Cancer is a dangerous disease caused by mutations in the host's genome that makes the cells proliferateuncontrollably and disrupts bodily functions. The immune system tries to prevent this, but tumours have methods ofdisrupting the immune system's ability to combat the cancer. These immunosuppression events can for examplehappen when the immune system interacts with the tumour to recognise it or try and destroy it. The tumours can bychanging their displayed proteins on the cell surface avoid detection or by excreting proteins, they can neutralisedangerous immune cells. This happens within the tumour microenvironment (TME), the immediate surrounding of atumour where there is a plethora of different cells both aiding and suppressing the tumour. Some of these cells arenot cancer cells but can still aid the tumour due to how the tumour has influenced them. For example, throughangiogenesis, where new blood vessels are formed which feeds the tumour. The interactions in the TME can be used as a target for immunotherapy, a field of treatments which improves theimmune system's own ability at defending against cancer. Immunotherapy can for example help the immune systemby guiding immune cells towards the tumour. It is therefore essential to understand the complex system ofinteractions within the TME to be able to create new methods of immunotherapy and thus treat cancers moreefficiently. Concurrently new methods of mapping what happens in a tissue have been developed in recent years,namely spatial transcriptomics (ST). It allows for the retrieval of transcriptomic information of cells throughsequencing while still retaining spatial information. However, the ST methods which capture the wholetranscriptome of the cells and reveal the cell-to-cell interactions are not of single-cell resolution yet. They capturemultiple cells in each spot, creating a mix of cells in the sequencing. This mix of cells can be detangled, and theproportions of each cell type revealed through the process of deconvolution. Deconvolution works by mapping thesingle cell expression profile of different cell types onto the ST data and figuring out what proportions of expressioneach cell type produces the expression of the mix. This reveals the cellular composition of the microenvironment.But since the interactions in the TME depend on the cells current expression we need to deconvolute according tophenotype and not just cell type. In this project we were able to create a tool which automatically finds phenotypes in the single-cell data and usesthose phenotypes to deconvolute ST data. Phenotypes are found using dimensionality reduction methods todifferentiate cells according to their contribution to the variability in the data. The resulting deconvoluted data wasthen used as the foundation for describing the growth of a cancer as a system of phenotype proportions in the tumourmicroenvironment. From this system a mathematical model was created which predicts the growth and couldprovide insight into how the phenotypes interact. The tool created worked as intended and the model explains thegrowth of a tumour in the TME with not just cancer cells phenotypes but other cell phenotypes as well. However, nonew interaction could be discovered by the final model and no phenotype found could provide us with new insightsto the structure of the TME. But our analysis was able to identify structures we expect to see in a tumour, eventhough they might not be so obvious, so an improved version of our tools might be able to find even more detailsand perhaps new, more subtle interactions.

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Ascetosporea is a class of poorly studied unicellular eukaryotes that function as parasites of marine invertebrates. These parasites cause mass mortality events in aquaculture species such as oysters and mussels. The economic importance of these aquaculture species should lead to more attention on the genomics of Ascetosporea and their place on the evolutionary tree of life. With the onset of global warming and rising sea levels and temperatures, many emerging pathogens have been seen and until these are sequenced and analysed, it is difficult to make any conclusions about their relationships and evolution. As there aren’t many genomes and transcriptomes available for Ascetosporea, their position in the larger eukaryotic tree of life remains hypothetical. To attempt to remedy this lack of information, the Burki lab has recently generated sequencing data through sample collection and sequencing for these organisms (genomes and transcriptomes). A curated dataset of the various eukaryotic species was previously created and newly sampled and sequenced Ascetosporean genomes of Paramarteilia sp., Marteilia pararefringens, Paramikrocytos canceri, etc. from multiple sampling locations like Ireland, Norway, Sweden, and the UK were included. These could increase the genomic and transcriptomic data available for Ascetosporea and help to resolve the relationships within Ascetosporea. A few reasons why this group has not yet been placed on the tree of life are that the samples are from host tissue, which makes it difficult to sequence these parasites. These Ascetosporeans have also been seen to be very fast-evolving. After building phylogenetic relationships with single gene trees to allow for the identification of possible contaminants and paralogs, it was seen that there was a lot of contamination in Ascetosporea, due to the sampling being from host tissue material (hosts are open to the environment). After cleaning and filtering the possible contaminated genes, the trees were remade and a possible link between a fungal group called Microsporidia and Ascetosporea was observed in a few genes. This was hypothesized to be lateral gene transfer between the two groups resulting from their similar lifestyles and infection of invertebrates. There were complications like contamination and short blast hits that arose during analysis, and these could be caused by problems by fragmentation in the genome. This fragmentation could have negative effects on genome annotation predictions and consequently phylogenetic and phylogenomic analysis. Due to this and the challenging nature of collecting samples, the read coverage for the genomes is low but it can be used to perform phylogenetic and phylogenomic studies using currently available data and methods. Another expected result was that the sequenced data had contaminants, and a thorough and comprehensive search would have to be conducted on a dataset-wide level to remove any contaminants.

