Reference : Inferring the metabolism of human orphan metabolites from their metabolic network con...
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
http://hdl.handle.net/10993/13035
Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity.
English
Rolfsson, Ottar [> >]
Paglia, Giuseppe [> >]
Magnusdottir, Manuela [> >]
Palsson, Bernhard O. [> >]
Thiele, Ines mailto []
2013
Biochemical Journal
449
2
427-435
Yes (verified by ORBilu)
0264-6021
1470-8728
England
[en] Adenosine Triphosphate/metabolism ; Chromatography, Liquid ; Computational Biology/methods ; Gluconates/metabolism ; HeLa Cells ; Humans ; Mass Spectrometry ; Metabolic Networks and Pathways ; NADP/metabolism ; Oxidation-Reduction ; Phosphotransferases (Alcohol Group Acceptor)/genetics/metabolism ; Substrate Specificity
[en] Metabolic network reconstructions define metabolic information within a target organism and can therefore be used to address incomplete metabolic information. In the present study we used a computational approach to identify human metabolites whose metabolism is incomplete on the basis of their detection in humans but exclusion from the human metabolic network reconstruction RECON 1. Candidate solutions, composed of metabolic reactions capable of explaining the metabolism of these compounds, were then identified computationally from a global biochemical reaction database. Solutions were characterized with respect to how metabolites were incorporated into RECON 1 and their biological relevance. Through detailed case studies we show that biologically plausible non-intuitive hypotheses regarding the metabolism of these compounds can be proposed in a semi-automated manner, in an approach that is similar to de novo network reconstruction. We subsequently experimentally validated one of the proposed hypotheses and report that C9orf103, previously identified as a candidate tumour suppressor gene, encodes a functional human gluconokinase. The results of the present study demonstrate how semi-automatic gap filling can be used to refine and extend metabolic reconstructions, thereby increasing their biological scope. Furthermore, we illustrate how incomplete human metabolic knowledge can be coupled with gene annotation in order to prioritize and confirm gene functions.
http://hdl.handle.net/10993/13035
10.1042/BJ20120980

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