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PubMed1998

Experimental pathology and breast cancer genetics: new technologies.

The goal is to understand the critical events in tumour development and to apply this understanding to new approaches to diagnosis, prevention and treatment. It is clear that breast cancer is a heterogeneous disease at the molecular level, raising the possibility of a future functional classification based on mechanisms rather than morphology. These molecular phenotypes will also confer predictive value on the potential of the tumour to invade, metastasise and respond to or resist new therapeutic strategies. Studies of the genome in individuals are predicted also to enable the identification of polymorphisms that are associated with increased susceptibility to environmental factors, in addition to possibly explaining de novo variations in responses to drugs and radiation. The difficulty is how to identify which, of the approximately 30,000 genes expressed by a typical cancer cell alone or in combination, are the ones involved in these processes. The majority of breast cancers have such a multitude of molecular changes that it is difficult to distinguish between those that are critical to tumour progression and those that are epiphenomena of genetic instability and abnormalities in DNA repair. The identification of the earliest events in carcinogenesis must be the best hope, as it will then be possible to target the events that predispose to other secondary changes before they occur. Genomics and proteomics is the current hope to take us forward. This involves the application of a number of new technologies to facilitate the profiling of individual tumours, including laser-guided microdissection of microscopic lesions, comparative genomic hybridisation and loss of heterozygosity analysis of DNA using microarray technology to study DNA and expressed RNAs and protein profiling using 2D gel mass spectroscopy. With over 100,000 mRNAs and proteins to examine in complex tissues and in various combinations, there is obviously going to be a requirement for a large investment in computing power (bioinformatics) to facilitate the analysis of these data in relation to the clinical characteristics of the individual tumour and the patient.

Bacteriophages↗

PubMed2006

In silico and microarray-based genomic approaches to identifying potential vaccine candidates against Leptospira interrogans.

BACKGROUND: Currently available vaccines against leptospirosis are of low efficacy, have an unacceptable side-effect profile, do not induce long-term protection, and provide no cross-protection against the different serovars of pathogenic leptospira. The current major focus in leptospirosis research is to discover conserved protective antigens that may elicit longer-term protection against a broad range of Leptospira. There is a need to screen vaccine candidate genes in the genome of Leptospira interrogans. RESULTS: Bioinformatics, comparative genomic hybridization (CGH) analysis and transcriptional analysis were used to identify vaccine candidates in the genome of L. interrogans serovar Lai strain #56601. Of a total of 4727 open reading frames (ORFs), 616 genes were predicted to encode surface-exposed proteins by P-CLASSIFIER combined with signal peptide prediction, alpha-helix transmembrane topology prediction, integral beta-barrel outer membrane protein and lipoprotein prediction, as well as by retaining the genes shared by the two sequenced L. interrogans genomes and by subtracting genes with human homologues. A DNA microarray of L. interrogans strain #56601 was constructed for CGH analysis and transcriptome analysis in vitro. Three hundred and seven differential genes were identified in ten pathogenic serovars by CGH; 1427 genes had high transcriptional levels (Cy3 signal > or = 342 and Cy5 signal > or = 363.5, respectively). There were 565 genes in the intersection between the set encoding surface-exposed proteins and the set of 307 differential genes. The number of genes in the intersection between this set of 565 and the set of 1427 highly transcriptionally active genes was 226. These 226 genes were thus identified as putative vaccine candidates. The proteins encoded by these genes are not only potentially surface-exposed in the bacterium, but also conserved in two sequenced L. interrogans. Moreover, these genes are conserved among ten epidemic serovars in China and have high transcriptional levels in vitro. CONCLUSION: Of the 4727 ORFs in the genome of L. interrogans, 226 genes were identified as vaccine candidates by bioinformatics, CGH and transcriptional analysis on the basis of the theory of reverse vaccinology. The proteins encoded by these genes might be useful as vaccine candidates as well as for diagnosis of leptospirosis.

Bacterial Outer Membrane Proteins↗

PubMed2005

Cloning and expression analysis of gonadogenesis-associated gene SPATA4 from rainbow trout (Oncorhynchus mykiss).

