This study investigates the analyses and clustering of Campylobacter spp., Listeria monocytogenes and Shiga toxin-producing Escherichia coli (STEC) at Livsmedelsverket. Livsmedelsverket is a control authority in Sweden. They work with eating habits, what food contains and safe food and good drinking water, where outbreak investigations of the above-mentioned bacterial types is a part of the work. For the investigations Livsmedelsverket uses a pipeline that is written in the programming language Python. The purpose of this project is to add identification of virulence genes and analysis of the STEC bacterium to the script. But also to develop the existing method to be able to cluster more isolates without losing information, enable the user to adjust parameters in the pipeline and write an ethical analysis to the work that is done. Our study shows the analysis and clustering of the three different types of bacteria, clustering of the samples from the analysis, both adaptively and statically, and that it can determine serotype, sequence type and virulence genes. We therefore conclude that STEC can be added to outbreak investigations at Livsmedelsverkets in-house pipeline. The clustering method has also been modified to be able to use more of the information given from the samples with the restriction of having lower accuracy.

Foodborne diseases are a growing health problem today and can be caused by eating food contaminated with bacteria. To monitor known foodborne diseases, institutions keep track of bacteria in surveillance projects. Whole genome sequencing is becoming the new standard method for comparing isolates, which generates large amounts of data. Today, the standard analyses are focused on conserved regions in genomes. The dynamics in less conserved regions can be studied by creating pan-genomes. A pan-genome consists of conserved genes, called core genes, and genes of varied conservation grade, called accessory genes. This thesis aimed to analyse pan-genomes of large datasets from six bacterial species coming from surveillance projects: Campylobacter coli, Campylobacter jejuni, Escherichia coli, Listeria monocytogenes, Salmonella enterica, and Streptococcus pneumoniae. The purpose was to investigate the species dynamics in the genomes and to look at properties of the genomes not included in the standard analyses that are used in surveillance projects today. Bacterial Pan Genome Analysis tool was used for the pan-genome analysis of the six species and datasets of 1,000-2,000 genomes per species were analysed. All species were estimated to have open pan-genomes, meaning the pan-genomes are increasing in size as more genomes are added. Escherichia coli and Salmonella enterica had more dynamic and open genomes compared to the other species. They had the highest number of accessory genes relative to their genome sizes and had the largest accessory segments between core genes. The synteny of the core genes showed high conservation for a part of the core genes in all species. Some core genes always sat directly after each other in the analysed genomes, never having accessory genes between them. Other core genes always had accessory genes between them, indicating very open regions in the genomes. The core genes were evenly distributed through the reference genomes with some regions showing increased gene density for all species. Some regions had a higher gene density for core genes often followed by core genes, and others for core genes often followed by accessory genes. However, the placement of genes needs to be investigated further with more reference genomes to be able to draw confident conclusions.

The non-coding space makes up around 98 % of the genome, but cancer-driving mutations have so far mostly been discovered in protein-coding regions. The majority of somatic non-coding mutations are neutral passenger mutations and identifying non-coding mutations with driving roles in cancer poses a challenge. In this work, evolutionary constraint was used to explore the non-coding space in human osteosarcoma to improve our understanding of how evolutionary constraint can be applied to identify non-coding driver mutations in cancer and describe the unknown role of non-coding mutations in osteosarcoma. Evolutionary constraint scores derived from an alignment of 33 mammals were used to extract non-coding mutations in functional elements from somatic variants of 38 osteosarcoma samples and genes with an enrichment of non-coding constraint mutations in their regulatory regions were identified. The investigation of those genes revealed that non-coding constraint mutations are likely involved in key osteosarcoma pathways. Furthermore, novel osteosarcoma genes and mechanisms were proposed based on the non-coding constraint mutation enrichment analysis. The regulatory potential of individual non-coding constraint mutations was evaluated based on regulatory annotations, functional evidence, transcription factor affinity predictions and electrophoretic mobility shift assays. We concluded that the analysis of non-coding constraint mutations is an efficient way to discover non-coding mutations with functional impact in osteosarcoma which likely play an important role in the disease.

Estimating selection is of key importance in evolutionary biology research. The recent price drop in sequencing and advances in NGS data analysis have opened up new avenues for novel methods that estimate selection quantitatively from time-series allele frequency data. However, it is not yet well understood which method performs best given specific model systems and experimental designs. Here, using popular quantitative metrics, we compared the performance of four prominent methods on a series of simulated data sets and on data from real biological experiments. We identified in three out of four methods the experi- mental conditions best suited for estimating selection. We also explored the limitations of these methods when estimating selection from complex patterns of allele frequency change in some relevant evolutionary scenarios. Our findings highlight the need for modification of population genomics models that are still used in inference of model parameters with the goal to develop new, more accurate methods for the quantitative estimation of selection in time-series genomic data.

