bluuebunny/crossref_metadata_2025 huggingface dataset:
A Sorghum bicolor × S. macrospermum hybrid recovered by embryo rescue and culture
Authors: Price H. James , Hodnett George L. , Burson Byron L. , Dillon Sally L. , Rooney William L.
Published: October 2005

Although exotic germplasm is extensively used in sorghum improvement programs, Sorghum species classified in sections other than Eu-sorghum have not been utilised as germplasm because of strong reproductive barriers involving pollen–pistil incompatibilities. S. macrospermum is of particular interest to sorghum breeders because of its close phylogenetic relationship and cytogenetic similarities to S. bicolor and its resistance to important sorghum pests and pathogens, such as sorghum midge and sorghum downy mildew. A vegetatively vigorous interspecific hybrid was obtained from a cross between a cytoplasmic male-sterile S. bicolor plant and S. macrospermum by using embryo rescue and in vitro culture techniques. The hybrid was morphologically intermediate to S. bicolor and S. macrospermum in leaf width, leaf pubescence, plant height, inflorescence morphology, chromosome number and nuclear DNA content. It was male-sterile like its ATx623 parent. The hybrid produced no offspring when used as the female parent in a backcross with S. bicolor. This is the first confirmed hybrid between S. bicolor and S. macrospermum, and to our knowledge, it is the first reported hybrid between S. bicolor and any Sorghum species outside the Eu-sorghum section.
Life histories, ecological tolerance limits, and the evolution of geographic range size in Eucalyptus (Myrtaceae)
Authors: Mathews Sally , Bonser Stephen P.
Published: October 2005

Current theories explaining variability in species geographic range sizes in plants tend to focus on how traits associated with either physiological tolerance limits or life histories are related to range size. In trees, aspects of both physiological tolerance (e.g. drought tolerance) and life history (e.g. life span and growth rate) are related to stem traits such as wood density and height relative to diameter. We examined how the evolution of stem traits is related to geographic range sizes in Eucalyptus at two spatial scales: across the Australian continent and within the wet forests near the east coast of Australia. Geographic range sizes were estimated from herbarium records. Stem trait data were collected from both natural populations and published sources. We used phylogenetically independent contrasts to test for evolutionary associations between stem traits and geographic range sizes. Across Australia, the evolution of stem traits conferring drought tolerance were not consistently associated greater range sizes. This was surprising since arid and semi-arid environments are geographically expansive. Within the eastern forests, the evolution of stem traits defining slow growing, competitively dominant life histories were associated with greater range sizes. These stem traits should confer both a capacity to disperse into previously unoccupied habitats and the ability to persist in habitats already occupied. Traits associated with physiological tolerance and life history had significant effects on the evolution of range sizes in Eucalyptus. However, we demonstrate that the impact of these traits on range size evolution depends on both environmental conditions and the scale at which these traits are examined.
TIM-2 is expressed on B cells and in liver and kidney and is a receptor for H-ferritin endocytosis
Authors: Chen Thomas T. , Li Li , Chung Dong-Hui , Allen Christopher D.C. , Torti Suzy V. , Torti Frank M. , Cyster Jason G. , Chen Chih-Ying , Brodsky Frances M. , Niemi Eréne C. , Nakamura Mary C. , Seaman William E. , Daws Michael R.
Published: October 2005

T cell immunoglobulin-domain and mucin-domain (TIM) proteins constitute a receptor family that was identified first on kidney and liver cells; recently it was also shown to be expressed on T cells. TIM-1 and -3 receptors denote different subsets of T cells and have distinct regulatory effects on T cell function. Ferritin is a spherical protein complex that is formed by 24 subunits of H- and L-ferritin. Ferritin stores iron atoms intracellularly, but it also circulates. H-ferritin, but not L-ferritin, shows saturable binding to subsets of human T and B cells, and its expression is increased in response to inflammation. We demonstrate that mouse TIM-2 is expressed on all splenic B cells, with increased levels on germinal center B cells. TIM-2 also is expressed in the liver, especially in bile duct epithelial cells, and in renal tubule cells. We further demonstrate that TIM-2 is a receptor for H-ferritin, but not for L-ferritin, and expression of TIM-2 permits the cellular uptake of H-ferritin into endosomes. This is the first identification of a receptor for ferritin and reveals a new role for TIM-2.
IL-18–induced CD83+CCR7+NK helper cells
Authors: Mailliard Robbie B. , Alber Sean M. , Shen Hongmei , Watkins Simon C. , Kirkwood John M. , Herberman Ronald B. , Kalinski Pawel
Published: October 2005

In addition to their cytotoxic activities, natural killer (NK) cells can have immunoregulatory functions. We describe a distinct “helper” differentiation pathway of human CD56+CD3− NK cells into CD56+/CD83+/CCR7+/CD25+ cells that display high migratory responsiveness to lymph node (LN)–associated chemokines, high ability to produce interferon-γ upon exposure to dendritic cell (DC)- or T helper (Th) cell–related signals, and pronounced abilities to promote interleukin (IL)-12p70 production in DCs and the development of Th1 responses. This helper pathway of NK cell differentiation, which is not associated with any enhancement of cytolytic activity, is induced by IL-18, but not other NK cell–activating factors. It is blocked by prostaglandin (PG)E2, a factor that induces a similar CD83+/CCR7+/CD25+ LN-homing phenotype in maturing DCs. The current data demonstrate independent regulation of the “helper” versus “effector” pathways of NK cell differentiation and novel mechanisms of immunoregulation by IL-18 and PGE2.
Lysophosphatidic acid inhibits cholera toxin-induced secretory diarrhea through CFTR-dependent protein interactions
Authors: Li Chunying , Dandridge Keanna S. , Di Anke , Marrs Kevin L. , Harris Erica L. , Roy Koushik , Jackson John S. , Makarova Natalia V. , Fujiwara Yuko , Farrar Patricia L. , Nelson Deborah J. , Tigyi Gabor J. , Naren Anjaparavanda P.
Published: October 2005

