VisHiC—hierarchical functional enrichment analysis of microarray data

Measuring gene expression levels with microarrays is one of the key technologies of modern genomics. Clustering of microarray data is an important application, as genes with similar expression profiles may be regulated by common pathways and involved in related functions. Gene Ontology (GO) analysis and visualization allows researchers to study the biological context of discovered clusters and characterize genes with previously unknown functions. We present VisHiC (Visualization of Hierarchical Clustering), a web server for clustering and compact visualization of gene expression data combined with automated function enrichment analysis. The main output of the analysis is a dendrogram and visual heatmap of the expression matrix that highlights biologically relevant clusters based on enriched GO terms, pathways and regulatory motifs. Clusters with most significant enrichments are contracted in the final visualization, while less relevant parts are hidden altogether. Such a dense representation of microarray data gives a quick global overview of thousands of transcripts in many conditions and provides a good starting point for further analysis. VisHiC is freely available at http://biit.cs.ut.ee/vishic

g:Profiler—a web server for functional interpretation of gene lists (2011 update)

Functional interpretation of candidate gene lists is an essential task in modern biomedical research. Here, we present the 2011 update of g:Profiler (http://biit.cs.ut.ee/gprofiler/), a popular collection of web tools for functional analysis. g:GOSt and g:Cocoa combine comprehensive methods for interpreting gene lists, ordered lists and list collections in the context of biomedical ontologies, pathways, transcription factor and microRNA regulatory motifs and protein–protein interactions. Additional tools, namely the biomolecule ID mapping service (g:Convert), gene expression similarity searcher (g:Sorter) and gene homology searcher (g:Orth) provide numerous ways for further analysis and interpretation. In this update, we have implemented several features of interest to the community: (i) functional analysis of single nucleotide polymorphisms and other DNA polymorphisms is supported by chromosomal queries; (ii) network analysis identifies enriched protein–protein interaction modules in gene lists; (iii) functional analysis covers human disease genes; and (iv) improved statistics and filtering provide more concise results. g:Profiler is a regularly updated resource that is available for a wide range of species, including mammals, plants, fungi and insects

Suuremahuliste andmete kasutamine geenidevaheliste seoste leidmiseks

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Geenid määravad ära, millistest RNA ja valgu molekulidest elusorganism koosneb. Ainult geenide tuvastamisest ei piisa, et aru saada kuidas organism toimib, millal ja kuidas erinevad geenide produktid avalduvad ja mida need teevad. Elusorganismi olemuse mõistmiseks ja bioloogiliste protsesside mõjutamiseks on vajalik aru saada geenide ja valkude omavahelistest seostest. Suure läbilaskevõimega tehnoloogiad võimaldavad hõlpsasti mõõta bioloogiliste protsesside erinevaid tahke. See omakorda on toonud kaasa andmemahtude üha kiireneva kasvutrendi ning vajaduse uute meetodite järele, mis aitaks toorandmeid analüüsida, andmeid omavahel kombineerida ning tulemusi visualiseerida. Samuti on kasvanud vajadus arvutuslike meetoditega katsetada, kas olemasolevad andmemudelid kirjeldavad bioloogilist uurimisobjekti piisavalt täpselt. Käesolevas uurimistöös on näidatud erinevaid bioinformaatilisi meetodeid, kuidas suuremahuliste ning eritüübiliste eksperimentaalsete andmete kombineerimist saab rakendada geenidevaheliste seoste leidmiseks. Suuremahulistele andmetele on integreerimise ja omavahel võrreldavaks tegemisega võimalik anda lisaväärtust. Töö käigus koondati kokku ja tehti avalikkusele ligipääsetavaks embrüonaalsete tüvirakkude regulatsiooni käsitlevate publikatsioonide lisafailides avaldatud info ESCDb andmebaasi näol. Neid andmeid kasutades on teadlaskonnal võimalik leida geenide vahelisi seoseid, mida eraldiseisvaid andmeid analüüsides ei ole võimalik välja selgitada. Andmebaasi kogutud info kombineerimisel arvutusliku mudeldamisega õnnestus leida käesoleva töö raames uus regulaator embrüonaalsetes tüvirakkudes — IL11. Lisaks võimaldas erinevate andmetüüpide kombineerimine leida embrüonaalsete tüvirakkude keskse regulaatori — OCT4 geeni alternatiivsed märklaudgeenide moodulid. Kasutades DNA konserveerumisinfot koos regulatoorsete motiivide analüüsiga leiti kolm uut rasvatüvirakkude diferentseerumise regulaatorvalku. Samuti käsitletakse töös automaatset grupeerimis- ja visualiseerimismetoodikat VisHiC, mis aitab esile tõsta huvitavaid geenigruppe, mida teiste meetoditega edasi uurida. Töös on näidatud erinevaid suuremahuliste andmestike integreerimise viise, mis võimaldavad leida selliseid geenidevahelisi seoseid, mida ei oleks võimalik leida kui analüüsiksime üht andmestikku korraga.In order to understand the basic principles of how organisms function, and to be able to affect the biological processes, we need to understand relationships between genes and proteins. Modern high-throughput technology enables to study different sides of biological processes in a rapid manner. This, however, has led to a steady growth of amount of data available. The need for more sophisticated methods for analysing raw data, for combining different data sources, and to visualise the results, has emerged. Additionally, computational modeling is required to test if our understanding of biological processes is supported by the available data. A variety of bioinformatics methods are used to demonstrate how to combine different type of high-throughput data for identifying relationships between genes. Furthermore, it was shown that through combining various data types from different sources adds value to already published data. In the thesis, data from publications about embryonic stem cell regulation were collected together and made available through Embryonic Stem Cell Database (ESCDb). Complementary data in the database allows researchers to find relationships between genes that would not be possible when analysing only one dataset at a time. One of the main findings of this study illustrates how using computational modelling on data from the ESCDb allowed to find a novel pluripotency regulator — IL11. Additionally, integration of different data types led to identification of alternative gene regulatory modules of core pluripotency regulator OCT4. Similarly, combination of conservation data and regulatory motif analysis led to identification of three new regulators of adipocyte differentiation. This thesis also covers innovative methodology, VisHiC, for automatic identification and visualisation of functionally related gene sets. This methodology allows to find relevant gene sets for further characterisation from large high-throughput datasets. This doctoral thesis demonstrates that integration of different high-throughput datasets enables establishing gene-gene relationships that would not be possible when looking at a single data type in isolation

