Nucleic Acids Research 2005 33(Database Issue):D321-D324; doi:10.1093/nar/gki042
Nucleic Acids Research, 2005, Vol. 33, Database issue D321-D324
© 2005, the authors
Nucleic Acids Research, Vol. 33, Database issue © Oxford University Press 2005; all rights reserved
Metagrowth: a new resource for the building of metabolic hypotheses in microbiology
Hiroyuki Ogata* and
Jean-Michel Claverie
Information Génomique et Structurale, CNRS UPR 2589, 31 Chemin Joseph Aiguier, 13402 Marseille Cedex 20, France
* To whom correspondence should be addressed. Tel: +33 491 16 45 48; Fax: +33 491 16 45 49; Email: Hiroyuki.Ogata{at}igs.cnrs-mrs.fr
Received August 7, 2004; Revised and Accepted September 28, 2004
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ABSTRACT
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Metagrowth is a new type of knowledge base developed to guide
the experimental studies of culture conditions of obligate parasitic
bacteria. We have gathered biological evidences giving possible
clues to the development of the axenic (i.e. ‘cell-free’)
growth of obligate parasites from various sources including
published literature, genomic sequence information, metabolic
databases and transporter databases. The database entries are
composed of those evidences and specific hypotheses derived
from them. Currently, 200 entries are available for
Rickettsia prowazekii,
Rickettsia conorii,
Tropheryma whipplei,
Treponema pallidum,
Mycobacterium tuberculosis and
Coxiella burnetii.
The web interface of Metagrowth helps users to design new axenic
culture media eventually suitable for those bacteria. Metagrowth
is accessible at
http://igs-server.cnrs-mrs.fr/axenic/.
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INTRODUCTION
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A number of bacteria resist axenic (i.e. ‘cell-free’)
culture in the laboratory. Those include obligate parasites
causing serious human diseases, such as
Rickettsia (
1,
2) and
Mycobacterium leprae (
3). They adapt to a limited environment
that provides appropriate physical conditions, nutriments and
other factors required for their replication and growth. Current
culture systems of obligate parasitic bacteria depend on eukaryotic
cells (e.g. for
Rickettsia) or even entire living animals (e.g.
for
M.leprae). The lack of cell-free culture media poses a critical
problem in studying these bacteria. Without cell-free culture,
it is impossible to use modern experimental approaches (e.g.
transcriptomics, proteomics) that depend on non-contaminated
RNA or protein extractions. Thus, the establishments of axenic
culture media for those pathogens would have a significant impact
on the medical and biological research communities working on
these diseases. In a more fundamental way, this type of study
might help to unraveling various type of signals involved in
their host–parasite relationships.
With the recent development of metabolic databases (4,5), genome-based metabolic reconstruction has become an efficient approach to tackle this problem (6,7). By examining the metabolic pathways predicted from genomic sequence analyses, one can generate testable hypotheses for the improvement of bacterial culture conditions. We recently analyzed the complete genome sequence of a human pathogen, Tropheryma whipplei strain Twist, and identified significant deficiencies in the biosynthesis of nine amino acids (8). Remarkably, this knowledge effectively guided the development of the first axenic culture medium to grow this fastidious microorganism (7) that had been previously cultured only in association with a fibroblast cell line (HEL) (9). We believe that this type of approach should be generalized and could allow more obligate parasitic bacteria to be grown in a cell-free culture medium.
It is clear that explicit hypotheses (e.g. ‘required nutriments’) and supporting evidences (e.g. ‘deficiencies of the de novo synthesis’) are determinants for this type of study. However, such a biological knowledge is usually dispersed in literature and various biological databases. Till date, no existing database exhaustively collects and systematically provides biological knowledge about the cultivation of obligate parasites. This prompted us to gather evidences and hypotheses that are relevant to the improvement of the culture conditions of obligate parasites and make them available in a knowledgebase named Metagrowth (http://igs-server.cnrs-mrs.fr/axenic/). In this paper, we describe the source of the data accessible in Metagrowth as well as its web interface guiding the user to design new cell-free culture media of obligate parasites.
