| 1. |
- Faust, Ellika
(författare)
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Genetic Identification of Corkwing Wrasse Cleaner Fish Escaping from Norwegian Aquaculture
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The genetic impact of farmed fish escaping aquaculture is a highly debated issue. However, non-target species, such as cleaner fish that are used in fish farms to remove parasitic sea lice, are rarely considered. Here, we report that wild corkwing wrasse (Symphodus melops), which are transported long distances to be used as cleaner fish in salmon farms, escape and hybridize with local wrasse populations. Recently, increasing numbers of corkwing wrasse have been reported north of its described distribution range, in Flatanger in Trøndelag in Norway, an area heavily relying on the import of cleaner fish from Skagerrak. Using a high number of nuclear genetic markers identified with 2bRAD sequencing, we show that, although the Flatanger population is largely a result of a northward range expansion, there is also evidence of considerable gene flow from southern populations in Skagerrak. Of the 40 corkwing wrasses first sampled in Flatanger, we discovered two individuals with clear southern genotypes, one first-generation hybrid, and 12 potential second-generation hybrids. Thus, we found clear evidence of gene flow from source populations of translocated cleaner fish at the edge of an ongoing northwards range expansion. To better understand the extent of gene flow we then greatly expanded our sampling. Based on patterns of genetic divergence and homogeneity, we identified a smaller battery of 84 SNPs which is able to detect escapees with a Skagerrak origin as well as first and secondgeneration hybrids with high accuracy and power. We then used these SNPs to investigate the magnitude and geographic extent of escaping and hybridizing cleaner fish along the Norwegian coast. We found that escapees and hybrids may constitute up to 20 % of the local populations at the northern edge of the species distribution. In other parts of the Norwegian coast where salmon farming is also common, we found surprisingly few escapees and hybrids. Possible causes for few escapees and hybrids found in these areas are difficult to evaluate with the current lack of reporting of translocations by aquaculture operators. Overall, these findings provide critical information both for aquaculture management and conservation of wild populations of non-target species, and have implications for the increasing use of cleaner fish as parasite control in fish farms, that is both poorly documented and regulated. Moving genetic material between isolated populations could drastically alter the genetic composition and erode population structure, potentially resulting in loss of local adaptation and hampering natural range expansion. Although the ecological and evolutionary significance of escapees warrant further investigation, these results should be taken into consideration in the use of translocated cleaner fish.
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| 2. |
- Berglund, Fanny
(författare)
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Identification of novel antibiotic resistance genes through large-scale data analysis
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Antibiotic resistance is increasing worldwide, and is considered a serious threat to public health by e.g. the World Health Organization. Antibiotic resistance genes are hypothesized to originate from harmless bacteria in and around us, from where they are horizontally transfered into human pathogens. It is therefore of great importance to explore human-associated and environmental bacterial communities to identify novel antibiotic resistance genes before they reach clinical settings. The three papers presented in this thesis aim to identify novel antibiotic resistance genes in large genomic and metagenomic datasets. In paper I, the aim was to identify novel genes of the clinically important subclass B1 metallo-β-lactamases. By analyzing whole bacterial genomes as well as metagenomes from environmental and human-associated bacterial communities, 76 novel putative B1 genes were predicted. Twenty-one of these were selected for experimental validation, whereof 18 expressed the predicted phenotype in E. coli. Phylogentic analysis revealed that the novel genes formed 59 previously undescribed gene families. In paper II, a large volume of genomic and metagenomic data was searched for novel plasmid-mediated quinolone resistance (qnr ) genes. In total, 611 qnr genes were predicted, of which 20 were putative novel. Nine of these were experimentally tested in E. coli, whereof eight expressed the predicted phenotype. In paper III, a new method for identification and reconstruction of novel antibiotic resistance genes from fragmented metagenomic data was presented. The method is based on gene specific models, which are optimized for a high sensitivity and specificity. The method is furthermore computationally efficient and can be applied to any class of resistance genes. The results of this thesis provides a deeper insight to the diversity and evolutionary history of two types of clinically relevant antibiotic resistance genes. It also provides new methods for efficient and reliable identification of novel resistance genes in fragmented metagenomic data.
