bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of the statin atorvastatin on intracellular signalling by the prostacyclin receptor in vitro and in vivo
Authors: O'Meara Sarah J , Kinsella B Therese 
Published: September 2004

Prostacyclin plays a central role within the vasculature. We have previously established that the prostacyclin receptor (IP) undergoes isoprenylation, a lipid modification obligate for its function. The aim of the current study was to investigate the effect of the hydroxy methyl glutaryl co‐enzyme A reductase inhibitor atorvastatin on signalling and function of the IP expressed in mammalian whole cells and in platelets isolated from patients undergoing therapeutic intervention with atorvastatin.
Initially, the effect of atorvastatin on signalling by the human (h) and mouse (m) IP overexpressed in human embryonic kidney 293 cells and the hIP endogenously expressed in human erythroleukaemic 92.1.7 cells was investigated. Atorvastatin significantly reduced IP‐mediated cAMP generation (IC50 6.6–11.1 μM) and [Ca2+]i mobilization (IC50 7.2–16.4 μM) in a concentration‐dependent manner, but had no effect on signalling by the nonisoprenylated β2 adrenergic receptor or the α or β isoforms of the human thromboxane A2 receptor (TP).
Moreover, atorvastatin significantly reduced IP‐mediated crossdesensitization of signalling by TPα (IC50 10.4 μM), but not by TPβ.
In contrast to the whole‐cell data, atorvastatin therapy did not interfere with IP‐mediated cAMP generation or IP‐induced inhibition of TP‐mediated aggregation of platelets isolated from human volunteers undergoing therapeutic intervention with atorvastatin (10–80 mg per daily dose).
In conclusion, while data generated in whole cells indicated that atorvastatin significantly impairs signalling by both the hIP and mP, the in vivo clinical data indicated that, at the administered therapeutic dose, atorvastatin does not significantly compromise IP signalling and function in humans.

British Journal of Pharmacology (2004) 143, 292–302. doi:10.1038/sj.bjp.0705947
Clann: investigating phylogenetic information through supertree analyses
Authors: Creevey C. J. , McInerney J. O. 
Published: September 2004

Abstract
Summary: Clann has been developed in order to provide methods of investigating phylogenetic information through the application of supertrees.
Availability: Clann has been precompiled for Linux, Apple Macintosh and Windows operating systems and is available from http://bioinf.may.ie/software/clann. Source code is available on request from the authors.
Supplementary information: Clann has been written in the C programming language. Source code is available on request.
Contact: chris.creevey@may.ie
Iterative Cluster Analysis of Protein Interaction Data
Authors: Arnau Vicente , Mars Sergio , Marín Ignacio 
Published: September 2004

AbstractMotivation: Generation of fast tools of hierarchical clustering to be applied when distances among elements of a set are constrained, causing frequent distance ties, as happens in protein interaction data.Results: We present in this work the program UVCLUSTER, that iteratively explores distance datasets using hierarchical clustering. Once the user selects a group of proteins, UVCLUSTER converts the set of primary distances among them (i.e. the minimum number of steps, or interactions, required to connect two proteins) into secondary distances that measure the strength of the connection between each pair of proteins when the interactions for all the proteins in the group are considered. We show that this novel strategy has advantages over conventional clustering methods to explore protein–protein interaction data. UVCLUSTER easily incorporates the information of the largest available interaction datasets to generate comprehensive primary distance tables. The versatility, simplicity of use and high speed of UVCLUSTER on standard personal computers suggest that it can be a benchmark analytical tool for interactome data analysis.Availability: The program is available upon request from the authors, free for academic users. Additional information available at http://www.uv.es/genomica/UVCLUSTERContact: ignacio.marin@uv.es
An experimental evaluation of a loop versus a reference design for two-channel microarrays
Authors: Vinciotti V. , Khanin R. , D'Alimonte D. , Liu X. , Cattini N. , Hotchkiss G. , Bucca G. , de Jesus O. , Rasaiyaah J. , Smith C. P. , Kellam P. , Wit E. 
Published: September 2004

