Development of polyaniline microarray electrodes for cadmium analysis

Disposable screen-printed electrodes (SPCE) were modified using a cosmetic product to partially block the electrode surface in order to obtain a microelectrode array. The microarrays formed were electropolymerized with aniline. Scanning electron microscopy was used to evaluate the modified and polymerized electrode surface. Electrochemical characteristics of the constructed sensor for cadmium analysis were evaluated by cyclic and square-wave voltammetry. Optimized stripping procedure in which the preconcentration of cadmium was achieved by depositing at –1.20 V (vs. Ag/AgCl) resulted in a well defined anodic peak at approximately –0.7 V at pH 4.6. The achieved limit of detection was 4 × 10−9 mol dm−3. Spray modified and polymerized microarray electrodes were successfully applied to quantify cadmium in fish sample digests.

Development of computational tools for the integrated analysis of DNA microarray data with applications in cancer research

The MAP-i Doctoral Program of the Universities of Minho, Aveiro and Porto

Microarray and RNAi Analysis of P450s in Anopheles gambiae Male and Female Steroidogenic Tissues: CYP307A1 Is Required for Ecdysteroid Synthesis.

In insects, the steroid hormone 20-hydroxyecdysone (20E) coordinates major developmental transitions. While the first and the final steps of 20E biosynthesis are characterized, the pathway from 7-dehydrocholesterol to 5β-ketodiol, commonly referred as the "black box", remains hypothetical and whether there are still unidentified enzymes is unknown. The black box would include some oxidative steps, which are believed to be mediated by P450 enzymes. To identify new enzyme(s) involved in steroid synthesis, we analyzed by small-scale microarray the expression of all the genes encoding P450 enzymes of the malaria mosquito Anopheles gambiae in active steroidogenic organs of adults, ovaries from blood-fed females and male reproductive tracts, compared to inactive steroidogenic organs, ovaries from non-blood-fed females. Some genes encoding P450 enzymes were specifically overexpressed in female ovaries after a blood-meal or in male reproductive tracts but only three genes were found to be overexpressed in active steroidogenic organs of both females and males: cyp307a1, cyp4g16 and cyp6n1. Among these genes, only cyp307a1 has an expression pattern similar to other mosquito steroidogenic genes. Moreover, loss-of-function by transient RNAi targeting cyp307a1 disrupted ecdysteroid production demonstrating that this gene is required for ecdysteroid biosynthesis in Anopheles gambiae.

GSVA: gene set variation analysis for microarray and RNA-seq data

Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

A microarray-based system for the simultaneous analysis of single nucleotide polymorphisms in human genes involved in the metabolism of anti-malarial drugs

Background: In order to provide a cost-effective tool to analyse pharmacogenetic markers in malaria treatment, DNA microarray technology was compared with sequencing of polymerase chain reaction (PCR) fragments to detect single nucleotide polymorphisms (SNPs) in a larger number of samples. Methods: The microarray was developed to affordably generate SNP data of genes encoding the human cytochrome P450 enzyme family (CYP) and N-acetyltransferase-2 (NAT2) involved in antimalarial drug metabolisms and with known polymorphisms, i.e. CYP2A6, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, and NAT2. Results: For some SNPs, i.e. CYP2A6*2, CYP2B6*5, CYP2C8*3, CYP2C9*3/*5, CYP2C19*3, CYP2D6*4 and NAT2*6/*7/*14, agreement between both techniques ranged from substantial to almost perfect (kappa index between 0.61 and 1.00), whilst for other SNPs a large variability from slight to substantial agreement (kappa index between 0.39 and 1.00) was found, e. g. CYP2D6*17 (2850C>T), CYP3A4*1B and CYP3A5*3. Conclusion: The major limit of the microarray technology for this purpose was lack of robustness and with a large number of missing data or with incorrect specificity.