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| 3. |
- Lund, David, 1994
(författare)
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Computational discovery of antibiotic resistance genes and their horizontal transfer
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Antibiotic resistance is increasing among clinical infections and represents one of the most serious threats to public health. Pathogens often become resistant by acquiring mobile antibiotic resistance genes (ARGs) via horizontal gene transfer (HGT). To limit the spread of new ARGs, it is important that we identify emerging threats early, and that we improve our understanding of what drives the HGT of ARGs. The three papers encompassing this thesis aim to increase our knowledge about ARGs and their mobility. In paper I, computational screening of large genomic datasets was used to identify new resistance genes for macrolide antibiotics, and to clarify their evolution. A large diversity of new erm and mph genes was identified, including six new families of mobile ARGs carried by pathogens, that showed varied phylogenetic origins. Of the tested genes, 70% induced resistance in Escherichia coli . In paper II, we identified previously undiscovered mobile genes giving resistance to aminoglycoside antibiotics in pathogens, further demonstrating how computational methods can discover potential emerging ARGs. Close to one million bacterial genomes were screened for aac and aph genes, and the mobility of each predicted gene was evaluated. A total of 50 families of new mobile ARGs were identified in pathogens. When new ARGs were tested in E. coli . 86% were functional, with 39% giving clinical resistance. In paper III, the factors influencing the HGT of ARGs were investigated. Phylogenetic analysis was used to identify HGT events from a large set of ARGs. For each event, the genetic compatibility of the involved gene(s) and genomes, as well as the co-occurrence of donor and recipient in different environments, were computed and used as input to train random forest classifiers. The resulting models suggested that the most important factor for determining if a mobile ARG successfully undergoes horizontal transfer is the genetic compatibility between the gene and the recipient genome. The findings presented in this thesis increase our knowledge about new genes giving resistance to two important classes of antibiotics. Furthermore, the results provide new insights into the horizontal transfer of resistance genes.
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| 4. |
- Shu, Nanjiang, 1981-
(författare)
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Prediction of zinc-binding sites in proteins and efficient protein structure description and comparison
- 2008
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A large number of proteins require certain metals to stabilize their structures or to function properly. About one third of all proteins in the Protein Data Bank (PDB) contain metals and it is estimated that approximately the same proportion of all proteins are metalloproteins. Zinc, the second most abundant transition metal found in eukaryotic organisms, plays key roles, mainly structural and catalytic, in many biological functions. Predicting whether a protein binds zinc and even the accurate location of binding sites is important when investigating the function of an experimentally uncharacterized protein. Describing and comparing protein structures with both efficiency and accuracy are essential for systematic annotation of functional properties of proteins, be it on an individual or on a genome scale. Dozens of structure comparison methods have been developed in the past decades. In recent years, several research groups have endeavoured in developing methods for fast comparison of protein structures by representing the three-dimensional (3D) protein structures as one-dimensional (1D) geometrical strings based on the shape symbols of clustered regions of φ/ψ torsion angle pairs of the polypeptide backbones. These 1D geometrical strings, shape strings, are as compact as 1D secondary structures but carry more elaborate structural information in loop regions and thus are more suitable for fast structure database searching, classification of loop regions and evaluation of model structures. In this thesis, a new method for predicting zinc-binding sites in proteins from amino acid sequences is described. This method predicts zinc-binding Cys, His, Asp and Glu (the four most common zinc-binding residues) with 75% precision (86% for Cys and His only) at 50% recall according to a solid 5-fold cross-validation on a non-redundant set of the PDB chains containing 2727 unique chains, of which 235 bind to zinc. This method predicts zinc-binding Cys and His with about 10% higher precision at different recall levels compared to a previously published method. In addition, different methods for describing and comparing protein structures are reviewed. Some recently developed methods based on 1D geometrical representation of backbone structures are emphasized and analyzed in details.
