Interspecies protein-protein interaction network construction for characterization of host-pathogen interactions: a Candida albicans-zebrafish interaction study

BMC Systems Biology

Bor-Sen Chen⁵  Yung-Jen Chuang¹  Chung-Yu Lan⁶  Wen-Ping Hsieh³  Ming-Ta Chuang⁵  Che Lin⁴  Yu-Chao Wang² 

[1] Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu 30013, Taiwan;Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan;Institute of Statistics, National Tsing Hua University, Hsinchu 30013, Taiwan;Institute of Communications Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan;Laboratory of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan;Institute of Molecular and Cellular Biology, National Tsing Hua University, Hsinchu 30013, Taiwan

关键词: Redox;    Multivariate dynamic modeling;    Infection;    Protein-protein interaction network;    Host-pathogen interaction;    Network construction;    Computational systems biology;   

Others  :  1142346
DOI  :  10.1186/1752-0509-7-79

 received in 2013-04-18, accepted in 2013-08-14,  发布年份 2013

【 摘 要 】

Background

Despite clinical research and development in the last decades, infectious diseases remain a top global problem in public health today, being responsible for millions of morbidities and mortalities each year. Therefore, many studies have sought to investigate host-pathogen interactions from various viewpoints in attempts to understand pathogenic and defensive mechanisms, which could help control pathogenic infections. However, most of these efforts have focused predominately on the host or the pathogen individually rather than on a simultaneous analysis of both interaction partners.

Results

In this study, with the help of simultaneously quantified time-course Candida albicans-zebrafish interaction transcriptomics and other omics data, a computational framework was developed to construct the interspecies protein-protein interaction (PPI) network for C. albicans-zebrafish interactions based on the inference of ortholog-based PPIs and the dynamic modeling of regulatory responses. The identified C. albicans-zebrafish interspecies PPI network highlights the association between C. albicans pathogenesis and the zebrafish redox process, indicating that redox status is critical in the battle between the host and pathogen.

Conclusions

Advancing from the single-species network construction method, the interspecies network construction approach allows further characterization and elucidation of the host-pathogen interactions. With continued accumulation of interspecies transcriptomics data, the proposed method could be used to explore progressive network rewiring over time, which could benefit the development of network medicine for infectious diseases.

【 授权许可】

   
2013 Wang et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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【 参考文献 】

