Bacterioplankton provide important ecosystem functions by carrying out biogeochemical cycling of organic matter. Playing an important role in the microbial loop they help remineralize carbon and nutrients. Bacteria also interact with phytoplankton during phytoplankton blooms. However, fundamental understanding on the underlying molecular mechanisms involved in the degradation of phytoplankton-derived organic matter is still in its infancy. Therefore, we analysed data from a mesocosm experiment following a natural phytoplankton-bloom from an upwelling system in the North- East Atlantic Ocean. The purpose was to contribute a mechanistic understanding based on functional gene expression analysis of natural microbial assemblages. Our results show the difference in functional gene expression within a bacterial metacommunity and how this functional response drastically switches between bloom build up and senescence. Transcripts showed a broad change in gene expression involving major SEED categories, with the bloom senescence phase exhibiting a higher relative abundance in major categories such as Carbohydrates, Protein Metabolism and Amino Acids and Derivatives. Within these categories genes connected to carbon utilization and transport systems (Ton and Tol) as well as chemotaxis showed a higher abundance during bloom senescence. The change in functionality based on transcripts showed a different bacterial community composition appearing over a very short time. We thus conclude that the bacterial functional gene expression response between build-up and degradation bloom phases is remarkably different and associated with a change in the identity of bacteria with active expression. Our findings highlight the importance of bacterial substrate specialists with different functional roles during different time points of phytoplankton blooms.

Using computational methods to identify protein-protein interactions (PPIs) supports experimental techniques by using less time and less resources. Identifying PPIs can be made through a template-based approach that describes how unstudied proteins interact by aligning a common structural template that exists in both interacting proteins. A pipeline that uses this is InterPred, that combines homology modelling and massive template comparison to construct coarse interaction models. These models are reviewed by a machine learning classifier that classifies models that shows traits of being true, which can be further refined with a docking technique. However, InterPred is dependent on using complex structural information, that might not be available from unstudied proteins, while it is suggested that PPIs are dependent of the shape and interface of proteins. A method that aligns structures based on the interface attributes is InterComp, which uses topological and sequence-order independent structural comparison. Implementing this method into InterPred will lead to restricting structural information to the interface of proteins, which could lead to discovery of undetected PPI models. The result showed that the modified pipeline was not comparable based on the receiver operating characteristic (ROC) performance. However, the modified pipeline could identify new potential PPIs that were undetected by InterPred.

The composition and diversity of the gastrointestinal microbiota and its interaction with human cells have been frequently associated with immune system functions and disease development, including autoimmunity and allergy. This is believed to be mediated in part through epigenetic modifications, mainly as DNA methylation. Several studies have collectively supported the beneficial effects of probiotics for the prevention of allergic disease. However, there have been few studies addressing the possibility for probiotic supplementation to induce epigenetic changes and its importance for allergy development. This study aims to investigate whether probiotic treatment with Lactobacillus reuteri, distributed during and after the pregnancy period, leads to epigenetic changes in the offspring and if this have any effect on the development of allergic disease. DNA methylation data received from a clinical allergy prevention study was analysed through a set of bioinformatics methods and basic network analysis. The obtained results suggests that supplementation with L. reuteri indeed induces some significant changes in DNA methylation. These changes did not exhibit any significant correlation with allergy outcome of the children. Furthermore, the methylation changes were found at positions located in genes not enriched for any allergy-related biological pathways. However, when taking the genes interactions with other genes into account an interconnected gene interaction module could be identified that showed enrichment for biological processes involved in the T cell receptor signaling pathway, central for immune response transduction. Further analyses did not fit into the time-frame of this thesis, but the obtained results gives a first informative view of the effects of L. reuteri on methylation patterns, and points out directions for the continuing project work.

Identifying homologous genes, that is genes from a common ancestor, is important in comparative genomic studies for understanding gene annotation and the predicted function of a gene. Several pieces of software, of which the most well-known is BLAST, have been developed for identifying homologues, but this can be challenging in non-model organisms where sometimes poor quality of genome assemblies and lack of annotation make it difficult to robustly identify homologues. The aim of this project was to build a bioinformatic framework for homology detection using genomes from non-model organisms. The approach developed used genome annotations, annotated polypeptide sequences and genome assembly sequences to detect homologous genes.The framework was applied to identify Drosophila melanogaster homologous wing development genes in the genomes of nine other insect species with the aim to understand the evolution of loss of wings. To identify changes related to wing loss, the homologous protein sequences obtained were aligned and phylogenetic trees were built from them. The aim of creating the multiple protein alignments and phylogenetic trees was to shed light on whether changes in gene sequences can be related to presence or absence of wings. From the set of 21 candidate wing development genes identified with literature and subsequent database searches, I tested eight and was successful in identifying homologues for all of them in eight of the 10 in sectgenomes. This was done using a combination of text searches in genome annotations, searches with Exonerate v. 2.4.0 alignment program in annotated polypeptide sequences and in genome assemblies. The eight genes chosen for testing the framework were based on initial finding of putative homologues in the eight insect genomes when using the first two steps of the framework. For the set of homologous wing development genes examined I was not able to identify any conclusive pattern of potential protein coding changes that correlated with loss of wings in these species. Improvement to the current pipeline could include using query sequences from closer relatives of the 8 test species than D. melanogaster and, of course, testing of the remaining wing development genes as well as further literature study of wing development genes. Together these could improve future studies on the evolution of wing loss in insects.

