TrypanoCyc: A community-led biochemical pathways database for Trypanosoma brucei

Abstract

The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites)
through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic
networks is increasing as we learn more about the enzymes that are active in particular cells under particular conditions
and as technologies advance to allow detailed measurements of the cellular metabolome. Metabolic network databases are of
increasing importance in allowing us to contextualise data sets emerging from transcriptomic, proteomic and metabolomic experiments.
Here we present a dynamic database, TrypanoCyc (http://www.metexplore.fr/trypanocyc/), which describes the generic and condition-specific metabolic network of Trypanosoma brucei, a parasitic protozoan responsible for human and animal African trypanosomiasis. In addition to enabling navigation through
the BioCyc-based TrypanoCyc interface, we have also implemented a network-based representation of the information through
MetExplore, yielding a novel environment in which to visualise the metabolism of this important parasite.

Full text

Nucleic Acids Research, 2014 1
doi: 10.1093/nar/gku944
TrypanoCyc: a community-led biochemical pathways
database for
Trypanosoma brucei
Sanu Shameer1, Flora J. Logan-Klumpler2, Florence Vinson1, Ludovic Cottret3,
Benjamin Merlet1, Fiona Achcar4, Michael Boshart5, Matthew Berriman2, Rainer Breitling6,
Fr´
ed´
eric Bringaud7, Peter B ¨
utikofer8, Amy M. Cattanach4, Bridget Bannerman-Chukualim2,
Darren J. Creek9, Kathryn Crouch4, Harry P. de Koning4, Hubert Denise10,
Charles Ebikeme11,AlanH.Fairlamb
12, Michael A. J. Ferguson12, Michael L. Ginger13,
Christiane Hertz-Fowler14, Eduard J. Kerkhoven15, Pascal M¨
aser16, Paul A. M. Michels17,
Archana Nayak4,DavidW.Nes
18, Derek P. Nolan19 , Christian Olsen20,
Fatima Silva-Franco14, Terry K. Smith21,MartinC.Taylor
22, Aloysius G. M. Tielens23, 24,
Michael D. Urbaniak13, Jaap J. van Hellemond24, Isabel M. Vincent4, Shane R. Wilkinson25,
Susan Wyllie12, Fred R. Opperdoes26, Michael P. Barrett4,* and Fabien Jourdan1,*
1Institut National de la Recherche Agronomique (INRA), UMR1331, TOXALIM (Research Centre in Food Toxicology),
Universit´
e de Toulouse, Toulouse, France, 2The Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA,
UK, 3Institut National de la Recherche Agronomique (INRA), UMR441, Laboratoire des Interactions
Plantes-Microorganismes (LIPM), Auzeville, France, 4University of Glasgow, Glasgow, Scotland, G12 8QQ, UK,
5Ludwig-Maximilians-Universit¨
at M¨
unchen, Biocenter, 82152-Martinsried, Germany, 6Manchester Institute of
Biotechnology, Faculty of Life Sciences, University of Manchester, Manchester, UK, 7CNRS, Bordeaux, 33076,
France, 8University of Bern, Bern, CH-3012, Switzerland, 9Monash Institute of Pharmaceutical Sciences, Monash
University, Parkville 3052, Australia, 10European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10
1SD, UK, 11ISSC, UNESCO, F-75732 CEDEX 15, Paris, France, 12University of Dundee, Dundee, Scotland, DD1
4HN, UK, 13Divisionof Biomedical and Life Sciences, Lancaster University, Bailrigg, Lancaster, LA1 4YG, UK,
14University of Liverpool, Liverpool, Merseyside L69 3BX, UK, 15Chalmers University of Technology, Kemiv ¨
agen 10,
412 96, G¨
oteborg, Sweden, 16Swiss Tropical and Public Health Institute, Socinstr. 57, Basel 4051, Switzerland,
17University of Edinburgh, Mayfield Road, Edinburgh EH9 3JU, UK, 18Texas Tech University, Lubbock, TX, USA,
19Trinity College Dublin, Dublin 2, Ireland, 20Biomatters Inc. 60 Park Place, Suite 2100, Newark, NJ, USA, 21University
of St Andrews, St Andrews, Scotland, KY16 9ST, UK, 22 LSHTM, London, WC1E 7HT, UK, 23Utrecht University,
Utrecht, 3508 TD, The Netherlands, 24Erasmus University Medical Center, Rotterdam, 3015 CE, The Netherlands,
