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  • 1.
    Abdelhalem, Marwa
    University of Skövde, School of Bioscience.
    Comparison of exosome isolation methods: Size exclusion chromatography versus ultracentrifugation2023Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    Extracellular vesicles are emitted by almost all cell types. They play an important role in cell-to-cell communication by passing biomolecules such as mRNAs to other cells by endocytosis. It is crucial to isolate and purify them from complex body fluids for studying exosomes. Various techniques, including ultracentrifugation, ultrafiltration, precipitation kits, and immunoprecipitation, are used to isolate exosomes (Bu et al., 2019). Each of these techniques has a significant impact on the properties and purity of the EVs obtained. This project aims to understand the effects of different isolation methods on exosome content by comparing the methods of UC and SEC, with three objectives. The first objective was to compare UC and SEC samples and identify DEGs from native EVs. The second objective was to analyze DEG functional annotation between isolation methods to understand their impact on EV content in biological processes and cellular components. The last objective was to investigate the protein-protien interactions (PPI) between the differentially expressed genes. To investigate the effects of EVs isolation methods at the transcriptional level, RNA-seq data were analyzed from a dataset of three different cell lines, including human lung epithelial cells (HTB-177), umbilical vein endothelial cells (HUVEC), and cardiac progenitor cells (CPC). RNA-seq analysis used an available transcriptomic dataset of EV samples isolated by UC and SEC methods. It identified 10, 15509, and 8995 DEGs from HTB, HUVEC, and CPC, respectively, and mapped them to pathways using EnrichR software. The study found that isolation methods and cell line sources affect analysis results. EnrichR analysis revealed the isolation method's impact on exosomal RNA content and regulation of biological processes.

  • 2.
    Ahammu, Sanuja
    University of Skövde, School of Bioscience.
    Quantitative gut microbiome profile of children growing up on farms2021Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    Low rates of allergy are observed in children growing up on a farm, but the factors that contribute to this protective effect are unclear. This study aimed to investigate if living on a farm alters the infant gut microbiota and thereby reduces the risk of developing allergic diseases in later childhood. This study was based on the prospective Farmflora cohort, which included 28 children living on a farm and 35 control children living in a rural area, who were followed from birth to 8 years of age. The gut microbiota was analyzed from 122 fecal samples collected from 48 children during the first 6 months after birth, using quantitative microbiome profiling. This novel method integrates 16S rRNA gene sequencing data with total bacterial load to obtain absolute counts of bacterial abundance. A significant increase in microbial diversity was seen in the gut microbiota of all the infants in the cohort over the first 6 months after birth. Similar alpha and beta diversity levels were observed in the farm and the control children. However, Sutterella, Megasphaera, and Dorea were more abundant in the gut microbiota of farm children. It has previously been observed that the farm environment was associated with low rates of allergy in children at 3 years. Taxa Akkermansia was more abundant in the gut microbiota of infants who were evaluated with allergy at 3 years. In addition, children who were healthy at 8 years had a higher abundance of Bifidobacterium in their gut microbiota at 6 months of age. However, the abundance of Bifidobacterium could not be linked to farm residence in this study. The findings were consistent with previous studies which link the higher abundance of Sutterella, Dorea, and Megasphaera with protection against allergic diseases. In conclusion, the study observed differences in the gut microbiota of children growing up on a farm, who have low rates of allergy, and showed Bifidobacterium may be protective against allergy development.

    The full text will be freely available from 2025-01-01 00:00
  • 3.
    Ahmad, Ansar
    University of Skövde, School of Bioscience.
    Evaluation of pipelines for analysis of next-generation sequencing data from CRISPR experiments2019Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
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  • 4.
    Ahmed, Suud
    University of Skövde, School of Bioscience.
    The battle against sepsis: exploring the genotypic diversity of pseudomonas and proteus clinical isolates2023Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    Sepsis is a dangerous and potentially fatal condition that has a mysterious origin, underscoring the significance of prompt and accurate diagnosis and treatment. Bacterial whole-genome sequencing, which is widely used in clinical microbiology, stands at the forefront of sequencing technologies, particularly to combat sepsis. The aim of this thesis is to improve sepsis treatment by examining the genetic characteristics and drug resistance patterns of the common sepsis-causing bacteria Pseudomonas and Proteus spp., by analyzing the whole-genome sequencing data of bacterial isolates using an in-house-developed pipeline. The result was compared with a commercial cloud-based platform from 1928 Diagnostic (Gothenburg, Sweden), as well as the results from a clinical laboratory. Using Illumina HiSeq X next-generation sequencing technology, whole-genome data from 88 isolates of Pseudomonas and Proteus spp. was obtained. The isolates were obtained during a prospective observational study of community-onset severe sepsis and septic shock in adults at Skaraborg Hospital in Sweden's western region. The collected isolates were characterized using approved laboratory techniques, such as phenotypic antibiotic susceptibility testing (AST) in accordance with EUCAST guidelines and species identification by MALDI-TOF MS analysis. The species identification result matched the phenotypic method, with the exception of two isolates from Pseudomonas samples and four isolates from Proteus samples. When benchmarking the in-house pipeline and 1928 platform for Pseudomonas spp., predicted 97% of the isolates were resistant to at least one class of the tested antibiotics, of which 94% shows multi-drug resistance. In phenotypes, 88% of the isolates had at least one antibiotic resistance future, of which 68% shows multi-drug resistance. The most prevalent sequence types (STs) identified were ST 3285 and ST111 (9.3%) and ST564 and ST17 (6.98%) each, and both pipelines accurately predicted the number of multilocus types. The in-house pipeline reported 9820 Pseudomonas virulence genes, with PhzB1, a metabolic factor, being the most common gene. It was discovered that there was a significant correlation between the virulence factor gene count and the multilocus sequence typing (MLST) (p = 0.00001). With a Simpson's Diversity Index of 0.98, the urine culture specimens showed the greatest ST diversity. Plasmids were detected in twelve samples (20.93%) in total. In general, this study provided a detailed description of the bacterial future for Pseudomonas and Proteus organisms using WGS data. This research shows the applicability of the in-house and 1928 pipelines in the identification of sepsis-causing organisms with accuracy. It also showed the need for an organized and easy-to-use international pipeline to implement and analyze WGS bacterial data and to compare it with laboratory results as needed.

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  • 5.
    Al Shobky, Mohamed
    University of Skövde, School of Bioscience.
    Utilization of cancer-specific genome-scale metabolic models in pancreatic ductal adenocarcinomas for biomarkers discovery and patient stratification2019Independent thesis Advanced level (degree of Master (Two Years)), 40 credits / 60 HE creditsStudent thesis
    Abstract [en]

    Pancreatic Ductal Adenocarcinomas initiates in the exocrine part of the pancreatic tissue and represents over 90% of all the pancreatic cancers. Pancreatic Ductal Adenocarcinomas are extremely aggressive and are one of the most lethal malignant neoplasms. The five-year relative survival is currently less than 8% of the patients. The main reason behind such a low survival rate is that most of the cases are diagnosed at a very late stage. Although substantial advancement in pancreatic cancer research has been done, there has not been any remarkable significance in the mortality to incidence ratio. This is mainly a result of the scarce of early diagnostic characteristic symptoms and reliable biomarkers besides the unresponsiveness to the treatments. In this study, transcriptomics and proteomics data were used for the construction of a genome-scale metabolic model that was used in the detection of altered metabolic pathways, genes and metabolites using gene set analysis and reporter metabolites analysis. As a result, altered metabolic pathways in PDAC tumours were detected, including the lipid metabolism-related pathways as well as carbohydrate metabolism, in addition to nucleotide metabolism, which are considered as potential candidates for diagnostic biomarkers. Moreover, classification of the filtered DIRAC tightly regulated network genes, based on their prognostic values from the pathology atlas, detected two groups of PDAC patients that have significantly different survival outcome. The differential expression analysis of the two groups showed that six of the eight genes used in clustering were showing significantly altered expression, which suggests their importance in PDAC patient stratification. As a conclusion, this study shows the valuable outcome of the GEM reconstructions and other systems-level analyses for elucidating the underlying altered metabolic mechanisms of PDAC. Such analyses results should provide more insights into the biomarker discovery and developing of potential treatments.

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  • 6.
    Alborgeba, Zainab
    University of Skövde, School of Bioscience.
    Development and evaluation of a cost-effectiveness analysis model for sepsis diagnosis2020Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    Sepsis is a life-threatening organ dysfunction that is caused by a dysregulated host response to infection. Sepsis is a substantial health care and economic burden worldwide and is one of the most common reasons for admission to the hospital and intensive care unit. Early diagnosis and targeted treatment of sepsis are the bases to reduce the mortality and morbidity. Conventional blood culturing is the gold standard method for sepsis diagnostics. However, blood culturing is a time consuming method, requiring at least 48 to 72 hours to get the first results with very low sensitivity and specificity. The aim of this study was to determine and assess the direct sepsis-related costs for PCR-based diagnostic strategies (SeptiFast and POC/LAB). A mathematical model was constructed to compare PCR-based diagnostic strategies with the conventional blood culturing. Three case scenarios were investigated based on data from the United Kingdom, Spain and the Czech Republic. It was found that, POC/LAB was the most cost effective strategy in all countries if it could reduce the hospitalization length of stay with at least 3 days in the normal hospital ward and 1 day in the intensive care unit. Reducing the hospitalization length of stay had the greatest impact on the economic outcomes. While, reducing the costs of the diagnostic strategies did not show a remarkable effect on the economic results. In conclusion, the findings suggest that PCR-rapid diagnostic methods could be cost-effective for the diagnosis of patients with sepsis if they could reduce the hospitalization length of stay.

