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

Greenhouse gas emissions from ships in ports – Case studies in four continents

Emissions of GHG from the transport sector and how to reduce them are major challenges for policy makers. The purpose of this paper is to analyse the level of greenhouse gas (GHG) emissions from ships while in port based on annual data from Port of Gothenburg, Port of Long Beach, Port of Osaka and Sydney Ports. Port call statistics including IMO number, ship name, berth number and time spent at berth for each ship call, were provided by each participating port. The IMO numbers were used to match each port call to ship specifications from the IHS database Sea-web. All data were analysed with a model developed by the IVL Swedish Environmental Research Institute for the purpose of quantifying GHG emissions (as CO2-equivalent) from ships in the port area. Emissions from five operational modes are summed in order to account for ship operations in the different traffic areas. The model estimates total GHG emissions of 150,000, 240,000, 97,000, and 95,000 tonnes CO2 equivalents per year for Gothenburg, Long Beach, Osaka, and Sydney, respectively. Four important emission-reduction measures are discussed: reduced speed in fairway channels, on-shore power supply, reduced turnaround time at berth and alternative fuels. It is argued that the potential to reduce emissions in a port area depends on how often a ship revisits a port: there it in general is easier to implement measures for high-frequent liners. Ships that call 10 times or less contribute significantly to emissions in all ports

Validation of a new WIND classification compared to ICC classification for weaning outcome

Abstract Background Although the WIND (Weaning according to a New Definition) classification based on duration of ventilation after the first separation attempt has been proposed, this new classification has not been tested in clinical practice. The objective of this cohort study was to evaluate the clinical relevance of WIND classification and its association with hospital mortality compared to the International Consensus Conference (ICC) classification. Methods All consecutive medical ICU patients who were mechanically ventilated for more than 24 h between July 2010 and September 2013 were prospectively registered. Patients were classified into simple, difficult, or prolonged weaning group according to ICC classification and Groups 1, 2, 3, or no weaning (NW) according to WIND classification. Results During the study period, a total of 1600 patients were eligible. These patients were classified by the WIND classification as follows: Group NW = 580 (36.3%), Group 1 = 617 (38.6%), Group 2 = 186 (11.6%), and Group 3 = 217 (13.6%). However, only 735 (45.9%) patients were classified by ICC classification as follows: simple weaning = 503 (68.4%), difficult weaning = 145 (19.7%), and prolonged weaning = 87 (11.8%). Clinical outcomes were significantly different across weaning groups by ICC classification and WIND classification. However, there were no statistical differences in successful weaning rate (96.6% vs. 95.2%) or hospital mortality (22.5% vs. 25.5%) between simple and difficult weaning groups by the ICC. Conversely, there were statistically significant differences in successful weaning rate (98.5% vs. 76.9%) and hospital mortality (21.2% vs. 33.9%) between Group 1 and Group 2 by WIND. Conclusions The WIND classification could be a better tool for predicting weaning outcomes than the ICC classification