Bayesian variable selection for gene expression modeling with regulatory motif binding sites in neuroinflammatory events

Multiple transcription factors (TFs) coordinately control transcriptional regulation of genes in eukaryotes. Although numerous computational methods focus on the identification of individual TF-binding sites (TFBSs), very few consider the interdependence among these sites. In this article, we studied the relationship between TFBSs and microarray gene expression levels using both family-wise and memberspecific motifs, under various combination of regression models with Bayesian variable selection, as well as motif scoring and sharing conditions, in order to account for the coordination complexity of transcription regulation. We proposed a three-step approach to model the relationship. In the first step, we preprocessed microarray data and used p-values and expression ratios to preselect upregulated and downregulated genes. The second step aimed to identify and score individual TFBSs within DNA sequence of each gene. A method based on the degree of similarity and the number of TFBSs was employed to calculate the score of each TFBS in each gene sequence. In the last step, linear regression and probit regression were used to build a predictive model of gene expression outcomes using these TFBSs as predictors. Given a certain number of predictors to be used, a full search of all possible predictor sets is usually combinatorially prohibitive. Therefore, this article considered the Bayesian variable selection for prediction using either of the regression models. The Bayesian variable selection has been applied in the context of gene selection, missing value estimation, and regulatory motif identification. In our modeling, the regressor was approximated as a linear combination of the TFBSs and a Gibbs sampler was employed to find the strongest TFBSs. We applied these regression models with the Bayesian variable selection on spinal cord injury gene expression data set. These TFs demonstrated intricate regulatory roles either as a family or as individual members in neuroinflammatory events. Our analysis can be applied to create plausible hypotheses for combinatorial regulation by TFBSs and avoiding false-positive candidates in the modeling process at the same time. Such a systematic approach provides the possibility to dissect transcription regulation, from a more comprehensive perspective, through which phenotypical events at cellular and tissue levels are moved forward by molecular events at gene transcription and translation levels.

Review: on the analysis and interpretation of correlations in metabolomic data

A remarkable inherent feature of cellular metabolism is that the concentrations of a small but significant number of metabolites are strongly correlated when measurements of biological replicates are performed. This review seeks to summarize the recent efforts to elucidate the origin of these observed correlations and points out several aspects concerning their interpretation. It is argued that correlations between metabolites differ profoundly from their transcriptomic and proteomic counterparts, and a straightforward interpretation in terms of the underlying biochemical pathways will unavoidably fail. It is demonstrated that the comparative correlations analysis offers a way to exploit the observed correlations to obtain additional information about the physiological state of the system.

High-throughput gene expression profiling--a work-in-progress with great potential for proteomics

Since the invention of Northern blot profiling more than 30 years ago, scientists have progressed from being able to analyze only a few genes at a time to whole-genome and global differential gene expression profiling. This progress has been made possible by the development of several high-throughput technologies. Such technologies have gained increasing popularity in recent years because of significant discoveries made in the field of proteomics, and have already led to huge advances in the medical field. This article reviews the high-throughput technologies that have been undergoing development during the last two years for the selection of specific cell subtypes in a tissue, for the discovery and identification of all of the genes expressed in a system without pre-selection (open systems), and for the validation of selected genes (closed systems).

DNA MICROARRAY TECHNOLOGY FOR TARGET IDENTIFICATION AND VALIDATION

1. Microarrays, a recent development, provide a revolutionary platform to analyse thousands of genes at once. They have enormous potential in the study of biological processes in health and disease and, perhaps, microarrays have become crucial tools in diagnostic applications and drug discovery. 2. Microarray based studies have provided the essential impetus for biomedical experiments, such as identification of disease-causing genes in malignancies and regulatory genes in the cell cycle mechanism. Microarrays can identify genes for new and unique potential drug targets, predict drug responsiveness for individual patients and, finally, initiate gene therapy and prevention strategies. 3. The present article reviews the principles and technological concerns, as well as the steps involved in obtaining and analysing of data. Furthermore, applications of microarray based experiments in drug target identifications and validation strategies are discussed. 4. To exemplify how this tool can be useful, in the present review we provide an overview of some of the past and potential future aspects of microarray technology and present a broad overview of this rapidly growing field.

