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The proteome of Toxoplasma gondii:: integration with the genome provides novel insights into gene expression and annotation
被引:92
作者:
Xia, Dong
[1
]
Sanderson, Sanya J.
[1
]
Jones, Andrew R.
[1
]
Prieto, Judith H.
[2
]
Yates, John R.
[2
]
Bromley, Elizabeth
[3
]
Tomley, Fiona M.
[3
]
Lal, Kalpana
[4
]
Sinden, Robert E.
[4
]
Brunk, Brian P.
[5
]
Roos, David S.
[5
]
Wastling, Jonathan M.
[1
,6
]
机构:
[1] Univ Liverpool, Dept Preclin Vet Sci, Fac Vet Sci, Liverpool L69 7ZJ, Merseyside, England
[2] Scripps Res Inst, Dept Cell Biol, La Jolla, CA 92037 USA
[3] Inst Anim Hlth, Div Microbiol, Compton RG20 7NN, Berks, England
[4] Univ London Imperial Coll Sci Technol & Med, Div Cell & Mol Biol, London SW7 2AZ, England
[5] Univ Penn, Dept Biol, Philadelphia, PA 19104 USA
[6] Univ Liverpool, Fac Vet Sci, Liverpool L69 7ZJ, Merseyside, England
基金:
英国生物技术与生命科学研究理事会;
关键词:
D O I:
10.1186/gb-2008-9-7-r116
中图分类号:
Q81 [生物工程学(生物技术)];
Q93 [微生物学];
学科分类号:
071005 ;
0836 ;
090102 ;
100705 ;
摘要:
Background: Although the genomes of many of the most important human and animal pathogens have now been sequenced, our understanding of the actual proteins expressed by these genomes and how well they predict protein sequence and expression is still deficient. We have used three complementary approaches (two-dimensional electrophoresis, gel-liquid chromatography linked tandem mass spectrometry and MudPIT) to analyze the proteome of Toxoplasma gondii, a parasite of medical and veterinary significance, and have developed a public repository for these data within ToxoDB, making for the first time proteomics data an integral part of this key genome resource. Results: The draft genome for Toxoplasma predicts around 8,000 genes with varying degrees of confidence. Our data demonstrate how proteomics can inform these predictions and help discover new genes. We have identified nearly one-third (2,252) of all the predicted proteins, with 2,477 intron-spanning peptides providing supporting evidence for correct splice site annotation. Functional predictions for each protein and key pathways were determined from the proteome. Importantly, we show evidence for many proteins that match alternative gene models, or previously unpredicted genes. For example, approximately 15% of peptides matched more convincingly to alternative gene models. We also compared our data with existing transcriptional data in which we highlight apparent discrepancies between gene transcription and protein expression. Conclusion: Our data demonstrate the importance of protein data in expression profiling experiments and highlight the necessity of integrating proteomic with genomic data so that iterative refinements of both annotation and expression models are possible.
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