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Recon 2.2: from reconstruction to model of human metabolism
Swainston, Neil,Smallbone, Kieran,Hefzi, Hooman,Dobson, Paul D.,Brewer, Judy,Hanscho, Michael,Zielinski, Daniel C.,Ang, Kok Siong,Gardiner, Natalie J.,Gutierrez, Jahir M.,Kyriakopoulos, Sarantos,Laksh Springer US 2016 METABOLOMICS Vol.12 No.7
<P><B>Introduction</B></P><P>The human genome-scale metabolic reconstruction details all known metabolic reactions occurring in humans, and thereby holds substantial promise for studying complex diseases and phenotypes. Capturing the whole human metabolic reconstruction is an on-going task and since the last community effort generated a consensus reconstruction, several updates have been developed.</P><P><B>Objectives</B></P><P>We report a new consensus version, Recon 2.2, which integrates various alternative versions with significant additional updates. In addition to re-establishing a consensus reconstruction, further key objectives included providing more comprehensive annotation of metabolites and genes, ensuring full mass and charge balance in all reactions, and developing a model that correctly predicts ATP production on a range of carbon sources.</P><P><B>Methods</B></P><P>Recon 2.2 has been developed through a combination of manual curation and automated error checking. Specific and significant manual updates include a respecification of fatty acid metabolism, oxidative phosphorylation and a coupling of the electron transport chain to ATP synthase activity. All metabolites have definitive chemical formulae and charges specified, and these are used to ensure full mass and charge reaction balancing through an automated linear programming approach. Additionally, improved integration with transcriptomics and proteomics data has been facilitated with the updated curation of relationships between genes, proteins and reactions.</P><P><B>Results</B></P><P>Recon 2.2 now represents the most predictive model of human metabolism to date as demonstrated here. Extensive manual curation has increased the reconstruction size to 5324 metabolites, 7785 reactions and 1675 associated genes, which now are mapped to a single standard. The focus upon mass and charge balancing of all reactions, along with better representation of energy generation, has produced a flux model that correctly predicts ATP yield on different carbon sources.</P><P><B>Conclusion</B></P><P>Through these updates we have achieved the most complete and best annotated consensus human metabolic reconstruction available, thereby increasing the ability of this resource to provide novel insights into normal and disease states in human. The model is freely available from the Biomodels database (http://identifiers.org/biomodels.db/MODEL1603150001).</P><P><B>Electronic supplementary material</B></P><P>The online version of this article (doi:10.1007/s11306-016-1051-4) contains supplementary material, which is available to authorized users.</P>
Characterizing posttranslational modifications in prokaryotic metabolism using a multiscale workflow
Brunk, Elizabeth,Chang, Roger L.,Xia, Jing,Hefzi, Hooman,Yurkovich, James T.,Kim, Donghyuk,Buckmiller, Evan,Wang, Harris H.,Cho, Byung-Kwan,Yang, Chen,Palsson, Bernhard O.,Church, George M.,Lewis, Nat National Academy of Sciences 2018 PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF Vol.115 No.43
<P>Understanding the complex interactions of protein posttranslational modifications (PTMs) represents a major challenge in metabolic engineering, synthetic biology, and the biomedical sciences. Here, we present a workflow that integrates multiplex automated genome editing (MAGE), genome-scale metabolic modeling, and atomistic molecular dynamics to study the effects of PTMs on metabolic enzymes and microbial fitness. This workflow incorporates complementary approaches across scientific disciplines; provides molecular insight into how PTMs influence cellular fitness during nutrient shifts; and demonstrates how mechanistic details of PTMs can be explored at different biological scales. As a proof of concept, we present a global analysis of PTMs on enzymes in the metabolic network of Escherichia coll. Based on our workflow results, we conduct a more detailed, mechanistic analysis of the PTMs in three proteins: enolase, serine hydroxymethyltransferase, and transaldolase. Application of this workflow identified the roles of specific PTMs in observed experimental phenomena and demonstrated how individual PTMs regulate enzymes, pathways, and, ultimately, cell phenotypes.</P>