| 1. |
- Wåhlén, Karin, 1986-
(författare)
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The pain profile in fibromyalgia : Painomic studies of pain characteristics and proteins in blood
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Chronic widespread pain (CWP), including fibromyalgia (FM), is a complex pain condition, where little is known about the molecular mechanisms contributing to its pathophysiology. To date, there are no established biomarkers for CWP/FM. This thesis has investigated potential molecular mechanisms and biomarkers in blood for chronic pain in women with CWP/FM. Furthermore, investigations are made to evaluate whether common pain characteristics such as pain intensity, sensitivity, and psychological distress in CWP/FM are correlated with specific proteins in blood.The pain profile of CWP/FM, which includes the plasma proteome and clinical characteristics, is analyzed using proteomics, advanced multivariate statistics, and bioinformatics. The results from paper I, III, and IV indicate that there are prominent systemic changes related to immunity, inflammation, and metabolic processes in women with CWP/FM compared to healthy controls. Furthermore, paper II and III show that in CWP/FM, pain intensity is related to protein profiles involved in immunity processes, psychological distress with metabolic and immunity processes, and pain sensitivity with inflammatory processes.In paper IV, the plasma proteome is investigated before and after a 15 weeks resistance exercise intervention in FM and healthy controls. Both at baseline and post exercise in FM and controls, prominent protein alterations are found that are involved in immunity, stress, mRNA stability, and muscle structure development. Exercise seems to influence clinical characteristics and circulating proteins in FM. Furthermore, specific plasma proteome profile is found related to grade of chronification, pain sensitivity, and improved muscle force of the quadriceps muscle.To summarize, the results from this thesis suggest that in CWP/FM there might be a dysregulation in the biological processes involved in the immune system and metabolic processes, which are tightly linked to several proteins in the complement system and blood coagulation cascade. These results shed light on potential ongoing mechanisms involved in the pathophysiology of the complex pain condition CWP/FM. This type of biomarker research has a large potential in increasing knowledge about mechanisms involved in CWP/FM and can hereby open for better clinical understanding and management of this and other chronic pain states. The clinical value of collecting a blood sample and measuring stable pain mechanism markers in combination with evaluation of anamnesis and clinical examination would in the future help clinicians and patients receive a faster and more precise diagnosis and ultimately better treatment strategies.
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| 2. |
- Badam, Tejaswi Venkata Satya, 1989-
(författare)
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Omic Network Modules in Complex diseases
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Biological systems encompass various molecular entities such as genes, proteins, and other biological molecules, including interactions among those components. Understanding a given phenotype, the functioning of a cell or tissue, aetiology of disease, or cellular organization, requires accurate measurements of the abundance profiles of these molecular entities in the form of biomedical data. The analysis of the interplay between these different entities at various levels represented in the form of biological network provides a mechanistic understanding of the observed phenotype. In order to study this interplay, there is a requirement of a conceptual and intuitive framework which can model multiple omics such as genome, transcriptome, or a proteome. This can be addressed by application of network-based strategies.Translational bioinformatics deals with the development of analytic and interpretive methods to optimize the transformation of different omics and clinical data to understanding of complex diseases and improving human health. Complex diseases such as multiple sclerosis (MS), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and non-small cell lung cancer (NSCLC) etc., are hypothesized to be a result of a disturbance in the omic networks rendering the healthy cells to be in a state of malfunction. Even though there are numerous methods to layout the relation of the interactions among omics in complex diseases, the output network modules were not clearly interpreted.In this PhD thesis, we showed how different omic data such as transcriptome and methylome can be mapped to the network of interactions to extract highly interconnected gene sets relevant to the disease, so called disease modules. First, we selected common module identification methods and assembled them into a unified framework of the methods implemented in an Rpackage MODifieR (Paper I). Secondly, we showed that the concept of the network modules can be applied on the whole genome sequencing data for developing a tested model for predicting myelosuppressive toxicity (Paper II).Furthermore, we demonstrated that network modules extracted using the methylome data helped identifying several genes that were associated with pregnancy-induced pathways and were enriched for disease-associated methylation changes that were also shared by three auto-immune and inflammatory diseases, namely MS, RA, and SLE (Paper III). Remarkably, those methylation changes correlated with the expected outcome from clinical experience in those diseases. Last, we benchmarked the omic network modules on 19 different complex diseases using both transcriptomic and methylomic data. This led to the identification of a multi-omic MS module that was highly enriched disease-associated genes identified by genome-wide association studies, but also genes associated with the most common environmental risk factors of MS (Paper IV).The application of the network modules concept on different omics is the centrepiece of the research presented in this PhD thesis. The thesis represents the application of omic network modules in complex diseases and how these modules should be integrated and interpreted. In particular, it aimed to show the importance of networks owing to the incomplete knowledge of the genes dysregulated in complex diseases and the contribution of this thesis that provides tools and benchmarks for the methods as well as insights into how a network module can be extracted and interpreted from the omic data in complex diseases.