Screening compounds with image-based analysis is an important part in the processof drug discovery. It is an efficient way to screen compounds as it gives moreinformation than for example HTS. High-content screening as it is also called, hasreally progressed in recent years, as the field of data science evolves, and with it sodoes the efficiency of how images can be processed into information. Anotherimportant part of the drug discovery field is the family of receptors GPCRs, a largefamily of over 800 different receptors in humans. The reason GPCRs are importantin drug discovery is because of the large number of drugs targeting them. In thisexperiment we wanted to use image-based analysis to challenge drugs orcompounds that were said to be specific and see if they actually are that specific, orif we can see indications of the drug also working somewhere else. While the drugswe tested did not appear to cause any morphological perturbations large enough todistinguish them from the control, some drugs appear to cluster differently. Thismight suggest that they affect multiple targets, but it needs to be followed up upon inorder to draw any substantial conclusions.

Cellular metabolism is one of the most fundamental systems for any living organisms, involving thousands of metabolites and reactions that forms large interconnected metabolic networks. Proper and comprehensive understanding of the metabolism in human cells has been a field of research for a long time. One of the key parameters in understanding the metabolism are the metabolic fluxes, which are the rates of conversion of metabolic intermediates. Currently, one of the main approaches for determining these fluxes is metabolic flux analysis (MFA), in which isotope-labelled compounds are introduced into the system and measured. Mathematical models are then used to calculate a prediction of the systems flux configuration. However, the current paradigm of MFA lack established methods for validating that a model can accurately predict quantities for which there are no experimental data. In this study, a model for the central human metabolism was created and evaluated with regards to the model’s ability to predict a validation dataset. Further, an uncertainty analysis of these predictions were performed with a prediction profile likelihood analysis. This study has conclusively shown that MFA models can be validated against experimental data that the model has never seen before. Additionally, such model predictions were shown to be observable with a well determined prediction uncertainty. These results shows that a systematic validation of MFA models is possible. This in turn allows for a greater trust to be placed in the models, and in any conclusions that are based on such models.

Coccidiosis caused by Eimeria parasites is a worldwide problem, affecting chickens and leading to great losses in the poultry industry. Current vaccines are costly and non-optimal, and the parasite has developed resistance to the anticoccidials in use. To be able to develop more efficient and cost-effective vaccines, further research into the immune response in poultry is needed. Here, we have analyzed immune chickens undergoing a secondary E. tenella infection using dual RNA-seq, as well as compared the immune response of the immune chickens to that of naïve chickens. Samples were taken from caecal tissue where the parasites replicate at six timepoints between 0 and 10 days post infection. The reads were put through a bioinformatic pipeline for preprocessing, mapping, counting and differential expression analysis. Using this we found 69 differentially expressed chicken genes (DEGs) in the secondary infections.The results show that DEGs are mainly found 1 and 2 days post infection (dpi), and a large proportion are interferon (IFN) stimulated genes. Compared to samples from naïve chickens, the immune chickens also expressed fewer cytokines and chemokines and the responses are lower at late time points (4 and 10 dpi). There are also lower counts of parasites in the immune chickens. These results show that immune chickens have a much faster response to E. tenellacompared to that of naïve chickens, and that there is a clear IFN-signature. We hypothesize that IFN-mediated inhibition of parasite replication is an important effector mechanism in protective immunity to Eimeria infection.

The Central Arctic Ocean is a repository of rich and diverse biota, whose major portion is one of the most important drivers of global biogeochemical cycles, including carbon cycling. In this study, the functional potential of the microbiota to fix carbon with alternative carbon fixation pathways were investigated along with their chemolithotrophic characteristics. Samples from two expeditions MOSAiC & SAS-Oden (2019-2021) resulted in metagenomic data consisting of about 1200 mOTUs (metagenomic Operational Taxonomic Units). Kofamscan based annotation explained by KEGG pathways database was analysed to explore prevalence of the alternative Carbon Fixation pathways across different taxa. From the six carbon fixation pathways, three were consolidated for their presence (rTCA, DC-HP, HP-HP). In order to explain the other metabolic processes that these organisms employ to survive, a functional annotation tool for metabolic pathways was used. that Reductive Tricarboxylic acid cycle pathway was found to be most present and observed in 5 out of 6 mOTUs selected from filtering the dataset. The taxa include bacterial phyla Proteobacteria, Actinobacteria, Chloroflexota and Marinisomatota and archaeal phyla Thermoplasmota. However, for the other pathways and less studied organisms less resolution were observed across the dataset for the presence of other pathways. These CFPs found were also supported by oxidation of inorganic compounds with high redox potentials. This study provides a glimpse of the metabolic potential of the Central Arctic Ocean microbiota, shines light on the importance of understanding and unravelling the intricacies of this rich and diverse environment.

