Gene Ontology - A Way Forwards
Ruth Lovering, Varsha Khodiyar, Pete Scambler, Mike Hubank, Rolf Apweiler and Philippa Talmud
Centre for Cardiovascular Genetics, UCL Department of Medicine, Rayne Institute 5 University Street London WC1E 6JF.
Molecular Medicine Unit, Institute of Child Health, 30 Guilford Street, London WC1N 1EH.
Molecular Hematology and Cancer Biology Unit, Institute of Child Health, 30 Guilford Street, London WC1N 1EH.
European Bioinformatics Institute, Hinxton, Cambridge, CB10 1SD.
(TNF alpha)
Inhibitory action of lipoxins on pro-inflammatory TNF-alpha signalling
The UCL-based GO annotation team aims to work with bench scientists to improve the
annotation of human proteins. Improvements in the GO annotation of your favourite
protein will lead to an improved public resource for everyone.
Proteomes and differentially regulated mRNAs can be analysed
with GO data, to provide an overview of the predominant activities
the constituent proteins are involved in or where they are normally
located1. Furthermore, often the generation of hypotheses to
explain proteome-wide alterations in response to certain diseases,
such as cardiac hypertrophy2, or stress states, such as hypoxia3,
relies on the use of GO annotation data. The ability to review
experimental results, with respect to known functional information,
has also proved useful when investigators need to select a subset
of proteins to analyse in greater depth in order to identify new sets
of disease biomarkers4,5. GO data also provides an indispensable
resource to indicate the success of subcellular enrichment
strategies or large scale confocal microscopy analyses6,7. Already,
drug treatments are being tailored according to molecular pathway
imbalances, detected through individual-specific microarray or
proteomic data.
PTPN11
For more information about contributing to the annotation of the human genome
contact GOannotation@UCL.ac.uk
(IKBKG)
Gene Ontology provides a systematic language for the description of
gene product attributes in three key domains
MAP3K14
(CHUK)
(SFN, YWHA family)
Cellular Component
FOXO1
CDKN1B
CCNE1
(NFKB1)
(IL-6)
Annotation
GO terms are associated with gene products (proteins)
MetaCore Map, GeneGO, www.genego.com
(CCNE1, Cyclin E1)
Distribution of Data
GO annotations are available through major biological databases
and numerous high-throughput analysis GO tools
Large number of uses
•  Biomarker discovery
•  Enhancing annotation of any genome
•  Validation of cell separation methodologies
•  Identification of disease-associated processes
•  Quick access to information about individual proteins
•  Validation of automated ways of deriving gene information
•  Drug therapies based on process variations between individuals
•  Identification of predominant activities within a specific group of proteins
•  Identification of common pathways targeted by different pathogens, proteins etc
Grant: SP/07/007/23671
www.cardiovasculargeneontology.com
Spot the Difference
KEY
Activation
Inhibition
Unspecified
Cytoplasm
Extracellular
Plasma Membrane
Nucleus
B Binding
CR Class relation
CS Complex subunit
IE Influence on expression
+P Phosphorylation
TR Transcription regulation
Z Catalysis
Associated with
Cardiovascular Disease
Kinase
Phosphatase
Phospholipase
Protein
Transfactor
Molecule
Phospholipid
Ligand
Binding protein
Receptor
GPCP
Protein Family
Completing the annotation of every gene product, using Gene Ontology (GO), is a substantial undertaking,
especially for highly investigated genes. Consequently, at present, there is a wide variation between the quality
and quantity of annotations associated with different proteins.
QuickGO (www.ebi.ac.uk/ego) views of the GO terms associated with TNF-alpha, IL-6 and CCNE1 (above) and
the histogram, to the right, illustrate the variation in the number of unique GO terms associated with human
proteins. This variation is not simply a reflection of the current knowledge about these proteins. Thousands of
publications describe TNF-alpha and IL-6 and yet there are over twice as many GO terms associated with TNFalpha (68) as there are with IL-6 (28). This difference is due to the time constraints facing GO curators. At
present there are only 2 projects (funding 4 curators) that prioritise the comprehensive annotation of human
genes. IL-1B, IL-6, PTPN11 and TNF-alpha have been prioritised for annotation by the Cardiovascular GO
Annotation Initiative, however, of these only TNF-alpha has been annotated by this project, to date.