The cystic fibrosis transmembrane conductance regulator (CFTR) is a cAMP-regulated chloride channel localized primarily at the apical or luminal surfaces of epithelial cells that line the airway, gut, and exocrine glands; it is well established that CFTR plays a pivotal role in cholera toxin (CTX)-induced secretory diarrhea. Lysophosphatidic acid (LPA), a naturally occurring phospholipid present in blood and foods, has been reported to play a vital role in a variety of conditions involving gastrointestinal wound repair, apoptosis, inflammatory bowel disease, and diarrhea. Here we show, for the first time, that type 2 LPA receptors (LPA2) are expressed at the apical surface of intestinal epithelial cells, where they form a macromolecular complex with Na+/H+ exchanger regulatory factor–2 and CFTR through a PSD95/Dlg/ZO-1–based interaction. LPA inhibited CFTR-dependent iodide efflux through LPA2-mediated Gi pathway, and LPA inhibited CFTR-mediated short-circuit currents in a compartmentalized fashion. CFTR-dependent intestinal fluid secretion induced by CTX in mice was reduced substantially by LPA administration; disruption of this complex using a cell-permeant LPA2-specific peptide reversed LPA2-mediated inhibition. Thus, LPA-rich foods may represent an alternative method of treating certain forms of diarrhea.
Removal of homeostatic cytokine sinks by lymphodepletion enhances the efficacy of adoptively transferred tumor-specific CD8+ T cells
Authors: Gattinoni Luca , Finkelstein Steven E. , Klebanoff Christopher A. , Antony Paul A. , Palmer Douglas C. , Spiess Paul J. , Hwang Leroy N. , Yu Zhiya , Wrzesinski Claudia , Heimann David M. , Surh Charles D. , Rosenberg Steven A. , Restifo Nicholas P.
Published: October 2005

Depletion of immune elements before adoptive cell transfer (ACT) can dramatically improve the antitumor efficacy of transferred CD8+ T cells, but the specific mechanisms that contribute to this enhanced immunity remain poorly defined. Elimination of CD4+CD25+ regulatory T (T reg) cells has been proposed as a key mechanism by which lymphodepletion augments ACT-based immunotherapy. We found that even in the genetic absence of T reg cells, a nonmyeloablative regimen substantially augmented CD8+ T cell reactivity to self-tissue and tumor. Surprisingly, enhanced antitumor efficacy and autoimmunity was caused by increased function rather than increased numbers of tumor-reactive T cells, as would be expected by homeostatic mechanisms. The γC cytokines IL-7 and IL-15 were required for augmenting T cell functionality and antitumor activity. Removal of γC cytokine–responsive endogenous cells using antibody or genetic means resulted in the enhanced antitumor responses similar to those seen after nonmyeloablative conditioning. These data indicate that lymphodepletion removes endogenous cellular elements that act as sinks for cytokines that are capable of augmenting the activity of self/tumor-reactive CD8+ T cells. Thus, the restricted availability of homeostatic cytokines can be a contributing factor to peripheral tolerance, as well as a limiting resource for the effectiveness of tumor-specific T cells.
The human macrophage mannose receptor directs Mycobacterium tuberculosis lipoarabinomannan-mediated phagosome biogenesis
Authors: Kang Peter B. , Azad Abul K. , Torrelles Jordi B. , Kaufman Thomas M. , Beharka Alison , Tibesar Eric , DesJardin Lucy E. , Schlesinger Larry S.
Published: October 2005

Mycobacterium tuberculosis (M.tb) survives in macrophages in part by limiting phagosome–lysosome (P-L) fusion. M.tb mannose-capped lipoarabinomannan (ManLAM) blocks phagosome maturation. The pattern recognition mannose receptor (MR) binds to the ManLAM mannose caps and mediates phagocytosis of bacilli by human macrophages. Using quantitative electron and confocal microscopy, we report that engagement of the MR by ManLAM during the phagocytic process is a key step in limiting P-L fusion. P-L fusion of ManLAM microspheres was significantly reduced in human macrophages and an MR-expressing cell line but not in monocytes that lack the receptor. Moreover, reversal of P-L fusion inhibition occurred with MR blockade. Inhibition of P-L fusion did not occur with entry via Fcγ receptors or dendritic cell–specific intracellular adhesion molecule 3 grabbing nonintegrin, or with phosphatidylinositol-capped lipoarabinomannan. The ManLAM mannose cap structures were necessary in limiting P-L fusion, and the intact molecule was required to maintain this phenotype. Finally, MR blockade during phagocytosis of virulent M.tb led to a reversal of P-L fusion inhibition in human macrophages (84.0 ± 5.1% vs. 38.6 ± 0.6%). Thus, engagement of the MR by ManLAM during the phagocytic process directs M.tb to its initial phagosomal niche, thereby enhancing survival in human macrophages.
Macrophage activation unveiled
Authors: Van Epps Heather L.
Published: October 2005