Review Article: Current Knowledge on Microarray Technology - An Overview

The completion of whole genome sequencing projects has led to a rapid increase in the availability of genetic information. In the field of transcriptomics, the emergence of microarray-based technologies and the design of DNA biochips allow high-throughput studies of RNA expression in cell and tissue at a given moment. It has emerged as one of the most important technology in the field of molecular biology and transcriptomics. Arrays of oligonucleotide or DNA sequences are being used for genome-wide genotyping and expression profiling, and several potential clinical applications have begun to emerge as our understanding of these techniques and the data they generate improves. From its emergence to date, several database, software and technology updates have been developed in the field of microarray technology. This paper reviews basics and updates of each microarray technology and serves to introduce newly compiled resources that will provide specialist information in this area.Keywords: Microarray, Databases, cDNA array, Oligonucleotide arra

Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence

BACKGROUND: Many studies have revealed correlations between breast tumour phenotypes, variations in gene expression, and patient survival outcomes. The molecular heterogeneity between breast tumours revealed by these studies has allowed prediction of prognosis and has underpinned stratified therapy, where groups of patients with particular tumour types receive specific treatments. The molecular tests used to predict prognosis and stratify treatment usually utilise fixed sets of genomic biomarkers, with the same biomarker sets being used to test all patients. In this paper we suggest that instead of fixed sets of genomic biomarkers, it may be more effective to use a stratified biomarker approach, where optimal biomarker sets are automatically chosen for particular patient groups, analogous to the choice of optimal treatments for groups of similar patients in stratified therapy. We illustrate the effectiveness of a biclustering approach to select optimal gene sets for determining the prognosis of specific strata of patients, based on potentially overlapping, non-discrete molecular characteristics of tumours. RESULTS: Biclustering identified tightly co-expressed gene sets in the tumours of restricted subgroups of breast cancer patients. The co-expressed genes in these biclusters were significantly enriched for particular biological annotations and gene regulatory modules associated with breast cancer biology. Tumours identified within the same bicluster were more likely to present with similar clinical features. Bicluster membership combined with clinical information could predict patient prognosis in conditional inference tree and ridge regression class prediction models. CONCLUSIONS: The increasing clinical use of genomic profiling demands identification of more effective methods to segregate patients into prognostic and treatment groups. We have shown that biclustering can be used to select optimal gene sets for determining the prognosis of specific strata of patients

Las especies reactivas de oxígeno mitocondriales de los astrocitos regulan el metabolismo cerebral y el comportamiento en ratón