Flux balance analysis in the era of metabolomics

Flux balance analysis (FBA) has emerged as an effective means to analyse biological networks in a quantitative manner. Much progress has been made on the extension of FBA to incorporate a priori biological knowledge, provide more practical descriptions of observed cell behaviours, and predict the outcome of network perturbations. Metabolomics is independently advancing as a set of high-throughput data acquisition tools providing dynamic profiles of metabolites in an unbiased manner. These data sets are neither yet sufficiently comprehensive nor accurate enough for generating large-scale kinetic models. Thus, there is a pressing need to develop quantitative techniques that can make use of the emerging data and embrace the associated uncertainties. This article reviews recent advances in FBA to meet this need and discusses the utility of FBA as a complement to metabolomics and the expected synergy as a result of combining these two techniques.

Imaging in cell-based therapy for neurodegenerative diseases

Fetal cell transplantation for the treatment of Parkinson's and Huntington's diseases has been developed over the past two decades and is now in early clinical testing phase. Direct assessment of the graft's survival, integration into the host brain and impact on neuronal functions requires advanced in vivo neuroimaging techniques. Owing to its high sensitivity, positron emission tomography is today the most widely used tool to evaluate the viability and function of the transplanted tissue in the brain. Nuclear magnetic resonance techniques are opening new possibilities for imaging neurochemical events in the brain. The ultimate goal will be to use the combination of multiple imaging modalities for complete functional monitoring of the repair processes in the central nervous system.

Gene therapy imaging in patients for oncological applications

Thus far, traditional methods for evaluating gene transfer and expression have been shown to be of limited value in the clinical arena. Consequently there is a real need to develop new methods that could be repeatedly and safely performed in patients for such purposes. Molecular imaging techniques for gene expression monitoring have been developed and successfully used in animal models, but their sensitivity and reproducibility need to be tested and validated in human studies. In this review, we present the current status of gene therapy-based anticancer strategies and show how molecular imaging, and more specifically radionuclide-based approaches, can be used in gene therapy procedures for oncological applications in humans. The basis of gene expression imaging is described and specific uses of these non-invasive procedures for gene therapy monitoring illustrated. Molecular imaging of transgene expression in humans and evaluation of response to gene-based therapeutic procedures are considered. The advantages of molecular imaging for whole-body monitoring of transgene expression as a way to permit measurement of important parameters in both target and non-target organs are also analyzed. The relevance of this technology for evaluation of the necessary vector dose and how it can be used to improve vector design are also examined. Finally, the advantages of designing a gene therapy-based clinical trial with imaging fully integrated from the very beginning are discussed and future perspectives for the development of these applications outlined.

Human gene therapy and imaging in neurological diseases

Molecular imaging aims to assess non-invasively disease-specific biological and molecular processes in animal models and humans in vivo. Apart from precise anatomical localisation and quantification, the most intriguing advantage of such imaging is the opportunity it provides to investigate the time course (dynamics) of disease-specific molecular events in the intact organism. Further, molecular imaging can be used to address basic scientific questions, e.g. transcriptional regulation, signal transduction or protein/protein interaction, and will be essential in developing treatment strategies based on gene therapy. Most importantly, molecular imaging is a key technology in translational research, helping to develop experimental protocols which may later be applied to human patients. Over the past 20 years, imaging based on positron emission tomography (PET) and magnetic resonance imaging (MRI) has been employed for the assessment and "phenotyping" of various neurological diseases, including cerebral ischaemia, neurodegeneration and brain gliomas. While in the past neuro-anatomical studies had to be performed post mortem, molecular imaging has ushered in the era of in vivo functional neuro-anatomy by allowing neuroscience to image structure, function, metabolism and molecular processes of the central nervous system in vivo in both health and disease. Recently, PET and MRI have been successfully utilised together in the non-invasive assessment of gene transfer and gene therapy in humans. To assess the efficiency of gene transfer, the same markers are being used in animals and humans, and have been applied for phenotyping human disease. Here, we review the imaging hallmarks of focal and disseminated neurological diseases, such as cerebral ischaemia, neurodegeneration and glioblastoma multiforme, as well as the attempts to translate gene therapy's experimental knowledge into clinical applications and the way in which this process is being promoted through the use of novel imaging approaches.