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  • 7.
    Alex, Dona
    University of Skövde, School of Bioscience.
    Transcriptomic analysis of stimulated and unstimulated naïve B cells of healthy donors and CVID patients2023Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    Common variable immunodeficiency (CVID) is a primary immune deficiency present in about 1 in 25,000 people, characterized by recurrent infections, low serum immunoglobulin (Ig) levels (IgG, IgA, and sometimes IgM), and reduced vaccine responses. It is genetically a heterogeneous illness that often affects adults or teenagers and requires lifetime clinical care. CVID patients experience recurrent or chronic sinopulmonary tract infections, gastrointestinal disorders, and malignancies. Ig reconstitution administered intravenously or subcutaneously is the main treatment. Although the fundamental causes of CVID are still undefined, studies suggest that a variety of variables, including impaired somatic hypermutation (SHM), B cell maturation, primary B cell dysfunctions, abnormalities in T cells, and antigen-presenting cells are implicated. Understanding the molecular mechanisms driving this disease's genome regulation requires a deep understanding of the gene expression. It is today possible to study both coding and non-coding sections of RNA using next-generation RNA-seq, which allows detecting differentially expressed genes in massive amounts of data, particularly in multifaceted illnesses like CVID. The aim of this study was to identify the differentially expressed genes between unstimulated (ex vivo) and stimulated (in vitro) naïve B cells of CVID patients and healthy donors (HD), and also to identify the underlying biological processes by gene enrichment analysis. The results of this study showed that both in CVID and HD, the stimulated and unstimulated cells were well separated. In the gene set analysis, it was discovered that significantly enriched CVID pathways were mostly involved in immune system-related processes such as adaptive immune response, cytoplasmic translation, granulocyte activation, lymphocyte activation, and lymphocyte differentiation. Therefore, the transcriptomic analysis of this study concluded that the majority of the genes that regulate the immune cell activation process function may have a greater impact on CVID patients than on HD which helps to understand the immunological defects in CVID patients. 

    The full text will be freely available from 2024-06-05 00:01
  • 8.
    Alexander, Suraj Thomas
    University of Skövde, School of Bioscience.
    Study of up-regulated genes in gene clusters during formation of mature hepatocytes from human induced pluripotent stem cells to identify transcription factors and mirnas2021Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    The multifunctional purpose of hepatocytes, the functional liver cells within the metabolic, endocrine and secretory functions highlights key importance in emphasizing the research and treatment methods that utilize these cells. Forming 80% of the liver's cells, hepatocytes are involved in many of the primary functions of the liver including the delivery of immune response against pathogens and aiding in the detoxification of drugs. As a result, it provides a valuable basis for medical research. Through the findings of Ghosheh et al. (2017), a method of generating mature hepatocytes was achieved through the human pluripotent stem cells (HPSC), but the generation of hepatocytes in which all the genes are expressed at the right amount through this method proves to be a difficult endeavor. The primary goal of this project is to utilize the established findings to enhance and improve the efficacy of the process that goes behind the generation of mature hepatocytes. The approach towards the current project was initiated with culturing and differentiating three human embryonic stem cell lines and three human-induced pluripotent stem cell lines into mature hepatocytes. In the study mentioned, k-means clustering along with Pearson correlation as the distant measure was run in R to subdivide the top 2000 genes with the highest differential expression into 10 clusters. The cluster data from this paper was used to do the current study, in which the up-regulated and down-regulated gene were first identified for clusters 2, 4 & 6 and 9. The interactions of up-regulated genes in these clusters were further analyzed using Enrichr to identify the different miRNAs for various genes from the clusters. Within cluster 2, a total of 8 genes showed the possibility of being regulated using 4 miRNAs. Transcription factors were also identified for cluster 2 and a combination of HNF1A, EP300, AHR, NFKB1 and HIF1A could repress 8 genes that were not repressed by miRNAs. In cluster 4 & 6, most of the up-regulated genes showcased tumorigenicity and all 20 genes identified could be regulated with the combination of 7 miRNAs. In cluster 9, a combination of 11 miRNAscould be used to regulate 26 out of 27 genes that were analyzed. Ensuring that stem cell products do not turn cancerous is a priority in the medical field. Conducting the analyses of the other clusters aside from 2, 4 & 6 and 9 will prove highly beneficial in reducing the risks pertaining to stem cell mutation due to overexpression of genes.

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  • 9.
    Ali, Sihaam
    University of Skövde, School of Bioscience.
    Genotypic biodiversity of clinical haemophilus influenzae isolates from patients with suspected community-onset sepsis, Sweden2023Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    Sepsis is defined as a syndrome of life-threatening organ dysfunction caused by a dysregulated host response to an infection. Early detection of sepsis and immediate treatment with antibiotics is critical for patient outcomes. Haemophilus influenzae (H. influenzae) is a gram-negative bacteria known to be a human-adapted pathogen that may cause a variety of communityacquired infections such as sepsis. A rapid increase in beta-lactam resistance in H. influenzae has been noticed and has become a major problem in clinical care. By implementing bacterial whole genome sequencing (WGS) in the clinical laboratory, it can provide a great amount of information such as species identification, serotype identification, antimicrobial resistance prediction, typing for epidemiologic purposes and tracking infectious disease outbreaks. The aim of this study was to analyze WGS data for clinical H. influenzae isolates using an in-house developed bioinformatic pipeline and an automated 1928 Diagnostics platform to evaluate and compare the predicted results in terms of species identification, prediction of resistance and virulence genes. Furthermore, the predicted genotypic antibiotic resistance genes were compared to the phenotypic antimicrobial susceptibility testing obtained from the clinical laboratory. For the in-house developed pipeline, the analysis of H. influenzae WGS data started with quality control and preprocessing (trimming) of FASTQ files. Following, de novo assembly and quality assessment of assembled contigs and lastly gene annotation tools were performed. For 1928 Diagnostics, the untrimmed FASTQ files were uploaded to the 1928 platform. Species identification resulted in a high agreement of predicted H. influenzae for both phenotypic and genotypic methods except for one sample that may have been contaminated. The analysis of antibiotic resistance genes resulted in both in-house developed pipeline and 1928 Diagnostics having a high agreement regarding the prediction of broad-spectrum beta-lactamase in six clinical isolates, all of which predicted bla TEM-1B. The four most common sequence types found in the MLST analysis from the in-house pipeline were ST159, ST388, ST14 and ST12. The analysis of virulence genes yielded a large number of different virulence genes and each of the identified virulence genes codes a specific function that is crucial to the pathogenesis of H. influenzae. In conclusion, the obtained results provide valuable insights into using WGS-based analysis as a reliable tool for determining the pathogen characteristics in clinical bacterial isolates.

  • 10.
    Aloysius Gomez, Sherin
    University of Skövde, School of Bioscience.
    CARD8 knockdown alters cholesterol crystal-induced inflammatory cytokine release in endothelial cells2023Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    One of the main components of atherosclerotic plaque is the production and accumulation of cholesterol crystals (CCs), which could serve as a biomarker of atherosclerosis. Atherosclerosis is a chronic inflammatory artery disease that is the root cause of myocardial infarction and stroke. Endothelial dysfunction is one of the main contributors to the development of atherosclerosis. The aim of the study was to investigate whether Human Umbilical Vein Endothelial Cells (HUVECs) can uptake CCs and to examine CCs-induced inflammatory response in HUVECs. Using molecular and functional techniques, the distinctive characteristic of CC-mediated immune response was discovered in HUVECs. CCs were mostly taken up by HUVECs by macropinocytosis and phagocytosis. CCs were found to induce Signal Transducer of Activators of Transcription (STAT) 3 phosphorylation and Interleukin (IL)-6 release in HUVEC. In addition, Caspase activation and recruitment domain 8 (CARD8) knockdown drastically reduced CCs uptake and CCs induced IL-6 expression in HUVECs. Moreover, the stem cell growth factor (SCGF)-b protein release was downregulated in response to CCs. IL-1A and Colony Stimulating Factor (CSF) 2 were identified as the topmost hub nodes interacting with all other differentially expressed proteins. A significant increase in neutrophil adhesion on HUVECS was found in response to CCs and conditioned medium from CCs-treated HUVECs. In conclusion, the study findings suggest that the CCs induceSTAT3-mediated IL-6 release and neutrophil adhesion, thereby promoting inflammation in HUVECs.

