than 5 cm (p = 0.038), and worse survival status during the follow-up (p = 0.036). Multivariate analysis showed that in addition to overall stage, co-overexpression of both proteins adversely affected the overall (hazard ratio, 2.40; p = 0.045) and disease-free survivals (hazard ratio, 2.27; p = 0.029).ConclusionsOverexpression of either COX-2 or mPGES-1 is common but unrelated in NSCLC. Co-overexpression of both COX-2 and mPGES-1 adversely affects postoperative overall and disease-free survivals

FLJ10540 is associated with tumor progression in nasopharyngeal carcinomas and contributes to nasopharyngeal cell proliferation, and metastasis via osteopontin/CD44 pathway

BACKGROUND: Nasopharyngeal carcinoma (NPC) is well-known for its highly metastatic characteristics, but little is known of its molecular mechanisms. New biomarkers that predict clinical outcome, in particular the ability of the primary tumor to develop metastatic tumors are urgently needed. The aim of this study is to investigate the role of FLJ10540 in human NPC development. METHODS: A bioinformatics approach was used to explore the potentially important regulatory genes involved in the growth/metastasis control of NPC. FLJ10540 was chosen for this study. Two co-expression strategies from NPC microarray were employed to identify the relationship between FLJ10540 and osteopontin. Quantitative-RT-PCR, immunoblotting, and immunohistochemistry analysis were used to investigate the mRNA and protein expression profiles of FLJ10540 and osteopontin in the normal and NPC tissues to confirm microarray results. TW01 and Hone1 NPC cells with overexpression FLJ10540 or siRNA to repress endogenous FLJ10540 were generated by stable transfection to further elucidate the molecular mechanisms of FLJ10540-elicited cell growth and metastasis under osteopontin stimulation. RESULTS: We found that osteopontin expression exhibited a positive correlation with FLJ10540 in NPC microarray. We also demonstrated comprehensively that FLJ10540 and osteopontin were not only overexpressed in NPC specimens, but also significantly correlated with advanced tumor and lymph node-metastasis stages, and had a poor 5-year survival rate, respectively. Stimulation of NPC parental cells with osteopontin results in an increase in FLJ10540 mRNA and protein expressions. Functionally, FLJ10540 transfectant alone, or stimulated with osteopontin, exhibited fast growth and increased metastasis as compared to vehicle control with or without osteopontin stimulation. Conversely, knockdown of FLJ10540 by siRNA results in the suppression of NPC cell growth and motility. Treatment with anti-CD44 antibodies in NPC parental cells not only resulted in a decrease of FLJ10540 protein, but also affected the abilities of FLJ10540-elicited cell growth and motility in osteopontin stimulated-NPC cells. CONCLUSIONS: These findings suggest that FLJ10540 may be critical regulator of disease progression in NPC, and the underlying mechanism may involve in the osteopontin/CD44 pathway

A Translational Regulator, PUM2, Promotes Both Protein Stability and Kinase Activity of Aurora-A

Aurora-A, a centrosomal serine-threonine kinase, orchestrates several key aspects of cell division. However, the regulatory pathways for the protein stability and kinase activity of Aurora-A are still not completely understood. In this study, PUM2, an RNA-binding protein, is identified as a novel substrate and interacting protein of Aurora-A. Overexpression of the PUM2 mutant which fails to interact with Aurora-A, and depletion of PUM2 result in a decrease in the amount of Aurora-A. PUM2 physically binds to the D-box of Aurora-A, which is recognized by APC/CCdh1. Overexpression of PUM2 prevents ubiquitination and enhances the protein stability of Aurora-A, suggesting that PUM2 protects Aurora-A from APC/CCdh1-mediated degradation. Moreover, association of PUM2 with Aurora-A not only makes Aurora-A more stable but also enhances the kinase activity of Aurora-A. Our study suggests that PUM2 plays two different but important roles during cell cycle progression. In interphase, PUM2 localizes in cytoplasm and plays as translational repressor through its RNA binding domain. However, in mitosis, PUM2 physically associates with Aurora-A to ensure enough active Aurora-A at centrosomes for mitotic entry. This is the first time to reveal the moonlight role of PUM2 in mitosis

Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online)

BACKGROUND: The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. RESULTS: Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO , to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. CONCLUSION: This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment

Observation of a near-threshold enhancement in th p pbar mass spectrum from radiative J/psi-->gamma p pbar decays

We observe a narrow enhancement near 2mp in the invariant mass spectrum of ppbar pairs from radiative J/psi-->gamma ppbar decays. The enhancement can be fit with either an S- or P-wave Breit Wigner fuction. In the case of the S-wave fit, the peak mass is below the 2mp threshold and the full width is less than 30 MeV. These mass and width values are not consistent with the properties of any known meson resonance.Comment: 5 pages, 4 figures, submitted to Phys. Rev. Let