| 1. |
- Andersson, Robin, 1980-
(författare)
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Decoding the Structural Layer of Transcriptional Regulation : Computational Analyses of Chromatin and Chromosomal Aberrations
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Gene activity is regulated at two separate layers. Through structural and chemical properties of DNA – the primary layer of encoding – local signatures may enable, or disable, the binding of proteins or complexes of them with regulatory potential to the DNA. At a higher level – the structural layer of encoding – gene activity is regulated through the properties of higher order DNA structure, chromatin, and chromosome organization. Cells with abnormal chromosome compaction or organization, e.g. cancer cells, may thus have perturbed regulatory activities resulting in abnormal gene activity. Hence, there is a great need to decode the transcriptional regulation encoded in both layers to further our understanding of the factors that control activity and life of a cell and, ultimately, an organism. Modern genome-wide studies with those aims rely on data-intense experiments requiring sophisticated computational and statistical methods for data handling and analyses. This thesis describes recent advances of analyzing experimental data from quantitative biological studies to decipher the structural layer of encoding in human cells. Adopting an integrative approach when possible, combining multiple sources of data, allowed us to study the influences of chromatin (Papers I and II) and chromosomal aberrations (Paper IV) on transcription. Combining chromatin data with chromosomal aberration data allowed us to identify putative driver oncogenes and tumor-suppressor genes in cancer (Paper IV). Bayesian approaches enabling the incorporation of background information in the models and the adaptability of such models to data have been very useful. Their usages yielded accurate and narrow detection of chromosomal breakpoints in cancer (Papers III and IV) and reliable positioning of nucleosomes and their dynamics during transcriptional regulation at functionally relevant regulatory elements (Paper II). Using massively parallel sequencing data, we explored the chromatin landscapes of human cells (Papers I and II) and concluded that there is a preferential and evolutionary conserved positioning at internal exons nearly unaffected by the transcriptional level. We also observed a strong association between certain histone modifications and the inclusion or exclusion of an exon in the mature gene transcript, suggesting a functional role in splicing.
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| 2. |
- Umer, Husen Muhammad
(författare)
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Computational Modelling of Gene Regulation in Cancer : Coding the noncoding genome
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Technological advancements have enabled quantification of processes within and around us. The information stored within our body converts into petabytes of data. Processing and learning from such data requires comprehensive computational programs and software systems. We developed software programs to systematically investigate the process of gene regulation in the human genome. Gene regulation is a complex process where several genomic elements control expression of a gene through recruiting many transcription factor (TF) proteins. The TFs recognize specific DNA sequences known as motifs. DNA mutations in regulatory elements and particularly in TF motifs may cause gene deregulation. Therefore, defining the landscape of regulatory elements and their roles in cancer and complex diseases is of major importance.We developed an algorithm (tfNet) to identify regulatory elements based on transcription factor binding sites. tfNet identified nearly 144,000 regulatory elements in five human cell lines. Investigating the elements we identified TF interaction networks and enrichment of many GWAS SNPs. We also defined the regulatory landscape for other conditions and species. Next, we investigated the role of regulatory elements in cancer. Cancer is initiated and developed by genetic aberrations in the genome. Genetic changes that are present in a cancer genome are obtained through whole genome sequencing technologies. We analyzed somatic mutations that had been detected in 326 whole genomes of liver cancer patients. Our results indicated 907 candidate mutations affecting TF motifs. Genome wide alignment of the mutated motifs revealed a significant enrichment of mutations in a highly conserved position of the CTCF motif. Gene expression analysis exhibited disruption of topologically associated domains in the mutated samples. We also confirmed the mutational pattern in pancreatic, gastric and esophagus cancers. Finally, enrichment of cancer associated gene sets and pathways suggested great role of noncoding mutations in cancer.To systematically analyze DNA mutations in TF motifs, we developed an online database system (funMotifs). Publicly available datasets were collected for thousands experiments. The datasets were integrated using a logistic regression model. Functionality annotations and scores for motifs of 519 TFs were derived. The database allows for identification of variants affecting functional motifs in a selected tissue type. Finally, a comprehensive analysis was performed to identify mutations overlapping functional TF motifs in 37 cancer types. Somatic mutations from a pan-cancer cohort of 2,515 cancer whole genomes were investigated. A significant enrichment of mutations in the CpG site of the CEBPB motif was identified. Overall, 10,806 mutated regulatory elements were identified including 406 highly recurrent ones. Genes associated to the mutated elements were highly enriched for cancer-related pathways. Our analyses provide further insights onto the role of regulatory elements and their impacts on cancer development.
