Extracellular vesicles (EVs) are released by almost  all types of cells. EVs play an    important role in cell-to-cell communication by sending biomolecules such as mRNAs to    other cells via endocytosis. This project aims to understand the roles of EVs and their    potential application as mRNA delivery vehicles by completing two objectives. One       objective was to investigate the EVs’ roles in cell proliferation by routinely removing EVs    from cell-conditioned media, transferring stress-induced EVs to recipient epithelial cells, and examining their cell number. Another objective was to analyzethe transcriptomic expression of HUVEC cells treated with breast-cancer-derived EVs. EVs were routinely removed from the cell culture using 100 kDa filters, 15 kDa filters, or by changing to new media. The result showed that the epithelial cancer cell line grows at a lower growth rate when EVs are removed using 100 kDa filters and 15 kDa filters compared to the control. The stressinduced EV transfer experiment showed no significant difference in cell number among different stress conditions and stress-EV treatment. The transcriptomic analysis revealed 765 differentially expressed genes, which were mapped onto pathways using the IPA software. The majority of the top 10 significant pathways were associated with cancer progression. IPA’s Biomarker Filter function revealed 35 cancer biomarkers, as well as 33 putative angiogenesis biomarkers. VEGFAtargeted genes were identified, and the ones that are upregulated and located in the extracellular matrix or plasma membrane were identified as putative biomarkers for VEGFA-induced angiogenesis. The top 10-pathways suggest that the recipient HUVEC may receive cancer-related messages from the EVs. It is important to evaluate the content of EVs derived from different origins thoroughly. Cancer-derived EVs may induced angiogenesis in HUVEC, as shownpreviously, but cancer EVs might also communicate cancer signals that can cause pathological effects. Extensive studies and validation are needed to fully understand the role of EVs and their application as an mRNA delivering system.

Acute myeloid leukaemia (AML) is a heterogeneous group of myeloid lineage malignancies arising from a spectrum of mutations. AML is characterized by the uncontrolled proliferation of clonal cells, which are unable to properly differentiate into mature myeloid cells. The present day standard treatment of care for AML patients under 60 years of age is the “7+3” induction chemotherapy. The survival rate beyond 5 years has been maintained at 40%. Most AML patients older than 60 years are unable to tolerate this intensive cytotoxic induction regimen and are treated with hypomethylating drugs. The two-year relapse-free survival rate of these patients remains at 40% (exceptionally low) with a median survival of 6.5 months. Thus, novel therapeutic strategies are urgently needed to improve the overall survival of both young and elderly AML patients. Therefore, elucidating the molecular mechanisms underlying AML has been of prime importance in the development of efficacious targeted therapeutics to treat subtypes of this heterogeneous haematological malignancy. Next-generation sequencing has uncovered that the most frequently mutated class of genes in AML encode epigenetic modifiers, suggesting that alterations in the epigenetic landscape contribute to the development of AML. H3K27me3 is an epigenetic alteration of the Histone H3, one of the five principal histones involved in chromatin construction in eukaryotic cells. In refractory acute myeloid leukaemia, a study found an epigenetic relationship between the decreased abundance of the Polycomb related protein MTF2 and chemotherapy resistance. MTF2 loss hampered PRC2 complex expression and H3K27me3 deposition at numerous genes, including the major target gene MDM2, resulting in enhanced MDM2 expression, which depleted p53 and conferred chemoresistance. This study aimed to elucidate the epigenetic dysregulation in chemorefractory and chemoresponsive AML.     A systems biology methodology was utilized here. Two AML sample types were collected from the bone marrow of AML patients. The first sample (AML_4) had basal levels of the epigenetic modification H3K27me3 and basal levels of MTF2. The second sample (AML_1) had low levels of H3K27me3 and basal levels of MTF2. ATAC-seq (Assay for Transposase Accessible Chromatin with High-Throughput Sequencing) is a powerful tool for revealing chromatin accessibility across the genome. By probing hyperactive Tn5 transposase to a DNA sequence, ATAC-seq generates reads from cells representing accessible regions that correspond to nucleosome positioning and transcription factor binding sites. ATAC-seq was performed on AML_4 and AML_1. Bioinformatics analysis was conducted on the two ATAC-seq data to identify transcription factors with differential activity in each of the AML types. 36 transcription factors with significant differential activity were identified in AML_1 while 4 transcription factors were identified in AML_4. These results reveal higher epigenetic dysregulation in AML_1. These results reveal higher epigenetic dysregulation in AML_1 compared to AML_4. These transcription factors with significant activity in the different AML types would be combined with CHIP-seq and RNA-seq data thereafter used for gene regulatory network analysis of key AML genes to uncover the underlying mechanisms of the regulation of these genes.

