bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exclusive core areas and intrasexual territoriality in Eurasian red squirrels (Sciurus vulgaris) revealed by incremental cluster polygon analysis
Authors: Pierro Erica Di , Molinari Ambrogio , Tosi Guido , Wauters Lucas A. 
Published: July 2007

AbstractWhen animal home ranges overlap extensively in species lacking overt territorial behaviours, identifying exclusive core areas within individual ranges can be difficult. By analysing the size and overlap of successively smaller core areas among individual Eurasian red squirrels (Sciurus vulgaris), we determined exclusive areas within the home ranges of resident males and females. Possible effects of habitat composition and food supplies were explored by monitoring squirrels in different conifer forests and during years with low and high tree seed production. Using outlier‐exclusive cores (OEC) revealed that the total ranges consisted of large sally zones (on average, 35% of the total minimum convex polygon [MCP] range) around home ranges with multi‐nucleate cores. The mean OEC home range size did not differ between the sexes but was larger with poor food availability. Home ranges (99% incremental cluster polygons [ICP]) overlapped extensively between sexes (average overlap high food–low food: males by females 21–40%, females by males 43–45%) and among males (males by males 26–44%), while intrasexual overlap among females was low (9–10%). The overlap of inner cores among females rapidly approached zero, suggesting the intrasexual territoriality of 75% core areas. This was not the case among male squirrels, for which intrasexual overlap averaged only 4% at 50% but 18% at 75% core areas. Even the smallest inner cores had some degree of intersexual overlap, indicating that complete territoriality did not occur in this species. Female home ranges were more strongly affected by annual fluctuations in food supplies than male ranges. Females reduced the size of their food‐based intrasexual territories when food availability increases. Males probably benefit from using larger home ranges and core areas, which overlap with the ranges of several females, by increasing their probability of successful mating.
Linoleic Acid Decreases Leptin and Adiponectin Secretion from Primary Rat Adipocytes in the Presence of Insulin
Authors: Pérez‐Matute P. , Martínez J. A. , Marti A. , Moreno‐Aliaga M. J. 
Published: July 2007

AbstractObesity rates have dramatically increased over the last few decades and, at the same time, major changes in the type of fatty acid intake have occurred. Linoleic acid, an n‐6 polyunsaturated fatty acid, is an essential fatty acid occurring in high amounts in several western diets. A potential role of this fatty acid on obesity has been suggested. Controversial effects of linoleic acid on insulin sensitivity have also been reported. Thus, the aim of this study was to examine the direct effects of linoleic acid on leptin and adiponectin production, two adipokines known to influence weight gain and insulin sensitivity. Because insulin‐stimulated glucose metabolism is an important regulator of leptin production, the effects of linoleic acid on adipocyte metabolism were also examined. For this purpose, isolated rat adipocytes were incubated with linoleic acid (1–200 μM) in the absence or presence of insulin. Linoleic acid (1–200 μM) significantly decreased insulin‐stimulated leptin secretion and expression (P< 0.05), however, no changes in basal leptin production were observed. Linoleic acid also induced a significant decrease (~20%) in adiponectin secretion (P< 0.05), but only in the presence of insulin and at the highest concentration tested (200 μM). This fatty acid did not modify either glucose uptake or lactate production and the percentage of glucose metabolized to lactate was not changed either. Together, these results suggest that linoleic acid seems to interfere with other insulin signalling pathway different from those controlling glucose uptake and metabolism, but involved in the regulation of leptin and adiponectin production.
Influence of β2‐adrenoceptor gene polymorphisms on β2‐adrenoceptor‐mediated responses in human lung mast cells
Authors: Kay L J , Rostami‐Hodjegan A , Suvarna S K , Peachell P T 
Published: July 2007

