| 1. |
- Gowers, G. O. F., et al.
(author)
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Starved Escherichia coli preserve reducing power under nitric oxide stress
- 2016
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In: Biochemical and Biophysical Research Communications. - : Elsevier BV. - 1090-2104 .- 0006-291X. ; 476:1, s. 29-34
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Journal article (peer-reviewed)abstract
- Nitric oxide (NO) detoxification enzymes, such as NO dioxygenase (NOD) and NO reductase (NOR), are important to the virulence of numerous bacteria. Pathogens use these defense systems to ward off immune-generated NO, and they do so in environments that contain additional stressors, such as reactive oxygen species, nutrient deprivation, and acid stress. NOD and NOR both use reducing equivalents to metabolically deactivate NO, which suggests that nutrient deprivation could negatively impact their functionality. To explore the relationship between NO detoxification and nutrient deprivation, we examined the ability of Escherichia coli to detoxify NO under different levels of carbon source availability in aerobic cultures. We observed failure of NO detoxification under both carbon source limitation and starvation, and those failures could have arisen from inabilities to synthesize Hmp (NOD of E. coli) and/or supply it with sufficient NADH (preferred electron donor). We found that when limited quantities of carbon source were provided, NO detoxification failed due to insufficient NADH, whereas starvation prevented Hmp synthesis, which enabled cells to maintain their NADH levels. This maintenance of NADH levels under starvation was confirmed to be dependent on the absence of Hmp. Intriguingly, these data show that under NO stress, carbon-starved E. con are better positioned with regard to reducing power to cope with other stresses than cells that had consumed an exhaustible amount of carbon. (C) 2016 Elsevier Inc. All rights reserved.
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| 2. |
- Robinson, Jonathan, 1986, et al.
(author)
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Integrative analysis of human omics data using biomolecular networks
- 2016
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In: Molecular Biosystems. - : Royal Society of Chemistry (RSC). - 1742-206X .- 1742-2051. ; 12:10, s. 2953-2964
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Research review (peer-reviewed)abstract
- High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their interpretation becomes increasingly cryptic and nontrivial as they continue to expand in size and complexity. Systems biology networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information from the data. Two of the most prevalent networks that have been used for such integrative analyses of omics data are genome-scale metabolic models (GEMs) and protein-protein interaction (PPI) networks, both of which have demonstrated success among many different omics and sample types. This integrative approach seeks to unite 'top-down' omics data with 'bottom-up' biological networks in a synergistic fashion that draws on the strengths of both strategies. As the volume and resolution of high-throughput omics data continue to grow, integrative network-based analyses are expected to play an increasingly important role in their interpretation.
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