Study of a novel evolutionarily conserved pattern of histone acetylation

Le génome eucaryote est empaqueté dans une structure hautement ordonnée appelée chromatine. Même si la structure de la chromatine est importante pour le maintien de l'intégrité génomique, elle constitue une barrière à de nombreux processus basés sur l'ADN tels que la réplication de l'ADN, la transcription et la réparation de l'ADN. Les histones contiennent une diversité déconcertante de modifications covalentes qui sont concentrées principalement, mais non exclusivement dans leurs queues amino-terminales. Les modifications des histones jouent un rôle central dans la régulation de la structure et de la fonction de la chromatine. Cependant, la détermination de la stoechiométrie des modifications à des sites spécifiques, l'identification des motifs de modifications et l'établissement de leurs rôles physiologiques restent des défis redoutables. Dans cette étude, nous avons utilisé la spectrométrie de masse pour déterminer la stoechiométrie de l'acétylation de résidus lysine spécifiques des histones. En général, les résidus lysine des histones dépourvus d'acétylation sont dérivatisés pour rendre les peptides résultants chimiquement équivalents à leurs homologues acétylés, mais pouvant être distingués par spectrométrie de masse. Cependant, cette méthode est insuffisante pour étudier les peptides contenant plus d'une lysine acétylable, tels que ceux dérivés de la queue N-terminale des histones H3 et H4. La digestion trypsique de tels peptides génère des «isomères de position», des isomères qui ont la même masse, mais qui portent des groupes acétyle à des positions différentes. La quantification précise de l'acétylation d'un site spécifique dans ces peptides est donc un défi analytique majeur. Dans le deuxième chapitre, nous décrivons une nouvelle méthode, pour quantifier l'acétylation à un site spécifique dans les peptides co-éluants isomériques et isobariques, qui combine des données LC-MS / MS à haute résolution avec un nouvel algorithme bioinformatique, Iso-PeptidAce. En utilisant des spectres de masse en tandem (MS/MS) de peptides synthétiques, les produits de fragmentation caractéristiques de chaque isomère de position ont été identifiés et utilisés pour déconvoluer des spectres provenant de mélanges d'isomères de position et quantifier l'abondance de chaque isomère. Nous avons ensuite testé l'applicabilité de l'Iso-PeptidAce pour quantifier les augmentations en fonction du temps de l'acétylation des histones des cellules d'érythroleucémie K562 traitées avec des inhibiteurs d’histone déacétylase (HDAC). En utilisant notre méthode, nous avons également trouvé que les histones H3 et H4 associées à CAF-1, un facteur d'assemblage de la chromatine, ont une stoechiométrie élevée d’acétylation sur plusieurs résidus de H3 et H4, par rapport aux histones totales. Dans le chapitre 3, nous avons appliqué Iso-PeptidAce pour déterminer la stoechiométrie de l'acétylation chez la levure de fission présentant un mutant d’histone désacétylase. Conformément aux études antérieures impliquant Clr3 et Sir2 dans la régulation de l’hétérochromatine, nous avons observé que les cellules dépourvues de ces HDAC présentaient une augmentation de l'acétylation H3-K14 uniquement sur les peptides coexistant avec H3-K9 di / tri méthylé, une marque caractéristique de l'hétérochromatine. Au chapitre 4, nous décrivons la découverte de très hauts niveaux d'acétylation sur deux résidus de lysine. Nous avons trouvé qu'une stoechiométrie élevée d’acétylation à H3-K14 et H3-K23 et une faible stoechiométrie d’acétylation à H3-K9 et H3-K18 est un profil global de H3 conservé sur le plan évolutif d’acétylation. En utilisant des souches de levures de fission (S. pombe) où la seule source de gènes d'histone porte des mutations H3-K14R et / ou H3-K23R qui empêchent l'acétylation, nous avons démontré que H3-K14 et H3-K23 ont des fonctions distinctes. De façon surprenante, nous avons trouvé que les phénotypes observés dans les cellules mutantes H3-K14R sont largement dus à la mutation du résidu lysine, plutôt qu'à la perte d'acétylation. En utilisant des souches de S. pombe dépourvues d'histone acétyltransférases (HAT), nous avons identifié les acétyltransférases qui contribuent à H3-K14ac et à H3-K23ac in vivo. Très peu d'études ont cherché à déterminer spécifiquement les stoechiométries d'acétylation des histones. Nos résultats suggèrent qu’en moyenne, sur l'ensemble du génome, chaque deuxième ou troisième