Background and purpose:Previous studies have shown that β
2‐adrenoceptor‐mediated responses in human lung mast cells are highly variable. The aims of the present study were to establish whether polymorphisms of the β
2‐adrenoceptor gene (ADRB2) influence this variability in (a) β
2‐adrenoceptor‐mediated inhibition and (b) desensitization of β
2‐adrenoceptor‐mediated responses in human lung mast cells.Experimental approach:Mast cells were isolated from human lung tissue. The inhibitory effects of the β‐adrenoceptor agonist, isoprenaline (10−10–10−5 M), on IgE‐mediated histamine release from mast cells were determined (n=92). Moreover, the inhibitory effects of isoprenaline were evaluated following a desensitizing treatment involving long‐term (24 h) incubation of mast cells with isoprenaline (10−6 M) (n=65). A potential influence of polymorphisms on these functional responses was determined by genotyping 11 positions, in the promoter and coding regions, of ADRB2 previously reported as polymorphic.Key results:There was no influence of any of the polymorphic positions of ADRB2 on the potency of isoprenaline to inhibit histamine release from mast cells with the exception of position 491C>T (Thr164Ile). There was no influence of any of the polymorphic positions of ADRB2 on the extent of desensitization of the isoprenaline‐mediated response following a desensitizing treatment except for position 46G>A (Gly16Arg). Analyses at the haplotype level indicated that there was no influence of haplotype on β
2‐adrenoceptor‐mediated responses in mast cells.Conclusions and implications:These data indicate that certain polymorphisms in ADRB2 influence β
2‐adrenoceptor‐mediated responses in human lung mast cells.British Journal of Pharmacology (2007) 152, 323–331; doi:10.1038/sj.bjp.0707400; published online 23 July 2007
Reconstruction of highly heterogeneous gene-content evolution across the three domains of life
Authors: Iwasaki Wataru , Takagi Toshihisa 
Published: July 2007

AbstractMotivation: Reconstruction of gene-content evolutionary history is fundamental in studying the evolution of genomes and biological systems. To reconstruct plausible evolutionary history, rates of gene gain/loss should be estimated by considering the high level of heterogeneity: e.g. genome duplication and parasitization, respectively, result in high rates of gene gain and loss. Gene-content evolution reconstruction methods that consider this heterogeneity and that are both effective in estimating the rates of gene gain and loss and sufficiently efficient to analyze abundant genomic data had not been developed.Results: An effective and efficient method for reconstructing heterogeneous gene-content evolution was developed. This method comprises analytically integrable modeling of gene-content evolution, analytical formulation of expectation-maximization and efficient calculation of marginal likelihood using an inside-outside-like algorithm. Simulation tests on the scale of hundreds of genomes showed that both the gene gain/loss rates and evolutionary history were effectively estimated within a few days of computational time. Subsequently, this algorithm was applied to an actual data set of nearly 200 genomes to reconstruct the heterogeneous gene-content evolution across the three domains of life. The reconstructed history, which contained several features consistent with biological observations, showed that the trends of gene-content evolution were not only drastically different between prokaryotes and eukaryotes, but were highly variable within each form of life. The results suggest that heterogeneity should be considered in studies of the evolution of gene content, genomes and biological systems.Availability: An R script that implements the algorithm is available upon request.Contact: iwasaki@cb.k.u-tokyo.ac.jp
Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants
Authors: Danziger Samuel A. , Zeng Jue , Wang Ying , Brachmann Rainer K. , Lathrop Richard H. 
Published: July 2007

Abstract
Motivation: Many biomedical projects would benefit from reducing the time and expense of in vitro experimentation by using computer models for in silico predictions. These models may help determine which expensive biological data are most useful to acquire next. Active Learning techniques for choosing the most informative data enable biologists and computer scientists to optimize experimental data choices for rapid discovery of biological function. To explore design choices that affect this desirable behavior, five novel and five existing Active Learning techniques, together with three control methods, were tested on 57 previously unknown p53 cancer rescue mutants for their ability to build classifiers that predict protein function. The best of these techniques, Maximum Curiosity, improved the baseline accuracy of 56–77%. This article shows that Active Learning is a useful tool for biomedical research, and provides a case study of interest to others facing similar discovery challenges.
A physical model for tiling array analysis
Authors: Chung Ho-Ryun , Kostka Dennis , Vingron Martin 
Published: July 2007

