Gyros Protein Technologies AB produce instruments designed to perform automated immunoassaying on plastic CDs with microstructures. While generally being a very robust process, the company had noticed that some runs on the instruments encountered problems. They hypothesised it had to do with the chamber on the CD in which the sample is added to. It was believed that the chamber was not being filled properly, leaving it completely empty or contained with a small amount of air, rather than liquid. This project aimed to investigate this hypothesis and to develop an image analysis solution that could reliably detect these occurrences. An image analysis script was developed which mainly utilised template matching and canny edge detection to assess the presence of air. The analysis had great success in detecting empty chambers and large bubbles of air, while it had some trouble with discerning small bubbles from dirt on top of the CD. Evaluating the analysis on a test set of 1305 images annotated by two people, the analysis managed to score an accuracy of 96.8 % and 99.5 % respectively.

Indiana University-Purdue University Indianapolis (IUPUI) / Malaria infects more than 240 million people every year, causing more than 640,000 deaths in 2021 alone. The complex interactions between the Plasmodium parasites that cause malaria and host immune system have made it difficult to identify specific mechanisms of vaccine-induced and naturally acquired immunity. After more than half a century of research into potential immunization methods, reliable immune correlates of malaria protection still have yet to be identified, and questions underlying the reduced protective efficacy of malaria vaccines in field studies of endemic populations relative to non-endemic populations still remain. In this thesis, I use computational methods to identify biological determinants of whole-parasite vaccine-induced immunity and immune correlates of protection from clinical malaria. Our systems analysis of a PfSPZ Vaccine clinical trial revealed that innate signatures were predictive of increased antibody response but also a decrease in the cytotoxic response required for sterilizing immunity. Conversely, these myeloid signatures predicted protection against parasitemia for subjects receiving a saline placebo, suggesting a role for myeloid-lineage cells in clearing pre-erythrocytic parasite stages. Based on these findings, I created a structural equation model to examine the interactions between cellular, humoral, and transcriptomic responses and the effects these have on protection outcome. This revealed a direct positive effect of CD11+ monocyte-derived cells on parasitemia outcome post-vaccination that was mediated by the presence of P. falciparum-specific antibodies at pre-vaccination baseline. Additionally, this model illustrates an indirect role of CD14+ monocyte activation in restricting immune priming by the PfSPZ Vaccine. Together, this data supports our hypothesis that innate immune activation and antigen presentation are uncoupled from cytotoxic cell-dependent immunity from the PfSPZ Vaccine and that this effect may be antibody-dependent.

Here we present a similarity-based pairing method for generating compound pairs to train a Siamese Neural Network. In comparison with the conventional exhaustive pairing of N2/2 pairs (N being the sizeof the training set), this method results in N-1 pairs, significantly reducing the training time. It exhibits a better prediction performance consistently on the three physicochemical property datasets, using a multilayer perceptron with the ECFP4 fingerprint. We further include into the Siamese Neural Network the pre-trained Chemformer which extracts task-specific chemical features from the input SMILES strings. With the n-shot learning, we propose a means to measure the prediction uncertainty. Our results demonstrate that the higher accuracy is indeed associated with the lower prediction uncertainty. In addition, we discuss implications of the similarity principle in machine learning.

The study explores the integration of mathematical modelling and machine learning to understand tumour-macrophage interactions in the tumour microenvironment. It details mathematical models based on biochemistry and physics for predicting tumour dynamics, highlighting the role of macrophages. Machine learning, particularly unsupervised and supervised techniques like K-means clustering, logistic regression, and support vector machines, are implemented to analyse simulation data. The thesis's integration of K-means clustering reveals distinct tumour behaviour patterns through the classification of tumour cells based on their microenvironmental interactions. This segmentation is crucial for understanding tumour heterogeneity and its implications for treatment. Additionally, the application of logistic regression provides insights into the probability of macrophage polarization states in the tumour microenvironment. This statistical model underscores the significant factors influencing macrophage behaviour and their consequent impact on tumour progression. These analytical approaches enhance the understanding of the complex dynamics within the tumour microenvironment, contributing to more effective tumour study strategies. The study presents a comprehensive analysis of tumour growth, macrophage polarization, and their impact on cancer treatment and prognosis. Ethical considerations and future directions focus on enhancing model accuracy and integrating experimental data for improved cancer diagnosis and treatment strategies. The thesis concludes with the potential of this hybrid approach in advancing cancer biology and therapeutic approaches. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p><p>There are other digital material (eg film, image or audio files) or models/artifacts that belongs to the thesis and need to be archived.</p>