[ES] La conservación de la energía cerebral requiere la cooperación entre dos tipos de células metabólicamente distantes; las neuronas y los astrocitos. Las neuronas dependen en gran medida de la fosforilación oxidativa mitocondrial para su función y supervivencia. Para ello, utilizan sustratos fácilmente oxidables como el lactato suministrado por los astrocitos, que son más glucolíticos. Así, la cadena respiratoria mitocondrial está estrechamente organizada y es eficiente en las neuronas, pero está poco ensamblada y es menos eficiente en los astrocitos. Como consecuencia, la generación mitocondrial de especies reactivas de oxígeno (ROS) es mucho menor en las neuronas que en los astrocitos. Sin embargo, se desconoce si los abundantes niveles de ROS mitocondriales (mROS) en los astrocitos desempeñan alguna función fisiológica. En esta tesis nos propusimos investigar esta posibilidad. Para abordar este problema, hemos generado un ratón que sobreexpresa una versión mitocondrial de la enzima catalasa (mitoCatalasa, mCAT) con la idea de disminuir persistentemente los niveles de mROS en los astrocitos durante la edad adulta. Comprobamos la eficiencia in vitro e in vivo de la reducción de mROS astrocíticos. Posteriormente, los resultados de transcriptómica, metabolómica, flujos metabólicos, junto con la inmunohistoquímica y el escrutinio del comportamiento de estos ratones revelaron alteraciones significativas a nivel cerebral en diversas vías metabólicas y modificaciones estructurales a nivel neuronal, compatibles con los leves defectos cognitivos observados. En concreto, los datos revelan que los mROS astrocíticos regulan, al menos, la utilización de la glucosa a través de la glucolisis y la vía de las pentosas fosfato (PPP). Este proceso es esencial para asegurar el apoyo metabólico que los astrocitos ejercen sobre las neuronas. Por tanto, nuestro trabajo demuestra que los mROS endógenos astrocíticos tienen una relevancia funcional que garantiza la correcta cooperación metabólica entre astrocitos y neuronas, necesaria para la bioenergética y supervivencia neuronal. Por último, este nuevo modelo animal podría ser una estrategia útil también como herramienta para combatir el estrés oxidativo en modelos de enfermedades neurológicas y estudiar aspectos celulares de este tipo de estrés. En resumen, nuestro trabajo revela propiedades de la señalización redox y su relación con la coordinación del metabolismo en el sistema nervioso central (SNC). También, pone de manifiesto la relevancia de los astrocitos en la regulación de funciones superiores del SNC como la conducta animal. Nuestros resultados sugieren que el estudio de la señalización redox requiere distinguir el origen celular de los ROS y explorar la función fisiológica como medio para comprender la intervención patológica. Por tanto, en nuestra opinión, este trabajo contribuye a la consideración de nuevos factores que deberán tenerse en cuenta en la búsqueda de nuevas estrategias terapéuticas basadas en el uso de sistemas antioxidantes. English summary Brain energy conservation requires cooperation between metabolically distant cell types, notably neurons and astrocytes. Neurons, which strictly depend on mitochondrial oxidative phosphorylation for function and survival, utilize easily oxidizable substrates supplied by astrocytes, which rely upon glycolysis. Therefore, the mitochondrial respiratory chain is tightly organized and efficient in neurons, but loosely assembled and less efficient in astrocytes. Consequently, the mitochondrial generation of reactive oxygen species (ROS) is minimized in neurons and relatively elevated in astrocytes. However, whether the naturally abundant mitochondrial ROS in astrocytes have any physiological function is unknown. To address this issue, a genetically-engineered mouse was herein generated to persistently lessen mitochondrial ROS in astrocytes during adulthood. Transcriptomics, metabolomics, biochemical, immunohistochemical and behavioural scrutiny of these mice revealed significant alterations in specific pathways of brain redox, carbohydrate, lipid and amino acid metabolic pathways affecting neuronal function and mouse behaviour. We find that astrocytic mitochondrial ROS (mROS) regulate at least glucose utilization via glycolysis and pentose-phosphate pathway. This process is essential to ensure the metabolic support that astrocytes exert on neurons, which modulates neuronal bioenergetics and, potentially, survival. Our data provide further molecular insight into the metabolic cooperation between astrocytes and neurons and demonstrate that astrocytic mitochondrial ROS are important regulators of organismal physiology sustaining brain metabolism and neuronal function in vivo

Implicación de perfiles inmuno-genéticos de la respuesta inmune citotóxica en la susceptibilidad y/o evolución de pacientes con cáncer de mama del sur de España

El objetivo de la tesis es identificar perfiles inmuno-genéticos de genes implicados en la respuesta inmune citotóxica antitumoral, que puedan estar implicados no solo en la susceptibilidad o protección en el cáncer de mama, también en su evolución y desarrollo de la enfermedad. Los genes del sistema inmune analizados son HLA B, MICA, LMP2y7, TAP1y2, NKG2D, CTLA-4