Genome-wide analysis reveals extensive functional interaction between DNA replication initiation and transcription in the genome of trypanosoma brucei

Identification of replication initiation sites, termed origins, is a crucial step in understanding genome transmission in any organism. Transcription of the Trypanosoma brucei genome is highly unusual, with each chromosome comprising a few discrete transcription units. To understand how DNA replication occurs in the context of such organization, we have performed genome-wide mapping of the binding sites of the replication initiator ORC1/CDC6 and have identified replication origins, revealing that both localize to the boundaries of the transcription units. A remarkably small number of active origins is seen, whose spacing is greater than in any other eukaryote. We show that replication and transcription in T. brucei have a profound functional overlap, as reducing ORC1/CDC6 levels leads to genome-wide increases in mRNA levels arising from the boundaries of the transcription units. In addition, ORC1/CDC6 loss causes derepression of silent Variant Surface Glycoprotein genes, which are critical for host immune evasion

Visual Analysis of Form and Function in Computational Biology

In the last years, the amount of available data in the field of computational biology steadily increased. In order to be able to analyze these data, various algorithms have been developed by bioinformaticians to process them efficiently. Moreover, computational models were developed to predict for instance biological relationships of species. Furthermore, the prediction of properties like the structure of certain biological molecules is modeled by complex algorithms. Despite these advances in handling such complicated tasks with automated workflows and a huge variety of freely available tools, the expert still needs to supervise the data analysis pipeline inspecting the quality of both the input data and the results. Additionally, choosing appropriate parameters of a model is quite involved. Visual support puts the expert into the data analysis loop by providing visual encodings of the data and the analysis results together with interaction facilities. In order to meet the requirements of the experts, the visualizations usually have to be adapted for the application purpose or completely new representations have to be developed. Furthermore, it is necessary to combine these visualizations with the algorithms of the experts to prepare the data. These in-situ visualizations are needed due to the amount of data handled within the analysis pipeline in this domain. In this thesis, algorithms and visualizations are presented that were developed in two different research areas of computational biology. On the one hand, the multi-replicate peak-caller Sierra Platinum was developed, which is capable of predicting significant regions of histone modifications occurring in genomes based on experimentally generated input data. This algorithm can use several input data sets simultaneously to calculate statistically meaningful results. Multiple quality measurements and visualizations were integrated into to the data analysis pipeline to support the analyst. Based on these in-situ visualizations, the analyst can modify the parameters of the algorithm to obtain the best results for a given input data set. Furthermore, Sierra Platinum and related algorithms were benchmarked against an artificial data set to evaluate the performance under specific conditions of the input data set, e.g., low read quality or undersequenced data. It turned out that Sierra Platinum achieved the best results in every test scenario. Additionally, the performance of Sierra Platinum was evaluated with experimental data confirming existing knowledge. It should be noticed that the results of the other algorithms seemed to contradict this knowledge. On the other hand, this thesis describes two new visualizations for RNA secondary structures. First, the interactive dot plot viewer iDotter is described that is able to visualize RNA secondary structure predictions as a web service. Several interaction techniques were implemented that support the analyst exploring RNA secondary structure dot plots. iDotter provides an API to share or archive annotated dot plots. Additionally, the API enables the embedding of iDotter in existing data analysis pipelines. Second, the algorithm RNApuzzler is presented that generates (outer-)planar graph drawings for all RNA secondary structure predictions. Previously presented algorithms failed in always producing crossing-free graphs. First, several drawing constraints were derived from the literature. Based on these, the algorithm RNAturtle was developed that did not always produced planar drawings. Therefore, some drawing constraints were relaxed and additional drawing constraints were established. Building on these modified constraints, RNApuzzler was developed. It takes the drawing generated by RNAturtle as an input and resolves the possible intersections of the graph. Due to the resolving mechanism, modified loops can become very large during the intersection resolving step. Therefore, an optimization was developed. During a post-processing step the radii of the heavily modified loops are reduced to a minimum. Based on the constraints and the intersection resolving mechanism, it can be shown that RNApuzzler is able to produce planar drawings for any RNA secondary structure. Finally, the results of RNApuzzler are compared to other algorithms

