Ek (Quercus spp.) är nationellt av stor betydelse för biologisk mångfald, arterna utgör centrala och grundläggande komponenter i lokala ekosystem. Stockholms län är en av Sveriges mest tätbefolkade regioner där en snabb förtätning av urbana miljöer och hög exploateringstakt utgör ett reellt hot mot värdefulla ekmiljöer. Syftet med studien är att undersöka vad som definierar värdefulla ekmiljöer och genom GIS-analys identifiera var i Stockholms län dessa återfinns, samt om det finns ett samband mellan parametrar kopplade till ekmiljöer, biologisk mångfald och storlek på värdetrakter. Via litteraturstudier noterades 18 parametrar som lämpliga för identifiering och rankning av värdefulla ekmiljöer. Ett system utvecklades för poängsättning av ekars attribut som ett indirekt mått på biologisk mångfald. Systemet bidrar med en relativt lättillgänglig identifiering av värdefulla ekmiljöer i tre skalor; värdeelement, värdekärna och värdetrakt. Klusteranalyser baserat på fyra spridningsavstånd, 119, 200, 477 och 656 meter utfördes för att lokalisera värdekärnor. Via buffertanalys baserat på spridningsavståndet två kilometer identifierades 20 värdetrakter i Stockholms län. En rankning av de 172 värdekärnorna samt de 20 värdetrakterna genomfördes baserat på ekarnas poängmedelvärde. För att undersöka om det fanns ett positivt samband mellan värdekärnors alternativt värdetrakters area och biologisk mångfald utfördes Spermans korrelationsanalys, som visade att det inte fanns något positivt samband mellan variablerna. Studiens resultat tyder på att höga naturvärden inte är synonymt med stora värdekärnor alternativt stora värdetrakter, utan att även mindre värdekärnor och värdetrakter kan vara mycket värdefulla. / Oak (Quercus spp.) is of great national importance for biodiversity, as central and fundamental components of local ecosystems. Stockholm County is one of Sweden's most densely populated regions where rapid urban densification and high rates of exploitation pose a real threat to valuable oak habitats. The purpose of the study is to investigate variables that define valuable oak habitats, and through GIS analysis locate their distribution in Stockholm County. Furthermore, the study examines whether there is a positive relationship between parameters associated with oak habitats, biodiversity, and the size of value areas. Through literature review, 18 parameters suitable for identifying and ranking valuable oak habitats were noted. A scoring system was developed to assess oak attributes as an indirect measure of biodiversity. The system provides a relatively accessible identification of valuable oak habitats across three scales: elements with high conservation value (Swedish: värdeelement), valuable core areas (Swedish: värdekärnor), and value areas at landscape scale (Swedish: värdetrakt). Cluster analyses based on four dispersal distances, 119, 200, 477 and 656 meters were conducted to locate valuable core areas. Moreover, a buffer analysis based on a two-kilometer dispersal distance, identified 20 value areas at landscape scale in Stockholm County. These 172 valuable core areas and 20 value areas were then ranked based on the mean scores of the oak trees. Spearman's correlation analysis was performed to examine whether there was a positive correlation between the size of the valuable core areas or value areas and biodiversity, with no positive correlation found. The findings of the study suggest that high nature values are not synonymous with large valuable core areas or value areas, and that small valuable core areas and value areas also can be of great importance.

Thyroid cancer, comprising well-differentiated follicular and papillary types, alongside less common medullary and anaplastic subtypes with poor prognoses, exhibits specific anaplastic cases resulting from papillary dedifferentiation, lacking precise molecular evidence. Utilizing Metascape, CTpathway and PathwAX II, the study integrates functional modules and pathway crosstalk for dedifferentiation analysis, conducting a comprehensive two-dimensional assessment of toolset’s functionality, compatibility, and interoperability. Results suggest that transitions between the cancer subtypes involve pathways related to cellular processes, extracellular matrix interactions, and genetic alterations. Metascape enriched crosstalk tool findings, providing extended lists of specific pathways, while CTpathway exhibited better sensitivity and specificity, offering more result customization options and database selection than PathwAX II. PathwAX II, with unique interactive features for network display and identifying depleted pathways, emerges a valuable component in a comprehensive pipeline integrating these three tools. Additional validation against previous clinical studies affirms the reliability of the results, reinforcing PathwAX II’s role as a key reference point in the creation of such a pipeline. The study also suggests future tool development directions, highlighting strengths and limitations across the platforms. The detailed pathway and gene analysis contributes concrete knowledge to the scientific community, serving as a hallmark for future studies.