nucléosome contient une molécule H3 avec une acétylation K14 et / ou K23. Cela nous amène à penser que l'acétylation de l'histone H3 à l'échelle du génome peut jouer un rôle important dans la fonction chromosomique. Il est impératif de comprendre la signification fonctionnelle de ce modèle d'acétylation étant donné que la thérapie épigénétique est activement étudiée comme stratégie pour traiter de nombreuses maladies.The eukaryotic genome is packaged into a highly ordered chromatin structure. Even though chromatin structure is important for maintaining genomic integrity, it is a barrier to numerous DNA-based processes such as DNA replication, transcription and DNA repair. Histones contain a bewildering diversity of covalent modifications that are mostly but not exclusively concentrated within their amino-terminal tails. Histone modifications play a central role in regulating chromatin structure and function. However, determining the stoichiometry of site-specific modifications, identifying patterns of modifications and establishing their physiological roles remain formidable challenges. In this study, we exploited mass spectrometry to determine the stoichiometry of acetylation at specific histone lysine residues. In general, histone lysine residues lacking acetylation are derivatized to render the resulting peptides chemically equivalent but distinguishable by mass from their acetylated counterparts. However, this method is insufficient to study peptides that contain more than one acetylatable lysine, such as those derived from the N-terminal tail of histones H3 and H4. Tryptic digestion of such peptides generates ‘positional isomers’, isomers that have the same mass but bearing acetyl groups located at different positions. Accurate quantification of site-specific acetylation in those peptides is, therefore, a major analytical challenge. In the second chapter, we describe a novel method for quantifying site-specific acetylation of co-eluting isomeric and isobaric peptides that combines high-resolution LC-MS/MS data with a novel bioinformatics algorithm, Iso-PeptidAce. Using tandem mass spectra (MS/MS) of synthetic peptides, fragmentation products diagnostic of each positional isomer were identified and were used to deconvolute spectra that arise from mixtures of positional isomers and quantify the abundance of each isomer. We then tested the applicability of Iso-PeptidAce to quantify time-dependent increases in histone acetylation of K562 erythroleukaemia cells treated with histone deacetylase (HDAC) inhibitors. Using our method, we also found that histones H3 and H4 associated with CAF-1, a chromatin assembly factor, have a high stoichiometry of acetylation on multiple residues of H3 and H4, compared with total histones. In Chapter 3, we applied Iso-PeptidAce to determine the stoichiometry of acetylation in fission yeast histone deacetylase mutants. Consistent with previous reports implicating Clr3 and Sir2 in heterochromatin function, we observed that cells lacking these HDACs showed an increase in H3-K14 acetylation only on those peptides where it co-exists with di/trimethylated H3-K9, a mark of heterochromatin. In chapter 4, we describe the discovery of very high levels of acetylation on two lysine residues. We found that a high stoichiometry of acetylation at H3-K14 and H3-K23, and low stoichiometry of acetylation at H3-K9 and H3-K18, is an evolutionarily conserved global pattern of H3 acetylation. Using fission yeast (S. pombe) strains harboring histone mutations H3-K14R and/or H3-K23R that prevent acetylation, we demonstrate that H3-K14 and H3-K23 have separable functions. Surprisingly, we found that the phenotypes observed in H3-K14R mutant cells are largely due to mutation of the lysine residue, rather than loss of acetylation. Using S. pombe strains that lack histone acetyltransferases (HATs) we identified the acetyltransferases that contribute to H3-K14ac and H3-K23ac in vivo. Very few studies have aimed at specifically determining the acetylation stoichiometries of histones. Our results suggest that, on average, over the entire genome, every second or third nucleosome contains an H3 molecule with K14 and/or K23 acetylation. This leads us to surmise that genome-wide acetylation of histone H3 may have an important role in chromosome function. It is imperative to understand the functional significance of this acetylation pattern given that epigenetic therapy is actively pursued as a strategy to treat many diseases