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| 3. |
- Magnusson, Rasmus, 1992-
(författare)
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High Confidence Network Predictions from Big Biological Data
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Biology functions in a most intriguing fashion, with human cells being regulated by multiplex networks of proteins and their dependent systems that control everything from proliferation to cell death. Notably, there are cases when these networks fail to function properly. In some diseases there are multiple small perturbations that push the otherwise healthy cells into a state of malfunction. These maladies are referred to as complex diseases, and include common disorders such as allergy, diabetes type II, and multiple sclerosis, and due to their complexity there is no universally defined approach to fully understand their pathogenesis or pathophysiology. While these perturbations can be measured using high-throughput technologies, the interplay of these perturbations is generally to complex to understand without any structured mathematical analysis. There is today numerous such methods that put the small perturbations of complex diseases into relation of interactions among each other. However, the methods have historically struggled with notable uncertainty in their predictions.This uncertainty can be addressed by at least two different approaches. First, mechanistically realistic mathematical modelling is an approach that has the capacity to accurately describe almost any biological system, but such models can to-date only describe small systems and networks. Secondly, large-scale mathematical modelling approaches exist, but the faithfulness of the models to the underlying biology has been compromised to achieve algorithms that are computationally effective.In this Ph.D. thesis, I suggest how high confidence predictions of network interactions can be extracted from big biological. First, I show how large-scale data can be used when building high-quality ODE models (Paper I). Secondly, by developing the software LASSIM, I show how ODE models can be expanded to the size of entire cell systems (Paper II). However, while LASSIM showed that powerful non-linear ODE-modelling can be applied to understand big biological data, it still remained a machine learning-based approach in contrast to hypothesis-driven model development.Instead, two more studies revolving around large-scale modelling approaches were initiated. The third study suggested that ambiguities in model selection and interaction identification greatly compromise the accuracy of available tools, and that the novel software of Paper III, LiPLike, can be used to remove such predictions. Intriguingly, while LiPLike was able to effectively discard false identifications, the accuracy of predictions remained relatively low. This low accuracy was thought to arise from model simplifications, and therefore the next study aimed at finding methods that come closer to the true biological system (Paper IV). In particular, the study aimed at predicting protein abundance -the true mediators of biological functionality- from the much more easily accessible mRNA levels, and found that such models could be used to get several new insights on protein mechanisms, which was exemplified by the identification of important biomarkers of autoimmune diseases.The analysis of big biological data and the underlying networks is a centrepiece of understanding both diseases and how cell functionality is orchestrated. The work that is presented in this Ph.D. thesis represents a journey between fields with different views on how these networks should be inferred. In particular, it aimed to combine the accuracy of small-scale mechanistic modelling with the system-spanning potential of large-scale linear system modelling, and this thesis thus provides a tool-bench of methods and insights on how knowledge can be extracted from big biological data, and in extension it is a small step towards a generation of new comprehensions of biological systems and complex diseases.