Data analysis tools and methods for DNA microarray and high-throughput sequencing data

The recent rapid development of biotechnological approaches has enabled the production of large whole genome level biological data sets. In order to handle thesedata sets, reliable and efficient automated tools and methods for data processingand result interpretation are required. Bioinformatics, as the field of studying andprocessing biological data, tries to answer this need by combining methods and approaches across computer science, statistics, mathematics and engineering to studyand process biological data. The need is also increasing for tools that can be used by the biological researchers themselves who may not have a strong statistical or computational background, which requires creating tools and pipelines with intuitive user interfaces, robust analysis workflows and strong emphasis on result reportingand visualization. Within this thesis, several data analysis tools and methods have been developed for analyzing high-throughput biological data sets. These approaches, coveringseveral aspects of high-throughput data analysis, are specifically aimed for gene expression and genotyping data although in principle they are suitable for analyzing other data types as well. Coherent handling of the data across the various data analysis steps is highly important in order to ensure robust and reliable results. Thus,robust data analysis workflows are also described, putting the developed tools andmethods into a wider context. The choice of the correct analysis method may also depend on the properties of the specific data setandthereforeguidelinesforchoosing an optimal method are given. The data analysis tools, methods and workflows developed within this thesis have been applied to several research studies, of which two representative examplesare included in the thesis. The first study focuses on spermatogenesis in murinetestis and the second one examines cell lineage specification in mouse embryonicstem cells.

Development of a Fully Automated Image Analysis Method for High Density cDNA and array CGH Microarray Based Genomic Studies

In this thesis, different techniques for image analysis of high density microarrays have been investigated. Most of the existing image analysis techniques require prior knowledge of image specific parameters and direct user intervention for microarray image quantification. The objective of this research work was to develop of a fully automated image analysis method capable of accurately quantifying the intensity information from high density microarrays images. The method should be robust against noise and contaminations that commonly occur in different stages of microarray development.

Production of DNA microarray and expression analysis of genes from Xylella fastidiosa in different culture media

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

BayGO: Bayesian analysis of ontology term enrichment in microarray data

Abstract Background The search for enriched (aka over-represented or enhanced) ontology terms in a list of genes obtained from microarray experiments is becoming a standard procedure for a system-level analysis. This procedure tries to summarize the information focussing on classification designs such as Gene Ontology, KEGG pathways, and so on, instead of focussing on individual genes. Although it is well known in statistics that association and significance are distinct concepts, only the former approach has been used to deal with the ontology term enrichment problem. Results BayGO implements a Bayesian approach to search for enriched terms from microarray data. The R source-code is freely available at http://blasto.iq.usp.br/~tkoide/BayGO in three versions: Linux, which can be easily incorporated into pre-existent pipelines; Windows, to be controlled interactively; and as a web-tool. The software was validated using a bacterial heat shock response dataset, since this stress triggers known system-level responses. Conclusion The Bayesian model accounts for the fact that, eventually, not all the genes from a given category are observable in microarray data due to low intensity signal, quality filters, genes that were not spotted and so on. Moreover, BayGO allows one to measure the statistical association between generic ontology terms and differential expression, instead of working only with the common significance analysis.

Analysis of ripening-related gene expression in papaya using an Arabidopsis-based microarray

Abstract Background Papaya (Carica papaya L.) is a commercially important crop that produces climacteric fruits with a soft and sweet pulp that contain a wide range of health promoting phytochemicals. Despite its importance, little is known about transcriptional modifications during papaya fruit ripening and their control. In this study we report the analysis of ripe papaya transcriptome by using a cross-species (XSpecies) microarray technique based on the phylogenetic proximity between papaya and Arabidopsis thaliana. Results Papaya transcriptome analyses resulted in the identification of 414 ripening-related genes with some having their expression validated by qPCR. The transcription profile was compared with that from ripening tomato and grape. There were many similarities between papaya and tomato especially with respect to the expression of genes encoding proteins involved in primary metabolism, regulation of transcription, biotic and abiotic stress and cell wall metabolism. XSpecies microarray data indicated that transcription factors (TFs) of the MADS-box, NAC and AP2/ERF gene families were involved in the control of papaya ripening and revealed that cell wall-related gene expression in papaya had similarities to the expression profiles seen in Arabidopsis during hypocotyl development. Conclusion The cross-species array experiment identified a ripening-related set of genes in papaya allowing the comparison of transcription control between papaya and other fruit bearing taxa during the ripening process.