Gonadogenesis is a complicated process which involves multi-gene interactions. A rainbow trout (Oncorhynchus mykiss) gene spermatogenesis associated 4 (SPATA4) was cloned and characterized from adult rainbow trout testis. The cDNA sequence of rainbow trout SPATA4 contains an open reading frame of 1, 081 nucleatides encoding a putative protein of 259 amino acids. The putative protein from rainbow trout shares a 76.8% homology with zebrafish SPATA4. No trans-membrane regions or signal peptide were detected using bioinformatics methods. Subcellular localization analysis revealed that rainbow trout SPATA4 was a nuclear protein with highest possibility (39.1%). Multi-tissue reverse transcriptase PCR (RT-PCR) was performed to examine the distribution of rainbow trout SPATA4 in eleven organs of adult rainbow trout. The result demonstrated that this gene express specifically in testis and slight amount of expression was detected in ovary. Further analysis of SPATA4 characterization and function in rainbow trout may provide insight into the understanding of gonadogenesis process.

Amino Acid Sequence↗

PubMed2005

Molecular evolution of enolase.

Enolase (EC 4.2.1.11) is an enzyme of the glycolytic pathway catalyzing the dehydratation reaction of 2-phosphoglycerate. In vertebrates the enzyme exists in three isoforms: alpha, beta and gamma. The amino-acid and nucleotide sequences deposited in the GenBank and SwissProt databases were subjected to analysis using the following bioinformatic programs: ClustalX, GeneDoc, MEGA2 and S.I.F.T. (sort intolerant from tolerant). Phylogenetic trees of enolases created with the use of the MEGA2 program show evolutionary relationships and functional diversity of the three isoforms of enolase in vertebrates. On the basis of calculations and the phylogenetic trees it can be concluded that vertebrate enolase has evolved according to the "birth and death" model of evolution. An analysis of amino acid sequences of enolases: non-neuronal (NNE), neuron specific (NSE) and muscle specific (MSE) using the S.I.F.T. program indicated non-uniform number of possible substitutions. Tolerated substitutions occur most frequently in alpha-enolase, while the lowest number of substitutions has accumulated in gamma-enolase, which may suggest that it is the most recently evolved isoenzyme of enolase in vertebrates.

Animals↗

PubMed2006

Structural bioinformatics of DNA: a web-based tool for the analysis of molecular dynamics results and structure prediction.

UNLABELLED: We report here the release of a web-based tool (MDDNA) to study and model the fine structural details of DNA on the basis of data extracted from a set of molecular dynamics (MD) trajectories of DNA sequences involving all the unique tetranucleotides. The dynamic web interface can be employed to analyze the first neighbor sequence context effects on the 10 unique dinucleotide steps of DNA. Functionality is included to build all atom models of any user-defined sequence based on the MD results. The backend of this interface is a relational database storing the conformational details of DNA obtained in 39 different MD simulation trajectories comprising all the 136 unique tetranucleotide steps. Examples of the use of this data to predict DNA structures are included. AVAILABILITY: http://humphry.chem.wesleyan.edu:8080/MDDNA. SUPPLEMENTARY INFORMATION: Supplementary data including color figures are available at Bioinformatics online.

Base Sequence↗

PubMed2004

Chemical probes and tandem mass spectrometry: a strategy for the quantitative analysis of proteomes and subproteomes.

Quantitative proteome profiling using mass spectrometry and stable isotope dilution is being widely applied for the functional analysis of biological systems and for the detection of clinical, diagnostic or prognostic marker proteins. Because of the enormous complexity of proteomes, their comprehensive analysis is unlikely to be routinely achieved in the near future. However, in recent years, significant progress has been achieved focusing quantitative proteomic analyses on specific protein classes or subproteomes that are rich in biologically or clinically important information. Such projects typically combine the use of chemical probes that are specific for a targeted group of proteins and may contain stable isotope signatures for accurate quantification with automated tandem mass spectrometry and bioinformatics tools for data analysis. In this review, we summarize technical and conceptual advances in quantitative subproteome profiling based on tandem mass spectrometry and chemical probes.

Amino Acids↗

PubMed2004

Microarrays and the gene expression profile of a single cell.

With completion of the human genome sequence, it is now possible to study the expression of the entire human gene complement of approximately 30,000-35,000 genes. To accomplish this goal, microarrays have become the leading methodology for the analysis of global gene expression. Improvements in technology have increased the sensitivity of microarrays to the point where it is feasible to study gene expression in a small number of cells and even at the single cell level. A summary of developments in the area of expression profiling in single cells will be described, and the rationale for these types of studies will be presented. In addition, from a biologist's point of view, some bioinformatic challenges of expression analysis of single cells will be discussed.

Cell Physiological Phenomena↗

PubMed2006

Integrative analysis of transcript and metabolite profiling data sets to evaluate the regulation of biochemical pathways during photomorphogenesis.