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| 4. |
- Rundquist, Olof, 1991-
(författare)
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Multi-omic time-series analysis of T-cells as a model for identification of biomarkers, treatments and upstream disease regulators
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- CD4+ T-cell function and their process of differentiation is a central piece of the puzzle in a multitude of diseases. CD4+ T-cells are part of the adaptive immune system and function by directing other immune cells to the site of infection and instructing B-cells to produce antibodies, among many other functions. CD4+ T-cells may differentiate into several different sub-types, such as T-helper 1, 2 and 17, with differing functions within the immune system. T-helper 1 (Th1) cells are most closely associated with the elimination of viral infections but are also associated with autoimmune diseases such as multiple sclerosis (MS) and rheumatoid arthritis (RA). T-cells develop in the thymus first as double-negative T-cells, that express neither CD4 nor CD8, going through multiple development stages before becoming double-positive T-cell that express both CD4 and CD8, before eventually giving rise to single positive CD4+ and CD8+ T-cells. This process of development is under tight control and if this control fails, cancer may result. Once CD4+ T-cells are fully developed, they may specialize as outlined above and if said process is not properly controlled, autoimmunity may result. As such, the proper understanding of these control mechanisms is of great importance for the understanding of diseases of the immune system and the discovery of biomarkers and treatments against said diseases. These control processes are often studied in a singular fashion using one omic technique, e.g., RNA sequencing (RNA-seq), with the assumption that a signal in one omic layer will be reflected in another. Recent studies attempting to integrate multiple omics have however cast doubt on this and it is becoming increasingly apparent that to gain a complete understanding of a system, the system needs to be studied at multiple levels of regulation, i.e., multiple omics.The aim of this thesis was to use multi-omics to investigate the development and differentiation process of CD4+ T-helper cells and relate it to disease mechanisms. To start, we studied T-cell development through the model of T-cell acute lymphoblastic leukaemia (T-ALL). More specifically, we studied the TET2 gene and investigated its importance in T-ALL for treatment susceptibility and mechanism in vitro. TET2 is a demethylase and functions through the removal of cytosine methylation on the DNA, a marker of gene silencing. Through treatment with decitabine, an inhibitor of DNA-methylation, and Vitamin C, a co-factor for TET2, we showed that TET2 deficient cancer cell lines were more vulnerable to treatment targeting DNA methylation and investigated the mechanistic effects of said treatment by RNA sequencing. We then moved on to study primary human naïve CD4+ T-cells and their differentiation into Th1-cells. First, we focused on T-cell activation and its importance to MS to understand the role of T-cells in mediating the lowered disease activity usually observed during pregnancy in MS. This showed that the major pregnancy hormone progesterone significantly dampens T-cell activation, providing a possible explanation for the beneficial effects of pregnancy on MS. Then, using ATAC sequencing (ATAC-seq), RNA-seq and proteomics we studied Th1-differentiation as a time series to elucidate regulatory events throughout the differentiation process and to study their implications for MS with the inclusion of progesterone treatment. The integration of several omic techniques presents unique challenges as one does not necessarily directly translate to the other. As such, we first focused on the integration of RNA-seq and proteomics by designing a model for the prediction of protein abundance from RNA-seq and validated it through biomarker discovery. Next, we focused on the integration of ATAC-seq and RNA-seq using correlation between time series of the two techniques. This thesis provides a thorough investigation of Th1-cell differentiation and its potential involvement in disease. Time series datasets were produced to study gene regulation (ATAC-seq), gene expression (RNA-seq) and protein expression (mass spectrometry) and the work focused on their integration. This profoundly showed that through combining multiple omic techniques it was possible to gain new insights that were not possible to discover with one or the other. Multi-omic analyses are becoming more and more common in medicine today as their power to produce new insight into the complexity of complex diseases is being increasingly recognized. As such, this work forms an important foundation for future discovery of biomarkers and treatments in such diseases.