Complement proteins play an important role in the body's immune processes and involve in the pathology of many diseases in human, such as major depressive disorder and schizophrenia. Understanding the genetics of those proteins is an important step toward unraveling their effects on psychiatric disorders.  The complement protein levels differ between neonates and adults. There are many studies investigating the genetic architecture of the complement proteins, but evidence about the genetics of neonatal complement proteins is scarce. In this study, I investigated the SNPs and haplotypes that are associated with the five complement proteins, C1q, C3, C4, CFB, and CFH among newborns. I also compared the effects of the identified SNPs among neonates and adults. This study uses 75 samples from Swedish newborns whose blood were primarily collected for Phenylketonuria screening. Genotype data and protein levels were measured from dried blood spots. To investigate the genetics of the complement proteins, I first conducted single SNPs association analyses. The single SNPs used in this study was derived from previous research of European adult samples and has been shown to be significantly associated with the target protein. Then, after imputing the SNPs for the MHC region, I conducted haplotype analysis in the MHC region for the five complement proteins. Finally, I compared the effects of the variants identified in the single SNPs association analysis with the effects that were reported for adult protein levels in previous studies. The results of single SNP association analysis showed that among the 14 SNPs that were associated with adult protein levels, SNP rs4151669 that associated with complement factor B (CFB) was significantly associated with the target protein in the neonatal population. Some SNPs (rs8283, rs4151669 and rs10737680) may have opposite effects in the two populations. This study found 86 haplotypes potentially associated with the complement proteins. Among them, the haplotype “H840” which located at chr6:32594217-32597172 was significantly associated with five complement proteins. This study provides evidence for the genetic component of the 5 complement protein levels among neonates. The results also suggested that the genetic influence of the complement protein among adult and neonatal population are different.

Lung cancer is one the most dangerous types of all cancer. Several studies have explored the use of machine learning methods to predict and diagnose this cancer. This study explored the potential of decision tree (DT) and random forest (RF) classification models, in the context of a small transcriptome dataset for outcome prediction of different subtypes on lung cancer. In the study we compared the three subtypes; adenocarcinomas (AC), small cell lung cancer (SCLC) and squamous cell carcinomas (SCC) with normal lung tissue by applying the two machine learning methods from caret R package. The DT and RF model and their validation showed different results for each subtype of the lung cancer data. The DT found more features and validated them with better metrics. Analysis of the biological relevance was focused on the identified features for each of the subtypes AC, SCLC and SCC. The DT presented a detailed insight into the biological data which was essential by classifying it as a biomarker. The identified features from this research may serve as potential candidate genes which could be explored further to confirm their role in corresponding lung cancer types and contribute to targeted diagnostics of different subtypes.

Nausea and vomiting are among the most common symptoms of early pregnancy. Its most extreme form Hyperemesis gravidarum often requires hospitalization and has been linked as a risk factor of perinatal depression. The emetic reflex is to a large extent triggered in the intestinal epithelium by the enterochromaffin cells, however the interplay between gut microbiome and pregnancy nausea is yet unclear. The aim of this study is to investigate the variation in gut microbiota diversity on second-trimester pregnant women with different levels of nausea, and to ascertain potential key species involved in that variation. Using shotgun sequencing to capture bacterial diversity from 1078 fecal samples, we found a reduction on species richness on women with strong nausea. There are measurable differences in the gut microbiota community composition based on the strength of nausea although depression seemed to be even more relevant to explain those differences. Our results provide evidence for the association of nausea and perinatal depression, but further studies are needed to elucidate the mechanisms underpinning the gut-brain axis cross-talk role in nausea and perinatal depression. No evidence of variation in species evenness or differential abundance of species were found. Finally, random forests results point at Lactococcus lactis as potentially displaying a key role determining the intensity of the nausea, although better models are needed to infer clear assumptions.