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Large scale genomic sequencing efforts have resulted in a massive inflow of raw sequence data. This raw data, when appropriately processed and analyzed, can provide insight to a trained biologist and aid in hypothesis-driven research. Given the time and resource requirements necessary for biological experiments, computational predictions of gene functions can aid in reducing a large list of candidate genes to a few promising targets. Various computational solutions have been proposed and developed for gene function prediction. These solutions utilize various forms of data, such as DNA/RNA/protein sequences, protein structures, interaction networks, literature mining, and a combination of these data sources. However, these methods do not always produce precise results as the underlying data sets used for training or modeling are quite sparse. We developed and used a massive sequence similarity network build over 108 million known protein sequences to aid in protein function prediction. Predictions are made through the alignment of query sequences to representative sequences for a given cluster derived from the massive sequence similarity network. Derived clusters aggregate information (particularly that from the Gene Ontology) from respective members, which we then consolidate through a novel weighted path method. We evaluate our method on four holdout datasets using CAFA evaluation metrics. Our results suggest that clustering significantly reduces the time and memory requirements, with a marginal impact on predictive power. At lower sequence similarity thresholds, our method outperforms other gold standard methods. / Master of Science / We often think of a protein as a nutritional requirement. However, proteins are far more than just food, they play countless and unappreciated roles in facilitating life. From transporting nutrients in the body, synthesis of hormones, functioning as enzymes to expediting chemical reactions, serving as the scaffold for cells and tissues, to protecting the body against foreign pathogens. On a molecular level, each protein is made up of chains of 20 different amino acids, just like a chain of beads, that are then folded to create a 3-dimensional structure. The variations in the ordering of amino acids result in different types of proteins. There are millions of genes across known life, and they perform different functions when translated into proteins. Nature has given us many proteins with interesting properties, and the low cost of sequencing their precursors (DNA) has resulted in large amounts of sequence data that is not yet associated with a function. Biological experiments to determine the function of a protein can be time consuming and expensive. We built a massive network encompassing 108 million protein sequences based on sequence similarity. This ensures that we make use of as much data as possible to make better predictions. Specifically, our work focuses on utilizing this information of similar proteins to aid in predicting the functions of a protein given its sequences. It is based on the idea of guilt by association, such that if two proteins are similar in sequences, they perform similar functions. We show that using computationally efficient methods and large datasets, one can achieve fast and highly precise predictions.

