Elucidating regulatory elements : studies in chronic lymphocytic leukemia and multiple myeloma
Sammanfattning: With next generation sequencing taking center stage in genetic and epigenetic research, its applications and challenges are many. This work revolves around the application of bioinformatics in different contexts: basic research in the understanding of diseases (biology), the effect of treatment on the target cells (clinics) and the assessment of a new wet-lab method (lab). Biology. Two studies fall under this topic, one on chronic lymphocytic leukemia, the other on multiple myeloma. Many coding mutations and chromosomal aberrations have long been identified in both diseases, yet they are only present in subsets of patients, and so it is puzzling that all this diversity results in a single diagnosis. We hypothesized that instead of a common genetic background, they might present with a common epigenetic background. For this we aimed to collect paired RNA-seq, histone ChIP-seq and ATAC-seq for patients and healthy controls, and had the following specific hypotheses: 1. Using H3K4me2 and H3K27Ac, we will be able to identify the regulatory elements altered between health and disease. 2. By looking at the interplay between those regulatory elemets, RNA-seq, ATAC-seq and database information, we will be able to describe the aberrant regulation in terms of transciption factors, regulatory elements and their target genes. Clinics Ibrutinib is a novel drug used in chronic lymphocytic leukemia treatment. Though it is shown to be beneficial to many patients, a lot of the early effects the treatment has on the malignant cells, especially in different parts of the body, are still unknown. We hypothesized that relevant changes in blood and lymph nodes would be visible soon after treatment, and collected RNA-seq data from both compartments, in additions to plasma values of inflammatory cytokines. We had to following specific hypotheses: 1. Early changes are visible, maybe even just hours after first treatment. 2. There will be differences in treatment effect between blood and lymph node compartments. Lab ChIP-seq is a very useful method to look at proteins bound to DNA, which, depending on the protein checked, can give a multitude of information. Yet, the amount of cells needed to perform these experiments is high, even in improved protocols like ChIPmentation. We hypothesized that the ChIPmentation protocol could be optimized, and that reducing time and steps would yield better data. For this we performed ChIP-seq with the original ChIPmentation protocol and with our adaptation, testing the following specific hypotheses: 1. Our version, high-throughput ChIPmentation, will perform equally well as the original method when high cell numbers are used, but be faster. 2. When low cell numbers are used, our method will give better results, as it involves less loss of material. My contribution lies in the development and execution of bioinformatics, pipelines, data handling etc, to test these hypotheses, which also includes discussions and planning of projects, samples and feasibility.
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