25Queen Mary University of London, London E1 4NS, UK and 26University of Louvain, Brussels, B-1200, Belgium
Received August 13, 2014; Revised September 26, 2014; Accepted September 26, 2014
ABSTRACT
The metabolic network of a cell represents the
catabolic and anabolic reactions that interconvert
small molecules (metabolites) through the activity of
enzymes, transporters and non-catalyzed chemical
reactions. Our understanding of individual metabolic
networks is increasing as we learn more about
the enzymes that are active in particular cells un-
der particular conditions and as technologies ad-
vance to allow detailed measurements of the cellu-
lar metabolome. Metabolic network databases are
of increasing importance in allowing us to con-
textualise data sets emerging from transcriptomic,
proteomic and metabolomic experiments. Here we
present a dynamic database, TrypanoCyc (http:
//www.metexplore.fr/trypanocyc/), which describes
the generic and condition-specific metabolic network
of
Trypanosoma brucei,
a parasitic protozoan re-
sponsible for human and animal African trypanoso-
miasis. In addition to enabling navigation through the
*To whom correspondence should be addressed. Tel: +33 561 28 57 15; Fax: +33 561 28 52 44; Email: Fabien.Jourdan@toulouse.inra.fr
Correspondence may also be addressed to Michael P. Barrett. Tel: +44 141 330 6904; Fax: +44 141 330 4077; Email: michael.barrett@glasgow.ac.uk
C
The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by-nc/4.0/), which
permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact
journals.permissions@oup.com
Nucleic Acids Research Advance Access published October 9, 2014
at Periodicals Dept on November 7, 2014http://nar.oxfordjournals.org/Downloaded from

2Nucleic Acids Research, 2014
BioCyc-based TrypanoCyc interface, we have also
implemented a network-based representation of the
information through MetExplore, yielding a novel en-
vironment in which to visualise the metabolism of
this important parasite.
INTRODUCTION
Trypanosoma brucei is the causative agent of African try-
panosomiasis (commonly known as sleeping sickness in hu-
mans and Nagana in animals). The disease is fatal if un-
treated in humans (1) and the economic impact of try-
panosomes on agriculture in Africa is immense. The drugs
available for the trypanosomiases are inadequate for a num-
ber of reasons and better therapeutic options are required
(2). Many drugs work through interfering with enzymes in-
volved in cellular metabolism. The only anti-trypanosomal
drug whose target is known is eornithine, an inhibitor of
ornithine decarboxylase (3), a key enzyme in the polyamine
biosynthetic pathway. A comprehensive understanding of
parasite metabolism therefore contributes to current efforts
in drug discovery and understanding drug resistance (4).
Global untargeted molecular proling data sets (e.g. tran-
criptomics, proteomics and metabolomics data) are now be-
ing generated for trypanosomes and the effects of life cycle,
environmental perturbation, specic genetic manipulation
and drug action are being dissected in a systematic man-
ner (5). Interpreting and integrating these data to allow bio-
logical inference and hypothesis generation is a major chal-
lenge. Metabolic network-based methods offer a means to
contextualise and integrate data to help inference of biolog-
ical function (6). Reliable, comprehensive databases collat-
ing information on metabolic networks and pathways are
therefore crucial to optimize understanding derived from
postgenomic data sets. Metabolic databases such as Leish-
Cyc (7)(forLeishmania), and the Library of Apicomplexan
Metabolic Pathways (8) (for apicomplexan parasites) are
among the few examples where this information is available
in parasitology.
