Systems approach to refining genome annotation

Abstract

Genome-scale models of Escherichia coli K-12 MG1655 metabolism have been able to predict growth phenotypes in most, but not all, defined growth environments. Here we introduce the use of an optimization-based algorithm that predicts the missing reactions that are required to reconcile computation and experiment when they disagree. The computer-generated hypotheses for missing reactions were verified experimentally in five cases, leading to the functional assignment of eight ORFs (yjjLMN, yeaTU, dctA, idnT, and putP) with two new enzymatic activities and four transport functions. This study thus demonstrates the use of systems analysis to discover metabolic and transport functions and their genetic basis by a combination of experimental and computational approaches.constraint-based ͉ flux balance analysis ͉ functional genomics ͉ metabolic reconstruction ͉ systems biology C urrent genome annotations include a substantial fraction of ORFs with unknown function (1, 2); methods are needed to provide insight into the possible function of these genes, without the need for screening individual gene products across a multitude of possible activities. Metabolic and regulatory networks are reconstructed from genome annotations and scientific literature to integrate and represent our current knowledge of network components and interactions (3). Dual-perturbation methods have been developed to study regulatory networks (4), which can be used to reconcile model predictions and experimental data, thus leading to possible iterative model refinements and experimentally testable hypotheses (4, 5). Iterative modelbuilding can be systematized through the use of computational algorithms (6). Such an approach is presented here, and it consists of four steps. First, computational analysis identified discrepancies between model predictions and growth phenotyping data by using a reconstructed genome-scale Escherichia coli metabolic network. Second, an algorithm then identified enzymatic and transport reactions that likely were missing from the current metabolic reconstruction that could reconcile model predictions and experimental observations. Third, ORFs that might be responsible for these missing activities then were identified by using literature searches, sequence-homology searches (7), context-based homology methods (8, 9), and in some cases unpublished microarray data. Fourth, experimental verification of the algorithm's predictions then were carried out by evaluating growth phenotypes of single-deletion strains available in the Keio collection (10) and gene-expression measurements. Here we present a comprehensive combined computational and experimental approach to analyze phenotypic data and genome annotation information in a global manner to uncover individual ORF function. ResultsGrowth phenotyping data (11), available from Biolog (Hayward CA; www.biolog.com), were used to identify missing reactions from the reconstructed genome-scale metabolic network of E. coli MG1655 (iJR904) (12). Using a flux balance model of E. coli, we ...

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

“…Applications of the E. coli GEM range from pragmatic to theoretical studies, and can be classified into five general categories ( Fig. 3): 1) metabolic engineering [20][21][22][23][24][25][26][27][28][29][30] ; 2) biological discovery [31][32][33][34][35][36][37] ; 3) assessment of phenotypic behavior 19, ; 4) biological network analysis [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] ; and 5) studies of bacterial evolution [80][81][82] . The in silico methods used to probe the E. coli GEM in each study are summarized in Fig.…”

“…Two recent studies have integrated a combined computational and experimental approach to aid the ORF discovery process in E. coli through utilizing the GEM and high-throughput phenotype data 35,37 . The first study utilized an iterative process 35 in which, 1) differences in modeling predictions and high-throughput growth phenotype data were identified, 2) potential missing reactions that remedy these disagreements were algorithmically determined, 3) bioinformatics was utilized to identify likely encoding ORFs, and 4) resulting targeted ORFs were cloned and experimentally characterized.…”

“…The first study utilized an iterative process 35 in which, 1) differences in modeling predictions and high-throughput growth phenotype data were identified, 2) potential missing reactions that remedy these disagreements were algorithmically determined, 3) bioinformatics was utilized to identify likely encoding ORFs, and 4) resulting targeted ORFs were cloned and experimentally characterized. Application of this process led to the functional characterization of eight ORFs that are involved in transport, regulatory and metabolic functions in E. coli 35 . The discovery process was aided by a high-throughput growth phenotyping analysis and the genome-wide single-gene mutant collection 95 , along with other characterization analyses such as targeted expression profiling.…”

“…Metabolic engineering studies have investigated in silico strain design using E. coli metabolic reconstructions to overproduce desired products [20][21][22][23][24][25][26][27][28][29][30] . (B) Recent studies utilizing the reconstruction in a prospective manner have aimed to use the current biochemical and genetic information included in the metabolic network along with additional data types to drive biological discovery, such as predicting genes encoding for orphan reactions 32,33,[35][36][37] . (C) Utilizing the reconstruction in phenotypic studies, computational analyses have examined gene 19,46,51,53,63 , metabolite 44,60 and reaction 39,47,48,58 essentiality along with considering thermodynamics 19,40,47,49,52,54,55,57,61 to make better predictions about the physiological state (i.e., the active pathways) of the cell for a given environmental condition.…”

“…In addition to these network topology-based methods, an optimization based procedure has also been developed to fill network gaps and evaluate reaction reversibility along with adding additional transport and intracellular reactions from databases of known metabolic reactions 36 . These studies produce specific targets for drill-down experiments needed for confirmation of these computationally generated hypotheses.Two recent studies have integrated a combined computational and experimental approach to aid the ORF discovery process in E. coli through utilizing the GEM and high-throughput phenotype data 35,37 . The first study utilized an iterative process 35 in which, 1) differences in modeling predictions and high-throughput growth phenotype data were identified, 2) potential missing reactions that remedy these disagreements were algorithmically determined, 3) bioinformatics was utilized to identify likely encoding ORFs, and 4) resulting targeted ORFs were cloned and experimentally characterized.…”