Protein-protein interactions build large networks which are essential in understanding complex diseases. Due to limitations of experimental methodology there are problems with large amounts of false negative and positive interactions; and a large gap in the amount of known interactions and structurally determined interactions. By using computational methods these problems can be alleviated. In this thesis the quality of a newly developed pipeline (InterPred) were investigated for its ability to generate coarse interaction models and score them. This ability was investigated by performing docking experiments in Rosetta on models generated in InterPred. The results suggest that InterPred is highly successful in generating good starting points for docking proteins in silico and to distinguish the quality of models.

Ökningen av sekvenseringsdata har skapat ett behov av att ta fram nya och flexibla analysmetoder för att kunna analysera datan. Många sekvenseringsteknologier har utvecklats genom åren, med olika syften och de är idag mer specialiserade. Kostnaden för att sekvensera har även sjunkit kraftigt och idag är kostnaden bara en bråkdel av kostnaden för 20 år sedan.   En av dessa heter Spatial Transcriptomics där mRNA kan analyseras med Spatiell upplösning. Experimenten skapar stora mängder data och analysmetoder som ursprungligen var utvecklade för scRNA-seq har nu ocksp blivit mer specialiserade mot spatial data. En analysmetod som använts länge är Seurat som utvecklades av Satija labbet under 2015. Men de senaste åren har även nya metoder utvecklats. Två av dessa, Giotto och Squidpy kommer att jämföras med Seurat som referens för att reda ut hur bra de presterar för Spatial Transcriptomics analyser. Datan som kommer användas kommer från hjärnvävnad från fyra olika möss som testades i NASAs RR3 mission. Två av mössen är av ”flight” skick och kommer jämföras med två stycken ”ground” kontroller. I data analysen kommer Quality Control, Normalization, Integration, Dimensional reduction, Clustering och Differential Expression analysis testas. Förutom de steg som testas i analysen kommer även parametrar som analysmetodernas flexibilitet, duration och prestation att testas och jämföras. Resultaten i detta projekt visade att Seurat presterar bättre än Giotto och Squidpy utifrån de parametrar som testas. / The increase in data received from sequencing has created a need for new and accurate frameworks to analyze the data. There are many sequencing technologies developed for different purposes. They have become more specialized and the cost compared to 20 years ago is just a fraction. One of the technologies is Spatial Transcriptomics, where mRNA can be analyzed with spatial resolution. The experiments has high throughput, and frameworks that was original developed for scRNA-seq has also started to be more specialised towards spatial data. Seurat has been widely used for that purpose for many years and was developed by the Satija Lab. But many more frameworks have been developed. In this project’s scope, two other frameworks, Giotto and Squidpy, will be benchmarked with Seurat as the golden standard and a referece to examine how the frameworks perform with Spatial Transcriptomics data as input. The dataset consists of four mouse brain tissue sections from the NASA RR3 mission. Two of the mouse brains are of ”flight” condition while the two others are used as ”ground” controls. The pipeline used in all three frameworks includes Quality Control, normalization, integration, dimensional reduction, clustering, and differential expression analysis. Except for the pipeline steps other parameters has been tested including: the flexibility of the frameworks, the duration of analysis, and the performance. The results showed that Seurat outperforms Giotto and Squidpy according to the tested parameters. Mainly because of more developed integration features when working with multiple data. But both Squidpy and Giotto shows great potential and has a lot of features that was not useful for this project, but however can for other projects be very promising.

