Sampling the Solution Space in Genome-Scale Metabolic Networks Reveals Transcriptional Regulation in Key Enzymes

Genome-scale metabolic models are available for an increasing number of organisms and can be used to define the region of feasible metabolic flux distributions. In this work we use as constraints a small set of experimental metabolic fluxes, which reduces the region of feasible metabolic states. Once the region of feasible flux distributions has been defined, a set of possible flux distributions is obtained by random sampling and the averages and standard deviations for each of the metabolic fluxes in the genome-scale model are calculated. These values allow estimation of the significance of change for each reaction rate between different conditions and comparison of it with the significance of change in gene transcription for the corresponding enzymes. The comparison of flux change and gene expression allows identification of enzymes showing a significant correlation between flux change and expression change (transcriptional regulation) as well as reactions whose flux change is likely to be driven only by changes in the metabolite concentrations (metabolic regulation). The changes due to growth on four different carbon sources and as a consequence of five gene deletions were analyzed for Saccharomyces cerevisiae. The enzymes with transcriptional regulation showed enrichment in certain transcription factors. This has not been previously reported. The information provided by the presented method could guide the discovery of new metabolic engineering strategies or the identification of drug targets for treatment of metabolic diseases

Immunoglobulin GM 3 23 5,13,14 phenotype is strongly associated with IgG1 antibody responses to Plasmodium vivax vaccine candidate antigens PvMSP1-19 and PvAMA-1

<p>Abstract</p> <p>Background</p> <p>Humoral immune responses play a key role in the development of immunity to malaria, but the host genetic factors that contribute to the naturally occurring immune responses to malarial antigens are not completely understood. The aim of the present investigation was to determine whether, in subjects exposed to malaria, GM and KM allotypes--genetic markers of immunoglobulin γ and κ-type light chains, respectively--contribute to the magnitude of natural antibody responses to target antigens that are leading vaccine candidates for protection against <it>Plasmodium vivax</it>.</p> <p>Methods</p> <p>Sera from 210 adults, who had been exposed to malaria transmission in the Brazilian Amazon endemic area, were allotyped for several GM and KM determinants by a standard hemagglutination-inhibition method. IgG subclass antibodies to <it>P. vivax </it>apical membrane antigen 1 (PvAMA-1) and merozoite surface protein 1 (PvMSP1-19) were determined by an enzyme-linked immunosorbent assay. Multiple linear regression models and the non-parametric Mann-Whitney test were used for data analyses.</p> <p>Results</p> <p>IgG1 antibody levels to both PvMSP1-19 and PvAMA-1 antigens were significantly higher (<it>P </it>= 0.004, <it>P </it>= 0.002, respectively) in subjects with the GM 3 23 5,13,14 phenotype than in those who lacked this phenotype.</p> <p>Conclusions</p> <p>Results presented here show that immunoglobulin GM allotypes contribute to the natural antibody responses to <it>P. vivax </it>malaria antigens. These findings have important implications for the effectiveness of vaccines containing PvAMA-1 or PvMSP1-19 antigens. They also shed light on the possible role of malaria as one of the evolutionary selective forces that may have contributed to the maintenance of the extensive polymorphism at the GM loci.</p

An Automated Phenotype-Driven Approach (GeneForce) for Refining Metabolic and Regulatory Models

Integrated constraint-based metabolic and regulatory models can accurately predict cellular growth phenotypes arising from genetic and environmental perturbations. Challenges in constructing such models involve the limited availability of information about transcription factor—gene target interactions and computational methods to quickly refine models based on additional datasets. In this study, we developed an algorithm, GeneForce, to identify incorrect regulatory rules and gene-protein-reaction associations in integrated metabolic and regulatory models. We applied the algorithm to refine integrated models of Escherichia coli and Salmonella typhimurium, and experimentally validated some of the algorithm's suggested refinements. The adjusted E. coli model showed improved accuracy (∼80.0%) for predicting growth phenotypes for 50,557 cases (knockout mutants tested for growth in different environmental conditions). In addition to identifying needed model corrections, the algorithm was used to identify native E. coli genes that, if over-expressed, would allow E. coli to grow in new environments. We envision that this approach will enable the rapid development and assessment of genome-scale metabolic and regulatory network models for less characterized organisms, as such models can be constructed from genome annotations and cis-regulatory network predictions

Chess databases as a research vehicle in psychology : modeling large data

The game of chess has often been used for psychological investigations, particularly in cognitive science. The clear-cut rules and well-defined environment of chess provide a model for investigations of basic cognitive processes, such as perception, memory, and problem solving, while the precise rating system for the measurement of skill has enabled investigations of individual differences and expertise-related effects. In the present study, we focus on another appealing feature of chess—namely, the large archive databases associated with the game. The German national chess database presented in this study represents a fruitful ground for the investigation of multiple longitudinal research questions, since it collects the data of over 130,000 players and spans over 25 years. The German chess database collects the data of all players, including hobby players, and all tournaments played. This results in a rich and complete collection of the skill, age, and activity of the whole population of chess players in Germany. The database therefore complements the commonly used expertise approach in cognitive science by opening up new possibilities for the investigation of multiple factors that underlie expertise and skill acquisition. Since large datasets are not common in psychology, their introduction also raises the question of optimal and efficient statistical analysis. We offer the database for download and illustrate how it can be used by providing concrete examples and a step-by-step tutorial using different statistical analyses on a range of topics, including skill development over the lifetime, birth cohort effects, effects of activity and inactivity on skill, and gender differences