Microarray based real-time analysis of nucleic acid hybridization kinetics and thermodynamics

Ziel dieser Dissertation ist die experimentelle Charakterisierung und quantitative Beschreibung der Hybridisierung von komplementären Nukleinsäuresträngen mit oberflächengebundenen Fängermolekülen für die Entwicklung von integrierten Biosensoren. Im Gegensatz zu lösungsbasierten Verfahren ist mit Microarray Substraten die Untersuchung vieler Nukleinsäurekombinationen parallel möglich. Als biologisch relevantes Evaluierungssystem wurde das in Eukaryoten universell exprimierte Actin Gen aus unterschiedlichen Pflanzenspezies verwendet. Dieses Testsystem ermöglicht es, nahe verwandte Pflanzenarten auf Grund von geringen Unterschieden in der Gen-Sequenz (SNPs) zu charakterisieren. Aufbauend auf dieses gut studierte Modell eines House-Keeping Genes wurde ein umfassendes Microarray System, bestehend aus kurzen und langen Oligonukleotiden (mit eingebauten LNA-Molekülen), cDNAs sowie DNA und RNA Targets realisiert. Damit konnte ein für online Messung optimiertes Testsystem mit hohen Signalstärken entwickelt werden. Basierend auf den Ergebnissen wurde der gesamte Signalpfad von Nukleinsärekonzentration bis zum digitalen Wert modelliert. Die aus der Entwicklung und den Experimenten gewonnen Erkenntnisse über die Kinetik und Thermodynamik von Hybridisierung sind in drei Publikationen zusammengefasst die das Rückgrat dieser Dissertation bilden. Die erste Publikation beschreibt die Verbesserung der Reproduzierbarkeit und Spezifizität von Microarray Ergebnissen durch online Messung von Kinetik und Thermodynamik gegenüber endpunktbasierten Messungen mit Standard Microarrays. Für die Auswertung der riesigen Datenmengen wurden zwei Algorithmen entwickelt, eine reaktionskinetische Modellierung der Isothermen und ein auf der Fermi-Dirac Statistik beruhende Beschreibung des Schmelzüberganges. Diese Algorithmen werden in der zweiten Publikation beschrieben. Durch die Realisierung von gleichen Sequenzen in den chemisch unterschiedlichen Nukleinsäuren (DNA, RNA und LNA) ist es möglich, definierte Unterschiede in der Konformation des Riboserings und der C5-Methylgruppe der Pyrimidine zu untersuchen. Die kompetitive Wechselwirkung dieser unterschiedlichen Nukleinsäuren gleicher Sequenz und die Auswirkungen auf Kinetik und Thermodynamik ist das Thema der dritten Publikation. Neben der molekularbiologischen und technologischen Entwicklung im Bereich der Sensorik von Hybridisierungsreaktionen oberflächengebundener Nukleinsäuremolekülen, der automatisierten Auswertung und Modellierung der anfallenden Datenmengen und der damit verbundenen besseren quantitativen Beschreibung von Kinetik und Thermodynamik dieser Reaktionen tragen die Ergebnisse zum besseren Verständnis der physikalisch-chemischen Struktur des elementarsten biologischen Moleküls und seiner nach wie vor nicht vollständig verstandenen Spezifizität bei.

An introduction to low-level analysis methods of DNA microarray data

This article gives an overview over the methods used in the low--level analysis of gene expression data generated using DNA microarrays. This type of experiment allows to determine relative levels of nucleic acid abundance in a set of tissues or cell populations for thousands of transcripts or loci simultaneously. Careful statistical design and analysis are essential to improve the efficiency and reliability of microarray experiments throughout the data acquisition and analysis process. This includes the design of probes, the experimental design, the image analysis of microarray scanned images, the normalization of fluorescence intensities, the assessment of the quality of microarray data and incorporation of quality information in subsequent analyses, the combination of information across arrays and across sets of experiments, the discovery and recognition of patterns in expression at the single gene and multiple gene levels, and the assessment of significance of these findings, considering the fact that there is a lot of noise and thus random features in the data. For all of these components, access to a flexible and efficient statistical computing environment is an essential aspect.

Survival Analysis with Large Dimensional Covariates: An Application in Microarray Studies

Use of microarray technology often leads to high-dimensional and low- sample size data settings. Over the past several years, a variety of novel approaches have been proposed for variable selection in this context. However, only a small number of these have been adapted for time-to-event data where censoring is present. Among standard variable selection methods shown both to have good predictive accuracy and to be computationally efficient is the elastic net penalization approach. In this paper, adaptation of the elastic net approach is presented for variable selection both under the Cox proportional hazards model and under an accelerated failure time (AFT) model. Assessment of the two methods is conducted through simulation studies and through analysis of microarray data obtained from a set of patients with diffuse large B-cell lymphoma where time to survival is of interest. The approaches are shown to match or exceed the predictive performance of a Cox-based and an AFT-based variable selection method. The methods are moreover shown to be much more computationally efficient than their respective Cox- and AFT- based counterparts.