Interspecies protein-protein interaction network construction for characterization of host-pathogen interactions: a Candida albicans-zebrafish interaction study
© Wang et al.; licensee BioMed Central Ltd. 2013
Received: 18 April 2013
Accepted: 14 August 2013
Published: 16 August 2013
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.
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.
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.
KeywordsComputational systems biology Network construction Host-pathogen interaction Protein-protein interaction network Infection Multivariate dynamic modeling Redox
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 [1–3]. Investigating the infection process in detail can aid understanding of the mechanisms that underlie infection and the control of infection disease. To obtain an in-depth understanding of the infectious process, the specific interactions between the virulence factors of the invasive pathogen and the defensive mechanisms of the host need to be elucidated. Candida albicans is one of the most common fungal pathogens of medical importance . In severe cases, C. albicans can penetrate through epithelial layers into deeper tissues and cause life-threatening systemic infections . C. albicans can grow in a budded yeast form or in a highly polarized hyphal form; its yeast-to-hyphal transition ability in response to environmental changes is one of its most well-known virulence characteristics . In addition to dimorphism, a number of fungal attributes, such as the expression of adhesion factors, directed growth/thigmotropism, stress adaptation, metabolic flexibility and the secretion of hydrolytic enzymes are also implicated in the infection process . However, the exact molecular mechanisms by which C. albicans attaches to epithelial surfaces, invades various epithelial barriers, causes cell and tissue damage, and disseminates within the host are not fully understood .
Recently, zebrafish have been increasingly used in biomedical research due to their high reproductive rate, comprehensive molecular tools, and low maintenance costs [9, 10]. Zebrafish are more similar to mammals than other mini-hosts (such as Drosophila melanogaster, Galleria mellonella and Caenorhabditis elegans) in terms of genetics, physiology, and anatomical structure, and most importantly, they have both innate and adaptive immune functions [11, 12]. As a result, the zebrafish model has been used to study human pathogens or closely related animal pathogens, either using adult fish with a fully developed adaptive immune system, or using embryos or larvae that rely solely on innate immunity [13, 14]. Chao et al. used zebrafish as a mini-vertebrate host system for their study of C. albicans infection, demonstrating that C. albicans can colonize and invade zebrafish at multiple anatomical sites and kill the fish in a dose-dependent manner . Hence, zebrafish are suitable for our study characterizing host-pathogen interactions with C. albicans.
Host-pathogen interactions are enormously complex processes. While traditional biological research, which isolates and studies small sets of components, may provide some insights, these approaches are not well suited to address interaction mechanisms on a larger and more general scale . To this end, a systems biology approach is an emerging strategy to better comprehend the underlying mechanisms that occur during host-pathogen interactions . Indeed, several different systems biology approaches have demonstrated their effectiveness . These approaches rely on an unbiased and global understanding of the transcriptomics of the host/pathogen. Further computational analyses of genome-wide gene expression profiles have partially revealed the mechanisms of interaction between host and pathogen, leading to a deeper understanding of the infection process . Nevertheless, the majority of these studies have addressed the pathogen or host transcriptomics individually rather than simultaneously analyzing both interaction partners. Consequently, in this study, we aimed to analyze the host and pathogen simultaneously and consider the interacting host and pathogen as an orchestrated system.
During the dynamically changing environment of host-pathogen interactions, both host and pathogen have evolved numerous strategies for adaptation. These adaptations are mediated by complex interaction networks, which lead to changes to gene expression patterns. Consequently, we intended to elucidate the adaptation mechanisms by understanding the underlying interaction networks. Although several network construction schemes have been successfully applied to many biological scenarios, these have focused mainly on a single species. Recently, some computational prediction methods to infer host-pathogen interactions have been developed based on interologs [20, 21] or gene expression profiles [22, 23]. In this study, we developed a computational framework that integrated ortholog-based protein-protein interaction (PPI) inference and dynamic modeling of regulatory responses during C. albicans infections to construct the interspecies PPI network for characterization of host-pathogen interactions. With PPI data for two well-studied organisms, S. cerevisiae and H. sapiens, and the cross-species ortholog information among these species, we first inferred the candidate interspecies PPI network consisting of putative interspecies and intracellular PPIs. We then used multivariate dynamic models to describe the regulatory responses between pathogen and host in the infection process and to prune the candidate network based on the simultaneously quantified C. albicans-zebrafish interaction transcriptomics . 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. With the accumulation of more interspecies transcriptomics data, the proposed interspecies network construction framework can be used to efficiently explore progressive network rewiring over time. Consequently, this proposed method could benefit the development of network medicine for infectious diseases.