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| 3. |
- Ameur, Adam, 1977-
(författare)
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A Bioinformatics Study of Human Transcriptional Regulation
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Regulation of transcription is a central mechanism in all living cells that now can be investigated with high-throughput technologies. Data produced from such experiments give new insights to how transcription factors (TFs) coordinate the gene transcription and thereby regulate the amounts of proteins produced. These studies are also important from a medical perspective since TF proteins are often involved in disease. To learn more about transcriptional regulation, we have developed strategies for analysis of data from microarray and massively parallel sequencing (MPS) experiments.Our computational results consist of methods to handle the steadily increasing amount of data from high-throughput technologies. Microarray data analysis tools have been assembled in the LCB-Data Warehouse (LCB-DWH) (paper I), and other analysis strategies have been developed for MPS data (paper V). We have also developed a de novo motif search algorithm called BCRANK (paper IV).The analysis has lead to interesting biological findings in human liver cells (papers II-V). The investigated TFs appeared to bind at several thousand sites in the genome, that we have identified at base pair resolution. The investigated histone modifications are mainly found downstream of transcription start sites, and correlated to transcriptional activity. These histone marks are frequently found for pairs of genes in a bidirectional conformation. Our results suggest that a TF can bind in the shared promoter of two genes and regulate both of them.From a medical perspective, the genes bound by the investigated TFs are candidates to be involved in metabolic disorders. Moreover, we have developed a new strategy to detect single nucleotide polymorphisms (SNPs) that disrupt the binding of a TF (paper IV). We further demonstrated that SNPs can affect transcription in the immediate vicinity. Ultimately, our method may prove helpful to find disease-causing regulatory SNPs.
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| 4. |
- Andersson, Claes, 1978-
(författare)
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Fusing Domain Knowledge with Data : Applications in Bioinformatics
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Massively parallel measurement techniques can be used for generating hypotheses about the molecular underpinnings of a biological systems. This thesis investigates how domain knowledge can be fused to data from different sources in order to generate more sophisticated hypotheses and improved analyses. We find our applications in the related fields of cell cycle regulation and cancer chemotherapy. In our cell cycle studies we design a detector of periodic expression and use it to generate hypotheses about transcriptional regulation during the course of the cell cycle in synchronized yeast cultures as well as investigate if domain knowledge about gene function can explain whether a gene is periodically expressed or not. We then generate hypotheses that suggest how periodic expression that depends on how the cells were perturbed into synchrony are regulated. The hypotheses suggest where and which transcription factors bind upstreams of genes that are regulated by the cell cycle. In our cancer chemotherapy investigations we first study how a method for identifiyng co-regulated genes associated with chemoresponse to drugs in cell lines is affected by domain knowledge about the genetic relationships between the cell lines. We then turn our attention to problems that arise in microarray based predictive medicine, were there typically are few samples available for learning the predictor and study two different means of alleviating the inherent trade-off betweeen allocation of design and test samples. First we investigate whether independent tests on the design data can be used for improving estimates of a predictors performance without inflicting a bias in the estimate. Then, motivated by recent developments in microarray based predictive medicine, we propose an algorithm that can use unlabeled data for selecting features and consequently improve predictor performance without wasting valuable labeled data.