Creation of a metabolic network database is achieved by
gathering information on all of the metabolic transforma-
tions an organism can perform (9). A rst outline of this
information is generally retrieved from genomic orthology.
Genes coding for enzymes are identied through sequence
similarity searches and then, using enzyme activity infor-
mation, metabolic reactions catalyzed by these enzymes are
added to the network database. Several automatic and semi-
automatic tools are available to perform these genome-
based metabolic network reconstructions (10–13).
In spite of their undoubted utility, genome-based recon-
struction has limitations since it is based primarily on se-
quence homology comparisons between the organism of
interest and databases encompassing information from a
multitude of organisms (14). Incorrect annotations readily
propagate across databases (15). Moreover, evolution works
through modication of function following alteration of
genes encoding proteins. For instance, trypanosomes use
N1,N8bis-glutathionyl spermidine (trypanothione) (16)as
a key cellular redox-associated metabolite. Trypanothione
is retained in its reduced form by the enzyme trypanoth-
ione reductase (EC 1.8.1.12). This enzyme is evolutionar-
ily derived from glutathione reductase (EC 1.8.1.7), with
which it shows great homology. In the absence of accom-
panying biochemical evidence, genome annotations would
simply predict trypanosomes as possessing a glutathione re-
ductase, and metabolic reconstructions would assume try-
panosomes employ canonical glutathione-based redox bal-
ancing. Cases like this highlight the necessity of rening
genome-based metabolic reconstructions by incorporating
advanced biochemical knowledge (15).
Moreover, simple genome reconstructions do not take
into account the sub-cellular localization of the enzymes
(although various methods are now being developed to
tackle this issue as canonical signals determining cellular lo-
calization come to light (17)). Finally, the genome provides
a view of the total metabolic capability of an organism, re-
gardless of environmental and genetic conditions. In try-
panosomes, however, different metabolic strategies are used
at different points in the life cycle. In the tsetse y, the try-
panosome’s main carbon source is proline (18) while in the
human-host it is glucose (19). Some reactions are active in
one condition but not in another. This information is par-
ticularly important when looking for potential drug targets.
Web servers such as KEGG (20) and BioCyc (14)rep-
resent metabolism as a set of pathways, reecting classical
textbook views of biochemistry. However, the pathway ap-
proach fragments metabolism in ways which constrain our
ability to decipher the broader impact on the metabolic net-
work; hence, methods that also enable connected network
views of metabolism are desirable. We have therefore com-
bined building a pathway-based TrypanoCyc database with
its integration into the MetExplore web server (21), to offer
both pathway and network-based inference and visualiza-
tion.
A HIGHLY CURATED DATABASE OF
T. BRUCEI
METABOLISM
The T. brucei TREU 927 genome is 26 Mb in size, with a
karyotype of 11 megabase chromosomes (22) and contain-
ing a predicted 9068 protein-coding genes. In a collaborative
project between the International Trypanotolerance Centre
in The Gambia and the Sanger Institute, the genome se-
quence was processed using the Pathologic metabolic net-
work reconstruction tool of Pathway Tools (23), creating
aPathway/Genome Database (PGDB) where gaps (called
‘pathway holes’) in the predicted metabolic pathways were
lled by hypothetical reactions, even without an obvious
gene association. The result of this rst automatic recon-
struction was the starting point of the current TrypanoCyc
database.
