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Characterization of Growth and Metabolism of the Haloalkaliphile Natronomonas pharaonis

Overview of attention for article published in PLoS Computational Biology, June 2010
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Title
Characterization of Growth and Metabolism of the Haloalkaliphile Natronomonas pharaonis
Published in
PLoS Computational Biology, June 2010
DOI 10.1371/journal.pcbi.1000799
Pubmed ID
Authors

Orland Gonzalez, Tanja Oberwinkler, Locedie Mansueto, Friedhelm Pfeiffer, Eduardo Mendoza, Ralf Zimmer, Dieter Oesterhelt

Abstract

Natronomonas pharaonis is an archaeon adapted to two extreme conditions: high salt concentration and alkaline pH. It has become one of the model organisms for the study of extremophilic life. Here, we present a genome-scale, manually curated metabolic reconstruction for the microorganism. The reconstruction itself represents a knowledge base of the haloalkaliphile's metabolism and, as such, would greatly assist further investigations on archaeal pathways. In addition, we experimentally determined several parameters relevant to growth, including a characterization of the biomass composition and a quantification of carbon and oxygen consumption. Using the metabolic reconstruction and the experimental data, we formulated a constraints-based model which we used to analyze the behavior of the archaeon when grown on a single carbon source. Results of the analysis include the finding that Natronomonas pharaonis, when grown aerobically on acetate, uses a carbon to oxygen consumption ratio that is theoretically near-optimal with respect to growth and energy production. This supports the hypothesis that, under simple conditions, the microorganism optimizes its metabolism with respect to the two objectives. We also found that the archaeon has a very low carbon efficiency of only about 35%. This inefficiency is probably due to a very low P/O ratio as well as to the other difficulties posed by its extreme environment.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 3%
Iran, Islamic Republic of 2 3%
Portugal 1 1%
Austria 1 1%
United Kingdom 1 1%
Japan 1 1%
United States 1 1%
Philippines 1 1%
Unknown 61 86%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 24%
Student > Ph. D. Student 15 21%
Student > Master 9 13%
Student > Bachelor 4 6%
Student > Doctoral Student 3 4%
Other 12 17%
Unknown 11 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 46%
Biochemistry, Genetics and Molecular Biology 8 11%
Computer Science 4 6%
Environmental Science 3 4%
Engineering 2 3%
Other 7 10%
Unknown 14 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 March 2012.
All research outputs
#23,154,082
of 25,806,080 outputs
Outputs from PLoS Computational Biology
#8,653
of 9,043 outputs
Outputs of similar age
#101,380
of 106,161 outputs
Outputs of similar age from PLoS Computational Biology
#58
of 60 outputs
Altmetric has tracked 25,806,080 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,043 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.