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| 5. |
- Baltzer, Nicholas
(författare)
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Predictive Healthcare : Cervical Cancer Screening Risk Stratification and Genetic Disease Markers
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The use of Machine Learning is rapidly expanding into previously uncharted waters. In the medicine fields there are vast troves of data available from hospitals, biobanks and registries that now are being explored due to the tremendous advancement in computer science and its related hardware. The progress in genomic extraction and analysis has made it possible for any individual to know their own genetic code. Genetic testing has become affordable and can be used as a tool in treatment, discovery, and prognosis of individuals in a wide variety of healthcare settings. This thesis addresses three different approaches to-wards predictive healthcare and disease exploration; first, the exploita-tion of diagnostic data in Nordic screening programmes for the purpose of identifying individuals at high risk of developing cervical cancer so that their screening schedules can be intensified in search of new dis-ease developments. Second, the search for genomic markers that can be used either as additions to diagnostic data for risk predictions or as can-didates for further functional analysis. Third, the development of a Ma-chine Learning pipeline called ||-ROSETTA that can effectively process large datasets in the search for common patterns. Together, this provides a functional approach to predictive healthcare that allows intervention at early stages of disease development resulting in treatments with reduced health consequences at a lower financial burden
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| 6. |
- Barrenäs, Fredrik, 1981-
(författare)
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Bioinformatic identification of disease associated pathways by network based analysis
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Many common diseases are complex, meaning that they are caused by many interacting genes. This makes them difficult to study; to determine disease mechanisms, disease-associated genes must be analyzed in combination. Disease-associated genes can be detected using high-throughput methods, such as mRNA expression microarrays, DNA methylation microarrays and genome-wide association studies (GWAS), but determining how they interact to cause disease is an intricate challenge. One approach is to organize disease-associated genes into networks using protein-protein interactions (PPIs) and dissect them to identify disease causing pathways. Studies of complex disease can also be greatly facilitated by using an appropriate model system. In this dissertation, seasonal allergic rhinitis (SAR) served as a model disease. SAR is a common disease that is relatively easy to study. Also, the key disease cell types, like the CD4+ T cell, are known and can be cultured and activated in vitro by the disease causing pollen.The aim of this dissertation was to determine network properties of disease-associated genes, and develop methods to identify and validate networks of disease-associated genes. First, we showed that disease-associated genes have distinguishing network properties, one being that they co-localize in the human PPI network. This supported the existence of disease modules within the PPI network. We then identified network modules of genes whose mRNA expression was perturbed in human disease, and showed that the most central genes in those network modules were enriched for disease-associated polymorphisms identified by GWAS. As a case study, we identified disease modules using mRNA expression data from allergen-challenged CD4+ cells from patients with SAR. The case study identified and validated a novel disease-associated gene, FGF2 using GWAS data and RNAi mediated knockdown.Lastly, we examined how DNA methylation caused disease-associated mRNA expression changes in SAR. DNA methylation, but not mRNA expression profiles, could accurately distinguish allergic patients from healthy controls. Also, we found that disease-associated mRNA expression changes were associated with a low DNA methylation content and absence of CpG islands. Specifically within this group, we found a correlation between disease-associated mRNA expression changes and DNA methylation changes. Using ChIP-chip analysis, we found that targets of a known disease relevant transcription factor, IRF4, were also enriched among non CpG island genes with low methylation levels.Taken together, in this dissertation the network properties of disease-associated genes were examined, and then used to validate disease networks defined by mRNA expression data. We then examined regulatory mechanisms underlying disease-associated mRNA expression changes in a model disease. These studies support network-based analyses as a method to understand disease mechanisms and identify important disease causing genes, such as treatment targets or markers for personalized medication.