The quality of annotations also varies between proteins. Proteins annotated mostly through automated methods
tend to have more general GO terms (see CCNE1). Whereas, proteins with annotations made by GO curators,
based on published experimental evidence, tend to have more specific GO terms (see TNF-alpha).
References
1. Pasini, E.M., Kirkegaard, M., Mortensen, P., et al. In-depth anyalysis of the membrane and cytosolic proteome of
red blood cells. Blood, 2006, 108, 791-801.
2. Pan, Y., Kislinger, T., Gramolini, A. O., et al. Identification of biochemical adaptations in hyper- or hypocontractile
hearts from phospholamban mutant mice by expression proteomics, Proc Natl Acad Sci U S A, 2004, 101: 2241-2246.
3. Boraldi, F., Annovi, G., Carraro, F., et al. Hypoxia influences the cellular cross-talk of human dermal fibroblasts. A
proteomic approach, Biochim Biophys Acta, 2007, 1774: 1402-1413.
4. Shi, M., Jin, J., Wang, Y., et al. Mortalin: a protein associated with progression of Parkinson disease?, J
Neuropathol Exp Neurol, 2008, 67: 117-124.
5. Perco, P., Wilflingseder, J., Bernthaler, A., et al. Biomarker candidates for cardiovascular disease and bone
metabolism disorders in chronic kidney disease: A systems biology perspective, J Cell Mol Med, 2008.
6. Kislinger, T., Rahman, K., Radulovic, D., et al. PRISM, a generic large scale proteomic investigation strategy for
mammals, Mol Cell Proteomics, 2003, 2: 96-106.
7. Barbe, L., Lundberg, E., Oksvold, P., et al. Toward a confocal subcellular atlas of the human proteome, Mol Cell
Proteomics, 2008, 7: 499-508.
Number of publications and GO terms associated with lipoxins/TNF-alpha
signalling pathway proteins
80
6
70
Unique GO terms
Publications
5
60
4
50
40
3
30
2
20
1
10
0
0
Gene Symbol
Log Number of Publications
Molecular Function
High-throughput technologies and research into multi-factorial
diseases are also highlighting how highly investigated proteins in
one field of biology are relevant to processes associated with
another field of biology. For example, in the central figure, several
genes (IL-6, IL-8, STAT3 and TNF-alpha) are associated with the
TNF-alpha pro-inflammatory signalling pathway and are also
associated with cardiovascular disease.
NFKBIE
ERLIN1
PPAPDC2
YWHAQ
YWHAZ
NFKBIB
PIK3R2
TNFRSF1
YWHAE
AKT2
MAP3K14
AKT3
CCNE1
YWHAB
YWHAG
FPRL1
PIK3CA
TRADD
FOXO1
YWHAH
PIK3CD
TRAF2
CDKN1B
CHUK
IKBKB
NFKB1
PDPK1
RIPK1
SOCS1
IKBKG
JAK1
PLD1
SFN
TNFRSF1
STAT3
PIK3CB
NFKBIA
CDK2
IL8
PRKCZ
PIK3R1
RELA
IL6
IL1B
PTPN11
AKT1
TNF
(NFKB1A)
Biological Process
How is GO used?
Number of Unique GO Terms
Gene Ontology (GO) provides a controlled vocabulary to describe the attributes of
genes and gene products in any organism. This resource is proving highly useful for
researchers investigating complex phenotypes such as cardiovascular disease, as well
as those interpreting results from high-throughput methodologies. By providing current
functional knowledge in a format that can be exploited by high-throughput
technologies, the GOC provides a freely available key public annotation resource that
can help bridge the gap between data collation and data analysis
(www.geneontology.org).