Novel opportunities for thymidylate metabolism as a therapeutic target

For over 40 years, the fluoropyrimidine 5-fluorouracil (5-FU) has remained the central agent in therapeutic regimens employed in the treatment of colorectal cancer and is frequently combined with the DNA-damaging agents oxaliplatin and irinotecan, increasing response rates and improving overall survival. However, many patients will derive little or no benefit from treatment, highlighting the need to identify novel therapeutic targets to improve the efficacy of current 5-FU-based chemotherapeutic strategies. dUTP nucleotidohydrolase (dUTPase) catalyzes the hydrolysis of dUTP to dUMP and PPi, providing substrate for thymidylate synthase (TS) and DNA synthesis and repair. Although dUTP is a normal intermediate in DNA synthesis, its accumulation and misincorporation into DNA as uracil is lethal. Importantly, uracil misincorporation represents an important mechanism of cytotoxicity induced by the TS-targeted class of chemotherapeutic agents including 5-FU. A growing body of evidence suggests that dUTPase is an important mediator of response to TS-targeted agents. In this article, we present further evidence showing that elevated expression of dUTPase can protect breast cancer cells from the expansion of the intracellular uracil pool, translating to reduced growth inhibition following treatment with 5-FU. We therefore report the implementation of in silico drug development techniques to identify and develop small-molecule inhibitors of dUTPase. As 5-FU and the oral 5-FU prodrug capecitabine remain central agents in the treatment of a variety of malignancies, the clinical utility of a small-molecule inhibitor to dUTPase represents a viable strategy to improve the clinical efficacy of these mainstay chemotherapeutic agents.

QUADrATiC: scalable gene expression connectivity mapping for repurposing FDA-approved therapeutics

Background: Gene expression connectivity mapping has proven to be a powerful and flexible tool for research. Its application has been shown in a broad range of research topics, most commonly as a means of identifying potential small molecule compounds, which may be further investigated as candidates for repurposing to treat diseases. The public release of voluminous data from the Library of Integrated Cellular Signatures (LINCS) programme further enhanced the utilities and potentials of gene expression connectivity mapping in biomedicine. Results: We describe QUADrATiC (http://go.qub.ac.uk/QUADrATiC), a user-friendly tool for the exploration of gene expression connectivity on the subset of the LINCS data set corresponding to FDA-approved small molecule compounds. It enables the identification of compounds for repurposing therapeutic potentials. The software is designed to cope with the increased volume of data over existing tools, by taking advantage of multicore computing architectures to provide a scalable solution, which may be installed and operated on a range of computers, from laptops to servers. This scalability is provided by the use of the modern concurrent programming paradigm provided by the Akka framework. The QUADrATiC Graphical User Interface (GUI) has been developed using advanced Javascript frameworks, providing novel visualization capabilities for further analysis of connections. There is also a web services interface, allowing integration with other programs or scripts.Conclusions: QUADrATiC has been shown to provide an improvement over existing connectivity map software, in terms of scope (based on the LINCS data set), applicability (using FDA-approved compounds), usability and speed. It offers potential to biological researchers to analyze transcriptional data and generate potential therapeutics for focussed study in the lab. QUADrATiC represents a step change in the process of investigating gene expression connectivity and provides more biologically-relevant results than previous alternative solutions.

Sistemas de informação para DNA microarrays

O projecto de sequenciação do genoma humano veio abrir caminho para o surgimento de novas áreas transdisciplinares de investigação, como a biologia computacional, a bioinformática e a bioestatística. Um dos resultados emergentes desde advento foi a tecnologia de DNA microarrays, que permite o estudo do perfil da expressão de milhares de genes, quando sujeitos a perturbações externas. Apesar de ser uma tecnologia relativamente consolidada, continua a apresentar um conjunto vasto de desafios, nomeadamente do ponto de vista computacional e dos sistemas de informação. São exemplos a optimização dos procedimentos de tratamento de dados bem como o desenvolvimento de metodologias de interpretação semi-automática dos resultados. O principal objectivo deste trabalho consistiu em explorar novas soluções técnicas para agilizar os procedimentos de armazenamento, partilha e análise de dados de experiências de microarrays. Com esta finalidade, realizou-se uma análise de requisitos associados às principais etapas da execução de uma experiência, tendo sido identificados os principais défices, propostas estratégias de melhoramento e apresentadas novas soluções. Ao nível da gestão de dados laboratoriais, é proposto um LIMS (Laboratory Information Management System) que possibilita a gestão de todos os dados gerados e dos procedimentos realizados. Este sistema integra ainda uma solução que permite a partilha de experiências, de forma a promover a participação colaborativa de vários investigadores num mesmo projecto, mesmo usando LIMS distintos. No contexto da análise de dados, é apresentado um modelo que facilita a integração de algoritmos de processamento e de análise de experiências no sistema desenvolvido. Por fim, é proposta uma solução para facilitar a interpretação biológica de um conjunto de genes diferencialmente expressos, através de ferramentas que integram informação existente em diversas bases de dados biomédicas.

