The casein kinases Yck1p and Yck2p act in the secretory pathway, in part, by regulating the Rab exchange factor Sec2p.

Sec2p is a guanine nucleotide exchange factor that activates Sec4p, the final Rab GTPase of the yeast secretory pathway. Sec2p is recruited to secretory vesicles by the upstream Rab Ypt32p acting in concert with phosphatidylinositol-4-phosphate (PI(4)P). Sec2p also binds to the Sec4p effector Sec15p, yet Ypt32p and Sec15p compete against each other for binding to Sec2p. We report here that the redundant casein kinases Yck1p and Yck2p phosphorylate sites within the Ypt32p/Sec15p binding region and in doing so promote binding to Sec15p and inhibit binding to Ypt32p. We show that Yck2p binds to the autoinhibitory domain of Sec2p, adjacent to the PI(4)P binding site, and that addition of PI(4)P inhibits Sec2p phosphorylation by Yck2p. Loss of Yck1p and Yck2p function leads to accumulation of an intracellular pool of the secreted glucanase Bgl2p, as well as to accumulation of Golgi-related structures in the cytoplasm. We propose that Sec2p is phosphorylated after it has been recruited to secretory vesicles and the level of PI(4)P has been reduced. This promotes Sec2p function by stimulating its interaction with Sec15p. Finally, Sec2p is dephosphorylated very late in the exocytic reaction to facilitate recycling

Preparation and properties of silver nanoparticles on collagen matrix

Cílem předložené diplomové práce byla in-situ příprava stříbrných nanočástic na kolagenové matrici jako antibakteriálního povlaku a studie vlivu podmínek přípravy na vlastnosti nanočástic, zejména jejich velikost, tvar, homogenita jejich distribuce a antibakteriální aktivita. V rámci práce byla rovněž sledována kinetika redukce stříbrných nanočástic z dusičnanu stříbrného a vliv teploty na její průběh. Připravený materiál a jeho vlastnosti byly analyzovány pomocí různých technik. UV-VIS absorpčních vlastností stříbra bylo využito pro kinetické studie redukce a uvolňování nanočástic. Pomocí rastrovací elektronové mikroskopie byla vyhodnocena homogenita stříbrného povlaku a přibližná velikost částic a jejich aglomerátů. Velikostní distribuce nanočástic byla pak přesně stanovena pomocí dynamického rozptylu světla. Pomocí infračervené spektrometrie s Fourierovou transformací s technikou úplného zeslabeného odrazu byla sledována interakce stříbra s funkčními, zejména karboxylovými skupinami. Termogravimetricky byla stanovena tepelná stabilita a procentuální obsah stříbra v materiálu. Vliv AgNPs povlaku na 3D strukturu kolagenního scaffoldu a fázový kontrast pro 3D zobrazovací techniky byl zkoumán pomocí rentgenové výpočetní nanotomografie. V neposlední řadě byla také stanovena antibakteriální aktivita připraveného materiálu a její závislost na koncentraci stříbra.Presented master thesis was focused on in-situ silver nanoparticles preparation on collagen matrix as an antibacterial coating and influence of preparation conditions on material properties, mainly nanoparticles size, shape, homogeneity of their distribution and antibacterial activity. Kinetics studies of silver nanoparticles reduction from silver nitrate were also studied within thesis. Prepared material was analysed using a variety of techniques. UV-VIS absorption properties of silver in neutral and ionic state were utilized for kinetics studies and nanoparticles release. Homogeneity of AgNPs layer and approximate size of NPs and their agglomerates were observed and captured using scanning electron microscopy. Their size was then exactly determined by dynamic lights scattering measurements. Infrared spectrometry with attenuated total reflection was used to study the interaction between silver and collagen carboxylic functional groups. Thermal stability and weight ratio of silver in prepared material was measured by thermal gravimetric analysis. Influence of AgNPs coating on 3D structure and phase resolution contrast of 3D visualization techniques was measured using X-ray computed nanotomography. Finally, antibacterial activity of silver coated collagen and its concentration dependence were evaluated.

Metabolic Changes by Wine Flor-Yeasts with Gluconic Acid as the Sole Carbon Source

Gluconic acid consumption under controlled conditions by a Saccharomyces cerevisiae flor yeast was studied in artificial media. Gluconic acid was the sole carbon source and the compounds derived from this metabolism were tracked by endo-metabolomic analysis using a Gas Chromatography-Mass Spectrometry (GC-MSD) coupled methodology. After 6 days, about 30% of gluconic acid (1.5 g/L) had been consumed and 34 endo-metabolites were identified. Metabolomic pathway analysis showed the TCA cycle, glyoxylate-dicarboxylate, glycine-serine-threonine, and glycerolipid metabolic pathway were significantly affected. These results contribute to the knowledge of intracellular metabolomic fluctuations in flor yeasts during gluconic acid uptake, opening possibilities for future experiments to improve their applications to control gluconic acid contents during the production of fermented beverages

Decoding microbial genomes to understand their functional roles in human complex diseases