Human gene therapy and imaging: cardiology

This review discusses the basics of cardiovascular gene therapy, the results of recent human clinical trials, and the rapid progress in imaging techniques in cardiology. Improved understanding of the molecular and genetic basis of coronary heart disease has made gene therapy a potential new alternative for the treatment of cardiovascular diseases. Experimental studies have established the proof-of-principle that gene transfer to the cardiovascular system can achieve therapeutic effects. First human clinical trials provided initial evidence of feasibility and safety of cardiovascular gene therapy. However, phase II/III clinical trials have so far been rather disappointing and one of the major problems in cardiovascular gene therapy has been the inability to verify gene expression in the target tissue. New imaging techniques could significantly contribute to the development of better gene therapeutic approaches. Although the exact choice of imaging modality will depend on the biological question asked, further improvement in image resolution and detection sensitivity will be needed for all modalities as we move from imaging of organs and tissues to imaging of cells and genes.

Microarray technology: beyond transcript profiling and genotype analysis

Understanding complex functional mechanisms requires the global and parallel analysis of different cellular processes. DNA microarrays have become synonymous with this kind of study and, in many cases, are the obvious platform to achieve this aim. They have already made important contributions, most notably to gene-expression studies, although the true potential of this technology is far greater. Whereas some assays, such as transcript profiling and genotyping, are becoming routine, others are still in the early phases of development, and new areas of application, such as genome-wide epigenetic analysis and on-chip synthesis, continue to emerge.

Modeling liver cancer using zebrafish: a comparative oncogenomics approach

Although the zebrafish has many attributes of a promising cancer model, one outstanding question is how similar zebrafish and human tumors are at the molecular level. To date, supporting data from histology and 'gene-to-gene' comparisons with human data offer limited insights. Using comparative microarray analyses, we found striking molecular similarities between zebrafish and human liver neoplasia. Our data indicate that zebrafish liver tumors possess the general molecular hallmarks of human liver cancer and some of the molecular similarities extend to the progression of liver tumors. The molecular conservation between fish and human liver tumors underscored the strong association and fundamental importance of these genes in liver neoplasia as well as their clinical potentials as diagnostic markers and/or therapeutic targets. In addition, our comparative oncogenomic work provides a general framework for comparing and validating microarray data of zebrafish model with human cancer, thus adding confidence of using the zebrafish to model human cancers.

Metabolomics technology and bioinformatics

Metabolomics is the global analysis of all or a large number of cellular metabolites. Like other functional genomics research, metabolomics generates large amounts of data. Handling, processing and analysis of this data is a clear challenge and requires specialized mathematical, statistical and bioinformatics tools. Metabolomics needs for bioinformatics span through data and information management, raw analytical data processing, metabolomics standards and ontology, statistical analysis and data mining, data integration and mathematical modelling of metabolic networks within a framework of systems biology. The major approaches in metabolomics, along with the modern analytical tools used for data generation, are reviewed in the context of these specific bioinformatics needs.

Improving cancer therapeutics by molecular profiling

The individualized medicine aims to identify the molecular basis of the individual's response to different therapeutic treatments. Individualized medicine is very relevant for human diseases such as cancer and it has become a major task to accomplish more efficient and specific therapeutics. An individualized response to treatment could underline therapeutic success or failure and, even more, could support the rationale for good or bad prognosis. The use of up to date genomic approaches is changing the way we understand modern medicine in terms of drug efficacy, toxicity and diagnosis. Results from genetic polymorphism studies, gene expression profiling and epigenetics illustrate how pharmacogenomic testing will contribute to the goal of individualized medicine. Antineoplastic drugs are designed to block the anomalous activity of specific molecules (therapeutic targets) that regulate cellular processes such as cell cycle. Understanding the relationship between molecular changes in therapeutic targets and enhanced antitumoral response or chemotherapeutic resistance is crucial to establish the clinical relevance of genomic approaches. The goal of this review is to discuss the basic and the clinical significance of genomic research on drug targets and its impact on the early diagnosis and treatment of cancer. We will also assess how these methodologies could contribute to individualized medicine in oncology. A special focus will be put on oncogenes and tumor suppressor genes. Aspects such as drug efficacy, side effects and the diagnostic value of antineoplastic pharmacogenomic research will be also considered.