As the field of next-generation sequencing (NGS) matures and the technology grows more advanced, it is becoming an increasingly strong tool for solving various biological problems. Harvesting and analysing the full genomic sequence of an individual and comparing it to a reference genome can unravel information about detrimental mutations, in particular ones that give rise to diseases such as cancer. At the Rudbeck Laboratory, Uppsala University, a fully automatic software pipeline for somatic mutational analysis of cancer patient sequence data is in development. This will increase the efficiency and accuracy of a process which today consists of several discrete computation steps. In turn, this will reduce the time to result and facilitate the process of making a diagnosis and delegate the optimal treatment for the patient. However, the genomic data of an individual is very sensitive and private, which demands that great security precautions are taken. Moreover, as more and more data are produced storage space is becoming increasingly valuable, which requires that data are handled and stored as efficiently as possible. In this project, I developed a Python pipeline for automatic compression and encryption of NGS alignment data, which aims to ensure full privacy protection of patient data while maintaining high computational and storage efficiency. The pipeline uses a state-of-the-art real-time compression algorithm combined with an Advanced Encryption Standard cipher. It offers security that meets rigorous modern standards, and performance which at least matches that of existing solutions. The system is made to be easily integrated in the somatic mutation analysis pipeline. This way, the data generated during the analysis, which are too large to be kept in operational memory, can safely be stored to disk.

Transcriptome reconstruction is an important component in the bioinformatical part of transcriptome studies. It is particulary interesting when a reference genome is missing, highly fragmented or incomplete, since in such situations, a simple alignment (or mapping) would not necessarily tell the full story. One species with such a highly fragmented reference genome is the Norway spruce (Picea abies (L.) H. Karst.) -- a conifer, which is very important for Swedish economy. Given its long juvenile phase and irregular cone setting, the demand of cultivated seeds are larger than the supply. This yields a desire to understand the transcriptomal biology behind the cone setting in P. abies. This thesis presents an introduction to this situation, and the biological and bioinformatical background in general, followed by two papers in which this is applied: Paper I introduces a novel de novo transcriptome assembler, with a focus on recovering isoforms, and paper II makes use of this assembler to be able to detect connections between scaffolds in the P. abies genome. Paper I also studies P. abies var acrocona, a mutant with shorter juvenile phase than the wild type, in order to detect how cone setting is initiated.  From differential expression studies of both mRNA and miRNA, a number of genes potentially involved in cone-setting in P. abies were found, and also a set of miRNAs that could be involved in their regulation. / Transkriptomrekonstruktion är en viktig komponent i den bioinformatiska delen av transkriptomstudier. Särskilt intressant är detta när ett referensgenom saknas, är kraftigt fragmenterat eller ofullständigt, ty i dessa situationer skulle inte en vanlig inpassning (eller mappning) kunna berätta allt. En art med ett kraftigt fragmenterat referensgenom är gran (Picea abies (L.) H. Karst.) -- ett barrträd, som är mycket viktigt för svensk ekonomi. På grund av dess långa uppväxtsfas och oregelbundna kottsättning, så är efterfrågan av förädlade fröer större än utbudet. Detta lämnar en önskan att förstå den transkriptomala biologin bakom granens kottsättning. Denna avhandling presenterar en introduktion till denna situation, den generella biologiska och bioinformatiska bakgrunden, följd av två artiklar i vilket detta är tillämpat: Artikel I introducerar en ny de novo transkriptomassembler med fokus på att återskapa isoformer, och artikel II tillämpar denna assembler för att kunna hitta länkar mellan scaffolder (genom-delar som hittills inte kunnat länkas med varandra) i grangenomet. Artikel II studerar även granmutanten acrocona (kottegran), vilken har kortare uppväxtsfas än vildtypen, för att kunna se vad som initierar kottsättning.  Från differentiella expressionsstudier av såväl mRNA som miRNA, hittades ett antal gener potentiellt involverade i granens kottsättning, samt några miRNA som kan vara involverade i dess reglering. / <p>QC 2021-02-12</p>

