A Systems Biological Approach Reveals Multiple Crosstalk Mechanism between Gram-Positive and Negative Bacterial Infections: An Insight into Core Mechanism and Unique Molecular Signatures
Background Bacterial infections remain a major threat and a leading cause of death worldwide. Most of the bacterial infections are caused by gram-positive and negative bacteria, which are recognized by Toll-like receptor (TLR) 2 and 4, respectively. Activation of these TLRs initiates multiple pathways that subsequently lead to effective immune response. Although, both the TLRs share common signaling mechanism yet they may exhibit specificity as well, resulting in the release of diverse range of inflammatory mediators which could be used as candidate biomolecules for bacterial infections. Results We adopted systems biological approach to identify signaling pathways mediated by TLRs to determine candidate molecules associated with bacterial infections. We used bioinformatics concepts, including literature mining to construct protein-protein interaction network, prioritization of TLRs specific nodes using microarray data and pathway analysis. Our constructed PPI network for TLR 2 (nodes: 4091 and edges: 66068) and TLR 4 (node: 4076 and edges: 67898) showed 3207 common nodes, indicating that both the TLRs might share similar signaling events that are attributed to cell migration, MAPK pathway and several inflammatory cascades. Our results propose the potential collaboration between the shared signaling pathways of both the receptors may enhance the immune response against invading pathogens. Further, to identify candidate molecules, the TLRs specific nodes were prioritized using microarray differential expressed genes. Of the top prioritized TLR 2 molecules, 70% were co-expressed. A similar trend was also observed within TLR 4 nodes. Further, most of these molecules were preferentially found in blood plasma for feasible diagnosis. Conclusions The analysis reveals the common and unique mechanism regulated by both the TLRs that provide a broad perspective of signaling events in bacterial infections. Further, the identified candidate biomolecules could potentially aid future research efforts concerning the possibility in differential diagnosis of gram-positive and negative bacterial infections.
References
[1]
Gardy JL, Lynn DJ, Brinkman FS, Hancock RE (2009) Enabling a systems biology approach to immunology: focus on innate immunity. Trends Immunol 30: 249–262. doi: 10.1016/j.it.2009.03.009
[2]
Erridge C (2010) Endogenous ligands of TLR2 and TLR4: agonists or assistants? J Leukoc Biol 87: 989–999. doi: 10.1189/jlb.1209775
[3]
Takeuchi O, Hoshino K, Kawai T, Sanjo H, Takada H, et al. (1999) Differential roles of TLR2 and TLR4 in recognition of Gram-negative and Gram-positive bacterial cell wall components. Immunity 11: 443–451. doi: 10.1016/s1074-7613(00)80119-3
[4]
Hajishengallis G, Lambris JD (2010) Crosstalk pathways between Toll-like receptors and the complement system. Trends Immunol 31: 154–163. doi: 10.1016/j.it.2010.01.002
[5]