published_or_final_version / Microbiology / Doctoral / Doctor of Philosophy

Hepatitis B virus (HBV), the prototype member of the family Hepadnaviridae, is hepatotropic and replicates by reverse transcription. HBV is responsible for the chronic infection of more than 240 million people worldwide, of which 65 million reside in Africa. The nine HBV genotypes (A to I) identified to date, are geographically distributed and exhibit different clinical manifestations and treatment responses. The term occult HBV infection (OBI) refers to a HBV infection in which HBV surface antigen (HBsAg) cannot be detected by conventional serological assays as has been defined by the Taormina expert panel. . HBV and human immune deficiency virus (HIV) are both endemic in many parts of the world and share common transmission routes. Worldwide, 10% of those infected with HIV are also chronically infected with HBV. HIV co-infection has been shown to be a risk factor for the development of OBI in individuals infected with HBV. The aim of this study was to characterize, at the molecular level, HBV from mono-infected and HBV/HIV co-infected individuals in Sudan The objectives of this study were the systematic and comparative analysis of HBV genotype D sequences, available in the public databases; the molecular characterization of HBV from mono-infected Sudanese liver disease patients and from HBV/HIV co-infected Sudanese patients; and the development and testing of bioinformatics tools to explore HBV sequence data generated using ultradeep pyrosequencing (UDPS) and comparison of UDPS results with those obtained from cloning based sequencing (CBS). All available complete genomes of genotype D of HBV from the GenBank database were analyzed. The intra-group divergence of the subgenotypes ranged from 0.8% + 0.5 for subgenotype D6 to 3.0% + 0.3 for subgenotype D8. Phylogenetic analysis of genotype D showed separation into six distinct clusters (subgenotypes D1, D2, D3/D6, D4, D5 and D7/D8), with good bootstrap support. The mean intergroup divergence between subgenotype D3 and subgenotype D6 was 2.6%, falling below the accepted threshold of 4% required to define a subgenotype. This suggests that subgenotypes D3 and D6 are the same subgenotype because they also share signature amino acids. Furthermore, subgenotype D8 is a genotype D/E recombinant, which clusters with subgenotype D7. This analysis provided an update on the classification of the subgenotypes of genotype D of HBV. Although HBsAg seroprevalence in Sudan, a central-African country, is greater than 8%, the only sequencing data for HBV, available prior to our study, was from asymptomatic blood donors, where genotype E predominates, followed by genotype D and subgenotype A2. Ninety-nine HBV-positive liver disease patients were enrolled in our study, including: 15 with hepatocellular carcinoma (HCC), 42 with cirrhosis, 30 asymptomatic carriers, 7 with acute hepatitis and 5 with chronic hepatitis. The surface and basic core promoter/precore (BCP/PC) regions, and the complete genome of HBV were sequenced. Eighty-two percent of the samples from HBV mono-infected liver disease patients were genotyped. Fifty-nine percent were infected with genotype D (74% D1, 10% D2, 3% D3 and 13% D6), 30% with genotype E, 8.5% with genotype A and 2.5% with a genotype D/E recombinant. Patients infected with genotype E had a higher frequency of HBeAg-positivity (29.2%) and higher viral loads compared to patients infected with genotype D. BCP/PC region mutations, including the G1896A mutation, seen in 37% of the HBeAg-negative individuals, could account for the HBeAg-negativity. A total of 358 Sudanese HIV-positive patients were enrolled. HBsAg was detected in 11.7% of the participants, indicating chronic HBV infection. HBV DNA was detected in 26.8% of the participants: 11.7% were HBsAg positive (overt infection) and the remaining 15.1% were HBsAg-negative (OBI). Fifty serum samples from the HBV/HIV DNA-positive co-infected participants were selected for genomic analysis of HBV. Of these, the HBV genotype of 37 was determined. The genotype distribution of HBV isolates from the HBV/HIV co-infected participants did not differ significantly from those from the HBV mono-infected patients: genotype D (46%), E (21.6%), A (18.9%) and a D/E recombinant (13.5%). Compared to the HBV isolates from mono-infected liver disease patients, the frequency of the D/E recombinant and genotype A was higher in HBV/HIV co-infected patients, as was the intragroup divergence of genotype E. No difference in BCP/PC mutations affecting HBeAg expression at the transcriptional and translational levels between genotype D and E was observed. The following mutations could account for the HBsAg-negativity: sM133T, sE164G, sV168G and sS174N. No primary drug resistance mutations were found. Two online bioinformatics tools, the ―Deep Threshold Tool (DDT)‖ and the ―Rosetta Tool‖, were built to analyze data generated from UDPS and CBS of the BCP/PC region of four Sudanese serum samples, infected with either genotype D or E of HBV, from HBeAgpositive and HBeAg negative patients. A total of 10952 reads were generated by UDPS on the 454 GS Junior platform. The Threshold was calculated using DDT based on probability of error of 0.5%. In total, 39 unique mutations were identified by UDPS, of which 25 were nonsynonymous. The ratio of nucleotide substitutions between isolates from HBeAg-negative and HBeAg-positive patients was 3.5:1. From the sequences analyzed, compared to genotype E isolates, genotype D isolates showed greater variation in the X, BCP/PC/C regions. Only 18 of the 39 positions identified by UDPS were detected by CBS. Using the specific criteria, that have been suggested previously, to define genotypes/subgenotypes of HBV, we determined that genotype D has six and not eight subgenotypes. The importance of HBV genotypes in clinical consequences of infection and response to antiviral treatment has led us to characterize HBV genotypes circulating in Sudan. HBV mono-infected patients and HBV/HIV co-infected individuals, were mainly infected with genotype D or E. HBV mono-infected patients, infected with genotype E, had higher HBeAg-positivity and higher viral loads than those infected with genotype D. The ratio of genotype A to non- A, as well as the genotype E intra-group divergence were higher in HBV/HIV co-infected individuals compared to HBV mono-infected individuals. OBI was found in 15.1% HBV/HIV co-infected patients and its clinical relevance remains to be determined. In order to overcome the limitations of Sanger sequencing, which include its high cost and inability to detect minor populations in quasispecies, next generation sequencing techniques have been developed. It was demonstrated that correct analysis of UDPS data required appropriate curation of read data, in order to clean the data and eliminate artefacts and that the appropriate consensus (reference) sequence should be used in order to identify variants correctly. CBS detected fewer than 50% of the substitutions detected by UDPS. This new technology may allow the detection of minor variants between the different genotypes of HBV and provide biomarkers for the prediction of clinical manifestation of HBV and response to antiviral therapy.