Overview of the interspecies PPI network construction framework
Data mining and integration
This section describes the sources of all the data used in this study. Both zebrafish and C. albicans genome-wide microarray data were downloaded from the GEO database (GSE32119). Microarray experiments were performed to simultaneously profile genome-wide gene expressions in both C. albicans and zebrafish during the infection process. Adult AB strain zebrafish were intraperitoneally injected with 1 × 108C. albicans (SC5314 strain) cells. Then, a two-step homogenization/mRNA extraction procedure was performing using the whole zebrafish infected with C. albicans. This approach could provide separate pools of gene transcripts from both the host and the pathogen, enabling individual estimation of specific gene expression profiles in either the host or the pathogen using sequence-targeted probes derived from the individual genome . Agilent in situ oligonucleotide microarrays, which cover 6,202 and 26,206 genes for C. albicans and zebrafish respectively, were used to profile time-course gene expression at 9 time-points (0.5, 1, 2, 4, 6, 8, 12, 16, 18 hours post-infection) with three replicates for both organisms . Manipulation of the animal model was approved by the Institutional Animal Care and Use Committee of National Tsing Hua University (IRB Approval No. 09808). In order to construct the interspecies PPI network for characterization of C. albicans-zebrafish interactions, the PPIs from S. cerevisiae and H. sapiens and the ortholog information among these species were used to infer the putative interspecies and intracellular PPI due to lack of sufficient information in the C. albicans, and zebrafish interactomes and their interspecies interactions. The PPI data for both S. cerevisiae and H. sapiens were obtained from the database of Biological General Repository for Interaction Datasets (BioGRID) (http://thebiogrid.org/) . In BioGRID version 3.2.95, there are 89,445 non-redundant physical interactions among 15,690 proteins for H. sapiens and 75,065 non-redundant physical interactions among 6,043 proteins for S. cerevisiae. The ortholog information for the four species, namely, zebrafish, H. sapiens, C. albicans and S. cerevisiae, were acquired from the following databases: InParanoid (http://inparanoid.sbc.su.se/) , ZFIN (http://zfin.org/) , and the Candida Genome Database (CGD) (http://www.candidagenome.org/) . The cellular information for both C. albicans and zebrafish proteins were retrieved from the Gene Ontology (GO) (http://www.geneontology.org/) , CGD, and ZFIN databases.
Selection of protein pool
The first step of interspecies PPI network construction is to select proteins of interest for both host and pathogen. Generally, selection of proteins can be divided into two categories: expression-based selection and function-based selection. For expression-based selection, statistical methods such as one-way analysis of variance (ANOVA) or simply fold change selection are usually applied to gene expression profiles from microarray experiments or RNA sequencing (RNA-seq) for global selection of genes/proteins of interest. In this case, the constructed network will represent the global scenario for all the dynamically regulated genes/proteins under the experimental condition. On the other hand, the function-based selection method is applied only if we want to construct the network for specific functions. GO annotations are useful tools for functional annotation of genes/proteins. In this study, one-way ANOVA was employed to detect significant gene expression variations across nine time-points for each gene. In this manner, the dynamically regulated genes in both C. albicans and zebrafish could be selected for global characterization of C. albicans-zebrafish interactions. The null hypothesis of ANOVA assumed that the average expression level of a gene would be the same at every time point . Genes with Bonferroni-adjusted p-values of less than 0.05 were identified as dynamically regulated genes and their corresponding gene products were selected in the protein pool as target proteins.