Abstract
Motivation: Chromatin immunoprecipitation (ChIP) is a powerful experimental approach to identify in vivo binding sites of sequence-specific transcription factors (TFs). These experiments are designed to specifically enrich DNA fragments that are bound to the TF. Tiling arrays have become more and more popular for the identification of these DNA fragments. However, many studies showed that only a fraction of the identified DNA fragments contains bona fide binding sites for the TF, suggesting that indirect binding mechanisms play a very important role. We explored the possibility that the lack of binding sites can also be explained by problems in identifying ChIP-enriched DNA fragments from the measured intensities.
Results: We derived a physical model that explains some (but not all) variation of the measured probe intensities of Affymetrix tilling arrays. We used the physical model to estimate the probe-specific behavior and corrected for it. Subsequently, we developed a method to identify ChIP-enriched DNA fragments. We termed it physical model for tiling array analysis (PMT). We applied PMT to the data of ChIP-chip experiments interrogating chromosome 21 and 22 of the human genome for binding of the TFs MYC, SP1 and P53. Almost all regions recovered by PMT showed evidence for sequence-specific binding of the TFs.
Contact: chung@molgen.mpg.de
SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments
Authors: Ourfali Oved , Shlomi Tomer , Ideker Trey , Ruppin Eytan , Sharan Roded 
Published: July 2007

Abstract
Motivation: The complex program of gene expression allows the cell to cope with changing genetic, developmental and environmental conditions. The accumulating large-scale measurements of gene knockout effects and molecular interactions allow us to begin to uncover regulatory and signaling pathways within the cell that connect causal to affected genes on a network of physical interactions.
Results: We present a novel framework, SPINE, for Signaling-regulatory Pathway INferencE. The framework aims at explaining gene expression experiments in which a gene is knocked out and as a result multiple genes change their expression levels. To this end, an integrated network of protein–protein and protein-DNA interactions is constructed, and signaling pathways connecting the causal gene to the affected genes are searched for in this network. The reconstruction problem is translated into that of assigning an activation/repression attribute with each protein so as to explain (in expectation) a maximum number of the knockout effects observed. We provide an integer programming formulation for the latter problem and solve it using a commercial solver.
We validate the method by applying it to a yeast subnetwork that is involved in mating. In cross-validation tests, SPINE obtains very high accuracy in predicting knockout effects (99%). Next, we apply SPINE to the entire yeast network to predict protein effects and reconstruct signaling and regulatory pathways. Overall, we are able to infer 861 paths with confidence and assign effects to 183 genes. The predicted effects are found to be in high agreement with current biological knowledge.
Availability: The algorithm and data are available at http://cs.tau.ac.il/~roded/SPINE.html
Contact: roded@post.tau.ac.il
Genomic characterization of perturbation sensitivity
Authors: Ohn Jung Hun , Kim Jihun , Kim Ju Han 
Published: July 2007

Abstract
Motivation: In determining the function of a gene, it provides much information to observe the changes in a biological system after disruption of the gene of interest through its knockout. Thanks to the microarray technology, it is now possible to profile transcriptional changes of the whole genome, thus differentiating genes that are significantly affected by the knockout. Based on microarray experiments of hundreds of different knockouts, we assigned the so called, ‘Perturbation Sensitivity’, to the Saccharomyces cerevisiae genome by the frequency of significant changes in the transcript level in hundreds of knockout conditions. Biologically, it reflects the degree of a gene's sensitivity to external perturbations.
Results: Through gradually enriching gene sets with more perturbation sensitive genes, we show that perturbation sensitive genes are usually not essential and their coding proteins have fewer physical interaction partners and more transcription factors bind to their upstream sequences. And the two extreme gene groups, perturbation sensitive versus perturbation resistant, have mutually exclusive functional annotations.
contact: juhan@snu.ac.kr
Annotating gene function by combining expression data with a modular gene network
Authors: Shiga Motoki , Takigawa Ichigaku , Mamitsuka Hiroshi 
Published: July 2007