Insectivorous bats face fragmentation and loss of suitable habitats. Bats numbers are declining in several parts of the world and scientists aim to construct distribution models that can predict occurrence, in order to understand species habitat requirements. Significant correlation has been found between species and habitat attributes but conditions differ across the world and between species. A predictive model will not work as well somewhere else. Bat occurrence in Vara, Alingsås, Vårgårda and Borås municipalities is only briefly inventoried. The aim of this study is to investigate if chosen habitat attributes affect bat occurrence in these municipalities. Inventories from wind energy establishments were used, and nine species were found. Multiple linear logistic regression analysis were used to compare species occurrence with vicinity to water, trees with special qualities “Skyddsvärda träd”, nature reserves, area of deciduous forest and urban area, and aspect. Five significant results are consistent with previous knowledge; positive effect of vicinity to skyddsvärda träd and small area of open habitats for Myotis brandtii/mustacinus and positive effect of vicinity to urban areas for Nyctalus noctula and Vespertilio murinus. Four significant results contradict known behaviour or cannot be explained. There is also a non-significant positive trend of vicinity to water for seven species. The results are unclear with low reliability. Type of bat activity is not known. Habitat requirements differ whether the area is used for roosting, hunting or transport, which is important to consider when “occurrence” is defined. Data was not collected at the same time of the year which affects both activity and species occurrence. This type of analysis along with modelling is a cost-effective way to monitor bat dispersion. Studies in other countries have found predictive correlations and this study can be improved. Therefore it should be possible to generate more accurate analyses in future Swedish studies. / Insektsätande fladdermöss hotas av fragmentering och förlust av boplatser. I flera världsdelar minskar fladdermöss i antal och forskare arbetar med att skapa modeller som förutsäger förekomst, för att förstå arters habitatkrav. Signifikanta samband har påvisats mellan arter och attribut i deras livsmiljöer men levnadsförhållanden skiljer sig mycket över världen och mellan arter. En modell kan inte antas fungera lika bra överallt. I Vara, Alingsås, Vårgårda och Borås kommuner är fladdermusförekomst endast översiktligt inventerad. Syftet med den här studien är att undersöka om utvalda miljöparametrar påverkar förekomst i nämnda kommuner. Inventeringar utförda i samband med vindkraftsutbyggnad användes, där nio arter påträffades. Arternas förekomst jämfördes genom multipel linjär logistisk regressionsanalys med avstånd till närmsta vatten, skyddsvärda träd, naturreservat samt tätort, mängden lövskog och öppen mark inom 300 meter från inventeringspunkten samt väderstreck på sluttningar. Fem signifikanta resultat kan kopplas till tidigare kända beteenden, såsom positiv effekt av närhet till skyddsvärda träd och minskande mängd öppen mark för Tajga/mustaschfladdermus (Myotis brandtii/mustacinus) och positiv effekt av närhet till tätort för Större brunfladdermus (Nyctalus noctula) och Gråskimlig fladdermus (Vespertilio murinus). Fyra signifikanta resultat strider mot arters beteenden eller inte kan förklaras. Bland icke signifikanta resultat syns en positiv trend av närhet till vatten för sju av nio arter. Resultatet spretar och tillförlitligheten är låg. Det är inte känt vilken aktivitet fladdermössen utövade under inventeringarna. Habitatkrav skiljer sig om området används som boplats, jaktmark eller förflyttningsstråk, vilket är viktigt att ta hänsyn tillvid definition av ”förekomst”. Data är dessutom insamlad under olika tider på året vilket påverkar både aktivitet och förekomst. Denna typ av analys tillsammans med modellering är ett kostnadseffektivt sätt att bevaka fladdermusförekomst. Studier i andra delar av världen har hittat tydliga samband och det finns förbättringsmöjligheter i denna metod. Därför finns förutsättningar för framtida mer precisa analyser även i Sverige.

This project was built upon a previous study (Johansson et al.2020) that tested multiple drugs on glioblastoma cell lines and found a big division between the drug response for proteasome inhibitors. The aim of this project was to try to obtain a better insight into the differences between the two drug response subgroups’ processes by creating and comparing two genome scale metabolic models (GEMs) of the two subgroups. To do this, genomescale metabolic models were made for each cell line and later merged after its proteasome inhibitor response to obtain two general models. After having multiple models for each cell line and two general drug response models, comparisons could be made. Overall, the differences between cell lines were larger than the differences between drug responses, but some differences could still be seen. Some differences in the number of reactions in subsystems were found between the two general GEMs, where the Ureacycle subsystem showed the largest difference between the two models. Another difference was in the metabolic activity of the models, where the sensitive model passed ten tasks which the resistant model could not. The last and the most important comparison was essentiality analysis which gave a multitude of essential genes but only twelve genes that were unique to the twogeneral GEMs. Nine genes for the resistant model and three for the sensitive. Out of these genes CYP51A1 and FDFT1, for the resistant model, and genes RBP1 and CYP27A1, for the sensitive model, had already been in at least one study regarding Glioblastoma or Proteasome Inhibitors. Since some of the found genes already seem to have been found interesting for PIs or glioblastoma treatment the unique genes from the essentiality analysis could be interesting to look more into in the future.