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| 7. |
- Bornelöv, Susanne, 1984-
(författare)
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Rule-based Models of Transcriptional Regulation and Complex Diseases : Applications and Development
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As we gain increased understanding of genetic disorders and gene regulation more focus has turned towards complex interactions. Combinations of genes or gene and environmental factors have been suggested to explain the missing heritability behind complex diseases. Furthermore, gene activation and splicing seem to be governed by a complex machinery of histone modification (HM), transcription factor (TF), and DNA sequence signals. This thesis aimed to apply and develop multivariate machine learning methods for use on such biological problems. Monte Carlo feature selection was combined with rule-based classification to identify interactions between HMs and to study the interplay of factors with importance for asthma and allergy.Firstly, publicly available ChIP-seq data (Paper I) for 38 HMs was studied. We trained a classifier for predicting exon inclusion levels based on the HMs signals. We identified HMs important for splicing and illustrated that splicing could be predicted from the HM patterns. Next, we applied a similar methodology on data from two large birth cohorts describing asthma and allergy in children (Paper II). We identified genetic and environmental factors with importance for allergic diseases which confirmed earlier results and found candidate gene-gene and gene-environment interactions.In order to interpret and present the classifiers we developed Ciruvis, a web-based tool for network visualization of classification rules (Paper III). We applied Ciruvis on classifiers trained on both simulated and real data and compared our tool to another methodology for interaction detection using classification. Finally, we continued the earlier study on epigenetics by analyzing HM and TF signals in genes with or without evidence of bidirectional transcription (Paper IV). We identified several HMs and TFs with different signals between unidirectional and bidirectional genes. Among these, the CTCF TF was shown to have a well-positioned peak 60-80 bp upstream of the transcription start site in unidirectional genes.
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| 8. |
- Diamanti, Klev, 1987-
(författare)
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Integrating multi-omics for type 2 diabetes : Data science and big data towards personalized medicine
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Type 2 diabetes (T2D) is a complex metabolic disease characterized by multi-tissue insulin resistance and failure of the pancreatic β-cells to secrete sufficient amounts of insulin. Cells recruit transcription factors (TF) to specific genomic loci to regulate gene expression that consequently affects the protein and metabolite abundancies. Here we investigated the interplay of transcriptional and translational regulation, and its impact on metabolome and phenome for several insulin-resistant tissues from T2D donors. We implemented computational tools and multi-omics integrative approaches that can facilitate the selection of candidate combinatorial markers for T2D.We developed a data-driven approach to identify putative regulatory regions and TF-interaction complexes. The cell-specific sets of regulatory regions were enriched for disease-related single nucleotide polymorphisms (SNPs), highlighting the importance of such loci towards the genomic stability and the regulation of gene expression. We employed a similar principle in a second study where we integrated single nucleus ribonucleic acid sequencing (snRNA-seq) with bulk targeted chromosome-conformation-capture (HiCap) and mass spectrometry (MS) proteomics from liver. We identified a putatively polymorphic site that may contribute to variation in the pharmacogenetics of fluoropyrimidines toxicity for the DPYD gene. Additionally, we found a complex regulatory network between a group of 16 enhancers and the SLC2A2 gene that has been linked to increased risk for hepatocellular carcinoma (HCC). Moreover, three enhancers harbored motif-breaking mutations located in regulatory regions of a cohort of 314 HCC cases, and were candidate contributors to malignancy.In a cohort of 43 multi-organ donors we explored the alternating pattern of metabolites among visceral adipose tissue (VAT), pancreatic islets, skeletal muscle, liver and blood serum samples. A large fraction of lysophosphatidylcholines (LPC) decreased in muscle and serum of T2D donors, while a large number of carnitines increased in liver and blood of T2D donors, confirming that changes in metabolites occur in primary tissues, while their alterations in serum consist a secondary event. Next, we associated metabolite abundancies from 42 subjects to glucose uptake, fat content and volume of various organs measured by positron emission tomography/magnetic resonance imaging (PET/MRI). The fat content of the liver was positively associated with the amino acid tyrosine, and negatively associated with LPC(P-16:0). The insulin sensitivity of VAT and subcutaneous adipose tissue was positively associated with several LPCs, while the opposite applied to branch-chained amino acids. Finally, we presented the network visualization of a rule-based machine learning model that predicted non-diabetes and T2D in an “unseen” dataset with 78% accuracy.
