DNA microarray integromics analysis platform

Background: The study of interactions between molecules belonging to different biochemical families (such as lipids and nucleic acids) requires specialized data analysis methods. This article describes the DNA Microarray Integromics Analysis Platform, a unique web application that focuses on computational integration and analysis of "multi-omics" data. Our tool supports a range of complex analyses, including - among others - low- and high-level analyses of DNA microarray data, integrated analysis of transcriptomics and lipidomics data and the ability to infer miRNA-mRNA interactions. Results: We demonstrate the characteristics and benefits of the DNA Microarray Integromics Analysis Platform using two different test cases. The first test case involves the analysis of the nutrimouse dataset, which contains measurements of the expression of genes involved in nutritional problems and the concentrations of hepatic fatty acids. The second test case involves the analysis of miRNA-mRNA interactions in polysaccharide-stimulated human dermal fibroblasts infected with porcine endogenous retroviruses. Conclusions: The DNA Microarray Integromics Analysis Platform is a web-based graphical user interface for "multi-omics" data management and analysis. Its intuitive nature and wide range of available workflows make it an effective tool for molecular biology research. The platform is hosted at https://lifescience.plgrid.pl

MicroRNA regulation and its effects on cellular transcriptome in Human Immunodeficiency Virus-1 (HIV-1) infected individuals with distinct viral load and CD4 cell counts

Background: Disease progression in the absence of therapy varies significantly in HIV-1 infected individuals. Both viral and host cellular molecules are implicated; however, the exact role of these factors and/or the mechanism involved remains elusive. To understand how microRNAs (miRNAs), which are regulators of transcription and translation, influence host cellular gene expression (mRNA) during HIV-1 infection, we performed a comparative miRNA and mRNA microarray analysis using PBMCs obtained from infected individuals with distinct viral load and CD4 counts.Methods: RNA isolated from PBMCs obtained from HIV-1 seronegative and HIV-1 positive individuals with distinct viral load and CD4 counts were assessed for miRNA and mRNA profile. Selected miRNA and mRNA transcripts were validated using in vivo and in vitro infection model.Results: Our results indicate that HIV-1 positive individuals with high viral load (HVL) showed a dysregulation of 191 miRNAs and 309 mRNA transcripts compared to the uninfected age and sex matched controls. The miRNAs miR-19b, 146a, 615-3p, 382, 34a, 144 and 155, that are known to target innate and inflammatory factors, were significantly upregulated in PBMCs with high viral load, as were the inflammatory molecules CXCL5, CCL2, IL6 and IL8, whereas defensin, CD4, ALDH1, and Neurogranin (NRGN) were significantly downregulated. Using the transcriptome profile and predicted target genes, we constructed the regulatory networks of miRNA-mRNA pairs that were differentially expressed between control, LVL and HVL subjects. The regulatory network revealed an inverse correlation of several miRNA-mRNA pair expression patterns, suggesting HIV-1 mediated transcriptional regulation is in part likely through miRNA regulation.Conclusions: Results from our studies indicate that gene expression is significantly altered in PBMCs in response to virus replication. It is interesting to note that the infected individuals with low or undetectable viral load exhibit a gene expression profile very similar to control or uninfected subjects. Importantly, we identified several new mRNA targets (Defensin, Neurogranin, AIF) as well as the miRNAs that could be involved in regulating their expression through the miRNA-mRNA interaction. © 2013 Duskova et al.; licensee BioMed Central Ltd

