The evolution of a large, diverse group of intracellular bacteria was previously very difficult to study. Recent advancements in both metagenomic methods and bioinformatics has made it possible. This thesis investigates the evolution of the order Legionellales. The study concentrates on a group of proteins essential for pathogenesis and host manipulation in the order, called effector proteins. The role of effectors in host adaptation, evolutionary history and the diversification of the order were investigated using a multitude of bioinformatics methods. First, the abundance and distribution of the known effector proteins in the orderwas found to cover newly discovered clades. There was a clear distinction between the proteins present in Legionellales and the outgoup, indicating the important role of the effectors in the order. Further, the effectors with known functions found in the new clades, particularly in Berkiella, revealed potential modes of host manipulation of this group. Secondly, the evolution of the effector gene content in the order shed light on theevolution of the order, as well as on the potential evolutionary differences between Legionellaceae and Coxiellaceae. In general, most of the effectors were gained early in the last common ancestor of Legionellales and Legionellaceae, as further indication of their role in the diversification of the order. New effector genes were acquired in the Legionellaceae even up to recent speciation events, whereas Coxiellacea have lost more protein coding genes with time. These differences may be due to horizontal gene transfer in the case of gene gains in Legionellaceae and loss of selection in the case of gene losses in Coxiellaceae. Third, the early evolution of core gained effector proteins for the order was studied.Two of the eight investigated core effectors seem to have a connection to eukaryotes, the rest to other bacteria, indicating both inter-domain and within bacteria horizontal gene transfer. In particular, one effector protein with eukaryotic motif gained at the last common ancestor of Legionellales, was found in all the clades and is therefore an important evolutionary link that may have allowed Legionellales to utilize eukaryotic hosts.

Protein-protein interactions occur in nature and have different functions. The interacting surface between two interacting proteins contains the respective protein's interface residues. In this thesis, a series of Python scripts are presented which can perform interface-interface comparisons with the method InterComp, to obtain a distance matrix of different protein interfaces. The distance matrix can be studied with the use of clustering algorithms such as DBSCAN. The result from clustering using DBSCAN shows that for the 77,017 protein interfaces studied, a majority of the protein interfaces are part of a single cluster while most of the remaining interfaces are noise for the tested parameters Eps and MinPts. The conclusion of this thesis is the effect on the number of clusters for the tested parameters Eps and MinPts when performing DBSCAN.

Sepsis is one of the leading causes for fatalities in the intensive care unit, and also one of the biggest health problems worldwide. It is a disease caused primarily by bacterial infections but can also be caused by viral or fungal infections. Since it is such a big health problem being associated with increased risk of sepsis, coupled with longer stays in the intensive care unit, the need for fast diagnosis and treatment is very important. Currently, culture is the leading diagnostic method for identification of bacteria, although other methods are currently being tested to improve identification time and decrease cost and workload. Next generation sequencing (NGS) has the capacity to output several million reads in a single experiment, making it very fast and relatively cheap compared to other older sequencing methods such as Sanger sequencing. The ability to analyze genes and even whole genomes, opens the possibilities to identify factors such as bacterial species, virulence genes and antibiotic resistance genes. The aim of this study was to find any possible correlations between 16 species of streptococci and clinical data in patients with suspected sepsis. Initial species identification was performed using MALDI-TOF before the samples were sequenced using NGS. Sequence files were then quality controlled and trimmed before being assembled. Following assembly, coverage was controlled for all assembled genomes before the downstream analysis started. Different tools such as 16S RNA species identification, multi locus sequence typing and antibiotic resistance finder were used, among other tools. The results were extremely mixed, with the overall quality of the data being of good quality, but the assembly and downstream analysis being worse. The most consistent species was S. pyogenes. No correlation between sepsis patients and relevant clinical data was found. The mixed quality of results from assembly and downstream analysis were most likely contributed to difficulties in culturing and sequencing of the streptococci. Finding ways to circumvent these problems would most likely aid in general sequencing of streptococcal species, and hopefully in clinical applications as well.

Gene expression analysis can be performed by a number of methods. One of the most common methods is using relative qPCR  to assess the relative expression of a determined set of genes compared to a reference gene. Analysis methods benefits from an as homogeneous sample set as possible, as great variety in original sample disease status, quality, type, or distribution may yield an uneven base expression between replicates. Additionally normalization of qPCR data will not work if there are missing values in the data. There are methods for handling non-detects (i.e. missing values) in the data, where most of them are only recommended to use when there is a single, or very few, value missing. By integrating microarray expression data with qPCR data, the data quality could be improved on, eradicating the need to redo an entire experiment when too much data is missing or sample data too is heterogeneous. In this project, publically available microarray data, with similar sample status of a given qPCR dataset, was downloaded and processed. The qPCR dataset included 51 genes, where a set of four DLG genes has been chosen for in-depth analysis. For handling missing values, mean imputation and inserting Cq value 40 were used, as well as a novel method initialized where microarray data was used to replace missing values. In summary replacing missing values with microarray data did not show any significant difference to the other two methods in three of the four DLG genes. From this project, it is also suggested an initialized approach towards testing the possibility of qPCR and microarray data integration.