New insights into functional regulation in MS-based drug profiling

We present a novel data analysis strategy which combined with subcellular fractionation and liquid chromatography-mass spectrometry (LC-MS) based proteomics provides a simple and effective workflow for global drug profiling. Five subcellular fractions were obtained by differential centrifugation followed by high resolution LC-MS and complete functional regulation analysis. The methodology combines functional regulation and enrichment analysis into a single visual summary. The workflow enables improved insight into perturbations caused by drugs. We provide a statistical argument to demonstrate that even crude subcellular fractions leads to improved functional characterization. We demonstrate this data analysis strategy on data obtained in a MS-based global drug profiling study. However, this strategy can also be performed on other types of large scale biological data.

Synchronization properties of multi-oscillator circadian systems

Dissertation presented to obtain the Ph.D degree in Computational Biology

Pervasive positive selection on duplicated and nonduplicated vertebrate protein coding genes.

A stringent branch-site codon model was used to detect positive selection in vertebrate evolution. We show that the test is robust to the large evolutionary distances involved. Positive selection was detected in 77% of 884 genes studied. Most positive selection concerns a few sites on a single branch of the phylogenetic tree: Between 0.9% and 4.7% of sites are affected by positive selection depending on the branches. No functional category was overrepresented among genes under positive selection. Surprisingly, whole genome duplication had no effect on the prevalence of positive selection, whether the fish-specific genome duplication or the two rounds at the origin of vertebrates. Thus positive selection has not been limited to a few gene classes, or to specific evolutionary events such as duplication, but has been pervasive during vertebrate evolution.

Recommendations for locus-specific databases and their curation.

Expert curation and complete collection of mutations in genes that affect human health is essential for proper genetic healthcare and research. Expert curation is given by the curators of gene-specific mutation databases or locus-specific databases (LSDBs). While there are over 700 such databases, they vary in their content, completeness, time available for curation, and the expertise of the curator. Curation and LSDBs have been discussed, written about, and protocols have been provided for over 10 years, but there have been no formal recommendations for the ideal form of these entities. This work initiates a discussion on this topic to assist future efforts in human genetics. Further discussion is welcome.

The European dimension for the mouse genome mutagenesis program.

The European Mouse Mutagenesis Consortium is the European initiative contributing to the international effort on functional annotation of the mouse genome. Its objectives are to establish and integrate mutagenesis platforms, gene expression resources, phenotyping units, storage and distribution centers and bioinformatics resources. The combined efforts will accelerate our understanding of gene function and of human health and disease.

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

Liderazgo y poder: una aproximación desde la ecología

El trabajo de grado se desarrollará a partir del análisis del liderazgo y el poder como característica de éste, desde una visión ecológica, lo cual constituye un aspecto de gran importancia en los estudios de administración. Primero, se abordará el significado de liderazgo y la importancia que este representa dentro de las organizaciones, a través de la generación de procesos que llevan a la organización a su evolución y desarrollo. Posteriormente se abordará el tema de poder en relación con la comprensión del efecto que este puede tener sobre las interacciones que se dan entre las personas de la organización. Finalmente, se estudiará también desde la ecología, como el poder ejercido por los líderes puede influir en la forma en que estos agentes, es decir personas, procesos e interacciones, interactúan para movilizar a la organización. De esta forma, se asume el poder como un aspecto importante dentro del estudio del liderazgo, en cuanto éste puede afectar la forma en que las personas son lideradas al interior de la organización.