One of the key developmental processes during photomorphogenesis is the differentiation of prolamellar bodies of proplastids into thylakoid membranes containing the photosynthetic pigment-protein complexes of chloroplasts. To study the regulatory events controlling pigment-protein complex assembly, including the biosynthesis of metabolic precursors and pigment end products, etiolated Arabidopsis thaliana seedlings were irradiated with continuous red light (Rc), which led to rapid greening, or continuous far-red light (FRc), which did not result in visible greening, and subjected to analysis by oligonucleotide microarrays and targeted metabolite profiling. An analysis using BioPathAt, a bioinformatic tool that allows the visualization of post-genomic data sets directly on biochemical pathway maps, indicated that in Rc-treated seedlings mRNA expression and metabolite patterns were tightly correlated (e.g., Calvin cycle, biosynthesis of chlorophylls, carotenoids, isoprenoid quinones, thylakoid lipids, sterols, and amino acids). K-means clustering revealed that gene expression patterns across various biochemical pathways were very similar in Rc- and FRc-treated seedlings (despite the visible phenotypic differences), whereas a principal component analysis of metabolite pools allowed a clear distinction between both treatments (in accordance with the visible phenotype). Our results illustrate the general importance of integrative approaches to correlate post-genomic data sets with phenotypic outcomes.

Arabidopsis↗

PubMed2000

Technical assessment of the affymetrix yeast expression GeneChip YE6100 platform in a heterologous model of genes that confer resistance to antimalarial drugs in yeast.

The advent of high-density gene array technology has revolutionized approaches to drug design, development, and characterization. At the laboratory level, the efficient, consistent, and dependable exploitation of this complex technology requires the stringent standardization of protocols and data analysis platforms. The Affymetrix YE6100 expression GeneChip platform was evaluated for its performance in the analysis of both global (6,000 yeast genes) and targeted (three pleiotropic multidrug resistance genes of the ATP binding cassette transporter family) gene expression in a heterologous yeast model system in the presence and absence of the antimalarial drug chloroquine. Critical to the generation of consistent data from this platform are issues involving the preparation of the specimen, use of appropriate controls, accurate assessment of experiment variance, strict adherence to optimized enzymatic and hybridization protocols, and use of sophisticated bioinformatics tools for data analysis.

ATP-Binding Cassette Transporters↗

PubMed2004

Environmentally induced foregut remodeling by PHA-4/FoxA and DAF-12/NHR.

Growth and development of the Caenorhabditis elegans foregut (pharynx) depends on coordinated gene expression, mediated by pharynx defective (PHA)-4/FoxA in combination with additional, largely unidentified transcription factors. Here, we used whole genome analysis to establish clusters of genes expressed in different pharyngeal cell types. We created an expectation maximization algorithm to identify cis-regulatory elements that activate expression within the pharyngeal gene clusters. One of these elements mediates the response to environmental conditions within pharyngeal muscles and is recognized by the nuclear hormone receptor (NHR) DAF-12. Our data suggest that PHA-4 and DAF-12 endow the pharynx with transcriptional plasticity to respond to diverse developmental and physiological cues. Our combination of bioinformatics and in vivo analysis has provided a powerful means for genome-wide investigation of transcriptional control.

Animals↗

PubMed2005

Coevolution between nonhomologous but functionally similar proteins and their conserved partners in the Legionella pathogenesis system.

Legionella pneumophila, the causative agent of Legionnaires' disease, and other pathogenic Legionella species multiply inside protozoa and human macrophages by using the intracellular multiplication (Icm)/defect in organelle trafficking (Dot) type-IV secretion system. The IcmQ protein, which possesses pore-forming activity, and IcmR, which regulates the IcmQ activity, are two essential components of this system. Analysis of the region expected to contain these two genes from 29 Legionella species revealed the presence of a conserved icmQ gene and a large hypervariable gene family [functional homologues of icmR (fir) genes], located at the icmR genomic position. Although hypervariable in their sequence, the fir genes from all 29 Legionella species were found, together with their corresponding icmQ genes, to function similarly during infection. In addition, all FIR proteins we examined were found to interact with their corresponding IcmQ proteins. Detailed bioinformatic, biochemical, and genetic analysis of the interaction between the variable FIR proteins and conserved IcmQ proteins revealed that their interaction depends on a variable region located between two conserved domains of IcmQ. This variable region was also found to be critical for IcmQ self-interaction, and the region probably coevolved with the corresponding FIR protein. A FIR-IcmQ pair was also found in Coxiella burnetii, the only known non-Legionella bacterium that contains an Icm/Dot system, indicating the significance of this protein pair for the function of this type-IV secretion system. We hypothesize that this gene variation, which is probably mediated by positive selection, plays an important role in the evolutionary arms race between the protozoan host cell and the pathogen.