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| 5. |
- Boulund, Fredrik, 1985
(författare)
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Analysis of large-scale metagenomic data
- 2013
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The topic of this thesis is the analysis of large data sets of DNA sequence data produced from modern high-throughput DNA sequencing machines. Using such machines to sequence the genetic content of a microbial community produces a metagenome. This thesis comprises three research papers, all connected to the study of large metagenomic data sets. In the first paper, we developed a method for discovering fragments of fluoroquinolone antibiotic resistance genes in short fragments of DNA. The method uses hidden Markov models for identifying qnr genes in short DNA fragments. Cross-validation showed that our method for classifying short fragments has high statistical power even for fragments as short as 100 base pairs, a length commonly encountered in modern next-generation sequencing data. In the second paper, the putative qnr genes identified in the first paper were verified using wet-lab experiments. This was a follow-up study to validate the findings from the first paper. An expression system for qnr genes in Escherichia coli hosts was developed and used to evaluate the resistance phenotype of the novel gene candidates discovered in the first paper. In the third paper, we developed an easy-to-use high performance method for distributed gene quantification in metagenomic sequence data. It leverages high-performance computing resources to provide high throughput while maintaining sensitivity. This enables efficient and accurate gene quantification, suitable for use in comparative metagenomics. Next-generation DNA sequencing has had a big impact on molecular biology. As the size of the produced data sets increases, there is an equally increasing need for methods suited for the analysis of such data sets. This thesis presents several new methods that are well adapted to analysis of modern terabase-sized metagenomic data sets.
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| 6. |
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| 7. |
- Brunnsåker, Daniel, 1992
(författare)
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Machine Learning Enabled Functional Discovery in Yeast Systems Biology
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Saccharomyces cerevisiae is a well-studied organism, yet roughly 20 percent of its proteins remain poorly characterized. Recent studies also seem to indicate that the pace of functional discovery is slow. Previous work has implied that the most probable path forward is via not only regular automation but fully autonomous systems that can automatically guide and perform high-throughput experimentation. This thesis explores various concepts to accelerate and perform functional discovery of gene and protein functions in Saccharomyces cerevisiae . It does so by combining ideas from artificial intelligence, such as active learning, with highthroughput analytical techniques like mass-spectrometry. The work performed as the basis for this thesis also served to aid in the further characterization of different aspects of yeast systems biology. Specifically, it delved into the diauxic shift and its regulators through the lens of untargeted metabolomics, as well as the regulatory patterns behind genome-wide intracellular proteomic abundances. We find that it is essential not only to develop tools and techniques for facilitating high-throughput experimentation, but also to ensure their optimal utilization of already existing knowledge. It is also of paramount importance to ensure a holistic and encompassing view of systems biology by more fully integrating and using different levels of cellular organization and analytical techniques.
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| 8. |
- Jauhiainen, Alexandra, 1981
(författare)
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Microarray Analysis of mRNA Decay Assays and Prediction of Drug Target Conservation
- 2008
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis contains two papers concerning (I) the evolutionary conservation of drug targets and its potential use in environmental risk assessments and (II) mRNA degradation as a control mechanism during osmotic stress in the yeast S. cerevisiae. Environmental risk assessments are needed for the approval of new pharmaceutical compounds. To date, the risk assessments are mainly focused on organisms like algae and Daphnia. The conservation of drug targets in species relevant for ecotoxicity testing is a key aspect in developing more targeted test strategies on higher organisms like fish or amphibians. With information on predicted proteomes for a wide range of species it is possible to extract and compile data on evolutionary conservation for drug targets. In paper I, orthology data is compiled and analyzed for a set of drug targets in several species, and the result evaluated based on an extensive literature search. mRNA degradation can be investigated on a genome-wide scale with the use of a transcriptional inhibitor and subsequent hybridization of RNA pools, isolated at a set of timepoints, to microarrays. Due to the complexity of the microarray methodology in this context, the data are in need of processing and transformation to deduce relevant information on changes in degradation rates. In paper II, mRNA degradation is investigated as a posttranscriptional control effect in connection to hyperosmotic stress. We conclude that mRNA degradation mechanisms are important regulatory keys in the stress response.