In the early 1960s, George Mackaness showed that macrophages from mice infected with intracellular bacteria could launch an indiscriminate attack against unrelated bacteria. Thus began an explosion of research on the biology of what Mackaness first termed “macrophage activation.”
Predicting post-synaptic activity in proteins with data mining
Authors: Pappa Gisele L. , Baines Anthony J. , Freitas Alex A.
Published: October 2005

Summary: The bioinformatics problem being addressed in this paper is to predict whether or not a protein has post-synaptic activity. This problem is of great intrinsic interest because proteins with post-synaptic activities are connected with functioning of the nervous system. Indeed, many proteins having post-synaptic activity have been functionally characterized by biochemical, immunological and proteomic exercises. They represent a wide variety of proteins with functions in extracellular signal reception and propagation through intracellular apparatuses, cell adhesion molecules and scaffolding proteins that link them in a web. The challenge is to automatically discover features of the primary sequences of proteins that typically occur in proteins with post-synaptic activity but rarely (or never) occur in proteins without post-synaptic activity, and vice-versa. In this context, we used data mining to automatically discover classification rules that predict whether or not a protein has post-synaptic activity. The discovered rules were analysed with respect to their predictive accuracy (generalization ability) and with respect to their interestingness to biologists (in the sense of representing novel, unexpected knowledge).

Contact:  A.A.Freitas@kent.ac.uk
Fast protein classification with multiple networks
Authors: Tsuda Koji , Shin HyunJung , Schölkopf Bernhard
Published: October 2005

Motivation: Support vector machines (SVMs) have been successfully used to classify proteins into functional categories. Recently, to integrate multiple data sources, a semidefinite programming (SDP) based SVM method was introduced. In SDP/SVM, multiple kernel matrices corresponding to each of data sources are combined with weights obtained by solving an SDP. However, when trying to apply SDP/SVM to large problems, the computational cost can become prohibitive, since both converting the data to a kernel matrix for the SVM and solving the SDP are time and memory demanding. Another application-specific drawback arises when some of the data sources are protein networks. A common method of converting the network to a kernel matrix is the diffusion kernel method, which has time complexity of O(n3), and produces a dense matrix of size n × n.

Results: We propose an efficient method of protein classification using multiple protein networks. Available protein networks, such as a physical interaction network or a metabolic network, can be directly incorporated. Vectorial data can also be incorporated after conversion into a network by means of neighbor point connection. Similar to the SDP/SVM method, the combination weights are obtained by convex optimization. Due to the sparsity of network edges, the computation time is nearly linear in the number of edges of the combined network. Additionally, the combination weights provide information useful for discarding noisy or irrelevant networks. Experiments on function prediction of 3588 yeast proteins show promising results: the computation time is enormously reduced, while the accuracy is still comparable to the SDP/SVM method.

Availability: Software and data will be available on request.

Contact:  shin@tuebingen.mpg.de
Evaluating the usefulness of protein structure models for molecular replacement
Authors: Giorgetti Alejandro , Raimondo Domenico , Miele Adriana Erica , Tramontano Anna
Published: October 2005

Motivation: We investigate the relationship between the quality of models of protein structure and their usefulness as search models in molecular replacement, a widely used method to experimentally determine protein structures by X-ray crystallography.

Results: We used the available models submitted to the Critical Assessment of Techniques for Protein Structure Prediction to verify in which cases they can be automatically used as search templates for molecular replacement. Our results show that there is a correlation between the quality of the models and their suitability for molecular replacement but that the traditional method of relying on sequence identity between the model and the template used to build it is not diagnostic for the success of the procedure.

Availability: Additional data are available at http://cassandra.bio.uniroma1.it/mr-results-casp.html

Contact:  anna.tramontano@uniroma1.it
Prediction of regulatory modules comprising microRNAs and target genes
Authors: Yoon Sungroh , De Micheli Giovanni
Published: October 2005

Motivation: MicroRNAs (miRNAs) are small endogenous RNAs that can play important regulatory roles via the RNA-interference pathway by targeting mRNAs for cleavage or translational repression. We propose a computational method to predict miRNA regulatory modules (MRMs) or groups of miRNAs and target genes that are believed to participate cooperatively in post-transcriptional gene regulation.

Results: We tested our method with the human genes and miRNAs, predicting 431 MRMs. We analyze a module with genes: BTG2, WT1, PPM1D, PAK7 and RAB9B, and miRNAs: miR-15a and miR-16. Review of the literature and annotation with Gene Ontology terms reveal that the roles of these genes can indeed be closely related in specific biological processes, such as gene regulation involved in breast, renal and prostate cancers. Furthermore, it has been reported that miR-15a and miR-16 are deleted together in certain types of cancer, suggesting a possible connection between these miRNAs and cancers. Given that most known functionalities of miRNAs are related to negative gene regulation, extending our approach and exploiting the insight thus obtained may provide clues to achieving practical accuracy in the reverse-engineering of gene regulatory networks.

