- Research article
- Open Access
Global screening of potential Candida albicans biofilm-related transcription factors via network comparison
© Wang et al; licensee BioMed Central Ltd. 2010
- Received: 21 August 2009
- Accepted: 26 January 2010
- Published: 26 January 2010
Candida albicans is a commonly encountered fungal pathogen in humans. The formation of biofilm is a major virulence factor in C. albicans pathogenesis and is related to antidrug resistance of this organism. Although many factors affecting biofilm have been analyzed, molecular mechanisms that regulate biofilm formation still await to be elucidated.
In this study, from the gene regulatory network perspective, we developed an efficient computational framework, which integrates different kinds of data from genome-scale analysis, for global screening of potential transcription factors (TFs) controlling C. albicans biofilm formation. S. cerevisiae information and ortholog data were used to infer the possible TF-gene regulatory associations in C. albicans. Based on TF-gene regulatory associations and gene expression profiles, a stochastic dynamic model was employed to reconstruct the gene regulatory networks of C. albicans biofilm and planktonic cells. The two networks were then compared and a score of relevance value (RV) was proposed to determine and assign the quantity of correlation of each potential TF with biofilm formation. A total of twenty-three TFs are identified to be related to the biofilm formation; ten of them are previously reported by literature evidences.
The results indicate that the proposed screening method can successfully identify most known biofilm-related TFs and also identify many others that have not been previously reported. Together, this method can be employed as a pre-experiment screening approach that reveals new target genes for further characterization to understand the regulatory mechanisms in biofilm formation, which can serve as the starting point for therapeutic intervention of C. albicans infections.
- Gene Regulatory Network
- Planktonic Cell
- Regulatory Association
- Filamentous Growth
- Graph Edit Distance
Candida albicans, the most commonly isolated opportunistic human fungal pathogen, can cause skin and mucosal infections as well as life-threatening systemic infections [1, 2]. In healthy individuals, C. albicans occurs as a dimorphic commensal colonizer of mucosal membranes in the oral cavity, gastrointestinal tract, urogenital mucosa, and vagina. In immunocompromised patients including those undergoing cancer chemotherapy, organ or bone marrow transplantation and those are AIDS sufferers, this organism can become pathogenic, resulting in proliferative growth on mucosal surfaces locally and systemically [3–5]. Candida infections, or candidiasis, are difficult to treat and create very serious challenge in medicine. Mortality rates among patients with candidiasis have been increasing and can be as high as 40% to 60%, especially for those who have bloodstream infections (candidemia) [6–8]. Therefore, to understand the molecular mechanisms underlying the pathogenicity of C. albicans is imperative for management of such infections.
Biofilm formation plays an important role in the pathogenicity of C. albicans. For example, biofilm can serve as reservoirs for the cells to continually seed infection. Moreover, C. albicans biofilm cells are much more resistant than free-living planktonic cells to many antifungal agents. As a result, the biofilm-specific property of C. albicans cells has prompted recent interests in the study of biofilm structure, physiology, and regulation, and research into the pathogenicity of Candida focusing on the prevention and management of biofilm development and antifungal resistance [6, 9]. Biofilms are defined as surface-associated communities of cells surrounded by an extracellular matrix and displaying phenotypic features that differ from their planktonic counterparts [10, 11]. The development of C. albicans biofilm can be divided into four sequential steps. First, the yeast cells adhere to a foreign substrate (host tissue or medical device). Second, the yeast cells proliferate across the substrate surface and pseudohyphae and hyphae begin to develop. Third, the extracellular matrix is produced and the network of pseudohyphae and hyphae cells is embedded within this matrix. Biofilm will then mature into a complex three-dimensional structure. Finally, the progeny biofilm cells disperse to enable remote surfaces to be populated [6, 9, 10]. Although previous studies have provided some insights, the details of molecular mechanisms that are responsible for biofilm formation still await to be elucidated.
Recently, the C. albicans genome for strain SC5314 was sequenced , revealing that almost two-thirds of its ~6000 open reading frames are orthologous to genes of Saccharomyces cerevisiae, a well-studied model organism and the first eukaryotic organism to have its entire genome sequenced [1, 4, 12]. In addition, the ease of genetic/molecular manipulation and the development of various tools for genome-wide functional analysis have led to accumulate a large amount of data from the study of S. cerevisiae. Since C. albicans and S. cerevisiae are closely related, i.e., both fall within the hemiascomycete group, the information from S. cerevisiae could be adapted and useful for our understanding in C. albicans biology and pathogenesis [1, 13].
Overview of the proposed screening method
The method of the global screening for biofilm-related TFs was divided into three key steps: (i) selection scheme for TFs and genes, (ii) scheme for gene regulatory network reconstruction, and (iii) comparison scheme between two networks of biofilm cells and planktonic cells. The output of the method is a score named relevance value (RV) for each TF. RV is computed to correlate the TF with regulation of biofilm formation. A higher score suggests that the particular TF is more likely involved in the regulatory network for C. albicans biofilm formation. Based on the RVs, the biofilm-related TFs are chosen. The whole process of the proposed screening method is shown in Figure 1. The data used and the details of each step are described in the following sections.