Retrieval of Gene Expression Measurements with Probabilistic Models

A crucial problem in current biological and medical research is how to utilize the diverse set of existing biological knowledge and heterogeneous measurement data in order to gain insights on new data. As datasets continue to be deposited in public repositories it is becoming important to develop search engines that can efficiently integrate existing data and search for relevant earlier studies given a new study. The search task is encountered in several biological applications including cancer genomics, pharmacokinetics, personalized medicine and meta-analysis of functional genomics.  Most existing search engines rely on classical keyword or annotation based retrieval which is limited to discovering known information and requires careful downstream annotation of the data. Data-driven model-based methods, that retrieve studies based on similarities in the actual measurement data, have a greater potential for uncovering novel biological insights. In particular, probabilistic modeling provides promising model-based tools due to its ability to encode prior knowledge, represent uncertainty in model parameters and handle noise associated to the data. By introducing latent variables it is further possible to capture relationships in data features in the form of meaningful biological components underlying the data.  This thesis adapts existing and develops new probabilistic models for retrieval of relevant measurement data in three different cases of background repositories. The first case is a background collection of data samples where each sample is represented by a single data type. The second case is a collection of multimodal data samples where each sample is represented by more than one data type. The third case is a background collection of datasets where each dataset, in turn, is a collection of multiple samples. In all three setups the proposed models are evaluated quantitatively and with case studies the models are demonstrated to facilitate interpretable retrieval of relevant data, rigorous integration of diverse information sources and learning of latent components from partly related dataset collections

Analysis of transcriptional networks and chromatin states in normal and abnormal blood cells

Altered myeloid differentiation can lead to a variety of haematological malignancies including the Myelodysplastic Syndrome (MDS), chronic myelomonocytic leukaemia (CMML) and acute myeloid leukaemia (AML). We have studied transcriptome regulation in haematopoietic stem and progenitor cells (HSPC) using different high-throughput technologies. In this thesis, I introduce bioinformatics pipelines and an algorithm for the analysis of next-generation sequencing (NGS) data and highlight methods to integrate different genome-wide datasets to derive chromatin states, transcriptional and post-transcriptional networks in normal and abnormal blood cells. Following an introduction to key concepts relevant to this thesis, in the second chapter, I detail the first genome-wide characterisation of small non-coding RNAs in HSPC in MDS patients. By profiling mRNA expression in the same patients, I developed a novel statistical model that integrated miRNA, transcription factors (TF) and gene expression to identify novel regulatory pathways in MDS. MDS and CMML patients often die following transformation into AML. In the third chapter, I present an analysis of a heptad of HSPC TFs that regulate their own expression by binding enhancers of these genes. The enhancer and the heptad are active in a subset of AMLs, normal HSPC and leukemic stem cells. The heptad and a gene signature derived from enhancer activity, predict clinical outcome in AML, while the expression of four heptad genes further correlated with the underlying genetic mutations in cytogenetically normal AML patients. In the fourth chapter, I describe a novel algorithm (LPCHP) to define histone states from NGS data. LPCHP makes use of signal characteristics such as peak shape, location and frequencies in contrast to other algorithms, which only evaluate read intensities. LPCHP was evaluated and performed well in terms of correlation with gene expression, prediction of histone states, parameter variations and signal-to-noise ratios. In the final chapter, I present preliminary data and outline plans for future work. I propose a systems biology approach to study networks of miRNAs and TFs in MDS and CMML. Sequencing of miRNA and mRNA facilitates network reconstruction where interactions between miRNA and mRNA are predicted at single nucleotide resolution, providing avenues for patient stratification and drug response prediction

Bayesian meta-analysis models for heterogeneous genomics data

<p>The accumulation of high-throughput data from vast sources has drawn a lot attentions to develop methods for extracting meaningful information out of the massive data. More interesting questions arise from how to combine the disparate information, which goes beyond modeling sparsity and dimension reduction. This dissertation focuses on the innovations in the area of heterogeneous data integration.</p><p>Chapter 1 contextualizes this dissertation by introducing different aspects of meta-analysis and model frameworks for high-dimensional genomic data.</p><p>Chapter 2 introduces a novel technique, joint Bayesian sparse factor analysis model, to vertically integrate multi-dimensional genomic data from different platforms. </p><p>Chapter 3 extends the above model to a nonparametric Bayes formula. It directly infers number of factors from a model-based approach.</p><p>On the other hand, chapter 4 deals with horizontal integration of diverse gene expression data; the model infers pathway activities across various experimental conditions. </p><p>All the methods mentioned above are demonstrated in both simulation studies and real data applications in chapters 2-4.</p><p>Finally, chapter 5 summarizes the dissertation and discusses future directions.</p>Dissertatio