AbstractMotivation: A promising and reliable approach to annotate gene function is clustering genes not only by using gene expression data but also literature information, especially gene networks.Results: We present a systematic method for gene clustering by combining these totally different two types of data, particularly focusing on network modularity, a global feature of gene networks. Our method is based on learning a probabilistic model, which we call a hidden modular random field in which the relation between hidden variables directly represents a given gene network. Our learning algorithm which minimizes an energy function considering the network modularity is practically time-efficient, regardless of using the global network property. We evaluated our method by using a metabolic network and microarray expression data, changing with microarray datasets, parameters of our model and gold standard clusters. Experimental results showed that our method outperformed other four competing methods, including k-means and existing graph partitioning methods, being statistically significant in all cases. Further detailed analysis showed that our method could group a set of genes into a cluster which corresponds to the folate metabolic pathway while other methods could not. From these results, we can say that our method is highly effective for gene clustering and annotating gene function.Contact: shiga@kuicr.kyoto-u.ac.jp
Prediction of DNA-binding residues from sequence
Authors: Ofran Yanay , Mysore Venkatesh , Rost Burkhard 
Published: July 2007

Abstract
Motivation: Thousands of proteins are known to bind to DNA; for most of them the mechanism of action and the residues that bind to DNA, i.e. the binding sites, are yet unknown. Experimental identification of binding sites requires expensive and laborious methods such as mutagenesis and binding essays. Hence, such studies are not applicable on a large scale. If the 3D structure of a protein is known, it is often possible to predict DNA-binding sites in silico. However, for most proteins, such knowledge is not available.
Results: It has been shown that DNA-binding residues have distinct biophysical characteristics. Here we demonstrate that these characteristics are so distinct that they enable accurate prediction of the residues that bind DNA directly from amino acid sequence, without requiring any additional experimental or structural information. In a cross-validation based on the largest non-redundant dataset of high-resolution protein–DNA complexes available today, we found that 89% of our predictions are confirmed by experimental data. Thus, it is now possible to identify DNA-binding sites on a proteomic scale even in the absence of any experimental data or 3D-structural information.
Availability: http://cubic.bioc.columbia.edu/services/disis
Contact: yo135@columbia.edu
Different mechanistic requirements for prokaryotic and eukaryotic chaperonins: a lattice study
Authors: Jacob Etai , Horovitz Amnon , Unger Ron 
Published: July 2007

Abstract
Motivation: The folding of many proteins in vivo and in vitro is assisted by molecular chaperones. A well-characterized molecular chaperone system is the chaperonin GroEL/GroES from Escherichia coli which has a homolog found in the eukaryotic cytosol called CCT. All chaperonins have a ring structure with a cavity in which the substrate protein folds. An interesting difference between prokaryotic and eukaryotic chaperonins is in the nature of the ATP-mediated conformational changes that their ring structures undergo during their reaction cycle. Prokaryotic chaperonins are known to exhibit a highly cooperative concerted change of their cavity surface while in eukaryotic chaperonins the change is sequential. Approximately 70% of proteins in eukaryotic cells are multi-domain whereas in prokaryotes single-domain proteins are more common. Thus, it was suggested that the different modes of action of prokaryotic and eukaryotic chaperonins can be explained by the need of eukaryotic chaperonins to facilitate folding of multi-domain proteins.
Results: Using a 2D square lattice model, we generated two large populations of single-domain and double-domain substrate proteins. Chaperonins were modeled as static structures with a cavity wall with which the substrate protein interacts. We simulated both concerted and sequential changes of the cavity surfaces and demonstrated that folding of single-domain proteins benefits from concerted but not sequential changes whereas double-domain proteins benefit also from sequential changes. Thus, our results support the suggestion that the different modes of allosteric switching of prokaryotic and eukaryotic chaperonin rings have functional implications as it enables eukaryotic chaperonins to better assist multi-domain protein folding.
Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles
Authors: Sysi-Aho Marko , Vehtari Aki , Velagapudi Vidya R. , Westerbacka Jukka , Yetukuri Laxman , Bergholm Robert , Taskinen Marja-Riitta , Yki-Järvinen Hannele , Orešič Matej 
Published: July 2007