• [1]Cohen ML: Changing patterns of infectious disease. Nature 2000, 406:762-767.
• [2]Fauci AS, Touchette NA, Folkers GK: Emerging infectious diseases: a 10-year perspective from the National Institute of Allergy and Infectious Diseases. Emerg Infect Dis 2005, 11:519-525.
• [3]Morens DM, Folkers GK, Fauci AS: The challenge of emerging and re-emerging infectious diseases. Nature 2004, 430:242-249.
• [4]Pfaller MA, Pappas PG, Wingard JR: Invasive fungal pathogens: current epidemiological trends. Clin Infect Dis 2006, 43:S3-S14.
• [5]Wachtler B, Wilson D, Haedicke K, Dalle F, Hube B: From attachment to damage: defined genes of Candida albicans mediate adhesion, invasion and damage during interaction with oral epithelial cells. PLoS One 2011, 6:e17046.
• [6]Whiteway M, Bachewich C: Morphogenesis in Candida albicans. Annu Rev Microbiol 2007, 61:529-553.
• [7]Martin R, Wachtler B, Schaller M, Wilson D, Hube B: Host-pathogen interactions and virulence-associated genes during Candida albicans oral infections. Int J Med Microbiol 2011, 301:417-422.
• [8]Dalle F, Wachtler B, L’Ollivier C, Holland G, Bannert N, Wilson D, Labruere C, Bonnin A, Hube B: Cellular interactions of Candida albicans with human oral epithelial cells and enterocytes. Cell Microbiol 2010, 12:248-271.
• [9]Zon LI, Peterson RT: In vivo drug discovery in the zebrafish. Nat Rev Drug Discov 2005, 4:35-44.
• [10]Lieschke GJ, Currie PD: Animal models of human disease: zebrafish swim into view. Nat Rev Genet 2007, 8:353-367.
• [11]Meeker ND, Trede NS: Immunology and zebrafish: spawning new models of human disease. Dev Comp Immunol 2008, 32:745-757.
• [12]Trede NS, Langenau DM, Traver D, Look AT, Zon LI: The use of zebrafish to understand immunity. Immunity 2004, 20:367-379.
• [13]van der Sar AM, Appelmelk BJ, Vandenbroucke-Grauls CM, Bitter W: A star with stripes: zebrafish as an infection model. Trends Microbiol 2004, 12:451-457.
• [14]Kanther M, Rawls JF: Host-microbe interactions in the developing zebrafish. Curr Opin Immunol 2010, 22:10-19.
• [15]Chao CC, Hsu PC, Jen CF, Chen IH, Wang CH, Chan HC, Tsai PW, Tung KC, Lan CY, Chuang YJ: Zebrafish as a model host for Candida albicans infection. Infect Immun 2010, 78:2512-2521.
• [16]Rizzetto L, Cavalieri D: Friend or foe: using systems biology to elucidate interactions between fungi and their hosts. Trends Microbiol 2011, 19:509-515.
• [17]Segata N, Boernigen D, Tickle TL, Morgan XC, Garrett WS, Huttenhower C: Computational meta’omics for microbial community studies. Mol Syst Biol 2013, 9:666.
• [18]Sturdevant DE, Virtaneva K, Martens C, Bozinov D, Ogundare O, Castro N, Kanakabandi K, Beare PA, Omsland A, Carlson JH, et al.: Host-microbe interaction systems biology: lifecycle transcriptomics and comparative genomics. Future Microbiol 2010, 5:205-219.
• [19]Westermann AJ, Gorski SA, Vogel J: Dual RNA-seq of pathogen and host. Nat Rev Microbiol 2012, 10:618-630.
• [20]Dyer MD, Murali TM, Sobral BW: Computational prediction of host-pathogen protein-protein interactions. Bioinformatics 2007, 23:i159-166.
• [21]Lee SA, Chan CH, Tsai CH, Lai JM, Wang FS, Kao CY, Huang CY: Ortholog-based protein-protein interaction prediction and its application to inter-species interactions. BMC Bioinformatics 2008, 12(9 Suppl):S11.
• [22]Shea PR, Virtaneva K, Kupko JJ 3rd, Porcella SF, Barry WT, Wright FA, Kobayashi SD, Carmody A, Ireland RM, Sturdevant DE, et al.: Interactome analysis of longitudinal pharyngeal infection of cynomolgus macaques by group A Streptococcus. Proc Natl Acad Sci U S A 2010, 107:4693-4698.
• [23]Reid AJ, Berriman M: Genes involved in host-parasite interactions can be revealed by their correlated expression. Nucleic Acids Res 2013, 41:1508-1518.
• [24]Chen YY, Chao CC, Liu FC, Hsu PC, Chen HF, Peng SC, Chuang YJ, Lan CY, Hsieh WP, Wong DSH: Dynamic transcript profiling of Candida albicans infection in zebrafish: a pathogen-host interaction study. PLoS Onein press
• [25]Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M: BioGRID: a general repository for interaction datasets. Nucleic Acids Res 2006, 34:D535-539.
• [26]Ostlund G, Schmitt T, Forslund K, Kostler T, Messina DN, Roopra S, Frings O, Sonnhammer EL: InParanoid 7: new algorithms and tools for eukaryotic orthology analysis. Nucleic Acids Res 2010, 38:D196-203.