  • 11.
    Andersson, Christoffer
    University of Skövde, School of Humanities and Informatics.
    PELICAN: a PipELIne, including a novel redundancy-eliminating algorithm, to Create and maintain a topicAl family-specific Non-redundant protein database2005Independent thesis Advanced level (degree of Master (One Year))Student thesis
    Abstract [en]

    The increasing number of biological databases today requires that users are able to search more efficiently among as well as in individual databases. One of the most widespread problems is redundancy, i.e. the problem of duplicated information in sets of data. This thesis aims at implementing an algorithm that distinguishes from other related attempts by using the genomic positions of sequences, instead of similarity based sequence comparisons, when making a sequence data set non-redundant. In an automatic updating procedure the algorithm drastically increases the possibility to update and to maintain the topicality of a non-redundant database. The procedure creates a biologically sound non-redundant data set with accuracy comparable to other algorithms focusing on making data sets non-redundant

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  • 12.
    Badam, Tejaswi V. S.
    et al.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Bioinformatics, Department of Physics, Chemistry and Biology, Linköping university, Sweden.
    de Weerd, Hendrik A.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Bioinformatics, Department of Physics, Chemistry and Biology, Linköping university, Sweden.
    Martínez-Enguita, David
    Bioinformatics, Department of Physics, Chemistry and Biology, Linköping university, Sweden.
    Olsson, Tomas
    Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden.
    Alfredsson, Lars
    Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden ; Institute of Environmental Medicine, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden.
    Kockum, Ingrid
    Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden.
    Jagodic, Maja
    Department of Clinical Neuroscience, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Gustafsson, Mika
    Bioinformatics, Department of Physics, Chemistry and Biology, Linköping university, Sweden.
    A validated generally applicable approach using the systematic assessment of disease modules by GWAS reveals a multi-omic module strongly associated with risk factors in multiple sclerosis2021In: BMC Genomics, E-ISSN 1471-2164, Vol. 22, no 1, article id 631Article in journal (Refereed)
    Abstract [en]

    Background: There exist few, if any, practical guidelines for predictive and falsifiable multi-omic data integration that systematically integrate existing knowledge. Disease modules are popular concepts for interpreting genome-wide studies in medicine but have so far not been systematically evaluated and may lead to corroborating multi-omic modules. Result: We assessed eight module identification methods in 57 previously published expression and methylation studies of 19 diseases using GWAS enrichment analysis. Next, we applied the same strategy for multi-omic integration of 20 datasets of multiple sclerosis (MS), and further validated the resulting module using both GWAS and risk-factor-associated genes from several independent cohorts. Our benchmark of modules showed that in immune-associated diseases modules inferred from clique-based methods were the most enriched for GWAS genes. The multi-omic case study using MS data revealed the robust identification of a module of 220 genes. Strikingly, most genes of the module were differentially methylated upon the action of one or several environmental risk factors in MS (n = 217, P = 10− 47) and were also independently validated for association with five different risk factors of MS, which further stressed the high genetic and epigenetic relevance of the module for MS. Conclusions: We believe our analysis provides a workflow for selecting modules and our benchmark study may help further improvement of disease module methods. Moreover, we also stress that our methodology is generally applicable for combining and assessing the performance of multi-omic approaches for complex diseases. 

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  • 13.
    Bakhtiyari, Elnaz
    University of Skövde, School of Bioscience.
    Analysis of differentially expressed genes (DEGs) in neuronal cells from the cerebral cortex of Alzheimer’s disease mouse model2020Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    Alzheimer’s disease (AD) is an aging-related neurodegenerative disorder with large implications for society and individuals. AD is a multi-factor disorder, with these factors having a direct or indirect correlation with each other. Despite many studies with different aspects on molecular and cellular pathways, there is still no specific treatment for AD. Identification of potential pathogenic factors can be done by transcriptomic studies of differentially expressed genes (DEGs), but the outcomes have been contradictory. Using both bioinformatics and meta-analysis methods can be useful for removing such inconsistencies. A useful and common approach for a better understanding of neurodegenerative disease is to assess its molecular causes, by comparing the gene expression levels in healthy and disease tissues. Next-generation RNA-sequencing is a valuable method for analyzing both coding and non-coding regions of RNA, and it has made it possible to identify differentially expressed genes in large-scale data. The aim of the current study was to get a better understanding of the transcriptional changes in AD models, and identify differentially expressed genes between healthy and AD individuals from the adult mouse brain model as well as detecting AD pathways. In this study, the transcriptomes of purified neuron, astrocyte and microglia cells from mouse brains were analyzed using publicly available RNA-seq datasets. The DEGs were identified for all three mentioned cell types using DESeq2 and EdgeR packages. All statistical analyses were performed by R software and the DEGs detected by DESeq2 and edgeR, respectively, were compared using Venn diagrams. Additionally, analyzing the AD pathway was performed using GOrilla tool for visualizing the enriched gene ontology (GO) terms in the list of ranked genes. From this project, it was found that there were very few significantly DEGs between AD and healthy samples in neuron cells, while there were more DEGs in astrocyte and microglia cells. In conclusion, comparing DESeq2 and egeR packages using Venn diagrams showed a slight advantage of DESeq2 in detection accuracy, since it was able to identify more DEGs than edgeR. Moreover, analyzing AD pathway using GOrilla tool indicated that identified enriched GO terms by each cell type differed from each other. For astrocytes, more enriched GO terms were identified than for microglia cells, while no significant enriched GO terms were detected for neuron cells.

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  • 14.
    Baxter, John
    University of Skövde, School of Bioscience.
    Evaluation of Oxford nanopore’s MinION: Use, functionality, and genome assembly2019Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    The rapid and reliable detection of pathogens is of utmost importance in healthcare settings to ensure the appropriate treatment thereby reducing morbidity and mortality for the patient. Current culturing, PCR based and NGS species detection methods are time consuming (Opota et al., 2015), limited in their detection (Buckley et al., 2015), or require specialist skills and are expensive (Basho and Eterovic., 2015). Oxford Technologies Nanopore devices could provide detailed genomic sequencing at a fraction of the cost and without the need for technical bioinformatic skills. This study evaluates the MinION device and analysis tools to suggest best practice. Classification and genotyping of 12 Klebsiella isolates were performed using EPI2ME automated workflows and manual de novo assembly.  Automated workflows using raw MinION reads provided clinically relevant information identified in ~6hrs. Manual de novo assembly and analysis used hybrid, and single source data took >24hrs. The inclusion of MinION long reads overcome problems assembling short reads. Hybrid genomes provided the most contiguous and highly detailed contigs. MinION only read assemblies contained more errors but still identified similar genotypic findings. Automated workflows are rapid and require minimal bioinformatic know-how. There should be a dialogue between clinicians and bioinformaticians to develop bespoke analysis tools.  Although challenges remain around compatible kits and vulnerable flowcells long read sequencing can be an effective tool for species detection and pathogen typing. Furthermore, hybrid assemblies have the potential to advance our genome detailing and discovery.

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  • 15.
    Benediktsson, Elís Ingi
    University of Skövde, School of Humanities and Informatics.
    Detection and analysis of megasatellites in the human genome using in silico methods2005Independent thesis Advanced level (degree of Master (One Year))Student thesis
    Abstract [en]

    Megasatellites are polymorphic tandem repetitive sequences with repeat-units longer than or equal to 1000 base pairs. The novel algorithm Megasatfinder predicts megasatellites in the human genome. A structured method of analysing the algorithm is developed and conducted. The analysis method consists of six test scenarios. Scripts are created, which execute the algorithm using various parameter settings. Three nucleotide sequences are applied; a real sequence extracted from the human genome and two random sequences, generated using different base probabilities. Usability and accuracy are investigated, providing the user with confidence in the algorithm and its output. The results indicate that Megasatfinder is an excellent tool for the detection of megasatellites and that the generated results are highly reliable. The results of the complete analysis suggest alterations in the default parameter settings, presented as user guidelines, and state that artificially generated sequences are not applicable as models for real DNA in computational simulations.

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  • 16.
    Bengtsson, Ola
    University of Skövde, School of Bioscience.
    Karakterisering av riskfaktorer kopplade till multipel skleros med hjälp av sjukdoms-moduler2019Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Multiple sclerosis is a common neurological disorder, characterized by increasing disability over time for the affected patient. The disease is considered an autoimmune disorder in which the immune system causes damage to nerves in the central nervous system through demyelination and inflammation. It is currently not understood what causes the disease, but both genetic susceptibility as well as environmental- and lifestyle factors are thought to contribute to disease development.In this project, data of known disease-associated risk factors were used to characterize the processes through which they may alter the risk of disease development. Modules for each risk factor was derived from experimental data, using the MODifieR disease-module inference algorithms. Of the five different risk factors included, each module was analysed using the PASCAL tool and disease specific GWAS data to evaluate the relevance towards the disease.Based on the modules derived using the Clique Sum Permutation module inference method a consensus module comprising 126 genes was identified, which proved to be significantly enriched for disease associated SNPs (single nucleotide polymorphisms). Additionally, the risk-factor consensus module was compared to disease specific consensus genes previously obtained within the research group. The comparison showed a significant overlap, which indicates that the methodology may provide means of examine the impact of risk-factors in the context of complex disease.

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  • 17.
    Bitioui M´Rabet, Mehdi
    University of Skövde, School of Bioscience.
    Dynamic DNA methylation and hydroxymethylation changes during in vitro hepatogenesis2023Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    The liver, the largest gland in the body, performs over 500 essential functions and displays a remarkable capacity for regeneration. When compromised, this regenerative ability can lead to severe conditions such as liver cirrhosis and hepatocellular carcinoma. A potential solution to these liver conditions lies in vitro hepatocyte differentiation from pluripotent stem cells, including human embryonic stem cells and induced pluripotent stem cells. Our study explored the role of epigenetic modifications in this process, specifically DNA methylation and hydroxymethylation. These modifications are crucial in modulating gene expression and cellular phenotypes, which influence the differentiation and function of cells. The results demonstrated significant differential methylation in key regions associated with hepatocyte differentiation, implicating their potential importance in successful in vitro hepatocyte differentiation. Additionally, the utility of the Infinium Methylation EPIC BeadChip for high-throughput analysis was confirmed, revealing a broad spectrum of methylation and hydroxymethylation changes during hepatocyte differentiation. The findings suggest a previously underappreciated role of DNA methylation and hydroxymethylation in influencing hepatocyte differentiation, with potential implications for enhancing the efficiency and stability of in vitro hepatocyte generation. This understanding can inform the development of improved protocols, providing valuable platforms for studying liver diseases and developing novel therapeutics. Future studies will continue to dissect the intricate network of epigenetic regulations, opening new avenues for understanding and treating liver diseases. 