Inference of putative interspecies and intracellular PPIs
After the selection of proteins of interest for both C. albicans and zebrafish, we then sought to identify the candidate interspecies PPI network among these selected proteins. However, due to extremely low coverage of the C. albicans and zebrafish interactomes and lack of interspecies PPIs between C. albicans and zebrafish, ortholog-based PPI prediction was used to infer the putative PPIs within and between C. albicans and zebrafish [20, 21, 31]. The PPI data of S. cerevisiae and H. sapiens from BioGRID and the ortholog information among these species from the InParanoid, CGD, and ZFIN databases were used to infer the putative interspecies and intracellular PPIs. The concept of the ortholog-based PPI inference is shown in Figure 1. For example, suppose that protein A’ and protein B’ of S. cerevisiae (or H. sapiens) are shown to interact based on the BioGRID database. From the InParanoid and CGD databases, we further identify that C. albicans protein A is orthologous to protein A’; from the InParanoid and ZFIN databases, we identify that zebrafish protein B is orthologous to protein B’. Based on the data mining via these databases, we infer that protein A in C. albicans and protein B in zebrafish are a putative interspecies PPI pair. Similarly, the putative intracellular PPIs can also be predicted for both C. albicans and zebrafish. Following this data mining methodology, the putative interspecies and intracellular PPIs were inferred and the candidate interspecies PPI network can be constructed by simply linking proteins inferred by our proposed method to interact with each other (Figure 1). It should be noted that the putative interspecies and intracellular PPIs inferred from the ortholog-based method were derived under many different experimental conditions, which cannot accurately reflect the actual condition of host-pathogen interactions during C. albicans infections; that is, false positives may be present among these putative PPIs. Therefore, these putative PPIs should be further validated by time-series microarray data of C. albicans-zebrafish interactions as described in the following section.
Multivariate dynamic modeling of regulatory responses during C. albicans infections
where x i [t] represents the mRNA expression level at time t for gene i of the corresponding target protein i (i = 1, 2, …, N), a ij denotes the regulatory ability of the j-th intracellular interactive protein to the i - th target protein, x j [t] represents the mRNA expression level of the j - th intracellular protein interacting with the target protein i, b ik denotes the regulatory ability of the k - th interspecies interactive protein to the i - th target protein, y k [t] represents the mRNA expression level of the k - th interspecies protein interacting with the target protein i, λ i indicates the degradation effect of the target protein i, h i represents the basal expression level, ϵ i [t] represents the stochastic noise, and J i and K i denote the numbers of intracellular and interspecies proteins interacting with target protein i in the candidate interspecies PPI network. In other words, only the proteins interacting with target protein i in the candidate interspecies PPI network were described in the multivariate linear dynamic model, therefore constraining the multivariate dynamic model based on the candidate interspecies network. In addition, it should be noted that only the mRNA expression level of the corresponding target protein were used in this equation, not the concentrations of the proteins. The biological interpretation of equation (1) is that the mRNA expression level for gene i of the corresponding target protein i at the next time t + 1 is determined by the current gene expression level, the regulatory effects of J i intracellular interactive proteins, the regulatory effects of K i interspecies interactive proteins, the degradation of the present state, the basal protein level from other sources beyond the interactive proteins in the system, and some stochastic noises. For each target protein with putative PPIs in the candidate interspecies network, a dynamic model was constructed. Consequently, a set of dynamic equations for all the target proteins can be used to describe the entire candidate interspecies PPI network.
Identification of regulatory abilities and determination of significant interactions
From the network point-of-view, the interspecies PPI network depicted by the multivariate linear dynamic models in equation (1) represents how C. albicans and zebrafish interact during the infection process. Once C. albicans invades zebrafish tissues and initiates the infection process as the interaction between pathogen and host, some interacting proteins between C. albicans and zebrafish become active. These interspecies interactions are captured by the term in equation (1); the response of intracellular protein interactions is instead reflected through the term . In other words, the regulatory abilities, specifically, the b ik and a ij terms, indicate the weighting of the edges in the interspecies PPI network. Hence, it is essential to identify these regulatory abilities and determine the significant interactions during C. albicans infections such that the candidate interspecies network can be further pruned into the refined interspecies PPI network that accurately captures C. albicans-zebrafish interactions during the infection process.
where and , constraining the parameters h i to be non-negative.
