Telomeres are repeated sequences at the ends of eukaryotic chromosomes that play prominent role in normal aging and disease development. They are dynamic structures that normally shorten over the lifespan of a cell, but can be elongated in cells with high proliferative capacity. Telomere elongation in stem cells is an advantageous mechanism that allows them to maintain the regenerative capacity of tissues, however, it also allows for survival of cancer cells, thus leading to development of malignancies.
Numerous studies have been conducted to explore the role of telomeres in health and disease. However, the majority of these studies have focused on consequences of extreme shortening of telomeres that lead to telomere dysfunction, replicative arrest or chromosomal instability. Very few studies have addressed the regulatory roles of telomeres, and the association of genomic, transcriptomic and epigenomic characteristics of a cell with telomere length dynamics. Scarcity of such studies is partially conditioned by the low-throughput nature of experimental approaches for telomere length measurement and the fact that they do not easily integrate with currently available high-throughput data.
In this thesis, we have attempted to build algorithms, in silico pipelines and software packages to utilize high-throughput –omics data for telomere biology research. First, we have developed a software package Computel, to compute telomere length from whole genome next generation sequencing data. We show that it can be used to integrate telomere length dynamics into systems biology research. Using Computel, we have studied the association of telomere length with genomic variations in a healthy human population, as well as with transcriptomic and epigenomic features of lung cancers.
Another aim of our study was to develop in silico models to assess the activity of telomere maintenance machanisms (TMM) based on gene expression data. There are two main TMMs: one based on the catalytic activity of ribonucleoprotein complex telomerase, and the other based on recombination events between telomeric sequences. Which type of TMM gets activated in a cancer cell determines the aggressiveness of the tumor and the outcome of the disease. Investigation into TMM mechanisms is valuable not only for basic research, but also for applied medicine, since many anticancer therapies attempt to inhibit the TMM in cancer cells to stop their growth. Therefore, studying the activation mechanisms and regulators of TMMs is of paramount importance for understanding cancer pathomechanisms and for treatment. Many studies have addressed this topic, however many aspects of TMM activation and realization still remain elusive. Additionally, current data-mining pipelines and functional annotation approaches of phenotype-associated genes are not adapted for identification of TMMs. To overcome these limitations, we have constructed pathway networks for the two TMMs based on literature, and have developed a methodology for assessment of TMM pathway activities from gene expression data. We have described the accuracy of our TMM-based approach on a set of cancer samples with experimentally validated TMMs. We have also applied it to explore TMM activity states in lung adenocarcinoma cell lines.
In summary, recent developments of high-throughput technologies allow for production of data on multiple levels of cellular organization – from genomic and transcriptiomic to epigenomic. This has allowed for rapid development of various directions in molecular and cellular biology. In contrast, telomere research, although at the heart of stem cell and cancer studies, is still conducted with low-throughput experimental approaches. Here, we have attempted to utilize the huge amount of currently accumulated multi-omics data to foster telomere research and to bring it to systems biology scale.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:16297 |
| Date | 20 September 2017 |
| Creators | Nersisyan, Lilit |
| Contributors | Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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