An international consortium of investigators, expert in
various aspects of trypanosome metabolism, was assembled
to produce a highly annotated TrypanoCyc database. As
recommended by Thiele & Palsson (24) we started the Try-
panoCyc initiative in 2012 with a two-day ‘jamboree’. Each
expert was offered a specic set of pathway(s) in his/her
area of expertise to curate. A dedicated web interface, called
TrypAnnot (a password protected part of the website avail-
able to annotators, not described here) stores submitted
annotations in a curation database, making it possible to
track all annotations, which are automatically taken from
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Nucleic Acids Research, 2014 3
Figure 1. TrypanoCyc page for the 6-phosphogluconate dehydrogenase (1.1.1.44) reaction. (a) Reaction name and GeneDB link (specic to TrypanoCyc),
(b) Detailed description of the reaction, (c) Localizations of the reactions as suggested by annotators, (d) Condence score for the reaction (specic to
TrypanoCyc), (e) Annotation tables displaying content of the TrypAnnot database (specic to TrypanoCyc).
the database and added to the web page of the correspond-
ing reaction. The TrypanoCyc project has so far had 1368
editing events, among which are 653 annotations made on
464 reactions. Furthermore, since the rst automated recon-
struction in 2008, 17 pathways, 35 enzymatic-reactions, 10
transport reactions, 41 enzymes, 2 protein complexes and
104 metabolites have been added to TrypanoCyc. Extended
summaries for some pathways have also been made avail-
able in the database.
T. brucei cells contain multiple membrane-bounded or-
ganelles, including the mitochondrion and an unusual
peroxisome-related organelle, the glycosome (25,26), in
which the rst seven steps of glycolysis occur, as well as a
series of other pathways (19). Annotators, therefore, spec-
ify the sub-cellular localization of reactions, if known, in
the annotation interface. Life cycle stage specicity for each
reaction is also important, since trypanosomes use differ-
ent metabolic pathways in different environments; hence
annotators can specify one or more developmental stages
in which reactions occur. Note that this information is not
available in the reconstruction provided by KEGG (see Ta-
ble 1for comparison). The level of knowledge on each reac-
tion varies from experimentally veried to indirect evidence
of activity regardless of manual curation. To reect the level
of condence of the annotation we have used the scoring
system proposed by Thiele & Palsson (9)(seeTable2). For
instance, of the 464 annotated reactions, 84 were annotated
based on direct evidence from protein purication, bio-
chemical assays or comparative gene expression studies and
hence can be considered with the highest condence. Dur-
ing curation we found numerous falsely predicted reactions
and pathways; 60 pathways, 14 enzymatic reactions, 20 en-
zymes and 56 metabolites have been removed from the origi-
nal reconstruction. Nevertheless we retained some reactions
if they are known to occur in related trypanosomatids, or
else when they have been proposed to exist, erroneously, in
the literature. Although such reactions are kept, they are
not linked to any pathway and they are assigned a nega-
tive condence score to highlight the fact that according to
our present knowledge they are not actually present. For ex-
ample, a methionine cycle that regenerates methionine from
methylthioadenosine resulting from polyamine biosynthe-
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4Nucleic Acids Research, 2014
Figure 2. Proteomics data loaded in TrypanoCyc using the cellular overview tool. (a) The diagram shows all the metabolic pathways in gray boxes. Colored
squares correspond to reactions with associated proteomics values. The color scale is displayed in the ‘Omics Viewer Control Panel’; it can be tuned using
dedicated parameters. The ‘REACTION’ dialog appears when clicking on a reaction. (b) It is then possible to get back to the corresponding reaction page
and read the annotators’ comments.
sis has been proposed (27). However, metabolic labelling
experiments have subsequently indicated that the pathway
is not active in trypanosomes, at least in the conditions
used (28). The reactions EC 4.2.1.109 (methylthioribulose
1-phosphate dehydratase) and EC 3.1.3.77 (5-(methylthio)-
2,3-dioxopentyl-phosphate phosphohydrolase), required to
complete the pathway, are included in the database, but
assigned negative scores to highlight that they are unde-
tectable in spite of previous predictions in the literature (27).
We consider it useful to keep such entries such that users of
the database can nd explicit reference to these reactions
they might seek upon reading literature pertaining to these
reactions.
