Introduction: Physiological microflora is characterised by wide diversity. The microbial community is mostly composed of bacteria, but also includes fungi, archaea and viruses. Anaerobic commensal bacteria (Firmicutes and Bacteroidetes) dominate 90% of the colon. The composition and products of the gut microbiota have a significant effect on an individual's immune system, and their interactions may ultimately promote immune tolerance or inflammatory immune response. Blood cell transplantation (HSCT) and its associated standard procedures of conditioning, antibiotic exposure and dietary prophylaxis represent modification and disruption of the gut microbiota leading to the development of some serious post-transplant complications affecting the OS (overall survival) and TRM (treatment related mortality) of patients. Objectives: The aim of this work was to investigate the representation of individual bacterial strains in patients undergoing allogeneic HSCT, as well as the effect of transplantation on the composition and diversity of their gut microbiota. Methods: Stool samples were obtained from 52 patients who underwent an allogeneic hematopoietic cell transplant at the Institute of Hematology and Blood Transfusion in Prague. A cut-off date for the first sample was set for the start of...

This thesis contains the development of a deformation monitoring software based on undifferenced GPS observations. Software like this can be used in alarm systems placed in areas where the earth is unstable. Systems like this can be used in areas where people are in risk of getting hurt, like in earthquake zones or in land slide areas, but they can also be useful when monitoring the movements in buildings, bridges and other artefacts. The main hypotheses that are tested are whether it is possible to detect deformations with undifferenced observations and if it is possible to reach the same accuracy in this mode as when working in a traditional mode where the observations are differenced. The development of a deformation monitoring software based on undifferenced GPS observations is presented. A complete mathematical model is given as well as implementation details. The software is developed in Matlab together with a GPS observation simulator. The simulator is mainly used for debugging purposes. The developed software is tested with both simulated and real observations. Results from tests with simulated observations show that it is possible to detect deformations in the order of a few millimetres with the software. Calculations with real observations give the same results. Further, the result from calculations in static mode indicates that the commercial software and the undifferenced software diverge a few millimetres, which probably depends on different implementations of the tropospheric corrections. In kinematic mode the standard deviation is about 1 millimetre larger in the undifferenced mode than in the double differenced mode. An initial test with different observation weighting procedures indicates that there is a lot of potential to improve the result by applying correct weights to the observations. This is one of the aims in the future work within this project. This thesis are sponsored by the Swedish Research Council for Enviroment, Agricultural Sciences and Spatial Planning, FORMAS within the framework “Monitoring of construction and detection of movements by GPS ref no. 2002-1257" / QC 20101108

In the last years, Genome-Wide Associations Studies (GWAS) found many variants associated with complex diseases. However, the biological and molecular links between these variants and phenotypes are still mostly unknown. Also, even if sample sizes are constantly increasing, the associated variants do not explain all the heritability estimated for many traits. Many hypotheses have been proposed to explain the problem: from variant-variant interactions, the effect of rare and ultra-rare coding variants and also technical biases related to sequencing or statistic on sexual chromosomes. In this thesis, we mainly explore the hypothesis of variant-variant interaction and, briefly, the rare coding variants hypothesis while also considering possible molecular effects like allele-specific expression and the effects of variants on protein interfaces. Some parts of the thesis are also devoted to explore the implementation of efficient computational tools to explore these effects and to perform scalable genotyping of germline single nucleotide polymorphisms (SNPs) in huge datasets. The main part of the thesis regards the development of a new resource to identify putative variant-variant interactions. In particular, we integrated ChIP-seq data from ENCODE, transcription factor binding motifs from several resources and genotype and transcript level data from GTeX and TCGA. This new dataset allows us to formalize new models, to make hypothesis and to find putative novel associations and interactions between (mainly non-coding) germline variants and phenotypes, like cancer-specific phenotypes. In particular, we focused on the characterization of breast cancer and Alzheimer’s Disease GWAS risk variants, looking for putative variants’ interactions. Recently, the study of rare variants has become feasible thanks to the biobanks that made available genotypes and clinical data of thousands of patients. We characterize and explore the possible effects of rare coding inherited polymorphisms on protein interfaces in the UKBioBank trying to understand if the change in structure of protein can be one of the causes of complex diseases. Another part of the thesis explores variants as causal molecular effect for allele-specific expression. In particular, we describe UTRs variants that can alter the post-transcriptional regulation in mRNA leading to a phenomenon where an allele is more expressed than the other. Finally, we show those variants can have prognostic significance in breast cancer. This thesis work introduces results and computational tools that can be useful to a broad community of researcher studying human polymorphisms effects.