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DATA IN METAGROWTH
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Metagrowth is gathering ‘evidences’ and derived
‘hypotheses’ relevant to the improvement of the
culture conditions of parasitic bacteria as follows:
- Example 1. Evidence: ‘The genome does not encode enzymes for the biosynthesis of compound X, but encodes a transporter for X’.
Hypothesis: ‘Adding X may improve the growth’.
- Example 2. Evidence: ‘The genome encodes enzymes requiring cofactor Y, but does not encode genes for the biosynthesis of cofactor Y’. Hypothesis: ‘Adding Y may improve the growth’.
These kinds of information are collected
from the published literature, genomic sequence databases (
4),
metabolic databases (
4,
5,
10,
11) and transporter databases (
12).
Table 1 shows a tentative Metagrowth entry describing the supplementation
of
S-adenosyl-
L-methionine for the improvement of
Rickettsia culture conditions. The ‘Evidence’ and ‘Hypothesis’
records are the two main components of the database entry. The
evidence record is a free text describing experimentally validated
facts or predicted metabolic features. Associated hypotheses
for the improvement of the culture condition are stored in the
hypothesis record. In the hypothesis record, we currently describe
the supplementation of organic or inorganic compounds in the
medium, or appropriate physical conditions such as oxygen concentration.
A prefix ‘IN=’ in the hypothesis record designates
a preferential input (a compound or a physical condition) to
the culture medium that could be experimentally tested. Hyperlinks
to relevant genes, pathways and literature in the source databases
are provided to direct the users to the original data. Currently,
we have accumulated 220 Metagrowth entries for 6 species of
bacteria (5 genera):
Rickettsia prowazekii and
Rickettsia conorii (agents of typhus and spotted fever, respectively; 40 entries),
T.whipplei (Whipple's disease; 38 entries),
Treponema pallidum (
13) (syphilis; 42 entries),
M.leprae (leprosy; 63 entries)
and
Coxiella burnetii (
14) (Q-fever; 37 entries).
Rating of the reliability of collected scientific evidences
is an important issue in constructing a database of biological
hypotheses (
15). In Metagrowth, the relationships between evidences
and hypotheses were classified into several categories. We refer
to the relationships as ‘evidence types’ in Metagrowth.
The evidence types could be used for the prioritization of different
hypotheses supported by different kinds of evidences. We defined
four major classes of evidence types. Class I evidences describe
the inability to synthesize a compound, either by metabolic
deficiencies or by general incapability of biosynthesis (e.g.
inorganic molecules such as metal ions). Class II evidences
refer to the importing capability of a compound, either by active
transporters or by passive membrane permeability. Class III
evidences refer to the requirement or utilization of a compound
by the bacteria. Those include cofactors required for known
or predicted enzymatic reactions in the cell, and basic building
blocks of macromolecules such as the 20 amino acids. Class IV
evidences refer to the other type of evidences, mostly experimentally
validated facts. Each class was further divided, leading to
a total of 10 subclasses. The precise definitions of these evidence
classes and subclasses are provided in the Supplementary Material
(Table S1). The evidence subclasses are identified in the hypothesis
record of Metagrowth entries with a ‘ET=’ prefix
(
Table 1).
Figure 1 shows the current status of the number of
hypotheses supported by different types of evidences.
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DESIGN OF A NEW CELL-FREE CULTURE MEDIUM
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Browsing Metagrowth entries, users can easily obtain a list
of nutriment compounds and the corresponding list of evidences
suggesting the supplementation of the medium by these compounds.
An important practical issue is the determination of the concentrations
of those supplemented molecules. To help the users with this
respect, Metagrowth proposes a range of concentrations for different
compounds. The ranges were determined according to the roles
of molecules in bacterial cells and the concentrations of the
molecules of the same role in several reference culture media.