Background: Multiple sclerosis (MS) is a neurological, autoimmune disease which mainly affects people in the age of 20 to 40. The disease course is unpredictable affecting each patient differently, leading to progressiveand irreversible degradation of the central nervous system. There is no treatment that cures this disease, however, there are treatments that either slows down the disease course or prevents progressive disabilities. A treatment called autologous hematopoietic stem cell transplantation (AHSCT) is thought to reset the immune system and induce a new, more tolerant one, thus haltering the disease course. However, the knowledge about the effects causing the improvement seen in patients treated with AHSCT is limited. Methods: To investigate the effect of AHSCT in MS patients, serum lipidomics data from 16 patients was collected at ten timepoints. The lipidomics data was collected for both positively and negatively charged molecules separately as well as within a single experiment called polarity switching, using mass spectrometry. Since the standard method requires two separate experiments to analyze both positively and negatively charged lipids it requires twice the time and resources compared to polarity switching. Results: Comparing the two mass spectrometry protocols showed that the coefficient of variation (CV) was slightly higher for polarity switching compared to the standard method. Nevertheless, the difference was not significant and both methods had in general a good CV, indicating low technical variation. In addition, this thesis showed that polarity switching has a slightly higher percentage of lipids with zero carryover compared to the standard method. The results also indicated that the expression levels of differentially expressed lipids follow two distinct patterns throughout the AHSCT treatment. The largest intensity variation arises after stem cellreinfusion and the lipid intensities are back to nearly initial levels atthe three month follow-up. Finally, many lipids were found to be associated with the change in c-protein levels as well as erythrocyte, leukocyte, and thrombocyte levels that occurred during treatment. Conclusions: This master thesis showed that polarity switching is a good alternative to the standard method, saving both time and resources without losing too much in specificity. In addition, this thesis has shown that differentially expressed lipids follow two distinct expression patterns through the treatment. The lipids levels for both differentially expressed lipids and lipids associated with clinical data were nearly back to baseline levels three months after AHSCT. Hence, AHSCT has a major but short-lasting impact on the lipid levels in peripheral blood.

Actinorhizal plants are in symbiosis with the nitrogen-fixating soil bacterium Frankia, which forms nodules in the plant root. However, several studies also report other endophytic bacteria appearing in the nodules, but their function and interaction with the host plant or Frankia is not yet understood. This thesis used a bioinformatic approach to investigate the metagenomes of eighteen actinorhizal nodule samples to find out which bacteria are present, how the microbiomes differed from each other, and if the genomes of non-Frankia inhabitants could give indications of any functions. The results showed that the bacterial composition, richness, and diversity differed among the samples, especially between the samples sequenced from the field versus those primarily cultivated in a greenhouse. All samples had a substantial number of sequencing reads belonging to potential endophytes, such as strains of Enterobacteria, Pseudomonas, Streptomyces, Micromonospora, Mycobacteria and Pseudonocardia. There seemed to be a common microbial community shared among the plants on a family level, since no significant difference was found in the core microbiomes between the field and greenhouse groups. Some sequences found in the metagenomes were annotated as potential functions of the fellow travellers, such as antibiotic synthesis, proteins involved in regulating abiotic stresses, but also probable plant damaging compounds rather associated with pathogens than symbionts.

Metabolomics is a rising field combining bioinformatics and cheminformatics together. A major component of research is having a reliable data source, which usually comes in the form of metabolomic databases. This paper documents arising issues revolving categorizing metabolome compounds within databases, and a possible solution in the form of an R package that is capable of matching up various metabolome identifiers that originate from various metabolome databases. Then, by using this package we reflect on the average coverage of external reference between metabolome databases to highlight the lack of a universal compound primary identifier. / <p>The thesis presentation was held over Zoom due to the recent COVID19 pandemic.</p>

The molecular mechanism of complementary sex determination in the haplodiploid organisms is poorly understood and has only been described in the honeybee Apis mellifera. In the haplodiploid system, males develop from unfertilized eggs and females from fertilized eggs. However, in some rare cases, diploid males develop from fertilized eggs. They can be distinguished from diploid and haploid males at the molecular level since they are heterozygous like females but are homozygous, like haploid males, at the sex determination locus. In this project, Osmia bicornis was chosen as the model organism, and the aim is to identify the complementary sex determination locus which should be homozygous in all diploid males. Bee nests were collected from the bees' natural habitat, and potential diploid males were identified. Data analysis of whole-genome sequencing on 17 potential diploid males was performed, which identified 80 candidate sex determination loci with 259 genes. Homologs of the Csd gene in A. mellifera were identified but not found in any candidate complementary sex determination loci.