| 1. |
- Gawel, Danuta R., 1988-
(författare)
-
Identification of genes and regulators that are shared across T cell associated diseases
- 2018
-
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.
|
|
| 2. |
- Lentini, Antonio, 1990-
(författare)
-
Dynamic regulation of DNA methylation in human T-cell biology
- 2019
-
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.
|
|
| 3. |
- Rundquist, Olof, 1991-
(författare)
-
Multi-omic time-series analysis of T-cells as a model for identification of biomarkers, treatments and upstream disease regulators
- 2021
-
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.
|
|
| 4. |
- Badam, Tejaswi Venkata Satya, 1989-
(författare)
-
Omic Network Modules in Complex diseases
- 2021
-
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.
|
|
| 5. |
- Björn, Niclas, 1990-, et al.
(författare)
-
Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients
- 2020
-
Ingår i: npj Systems Biology and Applications. - : Nature Publishing Group. - 2056-7189. ; 6:1
-
Tidskriftsartikel (refereegranskat)abstract
- Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression. Association of genetic variants in PLINK found 4594, 5019, and 5066 autosomal SNVs/INDELs with p ≤ 1 × 10−3 for neutropenia, leukopenia, and thrombocytopenia, respectively. Based on the SNVs/INDELs we identified the toxicity module, consisting of 215 unique overlapping genes inferred from MCODE-generated gene network modules of 350, 345, and 313 genes, respectively. These module genes showed enrichment for differentially expressed genes in rat bone marrow, human bone marrow, and human cell lines exposed to carboplatin and gemcitabine (p < 0.05). Then using 80% of the patients as training data, random LASSO reduced the number of SNVs/INDELs in the toxicity module into a feasible prediction model consisting of 62 SNVs/INDELs that accurately predict both the training and the test (remaining 20%) data with high (CTCAE 3–4) and low (CTCAE 0–1) maximal myelosuppressive toxicity completely, with the receiver-operating characteristic (ROC) area under the curve (AUC) of 100%. The present study shows how WGS, gene network modules, and random LASSO can be used to develop a feasible and tested model for predicting myelosuppressive toxicity. Although the proposed model predicts myelosuppression in this study, further evaluation in other studies is required to determine its reproducibility, usability, and clinical effect.
|
|
| 6. |
- Bruhn, Sören, 1976-, et al.
(författare)
-
Combining gene expression microarray- and cluster analysis with sequence-based predictions to identify regulators of IL-13 in allergy
- 2012
-
Ingår i: Cytokine. - : Elsevier. - 1043-4666 .- 1096-0023. ; 60:3, s. 736-740
-
Tidskriftsartikel (refereegranskat)abstract
- The Th2 cytokine IL-13 plays a key role in allergy, by regulating IgE, airway hyper secretion, eosinophils and mast cells. In this study, we aimed to identify novel transcription factors (TFs) that potentially regulated IL-13. We analyzed Th2 polarized naïve T cells from four different blood donors with gene expression microarrays to find clusters of genes that were correlated or anti-correlated with IL13. These clusters were further filtered, by selecting genes that were functionally related. In these clusters, we identified three transcription factors (TFs) that were predicted to regulate the expression of IL13, namely CEBPB, E2F6 and AHR. siRNA mediated knockdowns of these TFs in naïve polarized T cells showed significant increases of IL13, following knockdown of CEBPB and E2F6, but not AHR. This suggested an inhibitory role of CEBPB and E2F6 in the regulation of IL13 and allergy. This was supported by analysis of E2F6, but not CEBPB, in allergen-challenged CD4+ T cells from six allergic patients and six healthy controls, which showed decreased expression of E2F6 in patients. In summary, our findings indicate an inhibitory role of E2F6 in the regulation of IL-13 and allergy. The analytical approach may be generally applicable to elucidate the complex regulatory patterns in Th2 cell polarization and allergy.
|
|
| 7. |
- Das, Jyotirmoy, et al.
(författare)
-
Identification of DNA methylation patterns predisposing for an efficient response to BCG vaccination in healthy BCG-naive subjects
- 2019
-
Ingår i: Epigenetics. - : TAYLOR & FRANCIS INC. - 1559-2294 .- 1559-2308. ; 14:6, s. 589-601
-
Tidskriftsartikel (refereegranskat)abstract
- The protection against tuberculosis induced by the Bacille Calmette Guerin (BCG) vaccine is unpredictable. In our previous study, altered DNA methylation pattern in peripheral blood mononuclear cells (PBMCs) in response to BCG was observed in a subgroup of individuals, whose macrophages killed mycobacteria effectively (responders). These macrophages also showed production of Interleukin-1 beta (IL-1 beta) in response to mycobacterial stimuli before vaccination. Here, we hypothesized that the propensity to respond to the BCG vaccine is reflected in the DNA methylome. We mapped the differentially methylated genes (DMGs) in PBMCs isolated from responders/non-responders at the time point before vaccination aiming to identify possible predictors of BCG responsiveness. We identified 43 DMGs and subsequent bioinformatic analyses showed that these were enriched for actin-modulating pathways, predicting differences in phagocytosis. This could be validated by experiments showing that phagocytosis of mycobacteria, which is an event preceding mycobacteria-induced IL-1 beta production, was strongly correlated with the DMG pattern.
|
|
| 8. |
- de Weerd, Hendrik A., et al.
(författare)
-
MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data
- 2022
-
Ingår i: Bioinformatics Advances. - : Oxford University Press. - 2635-0041. ; 2:1
-
Tidskriftsartikel (refereegranskat)abstract
- MotivationNetwork-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators.ResultsWe developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data.Availability and implementationMODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/.Supplementary informationSupplementary data are available at Bioinformatics Advances online.
|
|
| 9. |
- de Weerd, Hendrik Arnold, 1986-
(författare)
-
Novel methods and software for disease module inference
- 2023
-
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.
|
|
| 10. |
|
|