Non-coding RNA regulatory networks

It is well established that the vast majority of human RNA transcripts do not encode for proteins and that non-coding RNAs regulate cell physiology and shape cellular functions. A subset of them is involved in gene regulation at different levels, from epigenetic gene silencing to post-transcriptional regulation of mRNA stability. Notably, the aberrant expression of many non-coding RNAs has been associated with aggressive pathologies. Rapid advances in network biology indicates that the robustness of cellular processes is the result of specific properties of biological networks such as scale-free degree distribution and hierarchical modularity, suggesting that regulatory network analyses could provide new insights on gene regulation and dysfunction mechanisms. In this study we present an overview of public repositories where non-coding RNA-regulatory interactions are collected and annotated, we discuss unresolved questions for data integration and we recall existing resources to build and analyse networks

Determining Protein Complex Connectivity Using a Probabilistic Deletion Network Derived from Quantitative Proteomics

Protein complexes are key molecular machines executing a variety of essential cellular processes. Despite the availability of genome-wide protein-protein interaction studies, determining the connectivity between proteins within a complex remains a major challenge. Here we demonstrate a method that is able to predict the relationship of proteins within a stable protein complex. We employed a combination of computational approaches and a systematic collection of quantitative proteomics data from wild-type and deletion strain purifications to build a quantitative deletion-interaction network map and subsequently convert the resulting data into an interdependency-interaction model of a complex. We applied this approach to a data set generated from components of the Saccharomyces cerevisiae Rpd3 histone deacetylase complexes, which consists of two distinct small and large complexes that are held together by a module consisting of Rpd3, Sin3 and Ume1. The resulting representation reveals new protein-protein interactions and new submodule relationships, providing novel information for mapping the functional organization of a complex

Learning the Regulatory Code of Gene Expression

Data-driven machine learning is the method of choice for predicting molecular phenotypes from nucleotide sequence, modeling gene expression events including protein-DNA binding, chromatin states as well as mRNA and protein levels. Deep neural networks automatically learn informative sequence representations and interpreting them enables us to improve our understanding of the regulatory code governing gene expression. Here, we review the latest developments that apply shallow or deep learning to quantify molecular phenotypes and decode the cis-regulatory grammar from prokaryotic and eukaryotic sequencing data. Our approach is to build from the ground up, first focusing on the initiating protein-DNA interactions, then specific coding and non-coding regions, and finally on advances that combine multiple parts of the gene and mRNA regulatory structures, achieving unprecedented performance. We thus provide a quantitative view of gene expression regulation from nucleotide sequence, concluding with an information-centric overview of the central dogma of molecular biology