Availability: A list of predicted modules is available from the authors upon request.

Contact:  sryoon@stanford.edu
Computational discovery of transcriptional regulatory rules
Authors: Pham Tho Hoan , Clemente José Carlos , Satou Kenji , Ho Tu Bao
Published: October 2005

Motivation: Even in a simple organism like yeast Saccharomyces cerevisiae, transcription is an extremely complex process. The expression of sets of genes can be turned on or off by the binding of specific transcription factors to the promoter regions of genes. Experimental and computational approaches have been proposed to establish mappings of DNA-binding locations of transcription factors. However, although location data obtained from experimental methods are noisy owing to imperfections in the measuring methods, computational approaches suffer from over-prediction problems owing to the short length of the sequence motifs bound by the transcription factors. Also, these interactions are usually environment-dependent: many regulators only bind to the promoter region of genes under specific environmental conditions. Even more, the presence of regulators at a promoter region indicates binding but not necessarily function: the regulator may act positively, negatively or not act at all. Therefore, identifying true and functional interactions between transcription factors and genes in specific environment conditions and describing the relationship between them are still open problems.

Results: We developed a method that combines expression data with genomic location information to discover (1) relevant transcription factors from the set of potential transcription factors of a target gene; and (2) the relationship between the expression behavior of a target gene and that of its relevant transcription factors. Our method is based on rule induction, a machine learning technique that can efficiently deal with noisy domains. When applied to genomic location data with a confidence criterion relaxed to P-value = 0.005, and three different expression datasets of yeast S.cerevisiae, we obtained a set of regulatory rules describing the relationship between the expression behavior of a specific target gene and that of its relevant transcription factors. The resulting rules provide strong evidence of true positive gene-regulator interactions, as well as of protein–protein interactions that could serve to identify transcription complexes.

Availability: Supplementary files are available from http://www.jaist.ac.jp/~h-pham/regulatory-rules

Contact:  h-pham@jaist.ac.jp
Identifying active transcription factors and kinases from expression data using pathway queries
Authors: Sohler Florian , Zimmer Ralf
Published: October 2005

Motivation: Although progress has been made identifying regulatory relationships from expression data in general, only few methods have focused on detecting biological mechanisms like active pathways using a single measurement. This is of particular importance when only few measurements are available, e.g. if special cell types or conditions are under investigation. Here we present a method to test user specified hypotheses (pathway queries) on expression data where prior knowledge is given in the form of networks and functional annotations. Based on this method, we develop a scoring function to identify active transcription factors or kinases, thus making a first step toward explaining the measured expression data.

Results: We apply the algorithm to the Rosetta Yeast Compendium dataset, finding that in many cases the results are in concordance with biological knowledge. We were able to confirm that transcription factors and to a lesser degree, kinases identified by our method play an important role in the biological processes affected by the respective knockouts. Furthermore, we show that correlation of inferred activities can provide evidence for a physical interaction or cooperation of transcription factors where correlation of plain expression data fails to do so.

Contact:  florian.sohler@bio.ifi.lmu.de
A fully Bayesian model to cluster gene-expression profiles
Authors: Vogl C. , Sanchez-Cabo F. , Stocker G. , Hubbard S. , Wolkenhauer O. , Trajanoski Z.
Published: October 2005

Motivation: With cDNA or oligonucleotide chips, gene-expression levels of essentially all genes in a genome can be simultaneously monitored over a time-course or under different experimental conditions. After proper normalization of the data, genes are often classified into co-expressed classes (clusters) to identify subgroups of genes that share common regulatory elements, a common function or a common cellular origin. With most methods, e.g. k-means, the number of clusters needs to be specified in advance; results depend strongly on this choice. Even with likelihood-based methods, estimation of this number is difficult. Furthermore, missing values often cause problems and lead to the loss of data.

Results: We propose a fully probabilistic Bayesian model to cluster gene-expression profiles. The number of classes does not need to be specified in advance; instead it is adjusted dynamically using a Reversible Jump Markov Chain Monte Carlo sampler. Imputation of missing values is integrated into the model. With simulations, we determined the speed of convergence of the sampler as well as the accuracy of the inferred variables. Results were compared with the widely used k-means algorithm. With our method, biologically related co-expressed genes could be identified in a yeast transcriptome dataset, even when some values were missing.

Availability: The code is available at http://genome.tugraz.at/BayesianClustering/

Contact:  claus.vogl@vu-wien.ac.at

Supplementary information: The supplementary material is available at http://genome.tugraz.at/BayesianClustering/
Fusing microarray experiments with multivariate regression
Authors: Gilks Walter R. , Tom Brian D. M. , Brazma Alvis
Published: October 2005

Motivation: It is widely acknowledged that microarray data are subject to high noise levels and results are often platform dependent. Therefore, microarray experiments should be replicated several times and in several laboratories before the results can be relied upon. To make the best use of such extensive datasets, methods for microarray data fusion are required. Ideally, the fused data should distil important aspects of the data while suppressing unwanted sources of variation and be amenable to further informal and formal methods of analysis. Also, the variability in the quality of experimentation should be taken into account.

Results: We present such an approach to data fusion, based on multivariate regression. We apply our methodology to data from a previous study on cell-cycle control in Schizosaccharomyces pombe.

Availability: The algorithm implemented in R is freely available from the authors on request.