As reference culture media, we selected two complex culture
media for fastidious (i.e. ‘difficult to grow’)
bacteria: BSK-H medium designed to support the growth of the
Lyme disease spirochete
Borrelia burgdorferi (
16), and TWH medium
supporting the growth of
T.whipplei (
7). The concentrations
of the components in the two media and the upper and lower limits
of the concentrations within each compound category are provided
in the Supplementary Material (Figure S1).
The users may specify one or more values within the suggested range of concentration of each molecule. If the users specify more than one concentration in the range, the combination of different concentrations for different molecules could lead to a huge number of experiments even if the number of molecules remains relatively small. The full combinatorial testing of 20 different nutriments, at two concentrations each, corresponds to 220 
106. Such a large number of screening experiments can be avoided by the use of the incomplete factorial design approach. Incomplete factorial design is a mathematical method to effectively reduce the number of experiments required by a full combinatorial-screening of parameters (17). SAmBA is an implementation of the incomplete factorial design, which has been extensively used for the determination of protein crystallization conditions (18), and more recently for optimizing recombinant protein experiments (19). Metagrowth outputs the list of molecules and their concentrations in a format compatible with the SAmBA program (http://igs-server.cnrs-mrs.fr/samba/). In the above example with 20 compounds, 40 representative experimental protocols are proposed using SAmBA. In theory, the incomplete factorial design provides a minimal set of experiments in which the influence of each parameter can be examined rationally by statistical methods such as a multiple linear regression analysis.
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FUTURE DIRECTIONS AND CONCLUSIONS
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Many evidences in Metagrowth originate in metabolic analyses
described in the literature such as whole genome sequencing
papers. They are usually based on the visual inspection of predicted
metabolic pathways. The use of
in silico simulation studies
with more sophisticated mathematical metabolic models (
20–
23)
than those available in the current metabolic databases (
4,
5)
is clearly the next improvement in the generation of metabolic
hypotheses. With these approaches, one may, more precisely,
examine Metagrowth evidences and derived hypotheses such as
‘a metabolic pathway from
X to
Y lacks an enzyme, thus
the addition of
Y in the medium may improve the culture of bacteria’.
In silico simulation studies may reveal an alternative pathway
to the metabolite
Y bypassing the missing reaction steps.
In the current Metagrowth, we only present predictions for preferential inputs to the culture conditions (designated by the ‘IN=’ prefix). We plan to incorporate other kinds of information such as an ‘unnecessary’ or ‘toxic’ in association with a compound. Compound nomenclature in Metagrowth is based on LIGAND (10), in which hierarchical relationships between individual and generic compound names are not well treated. Standardization of the compound name in Metagrowth will be required to facilitate data update and to automatically detect data redundancies.
Genome sequence analysis and metabolic reconstruction of T.whipplei led to the establishment of the first cell-free culture medium allowing this fastidious bacteria to grow outside its cellular host. Further improvement of the culture condition using Metagrowth may lead to an even faster growth, which would further facilitate the manipulation and study of this microorganism. The development of an axenic culture medium for other bacteria could be more challenging. For M.leprae and T.pallidum, a large body of research has been carried out to explore the possibility of axenic cultivation as can be seen in Metagrowth. The study of bacterial culture conditions offers new testable and valuable challenges for the whole cell metabolic modeling and simulation studies. It helps in improving genomic annotation by identifying deficient or alternative metabolic pathways. It helps better characterization of host–parasite relationships, eventually giving us clues about new therapeutic targets. We hope to help the scientific community working on those pathogens by providing comprehensive information about their culture conditions through Metagrowth.
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SUPPLEMENTARY MATERIAL
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Supplementary Material is available at NAR Online. Table S1
gives the definition of the classes and the subclasses of the
relationships between evidences and hypotheses. Figure S1 gives
the concentrations of the components in existing complex culture
medium.
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ACKNOWLEDGEMENTS
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We thank Dr Didier Raoult for helpful discussions and Dr Guillaume
Blanc for carefully reading the manuscript.
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Notes
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The online version of this article has been published under
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journals.permissions{at}oupjournals.org.
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