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| 5. |
- Zenere, Alberto, 1992-
(författare)
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Integration of epigenetic, transcriptomic and proteomic data
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- One of the scopes of Systems Biology is to propose mathematical models that best capture the dynamic behavior of intra-cellular processes. In this regard, the last two decades have brought up a shift in the field, with technological advances now allowing researchers to access a wide range of high-throughput technologies at an affordable cost. These techniques allow to simultaneously interrogate thousands of variables, such as genome-wide transcriptomics and proteomics. However, parallel to these technological advances, there is a growing need for mathematical models that are suited to integrate measurements obtained from different cellular processes.In this thesis we aim to model combinations of three commonly used high-throughput data: epigenetic (namely ATAC-seq and DNA methylation), transcriptomic (RNA-seq) and proteomic data (MASS-spectrometry). In the first work we analyze paired ATAC-seq and RNA-seq data to integrate measurements of (i) chromatin openness, (ii) transcription factors (TFs) availability and (iii) gene expression. To model these data, we use elementary causal motifs, a class of mathematical models which is suited to represent causal interactions between three nodes. Indeed, our analysis shows that the elementary causal motifs in the data are enriched for biologically relevant TF-gene interactions. Moreover, a significant overlap is observed between the causal motifs identified in datasets representing similar cell stimuli, suggesting that causal motifs represent a robust biological signal.This work is then extended to include another class of high-throughput data: MASS-spectrometry. More precisely, we propose a framework to model the flow of events that goes from chromatin remodeling to splice variants expression, and from splice variants to protein synthesis. As the underlying graph becomes more complex than the previous case, a more general mathematical framework is considered: Bayesian networks. Interestingly, this work shows that most putative associations between chromatin regions, splice variants and proteins that have been gathered by scientific community so far, are supported by the data. Moreover, similarly to the previous work, the causal interactions identified in the data highlight relevant biological features; more precisely, causal chains between chromatin regions, splice variants and proteins are enriched for splice variants that have a major role in protein synthesis.From a technical point of view, causal motifs are characterized by a property known as conditional independence, which can be used to identify causal interactions in the data. However, particularly when the data available is limited, it is challenging to assess conditional independencies in the data. It is therefore of interest to investigate the existence of properties that allow us to predict conditional independence. In particular, in our work we propose two properties: structural balance and inverse balance, which are closely connected to what is known in the literature as positive association and multivariate total positivity of order 2 (MTP2), respectively. Our analysis shows that both heuristics are useful in predicting conditional independence, both from a theoretical perspective and in experimental data.Lastly, a network-based approach is used to integrate DNA methylation and RNA-seq in a case-control study centered around multiple sclerosis, in order to identify common regulatory patterns in DNA methylation and gene expression during the course of pregnancy. The strategy is based on the rationale that proteins that are interconnected in the protein-protein network are more likely to be involved in similar cellular functions. Indeed, the analysis highlights that similar pathways are altered at epigenetic and transcriptomic level, leading to a set of genes that are likely involved in the modification of the disease symptoms that is observed during pregnancy.
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| 6. |
- de Weerd, Hendrik Arnold, 1986-
(författare)
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Novel methods and software for disease module inference
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cellular organization is believed to be modular, meaning cellular functions are carried out by modules composed of clusters of genes, proteins and metabolites that are interconnected, co-regulated or physically interacting. In turn, these modules interact together and thereby form complex networks that taken together is considered to be the interactome. Modern high-throughput biological techniques have made high-scale accurate quantification of these biological molecules possible, the so called omics. The simultaneous measurement of these molecules enables a picture of the state of a cell at a resolution that was never before possible. Mapping these measurements aids greatly to elucidate a network structure of interactions. The ever growing size of public repositories for omics data has ushered in the advent of biology as a (big) data science and opens the door for data hungry machine learning approaches in biology. Complex diseases are multi-factorial and arise from a combination of genetic, environmental and lifestyle factors. Additionally, diagnosis and treatment is complicated by the fact that these genetic, environmental and lifestyle factors can vary between patients and may or may not give rise to different disease phenotypes that still classify as the same disease. Genetically, there is substantial heterogeneity among patients and therefore the emergence of a disease phenotype cannot be attributed to a single genetic mutation but rather to a combination of various mutations that may vary from patient to patient. As complex diseases can have different root causes but give rise to a similar disease phenotype, the implication is that different root causes perturb similar components in the interactome. Most of the work in this thesis is aimed at developing methods and computational pipelines to identify, analyze and evaluate