Escherichia coli (E. coli) is among the gram-negative bacteria that can cause several infections including sepsis. Confirmed sepsis patients must show a sequential organ failure assessment (SOFA) score of ≥2 with verified infection. Understanding the genotypic characteristics of E. coliclinical isolates from sepsis patients can help directing treatment strategies, tracking antibiotic resistance, and monitoring acquired virulence factors that can contribute to the severity of sepsis. The isolates included in this thesis were collected from confirmed sepsis patients (SOFA 2-3)during a prospective observational study that was conducted in Sweden. The aim was to study the biodiversity in E. coli clinical isolates using whole genome sequencing (WGS) paired-end reads that were produced by the next-generation sequencer Illumina. To perform the WGS-based analysis, two bioinformatics pipelines were used. The first is the in-house developed pipeline and the second is the 1928Diagnostics E. coli pipeline. The obtained in silico results were compared with the phenotypic findings for species identification and the in vitro predictions of antibiotic resistance. Species identification by the bioinformatics pipelines matched the phenotypic method, except for three isolates that were highly contaminated with other species. Both pipelines predicted the exact multi locus sequence types, which revealed that the most common sequence types (STs) were ST73(17%), ST95(9%), and ST131(6%). The phenotype of the isolates resulted in 5% resistant to at least one of the assessed antibiotics. The 1928Diagnostics predicted 28% of the isolates were resistant to at least one class of the tested antibiotic classes, while the in-house pipeline predicted 33% of the isolates to be resistant. Out of the predicted resistant isolates, 52% coded for multi-drug resistance. The in-house pipeline reported virulence genes. The common reported genes were coding for iron reuptake, adhesins, cell outer membrane and increased serum survival. It was observed that the isolates that belonged to ST73 and ST95 showed a more susceptible antibiotic profile than isolates that belonged to ST131, those harbored the highest mean of virulence genes. In conclusion, the present study provided an evidence of the usefulness of the WGS-based analysis to study the biodiversity in E. coli. The obtained results are valuable for surveillances, tracking antibiotic resistance and identifying virulence factors, but with a limited use in clinical settings.

Rheumatoid arthritis (RA) is a systematic autoimmune disorder characterized by a persistent joint inflammation. A subset of HLA-DRB1 alleles known as shared epitope (SE) are the strongest genetic risk factors to develop anti-citrullinated protein antibody positive (ACPA-positive) RA. A strong enrichment of interactions exists between ACPA-positive RA-associated genetic variants and HLA-DRB1 SE alleles in disease development. Pathway analysis was performed to investigate how the interactions between risk variants (SNPs) with HLA-DRB1 from a previous study related to ACPA-positive RA. Gene-gene interactions analysis was performed between non-HLA risk variants and HLA-DRB1 SE alleles in SRQ biobank (SRQb) case-control cohort. We also evaluated whether the reported gene-gene interactions from a previous study relate to methotrexate (MTX) response for RA patients, at three and six months of follow-up in EIRA study. Interaction analysis based on an additive model was performed to understand the combined effect of two risk factors in the disease and treatment response. Two out of three genes from pathway analysis that were RXRA and NR3C1, pointed to ACPA-positive RA related important pathways including vitamin D receptor (VDR) pathway and adipocytokine signaling pathway. The replication analysis in SRQ-case-control study showed 2.627% of the evaluated SNPs insignificant additive interaction with HLA-DRB1 SE alleles. No interactions were significant in relation to the response to MTX monotherapy after 3 and 6 months follow-up. This project provides new insights into the gene-gene interactions in the study of ACPA-positive RA and suggests candidate genes for future functional studies.

