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 themetabolism 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. © The Authors Journal compilation © 2013 Biochemical Society

Rolfsson, O., Paglia, G., Magnusdottir, M., Palsson, B., Thiele, I. (2013). Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity. BIOCHEMICAL JOURNAL, 449(2), 427-435 [10.1042/BJ20120980].

Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity

Paglia G.
Secondo
;
2013

Abstract

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 themetabolism 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. © The Authors Journal compilation © 2013 Biochemical Society
Articolo in rivista - Articolo scientifico
Gap filling; Human metabolism; Metabolic network; RECON 1
English
2013
449
2
427
435
none
Rolfsson, O., Paglia, G., Magnusdottir, M., Palsson, B., Thiele, I. (2013). Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity. BIOCHEMICAL JOURNAL, 449(2), 427-435 [10.1042/BJ20120980].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/244099
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