these perturbed disease specific sub-networks in the interactome, so called disease modules. We started by collecting popular disease module inference methods and combined them in a unified framework, an R package called MODifieR (Paper I). The package uses standardized inputs and outputs, allowing for a more user-friendly way of running multiple disease module inference methods and the combining of modules. Next, we benchmarked the MODifieR methods on a compendium of transcriptomic and methylomic datasets and combined transcriptomic and methylomic disease modules for Multiple Sclerosis (MS) to a highly disease-relevant module greatly enriched with known risk factors for MS (Paper II). Subsequently, we extended the functionality of MODifieR with software for transcription factor hub detection in gene regulatory networks in a new framework with a graphical user interface, MODalyseR. We used MODalyseR to find upstream regulators and identified IKZF1 as an important upstream regulator for MS (Paper III). Lastly, we used the growing large-scale repositories of gene expression data to train a Variational Auto Encoder (VAE) to compress and decompress gene expression profiles with the aim of extracting disease modules from the latent space. Utilizing the continues nature of the latent space in VAE’s, we derived the differences in latent space representations between a compendium of complex disease gene expression profiles and matched healthy controls. We then derived disease modules from the decompressed latent space representation of this difference and found the modules highly enriched with disease-associated genes, generally outperforming the gold standard of transcriptomic analysis of diseases, top differentially expressed genes (Paper IV). To conclude, the main scientific contribution of this thesis lies in the development of software and methods for improving disease module inference, the evaluation of existing inference methods, the creation of new analysis workflows for multi-omics modules, and the introduction of a deep learning-based approach to the disease module inference toolkit.
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| 7. |
- Gawel, Danuta R., 1988-
(författare)
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Identification of genes and regulators that are shared across T cell associated diseases
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Genome-wide association studies (GWASs) of hundreds of diseases and millions of patients have led to the identification of genes that are associated with more than one disease. The aims of this PhD thesis were to a) identify a group of genes important in multiple diseases (shared disease genes), b) identify shared up-stream disease regulators, and c) determine how the same genes can be involved in the pathogenesis of different diseases. These aims have been tested on CD4+ T cells because they express the T helper cell differentiation pathway, which was the most enriched pathway in analyses of all disease associated genes identified with GWASs.Combining information about known gene-gene interactions from the protein-protein interaction (PPI) network with gene expression changes in multiple T cell associated diseases led to the identification of a group of highly interconnected genes that were miss-expressed in many of those diseases – hereafter called ‘shared disease genes’. Those genes were further enriched for inflammatory, metabolic and proliferative pathways, genetic variants identified by all GWASs, as well as mutations in cancer studies and known diagnostic and therapeutic targets. Taken together, these findings supported the relevance of the shared disease genes.Identification of the shared upstream disease regulators was addressed in the second project of this PhD thesis. The underlying hypothesis assumed that the determination of the shared upstream disease regulators is possible through a network model showing in which order genes activate each other. For that reason a transcription factor–gene regulatory network (TF-GRN) was created. The TF-GRN was based on the time-series gene expression profiling of the T helper cell type 1 (Th1), and T helper cell type 2 (Th2) differentiation from Native T-cells. Transcription factors (TFs) whose expression changed early during polarization and had many downstream predicted targets (hubs) that were enriched for disease associated single nucleotide polymorphisms (SNPs) were prioritised as the putative early disease regulators. These analyses identified three transcription factors: GATA3, MAF and MYB. Their predicted targets were validated by ChIP-Seq and siRNA mediated knockdown in primary human T-cells. CD4+ T cells isolated from seasonal allergic rhinitis (SAR) and multiple sclerosis (MS) patients in their non-symptomatic stages were analysed in order to demonstrate predictive potential of those three TFs. We found that those three TFs were differentially expressed in symptom-free stages of the two diseases, while their TF-GRN{predicted targets were differentially expressed during symptomatic disease stages. Moreover, using RNA-Seq data we identified a disease associated SNP that correlated with differential splicing of GATA3.A limitation of the above study is that it concentrated on TFs as main regulators in cells, excluding other potential regulators such as microRNAs. To this end, a microRNA{gene regulatory network (mGRN) of human CD4+ T cell differentiation was constructed. Within this network, we defined regulatory clusters (groups of microRNAs that are regulating groups of mRNAs). One regulatory cluster was differentially expressed in all of the tested diseases, and was highly enriched for GWAS SNPs. Although the microRNA processing machinery was dynamically upregulated during early T-cell activation, the majority of microRNA modules showed specialisation in later time-points.In summary this PhD thesis shows the relevance of shared genes and up-stream disease regulators. Putative mechanisms of why shared genes can be involved in pathogenesis of different diseases have also been demonstrated: a) differential gene expression in different diseases; b) alternative transcription factor splicing variants may affect different downstream gene target group; and c) SNPs might cause alternative splicing.