  • 18.
    Björn, Niclas
    et al.
    Clinical Pharmacology, Division of Drug Research, Department of Biomedical and Clinical Sciences, Linköping University, Sweden.
    Badam, Tejaswi
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Spalinskas, Rapolas
    Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden.
    Brandén, Eva
    Department of Respiratory Medicine, Gävle Hospital, Sweden / Centre for Research and Development, Uppsala University/Region Gävleborg, Gävle, Sweden.
    Koyi, Hirsh
    Department of Respiratory Medicine, Gävle Hospital, Sweden / Centre for Research and Development, Uppsala University/Region Gävleborg, Gävle, Sweden.
    Lewensohn, Rolf
    Thoracic Oncology Unit, Tema Cancer, Karolinska University Hospital, and Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
    De Petris, Luigi
    Thoracic Oncology Unit, Tema Cancer, Karolinska University Hospital, and Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Sahlén, Pelin
    Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden.
    Lundeberg,, Joakim
    Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden.
    Gustafsson, Mika
    Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Gréen, Henrik
    Clinical Pharmacology, Division of Drug Research, Department of Biomedical and Clinical Sciences, Linköping University, Sweden / Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden / Department of Forensic Genetics and Forensic Toxicology, National Board of Forensic Medicine, Linköping, Sweden.
    Whole-genome sequencing and gene network modules predict gemcitabine/carboplatin-induced myelosuppression in non-small cell lung cancer patients2020In: NPJ Systems Biology and Applications, E-ISSN 2056-7189, Vol. 6, no 1, article id 25Article in journal (Refereed)
    Abstract [en]

    Gemcitabine/carboplatin chemotherapy commonly induces myelosuppression, including neutropenia, leukopenia, and thrombocytopenia. Predicting patients at risk of these adverse drug reactions (ADRs) and adjusting treatments accordingly is a long-term goal of personalized medicine. This study used whole-genome sequencing (WGS) of blood samples from 96 gemcitabine/carboplatin-treated non-small cell lung cancer (NSCLC) patients and gene network modules for predicting myelosuppression. Association of genetic variants in PLINK found 4594, 5019, and 5066 autosomal SNVs/INDELs with p ≤ 1 × 10−3 for neutropenia, leukopenia, and thrombocytopenia, respectively. Based on the SNVs/INDELs we identified the toxicity module, consisting of 215 unique overlapping genes inferred from MCODE-generated gene network modules of 350, 345, and 313 genes, respectively. These module genes showed enrichment for differentially expressed genes in rat bone marrow, human bone marrow, and human cell lines exposed to carboplatin and gemcitabine (p < 0.05). Then using 80% of the patients as training data, random LASSO reduced the number of SNVs/INDELs in the toxicity module into a feasible prediction model consisting of 62 SNVs/INDELs that accurately predict both the training and the test (remaining 20%) data with high (CTCAE 3–4) and low (CTCAE 0–1) maximal myelosuppressive toxicity completely, with the receiver-operating characteristic (ROC) area under the curve (AUC) of 100%. The present study shows how WGS, gene network modules, and random LASSO can be used to develop a feasible and tested model for predicting myelosuppressive toxicity. Although the proposed model predicts myelosuppression in this study, further evaluation in other studies is required to determine its reproducibility, usability, and clinical effect.

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  • 19.
    Blom, Josefin
    University of Skövde, School of Bioscience.
    A transcriptional landscape of memory B cells in untreated, early rheumatoid arthritis2023Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    Rheumatoid arthritis is an autoimmune disease that affects smaller joints, resulting in bone erosion, joint inflammation, and significant disability. Current rheumatoid arthritis treatments are associated with the risk of relapse and side effects, the latter including immunosuppression.Therefore, developing more efficient therapies is of interest. Memory B cells have been implicated in the disease, given the presence of autoantibodies and discrepancies in memory B cell numbers when comparing patients with healthy individuals. Hence, this study aimed to characterize the memory B cell subsets present in patients with untreated, early rheumatoid arthritis, where no treatment would act as a confounding factor. To elucidate the immune cell heterogeneity, singlecell RNA sequencing was used. Data from three patients was integrated and compared to a public dataset from a healthy donor. Two memory B cell subsets were linked to the production of interleukin-6, with one of them also expressing tumor necrosis factor-α. These cytokines are known to be highly expressed in patients with rheumatoid arthritis. Furthermore, a third memory B cell subset expressed high levels of B cell activating factor receptor, associated with pro-survival signals and likely autoimmunity. In the equivalent subset of the healthy donor this receptor was not upregulated. Targeting these subsets specifically with treatments could improve patient prognosis and reduce side effects. However, further research is needed to evaluate the contribution of these memory B cells in the context of rheumatoid arthritis.

  • 20.
    Borgmästars, Emmy
    University of Skövde, School of Bioscience.
    Functional analysis of circulating microRNAs in pancreatic cancer2018Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
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  • 21.
    Borgmästars, Emmy
    et al.
    Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden.
    de Weerd, Hendrik Arnold
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Physics, Chemistry and Biology, Bioinformatics, Linköping University, Linköping, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Sund, Malin
    Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden.
    miRFA: an automated pipeline for microRNA functional analysis with correlation support from TCGA and TCPA expression data in pancreatic cancer2019In: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 20, no 1, p. 1-17, article id 393Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: MicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In silico-based functional analysis of miRNAs usually consists of miRNA target prediction and functional enrichment analysis of miRNA targets. Since miRNA target prediction methods generate a large number of false positive target genes, further validation to narrow down interesting candidate miRNA targets is needed. One commonly used method correlates miRNA and mRNA expression to assess the regulatory effect of a particular miRNA. The aim of this study was to build a bioinformatics pipeline in R for miRNA functional analysis including correlation analyses between miRNA expression levels and its targets on mRNA and protein expression levels available from the cancer genome atlas (TCGA) and the cancer proteome atlas (TCPA). TCGA-derived expression data of specific mature miRNA isoforms from pancreatic cancer tissue was used.

    RESULTS: Fifteen circulating miRNAs with significantly altered expression levels detected in pancreatic cancer patients were queried separately in the pipeline. The pipeline generated predicted miRNA target genes, enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways. Predicted miRNA targets were evaluated by correlation analyses between each miRNA and its predicted targets. MiRNA functional analysis in combination with Kaplan-Meier survival analysis suggest that hsa-miR-885-5p could act as a tumor suppressor and should be validated as a potential prognostic biomarker in pancreatic cancer.

    CONCLUSIONS: Our miRNA functional analysis (miRFA) pipeline can serve as a valuable tool in biomarker discovery involving mature miRNAs associated with pancreatic cancer and could be developed to cover additional cancer types. Results for all mature miRNAs in TCGA pancreatic adenocarcinoma dataset can be studied and downloaded through a shiny web application at https://emmbor.shinyapps.io/mirfa/ .

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  • 22.
    Bourbonnais, André
    University of Skövde, School of Bioscience.
    An assessment of the surrogate host metagenome-assembled genome decontamination for non-model host organisms: Proof-of-concept2023Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this study, a novel method has been assessed to bridge the gap between bioinformatics and ecological conservation efforts to gain evidence to further base conservational plans on. Herein, the validity of using a provisional host metagenome-assembled metagenome to decontaminate the data from host contamination was concluded. To achieve this, 11 samples of increasing host contamination were devised by simulating reads from 100 genomes representing Platanthera bifolia and Platanthera chlorantha endophytic root microbiomes. By following the Critical Interpretation of Metagenome Interpretation benchmarking framework, the method was evaluated on assembly and binning performance. The study concluded strong negative correlations with host contamination that is derived by the lowered proportion of endophytic sequence depth at the higher host contamination levels. Furthermore, statistically significant difference between the control and the perfect GHOST-MAGNET was determined when accounting for the proportion of bins being endophytic.

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  • 23.
    Candelli, Tito
    University of Skövde, School of Life Sciences.
    NOVEL APPROACH TO STORAGE AND STORTING OF NEXT GENERATION SEQUENCING DATA FOR THE PURPOSE OF FUNCTIONAL ANNOTATION TRANSFER2012Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    The problem of functional annotation of novel sequences has been a sigfinicant issue for many laboratories that decided to apply next generation sequencing techniques to less studied species. In particular experiments such as transcriptome analysis heavily suer from this problem due to the impossibility of ascribing their results in a relevant biological context. Several tools have been proposed to solve this problem through homology annotation transfer. The principle behind this strategy is that homologous genes share common functions in dierent organisms, and therefore annotations are transferable between these genes. Commonly, BLAST reports are used to identify a suitable homologousgene in a well annotated species and the annotation is then transferred fromthe homologue to the novel sequence. Not all homologues, however, possess valid functional annotations. The aim of this project was to devise an algorithm to process BLAST reports and provide a criterion to discriminate between homologues with a biologically informative and uninformative annotation, respectively. In addition, all data obtained from the BLAST report isto be stored in a relational database for ease of consultation and visualization. In order to test the solidity of the system, we utilized 750 novel sequences obtained through application of next generation sequencing techniques to Avena sativa samples. This species particularly suits our needs as it represents the typical target for homology annotation transfer: lack of a reference genome and diculty in attributing functional annotation. The system was able to perform all the required tasks. Comparisons between best hits asdetermined by BLAST and best hits as determined by the algorithm showed a significant increase in the biological significance of the results when thealgorithm sorting system was applied.