Common variable immunodeficiency (CVID) is a rare primary immune deficiency (1:25000) in which patients have a reduction in antibody production and very low titres in one or more of their Ig isotypes, (IgG, IgA and sometimes IgM). This disease can cause different symptoms such as: bronchiectasis, chronic lung disease and even autoimmunity, polyclonal lymphocytic infiltration, lymphoma and death. The underlying causes of CVID are still largely unknown but studies show that different factors like primary B-cell dysfunctions, defects in T cells and antigen-presenting cells are involved. Quantitative analysis of gene expression is of high importance in understanding the molecular mechanisms underlying this diseases´ genome regulation. Next-generation RNA-seq has enabled researchers to analyse both coding and non-coding regions of RNA, and therefore has made it possible to identify differentially expressed genes in large-scale data, especially in polygenic diseases like CVID. The aim for this study was to identify the differentially expressed genes between CVID patients and healthy donors to identify important genes and molecular mechanisms underlying this diseases´ genome regulation. For this matter, whole genome RNA-seq analysis was performed on RNA isolated from sorted peripheral blood naïve and CD27bright memory B cells from healthy donors (n=7) and CVID patients (n=5). The RNA-seq data for the samples was collected and undergone several bioinformatical and analytical steps to be processed. After quality control and trimming, the data files were assembled to the human genome. Then, the transcriptomic data of the CVID patients was compared with the healthy donors to identify differentially expressed genes (DEGs). From this study, it was found that PAX5, ETS1, POU2AF1, SPIB, BACH 2, EBF1 and PRDM1 play an important role on regulation of the B cells and especially this disease. Also, the Ikaros family, toll-like receptors and a number of chemokine and cytokine receptors were found out to have high importance regarding CVID.

Single cell RNA technology is a recent technical advancement used to understand the cancertumorgenicity at single cell resolution. In this study we have analyzed the scRNA data from thenon-small cell lung cancer (NSCLC) dataset to facilitate the early identification of NSCLCsubtypes namely, squamous cell carcinoma (SCC) and adenocarcinoma (AC). Non-immunecells, have a major role in tumorigenesis of the malignant tumors, in early stages. Therefore,we have analyzed the major non-immune cells, namely endothelial cells and fibroblast cellsfrom the GSE127465 dataset using SEURAT pipeline. Dimensionality reduction analysis andcluster analysis indicate that AC and SCC subtypes of NSCLC have different fibroblastcompositions. Differential gene expression analysis indicates that AC tumours have shownelevated content of MGP/PTGDS and INMT/MFAP4 fibroblast cells, whereas squamous cellcarcinoma showed an elevated content of COL6A1/COL6A2 and FNDC1/COL12A1 fibroblastcells. The statistical analysis shows that the clustering is statistically significant and not anartefact. Given that the tumour microenvironment is highly dynamic, in this study we haveattempted to understand the tumour microenvironment by scRNA analysis of non-immune cellsat single cell resolution.

Staphylococcus aureus are bacterial pathogens causing infectious diseases. Methicillin-resistant S. aureus, or MRSA, carry the mecA or mecC genes generating resistance to β-lactam antibiotics. MRSA is problematic to treat and crucial to rapidly detect while current diagnostic workflows are time-consuming. DNA sequencing offers an alternative to existing methods. Illumina, the leading sequencing platform, is time-consuming and generates short reads. The Oxford Nanopore Technologies MinION presents rapid sequencing protocols, generating long reads at reduced accuracy. Here, the MinION device was evaluated to rapidly identify and differentiate between MRSA and non-MRSA isolates, with estimations on turn-around-times for clinical implementation.DNA extracted from 12 bacterial isolates were sequenced on the MinION. The automated workflow for raw MinION reads EPI2ME called taxonomy and resistance genes. MinION reads and corresponding Illumina reads were also assembled with Unicycler with short-read, long-read and hybrid assembly, and analyzed with the Center for Genomic Epidemiology bioinformatic tools. MinION and Illumina reads analyzed with the 1928 Diagnostics S. aureus pipelines were also compared. All tools correctly identified mecA in confirmed MRSA isolates, and mecC in an unconfirmed isolate. Similar results in terms of species, virulence and resistance genes were observed. Strain typing was problematic for MinION reads compared to Illumina, due to increased error rates. The minimum estimated turn-around-time for the MinION in this project, from library preparation, was approximately 6.5 hours to 7.5 hours per sample. Although further investigations are required, the MinION offers an intriguing alternative to current methods for identifying MRSA, aiding in rapid diagnosis and treatment.