Acanthamoeba castellanii↗

PubMed2005

Quantitative modeling and data analysis of SELEX experiments.

SELEX (systematic evolution of ligands by exponential enrichment) is an experimental procedure that allows the extraction, from an initially random pool of DNA, of those oligomers with high affinity for a given DNA-binding protein. We address what is a suitable experimental and computational procedure to infer parameters of transcription factor-DNA interaction from SELEX experiments. To answer this, we use a biophysical model of transcription factor-DNA interactions to quantitatively model SELEX. We show that a standard procedure is unsuitable for obtaining accurate interaction parameters. However, we theoretically show that a modified experiment in which chemical potential is fixed through different rounds of the experiment allows robust generation of an appropriate dataset. Based on our quantitative model, we propose a novel bioinformatic method of data analysis for such a modified experiment and apply it to extract the interaction parameters for a mammalian transcription factor CTF/NFI. From a practical point of view, our method results in a significantly improved false positive/false negative trade-off, as compared to both the standard information theory based method and a widely used empirically formulated procedure.

Animals↗

PubMed2005

Identification of global gene expression differences between human lens epithelial and cortical fiber cells reveals specific genes and their associated pathways important for specialized lens cell functions.

PURPOSE: In order to identify specific genes that may play important roles in maintaining the specialized functions of lens epithelial and fiber cells, we have analyzed the global gene expression profiles of these two cell types in the human lens. This analysis will also reveal those genes that are exclusively expressed in the epithelial and cortical fiber cells and those genes that may play important roles in the differentiation of epithelial cells to mature fiber cells. METHODS: Oligonucleotide microarray hybridization was used to analyze the expression profiles of 22,215 genes between adult (average age greater than 56 years) human lens epithelial and cortical fiber cells. The expression levels of selected genes were further compared by semi-quantitative RT-PCR and selected genes were functionally clustered into common categories using the EASE bioinformatics software package. RESULTS: Analysis of three separate microarray hybridizations revealed 1,196 transcripts that exhibit increased expression and 1,278 transcripts that exhibit decreased expression at the 2 fold or greater level between lens epithelial cells and cortical fiber cells on all three of the arrays analyzed. Of these, 222 transcripts exhibited increased expression and 135 transcripts exhibited decreased expression by an average of 5 fold or greater levels on all three arrays. Semi-quantitative RT-PCR analysis of 21 randomly selected genes revealed identical expression patterns as those detected by microarray hybridization indicating that the microarray data are accurate. Functional clustering of the identified gene expression patterns using the EASE program revealed a wide variety of biological pathways that exhibited altered expression patterns between the two cell types including mRNA processing, cell adhesion, cell proliferation, translation, protein folding, oxidative phosphorylation, and apoptosis, among others. CONCLUSIONS: These data reveal novel and previously identified gene expression differences between lens epithelial and cortical fiber cells. The gene expression differences indicate distinct pathways and functions important for the specialization of lens epithelial and fiber cells and provide insight into potential mechanisms important for lens cell differentiation.

Aged↗

PubMed2003

Probabilistic graphical models for computational biomedicine.

BACKGROUND: As genomics becomes increasingly relevant to medicine, medical informatics and bioinformatics are gradually converging into a larger field that we call computational biomedicine. OBJECTIVES: Developing a computational framework that is common to the different disciplines that compose computational biomedicine will be a major enabler of the further development and integration of this research domain. METHODS: Probabilistic graphical models such as Hidden Markov Models, belief networks, and missing-data models together with computational methods such as dynamic programming, Expectation-Maximization, data-augmentation Gibbs sampling, and the Metropolis-Hastings algorithm provide the tools for an integrated probabilistic approach to computational biomedicine. RESULTS AND CONCLUSIONS: We show how graphical models have already found a broad application in different fields composing computational biomedicine. We also indicate several challenges that lie at the interface between medical informatics, statistical genomics, and bioinformatics. We also argue that graphical models offer a unified framework making it possible to integrate in a statistically meaningful way multiple models ranging from the molecular level to cellular and to clinical levels. Because of their versatility and firm statistical underpinning, we assert that probabilistic graphical models can serve as the lingua franca for many computationally intensive approaches to biology and medicine. As such, graphical models should be a foundation of the curriculum of students in these fields. From such a foundation, students could then build towards specific computational methods in medical informatics, medical image analysis, statistical genetics, or bioinformatics while keeping the communication open between these areas.

