Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity
Metabolic network reconstructions define metabolic information within a target organism and can therefore be used to address incomplete metabolic information. We employed 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 characterised 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. Our results 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 prioritise and confirm gene functions.