Inflammatory bowel diseases (IBD) including Crohn’s disease (CD) and ulcerative colitis (UC) are associated with various hepatobiliary disorders. Two of the chronic hepatobiliary disorders that may coexist with inflammatory bowel diseases are: primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC). Previous studies have hypothesized that IBD, PBC, and PSC might share an underlying mechanism which contributes to the pathogenesis of the three conditions. In this study, a module-based network analysis and pathway enrichment analysis was applied on IBD, PSC, and PBC differentially expressed genes (DEGs). The sample data were obtained from the study by Ostrowski et al. (2019). A network module-based approach was applied to examine generated results where additional information about biological processes, pathways and molecular functions can be inferred. FunRich and Enrichr were utilized as functional enrichment tools. A protein interaction network was constructed for the three conditions using STRING. Functional modules and overlapping modules of IBD, PSC, and PBC were identified using different plug-ins in Cytoscape. Some of the results were consistent with the findings of Ostrowski et al. (2019) such as the ATP synthesis and signal transduction that is shared among the overlapping genes in IBD, PBC, and PSC. ModuLand highlighted nodes that have been previously reported to have a role in the pathogenesis of autoimmune diseases. The proposed approach demonstrated that the module-based approach contributes to similar results regarding biological processes and pathway enrichment of generated modules, compared to enrichment analysis of DEGs. In addition, the utilization of the ModuLand plug-in to find hierarchal layers of disease genes is still poorly researched and would benefit from more in-depth comparison with related tools for module discovery. For instance, implementing ModuLand plug-in can potentially support research in elucidating complex diseases.

Copied selected text to selection primary: The development of new pharmaceuticals is a long and ardous process that typically requires more than 10 years from target identification to approved drug. This process often relies on high throughput screening of molecular libraries. However, this is a costly and time-intensive approach and the selection of molecules to screen is not obvious, especially in relation to the size of chemical space, which has been estimated to consist of 10 60 compounds. To accelerate this exploration, molecules can be obtained from virtual chemical libraries and tested in-silico using molecular docking. Still, such methods are incapable of handling the increasingly colossal virtual libraries, currently reaching into the billions. As the libraries continue to expand, a pre-selection of compounds will be necessitated to allow accurate docking-predictions. This project aims to investigate whether the search for ligands in vast molecular libraries can be made more efficient with the aid of classifiers extended with the conformal prediction framework. This is also explored in conjunction with a fragment based approach, where information from smaller molecules are used to predict larger, lead-like molecules. The methods in this project are retrospectively tested with two clinically relevant G protein-coupled receptor targets, A 2A and D 2 . Both of these targets are involved in devastating disease, including Parkinson’s disease and cancer. The framework developed in this project has the capacity to reduce a chemical library of > 170 million tenfold, while retaining the 80 % of molecules scoring among the top 1 % of the entire library. Furthermore, it is also capable of finding known ligands. This will allow for reduction of ultra-large chemical libraries to manageable sizes, and will allow increased sampling of selected molecules. Moreover, the framework can be used as a modular extension on top of almost any classifier. The fragment-based approaches that were tested in this project performed unreliably and will be explored further.

Proteins are crucial for all living organisms and they are involved in many different processes. The function of a protein is tightly coupled to its structure, yet to determine the structure experimentally is both non-trivial and expensive. Computational methods that are able to predict the structure are often the only possibility to obtain structural information for a particular protein. Structure prediction has come a long way since its inception. More advanced algorithms, refined mathematics and statistical analysis and use of machine learning techniques have improved this field considerably. Making a large number of protein models is relatively fast. The process of identifying and separating correct from less correct models, from a large set of plausible models, is also known as model quality assessment. Critical Assessment of Techniques for Protein Structure Prediction (CASP) is an international experiment to assess the various methods for structure prediction of proteins. CASP has shown the improvements of these different methods in model quality assessment, structure prediction as well as better model building. In the two studies done in this thesis, I have improved the model quality assessment part of this structure prediction problem for globular proteins, as well as trained the first such method dedicated towards membrane proteins. The work has resulted in a much-improved version of our previous model quality assessment program ProQ, and in addition I have also developed the first model quality assessment program specifically tailored for membrane proteins. / <p>QC 20120313</p>