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>

In this thesis, two algorithms that detect cross-contamination in tumor samples sequenced with next-generation sequencing (NGS) were evaluated. In genomic medicine, NGS is commonly used to sequence tumor DNA to detect disease-associated genetic variants and determine the most suitable treatment option. Targeted NGS panels are often employed to screen for genetic variations in a selection of specific tumor-associated genes. NGS handles samples from multiple patients in parallel, which poses a risk of cross-contamination between samples. Contamination is a significant issue in the interpretation of NGS results, as it can lead to the incorrect identification of genetic variants and, consequently, incorrect treatment. Therefore, contamination detection is a crucial quality control steps in the analysis of NGS data. Numerous algorithms for detection of cross-contamination have been developed, but many of these algorithms are not suited for small, targeted NGS panels, and several are not developed for tumor data. In this thesis, GATK's CalculateContamination and a self-created algorithm called ContaCheck were evaluated on simulated tumor NGS data. NGS samples were generated in silico with a Python script called BAMSynth and mixed to simulate cross-contamination with contamination rates between 1% and 50%. ContaCheck accurately detected contaminations ranging from 3% to 50% and identified the correct contaminant with an accuracy of 94%. CalculateContamination, on the other hand, detected contaminations ranging from 1% to 15% relatively accurately, but consistently failed to detect high level contaminations. The study showed that ContaCheck outperformed CalculateContamination on simulated NGS data, but to determine which algorithm is the best on real data and determine ContaCheck's applicability in a clinical setting, the algorithms need to be further evaluated on real tumor NGS samples.

Advances in multiplex fluorescence imaging now allow the measurement of more than 50protein markers in whole tissue sections at single-cell resolution. This promises to reveal tumor biology at an unprecedented level of detail, both in undisturbed growth and in therapy. However, to quantitatively analyze these images, the images must be broken down into the basic units of tumor biology: single cells and their types. In this study, we applied a graph-based unsupervised clustering method, Leiden, to perform cell type identification in highly multiplexed fluorescence images, and based on the annotated images, we ran the tumor microenvironment niches analysis in order to resolve the recurring patterns of tumor microarchitecture. This thesis first introduces several potentially feasible clustering methods selected based on the structure of the datasets studied. The performance and stability of these clustering methods were compared. The project involved benchmarking different dimensionality reduction and clustering techniques on manually annotated reference datasets and healthy tissue with known cellular composition. It was ultimately determined that appropriate data transformations combined with Leiden clustering methods with proper parameters could automatically identify cells in a way coherent with established marker profiles. The results imply that Leiden clustering can also identify clusters of cells with novel marker combinations. Careful examination of the multiplex images shows that the markers are indeed found in the tumor, leading to new hypotheses regarding tumor biology. Tumor microenvironment niches analysis found several archetypal niches with specific cellular composition, indicating active accumulation of immune cells after radiotherapy, and the less vascularized feature of rebound glioblastomas after treatment. We hope to further validate our analysis to provide new insights into the pathological process of glioblastoma. In future research, the analysis pipeline is planned to be improved so that it can be robustly used to analyze the growing data of multiplexed tumor images, both in mouse cancer models or patient samples.

It is currently understood that diseases are typically not caused by rogue errors in genetics but have both molecular and environmental causes from myriad overlapping interactions within an interactome. Genetic errors, such as that seen by a single-nucleotide polymorphism can lead to a dysfunctional cell, which in turn can lead to systemic disruptions that result in disease phenotypes. Perturbations within the interactome, as can be caused by many such errors, can be organized into a pathophenotype, or “disease module”. Disease modules are sets of correlated variables that can represent many of a disease’s activities with subgraphs of nodes and edges. Many methods for inferring disease modules are available today, but the results each one yields is not only variable between methods but also across datasets and trial attempts. In this study, several such inference methods for deriving disease modules are evaluated by combining them to create “consensus” modules. The method of focus is Double-Specific Betweenness (S2B), which uses betweenness centrality across separate diseases to derive new modules. This study, however, uses S2B to combine the results of independent inference methods rather than separate diseases to derive new modules. Pre-processed asthma and arthritis data are compared using various combinations of inference methods. The performance of each result is validated using Pathway Scoring Algorithm. The results of this study suggest that combining methods of inference using MODifieR or S2B may be beneficial for deriving meaningful disease modules.