Identification of infection-related and defense-related genes via a dynamic host-pathogen interaction network using a C. albicans-zebrafish infection model

J Innate Immun 2013;5:137–152

Zong-Yu Kuo1, Yung-Jen Chuang2, Chun-Cheih Chao2, Fu-Chen Liu2, Chung-Yu Lan3, Bor-Sen Chen1
1Lab of Control and Systems Biology, Department of Electrical Engineering, National Tsing Hua University, HsinChu, 30013, Taiwan

2Department of Medical Science & Institute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu 300, Taiwan, R.O.C.

3Institute of Molecular and Cellular Biology, National Tsing Hua University, Hsinchu 300, Taiwan, R.O.C.

Abstract

Candida albicans infections and candidiasis are difficult to treat and create very serious therapeutic challenges. In this study, based on interactive time profile microarray data of C. albicans and zebrafish during infection, the infection-related protein-protein interaction (PPI) networks of the two species and the intercellular PPI network between host and pathogen were simultaneously constructed by a dynamic interaction model, modeled as an integrated network consisting of intercellular invasion and cellular defense processes during infection. The signal transduction pathways in regulating morphogenesis and hyphal growth of C. albicans were further investigated based on significant interactions found in the intercellular PPI network. Two cellular networks were also developed corresponding to the different infection stages (adhesion and invasion), and then compared with each other to identify proteins from which we can gain more insight into the pathogenic role of hyphal development in the C. albicans infection process. Important defense-related proteins in zebrafish were predicted using the same approach. The hyphal growth PPI network, zebrafish PPI network, and host-pathogen intercellular PPI network were combined to form an integrated infectious PPI network that helps us understand the systematic mechanisms underlying the pathogenicity of C. albicans and the immune response of the host, and may help improve medical therapies and facilitate the development of new anti-fungal drugs.

PMID: 23406717

 

Supplement:

Construction of the integrated intercellular PPI network during infection

This study aimed to construct the integrated intercellular interaction network between the hyphal proteins of C. albicans and zebrafish proteins during the infection process. The flowchart detailing its construction is shown in Figure 1, and has three main routes, among which two separately construct the hyphal PPI network of C. albicans and the PPI network of zebrafish. The third constructs the host-pathogen intercellular PPI network. Based on the microarray data, we selected 4820 and 9665 proteins for inclusion in the source protein pools of C. albicans and zebrafish respectively. In addition, we selected 1002 proteins for inclusion in the hyphal growth protein pool from the C. albicans protein pool due to the need to investigate what factors are behind the transition from yeast form to hyphal form in the infection process. In the candidate C. albicans hyphal PPI network, there were 3604 protein-protein interactions; in the candidate zebrafish PPI network, there were 1129.

 

Bor-Sen Chen-1

Figure 1 –Flowchart of the construction of the integrated infection intercellular PPI network via database mining and integration

Bor-Sen Chen -2

Figure 2 –Experimental microscopy images of the infection process of C. albicans on zebrafish tissue.

We utilize the 9-time-point C. albicans time series microarray data to construct two dynamic networks for different infection stages. Since hyphae appear to begin to grow in the zebrafish body from 2 to 4 hours post-infection in the experimental microscopy images (figure 2), we collected two groups of data at different stages of infection to construct two separate networks. With the C. albicans microarray data spanning 0.5 to 4 hours, we constructed a network called the ‘adhesive stage network’, which represents C. albicans cells in the adhesion stage. Since cubic spline interpolation requires at least four data points to solve a cubic polynomial, we included the 4 hr data point to construct this network. With the C. albicans microarray data spanning 2 to 12 hours, we constructed another network called the ‘hyphal stage network’, which represents C. albicans cells transitioning to the hyphal form. Similarly, we collected two groups of data at different stages of infection to construct two separate PPI networks for zebrafish as well: one for microarray data from 0.5 to 4 hours, and another for data from 2 to 12 hours, named the zebrafish stage 1 network and zebrafish stage 2 network, respectively. By estimating the system parameters using the time series microarray data and selecting model order using the AIC measurement [1], the likelihood of false positive interactions in the potential PPI network for the infection process was reduced [2]. Network refinement yielded 550 proteins with 2725 PPIs in the adhesive stage network and 555 proteins with 3171 PPIs in the hyphal stage network: these two networks could then be combined into the C. albicans dynamic hyphal PPI network for the infection process. Similar refinements in the zebrafish data returned 1248 proteins with 2344 PPIs in the zebrafish stage 1 network and 1265 proteins with 2379 PPIs in the zebrafish stage 2 network, and these two networks could then be combined into the zebrafish dynamic PPI network for the defensive process. The C. albicans dynamic hyphal PPI network, the zebrafish dynamic PPI network, and the host-pathogen intercellular PPI network could be merged into an integrated infection intercellular PPI network [3].