where denotes the estimated expression profile of the i-th target protein, that is, , and is the estimated residual error. As the residual error decreases, the AIC decreases. In contrast, while the number of interactive proteins (or parameters) J i + K i increases, the AIC increases. Therefore, there is a tradeoff between residual error and model order. As the expected residual error decreases with increasing number of interactive proteins in models of inadequate complexity, there should be a minimum around the optimal interactive protein number. The minimization achieved in equation (5) will indicate the ideal model order (namely, the optimal number of proteins that interact with the target protein) of the protein interaction system. With the statistical selection of J i + K i interactive proteins by minimization of the AIC, the question of whether an interactive protein is a significant one or just a false positive for the i-th target protein can be determined. Hence, AIC can be adopted to select system order, filtering out insignificant protein interactions in the candidate interspecies network based on the estimated regulatory abilities (b ik and a ij terms). Once the estimated regulatory abilities were examined using the AIC model selection criteria, the Student’s t-test was further applied to determine the statistical significance of the parameters. The p-values for the regulatory abilities were calculated under the null hypothesis H0 : b ik = 0 or H0 : a ij = 0 . The interactions with Bonferroni-adjusted p-value≦0.05 were identified as significant interactions and preserved in the refined interspecies PPI network. In this manner, insignificant interactions in the candidate interspecies network were pruned to construct the refined host-pathogen interspecies PPI network.
Construction of interspecies PPI network
Novel interspecies interactions highlight the association between C. albicans pathogenesis and the zebrafish redox process
On the other hand, infection by some pathogens such as Entamoeba histolytica and Japanese encephalitis virus results in enhanced ROS formation [44, 45]. These pathogens have been reported to utilize enhanced ROS generation to induce host cell death, thus allowing themselves to escape the cell. This mechanism is likely to contribute to the spread of the pathogens . Recently, it has been demonstrated that Sclerotinia sclerotiorum, a fungal pathogen that infects virtually all dicotyledonous plants, can both suppress and induce host ROS formation during infection via the secretion of oxalic acid . During the initial stages of infection, S. sclerotiorum dampens the oxidative burst of the plant and generates a reducing environment in host cells. Once infection is established, the pathogen induces the generation of plant ROS (oxidizing conditions) leading to programmed cell death of the host, which directly benefits the pathogen . In this manner, Sclerotinia uses a novel strategy involving regulation of host redox status to establish infection. Although there is no evidence that C. albicans is capable of inducing ROS production in host cells to date (Wellington et al. demonstrated that C. albicans suppresses production of ROS in phagocytes within 180 minutes of infection ), we postulated that C. albicans may also modulate ROS levels to subvert immune defenses in the same way as S. sclerotiorum, that is, by suppressing host ROS production in the initial stages of infection and inducing ROS in the later stages. Further experiments measuring ROS at different infection stages are needed to validate this hypothesis. Taken together, novel interspecies interactions identified in this study highlight the association between C. albicans pathogenesis and the zebrafish redox process. The redox status in both the host and pathogen can be a critical factor that determines the outcome of the battle between the host and pathogen. From the perspective of the pathogen, it is essential to be in control of the redox environment and the cell death pathways of the host in order to subvert immune defenses by the host and support self-survival. In contrast, during an immune response, the host seeks to control the redox environment and the cell death pathways to the detriment of the pathogen.