Abstract
Motivation: Despite theoretical arguments that so-called ‘loop designs’ for two-channel DNA microarray experiments are more efficient, biologists continue to use ‘reference designs’. We describe two sets of microarray experiments with RNA from two different biological systems (TPA-stimulated mammalian cells and Streptomyces coelicolor). In each case, both a loop and a reference design were used with the same RNA preparations with the aim of studying their relative efficiency.
Results: The results of these experiments show that (1) the loop design attains a much higher precision than the reference design, (2) multiplicative spot effects are a large source of variability, and if they are not accounted for in the mathematical model, for example, by taking log-ratios or including spot effects, then the model will perform poorly. The first result is reinforced by a simulation study. Practical recommendations are given on how simple loop designs can be extended to more realistic experimental designs and how standard statistical methods allow the experimentalist to use and interpret the results from loop designs in practice.
Availability: The data and R code are available at http://exgen.ma.umist.ac.uk
Contact: veronica.vinciotti@brunel.ac.uk
VarMixt: efficient variance modelling for the differential analysis of replicated gene expression data
Authors: Delmar Paul , Robin Stéphane , Daudin Jean Jacques 
Published: September 2004

AbstractMotivation: Identifying differentially regulated genes in experiments comparing two experimental conditions is often a key step in the microarray data analysis process. Many different approaches and methodological developments have been put forward, yet the question remains open.Results: Varmixt is a powerful and efficient novel methodology for this task. It is based on a flexible and realistic variance modelling strategy. It compares favourably with other popular techniques (standard t-test, SAM and Cyber-T). The relevance of the approach is demonstrated with real-world and simulated datasets. The analysis strategy was successfully applied to both a ‘two-colour’ cDNA microarray and an Affymetrix Genechip. Strong control of false positive and false negative rates is proven in large simulation studies.Availability: The R package is freely available at http://www.inapg.inra.fr/ens_rech/mathinfo/recherche/mathematique/outil.htmlContact: delmar@inapg.inra.frSupplementary information: http://www.inapg.inra.fr/ens_rech/mathinfo/recherche/mathematique/outil.html
Prediction of splice sites with dependency graphs and their expanded bayesian networks
Authors: Chen Te-Ming , Lu Chung-Chin , Li Wen-Hsiung 
Published: September 2004

Abstract
Motivation: Owing to the complete sequencing of human and many other genomes, huge amounts of DNA sequence data have been accumulated. In bioinformatics, an important issue is how to predict the complete structure of genes from the genomic DNA sequence, especially the human genome. A crucial part in the gene structure prediction is to determine the precise exon–intron boundaries, i.e. the splice sites, in the coding region.
Results: We have developed a dependency graph model to fully capture the intrinsic interdependency between base positions in a splice site. The establishment of dependency between two position is based on a χ2-test from known sample data. To facilitate statistical inference, we have expanded the dependency graph (which is usually a graph with cycles that make probabilistic reasoning very difficult, if not impossible) into a Bayesian network (which is a directed acyclic graph that facilitates statistical reasoning).
When compared with the existing models such as weight matrix model, weight array model, maximal dependence decomposition, Cai et al.'s tree model as well as the less-studied second-order and third-order Markov chain models, the expanded Bayesian networks from our dependency graph models perform the best in nearly all the cases studied.
Availability: Software (a program called DGSplicer) and datasets used are available at http://csrl.ee.nthu.edu.tw/bioinf/
Contact: cclu@ee.nthu.edu.tw
Identifying time-lagged gene clusters using gene expression data
Authors: Ji Liping , Tan Kian-Lee 
Published: September 2004

Abstract
Motivation: Analysis of gene expression data can provide insights into the time-lagged co-regulation of genes/gene clusters. However, existing methods such as the Event Method and the Edge Detection Method are inefficient as they compare only two genes at a time. More importantly, they neglect some important information due to their scoring criterian. In this paper, we propose an efficient algorithm to identify time-lagged co-regulated gene clusters. The algorithm facilitates localized comparison and processes several genes simultaneously to generate detailed and complete time-lagged information for genes/gene clusters.
Results: We experimented with the time-series Yeast gene dataset and compared our algorithm with the Event Method. Our results show that our algorithm is not only efficient, but also delivers more reliable and detailed information on time-lagged co-regulation between genes/gene clusters.
Availability: The software is available upon request.
Contact: jiliping@comp.nus.edu.sg
Supplementary information: Supplementary tables and figures for this paper can be found at http://www.comp.nus.edu.sg/~jiliping/p2.htm.
Statistical methods of translating microarray data into clinically relevant diagnostic information in colorectal cancer
Authors: Kim Byung Soo , Kim Inyoung , Lee Sunho , Kim Sangcheol , Rha Sun Young , Chung Hyun Cheol 
Published: September 2004