Programming and reprogramming cellular identity

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2008.Includes bibliographical references.Every cell in the human body contains the same genetic information, with few exceptions, yet each cell type enacts a distinct gene expression program to allow for highly specialized functions. These tightly controlled programs are the results of transcriptional regulation, by transcription factors and chromatin regulators, as well as post-transcriptional regulation, mediated in part by microRNAs (miRNAs). Additionally, cells must respond to external cues, and signal transduction pathways converge on gene regulatory machinery to shape cellular identity. The work presented here focuses on the mechanisms by which transcription factors, chromatin regulators, miRNAs and signal transduction pathways coordinately regulate two particular medically important gene expression programs: (1) the program that controls pluripotency in embryonic stem (ES) cells, giving these cells the capacity to differentiate into every adult cell type, and (2) the program that allows regulatory T (Treg) cells to prevent autoimmunity by suppressing the response of self-reactive conventional T cells. Genomic investigations of the core regulatory circuitry of each of these cells types presented here provide new insight into the genetics of pluripotency and autoimmunity, and suggest a strategy for reprogramming based on chemical manipulation of the cellular programs that control cell identity.by Alexander Marson.Ph.D

Dissecting the transcriptional regulatory network of embryonic stem cells

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2008.Includes bibliographical references.The process by which a single fertilized egg develops into a human being with over 200 cell types, each with a distinct gene expression pattern controlling its cellular state, is poorly understood. An understanding of the transcriptional regulatory networks that establish and maintain gene expression programs in mammalian cells is fundamental to understand development and should provide the foundation for improved diagnosis and treatment of disease. Although it is not yet feasible to map the entirety of these networks in vertebrate cells, recent work in embryonic stem (ES) cells has demonstrated that core features of the network can be discovered by focusing on key transcriptional regulators and their target genes. Here, I describe important insights that have emerged from such studies and highlight how similar approaches can be used to discover the core networks of other vertebrate cell types. Knowledge of the regulatory networks controlling gene expression programs and cell states can guide efforts to reprogram cell states and holds great promise for both disease therapeutics and regenerative medicine.by Megan F. Cole.Ph.D

Genetic screening and molecular characterisation of biomarkers in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of liver cancer that accounts for 4.7% of the total number of new cases of cancer worldwide every year. HCC is a highly heterogeneous and complex disease with an estimated 5-year survival rate of only 18%. A better understanding of the mechanisms involved in the development, progression and recurrence of this tumour could not only guide us in the improvement of preventive strategies but also in the expansion of alternative target therapies for HCC patients. The aim of this thesis is to investigate new diagnostic and prognostic markers, both on genetic and molecular levels, in the context of HCC. The results section is divided in two, called Chapter I and Chapter II. HCC presents a distinct mutational landscape and Chapter I describes how we developed a HCC-specific custom made sequencing panel, containing the genes most commonly affected by somatic mutations and copy number alterations (CNAs) in the disease. We created a panel that was tested in different kinds of patient biopsies: frozen tissues, formalin-fixed paraffin-embedded (FFPE) tissues and also liquid biopsies. Moreover, to have reliable and reproducible sequencing data, we created a solid and user friendly somatic variant calling pipeline specific for Ion Torrent sequencing data. In Chapter II, we aimed to investigate the molecular mechanism of HMGA1 in HCC and to explore its molecular targets. HMGA1 is an architectural transcription factor that was found often overexpressed in HCCs. We explored its DNA-binding landscape and, after deregulating HMGA1 in a HCC in vitro environment, its expression signature both at the RNA and protein levels. With the analysis of the binding partners of HMGA1, we recognised the vast range of mechanisms of action of this complex protein. We identified several RNA regulators that bind HMGA1, including Alyref, which plays a role in the regulation of the transcription. Further work should aim to determine the non-canonical role of HMGA1 involved in the binding and the regulation not only at the DNA but also at the RNA level. Both chapters describe the steps of this work on the identification and the functional understanding of HCC biomarkers. This may lead in the future to more individualised treatment approaches, a need that in cancers with low survival rate such as HCC is not only highly desirable but is also a necessity