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| 9. |
- Abenius, Tobias, 1979
(författare)
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Towards Precision Medicine: Exploiting Genetic Variation in Tumours by Inferring Multitype Gene Regulatory Networks
- 2019
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Precision medicine aims to customize treatment to a patient given measured genetic or other molecular data for diagnostics. In cancer, optimal medical treatment, depends on how far the disease has progressed, type and subtype of cancer and, the individual tumor’s circumstances. Finding large-scale genome-wide models for cancer tumours comes with several challenges including, but not limited to, the relatively small size of the cohort compared to the vast number of genes. In this licentiate thesis, using DNA copy number aberration (CNA), mRNA expression data and survival data, methods were developed to adress some of the issues along the path towards useful large-scale models. In paper I, two related models were suggested that incorporate these data types. To allow large-scale computations a new LASSO solver based on Cyclic Coordinate Descent were coded in C/BLAS for both R and Matlab. A set of validation techniques were used and the solutions to the models could find both previously known genes involved in cancer as well as new candidate targets for intervention, predicting survival length and further elucidated the connectome. One of these candidate targets were verified in vitro. In paper II, the techniques and the software developed in paper I, were further refined in the form of R packages and exposed in a book chapter as a hands on tutorial. In paper III, efforts were made to increase the likelihood of reproducibility and save both human and machine time in calculations and report writing. Having calculations splitted into interdependent blocks and caching computations, results in a dynamic update of reports which change if data or analysis change. This allows for certain in silico issues in the reproducibility process to be mitigated. In paper IV, a model was developed to find similarites and differences between cancer types or subtypes. Potential benefits are to further elucidate the workings of the gene regulatory networks in cancer for multiple cohort clusters at any granularity by exploiting the accumulative statistical strength for coinciding cross-type subnetworks increasing the available sample size while keeping the resolution at a type or sub-type specific level. Known genes relevant to cancer appear in the models and the networks inferred disclose candidate hub genes, connections of interest for candidate sub-hub interventions predictors for survival important for selection of therapy. A generalization to pairwise fused LASSO were used as a model and a solver were implemented using Split Bregman optimization and parallel computations in C/BLAS. I conclude that the tools and models presented may aid in accelerating the system biology loop and provide insights into the biology of cancers, be it type, subtype or as more data come in, even smaller groups.
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| 10. |
- Inda Díaz, Juan Salvador, 1984
(författare)
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Data-Driven Methods for Surveillance and Diagnostics of Antibiotic-Resistant Bacteria
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Antimicrobial resistance is a rapidly growing challenge for the healthcare sector and multi-drug resistant bacterial infections are the cause of nearly a million deaths annually worldwide. Antibiotic resistance is conferred by either antibiotic resistance genes (ARGs) which can be acquired by horizontal gene transfer between bacterial cells or by mutations in pre-existing DNA. Large collections of ARGs are present in the bacterial communities hosted by humans and animals, and in the external environments. Most of these ARGs are uncharacterized and not well-studied. Furthermore, antimicrobial resistance has significantly hindered our ability to treat infections and novel diagnostic solutions are therefore needed to ensure efficient treatment. In paper I, the abundance and diversity of 24,074 ARGs of 17 classes were studied in metagenomic data. The majority of the ARGs were previously uncharacterized, of which several were commonly reoccurring and shared across the digestive system of humans and animals, suggesting that they are under strong selection pressures. The data-driven work in this paper showed that the analysis of all ARGs, including those that have previously not been described, is necessary to provide a comprehensive description of the resistance potential of bacterial communities. In paper II, an AI method for the prediction of bacterial susceptibility towards antibiotics is presented. The method is based on transformers and artificial neural networks and exploits the strong and highly non-trivial dependencies present in the resistance patterns of bacteria. The model was highly successful in predicting susceptibility for most antibiotics from the classes cephalosporins and quinolones but had a lower performance on penicillins and aminoglycosides. The AI-based methodology described in this paper may be used to improve the diagnostics chain of infectious diseases with the potential to reduce the morbidity and mortality of patients. This thesis provides methodologies for improved surveillance and diagnostics of antibiotic-resistant bacteria and, thereby, contributes to a more sustainable use of antibiotics.
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