AbstractMotivation: It is a common practice in cancer microarray experiments that a normal tissue is collected from the same individual from whom the tumor tissue was taken. The indirect design is usually adopted for the experiment that uses a common reference RNA hybridized both to normal and tumor tissues. However, it is often the case that the test material is not large enough for the experimenter to extract enough RNA to conduct the microarray experiment. Hence, collecting ncases does not necessarily end up with a matched pair sample of size n. Instead we usually have a matched pair sample of size n1, and two independent samples of sizes n2 and n3, respectively, for ‘reference versus normal tissue only’ and ‘reference versus tumor tissue only’ hybridizations (n = n1 + n2 + n3). Standard statistical methods need to be modified and new statistical procedures are developed for analyzing this mixed dataset.Results: We propose a new test statistic, t3, as a means of combining all the information in the mixed dataset for detecting differentially expressed (DE) genes between normal and tumor tissues. We employed the extended receiver operating characteristic approach to the mixed dataset. We devised a measure of disagreement between a RT–PCR experiment and a microarray experiment. Hotelling's T2 statistic is employed to detect a set of DE genes and its prediction rate is compared with the prediction rate of a univariate procedure. We observe that Hotelling's T2 statistic detects DE genes more efficiently than a univariate procedure and that further research is warranted on the formal test procedure using Hotelling's T2 statistic.Contact: bskim@yonsei.ac.kr
ESTminer: a Web interface for mining EST contig and cluster databases
Authors: Huang Yecheng , Pumphrey Janie , Gingle Alan R. 
Published: September 2004

Abstract
Summary: ESTminer is a Web application and database schema for interactive mining of expressed sequence tag (EST) contig and cluster datasets. The Web interface contains a query frame that allows the selection of contigs/clusters with specific cDNA library makeup or a threshold number of members. The results are displayed as color-coded tree nodes, where the color indicates the fractional size of each cDNA library component. The nodes are expandable, revealing library statistics as well as EST or contig members, with links to sequence data, GenBank records or user configurable links. Also, the interface allows ‘queries within queries’ where the result set of a query is further filtered by the subsequent query.
Availability: ESTminer is implemented in Java/JSP and the package, including MySQL and Oracle schema creation scripts, is available from http://cggc.agtec.uga.edu/Data/download.asp
Contact: agingle@uga.edu
A semiparametric approach for marker gene selection based on gene expression data
Authors: Guan Zhong , Zhao Hongyu 
Published: September 2004

AbstractMotivation: Identification of differentially expressed genes is a major issue in gene expression data analysis and selection of marker genes is critical in tumor classification using gene expression data. In this paper, we propose a semiparametric two-sample test to identify both differentially expressed genes and select marker genes for sample classification.Results: A simulation study shows that the proposed method is more robust and powerful than the methods, generally used such as t-tests and non-parametric rank-sum tests, when the sample size is small. Cross-validation shows that the sample classification based on genes selected using this semiparametric method has lower misclassification rates.Contact: hongyu.zhao@yale.edu
A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis
Authors: Statnikov Alexander , Aliferis Constantin F. , Tsamardinos Ioannis , Hardin Douglas , Levy Shawn 
Published: September 2004

Abstract
Motivation: Cancer diagnosis is one of the most important emerging clinical applications of gene expression microarray technology. We are seeking to develop a computer system for powerful and reliable cancer diagnostic model creation based on microarray data. To keep a realistic perspective on clinical applications we focus on multicategory diagnosis. To equip the system with the optimum combination of classifier, gene selection and cross-validation methods, we performed a systematic and comprehensive evaluation of several major algorithms for multicategory classification, several gene selection methods, multiple ensemble classifier methods and two cross-validation designs using 11 datasets spanning 74 diagnostic categories and 41 cancer types and 12 normal tissue types.
Results: Multicategory support vector machines (MC-SVMs) are the most effective classifiers in performing accurate cancer diagnosis from gene expression data. The MC-SVM techniques by Crammer and Singer, Weston and Watkins and one-versus-rest were found to be the best methods in this domain. MC-SVMs outperform other popular machine learning algorithms, such as k-nearest neighbors, backpropagation and probabilistic neural networks, often to a remarkable degree. Gene selection techniques can significantly improve the classification performance of both MC-SVMs and other non-SVM learning algorithms. Ensemble classifiers do not generally improve performance of the best non-ensemble models. These results guided the construction of a software system GEMS (Gene Expression Model Selector) that automates high-quality model construction and enforces sound optimization and performance estimation procedures. This is the first such system to be informed by a rigorous comparative analysis of the available algorithms and datasets.
Availability: The software system GEMS is available for download from http://www.gems-system.org for non-commercial use.
Contact: alexander.statnikov@vanderbilt.edu
Reliability analysis of microarray data using fuzzy c-means and normal mixture modeling based classification methods
Authors: Asyali Musa H. , Alci Musa 
Published: September 2004