Since metabolic databases focus mainly on pathways and
seldom consider sub-cellular compartments, they usually
lack information on intracellular transport reactions. Cur-
rently, TrypanoCyc contains only 35 such reactions. This is
because we did not incorporate transport reactions into our
annotation platform and because experimental knowledge
on intracellular transport processes is still sparse. However,
the dynamic nature of TrypanoCyc means additional anno-
tation and incorporation of measured and probable trans-
port reactions (e.g. taken from existing manually curated
metabolic models of the closely related organism Leishma-
nia major (29)) will form part of the iterative process of
database renement. We also perform gap lling in each
compartment using graph approaches and testing metabolic
scenarios as suggested (9,10) and successfully implemented
for other organisms (30).
Many additional databases provide information that can
complement metabolic network databases. Linking to these
other data sources enhances our ability to learn about
an organism’s metabolism. TrypanoCyc, therefore, links to
multiple databases including BRENDA (31,32), expasy.org
(33), ExplorEnz (34), Pubmed and UniProt (35). The Trit-
rypDB database (36) is the central resource for trypanoso-
matid genomes and associated functional genomics data,
while GeneDB houses the sequence information gathered
and annotated through the Wellcome Trust Sanger Institute
(37). For each gene, TrypanoCyc offers a direct link to the
corresponding TritrypDB and GeneDB pages.
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Nucleic Acids Research, 2014 5
Figure 3. Navigation between pathway and network representation using MetExplore and TrypanoCyc. (a) Each pathway page has an hyperlink allowing
to load and visualize the pathway in MetExplore (circled in red on the pathway page screenshot). (b) When clicking on this link in the Glycolysis page, it
is loaded in MetExplore; the red box corresponds to the cytosolic part and the green one to the glycosomal part. (c) Using MetExplore, it is then possible
to generate a combination of various pathways. TCA cycle, succinate shunt, glycolysis and the pentose phosphate pathway were selected. (d) All reactions
of these pathways are added to the cart (red box on the right). A third compartment, mitochondrion, appears (purple box). A reaction allowing transport
between cytosol and glycosome appears in the network (red arrow). (e) In the tabular view of MetExplore, a TrypanoCyc button (visible in the third column
of [c] table) allows to link back to TrypanoCyc.
Table 1. Description of the condence score system used in TrypanoCyc to evaluate the level of curation of each reaction
Reconstruction Compartments Life cycle stages Pathways Enzymatic reactions Unique metabolites
Draft reconstruction
2008
1 0 238 1120 796
KEGG August 2014 1 0 61 656 646
TrypanoCyc August
2014
9 4 209 1025 842
The BioCyc library is a collection of 3563 PGDBs. Based
on the quality of the PGDBs and the level of manual cu-
ration, this central repository classies them into Tier 1
(highly curated), Tier 2 (moderately curated) and Tier 3
(non-curated) categories. Prior to the release of BioCyc
v18.1, only 6 PGDBs (EcoCyc (38), MetaCyc (14), Human-
Cyc (39), AraCyc (40), YeastCyc and LeishCyc (7)) were
published in the Tier 1 category. Due to the quality of in-
formation being made available on TrypanoCyc, it was in-
cluded in BioCyc’s Tier 1 category with the release of Bio-
Cyc v18.1 in June 2014.
REACTIONS, PATHWAYS AND NETWORK MINING
Browsing TrypanoCyc content and expert annotations
As a Pathway Tools-based website, TrypanoCyc provides
a dedicated web page for each metabolic network entity
(pathways, reactions, metabolites, enzymes, proteins and
genes). The reaction page architecture was, however, mod-
ied in order to allow additional annotation information.
These include the annotation condence score (Figure 1d),
stage specicity and compartmentation with links to key lit-
erature (Figure 1e). A comment box is also included, con-
taining detailed free-text information on the reaction. Fig-
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6Nucleic Acids Research, 2014
Table 2. Overview of TrypanoCyc content before and after curation and comparison with the KEGG database
Evidence type Condence score Description
Biochemical data 4 Direct evidence for gene product function and
biochemical reaction: protein purication,
biochemical assays, experimentally solved protein
structures and comparative gene-expression
studies.
