Nucleic Acids Research 2007 35(Database issue):D533-D537; doi:10.1093/nar/gkl823
Nucleic Acids Research, 2007, Vol. 35, Database issue D533-D537
© 2006 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
SYSTOMONAS — an integrated database for systems biology analysis of Pseudomonas
Claudia Choi1,
Richard Münch1,
Stefan Leupold1,
Johannes Klein1,
Inga Siegel1,
Bernhard Thielen2,
Beatrice Benkert1,
Martin Kucklick1,
Max Schobert1,
Jens Barthelmes2,
Christian Ebeling2,
Isam Haddad1,
Maurice Scheer1,4,
Andreas Grote1,3,
Karsten Hiller1,
Boyke Bunk1,
Kerstin Schreiber1,
Ida Retter1,
Dietmar Schomburg2 and
Dieter Jahn1,*
1 Institut für Mikrobiologie, Technische Universität Braunschweig Spielmannstraße 7, D-38106 Braunschweig, Germany
2 Institut für Biochemie, Universität zu Köln Zülpicher Straße 47, D-50674 Köln, Germany
3 Institut für Bioverfahrenstechnik, Technische Universität Braunschweig Gaußstraße 17, D-38106 Braunschweig, Germany
4 Fachbereich Informatik, Fachhochschule Wolfenbüttel Am Exer 2, D-38302 Wolfenbüttel, Germany
*To whom correspondence should be addressed. Tel: +49 531 391 5801; Fax: +49 531 391 5854; Email: d.jahn{at}tu-bs.de
Received August 11, 2006. Revised October 5, 2006. Accepted October 5, 2006.
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ABSTRACT
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To provide an integrated bioinformatics platform for a systems
biology approach to the biology of pseudomonads in infection
and biotechnology the database SYSTOMONAS (SYSTems biology of
pseudOMONAS) was established. Besides our own experimental metabolome,
proteome and transcriptome data, various additional predictions
of cellular processes, such as gene-regulatory networks were
stored. Reconstruction of metabolic networks in SYSTOMONAS was
achieved via comparative genomics. Broad data integration is
realized using SOAP interfaces for the well established databases
BRENDA, KEGG and PRODORIC. Several tools for the analysis of
stored data and for the visualization of the corresponding results
are provided, enabling a quick understanding of metabolic pathways,
genomic arrangements or promoter structures of interest. The
focus of SYSTOMONAS is on pseudomonads and in particular
Pseudomonas aeruginosa, an opportunistic human pathogen. With this database
we would like to encourage the
Pseudomonas community to elucidate
cellular processes of interest using an integrated systems biology
strategy. The database is accessible at
http://www.systomonas.de.
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MOTIVATION
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Traditionally, metabolic and gene-regulatory networks were analysed
separately. There are various tools for metabolic network reconstruction
[e.g. (
1–
3)], and for the generation of gene-regulatory
networks (
4), i.e. the prediction of the regulation of certain
genes by specific transcription factors. However, there still
exist only a few tools combining both networks such as the Pathway
Tools Omics Viewer (
5). This poor connectivity between the two
outlined approaches might be due to the fact that the required
information is stored in different databases. Information on
transcription factor binding sites can be found for example
in RegulonDB (
6) or PRODORIC (
7), while metabolic reactions
or pathways need to be retrieved from other database, such as
BRENDA (
8), BioCyc (
5), KEGG (
9), PseudoCyc (
10), and UM-BDD
(
11). Combining knowledge from multiple disciplines and data
resources will drive our understanding of cellular processes
and lead to the prediction of the cellular behaviour in its
entirety.
Consequently, we constructed the database SYSTOMONAS, which provides the basis for a systems biology approach. Here we focus on data integration for the biotechnologically and medically relevant group of bacteria, the pseudomonads.
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CONTENT OF SYSTOMONAS
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The complexity of a systems biology approach requires the focus
on a certain well investigated organism. We have chosen the
Gram negative proteobacterium
Pseudomonas aeruginosa. This organism
is a versatile soil bacterium and an important opportunistic
pathogen causing persistent infection in immunocompromised patients
(
12). Our long term goal is the development of a dynamic model
simulating the behaviour of
P.aeruginosa during infection. The
basis of our approach is SYSTOMONAS, a comprehensive database
that includes data from all levels of analysis as microarray
and proteomics data, metabolite measurements, sequence data,
gene-regulatory networks and corresponding enzyme data.
Our database comprises information on eight different Pseudomonas species and strains, which genomes have been completely sequenced and functionally annotated. Besides the medically relevant P.aeruginosa the genera Pseudomonas contains various important plant pathogens and biotechnologically as well as ecologically interesting species.