Abstract
Motivation: A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, the elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from microarray data. In this study, we applied fuzzy c-means (FCM) and normal mixture modeling (NMM) based classification methods to separate microarray data into reliable and unreliable signal intensity populations.
Results: We compared the results of FCM classification with those of classification based on NMM. Both approaches were validated against reference sets of biological data consisting of only true positives and true negatives. We observed that both methods performed equally well in terms of sensitivity and specificity. Although a comparison of the computation times indicated that the fuzzy approach is computationally more efficient, other considerations support the use of NMM for the reliability analysis of microarray data.
Availability: The classification approaches described in this paper and sample microarray data are available as MatlabTM (The MathWorks Inc., Natick, MA) programs (mfiles) and text files, respectively, at http://rc.kfshrc.edu.sa/bssc/staff/MusaAsyali/Downloads.asp. The programs can be run/tested on many different computer platforms where Matlab is available.
Contact: asyali@kfshrc.edu.sa
Signaling via a novel integral plasma membrane pool of a serine/threonine protein kinase PRK1 in mammalian cells
Authors: Zhu Yimin , Stolz Donna B. , Guo Fengli , Ross Mark A. , Watkins Simon C. , Tan Bee Jen , Qi Robert Z. , Manser Ed , Li Qiu Tian , Bay Boon Huat , Teo Tian Seng , Duan Wei 
Published: September 2004

ABSTRACTMammalian serine/threonine protein kinases, except for TGF‐β receptor kinase family, are intracellular proteins. PRK1/PKN is a member of the protein kinase C superfamily of serine/threonine kinases and is one of the first identified effectors for RhoA GTPase. However, the role of PRK1 in mediating signaling downstream of activated RhoA is largely unknown. Here, we present evidence that identifies a novel plasma membrane pool of PRK1. This integral membrane form of PRK1 is catalytically active. The phosphorylation of serine377 of PRK1 is required for its integration into membranes. This integration is essential for PRK1 to function as a Rho effector as only the integral plasma membrane PRK1 is able to initiate RhoA‐mediated and ligand‐dependent transcriptional activation of the androgen receptor in human epithelial cells and to mediate RhoA‐induced neurite retraction in mouse neuronal cells. These results indicate that RhoA signals via the integral membrane pool of its effectors in its immediate vicinity at the plasma membrane, thus establishing a new paradigm in mammalian cell signaling.
RETRACTED: 5‐Lipoxygenase antagonizes genotoxic stress‐induced apoptosis by altering p53 nuclear trafficking
Authors: Catalano Alfonso , Caprari Paola , Soddu Silvia , Procopio Antonio , Romano Mario 
Published: September 2004

ABSTRACT5‐Lipoxygenase (5‐LO) promotes cancer cell proliferation and survival by unclear mechanisms. Here, we show that 5‐LO expression and activity were induced by genotoxic agents in a p53‐independent manner and antagonized p53‐ or genotoxic drug‐induced apoptosis in a variety of cancer cells. 5‐LO inhibited p53‐governed transactivation of the pro‐apoptotic genes bax and pig3 but not of p21WAF1/CIP1 or mdm2. This may be explained by 5‐LO capability to inhibit the binding of p53 to promyelocytic leukemia protein (PML) and p53 subnuclear relocalization into PML‐nuclear bodies in response to genotoxic stress. Interestingly, 5‐LO activity appears to be involved in nuclear retention and inactivation of wild‐type p53 in malignant mesothelioma cells. In these cells, genetic or pharmacological inhibition of 5‐LO enabled suppression of in vitro tumorigenicity by low doses of chemotherapeutic drugs. Together, these results uncover novel functions of 5‐LO and contribute to the understanding of 5‐LO involvement in tumor progression. Moreover, they provide a rationale to the therapeutic use of 5‐LO inhibitors to enhance cancer chemosensitivity in selected tumors.
AtHKT1 Facilitates Na+ Homeostasis and K+ Nutrition in Planta
Authors: Rus Ana , Lee Byeong-ha , Muñoz-Mayor Alicia , Sharkhuu Altanbadralt , Miura Kenji , Zhu Jian-Kang , Bressan Ray A. , Hasegawa Paul M. 
Published: September 2004

