Mycobacterium tuberculosis and Clostridium difficille interactomes: demonstration of rapid development of computational system for bacterial interactome prediction
Tóm tắt
Protein-protein interaction (PPI) networks (interactomes) of most organisms, except for some model organisms, are largely unknown. Experimental methods including high-throughput techniques are highly resource intensive. Therefore, computational discovery of PPIs can accelerate biological discovery by presenting "most-promising" pairs of proteins that are likely to interact. For many bacteria, genome sequence, and thereby genomic context of proteomes, is readily available; additionally, for some of these proteomes, localization and functional annotations are also available, but interactomes are not available. We present here a method for rapid development of computational system to predict interactome of bacterial proteomes. While other studies have presented methods to transfer interologs across species, here, we propose transfer of computational models to benefit from cross-species annotations, thereby predicting many more novel interactions even in the absence of interologs. Mycobacterium tuberculosis (Mtb) and Clostridium difficile (CD) have been used to demonstrate the work. We developed a random forest classifier over features derived from Gene Ontology annotations and genetic context scores provided by STRING database for predicting Mtb and CD interactions independently. The Mtb classifier gave a precision of 94% and a recall of 23% on a held out test set. The Mtb model was then run on all the 8 million protein pairs of the Mtb proteome, resulting in 708 new interactions (at 94% expected precision) or 1,595 new interactions at 80% expected precision. The CD classifier gave a precision of 90% and a recall of 16% on a held out test set. The CD model was run on all the 8 million protein pairs of the CD proteome, resulting in 143 new interactions (at 90% expected precision) or 580 new interactions (at 80% expected precision). We also compared the overlap of predictions of our method with STRING database interactions for CD and Mtb and also with interactions identified recently by a bacterial 2-hybrid system for Mtb. To demonstrate the utility of transfer of computational models, we made use of the developed Mtb model and used it to predict CD protein-pairs. The cross species model thus developed yielded a precision of 88% at a recall of 8%. To demonstrate transfer of features from other organisms in the absence of feature-based and interaction-based information, we transferred missing feature values from Mtb orthologs into the CD data. In transferring this data from orthologs (not interologs), we showed that a large number of interactions can be predicted. Rapid discovery of (partial) bacterial interactome can be made by using existing set of GO and STRING features associated with the organisms. We can make use of cross-species interactome development, when there are not even sufficient known interactions to develop a computational prediction system. Computational model of well-studied organism(s) can be employed to make the initial interactome prediction for the target organism. We have also demonstrated successfully, that annotations can be transferred from orthologs in well-studied organisms enabling accurate predictions for organisms with no annotations. These approaches can serve as building blocks to address the challenges associated with feature coverage, missing interactions towards rapid interactome discovery for bacterial organisms. The predictions for all Mtb and CD proteins are made available at:
http://severus.dbmi.pitt.edu/TB
and
http://severus.dbmi.pitt.edu/CD
respectively for browsing as well as for download.
Tài liệu tham khảo
Sears CL: A dynamic partnership: celebrating our gut flora. Anaerobe. 2005, 11 (5): 247-251. 10.1016/j.anaerobe.2005.05.001.
Organisation WH: Multidrug and extensively drug-resistant TB (M/XDR-TB): 2010 global report on surveillance and response. 2010
Koch R, Brock TD, Fred EB: The etiology of tuberculosis. Rev Infect Dis. 1982, 4 (6): 1270-1274. 10.1093/clinids/4.6.1270.
Walzl G, Ronacher K, Hanekom W, Scriba TJ, Zumla A: Immunological biomarkers of tuberculosis. Nat Rev Immunol. 2011, 11 (5): 343-354. 10.1038/nri2960.
Rachman H, Kaufmann SHE: Exploring functional genomics for the development of novel intervention strategies against tuberculosis. International Journal of Medical Microbiology. 2007, 297 (7-8): 559-567. 10.1016/j.ijmm.2007.03.003.
Corbett EL, Watt CJ, Walker N, Maher D, Williams BG, Raviglione MC, Dye C: The growing burden of tuberculosis: global trends and interactions with the HIV epidemic. Arch Intern Med. 2003, 163 (9): 1009-1021. 10.1001/archinte.163.9.1009.