Infectious disease is one of the leading causes of death worldwide, and complex interaction mechanisms between host and pathogen underlie the process of infection. However, most studies exploring host-pathogen interactions have predominately focused on the host or the pathogen individually rather than simultaneously analyzing both interaction partners. Although these single-species studies have provided insights into the pathogenic and defensive mechanisms for host-pathogen interactions, they did not give clues about interspecies functional associations between host and pathogen. Detailed knowledge of host-pathogen protein interactions may enable us to comprehend the mechanisms of infection and to identify better strategies to prevent or cure infection . Accordingly, in this study, we developed a computational framework to efficiently construct the interspecies PPI network focusing on the characterization of interspecies interactions between host and pathogen. Based on ortholog-based PPI inference and multivariate dynamic modeling of regulatory responses during C. albicans infections, several omics data were integrated for interspecies PPI network construction. The proposed computational method has been shown to be useful, emphasizing the association between C. albicans pathogenesis and the zebrafish redox process, and the idea that redox status is critical during the battle between the host and pathogen. According to our findings and evidence from other species, we also speculated that C. albicans may suppress host ROS production in the initial stages of infection and induce ROS formation in the later stages to subvert the host immune defense. However, further experiments are required to validate this hypothesis. Previous studies have demonstrated that hyphal morphogenesis is an important virulence factor in C. albicans[6, 48]. Therefore, in addition to the pathogenesis subnetwork, we also explored zebrafish proteins that interacted with C. albicans hyphae-related proteins, specifically, proteins annotated with GO term hyphal growth, in the constructed interspecies network. However, only the general GO terms, such as metabolic process and lipid biosynthetic process, were significantly enriched among those zebrafish proteins. Because these general biological processes add little to the understanding of the adaptation mechanisms during the battle between host and pathogen, they are not discussed in the current study. Although the proposed interspecies network construction method was shown to be useful, some improvements remain to be addressed. Due to extremely low coverage of the C. albicans and zebrafish interactomes and lack of interspecies PPIs between C. albicans and zebrafish, ortholog-based PPI prediction was used to infer the putative PPIs among and between C. albicans and zebrafish. Although using interologs to infer host-pathogen interaction has been shown to be a useful approach , the putative PPIs may still contain inaccuracies, which could lead to deviation of the constructed interspecies network from the actual network. Therefore, high coverage of the C. albicans and zebrafish interactomes or even the experimentally validated interspecies PPIs will improve the network construction scheme.
With the interspecies PPI network construction scheme, we are able to construct interspecies networks for all kinds of interacting organisms efficiently given the interspecies transcriptomics data. In addition, the constructed interspecies network can be easily scalable, that is, the use of the computational scheme is not limited by the number of proteins of interest. Recently, Tierney et al. used simultaneous RNA-seq to quantify C. albicans and Mus musculus gene expression dynamics during phagocytosis by dendritic cells and inferred an interspecies regulatory network that also identified novel interspecies host-pathogen interactions . On the basis of their inferred network, they proposed a mechanism whereby murine Ptx3 binding to C. albicans leads to cell wall remodeling via fungal Hap3 target genes, thereby altering recognition of the fungus by immune cells and attenuating host immune responses . Their work successfully demonstrated the usefulness of network inference approaches to decipher microbial pathogenesis mechanisms. Nevertheless, their network inference method was restricted to a limited number of genes with prior knowledge, which can be overcome by our proposed computational scheme. Advancing from the investigation of single species, the interspecies network construction approach can further help characterize and elucidate host-pathogen interactions. With the accumulation of interspecies transcriptomics data, the proposed framework can be used to explore progressive network rewiring over time. In this manner, the dynamics of the interspecies system can be comprehensively studied. It has been suggested that a disease is rarely a consequence of an abnormality in a single gene/protein given the functional interdependencies between molecular components in the cell , and that both network connectivity and dynamics are important targets for therapeutic intervention . Consequently, we believe that network medicine targeting network dynamics can be developed for infectious diseases with the help of the proposed interspecies protein interaction construction method.
In this study, a computational framework which integrated multiple omics data was proposed to construct an interspecies PPI network for characterization of host-pathogen interactions. The proposed method was shown to be useful, with results highlighting an association between C. albicans pathogenesis and the zebrafish redox process during C. albicans infection in zebrafish. Results further indicated that redox status is critical during the battle between the host and pathogen, which could determine the outcome of infection. While the pathogen can control the redox environment to subvert immune defenses and support self-survival, in contrast, the host controls the redox environment to the detriment of the pathogen during an immune response. With continued accumulation of interspecies transcriptomics data, the proposed method could be helpful in the development of network medicine for infectious diseases from an interspecies network perspective.
This work was supported by the National Science Council of Taiwan with grants NSC 100-2745-E-007-001-ASP, NSC 101-2745-E-007-001-ASP (to BSC), NSC 100-2627-B-007-004 (to CL), NSC 100-2627-B-007-002 (to CYL) and NSC 100-2627-B-007-003 (to YJC).
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