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Host-microbiota, pro-inflammatory immunity and physiological senescence in wild birds
Těšický, Martin
Triggered by microbial ligands, inflammation serves as a "double-edged sword" to fight infections on the one hand, but on the other hand causing tissue damage due to oxidative stress if it is dysregulated. For example, chronic inflammation can contribute to inflammaging, which is now widely regarded as one of the causes of ageing. In my interdisciplinary dissertation, my colleagues and I investigated three interrelated aspects of inflammation, using an evolutionary framework and various free-living birds as models: (1) ecological and evolutionary determinants of gut microbiota (GM) composition and diversity, a driver of wild bird immunity, (2) diversity in immune genes affecting inflammatory responses in wild birds and (3) inflammation-related physiological senescence in a free-living passerine bird, the great tit (Parus major). Firstly, using 16S rRNA gene metabarcoding, we revealed high intra- and interspecific variation in passerine gut microbiota (GM) dominated by the major phyla Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes. Although in mammals GM depends strongly on host phylogeny and diet, in birds we found only moderate effects of phylogeny and very limited effects of host geography and ecology on GM composition. While microbiota diverged between the upper and lower...
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Host-microbiota, pro-inflammatory immunity and physiological senescence in wild birds
Těšický, Martin ; Vinkler, Michal (advisor) ; Tschirren, Barbara (referee) ; Štěpánek, Ondřej (referee)
Triggered by microbial ligands, inflammation serves as a "double-edged sword" to fight infections on the one hand, but on the other hand causing tissue damage due to oxidative stress if it is dysregulated. For example, chronic inflammation can contribute to inflammaging, which is now widely regarded as one of the causes of ageing. In my interdisciplinary dissertation, my colleagues and I investigated three interrelated aspects of inflammation, using an evolutionary framework and various free-living birds as models: (1) ecological and evolutionary determinants of gut microbiota (GM) composition and diversity, a driver of wild bird immunity, (2) diversity in immune genes affecting inflammatory responses in wild birds and (3) inflammation-related physiological senescence in a free-living passerine bird, the great tit (Parus major). Firstly, using 16S rRNA gene metabarcoding, we revealed high intra- and interspecific variation in passerine gut microbiota (GM) dominated by the major phyla Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes. Although in mammals GM depends strongly on host phylogeny and diet, in birds we found only moderate effects of phylogeny and very limited effects of host geography and ecology on GM composition. While microbiota diverged between the upper and lower...
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Framework for retrieval and analysis of proteins apo and holo forms from PDB
Král, Adam ; Hoksza, David (advisor) ; Sanchez Rocha, Alma Carolina (referee)
We developed a software framework that allows the analysis of ligand-free (apo) and ligand-bound (holo) forms of proteins that are accessible in PDB. The software downloads the current version of the PDB, divides the structures into groups of the same molecules, and these into apo and holo forms. Finally, it is possible to analyze pairs of apo and holo structures with respect to their different structural characteristics. In addition to the software work itself, we also verify results against previous work on an equivalent dataset, and obtain results for the current version of PDB. Keywords: protein; structural bioinformatics; PDB
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Structural identification of protein-DNA interactions using machine learning
Gajdošová, Petra ; Hoksza, David (advisor) ; Feidakis, Christos (referee)
DNA-protein interactions are essential parts of cell life and cell cycle. Prediction of these interactions requires knowledge of DNA and a protein structure. Because machine learning approaches show adequate results in biological predictions, we chose to use it for the prediction of protein-DNA interactions. In this thesis, we use the machine learning tool P2Rank that was originally designed for prediction of ligand-binding sites and adapt it to predict DNA-binding sites. Apart of that, the thesis serves as a summary of existing prediction tools/methods and includes suggestions for further modifications of P2Rank.
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Algorithms for protein-ligand binding site discovery
Krivák, Radoslav ; Neruda, Roman (advisor) ; Mráz, František (referee)
Virtually all processes in living organisms are conducted by proteins. Proteins perform their function by binding to other proteins (protein-protein interactions) or small molecules - so called ligands (protein-ligand interactions). Active sites for protein-ligand interactions are pockets in protein structure where ligand can bind. Predicting of ligand binding sites is the first step to study and predict protein functions and structure based drug-design. In this thesis we reviewed current approaches for binding site prediction and proposed our own improvement. We have developed a novel pocket ranking function based on prediction model that predicts ligandability (ability to bind a ligand) of a given point inside of a pocket. Prediction is done considering only a local physicochemical and geometric properties derived from neighbourhood.
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