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| 8. |
- Gustafsson, Mika, 1977-
(författare)
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Gene networks from high-throughput data : Reverse engineering and analysis
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Experimental innovations starting in the 1990’s leading to the advent of high-throughput experiments in cellular biology have made it possible to measure thousands of genes simultaneously at a modest cost. This enables the discovery of new unexpected relationships between genes in addition to the possibility of falsify existing. To benefit as much as possible from these experiments the new inter disciplinary research field of systems biology have materialized. Systems biology goes beyond the conventional reductionist approach and aims at learning the whole system under the assumption that the system is greater than the sum of its parts. One emerging enterprise in systems biology is to use the high-throughput data to reverse engineer the web of gene regulatory interactions governing the cellular dynamics. This relatively new endeavor goes further than clustering genes with similar expression patterns and requires the separation of cause of gene expression from the effect. Despite the rapid data increase we then face the problem of having too few experiments to determine which regulations are active as the number of putative interactions has increased dramatic as the number of units in the system has increased. One possibility to overcome this problem is to impose more biologically motivated constraints. However, what is a biological fact or not is often not obvious and may be condition dependent. Moreover, investigations have suggested several statistical facts about gene regulatory networks, which motivate the development of new reverse engineering algorithms, relying on different model assumptions. As a result numerous new reverse engineering algorithms for gene regulatory networks has been proposed. As a consequent, there has grown an interest in the community to assess the performance of different attempts in fair trials on “real” biological problems. This resulted in the annually held DREAM conference which contains computational challenges that can be solved by the prosing researchers directly, and are evaluated by the chairs of the conference after the submission deadline.This thesis contains the evolution of regularization schemes to reverse engineer gene networks from high-throughput data within the framework of ordinary differential equations. Furthermore, to understand gene networks a substantial part of it also concerns statistical analysis of gene networks. First, we reverse engineer a genome-wide regulatory network based solely on microarray data utilizing an extremely simple strategy assuming sparseness (LASSO). To validate and analyze this network we also develop some statistical tools. Then we present a refinement of the initial strategy which is the algorithm for which we achieved best performer at the DREAM2 conference. This strategy is further refined into a reverse engineering scheme which also can include external high-throughput data, which we confirm to be of relevance as we achieved best performer in the DREAM3 conference as well. Finally, the tools we developed to analyze stability and flexibility in linearized ordinary differential equations representing gene regulatory networks is further discussed.
