Vitamin D3 modulated gene expression patterns in human primary normal and cancer prostate cells

The vitamin D receptor (VDR) is a member of the steroid/retinoid receptor superfamily of nuclear receptors and has potential tumor-suppressive functions in prostate and other cancer types. Vitamin D3 (VD3) exerts its biological actions by binding within cells to VDR. The VDR then interacts with specific regions of the DNA in cells, and triggers changes in the activity of genes involved in cell division, cell survival, and cellular function. Using human primary cultures and the prostate cancer (PCa) cell line, ALVA-31, we examined the effects of VD3 under different culture conditions. Complete G0/G1 arrest of ALVA-31 cells and approximately 50% inhibition of tumor stromal cell growth was observed. To determine changes in gene expression patterns related to VD3 activity, microarray analysis was performed. More than approximately 20,000 genes were evaluated for twofold relative increases and decreases in expression levels. A number of the gene targets that were up- and down-regulated are related to potential mechanisms of prostatic growth regulation. These include estrogen receptor (ER), heat shock proteins: 70 and 90, Apaf1, Her-2/neu, and paxillin. Utilizing antibodies generated against these targets, we were able to confirm the changes at the protein level. These newly reported gene expression patterns provide novel information not only potential markers, but also on the genes involved in VD3 induced apoptosis in PCa.     

Multiscale stochastic modelling of gene expression

Stochastic phenomena in gene regulatory networks can be modelled by the chemical master equation for gene products such as mRNA and proteins. If some of these elements are present in significantly higher amounts than the rest, or if some of the reactions between these elements are substantially faster than others, it is often possible to reduce the master equation to a simpler problem using asymptotic methods. We present examples of such a procedure and analyse the relationship between the reduced models and the original.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

Tumor-specific gene expression patterns with gene expression profiles

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, “RFE_Relief algorithm” was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.     

3D confocal reconstruction of gene expression in mouse

Three-dimensional computer reconstructions of gene expression data will become a valuable tool in biomedical research in the near future. However, at present the process of converting in situ expression data into 3D models is a highly specialized and time-consuming procedure. Here we present a method which allows rapid reconstruction of whole-mount in situ data from mouse embryos. Mid-gestation embryos were stained with the alkaline phosphotase substrate Fast Red, which can be detected using confocal laser scanning microscopy (CLSM), and cut into 70 microm sections. Each section was then scanned and digitally reconstructed. Using this method it took two days to section, digitize and reconstruct the full expression pattern of Shh in an E9.5 embryo (a 3D model of this embryo can be seen at genex.hgu.mrc.ac.uk). Additionally we demonstrate that this technique allows gene expression to be studied at the single cell level in intact tissue.     

Molecular imaging of the embryonic heart: Fables and facts on 3D imaging of gene expression patterns

Molecular imaging, which is the three-dimensional (3D) visualization of gene expression patterns, is indispensable for the study of the function of genes in cardiac development. The instrumentation, as well as the development of specific contrast agents for molecular imaging, has shown spectacular advances in the last decade. In this review, the spatial resolutions, contrast agents, and applications of these imaging methods in the field of cardiac embryology are discussed. Apart from 3D reconstructions from histological sections, not many of these methods have been applied in embryological research. This review shows that, for most methods, neither the spatial resolutions nor the specificity and applicability of the contrast agents are adequate for the reliable imaging of specific gene expression at the microscopic resolution required for embryological studies of small organs like the developing heart. Although a 3D reconstruction from sections will always suffer from imperfections, the resulting reconstructions meet the aim of most biological studies, especially since the original microscopic images are linked. With respect to imaging of gene expression, only histological sections and laser scanning microscopy provide the required resolution and specificity at the tissue and cellular level. Episcopic fluorescence image capturing and optical projection tomography are being used for microscopic phenotyping and lineage analysis, and both show potential for detailed molecular imaging. Other methods can be used very efficiently in rapid evaluation of biological experiments and high-throughput screens of large-scale gene expression profiling efforts when high spatial resolution is not required.     

A Java tool for dynamic web-based 3D visualization of anatomy and overlapping gene or protein expression patterns

The Gene Expression Viewer is a web-launched three-dimensional visualization tool, tailored to compare surface reconstructions of multi-channel image volumes generated by confocal microscopy or micro-CT.     

The manganese superoxide dismutase gene in bay scallop Argopecten irradians: cloning, 3D modelling and mRNA expression

A novel manganese superoxide dismutase (MnSOD) was cloned from bay scallop Argopecten irradians by 3' and 5' rapid amplification of cDNA ends (RACE) PCR. The full-length cDNA of MnSOD was of 1207bp with a 678bp open reading frame encoding 226 amino acids. The deduced amino acid sequence contained a putative signal peptide of 26 amino acids. Sequence comparison showed that the MnSOD of A. irradians shared high identity with MnSOD in invertebrates and vertebrates, such as MnSOD from abalone Haliotis discus discus (ABG88843) and frog Xenopus laevis (AAQ63483). Furthermore, the 3D structure of bay scallop MnSOD was predicted by SWISS-MODEL Protein Modelling Server and compared with those of other MnSODs. The overall structure of bay scallop MnSOD was similar to those of zebrafish Danio rerio, fruit fly Drosophila melanogaster, Chinese shrimp Fenneropenaeus chinensis, human Homo sapiens, and had the highest similarity to scallop Mizuhopecten yessoensis and abalone H. discus discus. A quantitative real-time PCR (qRT-PCR) assay was developed to detect the mRNA expression of MnSOD in different tissues and the temporal expression in haemocytes following challenge with the bacterium Vibrio anguillarum. A higher-level of mRNA expression of MnSOD was detected in gill and mantle. The expression of MnSOD reached the highest level at 3h post-injection with V. anguillarum and then slightly recovered from 6 to 48h. The results indicated that bay scallop MnSOD was a constitutive and inducible protein and thus could play an important role in the immune responses against V. anguillarum infection.     