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  • 24.
    Chawade, Aakash
    et al.
    Department of Cell and Molecular Biology, Göteborg University, Box 462, 403 20 Göteborg, Sweden.
    Bräutigam, Marcus
    Department of Cell and Molecular Biology, Göteborg University, Box 462, 403 20 Göteborg, Sweden.
    Lindlöf, Angelica
    University of Skövde, School of Humanities and Informatics.
    Olsson, Olof
    Department of Cell and Molecular Biology, Göteborg University, Box 462, 403 20 Göteborg, Sweden.
    Olsson, Björn
    University of Skövde, School of Humanities and Informatics.
    Putative cold acclimation pathways in Arabidopsis thaliana identified by a combined analysis of mRNA co-expression patterns, promoter motifs and transcription factors2007In: BMC Genomics, E-ISSN 1471-2164, Vol. 8, p. 304-Article in journal (Refereed)
    Abstract [en]

    Background

    With the advent of microarray technology, it has become feasible to identify virtually all genes in an organism that are induced by developmental or environmental changes. However, relying solely on gene expression data may be of limited value if the aim is to infer the underlying genetic networks. Development of computational methods to combine microarray data with other information sources is therefore necessary. Here we describe one such method.

    Results

    By means of our method, previously published Arabidopsis microarray data from cold acclimated plants at six different time points, promoter motif sequence data extracted from ~24,000 Arabidopsis promoters and known transcription factor binding sites were combined to construct a putative genetic regulatory interaction network. The inferred network includes both previously characterised and hitherto un-described regulatory interactions between transcription factor (TF) genes and genes that encode other TFs or other proteins. Part of the obtained transcription factor regulatory network is presented here. More detailed information is available in the additional files.

    Conclusion

    The rule-based method described here can be used to infer genetic networks by combining data from microarrays, promoter sequences and known promoter binding sites. This method should in principle be applicable to any biological system. We tested the method on the cold acclimation process in Arabidopsis and could identify a more complex putative genetic regulatory network than previously described. However, it should be noted that information on specific binding sites for individual TFs were in most cases not available. Thus, gene targets for the entire TF gene families were predicted. In addition, the networks were built solely by a bioinformatics approach and experimental verifications will be necessary for their final validation. On the other hand, since our method highlights putative novel interactions, more directed experiments could now be performed.

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  • 25.
    Curtsdotter, Alva
    et al.
    University of Skövde, School of Bioscience. Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden / Department of Environmental Sciences, Emory University, Atlanta, GA, Georgia, United States.
    Banks, H. Thomas
    Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC, United States.
    Banks, John E.
    Undergraduate Research Opportunities Center (UROC), California State University, Monterey Bay, Seaside, CA, United States.
    Jonsson, Mattias
    Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Jonsson, Tomas
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden .
    Laubmeier, Amanda N.
    Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC, United States.
    Traugott, Michael
    Mountain Agriculture Research Unit, Institute of Ecology, University of Innsbruck, Innsbruck, Austria.
    Bommarco, Riccardo
    Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden.
    Ecosystem function in predator-prey food webs: confronting dynamic models with empirical data2019In: Journal of Animal Ecology, ISSN 0021-8790, E-ISSN 1365-2656, Vol. 88, no 2, p. 196-210Article in journal (Refereed)
    Abstract [en]

    Most ecosystem functions and related services involve species interactions across trophic levels, for example, pollination and biological pest control. Despite this, our understanding of ecosystem function in multitrophic communities is poor, and research has been limited to either manipulation in small communities or statistical descriptions in larger ones. Recent advances in food web ecology may allow us to overcome the trade-off between mechanistic insight and ecological realism. Molecular tools now simplify the detection of feeding interactions, and trait-based approaches allow the application of dynamic food web models to real ecosystems. We performed the first test of an allometric food web model's ability to replicate temporally nonaggregated abundance data from the field and to provide mechanistic insight into the function of predation. We aimed to reproduce and explore the drivers of the population dynamics of the aphid herbivore Rhopalosiphum padi observed in ten Swedish barley fields. We used a dynamic food web model, taking observed interactions and abundances of predators and alternative prey as input data, allowing us to examine the role of predation in aphid population control. The inverse problem methods were used for simultaneous model fit optimization and model parameterization. The model captured >70% of the variation in aphid abundance in five of ten fields, supporting the model-embodied hypothesis that body size can be an important determinant of predation in the arthropod community. We further demonstrate how in-depth model analysis can disentangle the likely drivers of function, such as the community's abundance and trait composition. Analysing the variability in model performance revealed knowledge gaps, such as the source of episodic aphid mortality, and general method development needs that, if addressed, would further increase model success and enable stronger inference about ecosystem function. The results demonstrate that confronting dynamic food web models with abundance data from the field is a viable approach to evaluate ecological theory and to aid our understanding of function in real ecosystems. However, to realize the full potential of food web models, in ecosystem function research and beyond, trait-based parameterization must be refined and extended to include more traits than body size. © 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society

  • 26.
    Dafalla, Israa Yahia Al Hag Ibrahim
    University of Skövde, School of Bioscience.
    Improving SARS-CoV-2 analyses from wastewater2021Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Wastewater-based epidemiology (WBE) analyzes wastewater for the presence of biological and chemical substances to make public health conclusions. COVID-19 disease is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that infected individuals shed also in their feces, making WBE an alternative way to track SARS-CoV-2 in populations. There are many limitations to the detection and quantification of SARS-CoV-2 from wastewater, such as sample quality, storage conditions or viral concentration. This thesis aims to determine the extent of these limitations and the factors that contribute to them. Other viruses can help the measurements for example Bovine coronavirus (BCoV) can be spiked as a process surrogate, while Pepper mild mottle virus (PMMoV), a fecal biomarker is used to estimate the prevalence of SARS-CoV-2 infection. This study involved two distinct wastewater samples. For method comparison both samples were processed with two methods: virus concentration by electronegative (EN) filtration or direct RNA extraction method. From the RNA extracts RT-qPCR assays were performed to identify and quantify SARS-CoV-2, BCoV, and PMMoV. Based on the obtained cycle threshold (Ct) values, viral gene copy numbers and virus concentration of the original wastewater samples were calculated. Statistical tests were conducted to assess suggested hypothesizes and variations within the data. Results revealed differences in viral contents due to different sample qualities and as a result of freezing and thawing. Furthermore, different sample processing methods led to differences in quantification. In conclusion, improving analysis of SARS-CoV-2 in wastewater using methodologies with better detection efficiency leads to more reliable results.

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  • 27.
    Daran, Rukesh
    University of Skövde, School of Bioscience.
    Using Transcriptomic Data to Predict Biomarkers for Subtyping of Lung Cancer2021Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Lung cancer is one the most dangerous types of all cancer. Several studies have explored the use of machine learning methods to predict and diagnose this cancer. This study explored the potential of decision tree (DT) and random forest (RF) classification models, in the context of a small transcriptome dataset for outcome prediction of different subtypes on lung cancer. In the study we compared the three subtypes; adenocarcinomas (AC), small cell lung cancer (SCLC) and squamous cell carcinomas (SCC) with normal lung tissue by applying the two machine learning methods from caret R package. The DT and RF model and their validation showed different results for each subtype of the lung cancer data. The DT found more features and validated them with better metrics. Analysis of the biological relevance was focused on the identified features for each of the subtypes AC, SCLC and SCC. The DT presented a detailed insight into the biological data which was essential by classifying it as a biomarker. The identified features from this research may serve as potential candidate genes which could be explored further to confirm their role in corresponding lung cancer types and contribute to targeted diagnostics of different subtypes. 

  • 28.
    de Weerd, Hendrik A.
    et al.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
    Badam, Tejaswi V. S.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
    Martínez-Enguita, David
    Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
    Åkesson, Julia
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
    Muthas, Daniel
    Translational Science & Experimental Medicine, Early Respiratory, Inflammation and Autoimmunity, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
    Gustafsson, Mika
    Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    MODifieR: an ensemble R package for inference of disease modules from transcriptomics networks2020In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 36, no 12, p. 3918-3919Article in journal (Refereed)
    Abstract [en]

    MOTIVATION: Complex diseases are due to the dense interactions of many disease-associated factors that dysregulate genes that in turn form so-called disease modules, which have shown to be a powerful concept for understanding pathological mechanisms. There exist many disease module inference methods that rely on somewhat different assumptions, but there is still no gold standard or best performing method. Hence, there is a need for combining these methods to generate robust disease modules.

    RESULTS: We developed MODule IdentiFIER (MODifieR), an ensemble R package of nine disease module inference methods from transcriptomics networks. MODifieR uses standardized input and output allowing the possibility to combine individual modules generated from these methods into more robust disease-specific modules, contributing to a better understanding of complex diseases.

    AVAILABILITY: MODifieR is available under the GNU GPL license and can be freely downloaded from https://gitlab.com/Gustafsson-lab/MODifieR and as a Docker image from https://hub.docker.com/r/ddeweerd/modifier.

    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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  • 29.
    de Weerd, Hendrik A.
    et al.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Åkesson, Julia
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Guala, Dimitri
    Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden ; Merck AB, Solna, Sweden.
    Gustafsson, Mika
    Department of Physics, Chemistry and Biology, Linköping University, Sweden.
    Lubovac-Pilav, Zelmina
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    MODalyseR—a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data2022In: Bioinformatics Advances, E-ISSN 2635-0041, Vol. 2, no 1, article id vbac006Article in journal (Refereed)
    Abstract [en]

    MotivationNetwork-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators.

    ResultsWe developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data.

    Availability and implementationMODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/.

    Supplementary informationSupplementary data are available at Bioinformatics Advances online.