Antimicrobial resistance (AMR) is rapidly becoming a public health issue, as more and more infectious bacteria become resistant to our known antibiotics. Suggested reasons for the proliferation of these strains are misuse and overuse of antimicrobials, especially on an industrial level, in agriculture, livestock and aquaculture. These resistances are not unique for pathogenic bacteria but originates in nature where complicated systems of microbes interact with each other. Freshwater environments hold special interest as they provide drinking water, have unique biodiversity, and provide other ecosystem services. Soil environments have the most diverse microbial communities and are often the source of new discoveries in microbial functions and AMR interactions. Since the vast majority of microbes can’t be maintained in pure culture or replicated in the laboratory setting, metagenomic methods have proven to be vital for understanding the diversity and occurrence of AMR in the environment that would otherwise have remained unexplored and unaccounted for. By producing metagenomic pipelines that utilize parallel computing to handle vast amounts of data, a catalogue of AMR in 390 samples mainly from Scandinavia was created from two datasets. One of the datasets had been previously processed and published, while the other was managed from raw reads to metagenomic assembled bins. This revealed a difference in the distribution of resistance mechanisms that microbes utilize to achieve AMR based on lifestyle and that AMR can be found in most taxa as well as in any freshwater and soil environments.

Although transposable elements (TEs) have been subjected to detailed study in various organisms such as humans, maize, and drosophila, this is not the case for all organisms. Despite numerous studies on the effects of TEs in the field of evolution and functional genomics, there has not been many studies yet on how much variation these elements show in populations. To address these questions, we identified TEs in Leptidea sinapis based on a newly produced high-quality genome assembly and identified novel TEs in this project. In the first step of the project, we manually curated consensus sequences of the 150 most abundant TE subfamilies. We could identify 145 of these subfamilies: two of which were non-curatable because of bad consensus sequences, three that were uncertain where they start and end, and one of the subfamilies were divided into two different subfamilies. Hence, we ended up with 146 different TE subfamilies, and the remaining part of the project was carried out using these. In the second step, we examined how the manually curated 146 subfamilies were distributed in 83 different L. sinapis individuals in the Swedish population. Before performing manual curation for our selected TEs, we looked at the TE landscape of the long-read sequenced L. sinapis genome and showed that 58.2% of the L. sinapis genome consists of TEs. In a recent study, it has been shown that 40% of L. sinapis consists of TEs. So, when compared to previous studies, our result showed that the L. sinapis genome contained more TEs than previously reported. When we made the same analysis after manual curation, we showed that this amount increased to 62.4%. The distribution of classified TEs by groups is as follows: LINE 22.6%, DNA 7.43%, SINE 4.76%, LTR 3.10%.  After creating the final TE landscape for our reference genome, we analyzed 83 different individuals collected from different regions of Sweden such as Uppland, Östergötland, Västmanland, Närke, Värmland, Dalarna, Hälsingland, Småland, Medelpad, and Västerbotten for the individual number of non-reference insertions using RelocaTE2. We observed that these 146 subfamilies showed different distributions among individuals based on their sequence coverage. We couldn’t find any correlation between the number of insertions and the latitude of locations where individuals had been collected. When we look at the total number of insertions, we realized type I transposable elements were more abundant compared to type II transposable elements. Also, we checked the percentage of covered bases per individual in our dataset and observed that individuals with greater coverage had more TE insertions. After realizing this, when we analyzed individuals from different locations with very similar coverage, we could not see a significant correlation between the number of TE insertions and the latitude of locations of butterflies from different locations. For this reason, we can say that for the most abundant 146 TE subfamilies in the reference genome, there is not a significant difference between regions of Sweden. This study contributes to a better analysis of TE content in L. sinapis, and the know-how and possible problems with technical bias for individual TE insertion studies in general.