AbstractMotivation: Serum lipids have been traditionally studied in the context of lipoprotein particles. Today's emerging lipidomics technologies afford sensitive detection of individual lipid molecular species, i.e. to a much greater detail than the scale of lipoproteins. However, such global serum lipidomic profiles do not inherently contain any information on where the detected lipid species are coming from. Since it is too laborious and time consuming to routinely perform serum fractionation and lipidomics analysis on each lipoprotein fraction separately, this presents a challenge for the interpretation of lipidomic profile data. An exciting and medically important new bioinformatics challenge today is therefore how to build on extensive knowledge of lipid metabolism at lipoprotein levels in order to develop better models and bioinformatics tools based on high-dimensional lipidomic data becoming available today.Results: We developed a hierarchical Bayesian regression model to study lipidomic profiles in serum and in different lipoprotein classes. As a background data for the model building, we utilized lipidomic data for each of the lipoprotein fractions from 5 subjects with metabolic syndrome and 12 healthy controls. We clustered the lipid profiles and applied a regression model within each cluster separately. We found that the amount of a lipid in serum can be adequately described by the amounts of lipids in the lipoprotein classes. In addition to improved ability to interpret lipidomic data, we expect that our approach will also facilitate dynamic modelling of lipid metabolism at the individual molecular species level.Contact: marko.sysi-aho@vtt.fi
Biases induced by pooling samples in microarray experiments
Authors: Mary-Huard Tristan , Daudin Jean-Jacques , Baccini Michela , Biggeri Annibale , Bar-Hen Avner 
Published: July 2007

Abstract
Motivation: If there is insufficient RNA from the tissues under investigation from one organism, then it is common practice to pool RNA. An important question is to determine whether pooling introduces biases, which can lead to inaccurate results. In this article, we describe two biases related to pooling, from a theoretical as well as a practical point of view.
Results: We model and quantify the respective parts of the pooling bias due to the log transform as well as the bias due to biological averaging of the samples. We also evaluate the impact of the bias on the statistical differential analysis of Affymetrix data.
Contact: maryhuar@inapg.fr
Kernel-based data fusion for gene prioritization
Authors: De Bie Tijl , Tranchevent Léon-Charles , van Oeffelen Liesbeth M. M. , Moreau Yves 
Published: July 2007

Abstract
Motivation: Hunting disease genes is a problem of primary importance in biomedical research. Biologists usually approach this problem in two steps: first a set of candidate genes is identified using traditional positional cloning or high-throughput genomics techniques; second, these genes are further investigated and validated in the wet lab, one by one. To speed up discovery and limit the number of costly wet lab experiments, biologists must test the candidate genes starting with the most probable candidates. So far, biologists have relied on literature studies, extensive queries to multiple databases and hunches about expected properties of the disease gene to determine such an ordering. Recently, we have introduced the data mining tool ENDEAVOUR (Aerts et al., 2006), which performs this task automatically by relying on different genome-wide data sources, such as Gene Ontology, literature, microarray, sequence and more.
Results: In this article, we present a novel kernel method that operates in the same setting: based on a number of different views on a set of training genes, a prioritization of test genes is obtained. We furthermore provide a thorough learning theoretical analysis of the method's guaranteed performance. Finally, we apply the method to the disease data sets on which ENDEAVOUR (Aerts et al., 2006) has been benchmarked, and report a considerable improvement in empirical performance.
Availability: The MATLAB code used in the empirical results will be made publicly available.
Contact: tijl.debie@gmail.com or yves.moreau@esat.kuleuven.be