A COMPARISON OF THE WATER JET CUTTING AND LASER TECHNOLOGIES

Tato práce bude zaměřena, jak už její název napovídá, na seznámení s problematikou nekonvenčních technologií a to s řezáním vodním paprskem a laserem. Úvodní kapitoly se skládají z rešerše na již zmíněné metody obrábění. V praktické části, tvořené ve spolupráci s firmou Honeywell, bude zkoumána efektivita stroje na řezání laserem, který je starší, a nového stroje na řezání vodním paprskem.The thesis is focused on the introduction of non-conventional technology into the manufacturing workplace, mainly water jet and laser cutting. The first two chapters are research about water jet machining and laser. A practical part of my thesis was carried out with the assistance of Honeywell and involved comparing the efficiency of an older laser cutting machine against and new one for water jet machining.

Unique organization of photosystem II supercomplexes and megacomplexes in Norway spruce

Photosystem II (PSII) complexes are organized into large supercomplexes with variable amounts of light-harvesting proteins (Lhcb). A typical PSII supercomplex in plants is formed by four trimers of Lhcb proteins (LHCII trimers), which are bound to the PSII core dimer via monomeric antenna proteins. However, the architecture of PSII supercomplexes in Norway spruce[Picea abies (L.) Karst.] is different, most likely due to a lack of two Lhcb proteins, Lhcb6 and Lhcb3. Interestingly, the spruce PSII supercomplex shares similar structural features with its counterpart in the green alga Chlamydomonas reinhardtii [Kouřil et al. (2016) New Phytol. 210, 808–814]. Here we present a single-particle electron microscopy study of isolated PSII supercomplexes from Norway spruce that revealed binding of a variable amount of LHCII trimers to the PSII core dimer at positions that have never been observed in any other plant species so far. The largest spruce PSII supercomplex, which was found to bind eight LHCII trimers, is even larger than the current largest known PSII supercomplex from C. reinhardtii. We have also shown that the spruce PSII supercomplexes can form various types of PSII megacomplexes, which were also identified in intact grana membranes. Some of these large PSII supercomplexes and megacomplexes were identified also in Pinus sylvestris, another representative of the Pinaceae family. The structural variability and complexity of LHCII organization in Pinaceae seems to be related to the absence of Lhcb6 and Lhcb3 in this family, and may be beneficial for the optimization of light-harvesting under varying environmental conditions

Fluorescence-activated multi-organelle mapping of subcellular plant hormone distribution

Auxins and cytokinins are two major families of phytohormones that control most aspects of plant growth, development and plasticity. Their distribution in plants has been described, but the importance of cell- and subcellular-type specific phytohormone homeostasis remains undefined. Herein, we revealed auxin and cytokinin distribution maps showing their different organelle-specific allocations within the Arabidopsis plant cell. To do so, we have developed Fluorescence-Activated multi-Organelle Sorting (FAmOS), an innovative subcellular fractionation technique based on flow cytometric principles. FAmOS allows the simultaneous sorting of four differently labelled organelles based on their individual light scatter and fluorescence parameters while ensuring hormone metabolic stability. Our data showed different subcellular distribution of auxin and cytokinins, revealing the formation of phytohormone gradients that have been suggested by the subcellular localization of auxin and cytokinin transporters, receptors and metabolic enzymes. Both hormones showed enrichment in vacuoles, while cytokinins were also accumulated in the endoplasmic reticulum

SAMPI: Protein Identification with Mass Spectra Alignments

BACKGROUND: Mass spectrometry based peptide mass fingerprints (PMFs) offer a fast, efficient, and robust method for protein identification. A protein is digested (usually by trypsin) and its mass spectrum is compared to simulated spectra for protein sequences in a database. However, existing tools for analyzing PMFs often suffer from missing or heuristic analysis of the significance of search results and insufficient handling of missing and additional peaks. RESULTS: We present an unified framework for analyzing Peptide Mass Fingerprints that offers a number of advantages over existing methods: First, comparison of mass spectra is based on a scoring function that can be custom-designed for certain applications and explicitly takes missing and additional peaks into account. The method is able to simulate almost every additive scoring scheme. Second, we present an efficient deterministic method for assessing the significance of a protein hit, independent of the underlying scoring function and sequence database. We prove the applicability of our approach using biological mass spectrometry data and compare our results to the standard software Mascot. CONCLUSION: The proposed framework for analyzing Peptide Mass Fingerprints shows performance comparable to Mascot on small peak lists. Introducing more noise peaks, we are able to keep identification rates at a similar level by using the flexibility introduced by scoring schemes

Harvest: an open-source tool for the validation and improvement of peptide identification metrics and fragmentation exploration