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| 9. |
- Enroth, Stefan, 1976-
(författare)
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The Nucleosome as a Signal Carrying Unit : From Experimental Data to Combinatorial Models of Transcriptional Control
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The human genome consists of over 3 billion nucleotides and would be around 2 meters long if uncoiled and laid out. Each human somatic cell contains all this in their nucleus which is only around 5 µm across. This extreme compaction is largely achieved by wrapping the DNA around a histone octamer, the nucleosome. Still, the DNA is accessible to the transcriptional machinery and this regulation is highly dynamic and change rapidly with, e.g. exposure to drugs. The individual histone proteins can carry specific modifications such as methylations and acetylations. These modifications are a major part of the epigenetic status of the DNA which contributes significantly to the transcriptional status of a gene - certain modifications repress transcription and others are necessary for transcription to occur. Specific histone methylations and acetylations have also been implicated in more detailed regulation such as inclusion/exclusion of individual exons, i.e. splicing. Thus, the nucleosome is involved in chromatin remodeling and transcriptional regulation – both directly from steric hindrance but also as a signaling platform via the epigenetic modifications. In this work, we have developed tools for storage (Paper I) and normalization (Paper II) of next generation sequencing data in general, and analyzed nucleosome locations and histone modification in particular (Paper I, III and IV). The computational tools developed allowed us as one of the first groups to discover well positioned nucleosomes over internal exons in such wide spread organisms as worm, mouse and human. We have also provided biological insight into how the epigenetic histone modifications can control exon expression in a combinatorial way. This was achieved by applying a Monte Carlo feature selection system in combination with rule based modeling of exon expression. The constructed model was validated on data generated in three additional cell types suggesting a general mechanism.
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| 10. |
- Garbulowski, Mateusz
(författare)
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Patterns in big data bioinformatics : Understanding complex diseases with interpretable machine learning
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Alterations in the flow of genetic information may lead to complex diseases. Such changes are measured with various omics techniques that usually produce the so-called “big data”. Using interpretable machine learning (ML), we retrieved patterns from transcriptomics data sets. Specifically, we employed a rule-based ML to identify associations among features and a decision in a combinatorial manner, i.e. a co-prediction. We developed tools and methods that can be applied by a large community of bioinformaticians and proved their usability through a variety of studies.In paper I, we developed an R.ROSETTA package that provides an environment for rule-based ML relying on the rough sets. Basically, R.ROSETTA is an R wrapper of the ROSETTA toolkit; however, it extends its functions with various analytical solutions. The package was tested on a microarray gene expression case-control study of autism. Estimated models were highly accurate and provided lists of possible interactions among genes. Moreover, benchmarking revealed that R.ROSETTA was among the best performing rule- and decision tree-based methods.In paper II, we applied the R.ROSETTA together with a VisuNet package. We used both tools to perform a rule-based network analysis of autism spectrum disorder (ASD) subtypes. Here, we used microarray-based gene expression measures of ASD patients and controls from three data sets. We demonstrated that rule-based modelling is an efficient approach to merge multiple cohorts. Furthermore, we estimated centrality distances among produced subnetworks that revealed dissimilarities of ASD subtypes and controls. Finally, we discovered a highly probable interaction between EMC4 and TMEM30A genes.In paper III, we investigated our tools to perform an RNA-seq-based gene expression analysis of Acute Myeloid Leukemia (AML). We aimed at discovering gene expression patterns between the AML diagnosis and relapse. Specifically, we applied a rule-based network analysis to validate independent cohorts. Our study revealed that overexpressed CD6 and underexpressed INSR are highly co-predictive genes associated to the AML relapse. Finally, we demonstrated arc diagrams as a novel way of visualizing co-predictors.In paper IV, we analyzed glioma grading by performing a comprehensive ML analysis for RNA-seq data sets. We broadly preprocessed data sets and removed a strong batch effect that occurred between glioma grades. Afterwards, we performed ML evaluation on single-sample gene set enrichment scores that revealed topmost accurate collections and annotations that distinguish glioma grades. Among others, we found cell cycle, Fanconi anemia and cholesterol-related pathways associated to glioma progression. Finally, we discovered several co-enrichment mechanisms among annotations.
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