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  • 30.
    Deo, Ameya
    University of Skövde, School of Life Sciences.
    Normalization of microRNA expression levels in Quantitative RT-PCR arrays2010Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Background: Real-time quantitative Reverse Transcriptase Polymerase Chain Reaction (qRT-PCR) is recently used for characterization and expression analysis of miRNAs. The data from such experiments need effective analysis methods to produce reliable and high-quality data. For the miRNA prostate cancer qRT-PCR data used in this study, standard housekeeping normalization method fails due to non-stability of endogenous controls used. Therefore, identifying appropriate normalization method(s) for data analysis based on other data driven principles is an important aspect of this study.

    Results: In this study, different normalization methods were tested, which are available in the R packages Affy and qpcrNorm for normalization of the raw data. These methods reduce the technical variation and represent robust alternatives to the standard housekeeping normalization method. The performance of different normalization methods was evaluated statistically and compared against each other as well as with the standard housekeeping normalization method. The results suggest that qpcrNorm Quantile normalization method performs best for all methods tested.

    Conclusions: The qpcrNorm Quantile normalization method outperforms the other normalization methods and standard housekeeping normalization method, thus proving the hypothesis of the study. The data driven methods used in this study can be applied as standard procedures in cases where endogenous controls are not stable.

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  • 31.
    Ekberg, Sara
    University of Skövde, School of Bioscience.
    Evaluation and implementation of quality control parameters for genome-wide DNA methylome sequencing2022Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Epigenomics is the study of modifications to the genetic material without changes to the DNA sequence, one such modification is methylation of nucleotides. DNA methylation is associated with gene regulation and is studied in a variety of fields such as cancer and ageing. Quality control is essential when designing research studies to ensure that the end result is not affected by poor quality data. In this study, the aim was to define robust quality parameters for whole methylome sequencing for Illumina next generation sequencing data. Three different library preparation protocols, all designed for methylation analysis, has been compared: Accel-NGS Methyl-Seq DNA library, NEB Next Enzymatic Methyl-seq and SPlinted Ligation Adapter Tagging. All samples were sequenced on the Illumina NovaSeq 6000 with paired-end 150 bp. An evaluation of alignment software was also included in the study. The nf-core methylseq pipeline version 1.6.1 was used to process all samples in the study. The pipeline was run multiple times with different settings depending on library type and software choice. Throughout the study, the parameters puc19, lambda and alignment rate showed consistency whereas overall methylation rate and coverage were affected by origin of sample material and study design. In conclusion, not all proposed quality parameters were suitable for general quality control since study design and origin of sample material have impact, but alignment rate and the controls puc19 and lambda shows great promise for general quality control. Future work to establish sample material specific thresholds for methylation rate is encouraged.

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  • 32.
    Engström, Erik
    University of Skövde, School of Bioscience.
    Longitudinal study of memory B cells postpartum2021Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    The adaptive arm of the immune system protects from reinfection and is recognized by an immense diversity of cells that interact and coevolve as novel pathogens emerge. At gestation, the mother undergoes profound and complex changes of her immune system to maintain tolerance towards the fetus while providing proper protection against any intruders. Understanding the mechanisms behind this balancing act can help identify the hallmarks of pregnancy well-being and adverse outcomes. Here, the aim was to explore the dynamic changes at gestation and after delivery in sorted B cell populations including naïve and two memory B cell (MBC) subsets named herein CD27dull and CD27bright from three pregnant females. The subsets were analyzed using Illumina MiSeq HTS data derived from the full-length immunoglobulin heavy chain variable region transcript incorporated with unique molecular identifiers (UMIs). The data was processed using UMI-guided error corrections and reads with shared sequence identities were assembled into clonotypes. The samples were further analyzed by comparing baseline B cell repertoire properties including variable gene segment usage (IGHV), complementarity determining region 3 (CDR3) sizes and levels of somatic hypermutations (SHMs). The results revealed a dynamic change in IGHV usage profiles and an increase of CDR3 sizes between gestation and after delivery. CD27bright exhibited higher levels of SHMs than CD27dull, and SHMs tend to accumulate unequally across the region. CD27bright increased their replacement-to-silent SHM ratio after delivery. The results shed more light on maternal immune adaptation and provide a framework that can be refined and optimized in future works.

  • 33.
    Fagerlind, Magnus
    et al.
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Bioscience.
    Stålhammar, Hans
    VikingGenetics, Skara.
    Olsson, Björn
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Bioscience.
    Klinga-Levan, Karin
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Bioscience.
    Expression of miRNAs in Bull Spermatozoa Correlates with Fertility Rates2015In: Reproduction in domestic animals, ISSN 0936-6768, E-ISSN 1439-0531, Vol. 50, no 4, p. 587-594Article in journal (Refereed)
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  • 34.
    Feldhahn, Magdalena
    et al.
    University of Tübingen, Center for Bioinformatics, Applied Bioinformatics, Sand 14, 72076 Tübingen, Germany.
    Dönnes, Pierre
    University of Skövde, School of Life Sciences. University of Skövde, The Systems Biology Research Centre.
    Schubert, Benjamin
    University of Tübingen, Center for Bioinformatics, Applied Bioinformatics, Sand 14, 72076 Tübingen, Germany.
    Schilbach, Karin
    University Children's Hospital, Department of, Hematology/Oncology, Hoppe-Seyler Str. 1, 72076, Tübingen, Germany.
    Rammensee, Hans-Georg
    University of Tübingen, Department of Immunology, Auf der Morgenstelle 15, 72076 Tuebingen, Germany.
    Kohlbacher, Oliver
    University of Tübingen, Center for Bioinformatics, Applied Bioinformatics, Sand 14, 72076 Tübingen, Germany / University of Tübingen, Quantitative Biology Center, Tübingen, Germany.
    miHA-Match: Computational detection of tissue-specific minor histocompatibility antigens2012In: JIM - Journal of Immunological Methods, ISSN 0022-1759, E-ISSN 1872-7905, Vol. 386, no 1-2, p. 94-100Article in journal (Refereed)
    Abstract [en]

    Allogenic stem cell transplantation has shown considerable success in a number of hematological malignancies, in particular in leukemia. The beneficial effect is mediated by donor T cells recognizing patient-specific HLA-binding peptides. These peptides are called minor histocompatibility antigens (miHAs) and are typically caused by single nucleotide polymorphisms. Tissue-specific miHAs have successfully been used in anti-tumor therapy without causing unspecific graft-versus-host reactions. However, only a small number of miHAs have been identified to date, limiting the clinical use.

    Here we present an immunoinformatics pipeline for the identification of miHAs. The pipeline can be applied to large-scale miHA screening, for example, in the development of diagnostic tests. Another interesting application is the design of personalized miHA-based cancer therapies based on patient-donor pair-specific miHAs detected by this pipeline. The suggested method covers various aspects of genetic variant detection, effects of alternative transcripts, and HLA-peptide binding. A comparison of our computational pipeline and experimentally derived datasets shows excellent agreement and coverage of the computationally predicted miHAs.

  • 35.
    Gamalielsson, Jonas
    et al.
    University of Skövde, School of Humanities and Informatics.
    Olsson, Björn
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Life Sciences.
    Gene Ontology-based Semantic Alignment of Biological Pathways by Evolutionary Search2008In: Journal of Bioinformatics and Computational Biology, ISSN 0219-7200, E-ISSN 1757-6334, Vol. 6, no 4, p. 825-842Article in journal (Refereed)
    Abstract [en]

    A large number of biological pathways have been elucidated recently, and there is a need for methods to analyze these pathways. One class of methods compares pathways semantically in order to discover parts that are evolutionarily conserved between species or to discover intraspecies similarities. Such methods usually require that the topologies of the pathways being compared are known, i.e. that a query pathway is being aligned to a model pathway. However, sometimes the query only consists of an unordered set of gene products. Previous methods for mapping sets of gene products onto known pathways have not been based on semantic comparison of gene products using ontologies or other abstraction hierarchies. Therefore, we here propose an approach that uses a similarity function defined in Gene Ontology (GO) terms to find semantic alignments when comparing paths in biological pathways where the nodes are gene products. A known pathway graph is used as a model, and an evolutionary algorithm (EA) is used to evolve putative paths from a set of experimentally determined gene products. The method uses a measure of GO term similarity to calculate a match score between gene products, and the fitness value of each candidate path alignment is derived from these match scores. A statistical test is used to assess the significance of evolved alignments. The performance of the method has been tested using regulatory pathways for S. cerevisiae and M. musculus.

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  • 36.
    Gamalielsson, Jonas
    et al.
    University of Skövde, School of Humanities and Informatics.
    Olsson, Björn
    University of Skövde, The Systems Biology Research Centre. University of Skövde, School of Humanities and Informatics.
    On the (lack of) robustness of gene expression data clustering2004In: WSEAS Transactions on Biology and Biomedicine, ISSN 1109-9518, E-ISSN 2224-2902, Vol. 1, no 2, p. 198-204Article in journal (Refereed)
    Abstract [en]

    We assess the robustness of partitional clustering algorithms applied to gene expression data. A number of clusterings are made with identical parameter settings and input data using SOM and  k-means algorithms, which both rely on random initialisation and may produce different clusterings with different seeds. We define a reproducibility index and use it to assess the algorithms. The index is based on the number of pairs of genes consistently clustered together in different clusterings. The effect of noise applied to the original data is also studied. Our results show a lack of robustness for both classes of algorithms, with slightly higher reproducibility for SOM than for k-means.