Complex diseases such as cardiovascular disease (CVD), obesity, inflammatory bowel disease (IBD), kidney disease, type 2 diabetes (T2D), and cancer have become a major burden to public health and affect more than 20% of the population worldwide. The etiology of complex diseases is not yet clear, but they are traditionally thought to be caused by genetics and environmental factors (e.g., dietary habits), and by their interactions. Besides this, increasing pieces of evidence now highlight that the intestinal microbiota may contribute substantially to the health and disease of the human host via their metabolic molecules. Therefore, decoding the microbial genomes has been an important strategy to shed light on their functional potential. In this review, we summarize the roles of the gut microbiome in complex diseases from its functional perspective. We further introduce artificial tools in decoding microbial genomes to profile their functionalities. Finally, state-of-the-art techniques have been highlighted which may contribute to a mechanistic understanding of the gut microbiome in human complex diseases and promote the development of the gut microbiome-based personalized medicine.</p

T Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification

Cancer is a complex disease characterized by uncontrolled cell growth and proliferation. T cell receptors (TCRs) are essential proteins for the adaptive immune system, and their specific recognition of antigens plays a crucial role in the immune response against diseases, including cancer. The diversity and specificity of TCRs make them ideal for targeting cancer cells, and recent advancements in sequencing technologies have enabled the comprehensive profiling of TCR repertoires. This has led to the discovery of TCRs with potent anti-cancer activity and the development of TCR-based immunotherapies. In this study, we investigate the use of sparse coding for the multi-class classification of TCR protein sequences with cancer categories as target labels. Sparse coding is a popular technique in machine learning that enables the representation of data with a set of informative features and can capture complex relationships between amino acids and identify subtle patterns in the sequence that might be missed by low-dimensional methods. We first compute the k-mers from the TCR sequences and then apply sparse coding to capture the essential features of the data. To improve the predictive performance of the final embeddings, we integrate domain knowledge regarding different types of cancer properties. We then train different machine learning (linear and non-linear) classifiers on the embeddings of TCR sequences for the purpose of supervised analysis. Our proposed embedding method on a benchmark dataset of TCR sequences significantly outperforms the baselines in terms of predictive performance, achieving an accuracy of 99.8\%. Our study highlights the potential of sparse coding for the analysis of TCR protein sequences in cancer research and other related fields

Pharmacology of modulators of alternative splicing

More than 95% of genes in the human genome are alternatively spliced to form multiple transcripts, often encoding proteins with differing or opposing function. The control of alternative splicing is now being elucidated, and with this comes the opportunity to develop modulators of alternative splicing that can control cellular function. A number of approaches have been taken to develop compounds that can experimentally, and sometimes clinically, affect splicing control resulting in potential novel therapeutics. Here we develop the concepts that targeting alternative splicing can result in relatively specific pathway inhibitors/activators that result in dampening down of physiological or pathological processes, from changes in muscle physiology, to altering angiogenesis or pain. The targets and pharmacology of some of the current inhibitors/activators of alternative splicing are demonstrated and future directions discussed

PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding

We are now witnessing significant progress of deep learning methods in a variety of tasks (or datasets) of proteins. However, there is a lack of a standard benchmark to evaluate the performance of different methods, which hinders the progress of deep learning in this field. In this paper, we propose such a benchmark called PEER, a comprehensive and multi-task benchmark for Protein sEquence undERstanding. PEER provides a set of diverse protein understanding tasks including protein function prediction, protein localization prediction, protein structure prediction, protein-protein interaction prediction, and protein-ligand interaction prediction. We evaluate different types of sequence-based methods for each task including traditional feature engineering approaches, different sequence encoding methods as well as large-scale pre-trained protein language models. In addition, we also investigate the performance of these methods under the multi-task learning setting. Experimental results show that large-scale pre-trained protein language models achieve the best performance for most individual tasks, and jointly training multiple tasks further boosts the performance. The datasets and source codes of this benchmark are all available at https://github.com/DeepGraphLearning/PEER_BenchmarkComment: Accepted by NeurIPS 2022 Dataset and Benchmark Track. arXiv v2: source code released; arXiv v1: release all benchmark result

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical Covid-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalisation2-4 following SARS-CoV-2 infection. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from critically-ill cases with population controls in order to find underlying disease mechanisms. Here, we use whole genome sequencing in 7,491 critically-ill cases compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical Covid-19. We identify 16 new independent associations, including variants within genes involved in interferon signalling (IL10RB, PLSCR1), leucocyte differentiation (BCL11A), and blood type antigen secretor status (FUT2). Using transcriptome-wide association and colocalisation to infer the effect of gene expression on disease severity, we find evidence implicating multiple genes, including reduced expression of a membrane flippase (ATP11A), and increased mucin expression (MUC1), in critical disease. Mendelian randomisation provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5, CD209) and coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of Covid-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication, or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between critically-ill cases and population controls is highly efficient for detection of therapeutically-relevant mechanisms of disease