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response system and can occur when the immune system over- or under- reacts to an infection. Klebsiella spp. has been found to be one of the leading causes of sepsis, and the increasing occurrence of antibiotic resistance observed has become a major concern in clinical care. To study the genome and increase knowledge of the biodiversity of K. pneumoniae, K. variicola, and K. oxytoca, bacterial isolates were collected from blood, urine, nasopharynx, and wounds of patients with suspected sepsis. Next-generation sequencing was performed, and the presence of antibiotic resistance genes and plasmids were studied. Furthermore, a prediction of traits for each phylogroup was performed and the results from whole-genome sequencing were compared to phenotypic results. Among the K. pneumoniae isolates obtained, almost half had been misidentified by standard phenotypic methods and were found to be K. variicola, K. quasipneumoniae, and K. quasivariicola. A significant difference in the number of antibiotic resistance genes were observed between K. pneumoniae and K. variicola compared to K. oxytoca, however no significant difference was observed between K. pneumoniae and K. variicola, suggesting the underestimated pathogenicity of K. variicola. A genetic agreement was observed between the type of beta-lactamase harboured and presence or absence of nitrogen-fixation genes to the phylogroup, providing a way of species identification. Further studies should be conducted on the pathogenicity and virulence of K. variicola and K. quasipneumoniae to avoid misidentification, find organism-specific treatments, and narrow down the antibiotic prescription. / Biodiversitet vid Sepsis

The resistance of established medicines is rapidly increasing while the rate of discovery of new drugs and treatments have not increases during the last decades (Spiro et al. 2008). Systems pharmacology can be used to find new combinations or concentrations of established drugs to find new treatments faster (Borisy et al. 2003). A recent study aimed to use high resolution Liquid chromatography–mass spectrometry (LC-MS) for in vitro systems pharmacology, but encountered problems with unwanted variability and batch effects(Herman et al. 2017). This thesis builds on this work by improving the pipeline and comparing alternative methods and evaluating used methods. The evaluation of methods indicated that the data quality was often not improved substantially by complex methods and pipelines. Instead simpler methods such as binning for feature extraction performed best. In-fact many of the preprocessing method commonly used proved to have negative or neglect-able effects on resulting data quality. Finally the recently introduced Optimal Orthonormal System for Discriminant Analysis (OOS-DA) for batch removal was found to be a good alternative to the more complex Combat method.

Detta examensarbete är framtaget för att utforma moduler till programmet BioSpider som utvecklats vid ADIT avdelningen vid IDA institutionen Linköping Universitet med syfte att förenkla för biologer när de söker information om andra forskares resultat. Det finns ett stort antal databaser som innehåller forskningsdata kring proteiner, reaktioner, signalvägar etc. Exempel på databaser är UniProt, Reactome, IntAct, BioModels och KEGG. Det uppstår problem med att dessa databaser är uppbyggda på olika sätt och ej kan användas på ett universellt sätt, utan kräver individuellt utformning och anpassning för att kunna användas tillsammans med andra databaser.  Det är där BioSpider kommer in, BioSpider är ett program som försöker bygga upp ett träd utifrån olika databaser. Varje databas hanteras individuellt av BioSpider men presenteras på ett universellt sätt i form av ett träd för användaren. Examensarbetets del i BioSpider är att utforma moduler som behandlar ytterligare databaser utöver BioModels som redan stöds i BioSpider. Behovet av att tillgängliggöra stöd för fler databaser var nödvändigt för att kunna visa upp en användbar version av BioSpider med mer än en databas. Detta för att kunna visa att metoden fungerar i praktiken. Examensarbetet har utförts genom att en förstudie av ett antal databaser har gjorts och inom dessa valt ut relevant information som sedan implementerats i BioSpider med olika moduler för de olika databaserna. Det som fanns tillgängligt vid examensarbetets start var programmet BioSpider och implementation för en databas (BioModels). Nu stöds BioSpider av ytterligare fyra stycken databaser som är UniProt, Reactome, KEGG och IntAct som bygger ut trädet. BioSpider stöds även utav DIP, Ensembl, EMBL, FlyBase, GO, InterPro, OMIM, PDB, PIR, PROSIT och RefSeq för vidare länkning till webbsida där ytterligare information kan återfås. / The aim of this thesis was to develop modules for the system BioSpider that are developed by ADIT division at IDA institute at Linköping University. The objective is to simplify for biologist when they seek for information about research findings. There is a large number of databases that contains research results about proteins, reactions, pathways etc. Examples of these databases are UniProt, Reactome, IntAct, BioModels and KEGG. Problems emerges since the databases are constructed in different ways and cannot be used in a universal way, they must be individually tailored and adjusted to be compatible with other databases. This is where BioSpider comes in, BioSpider is a program that is supposed to build up a tree of the different databases. Each database is managed individually by BioSpider and is presented to the user in a universal way in the form of a tree. This thesis extends the BioSpider system so that more databases are supported than just the database BioModels. The need to support more databases was necessary to be able to produce a usable version of BioSpider with more than one database. This is important to show that the method works in practice. The work has been performed by a pilot study of a number of databases. Within these we selected appropriate information that was implemented in BioSpider with different modules for different databases. At start of this thesis one database was supported by BioSpider, this database is BioModels. Now BioSpider supports by additional four databases UniProt, Reactome, KEGG and IntAct. BioSpider also supports linking to websites where information can be retrieved, the supported databases are DIP, Ensembl, EMBL, FlyBase, GO, InterPro, OMIM, PDB, PIR, PROSIT and RefSeq.