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| 9. |
- Lentini, Antonio, 1990-
(författare)
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Dynamic regulation of DNA methylation in human T-cell biology
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- T helper cells play a central role in orchestrating immune responses in humans. Upon encountering a foreign antigen, T helper cells are activated followed by a differentiation process where the cells are specialised to help combating the infection. Dysregulation of T helper cell activation, differentiation and function has been implicated in numerous diseases, including autoimmunity and cancer. Whereas gene-regulatory networks help drive T-cell differentiation, acquisition of stable cell states require heritable epigenetic signals, such as DNA methylation. Indeed, the establishment of DNA methylation patterns is a key part of appropriate T-cell differentiation but how this is regulated over time remains unknown. Methylation can be directly attached to cytosine residues in DNA to form 5-methylcytosine (5mC) but the removal of DNA methylation requires multiple enzymatic reactions, commonly initiated by the conversion into 5-hydroxymethylcytosine (5hmC), thus creating a highly complex regulatory system. This thesis aimed to investigate how DNA methylation is dynamically regulated during T-cell differentiation.To this end, we employed large-scale profiling techniques combining gene expression as well as genome-wide 5mC and 5hmC measurements to construct a time-series model of epigenetic regulation of differentiation. This revealed that early T-cell activation was accompanied by extensive genome-wide deposition of 5hmC which resulted in demethylation upon proliferation. Early DNA methylation remodelling through 5hmC was not only indicative of demethylation events during T-cell differentiation but also marked changes persisting longterm in memory T-cell subsets. These results suggest that priming of epigenetic landscapes in T-cells is initiated during early activation events, preceding any establishment of a stable lineage, which are then maintained throughout the cells lifespan. The regions undergoing remodelling were also highly enriched for genetic variants in autoimmune diseases which we show to be functional through disruption of protein binding. These variants could potentially disrupt gene-regulatory networks and the establishment of epigenetic priming, highlighting the complex interplay between genetic and epigenetic layers. In the course of this work, we discovered that a commonly used technique to study genome-wide DNA modifications, DNA immunoprecipitation (DIP)-seq, had a false discovery rate between 50-99% depending on the modification and cell type being assayed. This represented inherent technical errors related to the use of antibodies resulting in off-target binding of repetitive sequences lacking any DNA modifications. These sequences are common in mammalian genomes making robust detection of rare DNA modifications very difficult due to the high background signals. However, offtarget binding could easily be controlled for using a non-specific antibody control which greatly improved data quality and biological insight of the data. Although future studies are advised to use alternative methods where available, error correction is an acceptable alternative which will help fuel new discoveries through the removal of extensive background signals.Taken together, this thesis shows how integrative use of high-resolution epigenomic data can be used to study complex biological systems over time as well as how these techniques can be systematically characterised to identify and correct errors resulting in improved detection.
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| 10. |
- Åkesson, Julia, 1994-
(författare)
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Network-based biomarker discovery for multiple sclerosis
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Complex autoimmune diseases, such as multiple sclerosis (MS), develop as a result of perturbations in the regulatory system controlling the function of immune cells. The disease course of MS is heterogeneous but is characterised by chronic inflammation in the central nervous system causing neurodegeneration resulting in gradual disability worsening. Disease biomarkers which are present at early stages of a disease can help clinicians to tailor treatment strategies to the expected disease course of individual persons. Gene products, i.e. RNA and proteins, serve as promising disease biomarkers due to the possibility to detect changes in abundance at early stages of a disease. Putative biomarkers can be identified by modelling different levels of gene regulation from high-throughput measurements of gene product abundance. Extracting information of disease relevance from high-throughput data is a complex problem which requires the use of efficient and targeted computational algorithms. The aim of this thesis was to develop and refine methods for identifying key biomarkers involved in the development and progression of complex diseases, with the main focus on MS. In Paper I, we used a machine learning approach to identify a combination of protein biomarkers, present in the cerebrospinal fluid, which could predict the disease trajectory of persons in the early stages of MS. The abundance of proteins is a result of an intricate network of multiple regulatory factors controlling the expression of genes. A large part of the expression of genes is controlled by a few key regulators, which are believed to be crucial for the development of diseases. In addition, disease-associated genes are believed to colocalise in these networks forming so called disease modules. In Paper II, we developed a method, named ComHub, for extracting the key regulators of gene expression. In Paper III, we combined ComHub with the tool MODifieR, for disease module predictions, in a network analysis pipeline for identifying a limited set of disease-associated genes. Using this network analysis pipeline we identified a set of MS-associated genes, as well as a promising key regulator of MS. The work performed in this doctoral thesis covers development of new and refined methods for modelling complex diseases, while simultaneously utilising these methods to identify disease biomarkers important for the development and progression of MS. The identified biomarkers can be used for understanding the pathology of MS, as candidate drug targets, and as promising biomarkers to aid clinicians in tailoring treatment strategies to individual persons.
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