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  • 37.
    Gardenalli, Luan
    University of Skövde, School of Bioscience.
    EVALUATING TRANSCRIPTOME ASSEMBLY POTENTIAL BY DIFFERENT DE NOVO SEQUENCE ASSEMBLER TYPES2023Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    With the rise of NGS technologies, the transcriptomes of non-model organisms can be reconstructed even with the absence of a reference genome, using de novo assembly tools. There is a wide range of de novo assembly tools frequently being developed, however, there is a still a knowledge gap about the different effects and efficiency of different de novo assembly software types for RNA-seq assembly. This study aims to assemble the transcriptome of two different mussel species, Anodonta anatina and Margaritifera margaritifera, using three different types of genomic assemblers and to evaluate their distinct performances. Here, the transcriptomes have been assembled using whole-genome, single-cell and RNA-seq specific assemblers, and the results have been evaluated and compared using reference-free transcriptome evaluation tools. Whole-genome assemblers are not designed to handle variable transcript expressions and splice variations, and have thus achieved poor performance at assembling the transcriptomes. Single-cell assemblers, however, are designed to assemble genomes with uneven coverage, which make them able to handle variable transcript expressions and have therefore achieved good efficiency at assembling the transcriptomes. Single-cell assembler SPAdes has matched the performance of the well stablished RNA-seq assembler Trinity and the single-cell version of IBDA performed just as well as their RNA version. Overall, the top performing assembler in the study was the RNA version of SPAdes.

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  • 38.
    Geirardsdottir, Kristin
    University of Skövde, School of Humanities and Informatics.
    Identifying and analysing alternative splice variants by aligning ESTs and mRNAs to the genomic sequence2005Independent thesis Advanced level (degree of Master (One Year))Student thesis
    Abstract [en]

    Questions have been raised about the genomic complexity of the human genome, since it was reported that it only consisted of 32,000 genes. Alternative splicing is considered the explanation of the enormous difference between the number of genes and the number of proteins. Aligning expressed sequence tags (ESTs) to the genomic sequence has become a popular approach for gene prediction, revealing alternative splice variants. The aim in this thesis is to identify and analyse splice variants of the adhesion family of G protein-coupled receptors using EST data. 75% of the genes in the data set of 33 sequences were found to have a total of 51 splice variants. About half of the variants were considered functional.

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  • 39.
    Ghosheh, Nidal
    et al.
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Küppers-Munther, Barbara
    Takara Bio Europe AB, Gothenburg, Sweden.
    Asplund, Annika
    Takara Bio Europe AB, Gothenburg, Sweden.
    Andersson, Christian X.
    Takara Bio Europe AB, Gothenburg, Sweden.
    Björquist, Petter
    VeriGraft AB, Gothenburg, Sweden.
    Andersson, Tommy B.
    Cardiovascular Renal and Metabolism, Innovative Medicines and Early Development Biotech Unit, AstraZeneca, Mölndal, Sweden / Department of Physiology and Pharmacology, Section of Pharmacogenetics, Karolinska Institutet, Stockholm, Sweden.
    Carén, Helena
    Sahlgrenska Cancer Center, Department of Pathology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Sweden.
    Simonsson, Stina
    Institute of Biomedicine, Department of Clinical Chemistry and Transfusion Medicine, Sahlgrenska Academy, University of Gothenburg, Sweden.
    Sartipy, Peter
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment. Late Stage Cardiovascular, Renal, and Metabolism, R&D BioPharmaceuticals, AstraZeneca, Mölndal, Sweden.
    Synnergren, Jane
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Human Pluripotent Stem Cell-Derived Hepatocytes Show Higher Transcriptional Correlation with Adult Liver Tissue than with Fetal Liver Tissue2020In: ACS Omega, E-ISSN 2470-1343, Vol. 5, no 10, p. 4816-4827Article in journal (Refereed)
    Abstract [en]

    Human pluripotent stem cell-derived hepatocytes (hPSC-HEP) display many properties of mature hepatocytes, including expression of important genes of the drug metabolizing machinery, glycogen storage, and production of multiple serum proteins. To this date, hPSC-HEP do not, however, fully recapitulate the complete functionality of in vivo mature hepatocytes. In this study, we applied versatile bioinformatic algorithms, including functional annotation and pathway enrichment analyses, transcription factor binding-site enrichment, and similarity and correlation analyses, to datasets collected from different stages during hPSC-HEP differentiation and compared these to developmental stages and tissues from fetal and adult human liver. Our results demonstrate a high level of similarity between the in vitro differentiation of hPSC-HEP and in vivo hepatogenesis. Importantly, the transcriptional correlation of hPSC-HEP with adult liver (AL) tissues was higher than with fetal liver (FL) tissues (0.83 and 0.70, respectively). Functional data revealed mature features of hPSC-HEP including cytochrome P450 enzymes activities and albumin secretion. Moreover, hPSC-HEP showed expression of many genes involved in drug absorption, distribution, metabolism, and excretion. Despite the high similarities observed, we identified differences of specific pathways and regulatory players by analyzing the gene expression between hPSC-HEP and AL. These findings will aid future intervention and improvement of in vitro hepatocyte differentiation protocol in order to generate hepatocytes displaying the complete functionality of mature hepatocytes. Finally, on the transcriptional level, our results show stronger correlation and higher similarity of hPSC-HEP to AL than to FL. In addition, potential targets for further functional improvement of hPSC-HEP were also identified. 

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  • 40.
    Gusenleitner, Daniel
    University of Skövde, School of Life Sciences.
    In silico modeling for uncertain biochemical data2009Independent thesis Advanced level (degree of Master (One Year)), 15 credits / 22,5 HE creditsStudent thesis
    Abstract [en]

    Analyzing and modeling data is a well established research area and a vast variety of different methods have been developed over the last decades. Most of these methods assume fixed positions of data points; only recently uncertainty in data has caught attention as potentially useful source of information. In order to provide a deeper insight into this subject, this thesis concerns itself with the following essential question: Can information on uncertainty of feature values be exploited to improve in silico modeling? For this reason a state-of-art random forest algorithm is developed using Matlab R. In addition, three techniques of handling uncertain numeric features are presented and incorporated in different modified versions of random forests. To test the hypothesis six realworld data sets were provided by AstraZeneca. The data describe biochemical features of chemical compounds, including the results of an Ames test; a widely used technique to determine the mutagenicity of chemical substances. Each of the datasets contains a single uncertain numeric feature, represented as an expected value and an error estimate. Themodified algorithms are then applied on the six data sets in order to obtain classifiers, able to predict the outcome of an Ames test. The hypothesis is tested using a paired t-test and the results reveal that information on uncertainty can indeed improve the performance of in silico models.

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  • 41.
    Guszpit, Emilia
    University of Skövde, School of Bioscience.
    Bioinformatics analysis on the drug supporting systems2023Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    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.

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  • 42.
    Helgadóttir, Hanna Sigrún
    University of Skövde, School of Humanities and Informatics.
    Using semantic similarity measures across Gene Ontology to predict protein-protein interactions2005Independent thesis Basic level (degree of Bachelor)Student thesis
    Abstract [en]

    Living cells are controlled by proteins and genes that interact through complex molecular pathways to achieve a specific function. Therefore, determination of protein-protein interaction is fundamental for the understanding of the cell’s lifecycle and functions. The function of a protein is also largely determined by its interactions with other proteins. The amount of protein-protein interaction data available has multiplied by the emergence of large-scale technologies for detecting them, but the drawback of such measures is the relatively high amount of noise present in the data. It is time consuming to experimentally determine protein-protein interactions and therefore the aim of this project is to create a computational method that predicts interactions with high sensitivity and specificity. Semantic similarity measures were applied across the Gene Ontology terms assigned to proteins in S. cerevisiae to predict protein-protein interactions. Three semantic similarity measures were tested to see which one performs best in predicting such interactions. Based on the results, a method that predicts function of proteins in connection with connectivity was devised. The results show that semantic similarity is a useful measure for predicting protein-protein interactions.

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  • 43.
    Hellner, Qarin
    University of Skövde, School of Life Sciences.
    Simultaneous MSY management of a predator and prey species, the Cod (Gadus morhua) and Herring (Clupea harengus) in the Baltic Sea2012Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    The European Commission manages fish stocks by applying a fishing mortality based on the maximum sustainable yield concept. So far most Baltic Sea fishing maximum sustainable yieldmodels have focused on one species at a time. The few existing multi-species models have assumed that a species’ maturity and growth is dependent on the availability of food. Our two-species models make it possible to investigate if there is a conflict between fishing maximum sustainable yield for cod and herring in the Baltic Sea. This two-species model of cod, as a predator and herring as prey, takes into account environmental drivers on cod and herring recruitment. Reproductive volume together with year-growth, (a year specific effect on growth of external variables like food availability) and predation by grey seals was included in the cod model. The herring model was dependent on cod spawning stock biomass and year-growth. The result shows that the reproductive volume is the main factor that affects the maximum sustainable yield for cod. The spawning stock biomass at maximum sustainable yield is more sensitive to reproductive volume than year-growth. When predation from seals is added in mortality and high environmental factors occurs the spawning stock biomass would be 50% compared to the spawning stock biomass at high environmental effects without seal predation. Four simulations of high cod spawning stock biomass were devastating for the herring population that was eradicated with high predation pressure. The herring maximum sustainable yield depends on the amount of cod spawning stack biomass i.e. the effect of high or low reproductive volume. Two analyses were made on a current environmental state for both species. The first analysis had a natural mortality of 0.2 for cod, which gave an fishing mortality of 0.20 and maximum sustainable yield of 410 000 tons. The herring had a fishing mortality of 0.03 and maximum sustainable yield of 11 000 tons. The second simulation included seal predation in cod mortality which decreased the cod maximum sustainable yield by 98% at a fishing mortality of 0.02, which gave a fishing mortality of 0.19 and maximum sustainable yield of 275 000 tons for herring. This gives a 25 times increase of herring maximum sustainable yield compared to the result without predation on cod. The cod population dynamics is vulnerable to environmental changes and to secure a healthy and productive cod population the target fishing mortality should be kept in phase with current reproductive volume. 