The global system view of the C. albicans– and zebrafish-integrated infection intercellular PPI network is illustrated in Figure 3. The entire integrated infection intercellular network can be divided into eight levels according to the location of protein action (i.e., nucleus, intracellular, cell surface, or extracellular) and species (i.e., C. albicans or zebrafish), and is composed of three subnetworks. The upper subnetwork is the dynamic hyphal PPI network of C. albicans. The middle subnetwork shows the host-pathogen intercellular interaction network. For simplicity, only the top five correlated interactions of the C. albicans cell surface proteins are listed. The bottom subnetwork is the dynamic defensive protein interaction network of zebrafish.

Inspection of the dynamic hyphal growth PPI network of C. albicans

In order to verify the accuracy of our dynamic hyphal growth protein interaction network, we investigated whether this network contains previously identified pathways related to hyphal growth. This figure displays signal transduction pathways involved in regulating morphogenesis in C. albicans. An inspection of the integrated infectious intercellular network seen in Figure 3 confirmed that our C. albicans dynamic hyphal PPI network includes the MAP kinase cascade, cyclic AMP/PKA pathway, and other hyphae-associated pathways. We isolated these pathways from Figure 3, and then constructed a new hyphae-related subnetwork as Figure 4. Our new hyphae-related subnetwork contains almost all of the proteins and interactions of the already known hyphae-related pathways. The GTP binding protein Ras1 was not contained in our dynamic hyphal growth PPI network because its p-value is greater than 0.01 in the original protein pool selection step for C. albicans; however, Ras2, which is in the same family as Ras1, appeared in the new subnetwork. Ras2 is similar to S. cerevisiae Ras2p, which can activate adenylate cyclase and is involved in S. cerevisiae pseudohyphal growth, and Ras2 mutants could have altered filamentous growth patterns. Similar to Ras1, Ras2 also stimulates Cyr1 (Cdc35), which in turn acts as an intracellular second messenger during morphological switching. Ras2 also stimulates Cdc42 through Ras-related protein (Rsr1), which is involved in budding, cell morphogenesis, and hyphal development processes. However, we can see that Cdc42 links to Wal1 via Myo2 and Rho3 in our new hyphae-related subnetwork. Myo2 is required for polarized cell growth and dimorphic switching in C. albicans and is also involved in hyphal development. Rho3 is required for polarized cell growth and cell separation and also involved in hyphal development. Fortunately, the previously uncertain pathway between Cdc42 and Wal1 was more completely elucidated in our dynamic hyphal growth PPI network.

 

Bor-Sen Chen-3

Figure 3 –C. albicans and zebrafish integrated intercellular dynamic PPI network during C. albicans infection of zebrafish.

Aside from these three well-known hyphae-related signaling pathways—i.e., the MAP kinase and cyclic AMP signaling pathways and the polarized cell growth pathway—the pH-dependent Rim101 pathway was also identified from the dynamic integrated infection intercellular network. In this pathway, Nrg1 was not identified for inclusion in Figure 4, due to its p-value being greater than 0.01. However, Tup1, which has the same function as Nrg1, fits into the pathways shown in Figure 4, and hence the pH-dependent pathway would seemingly be uninterrupted. In conclusion, 20 out of 22 proteins from already known pathways are included in our C. albicans dynamic hyphal PPI network, in which the major hyphae-related pathways are all visible. These results verify the high accuracy of our infection intercellular PPI network. These proteins are all related to hyphal growth or filamentous growth, and the figure reflects true pathways of these proteins.

Bor-Sen Chen-4

Figure 4 –Signaling cascades involved in the dynamic hyphal growth protein interaction subnetwork of C. albicans at different stages of infection.

 

Reference

[1]        R. Johansson, System modeling and identification. Englewood Cliffs, NJ: Prentice Hall, 1993.

[2]        W. S. Wu, W. H. Li, and B. S. Chen, “Identifying regulatory targets of cell cycle transcription factors using gene expression and ChIP-chip data,” BMC Bioinformatics, vol. 8, Jun 8 2007.

[3]        Y. C. Wang, C. Lin, M. T. Chuang, W. P. Hsieh, C. Y. Lan, Y. J. Chuang, et al., “Interspecies protein-protein interaction network construction for characterization of host-pathogen interactions: a Candida albicans-zebrafish interaction study,” BMC Syst Biol, vol. 7, p. 79, 2013.

 

Acknowledgements

The work was supported by the National Science Council of Taiwan under grants NSC 100-2745-E-007-001-ASP, NSC 101-2745-E-007-001-ASP, (to BSC), NSC 100-2627-B-007-002 (to CYL) and NSC 100-2627-B-007-003 (to YJC).

 

Contact

Bor-Sen Chen Ph.D.

Tsing Hua Distinguished Chain Professor

Dept. of Electrical Engineering

National Tsing Hua University

Hsinchu, Taiwan 30013

bschen@ee.nthu.edu.tw

 

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