- Paper report
- Open Access
Genome-wide analysis of bacterial metabolic pathways
- Mary Wildermuth
© BioMed Central Ltd 2000
Received: 20 December 1999
Published: 27 April 2000
Systems analysis of the complete genome sequence combined with biochemical information on metabolic pathways has been used to define and elucidate the relationship between genotype and phenotype for Haemophilus influenzae Rd.
Significance and context
With the advent of complete genome sequence information comes the challenge of applying this information to physiological questions. At issue is the relationship between genotype and phenotype - both for a given organism in different environments, and for different organisms. Edwards and Palsson apply systems-based analysis to information derived from the annotated genomic sequence of the bacterium Haemophilus influenzae Rd and the biochemistry of its metabolic reactions to elucidate the metabolic physiology of H. influenzae Rd, an approach that can be applied to genotype-phenotype relationships in general. Having constructed an in silico metabolic genotype, they use it to ascertain critical metabolic components, to distinguish metabolic phenotypes for a given growth variable, and to determine essential, critical and redundant metabolic genes through the use of in silico gene deletion. This approach should advance our understanding of the underlying design principles of organisms, including the regulatory logic controlling metabolic pathways.
From biochemical and genomic information,the H. influenzae Rd metabolic genotype was defined as including 343 metabolites (m) and 488 metabolic reactions (n), and its characteristics were studied on the basis of the properties of its stoichiometric matrix (m x n). Flux-balance analysis using this matrix determined feasible solutions as the intersection of the solution sets satisfying mass-balance constraints and physicochemical constraints (for example, minimum and maximum flux values). Key metabolites of the in silico H. influenzae metabolic genotype were elucidated by examining the degree of connectivity of each of the metabolites, as determined by the number of metabolic reactions in which each of the 343 metabolites was used. Metabolites participating in the greatest number of reactions were deemed to be critical and included ATP, ADP, inorganic phosphate, pyrophosphate, carbon dioxide, glutamate, NADP, and NADPH, indicating that the generation of charged cofactors is critical. The capacity of a metabolic genotype to produce these cofactors was determined by optimizing their production in the linear programming problem and comparing the charged cofactor capacities of H. influenzae and E. coli genotypes (see Figure 1).
LINDO 6.1, the linear programming software ia available from LINDO Systems, Inc (Chicago) and genomic information is organized into known biochemical pathways at the Kyoto encyclopedia of genes and genomes (KEGG), which also contains details of Haemophilus. influenzae Rd genes. Bernhard Palsson's current funding page contains further details of work carried out in his lab.
Use of this methodology requires complete, annotated genomic sequence and extensive biochemical information for the prokaryote in question. Tools such as KEGG (see above) are available to aid in defining the stoichiometric matrix. This paper beautifully couples phenotype to genotype and provides a mathematical framework for this coupling using flux-balance analysis that should be applicable not only for any prokaryote, but also for diverse phenotypes (that is, not just maximal growth). In addition, it shows the utility of in silico biology to elucidate complex relationships and to direct experimental work.
Table of links
The Full text of J Biol Chem 274:17410-17416 is available to subscribers and non-subscribers online.