No description available.

This research project investigates the interactions of staurosporine, a potent kinase inhibitor, with 11 ligands, highlighting its role in drug design and bioinformatics. Focusing on the selectivity and promiscuity of staurosporine in binding to protein kinases, the study employs the MANORAA database for data extraction. A Python script was developed to automate the retrieval and organisation of data, particularly targeting ligands with known affinity numbers. This method efficiently structures complex biochemical information into a comprehensible format. The research culminated in the creation of a website that presents detailed data on staurosporine’s molecular interactions and binding affinities. This website can serve as a valuable tool for researchers, offering insights into the drug's mechanism of action and its implications in therapeutic applications. The study methods included Python scripting for data handling and API integration for efficient data extraction, emphasising the importance of computational tools in bioinformatics. The findings reveal significant insights into the binding dynamics of staurosporine, identifying conserved and variable regions in kinase binding pockets that influence drug efficacy. These results contribute to a deeper understanding of staurosporine's broad spectrum of kinase inhibition and provide a model for future research in drug-protein interaction analysis. This project underscores the significance of accessible data presentation in bioinformatics, facilitating advanced research and development in drug design.

Sepsis, a life-threatening condition with alarmingly high mortality rates, demands the development of improved diagnostic methods to better understand and manage the disease. Enterococcus spp., significant contributors to sepsis and known for their multidrug resistance, urgently require thorough and detailed investigation to devise effective treatment strategies and healthcare interventions. This study explores the genomic characterization of Enterococcus spp. isolates from patients with suspected community-onset sepsis in Sweden, using whole-genome sequencing. Data was processed through an in-house developed bioinformatics pipeline, including quality control with FastQC, sequence trimming with Trimmomatic, and assembly with Unicycler and QUAST, generating reliable FASTA files for the downstream analysis. Subsequently, different gene annotation tools were applied for the genotypic species identification, prediction of antibiotic resistance genes, plasmid replicons, and determination of sequence type. Moreover, the results from the genotypic characterization were compared to those obtained using routine microbiological methods based on cultures followed by MALDI-TOF MS for species identification and disk diffusion for antimicrobial resistance testing. The results revealed a high prevalence of resistance genes, particularly for macrolide, lincosamide, and streptogramin antibiotics. A notable finding was the high discordance (83.2%) between phenotypic and genotypic methods in detecting resistance, highlighting the complexity of correlating phenotypic antibiotic resistance with genotypic predictions. Additionally, a statistically significant higher prevalence of antibiotic resistance genes was observed in E. faecium compared to E. faecalis (p=0.001). Furthermore, a high diversity of sequence types among Enterococcus spp. isolates was detected by multi-locus sequencing typing, with ST6 (14%) as the most prevalent for E. faecalis and ST192 (37%) for E. faecium. In conclusion, this comprehensive genomic approach enhances the understanding of antibiotic resistance spread and informs strategies for improved clinical and public health interventions in sepsis management. The study also underscores the importance of integrating genomic data with traditional diagnostic methods to develop effective strategies for managing antibiotic resistant infections. / <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>

