Mazumdar2008 - Genome-scale metabolic network of Porphyromonas gingivalis (iVM679)

Mazumdar2008 - Genome-scale metabolic network
of Porphyromonas gingivalis (iVM679)

This model is described in the article:

Metabolic network model of a
human oral pathogen.

Mazumdar V, Snitkin ES, Amar S,
Segrè D.
J. Bacteriol. 2009 Jan; 191(1):
74-90
Abstract:

The microbial community present in the human mouth is
engaged in a complex network of diverse metabolic activities.
In addition to serving as energy and building-block sources,
metabolites are key players in interspecies and host-pathogen
interactions. Metabolites are also implicated in triggering the
local inflammatory response, which can affect systemic
conditions such as atherosclerosis, obesity, and diabetes.
While the genome of several oral pathogens has been sequenced,
quantitative understanding of the metabolic functions of any
oral pathogen at the system level has not been explored yet.
Here we pursue the computational construction and analysis of
the genome-scale metabolic network of Porphyromonas gingivalis,
a gram-negative anaerobe that is endemic in the human
population and largely responsible for adult periodontitis.
Integrating information from the genome, online databases, and
literature screening, we built a stoichiometric model that
encompasses 679 metabolic reactions. By using flux balance
approaches and automated network visualization, we analyze the
growth capacity under amino-acid-rich medium and provide
evidence that amino acid preference and cytotoxic by-product
secretion rates are suitably reproduced by the model. To
provide further insight into the basic metabolic functions of
P. gingivalis and suggest potential drug targets, we study
systematically how the network responds to any reaction
knockout. We focus specifically on the lipopolysaccharide
biosynthesis pathway and identify eight putative targets, one
of which has been recently verified experimentally. The current
model, which is amenable to further experimental testing and
refinements, could prove useful in evaluating the oral
microbiome dynamics and in the development of novel biomedical
applications.

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