Elson G, Dunn-Siegrist I, Daubeuf B, Pugin J (2007) Contribution of toll-like receptors to the innate immune response to Gram-negative and Gram-positive bacteria. Blood 109: 1574–1583. doi: 10.1182/blood-2006-06-032961
[6]
Schrag B, Roux-Lombard P, Schneiter D, Vaucher P, Mangin P, et al. (2012) Evaluation of C-reactive protein, procalcitonin, tumor necrosis factor alpha, interleukin-6, and interleukin-8 as diagnostic parameters in sepsis-related fatalities. Int J Legal Med 126: 505–512. doi: 10.1007/s00414-011-0596-z
[7]
Verboon-Maciolek MA, Thijsen SF, Hemels MA, Menses M, van Loon AM, et al. (2006) Inflammatory mediators for the diagnosis and treatment of sepsis in early infancy. Pediatr Res 59: 457–461. doi: 10.1203/01.pdr.0000200808.35368.57
[8]
Andaluz-Ojeda D, Bobillo F, Iglesias V, Almansa R, Rico L, et al. (2012) A combined score of pro- and anti-inflammatory interleukins improves mortality prediction in severe sepsis. Cytokine 57: 332–336. doi: 10.1016/j.cyto.2011.12.002
[9]
Vandenbon A, Teraguchi S, Akira S, Takeda K, Standley DM (2012) Systems biology approaches to toll-like receptor signaling. WIREs Syst Biol Med 4: 497–507. doi: 10.1002/wsbm.1178
[10]
Aderem A, Adkins JN, Ansong C, Galagan J, Kaiser S, et al. (2011) A systems biology approach to infectious disease research: innovating the pathogen-host research paradigm. MBio 2(1): e00325–10. doi: 10.1128/mbio.00325-10
[11]
Jegga AG, Schneider L, Ouyang X, Zhang J (2011) Systems biology of the autophagy-lysosomal pathway. Autophagy 5: 477–489. doi: 10.4161/auto.7.5.14811
[12]
Aittokallio T, Schwikowski B (2006) Graph-based methods for analysing networks in cell biology. Brief Bioinform 7: 243–255. doi: 10.1093/bib/bbl022
[13]
Estrada E (2010) Quantifying network heterogeneity. Phys RevE Stat Nonlin Soft Matter Phys 82(6 Pt 2): 066102.
[14]
Xue M, Zhang S, Cai C, Yu X, Shan L, et al.. (2013) Predicting the drug safety for traditional Chinese medicine through a comparative analysis of withdrawn drugs using pharmacological network. Evid Based Complement Alternat Med 256782.
[15]
Sabroe I, Prince LR, Jones EC, Horsburgh MJ, Foster SJ, et al. (2003) Selective roles for toll-like receptor (TLR) 2 and TLR4 in the regulation of neutrophil activation and life span. J Immunol 170: 5268–5275. doi: 10.4049/jimmunol.170.10.5268
[16]
Tang BM, McLean AS, Dawes IW, Huang SJ, Cowley MJ, et al. (2008) Gene-expression profiling of Gram-positive and Gram-negative sepsis in critically ill patients. Crit Care Med 36: 1125–1128. doi: 10.1097/ccm.0b013e3181692c0b
[17]
Zhang BH, Liu J, Zhou QX, Zuo D, Wang Ys (2013) Analysis of differentially expressed genes inductal carcinoma with DNA microarray. Eur Rev Med Pharmacol Sci 17: 758–766.
[18]
Tu CT, Chen BS (2013) New measurement methods of network robustness and response ability via microarray data. PLoS One 8(1): e55230. doi: 10.1371/journal.pone.0055230
[19]
D Schlaepfer, K Jones, T Hunter (1998) Multiple Grb2-Mediated Integrin-Stimulated Signaling Pathways to ERK2/Mitogen-Activated Protein Kinase: Summation of Both c-Src- and Focal Adhesion Kinase-Initiated Tyrosine Phosphorylation Events. Mol Cell Biol 18: 2571–2585.