Swedish populations of Wels catfish (Silurus glanis) experienced severe declines during the 19th and 20th centuries. The main causes for the decline were loss of suitable spawning habitat and fragmentation of populations. Currently, three native and two reintroduced populations remain in Sweden. Thanks to national protection and progress in restoring habitats, population sizes have increased over the past three decades. Previous studies that used microsatellite loci have found that genetic diversity and effective population sizes in Sweden are low, while population differentiation is high. A study that used whole genome sequencing (WGS) confirmed these results for native Swedish populations (those found in the Båven, Emån, and Möckeln water systems). The current project uses the same WGS methods and expands on the previous study by considering samples from non-Swedish populations (river Garonne, France; river Ebro, Spain; hatchery, Czech Republic), as well as improving read-depth coverage and sampling from the introduced Swedish population in the Helge å water system. Both a genome-wide SNP-set and full mitochondrial sequences were used to assess genetic diversity within each population, and differentiation among them. Genetic diversity in Swedish populations is lower than in non-Swedish populations. Native Swedish populations are strongly differentiated from one another. The introduced Helge å population is strongly differentiated from Emån and Möckeln, but less so from Båven. Despite Helge å individuals having heritage that can be predominantly traced to Båven, there are clear signs of admixture with the other two native populations. Swedish populations are all strongly differentiated from the non-Swedish populations. Altogether, evidence of admixture and slightly greater genetic diversity than native Swedish populations in Helge å can at the surface be seen as promising signs. However, it remains uncertain whether these improvements are durable over multiple generations. Considering the poor genetic status of Sweden taken as a whole, and the questionable nature of the improvements seen in Helge å, the long-term viability of Swedish catfish populations remains uncertain.

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<p>  </p> <p>Forward genetics discovery of the molecular basis of induced mutants has fundamentally contributed to our understanding of basic biological processes such as metabolism, cell dynamics, growth, and development. Advances in Next-Generation Sequencing (NGS) technologies enabled rapid genome sequencing but also come with limitations such as sequencing errors, dependence on reference genome accuracy, and alignment errors. By incorporating pedigree information to help correct for some errors I optimized variant calling and filtering strategies to respond to experimental design. This led to the identification of multiple causative alleles, the detection of pedigree errors, and an ability to explore the mutational spectrum of multiple mutagens in Arabidopsis. Similar to the problems in forward genetic discovery of mutant alleles, variation in genomes complicates the analysis of gene expression affected by natural variation. The plant hypersensitive response (HR) is a highly localized and rapid form of programmed cell death that plants use to contain biotrophic pathogens. Substantial natural variation exists in the mechanisms that trigger and control HR, yet a complete understanding of the molecular mechanisms modulating HR is lacking. I explored the gene expression consequences of the plant HR in maize using a semi-dominant mutant encoding a constitutively active HR-inducing Nucleotide Binding Site Leucine Rich Repeat protein, <em>Rp1-D21,</em> derived from the receptor responsible for perceiving certain strains of the common rust <em>Puccinia sorghi</em>. Differentially expressed genes (DEG) in response to <em>Rp1-D21</em> were identified in different genetic backgrounds and hybrids that exhibit divergent enhancing (NC350) or suppressing (H95, B73) effects on the visual manifestations of HR. To enable this analysis, I created anonymized reference genomes for each comparison, so that the reference genome induced less bias in the mapping steps. Comprehensive identification of DEG corroborated the visual phenotypes and provided the identities of genes influential in plant hypersensitive response for further studies. The locations of expression quantitative trait loci (eQTL) that determined the differential response of NC350 and B73 were identified using 198 F1 families generated by crossing B73 x NC350 RIL population and <em>Rp1-D21</em>/+ in H95. This identified 3514 eQTL controlling the variability in differential expression between mutant versus wild-type. <em>Trans-</em>eQTL were dramatically arranged in the genome and identified 17 hotspots with more than 200 genes influenced by each locus. A single locus significantly affected expression variation in 5700 genes, 5396 (94.7%) of which were DGE. An allele specific expression analysis of NC350 x H95 and B73 x H95 F1 hybrids with and without <em>Rp1-D21</em> identified <em>cis-</em>eQTL and ASE at a subset of these genes. Bias in the confirmation of eQTL by ASE was still present despite the anonymized reference genomes indicating that additional efforts to improve signal processing in these experiments is needed.</p>