Frontotemporal dementia is a neurodegenerative disorder with high heterogeneity on the genetic, pathological and clinical level. The familial form of the disease is mainly caused by pathogenic variants of three genes: C9orf72, MAPT and GRN. As there is no clear correlation between the mutation and the clinical phenotype, symptom severity or age of onset, the demand for predictive biomarkers is high. While there is no fluid biomarker for frontotemporal dementia in use yet, there is strong hope that changes of protein concentrations in the blood or cerebrospinal fluid can aid prognostics many years before symptoms develop. Increasing amounts of data are becoming available because of long-term studies of families affected by familial frontotemporal dementia, but its analysis is time-consuming and work intensive. In the scope of this project a pipeline was built for the automated analysis of proteomics data. Specifically, it aims to identify proteins useful for differentiation between two groups by using random forest, a supervised machine learning method. The analysis results of the pipeline for a data set containing blood plasma protein concentration of healthy controls and participants affected by frontotemporal dementia were promising and the generalized functioning of the pipeline was proven with an independent breast cancer proteomics data set.

Background: Knowledge within organizations can play a vital role for organizational development. The role of sharing knowledge in public organizations by means of the use of information systems have not been studied to a larger extent. During 2016 the thirteen municipalities within Region Jönköping’s län adhered to a so-called digital agenda to develop the municipal organizations and service delivery. One of the goals of the digital agenda was to increase knowledge sharing by digital means between municipalities.  Purpose: The purpose of the thesis was to investigate how knowledge sharing practices taking place between municipalities in region Jönköping’s län. The authors performed a pilot case study in the educational department within three municipalities.  Method: This study based on qualitative research and data were gathered through semi-structured interviews and analyzed according to the conventional content analysis. Semi-structured interviews were performed based on the theoretical frameworks of Nonaka’s Model of Knowledge Management, which resulted in an interview guide with open-ended questions. Conventional content was used for qualitative data analysis.  Conclusion: According to our analysis we have found that knowledge sharing in public organization is generally seen as one of the most important elements that should be wisely managed. Collaboration in public sector basically depend on the so many things and it starts with the government initiative but ends with public awareness. It is also very important that organizations can manage knowledge resources more successfully if employees are willingly to share their knowledge with colleagues. People of organizations are quite comfortable with collaborative technologies because the advance of the internet and related technologies. In the public sector worker or employees should motivated, get more encouragement and support by the leaders.

The brain – the organ that allows us to be aware of our surroundings – consists of a complex network of neurons, which seemingly allows the human brain to be able of abstract thinking, emotions, and cognitive function. To learn how the brain is capable of this, the two main branches of neuroscience study either neurons in detail, or how they communicate within neuronal networks. Both these branches often tackle the complexity using a combination of experiments and mathematical modelling. A third and less studied aspect of neuroscience concerns the neurovascular coupling (NVC), for which my research group has previously developed mathematical models. However, these NVC models have still not integrated valuable data from rodents and primates, and the NVC models are also not connected to existing neuronal network models. In this project, I address both of these two shortcomings. First, an existing model for the NVC was connected with a simple model for neuronal networks, establishing a connection between the NVC models and the software NEURON. Second, we established a way to preserved information from NVC data from rodents and mice into NVC models humans. This work thus connects the previously developed NVC model both with data from other species and with other types of models. This brings us one step closer to a more holistic and interconnected understanding of the brain and its many intriguing cognitive and physiological functions.