Contact:  wally.gilks@mrc-bsu.cam.ac.uk
A Gamma mixture model better accounts for among site rate heterogeneity
Authors: Mayrose Itay , Friedman Nir , Pupko Tal
Published: October 2005

Motivation: Variation of substitution rates across nucleotide and amino acid sites has long been recognized as a characteristic of molecular sequence evolution. Evolutionary models that account for this rate heterogeneity usually use a gamma density function to model the rate distribution across sites. This density function, however, may not fit real datasets, especially when there is a multimodal distribution of rates. Here, we present a novel evolutionary model based on a mixture of gamma density functions. This model better describes the among-site rate variation characteristic of molecular sequence evolution. The use of this model may improve the accuracy of various phylogenetic methods, such as reconstructing phylogenetic trees, dating divergence events, inferring ancestral sequences and detecting conserved sites in proteins.

Results: Using diverse sets of protein sequences we show that the gamma mixture model better describes the stochastic process underlying protein evolution. We show that the proposed gamma mixture model fits protein datasets significantly better than the single-gamma model in 9 out of 10 datasets tested. We further show that using the gamma mixture model improves the accuracy of model-based prediction of conserved residues in proteins.

Availability: C++ source codes are available from the authors upon request.

Contact:  talp@post.tau.ac.il
Computing recombination networks from binary sequences
Authors: Huson Daniel H. , Kloepper Tobias H.
Published: October 2005

Motivation: Phylogenetic networks are becoming an important tool in molecular evolution, as the evolutionary role of reticulate events, such as hybridization, horizontal gene transfer and recombination, is becoming more evident, and as the available data is dramatically increasing in quantity and quality.

Results: This paper addresses the problem of computing a most parsimonious recombination network for an alignment of binary sequences that are assumed to have arisen under the ‘infinite sites’ model of evolution with recombinations. Using the concept of a splits network as the underlying datastructure, this paper shows how a recent method designed for the computation of hybridization networks can be extended to also compute recombination networks. A robust implementation of the approach is provided and is illustrated using a number of real biological datasets.

Availability: Our implementation of this approach is freely available as part of the SplitsTree4 software, downloadable from www.splitstree.org

Contact:  huson@informatik.uni-tuebingen.de
A quadratic programming approach for decomposing steady-state metabolic flux distributions onto elementary modes
Authors: Schwartz Jean-Marc , Kanehisa Minoru
Published: October 2005

Motivation: It is known that steady-state flux distributions in metabolic networks can be expressed as non-negative combinations of elementary modes. However, little understanding has been achieved so far in how individual elementary modes contribute to the reconstruction of actual physiological flux distributions.

Results: We introduce an approach for decomposing steady-state flux distributions onto elementary modes based on quadratic programming. The decomposition is performed so as to favour modes that are closest to the actual state of the system, i.e. most relevant for biological interpretation. As an illustration, an application of this approach to a model of yeast glycolysis is presented.

Availability: Software is available upon request from the authors.

Contact:  jean@kuicr.kyoto-u.ac.jp
Estimating gene regulatory networks and protein–protein interactions of Saccharomyces cerevisiae from multiple genome-wide data
Authors: Nariai Naoki , Tamada Yoshinori , Imoto Seiya , Miyano Satoru
Published: October 2005

Motivation: Biological processes in cells are properly performed by gene regulations, signal transductions and interactions between proteins. To understand such molecular networks, we propose a statistical method to estimate gene regulatory networks and protein–protein interaction networks simultaneously from DNA microarray data, protein–protein interaction data and other genome-wide data.

Results: We unify Bayesian networks and Markov networks for estimating gene regulatory networks and protein–protein interaction networks according to the reliability of each biological information source. Through the simultaneous construction of gene regulatory networks and protein–protein interaction networks of Saccharomyces cerevisiae cell cycle, we predict the role of several genes whose functions are currently unknown. By using our probabilistic model, we can detect false positives of high-throughput data, such as yeast two-hybrid data. In a genome-wide experiment, we find possible gene regulatory relationships and protein–protein interactions between large protein complexes that underlie complex regulatory mechanisms of biological processes.

Contact:  nariai@ims.u-tokyo.ac.jp
A probabilistic model for mining implicit ‘chemical compound–gene’ relations from literature
Authors: Zhu Shanfeng , Okuno Yasushi , Tsujimoto Gozoh , Mamitsuka Hiroshi
Published: October 2005

Motivation: The importance of chemical compounds has been emphasized more in molecular biology, and ‘chemical genomics’ has attracted a great deal of attention in recent years. Thus an important issue in current molecular biology is to identify biological-related chemical compounds (more specifically, drugs) and genes. Co-occurrence of biological entities in the literature is a simple, comprehensive and popular technique to find the association of these entities. Our focus is to mine implicit ‘chemical compound and gene’ relations from the co-occurrence in the literature.