Previous studies have shown that genes can be switched on or off by age, environmental factors, diseases, and lifestyles. The open or compact structures of chromatin is a crucial factor that affects gene expression. Epigenetics refers to hereditary mechanisms that change gene expression and regulations without changing DNA sequences. Epigenetic modifications, such as DNA methylation, histone modification, and non-coding RNA interaction, play critical roles in cell differentiation and disease processes. The conventional approach requires the use of a few million or more cells as starting material. However, such quantity is not available when samples from patients and small lab animals are examined. Microfluidic technology offers advantages to utilize low-input starting material and for high-throughput. In this thesis, I developed novel microfluidic technologies to study epigenomic regulations, including 1) profiling epigenomic changes associated with LPS-induced murine monocytes for immunotherapy, 2) examining cell-type-specific epigenomic changes associated with BRCA1 mutation in breast tissues for breast cancer treatment, and 3) developing a novel microfluidic oscillatory hybridized ChIRP-seq assay to profile genome-wide lncRNA binding for numerous human diseases. We used 20,000 and 50,000 primary cells to study histone modifications in inflammation and breast cancer of BRCA1 mutation, respectively. In the project of whole-genome lncRNA bindings, our microfluidic ChIRP-seq assay, for the first time, allowed us to probe native lncRNA bindings in mouse tissue samples successfully. The technology is a promising approach for scientists to study lncRNA bindings in primary patients. Our works pave the way for low-input and high-throughput epigenomic profiling for precision medicine development. / Doctor of Philosophy / Traditionally, physicians treat patients with a one-size-fits-all approach, in which disease prevention and treatment are designed for the average person. The one-size-fits-all approach fits many patients, but does not work on some. Precision medicine is launched to improve the low efficiency and diminish side effects, and all of these drawbacks are happening in the traditional approaches. The genomic, transcriptomic, and epigenomic data from patients is a valuable resource for developing precision medicine. Conventional approaches in profiling functional epigenomic regulation use tens to hundreds of millions cells per assay, that is why applications in clinical samples are restricted for several decades. Due to the small volume manipulated in microfluidic devices, microfluidic technology exhibits high efficiency in easy operation, reducing the required number of cells, and improving the sensitivity of assays. In order to examine functional epigenomic regulations, we developed novel microfluidic technologies for applications with the small number of cells. We used 20,000 cells from mice to study the epigenomic changes in monocytes. We also used 50,000 cells from patients and mice to study epigenomic changes associated with BRCA1 mutation in different cell types. We developed a novel microfluidic technology for studying lncRNA bindings. We used 100,000-500,000 cells from cell lines and primary tissues to test several lncRNAs. Traditional approaches require 20-100 million cells per assay, and these cells are infected by virus for over-producing specific lncRNA. However, our technology just needs 100,000 cells (non-over-producing state) to study lncRNA bindings. To the best of our knowledge, this is the first allowed us to study native lncRNA bindings in mouse samples successfully. Our efforts in developing microfluidic technologies and studying epigenomic regulations pave the way for precision medicine development.

Computational biology currently faces challenges in a big data world with thousands of data samples across multiple disease types including cancer. The challenging problem is how to extract biologically meaningful information from large-scale genomic data. Next-generation Sequencing (NGS) can now produce high quality data at DNA and RNA levels. However, in cells there exist a lot of non-specific (background) signals that affect the detection accuracy of true (foreground) signals. In this dissertation work, under Bayesian framework, we aim to develop and apply approaches to learn the distribution of genomic signals in each type of NGS data for reliable identification of specific foreground signals. We propose a novel Bayesian approach (ChIP-BIT) to reliably detect transcription factor (TF) binding sites (TFBSs) within promoter or enhancer regions by jointly analyzing the sample and input ChIP-seq data for one specific TF. Specifically, a Gaussian mixture model is used to capture both binding and background signals in the sample data; and background signals are modeled by a local Gaussian distribution that is accurately estimated from the input data. An Expectation-Maximization algorithm is used to learn the model parameters according to the distributions on binding signal intensity and binding locations. Extensive simulation studies and experimental validation both demonstrate that ChIP-BIT has a significantly improved performance on TFBS detection over conventional methods, particularly on weak binding signal detection. To infer cis-regulatory modules (CRMs) of multiple TFs, we propose to develop a Bayesian integration approach, namely BICORN, to integrate ChIP-seq and RNA-seq data of the same tissue. Each TFBS identified from ChIP-seq data can be either a functional binding event mediating target gene transcription or a non-functional binding. The functional bindings of a set of TFs usually work together as a CRM to regulate the transcription processes of a group of genes. We develop a Gibbs sampling approach to learn the distribution of CRMs (a joint distribution of multiple TFs) based on their functional bindings and target gene expression. The robustness of BICORN has been validated on simulated regulatory network and gene expression data with respect to different noise settings. BICORN is further applied to breast cancer MCF-7 ChIP-seq and RNA-seq data to identify CRMs functional in promoter or enhancer regions. In tumor cells, the normal regulatory mechanism may be interrupted by genome mutations, especially those somatic mutations that uniquely occur in tumor cells. Focused on a specific type of genome mutation, structural variation (SV), we develop a novel pattern-based probabilistic approach, namely PSSV, to identify somatic SVs from whole genome sequencing (WGS) data. PSSV features a mixture model with hidden states representing different mutation patterns; PSSV can thus differentiate heterozygous and homozygous SVs in each sample, enabling the identification of those somatic SVs with a heterozygous status in the normal sample and a homozygous status in the tumor sample. Simulation studies demonstrate that PSSV outperforms existing tools. PSSV has been successfully applied to breast cancer patient WGS data for identifying somatic SVs of key factors associated with breast cancer development. In this dissertation research, we demonstrate the advantage of the proposed distributional learning-based approaches over conventional methods for NGS data analysis. Distributional learning is a very powerful approach to gain biological insights from high quality NGS data. Successful applications of the proposed Bayesian methods to breast cancer NGS data shed light on underlying molecular mechanisms of breast cancer, enabling biologists or clinicians to identify major cancer drivers and develop new therapeutics for cancer treatment. / Ph. D.