Modeling chemotaxis reveals the role of reversed phosphotransfer and a bi-functional kinase-phosphatase

Understanding how multiple signals are integrated in living cells to produce a balanced response is a major challenge in biology. Two-component signal transduction pathways, such as bacterial chemotaxis, comprise histidine protein kinases (HPKs) and response regulators (RRs). These are used to sense and respond to changes in the environment. Rhodobacter sphaeroides has a complex chemosensory network with two signaling clusters, each containing a HPK, CheA. Here we demonstrate, using a mathematical model, how the outputs of the two signaling clusters may be integrated. We use our mathematical model supported by experimental data to predict that: (1) the main RR controlling flagellar rotation, CheY6, aided by its specific phosphatase, the bifunctional kinase CheA3, acts as a phosphate sink for the other RRs; and (2) a phosphorelay pathway involving CheB2 connects the cytoplasmic cluster kinase CheA3 with the polar localised kinase CheA2, and allows CheA3-P to phosphorylate non-cognate chemotaxis RRs. These two mechanisms enable the bifunctional kinase/phosphatase activity of CheA3 to integrate and tune the sensory output of each signaling cluster to produce a balanced response. The signal integration mechanisms identified here may be widely used by other bacteria, since like R. sphaeroides, over 50% of chemotactic bacteria have multiple cheA homologues and need to integrate signals from different sources.

The Origins of Lactase Persistence in Europe

Lactase persistence (LP) is common among people of European ancestry, but with the exception of some African, Middle Eastern and southern Asian groups, is rare or absent elsewhere in the world. Lactase gene haplotype conservation around a polymorphism strongly associated with LP in Europeans (-13,910 C/T) indicates that the derived allele is recent in origin and has been subject to strong positive selection. Furthermore, ancient DNA work has shown that the -13,910*T (derived) allele was very rare or absent in early Neolithic central Europeans. It is unlikely that LP would provide a selective advantage without a supply of fresh milk, and this has lead to a gene-culture coevolutionary model where lactase persistence is only favoured in cultures practicing dairying, and dairying is more favoured in lactase persistent populations. We have developed a flexible demic computer simulation model to explore the spread of lactase persistence, dairying, other subsistence practices and unlinked genetic markers in Europe and western Asia's geographic space. Using data on -13,910*T allele frequency and farming arrival dates across Europe, and approximate Bayesian computation to estimate parameters of interest, we infer that the -13,910*T allele first underwent selection among dairying farmers around 7,500 years ago in a region between the central Balkans and central Europe, possibly in association with the dissemination of the Neolithic Linearbandkeramik culture over Central Europe. Furthermore, our results suggest that natural selection favouring a lactase persistence allele was not higher in northern latitudes through an increased requirement for dietary vitamin D. Our results provide a coherent and spatially explicit picture of the coevolution of lactase persistence and dairying in Europe.

Predicting functional gene links from phylogenetic-statistical analyses of whole genomes

An important element of the developing field of proteomics is to understand protein-protein interactions and other functional links amongst genes. Across-species correlation methods for detecting functional links work on the premise that functionally linked proteins will tend to show a common pattern of presence and absence across a range of genomes. We describe a maximum likelihood statistical model for predicting functional gene linkages. The method detects independent instances of the correlated gain or loss of pairs of proteins on phylogenetic trees, reducing the high rates of false positives observed in conventional across-species methods that do not explicitly incorporate a phylogeny. We show, in a dataset of 10,551 protein pairs, that the phylogenetic method improves by up to 35% on across-species analyses at identifying known functionally linked proteins. The method shows that protein pairs with at least two to three correlated events of gain or loss are almost certainly functionally linked. Contingent evolution, in which one gene's presence or absence depends upon the presence of another, can also be detected phylogenetically, and may identify genes whose functional significance depends upon its interaction with other genes. Incorporating phylogenetic information improves the prediction of functional linkages. The improvement derives from having a lower rate of false positives and from detecting trends that across-species analyses miss. Phylogenetic methods can easily be incorporated into the screening of large-scale bioinformatics datasets to identify sets of protein links and to characterise gene networks.

GeneMCL in microarray analysis

Accurately and reliably identifying the actual number of clusters present with a dataset of gene expression profiles, when no additional information on cluster structure is available, is a problem addressed by few algorithms. GeneMCL transforms microarray analysis data into a graph consisting of nodes connected by edges, where the nodes represent genes, and the edges represent the similarity in expression of those genes, as given by a proximity measurement. This measurement is taken to be the Pearson correlation coefficient combined with a local non-linear rescaling step. The resulting graph is input to the Markov Cluster (MCL) algorithm, which is an elegant, deterministic, non-specific and scalable method, which models stochastic flow through the graph. The algorithm is inherently affected by any cluster structure present, and rapidly decomposes a graph into cohesive clusters. The potential of the GeneMCL algorithm is demonstrated with a 5730 gene subset (IGS) of the Van't Veer breast cancer database, for which the clusterings are shown to reflect underlying biological mechanisms. (c) 2005 Elsevier Ltd. All rights reserved.