Listeria monocytogenes and Campylobacter spp. are bacteria that sometimes can cause severe illness in humans. Both can be found as contaminants in food that has been produced, stored or prepared improperly, which is why it is important to ensure that the handling of food is done correctly. The National Food Agency (Livsmedelsverket) is the Swedish authority responsible for food safety. One important task is to, in collaboration with other authorities, track and prevent food-related disease outbreaks. For this purpose bacterial samples are regularly collected from border control, at food production facilities and retail as well as from suspected food items and drinking water during outbreaks, and epidemiological analyses are employed to determine the type of bacteria present and whether they can be linked to a common source. One part of these epidemiological analyses involve bioinformatic analyses of the bacterial DNA. This includes determination of sequence type and serotype, as well as calculations of similarities between samples. Such analyses require data processing in several different steps which are usually performed by a bioinformatician using different computer programs. Currently the National Food Agency outsources most of these analyses to other authorities and companies, and the purpose of this project was to develop a pipeline that would allow for these analyses to be performed in-house. The result was a pipeline named BacIL - Bacterial Identification and Linkage which has been developed to automatically perform sequence typing, serotyping and SNP-analysis of Listeria monocytogenes as well as sequence typing and SNP-analysis of Campylobacter jejuni, C. coli and C. lari. The result of the SNP-analysisis is used to create clusters which can be used to identify related samples. The pipeline decreases the number of programs that have to be manually started from more than ten to two.

In this report, we rank possible protein biomarkers based on different criteria for use in Olink Proteomics’ protein panels. We started off with a list compiled through the Human Plasma Proteome Project (HPPP) and have in different ways used this to obtain the final results. To complete this task we compared the list with Olink’s and its competitors’ protein catalogs, identified diseases beyond Olink’s coverage and the proteins linked with these. We also created a scoring system used to fa- cilitate detection of good biomarkers. From this, we have concluded that Olink should focus on proteins that the competitors have in their catalogs and proteins that can be found in many pathways and are linked with many diseases. From each of the methods used, we have been able to identify a number of proteins that we recommend Olink to investigate further.