• [27]Bradford Y, Conlin T, Dunn N, Fashena D, Frazer K, Howe DG, Knight J, Mani P, Martin R, Moxon SA, et al.: ZFIN: enhancements and updates to the Zebrafish Model Organism Database. Nucleic Acids Res 2011, 39:D822-829.
• [28]Inglis DO, Arnaud MB, Binkley J, Shah P, Skrzypek MS, Wymore F, Binkley G, Miyasato SR, Simison M, Sherlock G: The Candida genome database incorporates multiple Candida species: multispecies search and analysis tools with curated gene and protein information for Candida albicans and Candida glabrata. Nucleic Acids Res 2012, 40:D667-674.
• [29]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, 25:25-29.
• [30]Pagano M, Gauvreau K: Principles of biostatistics. 2nd edition. Pacific Grove, CA: Duxbury; 2000.
• [31]Wang YC, Lan CY, Hsieh WP, Murillo LA, Agabian N, Chen BS: Global screening of potential Candida albicans biofilm-related transcription factors via network comparison. BMC Bioinformatics 2010, 11:53. BioMed Central Full Text
• [32]Johansson R: System modeling and identification. Englewood Cliffs, NJ: Prentice Hall; 1993.
• [33]Wang YC, Chen BS: Integrated cellular network of transcription regulations and protein-protein interactions. BMC Syst Biol 2010, 4:20. BioMed Central Full Text
• [34]Akaike H: A new look at the statistical model identification. IEEE Trans Automatic Control 1974, 19:716-723.
• [35]Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003, 13:2498-2504.
• [36]Spooner R, Yilmaz O: The role of reactive-oxygen-species in microbial persistence and inflammation. Int J Mol Sci 2011, 12:334-352.
• [37]Schoonbroodt S, Piette J: Oxidative stress interference with the nuclear factor-kappa B activation pathways. Biochem Pharmacol 2000, 60:1075-1083.
• [38]Allen LA, Beecher BR, Lynch JT, Rohner OV, Wittine LM: Helicobacter pylori disrupts NADPH oxidase targeting in human neutrophils to induce extracellular superoxide release. J Immunol 2005, 174:3658-3667.
• [39]Harada T, Miyake M, Imai Y: Evasion of Legionella pneumophila from the bactericidal system by reactive oxygen species (ROS) in macrophages. Microbiol Immunol 2007, 51:1161-1170.
• [40]Tsunawaki S, Yoshida LS, Nishida S, Kobayashi T, Shimoyama T: Fungal metabolite gliotoxin inhibits assembly of the human respiratory burst NADPH oxidase. Infect Immun 2004, 72:3373-3382.
• [41]Wellington M, Dolan K, Krysan DJ: Live Candida albicans suppresses production of reactive oxygen species in phagocytes. Infect Immun 2009, 77:405-413.
• [42]Nakagawa Y, Kanbe T, Mizuguchi I: Disruption of the human pathogenic yeast Candida albicans catalase gene decreases survival in mouse-model infection and elevates susceptibility to higher temperature and to detergents. Microbiol Immunol 2003, 47:395-403.
• [43]Frohner IE, Bourgeois C, Yatsyk K, Majer O, Kuchler K: Candida albicans cell surface superoxide dismutases degrade host-derived reactive oxygen species to escape innate immune surveillance. Mol Microbiol 2009, 71:240-252.
• [44]Sim S, Yong TS, Park SJ, Im KI, Kong Y, Ryu JS, Min DY, Shin MH: NADPH oxidase-derived reactive oxygen species-mediated activation of ERK1/2 is required for apoptosis of human neutrophils induced by Entamoeba histolytica. J Immunol 2005, 174:4279-4288.
• [45]Yang TC, Lai CC, Shiu SL, Chuang PH, Tzou BC, Lin YY, Tsai FJ, Lin CW: Japanese encephalitis virus down-regulates thioredoxin and induces ROS-mediated ASK1-ERK/p38 MAPK activation in human promonocyte cells. Microbes Infect 2010, 12:643-651.
• [46]Williams B, Kabbage M, Kim HJ, Britt R, Dickman MB: Tipping the balance: Sclerotinia sclerotiorum secreted oxalic acid suppresses host defenses by manipulating the host redox environment. PLoS Pathog 2011, 7:e1002107.
• [47]Durmus Tekir SD, Ulgen KO: Systems biology of pathogen-host interaction: Networks of protein-protein interaction within pathogens and pathogen-human interactions in the post-genomic era. Biotechnol J 2013, 8:85-96.
• [48]Calderone RA, Fonzi WA: Virulence factors of Candida albicans. Trends Microbiol 2001, 9:327-335.
• [49]Tierney L, Linde J, Muller S, Brunke S, Molina JC, Hube B, Schock U, Guthke R, Kuchler K: An interspecies regulatory network inferred from simultaneous RNA-seq of Candida albicans invading innate immune cells. Front Microbiol 2012, 3:85.
• [50]Barabasi AL, Gulbahce N, Loscalzo J: Network medicine: a network-based approach to human disease. Nat Rev Genet 2011, 12:56-68.
• [51]Pawson T, Linding R: Network medicine. FEBS Lett 2008, 582:1266-1270.