Genetic data 3 Direct and indirect evidence for gene function:
knock-out characterization, knock-in
characterization and over expression.
Physiological data 2 Indirect evidence for biochemical reaction based
on physiological data: secretion products or
dened medium components serve as evidence for
transport and metabolic reactions.
Sequence data 2 Evidence for gene function: genome annotation,
SEED annotation.
Modelling data 1 No evidence is available but reaction is required for
modelling. The included function is a hypothesis
and needs experimental verication. The reaction
mechanism may be different from the included
reaction(s).
Not evaluated 0
Negative hypothesis –1 Although there is no evidence against this
reaction, it is expected to not exist
Evidence against the reaction –2 Direct/indirect evidence against the hypothesis is
available
ure 1shows the webpage for the pentose phosphate pathway
enzyme, 6-phosphogluconate dehydrogenase (EC. 1.1.1.44).
Search requests on database content can be made
through a quick search box found at the top right-hand cor-
ner of the interface page, as well as through the advanced
search options available from the menu bar. Each pathway
representation is available with different levels of detail, the
simplest view displaying only the reactions and metabolites
while the detailed view displays all available information in-
cluding the molecular structure of all metabolites involved.
Additionally, for every pathway in TrypanoCyc, we provide
a link to visualize the pathway in MetExplore.
Mining stage specic metabolism using cellular overview
To exemplify the integration of molecular proling data in
the TrypanoCyc database we used published results from
a Stable Isotope Labelling of Amino acids in Cell culture
experiment, comparing protein levels in bloodstream form
(BSF) and procyclic form (PCF) trypanosomes (41). The
data set contains 3552 gene IDs along with their relative
protein levels in the two tested stages of T. brucei (expressed
as log PCF/BSF values). A TrypanoCyc cellular overview
shows enzymes that differ in abundance between the two
life cycle forms (Figure 2; for step by step instructions see
Supplementary Data S1).
Mapping other molecular proling data in TrypanoCyc
can be achieved using the Pathway Tools Omics Viewer (42),
which displays all pathways in a single representation. Data
sets can be loaded using the options listed on the right-hand
side of the page (Supplementary Material S2 is a version of
this data set in an Omics Viewer-compliant format). Figure
2a shows an image of the overview after loading the pro-
teomics data of (41). Individual reactions can be viewed by
moving the mouse over them and clicking the link in the
pop-up dialog box. This opens the related reaction page
containing the annotation table, giving access to specic
TrypanoCyc annotators’ comments about the enzyme and
its activity as well as the generic information pertaining to
that reaction in the MetaCyc database. For example, the
overlaid data clearly show that the respiratory chain is up-
regulated in procyclic stages. Browsing the reaction page of
any of those up-regulated proteins shows additional infor-
mation from the annotators. For example, for ubiquinone-
cytochrome C reductase (EC 1.10.2.2), two TrypanoCyc an-
notators report that this reaction is active in the PCF but
not in the long slender BSF of T. brucei (see Figure 2b), thus
agreeing with the observations from the proteomics experi-
ment.
Using MetExplore to create user-dened sub-networks from
TrypanoCyc
To complement the classical pathway-oriented BioCyc rep-
resentation of data, we also offer a novel way to visualize the
content of TrypanoCyc via our MetExplore web server (21)
(for step by step instructions see Supplementary Data S3).
Each pathway page contains a hyperlink (Figure 3a), that
opens MetExplore with the selected pathway (Figure 3b).
Importantly, the MetExplore viewer takes into account the
localization of reactions. For example, Figure 3b shows how
the glycolytic pathway is divided into two compartments
(glycosome and cytosol represented by green and red boxes,
respectively).