Results: We propose a probabilistic model, called the mixture aspect model (MAM), and an algorithm for estimating its parameters to efficiently handle different types of co-occurrence datasets at once. We examined the performance of our approach not only by a cross-validation using the data generated from the MEDLINE records but also by a test using an independent human-curated dataset of the relationships between chemical compounds and genes in the ChEBI database. We performed experimentation on three different types of co-occurrence datasets (i.e. compound–gene, gene–gene and compound–compound co-occurrences) in both cases. Experimental results have shown that MAM trained by all datasets outperformed any simple model trained by other combinations of datasets with the difference being statistically significant in all cases. In particular, we found that incorporating compound–compound co-occurrences is the most effective in improving the predictive performance. We finally computed the likelihoods of all unknown compound–gene (more specifically, drug–gene) pairs using our approach and selected the top 20 pairs according to the likelihoods. We validated them from biological, medical and pharmaceutical viewpoints.

Contact:  mami@kuicr.kyoto-u.ac.jp
Expert knowledge without the expert: integrated analysis of gene expression and literature to derive active functional contexts
Authors: Küffner Robert , Fundel Katrin , Zimmer Ralf
Published: October 2005

Motivation: The interpretation of expression data without appropriate expert knowledge is difficult and usually limited to exploratory data analysis, such as clustering and detecting differentially regulated genes. However, comparing experimental results against manually compiled knowledge resources might limit or bias the perspective on the data. Thus, manual analysis by experts is required to obtain confident predictions about involved processes.

Results: We present an algorithm to simultaneously derive interpretations of expression measurements together with biological hypotheses from biomedical publications. It identifies active functional contexts (‘concepts’), i.e. gene clusters that exhibit both a significant gene expression as well as a coherent literature profile. Manual intervention by an expert in specifying prior knowledge is not required. The approach scales to realistic applications and does not rely on controlled vocabularies or pathway resources.

We validated our algorithm by analyzing a current juvenile arthritis dataset. A number of gene clusters and accompanying literature topics are identified as an interpretation of the data that coincide well with the phenotype and biological processes known to be involved in the disease. We demonstrate that generated clusters are both more sensitive and more specific than Gene Ontology categories detected on the same data. The method allows for in-depth investigation of subsets of genes, the associated literature topics and publications.

Availability: Supplementary data on clusters is available upon request.

Contact:  Robert.Kueffner@bio.ifi.lmu.de
DNA looping-mediated repression by histone-like protein H-NS: specific requirement of Eσ70 as a cofactor for looping
Authors: Shin Minsang , Song Miryoung , Rhee Joon Haeng , Hong Yeongjin , Kim You-Jin , Seok Yeong-Jae , Ha Kwon-Soo , Jung Se-Hui , Choy Hyon E.
Published: October 2005

Transcription initiation by RNA polymerase (RNP) carrying the house-keeping σ subunit, σ70 (Eσ70), is repressed by H-NS at a number of promoters including hdeABp in Escherichia coli, while initiation with RNP carrying the stationary phase σ, σ38 (Eσ38), is not. We investigated the molecular mechanism of selective repression by H-NS to identify the differences in transcription initiation by the two forms of RNPs, which show indistinguishable promoter selectivities in vitro. Using hdeABp as a model promoter, we observed with purified components that H-NS, acting at a sequence centered at -118, selectively repressed transcription by Eσ70. This selective repression is attributed to the differences in the interactions between hdeABp and the two forms of RNPs, since no other factor is required for the repression. We observed that the two forms of RNPs could form an open initiation complex (RPO) at hdeABp, but that Eσ70 failed to initiate transcription in the presence of H-NS. Interestingly, KMnO4 assays and high-resolution atomic force microscopy (AFM) revealed that hdeABp DNA wrapped around Eσ70 more tightly than around Eσ38, resulting in the potential crossing over of the DNA arms that project out of Eσ70 · RPO but not out of Eσ38 · RPO. Based on these observations, we postulated that H-NS bound at -118 laterally extends by the cooperative recruitment of H-NS molecules to the promoter-downstream sequence joined by wrapping of the DNA around Eσ70 · RPO, resulting in effective sealing of the DNA loop and trapping of Eσ70. Such a ternary complex of H-NS · Eσ70hdeABp was demonstrated by AFM. In this case, therefore, Eσ70 acts as a cofactor for DNA looping. Expression of this class of genes by Eσ38 in the stationary phase is not due to its promoter specificity but to the architecture of the promoter · Eσ38 complex.
Response of RNA polymerase to ppGpp: requirement for the ω subunit and relief of this requirement by DksA
Authors: Vrentas Catherine E. , Gaal Tamas , Ross Wilma , Ebright Richard H. , Gourse Richard L.
Published: October 2005

Previous studies have come to conflicting conclusions about the requirement for the ω subunit of RNA polymerase in bacterial transcription regulation. We demonstrate here that purified RNAP lacking ω does not respond in vitro to the effector of the stringent response, ppGpp. DksA, a transcription factor that works in concert with ppGpp to regulate rRNA expression in vivo and in vitro, fully rescues the ppGpp-unresponsiveness of RNAP lacking ω, likely explaining why strains lacking ω display a stringent response in vivo. These results demonstrate that ω plays a role in RNAP function (in addition to its previously reported role in RNAP assembly) and highlight the importance of inclusion of ω in RNAP purification protocols. Furthermore, these results suggest that either one or both of two short segments in the β′ subunit that physically link ω to the ppGpp-binding region of the enzyme may play crucial roles in ppGpp and DksA function.
The ubiquitin ligase Rnf6 regulates local LIM kinase 1 levels in axonal growth cones
Authors: Tursun Baris , Schlüter Anne , Peters Marvin A. , Viehweger Birte , Ostendorff Heather P. , Soosairajah Juliana , Drung Alexander , Bossenz Michael , Johnsen Steven A. , Schweizer Michaela , Bernard Ora , Bach Ingolf
Published: October 2005

