title: A framework for application of metabolic modeling in yeast to predict the effects of nsSNV in human orthologs.
abstract: BACKGROUND
  We have previously suggested a method for proteome wide analysis of variation at functional residues wherein we identified the set of all human genes with nonsynonymous single nucleotide variation (nsSNV) in the active site residue of the corresponding proteins. 34 of these proteins were shown to have a 1:1:1 enzyme:pathway:reaction relationship, making these proteins ideal candidates for laboratory validation through creation and observation of specific yeast active site knock-outs and downstream targeted metabolomics experiments. Here we present the next step in the workflow toward using yeast metabolic modeling to predict human metabolic behavior resulting from nsSNV.
  
  
  RESULTS
  For the previously identified candidate proteins, we used the reciprocal best BLAST hits method followed by manual alignment and pathway comparison to identify 6 human proteins with yeast orthologs which were suitable for flux balance analysis (FBA). 5 of these proteins are known to be associated with diseases, including ribose 5-phosphate isomerase deficiency, myopathy with lactic acidosis and sideroblastic anaemia, anemia due to disorders of glutathione metabolism, and two porphyrias, and we suspect the sixth enzyme to have disease associations which are not yet classified or understood based on the work described herein.
  
  
  CONCLUSIONS
  Preliminary findings using the Yeast 7.0 FBA model show lack of growth for only one enzyme, but augmentation of the Yeast 7.0 biomass function to better simulate knockout of certain genes suggested physiological relevance of variations in three additional proteins. Thus, we suggest the following four proteins for laboratory validation: delta-aminolevulinic acid dehydratase, ferrochelatase, ribose-5 phosphate isomerase and mitochondrial tyrosyl-tRNA synthetase. This study indicates that the predictive ability of this method will improve as more advanced, comprehensive models are developed. Moreover, these findings will be useful in the development of simple downstream biochemical or mass-spectrometric assays to corroborate these predictions and detect presence of certain known nsSNVs with deleterious outcomes. Results may also be useful in predicting as yet unknown outcomes of active site nsSNVs for enzymes that are not yet well classified or annotated.
authors.forename: Hayley
authors.initials: H
authors.lastname: Dingerdissen
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Weaver
authors.forename: Peter D
authors.initials: PD
authors.lastname: Karp
authors.forename: Yang
authors.initials: Y
authors.lastname: Pan
authors.forename: Vahan
authors.initials: V
authors.lastname: Simonyan
authors.affiliation: Department of Biochemistry and Molecular Biology, The George Washington University Medical Center, Ross Hall, Room 540, 2300 Eye Street NW, Washington, DC 20037, USA. mazumder@gwu.edu.
authors.forename: Raja
authors.initials: R
authors.lastname: Mazumder
mesh_terms: D005656:Fungal Proteins / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D020543:Proteome / Q000737:chemistry / Q000235:genetics* / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism
chemical_list: D005656:Fungal Proteins; D020543:Proteome