ABSTRACT:
Fang2010 - Genome-scale metabolic network of
Mycobacterium tuberculosis (iNJ661m)
This model is described in the article:
Development and analysis of
an in vivo-compatible metabolic network of Mycobacterium
tuberculosis.
Fang X, Wallqvist A, Reifman
J.
BMC Syst Biol 2010; 4: 160
Abstract:
BACKGROUND: During infection, Mycobacterium tuberculosis
confronts a generally hostile and nutrient-poor in vivo host
environment. Existing models and analyses of M. tuberculosis
metabolic networks are able to reproduce experimentally
measured cellular growth rates and identify genes required for
growth in a range of different in vitro media. However, these
models, under in vitro conditions, do not provide an adequate
description of the metabolic processes required by the pathogen
to infect and persist in a host. RESULTS: To better account for
the metabolic activity of M. tuberculosis in the host
environment, we developed a set of procedures to systematically
modify an existing in vitro metabolic network by enhancing the
agreement between calculated and in vivo-measured gene
essentiality data. After our modifications, the new in vivo
network contained 663 genes, 838 metabolites, and 1,049
reactions and had a significantly increased sensitivity (0.81)
in predicted gene essentiality than the in vitro network
(0.31). We verified the modifications generated from the purely
computational analysis through a review of the literature and
found, for example, that, as the analysis suggested, lipids are
used as the main source for carbon metabolism and oxygen must
be available for the pathogen under in vivo conditions.
Moreover, we used the developed in vivo network to predict the
effects of double-gene deletions on M. tuberculosis growth in
the host environment, explore metabolic adaptations to life in
an acidic environment, highlight the importance of different
enzymes in the tricarboxylic acid-cycle under different
limiting nutrient conditions, investigate the effects of
inhibiting multiple reactions, and look at the importance of
both aerobic and anaerobic cellular respiration during
infection. CONCLUSIONS: The network modifications we
implemented suggest a distinctive set of metabolic conditions
and requirements faced by M. tuberculosis during host infection
compared with in vitro growth. Likewise, the double-gene
deletion calculations highlight the importance of specific
metabolic pathways used by the pathogen in the host
environment. The newly constructed network provides a
quantitative model to study the metabolism and associated drug
targets of M. tuberculosis under in vivo conditions.
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