<p>Abstract</p> <p>Background</p> <p>Protein identification using mass spectrometry is an important tool in many areas of the life sciences, and in proteomics research in particular. Increasing the number of proteins correctly identified is dependent on the ability to include new knowledge about the mass spectrometry fragmentation process, into computational algorithms designed to separate true matches of peptides to unidentified mass spectra from spurious matches. This discrimination is achieved by computing a function of the various features of the potential match between the observed and theoretical spectra to give a numerical approximation of their similarity. It is these underlying "metrics" that determine the ability of a protein identification package to maximise correct identifications while limiting false discovery rates. There is currently no software available specifically for the simple implementation and analysis of arbitrary novel metrics for peptide matching and for the exploration of fragmentation patterns for a given dataset.</p> <p>Results</p> <p>We present Harvest: an open source software tool for analysing fragmentation patterns and assessing the power of a new piece of information about the MS/MS fragmentation process to more clearly differentiate between correct and random peptide assignments. We demonstrate this functionality using data metrics derived from the properties of individual datasets in a peptide identification context. Using Harvest, we demonstrate how the development of such metrics may improve correct peptide assignment confidence in the context of a high-throughput proteomics experiment and characterise properties of peptide fragmentation.</p> <p>Conclusions</p> <p>Harvest provides a simple framework in C++ for analysing and prototyping metrics for peptide matching, the core of the protein identification problem. It is not a protein identification package and answers a different research question to packages such as Sequest, Mascot, X!Tandem, and other protein identification packages. It does not aim to maximise the number of assigned peptides from a set of unknown spectra, but instead provides a method by which researchers can explore fragmentation properties and assess the power of novel metrics for peptide matching in the context of a given experiment. Metrics developed using Harvest may then become candidates for later integration into protein identification packages.</p

A53T-alpha-synuclein-overexpression in the mouse nigrostriatal pathway leads to early increase of 14-3-3 epsilon and late increase of GFAP

Parkinson’s disease (PD) is a neurodegenerative disorder frequent at old age characterized by atrophy of the nigrostriatal projection. Overexpression and A53T-mutation of the presynaptic, vesicle-associated chaperone alpha-synuclein are known to cause early-onset autosomal dominant PD. We previously generated mice with transgenic overexpression of human A53T-alpha-synuclein (A53T-SNCA) in dopaminergic substantia nigra neurons as a model of early PD. To elucidate the early and late effects of A53T-alpha-synuclein on the proteome of dopaminergic nerve terminals in the striatum, we now investigated expression profiles of young and old mice using two-dimensional fluorescence difference in gel electrophoresis (2D-DIGE) and mass spectrometry. In total, 15 proteins were upregulated and 2 downregulated. Mice before the onset of motor anomalies showed an upregulation of the spot containing 14-3-3 proteins, in particular the epsilon isoform, as well as altered levels of chaperones, vesicle trafficking and bioenergetics proteins. In old mice, the persistent upregulation of 14-3-3 proteins was aggravated by an increase of glial fibrillary acidic protein (GFAP) suggesting astrogliosis due to initial neurodegeneration. Independent immunoblots corroborated GFAP upregulation and 14-3-3 upregulation for the epsilon isoform, and also detected significant eta and gamma changes. Only for 14-3-3 epsilon a corresponding mRNA increase was observed in midbrain, suggesting it is transcribed in dopaminergic perikarya and accumulates as protein in presynapses, together with A53T-SNCA. 14-3-3 proteins associate with alpha-synuclein in vitro and in pathognomonic Lewy bodies of PD brains. They act as chaperones in signaling, dopamine synthesis and stress response. Thus, their early dysregulation probably reflects a response to alpha-synuclein toxicity

Computational Methods for Protein Identification from Mass Spectrometry Data

Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology

Immunogenic Salivary Proteins of Triatoma infestans: Development of a Recombinant Antigen for the Detection of Low-Level Infestation of Triatomines

Chagas disease, caused by Trypanosoma cruzi, is a neglected disease with 20 million people at risk in Latin America. The main control strategies are based on insecticide spraying to eliminate the domestic vectors, the most effective of which is Triatoma infestans. This approach has been very successful in some areas. However, there is a constant risk of recrudescence in once-endemic regions resulting from the re-establishment of T. infestans and the invasion of other triatomine species. To detect low-level infestations of triatomines after insecticide spraying, we have developed a new epidemiological tool based on host responses against salivary antigens of T. infestans. We identified and synthesized a highly immunogenic salivary protein. This protein was used successfully to detect differences in the infestation level of T. infestans of households in Bolivia and the exposure to other triatomine species. The development of such an exposure marker to detect low-level infestation may also be a useful tool for other disease vectors