Aromatase inhibitor-associated bone and musculoskeletal effects: new evidence defining etiology and strategies for management

Aromatase inhibitors are widely used as adjuvant therapy in postmenopausal women with hormone receptor-positive breast cancer. While the agents are associated with slightly improved survival outcomes when compared to tamoxifen alone, bone and musculoskeletal side effects are substantial and often lead to discontinuation of therapy. Ideally, the symptoms should be prevented or adequately treated. This review will focus on bone and musculoskeletal side effects of aromatase inhibitors, including osteoporosis, fractures, and arthralgias. Recent advances have been made in identifying potential mechanisms underlying these effects. Adequate management of symptoms may enhance patient adherence to therapy, thereby improving breast cancer-related outcomes

Optimizing the Protection of Cattle against Escherichia coli O157: H7 Colonization through Immunization with Different Combinations of H7 Flagellin, Tir, Intimin-531 or EspA

Enterohemorrhagic Escherichia coli (EHEC) are important human pathogens, causing hemorrhagic colitis and hemolytic uraemic syndrome in humans. E. coli O157:H7 is the most common serotype associated with EHEC infections worldwide, although other non-O157 serotypes cause life-threatening infections. Cattle are a main reservoir of EHEC and intervention strategies aimed at limiting EHEC excretion from cattle are predicted to lower the risk of human infection. We have previously shown that immunization of calves with recombinant versions of the type III secretion system (T3SS)-associated proteins EspA, intimin and Tir from EHEC O157:H7 significantly reduced shedding of EHEC O157 from experimentally-colonized calves, and that protection could be augmented by the addition of H7 flagellin to the vaccine formulation. The main aim of the present study was to optimize our current EHEC O157 subunit vaccine formulations by identifying the key combinations of these antigens required for protection. A secondary aim was to determine if vaccine-induced antibody responses exhibited cross-reactive potential with antigens from other EHEC serotypes. Immunization with EspA, intimin and Tir resulted in a reduction in mean EHEC O157 shedding following challenge, but not the mean proportion of calves colonized. Removal of Tir resulted in more prolonged shedding compared with all other groups, whereas replacement of Tir with H7 flagellin resulted in the highest levels of protection, both in terms of reducing both mean EHEC O157 shedding and the proportion of colonized calves. Immunization of calves with recombinant EHEC O157 EspA, intimin and Tir resulted in the generation of antibodies capable of cross-reacting with antigens from non-O157 EHEC serotypes, suggesting that immunization with these antigens may provide a degree of cross-protection against other EHEC serotypes. Further studies are now required to test the efficacy of these vaccines in the field, and to formally test the cross-protective potential of the vaccines against other non-O157 EHEC

Genome-Scale 13C Fluxomics Modeling for Metabolic Engineering of Saccharomyces cerevisiae.

Synthetic biology is a rapidly developing field that pursues the application of engineering principles and development approaches to biological engineering. Synthetic biology is poised to change the way biology is practiced, and has important practical applications: for example, building genetically engineered organisms to produce biofuels, medicines, and other chemicals. Traditionally, synthetic biology has focused on manipulating a few genes (e.g., in a single pathway or genetic circuit), but its combination with systems biology holds the promise of creating new cellular architectures and constructing complex biological systems from the ground up. Enabling this merge of synthetic and systems biology will require greater predictive capability for modeling the behavior of cellular systems, and more comprehensive data sets for building and calibrating these models. The so-called "-omics" data sets can now be generated via high throughput techniques in the form of genomic, proteomic, transcriptomic, and metabolomic information on the engineered biological system. Of particular interest with respect to the engineering of microbes capable of producing biofuels and other chemicals economically and at scale are metabolomic datasets, and their insights into intracellular metabolic fluxes. Metabolic fluxes provide a rapid and easy to understand picture of how carbon and energy flow throughout the cell. Here, we present a detailed guide to performing metabolic flux analysis and modeling using the open source JBEI Quantitative Metabolic Modeling (jQMM) library. This library allows the user to transform metabolomics data in the form of isotope labeling data from a 13C labeling experiment into a determination of cellular fluxes that can be used to develop genetic engineering strategies for metabolic engineering.The jQMM library presents a complete toolbox for performing a range of different tasks of interest in metabolic engineering. Various different types of flux analysis and modeling can be performed such as flux balance analysis, 13C metabolic flux analysis, and two-scale 13C metabolic flux analysis (2S-13C MFA). 2S-13C MFA is a novel method that determines genome-scale fluxes without the need of every single carbon transition in the metabolic network. In addition to several other capabilities, the jQMM&nbsp;library can make model based predictions for how various genetic engineering strategies can be incorporated toward bioengineering goals: it can predict the effects of reaction knockouts on metabolism using both the MoMA and ROOM methodologies. In this chapter, we will illustrate the use of the jQMM library through a step-by-step demonstration of flux determination and knockout prediction in a complex eukaryotic model organism: Saccharomyces cerevisiae (S. cerevisiae). Included with this chapter is a digital Jupyter Notebook file that provides a computable appendix showing a self-contained example of jQMM usage, which can be changed to fit the user's specific needs. As an open source software project, users can modify and extend the code base to make improvements at will, allowing them to share their development work and contribute back to the jQMM modeling community