Topological analysis of protein co-abundance networks identifies novel host targets important for HCV infection and pathogenesis

<p>Abstract</p> <p>Background</p> <p>High-throughput methods for obtaining global measurements of transcript and protein levels in biological samples has provided a large amount of data for identification of 'target' genes and proteins of interest. These targets may be mediators of functional processes involved in disease and therefore represent key points of control for viruses and bacterial pathogens. Genes and proteins that are the most highly differentially regulated are generally considered to be the most important. We present topological analysis of co-abundance networks as an alternative to differential regulation for confident identification of target proteins from two related global proteomics studies of hepatitis C virus (HCV) infection.</p> <p>Results</p> <p>We analyzed global proteomics data sets from a cell culture study of HCV infection and from a clinical study of liver biopsies from HCV-positive patients. Using lists of proteins known to be interaction partners with pathogen proteins we show that the most differentially regulated proteins in both data sets are indeed enriched in pathogen interactors. We then use these data sets to generate co-abundance networks that link proteins based on similar abundance patterns in time or across patients. Analysis of these co-abundance networks using a variety of network topology measures revealed that both degree and betweenness could be used to identify pathogen interactors with better accuracy than differential regulation alone, though betweenness provides the best discrimination. We found that though overall differential regulation was not correlated between the cell culture and liver biopsy data, network topology was conserved to an extent. Finally, we identified a set of proteins that has high betweenness topology in both networks including a protein that we have recently shown to be essential for HCV replication in cell culture.</p> <p>Conclusions</p> <p>The results presented show that the network topology of protein co-abundance networks can be used to identify proteins important for viral replication. These proteins represent targets for further experimental investigation that will provide biological insight and potentially could be exploited for novel therapeutic approaches to combat HCV infection.</p

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production

Genome-scale metabolic reconstruction and in silico analysis of methylotrophic yeast Pichia pastoris for strain improvement

<p>Abstract</p> <p>Background</p> <p><it>Pichia pastoris </it>has been recognized as an effective host for recombinant protein production. A number of studies have been reported for improving this expression system. However, its physiology and cellular metabolism still remained largely uncharacterized. Thus, it is highly desirable to establish a systems biotechnological framework, in which a comprehensive <it>in silico </it>model of <it>P. pastoris </it>can be employed together with high throughput experimental data analysis, for better understanding of the methylotrophic yeast's metabolism.</p> <p>Results</p> <p>A fully compartmentalized metabolic model of <it>P. pastoris </it>(<it>iPP</it>668), composed of 1,361 reactions and 1,177 metabolites, was reconstructed based on its genome annotation and biochemical information. The constraints-based flux analysis was then used to predict achievable growth rate which is consistent with the cellular phenotype of <it>P. pastoris </it>observed during chemostat experiments. Subsequent <it>in silico </it>analysis further explored the effect of various carbon sources on cell growth, revealing sorbitol as a promising candidate for culturing recombinant <it>P. pastoris </it>strains producing heterologous proteins. Interestingly, methanol consumption yields a high regeneration rate of reducing equivalents which is substantial for the synthesis of valuable pharmaceutical precursors. Hence, as a case study, we examined the applicability of <it>P. pastoris </it>system to whole-cell biotransformation and also identified relevant metabolic engineering targets that have been experimentally verified.</p> <p>Conclusion</p> <p>The genome-scale metabolic model characterizes the cellular physiology of <it>P. pastoris</it>, thus allowing us to gain valuable insights into the metabolism of methylotrophic yeast and devise possible strategies for strain improvement through <it>in silico </it>simulations. This computational approach, combined with synthetic biology techniques, potentially forms a basis for rational analysis and design of <it>P. pastoris </it>metabolic network to enhance humanized glycoprotein production.</p

Technical Advances in Chloroplast Biotechnology

Chloroplasts are highly organized cellular organelles after master organelle nucleus. They not only play a central role in photosynthesis but are also involved in several crucial cellular activities. Advancements in molecular biology and transgenic technology have further groomed importance of the organelle, and they are the most ideal ones for the expression of transgene. No doubt, limitations are there, but still research is advancing to resolve those. Certain valuable traits have been engineered for improved agronomic performance of crop plants. Industrial enzymes and therapeutic proteins have been expressed using plastid transformation system. Synthetic biology has been explored to play a key role in engineering metabolic pathways. Further, producing dsRNA in a plant’s chloroplast rather than in its cellular cytoplasm is more effective way to address desired traits. In this chapter, we highlight technological advancements in chloroplast biotechnology and its implication to develop biosafe engineered plants

Carbon Partitioning in Green Algae (Chlorophyta) and the Enolase Enzyme

The exact mechanisms underlying the distribution of fixed carbon within photoautotrophic cells, also referred to as carbon partitioning, and the subcellular localization of many enzymes involved in carbon metabolism are still unknown. In contrast to the majority of investigated green algae, higher plants have multiple isoforms of the glycolytic enolase enzyme, which are differentially regulated in higher plants. Here we report on the number of gene copies coding for the enolase in several genomes of species spanning the major classes of green algae. Our genomic analysis of several green algae revealed the presence of only one gene coding for a glycolytic enolase [EC 4.2.1.11]. Our predicted cytosolic localization would require export of organic carbon from the plastid to provide substrate for the enolase and subsequent re-import of organic carbon back into the plastids. Further, our comparative sequence study of the enolase and its 3D-structure prediction may suggest that the N-terminal extension found in green algal enolases could be involved in regulation of the enolase activity. In summary, we propose that the enolase represents one of the crucial regulatory bottlenecks in carbon partitioning in green algae