LIM kinase 1 (LIMK1) controls important cellular functions such as morphogenesis, cell motility, tumor cell metastasis, development of neuronal projections, and growth cone actin dynamics. We have investigated the role of the RING finger protein Rnf6 during neuronal development and detected high Rnf6 protein levels in developing axonal projections of motor and DRG neurons during mouse embryogenesis as well as cultured hippocampal neurons. RNAi-mediated knock-down experiments in primary hippocampal neurons identified Rnf6 as a regulator of axon outgrowth. Consistent with a role in axonal growth, we found that Rnf6 binds to, polyubiquitinates, and targets LIMK1 for proteasomal degradation in growth cones of primary hippocampal neurons. Rnf6 is functionally linked to LIMK1 during the development of axons, as the changes in axon outgrowth induced by up- or down-regulation of Rnf6 levels can be restored by modulation of LIMK1 expression. Thus, these results assign a specific role for Rnf6 in the control of cellular LIMK1 concentrations and indicate a new function for the ubiquitin/proteasome system in regulating local growth cone actin dynamics.
The Drosophila MSL complex activates the transcription of target genes
Authors: Straub Tobias , Gilfillan Gregor D. , Maier Verena K. , Becker Peter B.
Published: October 2005

The mechanism through which gene expression originating from the single male or the two female X chromosomes in Drosophila is adjusted to autosomal gene expression has remained controversial. According to the prevalent model, transcription of the male X is increased twofold by the male-specific-lethal (MSL) complex. However, a significant body of data supports an alternative model, whereby compensation involves a global repression of autosomal gene expression in males by sequestration and neutralization of an activator onto the X chromosome. In order to rigorously discriminate between these models we identified direct target genes for the MSL complex and quantified transcription in absolute terms after knockdown of MSL2. The results unequivocally document an approximate twofold activation of target genes by the MSL complex.
Hfq-dependent regulation of OmpA synthesis is mediated by an antisense RNA
Authors: Udekwu Klas I. , Darfeuille Fabien , Vogel Jörg , Reimegård Johan , Holmqvist Erik , Wagner E. Gerhart H.
Published: October 2005

This paper shows that the small RNA MicA (previously SraD) is an antisense regulator of ompA in Escherichia coli. MicA accumulates upon entry into stationary phase and down-regulates the level of ompA mRNA. Regulation of ompA (outer membrane protein A), previously attributed to Hfq/mRNA binding, is lost upon deletion of the micA gene, whereas overexpression of MicA inhibits the synthesis of OmpA. In vitro, MicA binds to the ompA mRNA leader. Enzymatic and chemical probing was used to map the structures of MicA, the ompA mRNA leader, and the complex formed upon binding. MicA binding generates a footprint across the ompA Shine-Dalgarno sequence, consistent with a 12 + 4 base-pair interaction, which is additionally supported by the effect of mutations in vivo and by bioinformatics analysis of enterobacterial micA/ompA homolog sequences. MicA is conserved in many enterobacteria, as is its ompA target site. In vitro toeprinting confirmed that binding of MicA specifically interferes with ribosome binding. We propose that MicA, when present at high levels, blocks ribosome binding at the ompA translation start site, which—in line with previous work—secondarily facilitates RNase E cleavage and subsequent mRNA decay. MicA requires the presence of the Hfq protein, although the mechanistic basis for this remains unclear.
Gene enrichment in maize with hypomethylated partial restriction (HMPR) libraries
Authors: Emberton John , Ma Jianxin , Yuan Yinan , SanMiguel Phillip , Bennetzen Jeffrey L.
Published: October 2005

A new technology was developed to assist gene-enrichment sequencing of any complex plant genome, employing maize as the test organism. Hypomethylated partial restriction (HMPR) libraries were constructed by using independent partial digestions with methylation-sensitive restriction enzymes HpaII (5′-CCGG-3′) and HpyCH4IV (5′-ACGT-3′). Fragments of 1-4 kb were purified and cloned, followed by sequence analysis of >2000 clones from 10 separate libraries. Organellar clones comprised ∼10% of each library but were useful in showing that no chimeric clones were generated and that digestion efficiencies were 10%-25% in different libraries. Four separate HMPR libraries, analyzed in detail, exhibited very similar degrees of gene enrichment and repeat depletion. Known gene homologies were found in ∼25% of the HMPR clones, compared with <4% in clones from a fully random set of unfiltered maize shotgun sequences. This six- to sevenfold enrichment for genes compares favorably with the best previous gene enrichment techniques in maize, High Cot analysis and methylation filtration. Compared with High Cot and methylation filtration, HMPR is exceptional in depleting retrotransposons' content to the lowest level yet observed (<5%, compared with >70% for unfiltered maize sequences) and in providing an unmatched enrichment for the “unknown” sequences that contain promoters, introns, and other gene-adjacent regions.
Long-range DNase I hypersensitivity mapping reveals the imprinted Igf2r and Air promoters share cis-regulatory elements
Authors: Pauler Florian M. , Stricker Stefan H. , Warczok Katarzyna E. , Barlow Denise P.
Published: October 2005