Changing perspectives in yeast research nearly a decade after the genome sequence

Research with budding yeast (Saccharomyces cerevisiae) has been transformed by the publication, nearly a decade ago, of the entire genome DNA sequence. The introduction of this first eukaryotic genomic sequence changed the yeast research environment significantly, not just because of dramatic progress in technical means but also because the sequence made accessible a new class of scientific questions. A central goal of yeast research remains the determination of the biological role of every sequence feature in the yeast genome. The most remarkable change has been the shift in perspective from focus on individual genes and functionalities to a more global view of how the cellular networks and systems interact and function together to produce the highly evolved organism we see today.

Strategies for high-throughput gene cloning and expression

High-throughput approaches for gene cloning and expression require the development of new, nonstandard tools for use by molecular biologists and biochemists. We have developed and implemented a series of methods that enable the production of expression constructs in 96-well plate format. A screening process is described that facilitates the identification of bacterial clones expressing soluble protein. Application of the solubility screen then provides a plate map that identifies the location of wells containing clones producing soluble proteins. A series of semi-automated methods can then be applied for validation of solubility and production of freezer stocks for the protein production group. This process provides an 80% success rate for the identification of clones producing soluble protein and results in a significant decrease in the level of effort required for the labor-intensive components of validation and preparation of freezer stocks. This process is customized for large-scale structural genomics programs that rely on the production of large amounts of soluble proteins for crystallization trials.

Neuronal and glial cell lines as model systems for studying P2Y receptor pharmacology

Investigation of the role of extracellular nucleotides in nervous system has been one of the main topics of the P2Y receptor research throughout the years. In parallel to numerous studies on primary culture systems, various neuronal and non-neuronal cell lines have been used to model in vitro the processes mediated by extracellular nucleotides. In this review article, a survey of expression profiles of G protein-coupled P2Y receptor subtypes in nervous-system-derived cell lines is presented, by analysing the receptor expression at the mRNA, protein, and functional level. The variability of receptor expression profiles in established cell lines is further discussed, bringing forward some general properties for neuronal and glial malignant cell lines.

Metabolomics and its role in understanding cellular responses in plants

A natural shift is taking place in the approaches being adopted by plant scientists in response to the accessibility of systems-based technology platforms. Metabolomics is one such field, which involves a comprehensive non-biased analysis of metabolites in a given cell at a specific time. This review briefly introduces the emerging field and a range of analytical techniques that are most useful in metabolomics when combined with computational approaches in data analyses. Using cases from Arabidopsis and other selected plant systems, this review highlights how information can be integrated from metabolomics and other functional genomics platforms to obtain a global picture of plant cellular responses. We discuss how metabolomics is enabling large-scale and parallel interrogation of cell states under different stages of development and defined environmental conditions to uncover novel interactions among various pathways. Finally, we discuss selected applications of metabolomics.

Translational studies for target-based drugs

The biological background for the clinical and prognostic heterogeneity among tumors within the same histological subgroup is due to individual variations in the biology of tumors. The number of investigations looking at the application of novel technologies within the setting of clinical trials is increasing. The most promising way to improve cancer treatment is to build clinical research strategies on intricate biological evidence. New genomic technologies have been developed over recent years. These techniques are able to analyze thousands of genes and their expression profiles simultaneously. The purpose of this approach is to discover new cancer biomarkers, to improve diagnosis, predict clinical outcomes of disease and response to treatment, and to select new targets for novel agents with innovative mechanisms of action. Gene expression profiles are also used to assist in selecting biomarkers of pharmacodynamic effects of drugs in the clinical setting. Biomarker monitoring in surrogate tissues may allow researchers to assess "proof of principle" of new treatments. Clinical studies of biomarkers monitoring toxicity profiles have also been done. Such pharmacodynamic markers usually respond to treatment earlier than clinical response, and as such may be useful predictors of efficacy. Epidermal growth factor receptor (EGFR) mutation in lung cancer tissues is a strong predictive biomarker for EGFR-targeted protein tyrosine kinase inhibitors. Monitoring of EGFR mutation has been broadly performed in retrospective and prospective clinical studies. However, global standardization for the assay system is essential for such molecular correlative studies. A more sensitive assay for EGFR mutation is now under evaluation for small biopsy samples. Microdissection for tumor samples is also useful for the sensitive detection of EGFR mutation. Novel approaches for the detection of EGFR mutation in other clinical samples such as cytology, pleural effusion and circulating tumor cells are ongoing.