Another advantage of MetExplore is that it provides a
tabular representation of compartments, pathways, reac-
tions, enzymes, genes and metabolites in the database. It is
also possible to lter these tables by compartments, path-
ways or reactions. For instance, by ltering simultaneously
on the pentose phosphate pathway, TCA cycle, succinate
shunt and glycolysis, only reactions and metabolites related
to these pathways are displayed in their respective tables
(Figure 3c). The user can also add reactions of interest to a
‘cart’ (red box on Figure 3d). It is then possible to visualize
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Nucleic Acids Research, 2014 7
the content of this cart in the network representation. From
Figure 3d, it is evident that the network perspective is much
more effective in representing compartments and transport
reactions. Furthermore, the glycosome (green box) and cy-
tosol (red box) are demonstrably connected by a reaction
involved in the succinate shunt (marked by a red arrow on
Figure 3d). For a more exible representation MetExplore
also offers a downloadable version of the Cytoscape visual-
ization software (43), pre-loaded with the cart content.
Finally, each MetExplore reaction/pathway with a de-
scription in TrypanoCyc has been hyperlinked to the corre-
sponding reaction/pathway pages, allowing the user to go
back to the expert annotations anytime (Figure 3e).
CONCLUSION
Since 2012, TrypanoCyc has been under extensive cura-
tion with the help of the scientic community and is now
counted among the seven Tier 1 databases within the Bio-
Cyc repository. Collaborative annotations help in improv-
ing the quality of the database by reducing errors, reduc-
ing the workload for individual annotators and also provid-
ing inferences from multiple perspectives given the various
types of experts in the community.
T. brucei metabolic plasticity allows the parasite to adapt
to divergent nutritional environments offered by different
hosts. For drug target identication, for example, focus-
ing on enzymes and metabolic pathways expressed in the
parasite-stages that are replicative in the mammalian host
is critical. TrypanoCyc is the rst comprehensive metabolic
network database for parasites including stage specicity as
a key component of the collected data. LeishCyc (7), for
the related parasite L. major, has also been established, and
in the future these two databases should, ideally, be linked,
given the signicant degree of similarity in the metabolic
networks of these evolutionarily related parasites.
TrypanoCyc and the related annotation database allow
anyone with an interest to join the annotation team. The
size of the consortium helps guarantee the sustainabil-
ity of TrypanoCyc as does the involvement of permanent
staff both at INRA, Toulouse, and the University of Glas-
gow. The Toulouse bioinformatics facility provides the Try-
panoCyc server. TrypanoCyc is freely available and is not
password protected.
TrypanoCyc database content can be mined in a
pathway-oriented manner using the BioCyc-like web inter-
face but also in a network perspective using the MetExplore
web server, which allows tailored building and visualization
of sub-networks. Two options are available to programmati-
cally access TrypanoCyc: through pathway tools using Java-
Cyc or PerlCyc and through MetExplore using its web ser-
vice.
TrypanoCyc is a unique knowledge source for people in-
vestigating T. brucei metabolism. The availability of SBML
(44) les (provided as Supplementary Material S4) based
on the curated network reconstruction in TrypanoCyc will
underpin efforts to explore trypanosome metabolism using
ux balance analysis (45) or other constraints-based tech-
niques. It will also serve as a potential model organism for
early eukaryotes.
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.
ACKNOWLEDGEMENT
We are grateful to the genotoul bioinformatics platform
Toulouse Midi-Pyrenees for providing computing and stor-
age resources requiredby TrypanoCyc Database. This study
was initiated by the BBSRC-ANR Systryp grant.
FUNDING
European Commission FP7 Marie Curie Initial Train-
ing Network ‘ParaMet’ [290080 to S.S.]; ANR project
MetaboHub [ANR-11-INBS-0010 to B.M.]; Wellcome
Trust [085349]; The work of Fiona Achcar was part of the
SysMO SilicoTryp project coordinated by R.B. Funding
for open access charge: European Commission FP7 Marie
Curie Initial Training Network ‘ParaMet’ [290080].
Conict of interest statement. None declared.
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