Epigenetic mechanisms restrict the expression of imprinted genes to one parental allele in diploid cells. At the Igf2r/Air imprinted cluster on mouse chromosome 17, paternal-specific expression of the Air noncoding RNA has been shown to silence three genes in cis: Igf2r, Slc22a2, and Slc22a3. By an unbiased mapping of DNase I hypersensitive sites (DHS) in a 192-kb region flanking Igf2r and Air, we identified 21 DHS, of which nine mapped to evolutionarily conserved sequences. Based on the hypothesis that silencing effects of Air would be directed towards cis regulatory elements used to activate genes, DHS are potential key players in the control of imprinted expression. However, in this 192-kb region only the two DHS mapping to the Igf2r and Air promoters show parental specificity. The remaining 19 DHS were present on both parental alleles and, thus, have the potential to activate Igf2r on the maternal allele and Air on the paternal allele. The possibility that the Igf2r and Air promoters share the same cis-acting regulatory elements, albeit on opposite parental chromosomes, was supported by the similar expression profiles of Igf2r and Air in vivo. These results refine our understanding of the onset of imprinted silencing at this cluster and indicate the Air noncoding RNA may specifically target silencing to the Igf2r promoter.
Parallel adaptive evolution cultures ofEscherichia colilead to convergent growth phenotypes with different gene expression states
Authors: Fong Stephen S. , Joyce Andrew R. , Palsson Bernhard Ø.
Published: October 2005

Laboratory evolution can be used to address fundamental questions about adaptation to selection pressures and, ultimately, the process of evolution. In this study, we investigated the reproducibility of growth phenotypes and global gene expression states during adaptive evolution. The results from parallel, replicate adaptive evolution experiments ofEscherichia coliK-12 MG1655 grown on either lactate or glycerol minimal media showed that (1) growth phenotypes at the endpoint of evolution are convergent and reproducible; (2) endpoints of evolution have different underlying gene expression states; and (3) the evolutionary gene expression response involves a large number of compensatory expression changes and a smaller number of adaptively beneficial expression changes common across evolution strains. Gene expression changes initially showed a large number of differentially expressed genes in response to an environmental change followed by a return of most genes to a baseline expression level, leaving a relatively small set of differentially expressed genes at the endpoint that varied between evolved populations.
The function and expansion of the Patched- and Hedgehog-related homologs in C. elegans
Authors: Zugasti Olivier , Rajan Jeena , Kuwabara Patricia E.
Published: October 2005

The Hedgehog (Hh) signaling pathway promotes pattern formation and cell proliferation in Drosophila and vertebrates. Hh is a ligand that binds and represses the Patched (Ptc) receptor and thereby releases the latent activity of the multipass membrane protein Smoothened (Smo), which is essential for transducing the Hh signal. In Caenorhabditis elegans, the Hh signaling pathway has undergone considerable divergence. Surprisingly, obvious Smo and Hh homologs are absent whereas PTC, PTC-related (PTR), and a large family of nematode Hh-related (Hh-r) proteins are present. We find that the number of PTC-related and Hh-r proteins has expanded in C. elegans, and that this expansion occurred early in Nematoda. Moreover, the function of these proteins appears to be conserved in Caenorhabditis briggsae. Given our present understanding of the Hh signaling pathway, the absence of Hh and Smo raises many questions about the evolution and the function of the PTC, PTR, and Hh-r proteins in C. elegans. To gain insights into their roles, we performed a global survey of the phenotypes produced by RNA-mediated interference (RNAi). Our study reveals that these genes do not require Smo for activity and that they function in multiple aspects of C. elegans development, including molting, cytokinesis, growth, and pattern formation. Moreover, a subset of the PTC, PTR, and Hh-r proteins have the same RNAi phenotypes, indicating that they have the potential to participate in the same processes.
Metabolic functions of duplicate genes in Saccharomyces cerevisiae
Authors: Kuepfer Lars , Sauer Uwe , Blank Lars M.
Published: October 2005

The roles of duplicate genes and their contribution to the phenomenon of enzyme dispensability are a central issue in molecular and genome evolution. A comprehensive classification of the mechanisms that may have led to their preservation, however, is currently lacking. In a systems biology approach, we classify here back-up, regulatory, and gene dosage functions for the 105 duplicate gene families of Saccharomyces cerevisiae metabolism. The key tool was the reconciled genome-scale metabolic model iLL672, which was based on the older iFF708. Computational predictions of all metabolic gene knockouts were validated with the experimentally determined phenotypes of the entire singleton yeast library of 4658 mutants under five environmental conditions. iLL672 correctly identified 96%-98% and 73%-80% of the viable and lethal singleton phenotypes, respectively. Functional roles for each duplicate family were identified by integrating the iLL672-predicted in silico duplicate knockout phenotypes, genome-scale carbon-flux distributions, singleton mutant phenotypes, and network topology analysis. The results provide no evidence for a particular dominant function that maintains duplicate genes in the genome. In particular, the back-up function is not favored by evolutionary selection because duplicates do not occur more frequently in essential reactions than singleton genes. Instead of a prevailing role, multigene-encoded enzymes cover different functions. Thus, at least for metabolism, persistence of the paralog fraction in the genome can be better explained with an array of different, often overlapping functional roles.