Common variable immunodeficiency (CVID) encompasses a heterogeneous group of inborn immune system errors characterized by a failure in antibody production. The defective immune response in CVID subjects results in poor clearance of infectious agents and higher susceptibility to severe diseases, mainly caused by encapsulated extracellular bacteria in the respiratory tract. On top of that, autoimmunity, and inflammatory complications are common clinical manifestations, leading to important impairment in the overall health status and lifespan of these patients. This study aimed to understand, at the genetic level, the particularities of the immune pathways from a CVID cohort in contrast to a healthy control group. For that, transcriptomic analyses from bulk RNA-sequencing from in vitro activated B cells were performed. Results from differentially expressed genes and enrichment analyses involving both naïve and memory cells unveiled the peculiarities of the expression network from the pathological B-cell environment. For instance, pathways involving mTOR1, MYC, E2F, and IL-2/STAT5 signaling were downregulated in activated naïve B cells from the CVID group, following the lower expression of genes related to the metabolic processes. Nevertheless, the activated memory B cells revealed an opposite pattern: enrichment in genes related to cell metabolism, as well as the enhancement of mTOR1, p53, STAT3, and MYC targets. Markers of inflammation such as type I interferons and complement, immunosurveillance, and cellular stress response, the latter represented by processes relative to unfolded protein responses, apoptosis, and autophagy, were found over-represented in all activated B cells, naïve and memory, in the CVID group. In summary, these results could indicate major problems in the germinal center reactions from secondary lymphoid organs causing a defective transition from naïve to memory and long-lived plasma cells in patients with CVID, but further studies are needed to validate these assumptions. Finally, since epigenetic mechanisms were also found more expressed in the disease group, the genetic signature solely may not determine an illness’ fate. If future researchers could determine how environmental factors could influence the disease phenotype, a personalized and maybe curative approach for these patients would be a reality.

In this thesis, I present a comprehensive framework for the analysis of single-cell (SC)morphological data, specifically focusing on the Cell Painting assay. SC technologies haverevolutionized biological research by enabling high-throughput and high-content screeningat the cellular level. Here, the computational challenges and opportunities associated withSC morphological profiling are explored, leveraging both traditional tools like CellProfilerand advanced deep learning methods such as DeepProfiler. This study investigates the potential of SC morphological data to uncover cellular hetero-geneity and identify distinct sub populations within complex datasets. To attain this goal, various feature extraction, normalization, and filtering techniques are employed, followedby unsupervised and supervised learning methods to analyze the extracted features. The results demonstrate the effectiveness of the deep-learning model DeepProfiler in cap-turing intricate cellular features, outperforming the traditional method CellProfiler in most tasks including mechanism of action predictions by as much as 30% macro F1. Thiswork also highlights the importance of efficient computational resources and robust dataprocessing pipelines to handle the large-scale datasets typically generated in SC research.Additionally, I propose a combination of metrics, namely e-distance and SC grit score,for evaluating perturbation strength and filtering morphological data. These metrics, inconjunction with advanced analysis tools such as UMAP and the introduced CellViewer,enhance the interpretability of results, offering a deeper insight into the morphologicalchanges induced by various treatments and subsequent biological implications.

Complex diseases such as cancer or obesity are thought to be caused by abnormalities in multiple  genes and cannot be derived to one specific location in the genome. It has been shown that  identification of disease associated genes can be made through looking at interaction patterns in a  protein‐protein interaction network, where the disease associated genes are represented in clusters,  or disease modules. There are several algorithms developed to infer these disease modules, but  studies have shown that the reliability of the results increase if multiple algorithms are used and a  consensus module is derived from them. MODifieR is an R package developed to combine the results  of multiple  disease module inferring algorithms and has proven to provide a stable result. To  increase usability of the R package and make it available not only for users with programmatic skills,  MODifieR Web was developed as a web based tool with a graphical user interface. The tool was built  using Angular and .NET core, invoking the MODifieR R package in the backend. The interface requires  input in the form of an expression matrix and a probe map from the user, easily uploadable in a  drag‐and‐drop  interface.  It  gives  the  user  the  possibility  to  analyze  data  using  seven  different  algorithms and provide results as gene lists and visualizes the consensus module in a network image.  MODifieR Web is a first version of an application that is a novel contribution to the existing tools for  identification of disease modules, although in need of further improvements to be able to serve a  greater  pool  of  users  in  a  more  effective  way.  The  tool  is  available  to  try  out  at   http://transbioinfo.liu.se/modifier#/home and the source code is released as an open‐source project  in Github (https://github.com/emmape/MODifieRProject).