Stabler RA, Gerding DN, Songer JG, Drudy D, Brazier JS, Trinh HT, Witney AA, Hinds J, Wren BW: Comparative phylogenomics of Clostridium difficile reveals clade specificity and microevolution of hypervirulent strains. J Bacteriol. 2006, 188 (20): 7297-7305. 10.1128/JB.00664-06.
Viswanathan VK, Mallozzi MJ, Vedantam G: Clostridium difficile infection: An overview of the disease and its pathogenesis, epidemiology and interventions. Gut Microbes. 2010, 1 (4): 234-242. 10.4161/gmic.1.4.12706.
Chandra N, Kumar D, Rao K: Systems biology of tuberculosis. Tuberculosis. 2011, 91 (5): 487-496. 10.1016/j.tube.2011.02.008. doi: 10.1016/j.tube.2011.02.008. 1472-9792
Raman K, Bhat AG, Chandra N: A systems perspective of host-pathogen interactions: predicting disease outcome in tuberculosis. Mol Biosyst. 2010, 6 (3): 516-530. 10.1039/b912129c.
Browne F, Zheng H, Wang H, Azuaje F: From Experimental Approaches to Computational Techniques: A Review on the Prediction of Protein-Protein Interactions. Advances in Artificial Intelligence. 2010, 2010: 15-924529
Raman K, Chandra N: Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance. BMC Microbiol. 2008, 8 (1): 234-10.1186/1471-2180-8-234.
Osmanbeyoglu HU, Ganapathiraju M: Rapid development of computational prediction system of virus-host protein-protein interactions for new viruses. American Medical Informatics Association Translational Bioinformatics Summit. 2011, San Francisco, USA
Tastan O, Qi Y, Carbonell JG, Klein-Seetharaman J: Prediction of interactions between HIV-1 and human proteins by information integration. Pac Symp Biocomput. 2009, 516-527.
Venkatesan K, Rual JF, Vazquez A, Stelzl U, Lemmens I, Hirozane-Kishikawa T, Hao T, Zenkner M, Xin X, Goh KI, et al: An empirical framework for binary interactome mapping. Nat Methods. 2009, 6 (1): 83-90. 10.1038/nmeth.1280.
Jothi R, Przytycka T: Computational approaches to predict protein-protein and domain-domain interactions. Bioinformatics Algorithms: Techniques and Applications. Edited by: Mandoiu II. 2008, Zelikovsky A: Wiley Press
Zhong W, Sternberg PW: Genome-wide prediction of C. elegans genetic interactions. Science. 2006, 311 (5766): 1481-1484. 10.1126/science.1123287.
Sharan R, Suthram S, Kelley RM, Kuhn T, McCuine S, Uetz P, Sittler T, Karp RM, Ideker T: Conserved patterns of protein interaction in multiple species. Proc Natl Acad Sci USA. 2005, 102 (6): 1974-1979. 10.1073/pnas.0409522102.
Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO: Assigning protein functions by comparative genome analysis: Protein phylogenetic profiles. Proc Natl Acad Sci. 1999, 96 (8): 4285-4288. 10.1073/pnas.96.8.4285.
Dandekar T, Snel B, Huynen M, Bork P: Conservation of gene order: a fingerprint of proteins that physically interact. Trends in Biochemical Sciences. 1998, 23 (9): 324-328. 10.1016/S0968-0004(98)01274-2.
Pazos F, Valencia A: In silico two-hybrid system for the selection of physically interacting protein pairs. Proteins: Structure, Function, and Bioinformatics. 2002, 47 (2): 219-227. 10.1002/prot.10074.
Wojcik J, Schächter V: Protein-protein interaction map inference using interacting domain profile pairs. Bioinformatics (Oxford, England). 2001, 17 (suppl 1): S296-S305. 10.1093/bioinformatics/17.suppl_1.S296.
Ezkurdia I, Bartoli L, Fariselli P, Casadio R, Valencia A, Tress ML: Progress and challenges in predicting protein-protein interaction sites. Briefings in Bioinformatics. 2009, 10 (3): 233-246.