Comparative proteomics analysis of human pituitary adenomas: current status and future perspectives

This article will review the published research on the elucidation of the mechanisms of pituitary adenoma formation. Mass spectrometry (MS) plays a key role in those studies. Comparative proteomics has been used with the long-term goal to locate, detect, and characterize the differentially expressed proteins (DEPs) in human pituitary adenomas; to identify tumor-related and -specific biomarkers; and to clarify the basic molecular mechanisms of pituitary adenoma formation. The methodology used for comparative proteomics, the current status of human pituitary proteomics studies, and future perspectives are reviewed. The methodologies that are used in comparative proteomics studies of human pituitary adenomas are readily exportable to other different areas of cancer research.

Gene Expression Analysis of Human Tissue from Patients with Cardiomyopathies: A New Tool for Guiding Therapies in the Future?

The complete sequencing of the human genome led to the development of a number of new molecular technologies. DNA microarrays represent an exciting new tool for gene expression analysis in human tissue. Measurements of the expressions of many thousands of genes in parallel is possible now. Microarrays may be used for various applications in medicine. They can be used to find novel prognostic and predictive markers as well as new disease classifications into clinically relevant subgroups. While there has been great progress in cancer research in this field, there are significantly less expression data available concerning the heart. In order to identify genes and pathways involved in the pathogenesis of cardiomyopathies, we have looked recently for alterations at cellular and molecular levels in heart tissue from cardiomyopathy patients. We showed that a special group of genes is differentially regulated in dilated cardiomyopathy. The first results in hypertrophic cardiomyopathy also showed similar findings. The surgeon's role in the clinical application of gene expression profiling is crucial. He provides a link between the patient and the laboratory scientists playing a significant role in focusing research on the clinically relevant problems. Gene expression profiles may help to better characterize the course and prognosis of the individual patient in the future. The long-term goal is to find a tool that will help to guide medical and surgical therapies in cardiomyopathies and other heart diseases.

Gene expression profiling in breast cancer: towards individualising patient management

Breast cancer is a complex and clinically heterogeneous disease. The increase in knowledge of breast cancer biology has led to a number of clinical advances in the treatment of breast cancer, most notably the implementation of widespread mammography screening and advances in adjuvant treatment of early-stage disease. In the last 20 years, arrays of potential prognostic and/or predictive markers of breast cancer have been analysed. However, relatively few have proven to be clinically useful. To date, the only widely accepted markers for routine use in breast cancer are the oestrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor, HER-2 (c-erbB2/neu). Expression microarray technology and laser capture microdissection have now been employed to further our understanding of the molecular pathogenesis of breast cancer. Recently reported advances in array technology and RNA amplification methods are having a considerable impact in this field, allowing the analysis of pre-malignant and pre-invasive lesions. A number of studies have identified prognostic and predictive gene 'signatures', whose prediction of disease outcome and response to treatment is superior to conventional prognostic indicators. Despite major technological advances, a number of confounding issues remain concerning the potential clinical utility of gene expression profiling, including differences in study design, patient selection, array technology, chemistry, and methods of analysis. It seems likely, however, that following careful 'hypothesis driven' validation studies and clinical trials, expression profiling will be applied in the future to identify patient-specific disease profiles and provide rationale for individualised treatment. This review focuses on the current use and future potential of microarray profiling in breast cancer.