Conserved host response to highly pathogenic avian influenza virus infection in human cell culture, mouse and macaque model systems

<p>Abstract</p> <p>Background</p> <p>Understanding host response to influenza virus infection will facilitate development of better diagnoses and therapeutic interventions. Several different experimental models have been used as a proxy for human infection, including cell cultures derived from human cells, mice, and non-human primates. Each of these systems has been studied extensively in isolation, but little effort has been directed toward systematically characterizing the conservation of host response on a global level beyond known immune signaling cascades.</p> <p>Results</p> <p>In the present study, we employed a multivariate modeling approach to characterize and compare the transcriptional regulatory networks between these three model systems after infection with a highly pathogenic avian influenza virus of the H5N1 subtype. Using this approach we identified functions and pathways that display similar behavior and/or regulation including the well-studied impact on the interferon response and the inflammasome. Our results also suggest a primary response role for airway epithelial cells in initiating hypercytokinemia, which is thought to contribute to the pathogenesis of H5N1 viruses. We further demonstrate that we can use a transcriptional regulatory model from the human cell culture data to make highly accurate predictions about the behavior of important components of the innate immune system in tissues from whole organisms.</p> <p>Conclusions</p> <p>This is the first demonstration of a global regulatory network modeling conserved host response between <it>in vitro </it>and <it>in vivo </it>models.</p

The molecular basis for pore pattern morphogenesis in diatom silica

Biomineral-forming organisms produce inorganic materials with complex, genetically encoded morphologies that are unmatched by current synthetic chemistry. It is poorly understood which genes are involved in biomineral morphogenesis and how the encoded proteins guide this process. We addressed these questions using diatoms, which are paradigms for the self-assembly of hierarchically meso- and macroporous silica under mild reaction conditions. Proteomics analysis of the intracellular organelle for silica biosynthesis led to the identification of new biomineralization proteins. Three of these, coined dAnk1-3, contain a common protein–protein interaction domain (ankyrin repeats), indicating a role in coordinating assembly of the silica biomineralization machinery. Knocking out individual dank genes led to aberrations in silica biogenesis that are consistent with liquid–liquid phase separation as underlying mechanism for pore pattern morphogenesis. Our work provides an unprecedented path for the synthesis of tailored mesoporous silica materials using synthetic biology

Genome Mining and Synthetic Biology in Marine Natural Products Discovery

In recent years, marine genomics has become a growning rapidly field, helped by the large amount of information that is becoming available to the international scientific community. Taking into account the current excitement in the field of marine biotechnology, this Special Issue entitled “Genome Mining and Synthetic Biology in Marine Natural Product Discovery” aims to to assess the impact of these molecular approaches on the discovery of bioactive compounds from marine organisms. The term “genome mining” is used to identify all bioinformatic investigations aimed at detecting the biosynthetic pathways of bioactive natural products and their possible functional and chemical interactions. Several studies are now reporting on marine organisms. Oceans cover nearly 70% of the Earth’s surface and host a huge ecological, chemical, and biological diversity. The natural conditions of the sea favor, in marine organisms, the production of a large variety of novel molecules with great pharmaceutical potential. Marine organisms are unique in their structural and functional features compared to terrestrial ones. Innovation in this field is very rapid, as revealed by the funding of several Seventh Framework Programme (FP7) and Horizon 2020 projects under the topic “Blue Growth”, with the urgent goal of discovering new drugs

Morphogenesis and Protein Composition of Valve Silica Deposition Vesicles from Diatoms