SammanfattningSyftet med denna studie var att jämföra NADH-kopplade mätsystem och in vitro motilitets-analys (IVMA) för att bestämma aktiviteten hos isolerat myosin. Från NADH-kopplade analysmätningar bestämdes tre parameter: den maximala hastigheten med vilken myosin hydrolyserar ATP i frånvaro av F-aktin (V0), den maximala ATPas-hastigheten för myosin i närvaro av mättande aktin (kcat) och den koncentration av aktin som behövs för att nå halv maximal aktivering av myosin ATPas-aktivitet (KATPas). Från in vitro-motilitets-analys (IVMA) bestämdes två parametrar: fraktion av rörliga filament (FMF) och totala antalet rörliga filament (TMF). Från detta kunde vi uppskatta den fraktion av aktiva huvuden i myosinpreparationer som behövs för en lyckad IVMA.Myosin är ett protein som tillsammans med aktin är ansvarigt för muskelkontraktionen. I denna studie används två myosin preparationer (HMM-fragment) som vi betecknade ”bra HMM” och ”dåligt HMM” på grund av deras kvalitet för aktin motilitet. Först mättes ATPas-aktiviteten hos myosinmotorer med hjälp av ett NADH-kopplat mätsystem som bygger på övervakning av förändringen i absorbans av NADH. Därefter bestämdes V0, kcat, och KATPas för aktin-beroende av myosin-ATPast genom att mäta myosinaktivitet vid olika aktinkoncentrationer, följt av anpassning av data till Michaelis-Menten ekvationen.Parallellt utfördes IVMA-studier genom att HMM immobiliserades på ett objektglas som derivatiserats med trimetylklorsilan. Sedan observerades när HMM flyttar fram fluorescensmärkta aktinfilament i närvaro av ATP. Under samma förhållanden gav resultaten för basalt myosin ATPas aktivitet V0 värden som var ~0,03 ATP s-1 myosinhuvud -1for både dåligt och bra HMM. I en jämförelse mellan de två HMM vid olika F-aktin-koncentrationer var hastighet i ATP-förbrukningen högre för bra än för dåligt HMM. Anpassning av data till Michaelis-Menten-ekvationen gav kcat på 7,18 ATP s-1myosinhuvud-1för dåligt HMM jämfört med 11,21 ATP s-1 myosinhuvud-1för bra HMM (35 % högre). KATPas (Km) för dåligt HMM var lite högre jämfört med den för bra HMM. Vid IVMA-studierna var FMF och TMF 80 % respektive 98 % lägre för dåligt än bra HMM. Slutsatsen var att de två metoderna karakteriserar HMM-funktionen på olika sätt och med olika känslighet. Om man antar att bra HMM har nästan 100 % aktiva huvuden och eftersom man vet att uppmätt kcat är direkt proportionellt mot antalet aktiva myosinhuvuden ser man från dessa mätningar att mycket mer än 65 % av totalt myosin måste vara aktivt för att ge god aktinmotilitet i en IVMA.

In many countries, cancer is one of the biggest threats for citizens’ health, especially among aged people. Genomic mutations play a crucial role in cancer cell development. In previous decades, cancer research has been mainly focused on mutations in coding regions. These mutations can directly change the encoded protein sequences and influence their functions. In recent years, as the function of non-coding regions has been gradually understood, a growing number of studies have focused on the role of non-coding mutations in cancer. Transcription factor (TF) is an important group of gene regulatory factors. These factors only bind to specific sequences called transcription factor binding motifs (TFBMs) in the genome. Mutations in these motifs can disrupt the TF binding and thus influence gene regulation. A framework called funMotifs was made to predict and annotate functional TFBMs in the human genome. And a research has been made to intersect the mutation information from Pan-Cancer Analysis of Whole Genomes (PCAWG) to motifs in funMotifs, aiming to give a general view of influence of cancer-related mutations on functional TF motifs. But the research only focused on the existing motifs that were identified previously from the normal genome, while de novo motifs that could be potentially created by mutations were disregarded. An instance near the TERT promoter has been found, showing that mutations create a de novo ETS binding site and up-regulate the TERT expression.  My study aims to extend the borderline of funMotifs, from existing motifs to de novo motifs created by cancer-related mutations. I extended the original motifs in funMotifs database and merged the overlapping motifs into longer regulatory elements. Then I mutated these elements according to the mutation data from PCAWG. Next I scan through the mutated elements and identify TF motifs. These motifs were then intersected with original motifs in funMotifs database to remove the redundant results. After intersection and filtering, 2,525,771 de novo motifs were retained. These motifs mainly come from C2H2 zinc finger factors, tryptophan cluster factors, STAT domain factors, fork head/winged helix factors, MADS box factors and homeo domain factors. Even though the de novo motifs I found in this study still need further verification and analysis, for example the change of information content in the mutated sites of the motifs, the result I obtained can be a useful data source for further research on regulatory impact from cancer-related mutations. / <p></p><p></p><p></p><p></p><p></p>