[20]
Xu H, An H, Hou J, Han C, Wang P, et al. (2008) Phosphatase PTP1B negatively regulates MyD88- and TRIF-dependent proinflammatory cytokine and type I interferon production in TLR-triggered macrophages. Mol Immunol 45: 3545–52. doi: 10.1016/j.molimm.2008.05.006
Sookoian S, Pirola CJ (2013) Systems biology elucidates common pathogenic mechanisms between nonalcoholic and alcoholic-fatty liver disease. PLoS One 8(3): e5889. doi: 10.1371/journal.pone.0058895
[23]
Chen J, Bardes EE, Aronow BJ, Jegga AG (2009) ToppGene suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37: 305–311. doi: 10.1093/nar/gkp427
[24]
Jia P, Kao CF, Kuo PH, Zhao Z (2011) A comprehensive network and pathway analysis of candidate genes in major depressive disorder. BMC Syst Biol 23: 5. doi: 10.1186/1752-0509-5-s3-s12
[25]
Bauer-Mehren A, Furlong LI, Sanz F (2009) Pathway databases and tools for their exploitation:benefits, current limitations and challenges. Mol Syst Biol 5: 290. doi: 10.1038/msb.2009.47
[26]
Krishnan J, Choi S (2012) Systems biological approaches reveal non-additive responses and multiple crosstalk mechanisms between TLR and GPCR signaling. Genomics Inform 10(3): 153–166. doi: 10.5808/gi.2012.10.3.153
[27]
Diaz-Beltran L, Cano C, Wall DP, Esteban FJ (2013) Systems biology as a comparative approach to understand complex gene expression in neurological diseases. Behav. Sci 3: 253–272. doi: 10.3390/bs3020253
[28]
Van Dam S, Cordeiro R, Craig T, van Dam J, Wood SH, et al. (2012) GeneFriends: An online co-expression analysis tool to identify novel gene targets for aging and complex diseases. BMC Genomics 13: 535. doi: 10.1186/1471-2164-13-535
[29]
Huang QY, Li GH, Cheung WM, Song YQ, Kung AW (2008) Prediction of osteoporosis candidate genes by computational disease-gene identification strategy. J Hum Genet 53: 644–655. doi: 10.1007/s10038-008-0295-x
[30]
Patil S, Pincas H, Seto J, Nudelman G, Nudelman I, et al. (2010) Signaling network of dendritic cells in response to pathogens: a community-input supported knowledgebase. BMC Syst Biol 4: 137. doi: 10.1186/1752-0509-4-137
[31]
Chang JH, Park JY, Kim SK (2006) Dependence on p38 MAPK signalling in the up-regulation of TLR2,TLR4 and TLR9 gene expression in Trichomonas vaginalis-treated HeLa cells. Immunology 118: 164–170. doi: 10.1111/j.1365-2567.2006.02347.x
Cahill CM, Rogers JT, Walker WA (2012) The role of phosphoinositide 3-kinase signaling in intestinal inflammation. J Signal Transduct 2012: 358476. doi: 10.1155/2012/358476
[34]
Derek CA, Tom P (2013) Severe sepsis and septic shock. N Engl J Med 369: 840–851. doi: 10.1056/nejmra1208623
[35]
Fontaine JF, Barbosa-Silva A, Schaefer M, Huska MR, Muro EM, et al. (2009) MedlineRanker: flexible ranking of biomedical literature. Nucleic Acids Res 37: 141–146. doi: 10.1093/nar/gkp353
[36]
Barbosa-Silva A, Soldatos TG, Magalh？es IL, Pavlopoulos GA, Fontaine JF, et al. (2010) LAITOR–literature assistant for identification of terms co-occurrences and relationships. BMC.Bioinformatics 11: 70. doi: 10.1186/1471-2105-11-70
[37]
Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27: 431–432. doi: 10.1093/bioinformatics/btq675
[38]
Martin A, Ochagavia ME, Rabasa LC, Miranda J, Fernandez-de-Cossio J, et al. (2010) BisoGenet: a new tool for gene network building, visualization and analysis. BMC Bioinformatics 11: 91. doi: 10.1186/1471-2105-11-91
[39]
Edgar R, Domrachev M, Lash AE (2002) Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30: 207–210. doi: 10.1093/nar/30.1.207
[40]
Schaefer CF, Anthony K, Krupa S, Buchoff J, Day M, et al. (2009) PID: the pathway interaction database. Nucleic Acids Res 37: 674–679. doi: 10.1093/nar/gkn653
[41]
Ahmed SS, Ahameethunisa AR, Santosh W, Chakravarthy S, Kumar S (2011) Systems biological approach on neurological disorders: a novel molecular connectivity to aging and psychiatric diseases. BMC Syst Biol 5: 6. doi: 10.1186/1752-0509-5-6
[42]
Peri S, Navarro JD, Kristiansen TZ, Amanchy R, Surendranath V, et al. (2004) Human protein reference database as a discovery resource for proteomics. Nucleic Acids Res 32: 497–501. doi: 10.1093/nar/gkh070