Matthews LR, Vaglio P, Reboul J, Ge H, Davis BP, Garrels J, Vincent S, Vidal M: Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or 'interologs'. Genome Res. 2001, 11 (12): 2120-2126. 10.1101/gr.205301.
Liu Y, Liu N, Zhao H: Inferring protein-protein interactions through high-throughput interaction data from diverse organisms. Bioinformatics (Oxford, England). 2005, 21 (15): 3279-3285. 10.1093/bioinformatics/bti492.
Maggi S, Massidda O, Luzi G, Fadda D, Paolozzi L, Ghelardini P: Division protein interaction web: identification of a phylogenetically conserved common interactome between Streptococcus pneumoniae and Escherichia coli. Microbiology (Reading, England). 2008, 154 (Pt 10): 3042-3052.
Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, et al: The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 2011, D561-D568. 39 Database
Stark C, Breitkreutz BJ, Chatr-Aryamontri A, Boucher L, Oughtred R, Livstone MS, Nixon J, Van Auken K, Wang X, Shi X, et al: The BioGRID Interaction Database: 2011 update. Nucleic Acids Res. 2011, D698-D704. 39 Database
Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006, D535-D539. 34 Database
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000, 25 (1): 25-29. 10.1038/75556.
Aoki-Kinoshita KF, Kanehisa M: Gene annotation and pathway mapping in KEGG. Methods in molecular biology (Clifton, NJ). 2007, 396: 71-91. 10.1007/978-1-59745-515-2_6.
Lin N, Wu B, Jansen R, Gerstein M, Zhao H: Information assessment on predicting protein-protein interactions. BMC Bioinforma. 2004, 5: 154-10.1186/1471-2105-5-154.
Qi Y, Bar-Joseph Z, Klein-Seetharaman J: Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins. 2006, 63 (3): 490-500. 10.1002/prot.20865.
Sambourg L, Thierry-Mieg N: New insights into protein-protein interaction data lead to increased estimates of the S. cerevisiae interactome size. BMC Bioinforma. 2010, 11: 605-10.1186/1471-2105-11-605.
Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, Muthusamy B, Gandhi TK, Chandrika KN, Deshpande N, Suresh S, et al: Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res. 2004, D497-D501. 32 Database
Wang Y, Cui T, Zhang C, Yang M, Huang Y, Li W, Zhang L, Gao C, He Y, Li Y, et al: Global Protein-Protein Interaction Network in the Human Pathogen Mycobacterium tuberculosis H37Rv. Journal of Proteome Research. 2010, 9 (12): 6665-6677. 10.1021/pr100808n.
Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28 (1): 27-30. 10.1093/nar/28.1.27.
Alifano P, Fani R, Lio P, Lazcano A, Bazzicalupo M, Carlomagno MS, Bruni CB: Histidine biosynthetic pathway and genes: structure, regulation, and evolution. Microbiol Rev. 1996, 60 (1): 44-69.
Cho Y, Ioerger TR, Sacchettini JC: Discovery of novel nitrobenzothiazole inhibitors for Mycobacterium tuberculosis ATP phosphoribosyl transferase (HisG) through virtual screening. J Med Chem. 2008, 51 (19): 5984-5992. 10.1021/jm800328v.
Hinshelwood S, Stoker NG: Cloning of mycobacterial histidine synthesis genes by complementation of a Mycobacterium smegmatis auxotroph. Mol Microbiol. 1992, 6 (19): 2887-2895. 10.1111/j.1365-2958.1992.tb01468.x.
Jennings MP, Beacham IR: Co-dependent positive regulation of the ansB promoter of Escherichia coli by CRP and the FNR protein: a molecular analysis. Mol Microbiol. 1993, 9 (1): 155-164. 10.1111/j.1365-2958.1993.tb01677.x.
Raman S, Song T, Puyang X, Bardarov S, Jacobs WR, Husson RN: The alternative sigma factor SigH regulates major components of oxidative and heat stress responses in Mycobacterium tuberculosis. J Bacteriol. 2001, 183 (20): 6119-6125. 10.1128/JB.183.20.6119-6125.2001.
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH: The WEKA Data Mining Software: An Update. SIGKDD Explorations: 2009. 2009