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  • 44.
    Holmgren, Gustav
    Sahlgrenska Academy at University of Gothenburg.
    In vitro toxicity testing using human pluripotent stem cell derivatives2016Doctoral thesis, comprehensive summary (Other academic)
  • 45.
    Imran, Saima
    University of Skövde, School of Bioscience.
    Comprehensive Analysis of lncRNA and circRNA Mediated ceRNA network in Psoriasis2022Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Evidence is accumulating that noncoding RNAs and circRNA are involved in psoriasis; however, the competing endogenous RNA (ceRNA) mediated regulatory mechanisms in psoriasis are rarely reported. The research study aimed to comprehensively investigate the differences in the expression levels of circular RNA (circRNA), long non-coding RNA (lncRNA), microRNA (miRNA/miR), and mRNA in psoriasis. In addition, key lncRNA/circRNA-miRNA-mRNA-ceRNA interactions were screened using the GSE145305 microarray dataset from the Gene Expression Omnibus database. After data preprocessing, differentially expressed circRNAs (DECs), lncRNAs (DELs), miRNAs (DEMs), or genes (DEGs) were identified, and normal controls using the linear models for the microarray data method. A protein-protein interaction (PPI) network was constructed for DEGs based on protein databases, followed by a module analysis. The ceRNA network was constructed based on the interaction between miRNAs and mRNAs and lncRNAs/circRNAs and miRNAs. The present study identified that in the case of mRNA 10 genes are significantly down-regulated, 86 genes are significantly up-regulated and in the case of miRNA 48 are significantly down-regulated and 75 genes are significantly up-regulated between patients with psoriasis and controls. miRNA, mRNA, lncRNA, and circRNA target predictionswere made. Then combined construction of a ceRNA network using mRNA-miRNA-lncRNA and mRNAmiRNA-circRNA. The current research has employed the knowledge of bioinformatics tools and software to determine the hub module and PPI network. Taken together, these identified ceRNA interactions may be crucial targets for the treatment of psoriasis.

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  • 46.
    Jadhav, Trishul
    University of Skövde, School of Life Sciences.
    Knowledge Based Gene Set analysis (KB-GSA): A novel method for gene expression analysis2010Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Microarray technology allows measurement of the expression levels of thousand of genes simultaneously. Several gene set analysis (GSA) methods are widely used for extracting useful information from microarrays, for example identifying differentially expressed pathways associated with a particular biological process or disease phenotype. Though GSA methods like Gene Set Enrichment Analysis (GSEA) are widely used for pathway analysis, these methods are solely based on statistics. Such methods can be awkward to use if knowledge of specific pathways involved in particular biological processes are the aim of the study. Here we present a novel method (Knowledge Based Gene Set Analysis: KB-GSA) which integrates knowledge about user-selected pathways that are known to be involved in specific biological processes. The method generates an easy to understand graphical visualization of the changes in expression of the genes, complemented with some common statistics about the pathway of particular interest.

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  • 47.
    Jamal, Noor Haval
    University of Skövde, School of Bioscience.
    Gene Biomarker Identification by Distinguishing Between Small-Cell and Non-Small Cell Lung Cancer Through a Module-Based Approach2023Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Lung cancer is the leading cause of cancer-related deaths worldwide and is divided into two broad histological types, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). Network module-based approach is applied to lung cancer subtypes in order to analyze and compare the results with previous literature and thus discover new genetic biomarkers and/or confirm previously discovered ones. Data were extracted and analyzed in GEO2R, later protein-protein interaction (PPI)networks were generated through STRING. Functional modules and genesoverlapping between modules were identified using Cytoscape plugins MCODE and ModuLand, which were compared subsequently. The tools complement each other as MCODE can help visualize the neighbors of nodes identified by ModuLand while ModuLand can help identify significant genes as MCODE identifies all genes equally. Venny was used to analyze the overlapping genes between the subtypes and FunRichfor functional enrichment. The results were consistent with findings of previous literature. ModuLand highlighted nodes previously reported to have a role in various types of cancer including lung cancer, which involved two common proteins: CDK1and HIGD1B. The two functional networks showed clusters belonging to the mitoticsister chromatid segregation. Perhaps the main defective part in the cell cycle of lungcancer is chromatin-related. In conclusion by establishing functional modules and highlighting common genes between the modules for each subtype can shed light on potential mechanisms and further support previous discoveries. Several important genes have been identified at the centre of highly interconnected biological complexes that could serve as candidate biomarkers and hallmarks for future studies.

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  • 48.
    Jansson, Andreas
    University of Skövde, School of Life Sciences.
    Modelling T helper cell activation and development2006Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    T helper (Th) cell activation and development is one of the most critical events in regulating the adaptive immune response. Understanding its regulation could be of great therapeutical value as many severe diseases are associated with failure in controlling T cell activation and development. However, the regulation of T cell activation appears to be one of the most complex set of cellular and molecular interactions known in the immune system. There is therefore an urgent need for tools to unravel this complexity, and to make use of the quantitative experimental data. To address this issue, mathematical and computational models, based on rigorous biophysical and kinetic data, were developed to study the specific role of some of the major costimulatory molecules involved in Th cell activation, and others developed to investigate proposed theories about mechanisms involved in Th cell differentiation. The simulations of costimulation reveal new implications for the function of the costimulatory molecules CD28 and CTLA-4, and their ligands B7-1 and B7-2, and show how binding affinity, stoichiometric properties, expression levels, and, in particular, competition effects, all profoundly influence complex formation at the immunological synapse. The results support the concept that B7-2 and B7-1 are the dominant ligands of CD28 and CTLA-4, respectively, and indicate that the inability of B7-2 to recruit CTLA-4 to the synapse cannot be, as has been previously proposed, due to the different binding properties of B7-1 and B7-2. Simulations of Th cell development reveal that both instructive and selective processes are likely to be involved in Th cell differentiation. In addition, further simulations indicate that Th2 cells are more likely to become dominant by inhibiting Th1 cells (negative selection), rather than selecting their own growth (positive selection). This thesis also includes an experimental work in which the immunomodulatory role of the bacterial signalling molecule N-3-(oxododecanoyl)-L-homoserine lactone (OdDHL) was analysed. This study strongly suggests that OdDHL suppresses Th cell activation and development, and that it is likely targeting the intracellular signalling events involved in the early stages of Th cell activation.

  • 49.
    Jantscher, Yvonne
    University of Skövde, School of Life Sciences. University of Skövde.
    Identification of miRNAs and their target genes in stem cell derived cardiomyocytes2011Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Stem cell research, especially the one dealing with human embryonic stem cells, is a major topic nowadays. In the last few years studies about human embryonic stem cell derived cardiomyocytes highlighted the importance of those, as their characteristics are almost identical as of the cardiomyocytes in the heart (i.e. the contraction of those cells). The studies concentrate on the ability of using cardiomyocytes in the drug development for cardiac diseases or in regenerative medicine and cell replacement therapies. In contrast some researchers concentrate on microRNAs (miRNAs) as regulators in the development of cardiomyocytes. This study combines both research topics as it deals with stem cells and miRNAs (as well as their target mRNAs). A main objective is to find differentially expressed genes by using Significance Analysis of Microarrays (SAM) as method. Furthermore miRNA target prediction is applied and the identified targets are compared with the ones found by SAM. With an intersection approach we derived 41 targets of up-regulated miRNAs and 25 targets of down-regulated miRNAs, which can be the basis for further studies (i.e. knock-out experiments).

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  • 50.
    Johansson, Anna
    University of Skövde, School of Bioscience.
    Plasma metabolite biomarkers associated with different meat consumption and risk of type 2 diabetes: A case-control study nested within a northern Swedish cohort2019Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    Numerous epidemiological studies show that processed meat consumption is associated to type 2 diabetes (T2D), but the results are less consistent for unprocessed meat and poultry intake. However, in the studies the meat consumption has been estimated through self-reporting using questionnaires. This method is known to suffer from measurement errors, which may lead to dubious observed diet-disease associations. Dietary biomarkers may be used as more objective instruments of specific meat intake. Therefore, the aim in this study was to (1) identify plasma metabolites associated to total, processed, unprocessed meat and poultry intake, and (2) to investigate their associations with the risk of developing T2D. Furthermore, a correlation analysis was performed to elucidate dietary patterns related to the reported meat intakes and their metabolites. Through multivariate modelling, panels of 22, 22, 15 and 19 metabolites where selected, that mirrors total, processed, unprocessed meat and poultry intake, respectively. Ridge predictive modelling was performed generating metabolite scores reflective of the diverse meat consumptions. These scores and the reported meat intakes were used in conditional logistic regression for the assessment of their association with risk of developing T2D. Both the processed metabolite scores and reported intakes were associated with higher risk of developing T2D, whereas the unprocessed meat, poultry scores and associated intakes were not. In conclusion, assessments of plasma metabolite scores appears promising as objective measurements of dietary meat intakes and could be used as a complement to the questionnaires. Results need to be validated in independent cohorts.

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