Our initial focus was on metabolomics. However, all current information coming from genomics, transcriptomics and proteomics (13) is also stored in our database as data warehouse (14) or dynamically accessible via web services using SOAP interfaces (15), a platform-independent data transfer protocol (see section ‘Database Techniques’). Besides other research groups and our own experimental results further data are retrieved from major general data resources. Major external sources of SYSTOMONAS are KEGG [Kyoto Encyclopedia of Genes and Genomes (9)], Pseudomonas Genome Database v2 [PGDv2 (16)], PRODORIC [PROcaryotIC Database Of gene-Regulation (4)], and BRENDA (8). KEGG provided metabolic reactions, compounds, glycans and pathways; PGDv2 and PRODORIC supplied protein, gene annotation, gene-regulatory and genome structure data. BRENDA supports kinetic and disease information. ENZYME (17) and BioCyc (5) provide further functional characterization of proteins.
Currently, SYSTOMONAS contains 10 034 proteins identified as enzymes, 195 transcription factor–gene relations, 14 250 measuring points of three independent metabolome experiments. Moreover, 11 exemplary protein spots from one proteome experiment were entered. Transcriptome data are provided by PRODORIC via SOAP (see section ‘Database Techniques’). For P.aeruginosa PAO1 1509 unique proteins were annotated in SYSTOMONAS as enzymes. The 1509 annotated enzymes were retrieved from KEGG (1003), PGDv2 (1017), BioCyc (493), ENZYME (393) and from our own annotation (241). The corresponding annotation process is described in the following sections. By comparison, PseudoCyc contains 738 enzymes (version 9.6, http://v2.pseudomonas.com:1555/).
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COMPARATIVE GENOMICS AND REGULATORY NETWORK PREDICTION
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Comparing a
Pseudomonas protein of interest with other well-characterized
proteins may deliver useful insights into the evolution, distribution
and species specific function. Therefore, we searched for all
deduced proteins of the SYSTOMONAS database for orthologous
proteins in other
Pseudomonas species to obtain orthologous
protein clusters. First, a restricted BLAST analysis (
18) was
performed on the protein sequences followed by a pairwise global
alignment using the tool stretcher of the EMBOSS package (
19).
The homologous protein pairs can be obtained from the SYSTOMONAS
protein table and visualized as multiple alignments, which are
produced by MUSCLE [MUltiple Sequence Comparison by Log-Expectation
(
20)]. A more dynamic and flexible tool for the visualization
of multiple alignments is provided by Jalview (
21), which is
also accessible from SYSTOMONAS. This tool does not only display
multiple alignments but is also able to generate a phylogenetic
tree for the protein group by different algorithms. The
E-value
of BLAST and the identity calculated by stretcher can be retrieved
by activating the two multiple alignment tools.
Graphical maps of corresponding gene regions can be retrieved via a hyperlink to the BRENDA Genome Explorer, a tool within the BRENDA package (8). BRENDA Genome Explorer visualizes orthologous gene regions, which have a sequence identity of at least 50% in different organisms.
If the user is interested in the prediction of transcription factor binding sites and the deduction of corresponding regulons the tool Virtual Footprint (4) can be employed. We adapted this tool to SYSTOMONAS by limiting the analysis on Pseudomonas species.
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Metabolic network reconstruction
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Genes and proteins of eight
Pseudomonas species and strains
are carefully annotated by the
Pseudomonas community and involved
genome projects (
16). PGDv2 is the resource for the continually
updated
P.aeruginosa PAO1 genome annotation. It also refers
to genome annotation web sites of other
Pseudomonas genome projects
for the most up-to-date information. To reconstruct metabolic
networks, we transferred known EC numbers of each
Pseudomonas protein to its homologous partners. The enzyme designation of
proteins was determined in three steps. First, the external
databases KEGG (
9), PGDv2 (
16), ENZYME (
17), BioCyc (
5) provide
EC numbers for the proteins. Second, homology analyses lead
to putative orthologous protein pairs (see section ‘Comparative
Genomics’). Third, if the identity of the global alignment
(stretcher) equals or exceeds 60%, all EC number connections
to proteins were transferred to their orthologous protein partners.
The EC number source on the website is indicated accordingly.
EC numbers newly identified by our method are declared as ‘predicted’.
In order to improve the metabolic network reconstruction by
applying another method, we also used the tool metaSHARK (
1).
This tool is able to identify potential enzyme-encoding genes
in raw DNA sequences, which are not annotated yet. All newly
detected EC numbers by metaSHARK are also indicated as ‘predicted’
in SYSTOMONAS.
Table 1 lists parts of the corresponding database
content.
Next, we imported KEGG-pathway data and enabled links to pathway
maps via the SOAP interface provided by KEGG (
9), in which
P.aeruginosa enzymes are highlighted. A metabolic network including all involved
enzymes, metabolic reactions and metabolites of all pseudomonads
is delivered by our own adapted tool, which is based on GraphViz
(
22) and creates clickable image maps (
Figure 1). The user immediately
recognizes enzymatic reactions unique to one
Pseudomonas species.
All necessary information concerning this reaction is provided
by mouse click.