The shift of medical technology from a doctor's application of individualized medicine toward precision medicine has been accelerated by the advent of Next Generation Sequencing. Individualized medicine is where a doctor tries to understand the intricacies of a patient's medical state, where precision medicine uses a wealth of data to understand the individuality of a patient on a biological level to determine treatment course. Next Generation Sequencing allows for the collection of genome wide analyses such as genomic, transcriptomic, and epigenomic sequencing, which provides the backbone of the data driven precision medicine. In order to obtain and use this data, it needs to be produced from minimal amounts of patient tissue, such as the amount from a needle biopsy. In order to perform so many different assays it is paramount that we develop high sensitivity methodologies, such that we can gain an understanding of the patient's physiology without causing much discomfort in gathering large amounts of sample. In pursuit of making more tests, data, and assays available for use in precision medicine, we have developed 3 different microfluidic technologies, which automate and simplify the assays needed for the data collection at a high sensitivity, as well as a versatile platform for therapeutic production. First, we developed a epigenomic assay for chromatin immunoprecipitation, which gives us information on histone modifications across the genome. These histone modifications heavily impact gene expression and how the chromatin is organized, as well promoting or inhibiting transcription of genes. Our technology allowed us to perform multiple parallel assays from as few as 50 cells quickly and reliably using our fluidized bed technology. Next, we developed a library preparation system, which reduces the cost of library preparation by 20x and reduces operator pipetting by 100x. Our system uses a droplet based reactor to quickly and reliably prepare sequencing libraries using the lowest amount of DNA to date, 10 pg. Finally, we designed a therapeutics-on-a-chip platform which is capable of producing clinically relevant proteins on demand from temperature stable components. Using our system, we are capable of producing a number of different therapeutics on demand quickly without rearrangement of the system. / Doctor of Philosophy / Technical advances in the healthcare industry have made a range of new data available to physicians and patients. Home use DNA testing kits have made it possible to examine one’s predisposition to certain genetic diseases. Using these advanced methods, we are able to gain insights into a patient’s disease state where we were previously unable. Unfortunately, some of these new analyses currently require large amounts of patient sample, which make the examinations largely impractical to perform. In order to overcome the sample requirements, which make these analyses impractical, we develop microscale reactor systems capable of reducing the amount of material required for these new analyses. Here I demonstrate our developed technologies to automate 3 different processes aimed at enabling the study of protein-DNA interactions and produce therapeutics at the point of care. First, we developed an analytical system to study protein-DNA interactions (which are important to understanding patient responses to treatment), that allow for parallel analyses which can be done with sample from less than one needle biopsy, where existing methods would require dozens or more (50 vs 10,000,000 cells.) Next, we developed automated system for preparing DNA sequencing libraries using as little as 10 pg DNA (~2 cells of DNA). The device run multiple reactions simultaneously while reducing batch to batch variation and operator hands-on time. Finally, we developed a v Therapeutics-On-a-Chip platform that produces clinically relevant therapeutic proteins in clinically relevant dosages using a cell-free approach, while saving the trouble and cost associated with protein storage and transportation.