Volume 2, Issue 1

We present a novel tool for processing and analysis of images of gene expression patterns stored in a relational database. This tool, known as ImageServer, is portable across different software/hardware platforms and supports both basic and subject domain oriented operations on images. The tool is used to process and analyze images stored in FlyEx database (http://urchin.spbcas.ru/flyex); software and documentation are available for download from http://urchin.spbcas.ru/downloads/IS/IS.htm.

Two-component systems (TCS) are the most prevalent gene regulatory mechanism in bacteria. A typical TCS is comprised of a histidine kinase (HK) and a partner response regulator (RR). Specific environment signals lead to autophosphorylation of different HKs, which in turn act as phosphoryl donors for autophosphorylation of their partner RRs. Nonpartner HKs and RRs also interact, giving rise to cross regulation among TCSs in response to diverse signals. PhoR (HK) and PhoB (RR) constitute the TCS for detection of environmental (extracellular) inorganic phosphate (Pi). The PhoR/PhoB TCS controls the expression of a large number of genes for acquisition of alternative phosphorus sources, including phoA, which encodes the non-specific phosphohydrolase bacterial alkaline phosphatase (Bap). Cross activation of PhoB by the nonpartner HK CreC is now a classic example of cross regulation among TCSs. A systematic search for other cross talking HKs revealed five additional HKs that activate (phosphorylate) PhoB (J. M. B. and B. L. W., unpublished data). Examination of cross activation of PhoB by these non-partner HKs by flow cytometry at the single-cell level revealed a bimodal, “all-or-none,” distribution pattern for expression of a phoAp-gfp (green fluorescent protein) reporter fusion. Although the basis of the observed stochastic behavior is unclear, it seems to reflect an inherent property of TCSs. We propose that cells exploit the stochastic character of TCSs to achieve nongenetic (epigenetic) diversity within genetically homogeneous cell populations in order to facilitate adaptation to environmental changes.

Proteomic analysis is intrinsically an iterative, incremental process. Information is usually acquired gradually by researchers, and in different projects. At the same time, there are relatively few examples of biological data management systems which take into account this reality, most of them usually treat the experiment generated data as static and unchangeable: data are never reconsidered, or seldom, whereas technology becomes more powerful or that other researchers have brought information on data correction. And yet, post-planned analysis [21] which involves multiple iterations and subsequent re-investigations of previously prepared data might bring tremendous benefits. Named PARIS (Proteomic Analysis and Resources Indexation System), the system we developed here seeks to address this requirement. Compliant with the majority of 2-DE analysis and MALDI-TOF based protein identification softwares, it automatically takes data from them and stores the raw and processed data in a relational database suitable for advanced exploration. Taking into account the standards proposed by PSI (Proteomics Standard Initiative), the system exports the stored data in XML format for data exchange and knowledge sharing. PARIS also manages information about experiments and their biological contexts, and allows the user to search and analyze a large data collection in a global manner. It provides tools for data integration and advanced, cross multi-experiment, multi-experimenter data exploration, and supports visual verification and correction of the analysis results. Implemented in Java, the system is platform independent, accessible to multiple users through Internet. It is also scalable for use for one or many laboratories, and therefore suitable to inter-institute collaborative work. PARIS can be tested and downloaded at http://genome.jouy.inra.fr/paris

Given the increasing number of available genomic sequences, one now faces the task of identifying their functional parts, like the protein coding regions. The gene prediction problem can be addressed in several ways. One of the most promising methods makes use of similarity information between the genomic DNA and previously annotated sequences (proteins, cDNAs and ESTs). Recently, given the huge amount of newly sequenced genomes, new similarity-based methods are being successfully applied in the task of gene prediction. The so-called comparative-based methods lie in the similarities shared by regions of two evolutionary related genomic sequences. Despite the number of different gene prediction approaches in the literature, this problem remains challenging. In this paper we present a new comparative-based approach to the gene prediction problem. It is based on a syntenic alignment of three or more genomic sequences. With syntenic alignment we mean an alignment that is constructed taking into account the fact that the involved sequences include conserved regions intervened by unconserved ones. We have implemented the proposed algorithm in a computer program and confirm the validity of the approach on a benchmark including triples of human, mouse and rat genomic sequences.

The complete genome sequences have provided a plethora of potential drug targets. Gene network technique holds the promise of providing a conceptual framework for analysis of the profusion of biological data being generated on potential drug targets and providing insights to understand the biological regulatory mechanisms in diseases, which are playing an increasingly important role in searching for novel drug targets from the information contained in genomics. In this paper, we discuss some of the network-based approaches for identifying drug targets, with the emphasis on the gene network strategy. In addition, some of the relevant data resources and computational tools are given.

Wnt/β-catenin signalling plays an important role in zonation of liver parenchyma and in patterning of hepatocyte heterogeneity. A characteristic marker of this heterogeneity is glutamine synthetase, which is expressed only in a subset of pericentrally located hepatocytes. To investigate, whether and how the Wnt/β-catenin signalling pathway is involved a culture of hepatocytes was stimulated by LiCl. This resulted in an increase in the specific GS activity, indicating that the Wnt/β-catenin pathway may participate in regulating GS levels. Affymetrix GeneChip oligonucleotide arrays were used to monitor the gene expression changes during a period from 2 to 24 hours after stimulation by LiCl. Samples from a cultivation without stimulation were used as controls. Based on the gene expression profiles a hypothetic signal transduction network was constructed by a reverse engineering algorithm. The network robustness was tested and the most stable structure was identified.

While biological processes underlying gene expression are still under experimental research, computational gene prediction techniques have reached high level of sophistication with the employment of efficient intrinsic and extrinsic methods that identify protein-coding regions within query genomic sequences. Their ability though to delineate the exact exon boundaries is characterized by a trade off between sensitivity and specificity and still is prone to alternations in gene regulation during transcription and splicing and to inherent complexities introduced by the implemented methodology. Evaluation studies have shown that combinatorial approaches exhibit improved accuracy levels through the integration of evidence data from multiple resources that are further assessed in order to end up with the most probable gene assembly. In this work, we present an integration and information handling architecture that exploits evidence derived from multiple gene finding resources, in order to generate machine-readable representations of optimal/suboptimal gene structure predictions, signal features identification and high scoring similarity matches. Unlike most combinatorial techniques, which end up with the most probable gene assembly, the objective of this architecture is to support advanced information handling mechanisms that may give more in depth insights on the underlying gene expression machinery and the alternations that may occur. Technically, XML was adopted to build and interchange structured data among the architecture’s components together with relevant technologies offering graphical representations and queries formulation/execution over single/multiple information sources.