Computational Biology↗

PubMed2002

A transcript map encompassing a susceptibility locus for bipolar affective disorder on chromosome 4q35.

Bipolar affective disorder is one of the most common mental illnesses with a population prevalence of approximately 1%. The disorder is genetically complex, with an increasing number of loci being implicated through genetic linkage studies. However, the specific genetic variations and molecules involved in bipolar susceptibility and pathogenesis are yet to be identified. Genetic linkage analysis has identified a bipolar disorder susceptibility locus on chromosome 4q35, and the interval harbouring this susceptibility gene has been narrowed to a size that is amenable to positional cloning. We have used the resources of the Human Genome Project (HGP) and Celera Genomics to identify overlapping sequenced BAC clones and sequence contigs that represent the region implicated by linkage analysis. A combination of bioinformatic tools and laboratory techniques have been applied to annotate this DNA sequence data and establish a comprehensive transcript map that spans approximately 5.5 Mb. This map encompasses the chromosome 4q35 bipolar susceptibility locus, which localises to a "most probable" candidate interval of approximately 2.3 Mb, within a more conservative candidate interval of approximately 5 Mb. Localised within this map are 11 characterised genes and eight novel genes of unknown function, which together provide a collection of candidate transcripts that may be investigated for association with bipolar disorder. Overall, this region was shown to be very gene-poor, with a high incidence of pseudogenes, and redundant and novel repetitive elements. Our analysis of the interval has demonstrated a significant difference in the extent to which the current HGP and Celera sequence data sets represent this region.

Bipolar Disorder↗

PubMed2004

FLOSYS--a web-accessible workflow system for protocol-driven biomolecular sequence analysis.

FLOSYS is an interactive web-accessible bioinformatics workflow system designed to assist biologists in multi-step data analyses. FLOSYS allows the user to create complex analysis pathways (protocols) graphically, similar to drawing a flowchart: icons representing particular bioinformatics tools are dragged and dropped onto a canvas and lines connecting those icons are drawn to specify the relationships between the tools. In addition, FLOSYS permits to select input-data, execute the protocol and store the results in a personal workspace. The three-tier architecture of FLOSYS has been implemented in Java and uses a relational database system together with new technologies for distributed and web computing such as CORBA, RMI, JSP and JDBC. The prototype of FLOSYS, which is part of the bioinformatics workbench AnaBench, is accessible on-line at http://malawimonas.bcm.umontreal.ca: 8091/anabench. The entire package is available on request to academic groups who wish to have a customized local analysis environment for research or teaching.

Computational Biology↗

PubMed2005

Peptides in body fluids and tissues as markers of disease.

The general awareness of the importance of peptides in physiology and pathophysiology has increased strongly over the last few years. With worldwide progress in the analysis of whole genomes, the knowledge base in gene sequence and expression data useful for protein and peptide analysis has drastically increased. The medical need for relevant biomarkers is enormous. This is particularly true for the many types of cancer, but other diseases such as Type 2 diabetes also lack useful and adequate diagnostic markers with high specificity and sensitivity. Despite advances in imaging technologies for early detection of diseases, proteomic and peptidomic multiplex techniques have evolved in recent years. This review focuses on the application of peptidomics technologies to peptides in health and disease. Peptidomics technologies provide new opportunities for the detection of low-molecular-weight proteome biomarkers (peptides) by mass spectrometry. Improvements in peptidomics research are based on separation of peptides and/or proteins by their physicochemical properties in combination with mass spectrometric detection, identification and sophisticated bioinformatics tools for data analysis. Therefore, peptidomics technologies offer an opportunity to discover novel biomarkers for diagnosis and management of disease (e.g., prognosis, treatment decision and monitoring response to therapy).

Animals↗

PubMed2002

[Recent advances in molecular diagnostic tests].

Recent advances in molecular biotechnologies have facilitated laboratory use of molecular diagnostic tests. Automated systems for amplification and detection, and lately for extraction, of nucleic acid sequences have been developed. The automated systems have allowed improvement of not only assay efficiency but also quality control of the tests. The information on the genome sequence from the human genome project has been studied to elucidate functions of genes and proteins and the clinical significance of nucleic acid sequences with post-genomics such as expression profiling using DNA microarray, proteomics, and single nucleotide polymorphism analysis, in conjunction with bioinformatics. Now the challenge is the development and application of these new technologies as clinical diagnostic tools. Design of a diagnostic array or panel must be developed with defined sequences based on interpretation of a huge quantity of experimental data to meet clinical demands. There is a need for development of generally available instruments that are inexpensive, practical and more importantly reproducible and reliable.

Computational Biology↗