The silica-based cell walls of diatoms are outstanding examples of nature’s capability to synthesize complex porous structures with genetically controlled patterns from the nanometer scale to the range of hundreds of micrometers. Formation of the cell wall building blocks (valves and girdle bands) occurs in membrane-bound compartments, termed silica deposition vesicles (SDVs), which are unique organelles in silica-forming protists. Isolation of the SDVs has not yet been achieved, which has severely hampered the efforts to understand the mechanisms of biological silica morphogenesis. The present thesis aimed to address this shortcoming. The foundation was the development of an improved cell cycle synchronization and a fluorescence labeling method for the model diatom Thalassiosira pseudonana that enabled rapid identification of valve SDVs in a cell lysate. Correlative fluorescence and electron microscopy allowed visualizing the development of valve silica with unprecedented spatio-temporal resolution. Elemental analysis and demineralization of immature valves provided the first direct chemical evidence that silica morphogenesis is an interplay of inorganic and organic molecules inside the valve SDVs. Cryo TEM imaging of valve SDVs indicated the formation of organic patterns that precede silica depostion. From these observations, an organic biomolecule dependent, liquid-liquid phase separation based model for pore formation in the diatom T. pseudonana was proposed. The second part of this thesis was focused on the enrichment of valve SDVs from T. pseudonana and the subsequent proteomics based identification of more than 40 potential valve SDV proteins. Among these, three diatom-specific proteins contained conserved protein protein interaction domains (ankyrin-repeats) and were surprisingly predicted to be located in the cytoplasm. The fluorescent tagging of the three proteins (termed dANK1-3) confirmed their association with the valve SDVs. When the respective dank genes were knocked out by CRISPR/Cas9, the valves displayed permanent anomalies in the quantity and the pattern of ~22 nm sized pores. Double knockout mutants lacking both dank1 and dank3 were almost completely devoid of pores. The analysis of valve morphogenesis in the single and double knockout mutants revealed phenotypic changes that were consistent with the liquid-liquid phase separation based model for pore pattern formation in diatom biosilica. The work of this thesis has provided for the first time direct access to valve SDVs, which has opened entirely new possibilities for studying the composition, properties, and working mechanism of an organelle that forms a complex shaped mineral

Spectral clustering gene ontology terms to group genes by function

Abstract. With the invention of biotechnological high throughput methods like DNA microarrays, biologists are capable of producing huge amounts of data. During the analysis of such data the need for a grouping of the genes according to their biological function arises. In this paper, we propose a method that provides such a grouping. As functional information, we use Gene Ontology terms. Our method clusters all GO terms present in a data set using a Spectral Clustering method. Then, mapping the genes back to their annotation, genes can be associated to one or more clusters of defined biological processes. We show that our Spectral Clustering method is capable of finding clusters with high inner cluster similarity.

Analytical Techniques for the Improvement of Mass Spectrometry Protein Profiling

Bioinformatics is rapidly advancing through the "post-genomic" era following the sequencing of the human genome. In preparation for studying the inner workings behind genes, proteins and even smaller biological elements, several subdivisions of bioinformatics have developed. The subdivision of proteomics, concerning the structure and function of proteins, has been aided by the mass spectrometry data source. Biofluid or tissue samples are rapidly assayed for their protein composition. The resulting mass spectra are analyzed using machine learning techniques to discover reliable patterns which discriminate samples from two populations, for example, healthy or diseased, or treatment responders versus non-responders. However, this data source is imperfect and faces several challenges: unwanted variability arising from the data collection process, obtaining a robust discriminative model that generalizes well to future data, and validating a predictive pattern statistically and biologically.This thesis presents several techniques which attempt to intelligently deal with the problems facing each stage of the analytical process. First, an automatic preprocessing method selection system is demonstrated. This system learns from data and selects a combination of preprocessing methods which is most appropriate for the task at hand. This reduces the noise affecting potential predictive patterns. Our results suggest that this method can help adapt to data from different technologies, improving downstream predictive performance. Next, the issues of feature selection and predictive modeling are revisited with respect to the unique challenges posed by proteomic profile data. Approaches to model selection through kernel learning are also investigated. Key insights are obtained for designing the feature selection and predictive modeling portion of the analytical framework. Finally, methods for interpreting the resultsof predictive modeling are demonstrated. These methods are used to assure the user of various desirable properties: validation of the strength of a predictive model, validation of reproducible signal across multiple data generation sessions and generalizability of predictive models to future data. A method for labeling profile features with biological identities is also presented, which aids in the interpretation of the data. Overall, these novel techniques give the protein profiling community additional support and leverage to aid the predictive capability of the technology