Clustering gene expression patterns.

Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. The corresponding algorithmic problem is to cluster multicondition gene expression patterns. In this paper we describe a novel clustering algorithm that was developed for analysis of gene expression data. We define an appropriate stochastic error model on the input, and prove that under the conditions of the model, the algorithm recovers the cluster structure with high probability. The running time of the algorithm on an n-gene dataset is O[n2[log(n)]c]. We also present a practical heuristic based on the same algorithmic ideas. The heuristic was implemented and its performance is demonstrated on simulated data and on real gene expression data, with very promising results.     

Integrative modelling of gene expression and cell metabolism

The goal of modelling biochemical networks is to understand the system's behaviour (dynamics and control) of these networks in terms of the properties of the individual molecules. Most modelling approaches have dealt with either gene expression or cell metabolism. In light of the widespread use of robotic technologies in laboratories and improved computational power, it is now time to incorporate information from all biochemical levels--gene expression, protein interactions and metabolism--into integrated models. Here, we review the literature on modelling gene expression with cell metabolism. At the end we describe some analytic methods to deal with these systems.     

Integrating heterogeneous gene expression data for gene regulatory network modelling

Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors' knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets.     

Root organization and gene expression patterns

New tools of microscopy, molecular biology, and genetics aremaking it possible for biologists to study roots with new vigour.Such investigations have enabled plant biologists to noticethe symmetry, pattern, and simplicity of root structures sothat there is now an exciting rebirth in the study of roots.The literature of root biology, development and structure isvast. In this short review we will concentrate on describingour notion of how roots are organized structurally, and thendiscuss what is known about tissue- and zone-specific gene expressionin roots. Key words: Root apex, root anatomy, root development, gene expression     

Elucidating multi-input processing 3-node gene regulatory network topologies capable of generating striped gene expression patterns

A central problem in developmental and synthetic biology is understanding the mechanisms by which cells in a tissue or a Petri dish process external cues and transform such information into a coherent response, e.g., a terminal differentiation state. It was long believed that this type of positional information could be entirely attributed to a gradient of concentration of a specific signaling molecule (i.e., a morphogen). However, advances in experimental methodologies and computer modeling have demonstrated the crucial role of the dynamics of a cell’s gene regulatory network (GRN) in decoding the information carried by the morphogen, which is eventually translated into a spatial pattern. This morphogen interpretation mechanism has gained much attention in systems biology as a tractable system to investigate the emergent properties of complex genotype-phenotype maps. In this study, we apply a Markov chain Monte Carlo (MCMC)-like algorithm to probe the design space of three-node GRNs with the ability to generate a band-like expression pattern (target phenotype) in the middle of an arrangement of 30 cells, which resemble a simple (1-D) morphogenetic field in a developing embryo. Unlike most modeling studies published so far, here we explore the space of GRN topologies with nodes having the potential to perceive the same input signal differently. This allows for a lot more flexibility during the search space process, and thus enables us to identify a larger set of potentially interesting and realizable morphogen interpretation mechanisms. Out of 2061 GRNs selected using the search space algorithm, we found 714 classes of network topologies that could correctly interpret the morphogen. Notably, the main network motif that generated the target phenotype in response to the input signal was the type 3 Incoherent Feed-Forward Loop (I3-FFL), which agrees with previous theoretical expectations and experimental observations. Particularly, compared to a previously reported pattern forming GRN topologies, we have uncovered a great variety of novel network designs, some of which might be worth inquiring through synthetic biology methodologies to test for the ability of network design with minimal regulatory complexity to interpret a developmental cue robustly.     

3D docking device for molecular modelling

A device to aid in the process of docking molecules has been incorporated into a molecular modelling system. It provides a superb correspondence between motions of a single electromagnetic control wand and motions of a molecule displayed on the graphics screen. The commercially available hardware device is described, the new algorithms necessary to implement it are also described, and an example is given of how it has been used to dock a drug with its receptor.     

Mathematical characterization of three-dimensional gene expression patterns

MOTIVATION: The importance of a systematic methodology for the mathematical characterization of three-dimensional gene expression patterns in embryonic development. METHODS: By combining lacunarity and multiscale fractal dimension analyses with computer-based methods of three-dimensional reconstruction, it becomes possible to extract new information from in situ hybridization studies. Lacunarity and fractality are appropriate measures for the cloud-like gene activation signals in embryonic tissues. The newly introduced multiscale method provides a natural extension of the fractal dimension concept, being capable of characterizing the fractality of geometrical patterns in terms of spatial scale. This tool can be systematically applied to three-dimensional patterns of gene expression. RESULTS: Applications are illustrated using the three-dimensional expression patterns of the myogenic marker gene Myf5 in a series of differentiating somites of a mouse embryo.     

Mixture modelling of gene expression data from microarray experiments

MOTIVATION: Hierarchical clustering is one of the major analytical tools for gene expression data from microarray experiments. A major problem in the interpretation of the output from these procedures is assessing the reliability of the clustering results. We address this issue by developing a mixture model-based approach for the analysis of microarray data. Within this framework, we present novel algorithms for clustering genes and samples. One of the byproducts of our method is a probabilistic measure for the number of true clusters in the data. RESULTS: The proposed methods are illustrated by application to microarray datasets from two cancer studies; one in which malignant melanoma is profiled (Bittner et al., Nature, 406, 536-540, 2000), and the other in which prostate cancer is profiled (Dhanasekaran et al., 2001, submitted).