유전체 서열 분석에서 고차 관계의 진화적 기계학습

학위논문 (박사)-- 서울대학교 대학원 : 협동과정 생물정보학전공, 2014. 2. 장병탁.One of the basic research goals in life science is to understand the complex relationships between biological factors and phenotypes, and to identify the various factors affecting the phenotype. In particular, genomic sequences play a significant role in determining the phenotype, such as gene expression and a susceptibility to disease, so the studies for the fundamental information stored in genome is essential to understanding biological processes. Previous genomic sequence analyses mainly focused on identification of a single associated factor or pairwise relationships with significant effects. Recent development of high-throughput technologies has made it possible to identify the causal factors by carrying out genome-wide analysis. However, it still remains as a challenge to discover higher-order interactions of multiple factors because this involves huge search spaces and computational costs. In this dissertation, we develop effective methods for identifying the higher-order relationships of sequence elements affecting the phenotype, by combining statistical learning with evolutionary computation. The methods are applied to finding the associated combinatorial factors and dysfunctional modules in various genome-wide sequence analysis problems. Firstly, we show statistical learning-based methods to detect co-regulatory sequence motifs and to investigate combinatorial effects of DNA methylation, affecting on downstream gene expression. Next, to examine the sequence datasets with a huge number of attributes on human genome, we apply evolutionary computation approaches. Our methods search the problem feature space based on machine learning techniques using training datasets in evolutionary computation processes and are able to find candidate solution well in computationally expensive optimization problems. The experimental results show that the approaches are useful to find the higher-order relationships associated to disease using genomic and epigenomic datasets. In conclusion, our studies would provide practical methods to analyze complex interactions among sequence elements in genomic/epigenomic studies.Abstract i 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Organization of the dissertation . . . . . . . . . . . . . . . . . . . . . 7 2 Genome biology and computational analysis 9 2.1 Fundamentals of genome biology . . . . . . . . . . . . . . . . . . . . 9 2.1.1 DNA, gene, chromosomes and cell biology . . . . . . . . . . . 9 2.1.2 Gene expression and regulation . . . . . . . . . . . . . . . . . 10 2.1.3 Genomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1.4 Epigenomics . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Evolutionary machine learning . . . . . . . . . . . . . . . . . . . . . 13 2.2.1 Machine learning and evolutionary computation . . . . . . . 13 2.2.2 Evolutionary computation in biology . . . . . . . . . . . . . . 13 3 Identifying co-regulatory sequence motifs 16 3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.2.1 Investigation of the relationship between regulatory sequence motifs and expression prolfies . . . . . . . . . . . . . . . . . . 18 3.2.2 Preparation of the gene expression datasets . . . . . . . . . . 21 3.2.3 Preparation of the gene sequence datasets . . . . . . . . . . . 22 3.2.4 Measurement of the eect of motif combinations . . . . . . . 23 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.3.1 Identication of the relationship between gene expression and known motifs . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.3.2 Identification of cell cycle-related motifs . . . . . . . . . . . . 28 3.3.3 Combinational effects of regulatory motifs . . . . . . . . . . . 30 3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4 Investigation of combinatorial eects of DNA methylation 35 4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.2.2 Proling of DNA methylation patterns . . . . . . . . . . . . . 39 4.2.3 Identifying differentially methylated/expressed genes by information theoretic analysis . . . . . . . . . . . . . . . . . . . . 39 4.2.4 Identifying downregulated genes in each subtype for integrative analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.2.5 Correlation between DNA methylation and gene expression . 41 4.2.6 Combinatorial effects of DNA methylation in various genomic regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.2.7 Analysis of transcription factor binding regions possibly blocked by DNA methylation . . . . . . . . . . . . . . . . . . . . . . . 43 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.3.1 DNA methylation in 30 ICBP cell lines . . . . . . . . . . . . 44 4.3.2 Information theoretic analysis of phenotype-differentially methylated and expressed genes . . . . . . . . . . . . . . . . . . . . 45 4.3.3 Integrated analysis of DNA methylation and gene expression 47 4.3.4 Investigation of the combinatorial eects of DNA methylation in various regions on downstream gene expression levels . . . 52 4.3.5 Integrative analysis of transcription factors, DNA methylation and gene expression . . . . . . . . . . . . . . . . . . . . . . . 56 4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5 Detecting multiple SNP interaction via evolutionary learning 63 5.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . 65 5.2.1 Identifying higher-order interaction of SNPs . . . . . . . . . . 65 5.2.2 Algorithm Description . . . . . . . . . . . . . . . . . . . . . . 66 5.2.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 5.3.1 Identifying interaction between features in simulation data . 72 5.3.2 Identifying higher-order SNP interactions in Korean population 74 5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6 Identifying DNA methylation modules by probabilistic evolution- ary learning 85 6.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 6.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 6.2.1 Evolutionary learning procedure to identify a set of DNA methylation sites associated to disease . . . . . . . . . . . . . . . . 87 6.2.2 Learning dependency graph . . . . . . . . . . . . . . . . . . . 88 6.2.3 Fitness evaluation in population . . . . . . . . . . . . . . . . 90 6.2.4 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6.3.1 DNA methylation modules associated to breast cancer . . . 92 6.3.2 Modules associated to colorectal cancer using high-throughput sequencing data . . . . . . . . . . . . . . . . . . . . . . . . . . 96 6.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 7 Conclusion 104 Bibliography 106 초록 133Docto