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Book of Abstracts Sevilla, 21-24 septiembre 2014 Book of Abstracts Supported by: #JdBI2014 Book of Abstracts Index of Kenynote Lectures ...............................................................................................3 of Oral Presentations per Topics Highlights ...........................................................................................................5 Metagenomics ...................................................................................................8 Integrative Biology ....................................................................................... 10 Medical Informatics ................................................................................... 13 Phylogeny / Evolution ................................................................................. 16 Structure / Function ..................................................................................... 19 Student Symposium ..................................................................................... 22 of Posters per Topics Highlights ........................................................................................................ 25 Metagenomics .................................................................................................27 Integrative Biology ....................................................................................... 28 Medical Informatics ...................................................................................... 49 Phylogeny / Evolution ................................................................................. 64 Structure / Function ..................................................................................... 71 Student Symposium ..................................................................................... 81 Page 2 #JdBI2014 Keynotes K1-01 The gut microbiome - A new target for understanding, diagnosing and treating disease Jeroen Raes VIB - K.U. Leuven, Leuven, BE The functioning of the human body constitutes a complex interplay of human processes and ‘services’ rendered to us by the 1000 trillion microbial cells we carry. Disruption of this natural microbial flora is linked to infection, autoimmune diseases and cancer, but detailed knowledge about our microbial component remains scarce. Recent technological advances such as metagenomics and next-generation sequencing permit the study of the various microbiota of the human body at a previously unseen scale. These advances have allowed the initiation of the Inter- national Human Microbiome Project, aiming at genomically characterizing the totality of human-associated microor- ganisms (the “microbiome”). Here, I will present our work on characterizing the human intestinal flora based upon the analysis of high-throughput meta-omics (metagenomics, metatranscriptomics, metaproteomics) data. I will show how the healthy gut flora can be classified “enterotypes” that are independent from host nationality, age, bmi and gender. I will also show how meta- genome-wide association studies (MGWAS) can lead to the detection of diagnostic markers for host properties and disease (e.g. in IBD, diabetes and obesity), and aid in further understanding on how the gut flora disturbances contribute to these pathologies. Finally, I will illustrate how gut microbiota-based treatment strategies are emerging, for example through Faecal Microbiota Transplantation (FMT). References: Hildebrand F et al. (2013) Inflammation-associated enterotypes, host genotype, cage and interindividual effects drive gut microbiota variation in common laboratory mice. Genome Biol, 14(1):R4 Qin et al. (2012) A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490:55-60 Arumugam*, Raes* et al. (2011) Enterotypes of the human gut microbiome. Nature 473, 174-180 K2-01 Cellular resolution models for gene regulation in fly embryos Nick Luscombe Cancer Research UK London Research Institute, London, UK Transcriptional control ensures genes are expressed in the right amounts at the correct times and locations. Understan- ding quantitatively how regulatory systems convert input signals to appropriate outputs remains a challenge. For the first time, we successfully model even skipped (eve) stripes 2 and 3+7 across the entire fly embryo at cellular resolution. A straightforward statistical relationship explains how transcription factor (TF) concentrations define eve’s complex spatial expression, without the need for pairwise interactions or cross-regulatory dynamics. Simulating thousands of TF combinations, we recover known regulators and suggest new candidates. Finally, we accurately predict the intricate effects of perturbations including TF mutations and misexpression. Our approach imposes minimal assumptions about regulatory function; instead we infer underlying mechanisms from models that best fit the data, like the lack of TF- specific thresholds and the positional value of homotypic interactions. Our study provides a general and quantitative method for elucidating the regulation of diverse biological systems. Page 3 #JdBI2014 Keynotes K3-01 Functions of miRNAs within gene expression regulatory networks Mihaela Zavolan Biozentrum - University of Basel, Basel, CH Among the many mechanisms that regulate gene expression, miRNAs have emerged in the past decade as an impor- tant class of post-transcriptional regulators of mRNA decay and protein translation. Through complementarity invol- ving 7-8 nucleotides at their 5’end miRNAs guide Argonaute proteins to ‘canonical’ target mRNAs. Recent studies have suggested that the miRNA-induced target degradation can further give rise to additional behaviors. These include the threshold-linear response of the targets to their transcriptional induction, reduction of the ‘noise’ in target expression and induction of correlations in the expression of the targets of a given miRNA. Here I will discuss experimental and computational approaches to studying these behaviors, including single cell gene expression profiling, as well as the insights that were derived about the functions of miRNAs in the regulation of gene expression. K4-01 Why are individuals different? Ben Lehner Centre for Genomic Regulation, Barcelona, ES We study the causes of phenotypic variation amongst individuals, including the distribution and effects of genetic variation, somatic mutations and epigenetic differences (stochastic/environmental influences). I will present some our recent work on how inherited genetic variation influences dynamic processes, on the causes of phenotypic variation in the absence of genetic variation, and on somatic mutation processes in human cancers. Page 4 Oral presentations Highlights H1-01 The Functional Topography of the Arabidopsis Genome Is Organized in a Reduced Number of Linear Motifs of Chromatin States Joana Sequeira-Mendes1, Irene Araguez1, Ramon Peiró1, Raul Mendez-Giraldez1, Xiaoyou Zhang2, Steven Jacob- sen2, Ugo Bastolla1, Crisanto Gutierrez1 1Centro de Biología Molecular Severo Ochoa, Madrid, ES, 2University of California Los Angeles, Los Angeles, US Chromatin is of major relevance for gene expression, cell division, and differentiation. Here, we determined the lands- cape of Arabidopsis thaliana chromatin states using 16 features, including DNA sequence, CG methylation, histone variants, and modifications. The combinatorial complexity of chromatin can be reduced to nine states that describe chromatin with high resolution and robustness. Each chromatin state has a strong propensity to associate with a subset of other states defining a discrete number of chromatin motifs. These topographical relationships revealed that an in- tergenic state, characterized by H3K27me3 and slightly enriched in activation marks, physically separates the canonical Polycomb chromatin and two heterochromatin states from the rest of the euchromatin domains. Genomic elements are distinguished by specific chromatin states: four states span genes from transcriptional start sites (TSS) to termination sites and two contain regulatory regions upstream of TSS. Polycomb regions and the rest of the euchromatin can be connected by two major chromatin paths. Sequential chromatin immunoprecipitation experiments demonstrated the occurrence of H3K27me3 and H3K4me3 in the same chromatin fiber, within a two to three nucleosome size range. Our data provide insight into the Arabidopsis genome topography and the establishment of gene expression patterns, specification of DNA replication origins, and definition of chromatin domains. H1-02 Do long non-coding RNAs make proteins? Jorge Ruiz-Orera1, Xavier Messeguer2, Juan A Subirana3, M. Mar Albà4 1Evolutionary Genomics Group, Research Programme on Biomedical Informatics (GRIB) - Hospital del Mar Research Institute (IMIM) - Uni- versitat Pompeu Fabra (UPF), Barcelona, ES, 2Universitat Politècnica de Catalunya (UPC), Barcelona, Barcelona, ES, 3Evolutionary Genomics Group, Research Programme on Biomedical Informatics (GRIB) - Hospital del Mar Research Institute (IMIM) - Universitat Pompeu Fabra (UPF), Real Academia de Ciències i Arts de Barcelona (RACAB), Barcelona, ES, 4Evolutionary Genomics Group, Research Programme on Bio- medical Informatics (GRIB) - Hospital del Mar Research Institute (IMIM) - Universitat Pompeu Fabra (UPF), Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, ES Deep transcriptome sequencing has revealed the existence of thousands of transcripts that lack conserved open rea- ding frames and which have been termed long non-coding
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