Abstract
Genetic and physiological data establish that Arabidopsis AtHKT1 facilitates Na+ homeostasis in planta and by this function modulates K+ nutrient status. Mutations that disrupt AtHKT1 function suppress NaCl sensitivity of sos1-1 and sos2-2, as well as of sos3-1 seedlings grown in vitro and plants grown in controlled environmental conditions. hkt1 suppression of sos3-1 NaCl sensitivity is linked to higher Na+ content in the shoot and lower content of the ion in the root, reducing the Na+ imbalance between these organs that is caused by sos3-1. AtHKT1 transgene expression, driven by its innate promoter, increases NaCl but not LiCl or KCl sensitivity of wild-type (Col-0 gl1) or of sos3-1 seedlings. NaCl sensitivity induced by AtHKT1 transgene expression is linked to a lower K+ to Na+ ratio in the root. However, hkt1 mutations increase NaCl sensitivity of both seedlings in vitro and plants grown in controlled environmental conditions, which is correlated with a lower K+ to Na+ ratio in the shoot. These results establish that AtHKT1 is a focal determinant of Na+ homeostasis in planta, as either positive or negative modulation of its function disturbs ion status that is manifested as salt sensitivity. K+-deficient growth of sos1-1, sos2-2, and sos3-1 seedlings is suppressed completely by hkt1-1. AtHKT1 transgene expression exacerbates K+ deficiency of sos3-1 or wild-type seedlings. Together, these results indicate that AtHKT1 controls Na+ homeostasis in planta and through this function regulates K+ nutrient status.
Cellular and Subcellular Localization of Endogenous Nitric Oxide in Young and Senescent Pea Plants
Authors: Corpas Francisco J. , Barroso Juan B. , Carreras Alfonso , Quirós Miguel , León Ana M. , Romero-Puertas María C. , Esteban Francisco J. , Valderrama Raquel , Palma José M. , Sandalio Luisa M. , Gómez Manuel , del Río Luis A. 
Published: September 2004

AbstractThe cellular and subcellular localization of endogenous nitric oxide (NO ˙) in leaves from young and senescent pea (Pisum sativum) plants was studied. Confocal laser scanning microscopy analysis of pea leaf sections with the fluorescent probe 4,5-diaminofluorescein diacetate revealed that endogenous NO ˙ was mainly present in vascular tissues (xylem and phloem). Green fluorescence spots were also detected in the epidermal cells, palisade and spongy mesophyll cells, and guard cells. In senescent leaves, NO ˙ generation was clearly reduced in the vascular tissues. At the subcellular level, by electron paramagnetic resonance spectroscopy with the spin trap Fe(MGD)2 and fluorometric analysis with 4,5-diaminofluorescein diacetate, NO ˙ was found to be an endogenous metabolite of peroxisomes. The characteristic three-line electron paramagnetic resonance spectrum of NO ˙, with g = 2.05 and aN = 12.8 G, was detected in peroxisomes. By fluorometry, NO ˙ was also found in these organelles, and the level measured of NO ˙ was linearly dependent on the amount of peroxisomal protein. The enzymatic production of NO ˙ from l-Arg (nitric oxide synthase [NOS]-like activity) was measured by ozone chemiluminiscence. The specific activity of peroxisomal NOS was 4.9 nmol NO ˙ mg−1 protein min−1; was strictly dependent on NADPH, calmodulin, and BH4; and required calcium. In senescent pea leaves, the NOS-like activity of peroxisomes was down-regulated by 72%. It is proposed that peroxisomal NO ˙ could be involved in the process of senescence of pea leaves.