View larger version (25K):
[in this window]
[in a new window]
[Download PowerPoint slide]
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Figure 1 The visualization of metabolic pathways from KEGG in SYSTOMONAS is based on GraphViz using the dot layout. All known metabolic reactions are depicted here for the ‘Urea cycle and metabolism of amino groups’ pathway. Rectangles depict metabolic reactions, ellipses represent metabolites whose names are abbreviated with an asterisk * when the length exceeds 10 letters. Both types of nodes are clickable. Different colours for rectangles specify distinct Pseudomonas species, which catalyse the corresponding reaction. These pathways can be obtained from metabolic pathway entries. An abbreviation code for the species is provided with the visualization output (AO1 = P.aeruginosa PAO1, A14 = P.aeruginosa PA14, P = P.putida KT2440, Pf-5 = P.fluorescens F5, F01 = P.fluorescens PfO-1, ST = P.syringae pv tomato, SP = P.syringae pv phaseolicola, SS = P.syringae pv syringae)
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METABOLOMICS DATA
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The database structure of SYSTOMONAS is suitable for the simple
storage of various types of experimental data. All currently
available transcriptome and proteome data are deposited in the
database. For a start, we included our own experimental data
obtained for the
P.aeruginosa strain PAO1 measured under different
growth conditions. Our raw data analysed by GC/MS can be accessed
via the query form ‘omics data’. As an extra feature,
the metabolomics data obtained for one specific growth condition
can be plotted against another dataset using gnuplot (
www.gnuplot.info).
Data points are clickable and lead to the corresponding metabolite
of the database (
Figure 2). If the levels of specific metabolites
differ significantly between different conditions, the measured
values are found distinct to a fictive diagonal line. Experimental
conditions and methods are indicated along with the raw data.
View larger version (17K):
[in this window]
[in a new window]
[Download PowerPoint slide]
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Figure 2 Semi-quantitative scatter plot for the comparison of metabolic profiles measured for P.aeruginosa PAO1 grown under aerobic conditions. Metabolites were analysed by GC/MS. Mean peak areas and standard deviations for the metabolites were calculated and plotted on a logarithmic scale using gnuplot (www.gnuplot.info). Metabolites measured from samples of exponentially growing cells under aerobic conditions are plotted along the x-axis against metabolites from samples of resting cells along the y-axis. The metabolite name for every data point is shown as tooltip while moving the mouse over the point (e.g. for the data point ‘Lactate’) and linked back to the corresponding database entry. If the metabolic profile during one experimental condition is similar to the condition compared, data points will arrange closely to the diagonal line.
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DATABASE TECHNIQUES
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We have chosen the open source object-relational database management
system PostgreSQL 8.0.3 (
www.posgresql.org) for our database.
This database is accessed with the scripting language PHP (
www.php.net),
which also allows the dynamic generation of the web interface.
The web server is Apache 2.0 (
http://httpd.apache.org).
Data integration with SYSTOMONAS combines two different principle concepts, the data warehouse concept (14) and dynamic web services via SOAP. The advantage of a data warehouse is mainly its fast performance during the data retrieval. For this purpose, a major portion of the data of SYSTOMONAS, such as KEGG compounds, reactions or PRODORIC transcription factor—gene interactions is locally stored. The major advantage of SOAP is its up-to-dateness, since SOAP-transmitted information is corresponding to the most recent data of the consulted database. Several databases provide web services via SOAP, such as the major sequence databases (23–25). Several other databases, such as Atlas (26) are organized as data warehouses. The main data sources of SYSTOMONAS are stored and matched in an intermediate data container ‘metabold’, which supplies data to SYSTOMONAS (Figure 3). The web services via SOAP, which are used on the websites of SYSTOMONAS, are listed in Table 2. Whenever a webpage with these web services is accessed, the data is retrieved from the actual external database and amends the locally stored data of SYSTOMONAS. The API (application programming interface) is constructed by the SOAP extension of PHP.
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AVAILABILITY
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Currently, the data of SYSTOMONAS along with its visualization
tools and web services can be accessed freely via a web-based
user interface (
http://www.systomonas.de). Additional information,
such as kinetic data, operon structures or transcriptomics data
are retrieved on-the-fly via web services from PRODORIC, BRENDA
and KEGG (
Table 2). We provide SBML (
27) formatted files for
downloading our metabolic and gene-regulatory networks along
with a database copy at the SYSTOMONAS website.
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ACKNOWLEDGEMENTS
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The authors are much obliged to Frank Klawonn for advising in
the statistical part of metabolomics comparison. Many thanks
go to Mathias Krull and Barbara Schulz, who proof-read this
paper. This work was funded by the German Bundesministerium
für Bildung und Forschung (BMBF) for the National Genome
Research Network (NGFN2-EP, grant no. 0313398A), BMBF for the
Bioinformatics Competence Center Intergenomics (Grant No. 031U110A/031U210A)
and the Volkswagen Foundation.
Conflict of interest statement. None declared.
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