Plant Genetics and Biotechnology in Biodiversity

Plant genetic resources for food and agriculture (PGRFA) have been collected and exchanged for centuries. The rapid development of novel tools for genetic and phenotypic analysis is changing the way we can uncover diversity and exploit its value in modern agriculture. The integration of novel analytical tools is crucial for translating research into much-needed, more efficient management and use of PGRFA. This Special Issue provides an overview of recent topics on plant genetics and biotechnology in biodiversity. The proposed reviews and research papers present current trends and examples of genetic resources’ description, conservation, management, and exploitation, highlighting that new approaches and methodogies can increase our understanding and efficient use of PGRFA to address the agricultural challenges that lie ahead

Evolution du régulateur floral LEAFY dans la lignée verte

LEAFY (LFY) is a unique transcription factor, highly conserved within land plants. LFY directly regulates a set of genes participating in floral development in angiosperms (flowering plants), but its role in the other groups of land plants is unknown, except in the moss Physcomitrella patens where the LFY ortholog (PpLFY) regulates the first cell division in the zygote. PpLFY does not bind to the same DNA sequences as LFY from Arabidopsis thaliana, in spite of the very high degree of conservation of their DNA binding domains. Thus, it appears that the properties of LFY have changed during evolution ; the goal of my thesis was to find out if such changes had occurred frequently in land plants, and what are their origins and consequences on target genes regulation. I performed SELEX experiments on LFY orthologs from all land plants, which revealed that their DNA binding specificty was highly conserved, except in the case of PpLFY. These results allowed us to build an accurate biophysical model to predict LFY binding on DNA fragments at a genomic level, which we applied on the evolution of the regulation of key target genes by LFY. We were able to predict the regulation of the floral gene AGAMOUS by LFY in various angiosperm species, et we could also show that LFY was very likely regulating gymnosperm orthologs of genes involved in floral organ identity, even before the appearance of the flower. The change in DNA binding specificity observed for PpLFY led us to study more precisely the consequences of this change for the regulation of target genes : for this, I initiated bioinformatic and experimental work in P. patens. Finally, to understand how this change in DNA binding specificity had occurred during evolution, we looked for the ancestor of LFY and found out that LFY already existed in green algae. We are currently investigating the ancestral specificity of LFY in these species.LEAFY (LFY) est un facteur de transcription unique et très conservé chez les plantes terrestres. Il contrôle le développement floral chez les angiospermes (plantes à fleurs), mais son rôle est encore mal connu chez toutes les autres plantes terrestres à l'exception de la mousse Physcomitrella patens où l'orthologue de LFY (PpLFY) est requis pour la première division cellulaire du zygote. PpLFY ne reconnaît pas les mêmes séquences d'ADN que LFY d'Arabidopsis thaliana, malgré la très forte conservation de leurs domaines de liaison à l'ADN. LFY semble donc avoir changé de propriétés au cours de l'évolution ; l'objectif de ma thèse a été de déterminer si de tels changements s'étaient produits fréquemment chez les plantes terrestres, et de comprendre leur origine et leur impact sur la régulation des gènes cibles de LFY. Pour cela, j'ai étudié la spécificité de liaison à l'ADN des orthologues de LFY chez les grands groupes de plantes terrestres par des expériences de SELEX, et cette spécificité s'est révélée très fortement conservée, excepté dans le cas de PpLFY. Ces résultats nous ont permis de construire un modèle biophysique performant pour prédire la liaison de LFY à l'échelle génomique, ce que nous avons appliqué à l'étude de l'évolution de la régulation de quelques gènes clés par LFY. Nous avons ainsi pu prédire la régulation du gène floral AGAMOUS par LFY chez différentes espèces angiospermes, et nous avons pu montrer que LFY régulait très vraisemblablement les orthologues des gènes d'identité florale chez les gymnospermes, c'est-à-dire avant l'apparition de la fleur. La divergence de spécificité de PpLFY nous a poussés à étudier les gènes cibles de PpLFY : pour cela, j'ai initié des approches bioinformatiques et expérimentales chez P. patens. Enfin, pour comprendre comment ce changement de spécificité s'est déroulé au cours de l'évolution, nous nous sommes penchés sur l'ancêtre de LFY et avons découvert que LFY était déjà présent chez les algues vertes. Des études pour déterminer la spécificité ancestrale de LFY chez ces espèces ont été initiées