Data used in the proposed screening method
In this study, four kinds of data are integrated-microarray gene expression profiles, regulatory associations between TFs and genes, ortholog data between C. albicans and S. cerevisiae genes, and Gene Ontology annotation information. The microarray data were obtained from Murillo et al., in which genome-wide transcription analysis of biofilm formation are profiled using Affymetrix oligonucleotide GeneChips representative of the entire genome of C. albicans. Briefly, the DNA microarray includes 7116 ORFs and each microarray experiment was performed in duplicate . The resulting time-course microarray data contain two sets of information for biofilm and planktonic cells, generated from early stages of biofilm formation (0-390 mins, 6 time points). The regulatory associations between TFs and genes were obtained from S. cerevisiae using YEASTRACT database http://www.yeastract.com/ and genome-wide location analysis of yeast TFs from Harbison et al.. YEASTRACT (Yea st S earch for T ranscriptional R egulators A nd C onsensus T racking) deposits more than 34469 regulatory associations between TFs and target genes in S. cerevisiae, based on more than 1000 bibliographic references . The genome-wide location analysis allows protein-DNA interactions to be monitored across the entire yeast genome by combing a modified Chromatin Immunoprecipitation (ChIP) procedure with DNA microarray analysis. In Harbison et al., the genomic occupancy of 203 DNA-binding TFs in S. cerevisiae was determined. The p-value threshold for significant binding was selected as p ≦ 0.001 since their analysis indicated that the threshold maximizes inclusion of legitimate regulator-DNA interactions and minimizes false positives . The ortholog data between C. albicans and S. cerevisiae genes were retrieved from Candida Genome Database or CGD http://www.candidagenome.org/. Gene orthology and its best hit mappings were used to correlate S. cerevisiae genes with C. albicans genes using the InParanoid program . The annotations for C. albicans genes were acquired from the Gene Ontology (GO) . The GO annotations were facilitated to query for molecular function or biological process of a gene-of-interest in this study. The way we used these data for screening of biofilm-related TFs are further described in the following sections.
Selection scheme for transcription factors and target genes
As for the selection of target genes, GO annotations were used . An assumption of the proposed screening method is that if a TF regulates gene expression in biofilm cells rather than in planktonic cells, this particular TF is more likely involved in the regulatory machinery that governs biofilm formation. Therefore, the genes annotated with GO terms such as biofilm formation, or those possibly related to different steps of biofilm formation and development, such as cell adhesion, and filamentous growth, were selected for further analysis. However, if the selected target genes of C. albicans are not included in gene expression profiles or have no ortholog mapping data with S. cerevisiae genes, they were excluded for the subsequent steps.
The regulatory associations between TFs and genes in S. cerevisiae from YEASTRACT database  and Harbison et al. were used to infer the possible TF-gene regulatory associations in C. albicans. An example for this step is illustrated in Figure 2. Borneman et al. identified Ste12-MUC1 association by chromatin immunoprecipitation (ChIP)-chip experiment with a p-value = 2e-15 and the result is deposited in the YEASTRACT database. According to CGD, the TF Ste12 and its target gene MUC1 in S. cerevisiae have orthologs Cph1 and HWP1 in C. albicans, respectively. Consequently, based on the experimental results from S. cerevisiae, the possible associations between Cph1 and HWP1 in C. albicans were inferred in our study.
Gene regulatory network reconstruction scheme
From the first step described above, we have selected TFs, their potential target genes, and their possible regulatory associations. This information was used to further constitute the candidate gene regulatory network [Additional file 1]. A stochastic dynamic model was then applied to prune the candidate network to obtain the gene regulatory networks independently for biofilm cells and planktonic cells, according to their respective data sets. For a target gene i in the candidate gene regulatory network, the gene was described using the stochastic discrete dynamic equation (1) .
where f j denotes the sigmoid function, μ j and σ j represent the mean and standard deviation of protein concentration level of TF j. The biological implication of the equation (1) is that the gene expression of the target gene i at the next time t+1 is determined by the present gene expression, the present regulation function of N i TFs binding to this target gene, the degradation effect of the present time, the basal level of gene expression, and some stochastic noises. For each target gene selected from the previous scheme, a stochastic dynamic model was constructed. Consequently, the stochastic dynamic equations for all the target genes constituted the mathematical model of the candidate gene regulatory network.
After constructing the stochastic dynamic model of the candidate gene regulatory network, the microarray gene expression profiles were then overlaid to identify the regulatory parameters in equation (1). Since the DNA microarray data for gene expression profiles of biofilm and planktonic cells are collected separately, the gene regulatory networks of biofilm and planktonic cells can be independently reconstructed. The identification of the gene regulatory network was performed gene by gene, so that the process was not limited by the number of target genes. Due to the non-negativity of basal level of expression (k i ≥ 0 in equation (1)), the constrained least squares regression method was used to identify the regulatory parameters [25, 26] (see Additional file 2 for details). Moreover, since there are no good data available for genome-wide protein concentration levels in C. albicans, gene expression profiles were used instead for identifying the regulatory parameters. Once the regulatory parameters were identified, the significant TF-gene interactions were determined based on the identified a ij 's. By means of Akaike Information Criterion (AIC) [27, 28] and student's t-test [29, 30], we determined the statistical significance of the interactions between TFs and genes, pruned the candidate gene regulatory network and reconstructed the gene regulatory networks for biofilm and planktonic cells (see Additional files 2, 3, 4 and 5 for details). The resulting biofilm and planktonic gene regulatory networks and the significant TF-gene interactions among them were then used for comparison scheme.
Comparison scheme between two networks of biofilm and planktonic cells
Construction of gain-of-function and loss-of-function subnetworks
Screening of potential C. albicans biofilm-related TFs
Identification of potential C. albicans biofilm-related TFs
The potential biofilm-related TFs
Efg1, Cph1 and Efh1: Both cell adhesion and morphogenesis to form hyphae play important roles in biofilm formation and maturation . Efg1 is a downstream transcription factor of Ras-protein kinase A signaling pathway and governs multiple different morphogenetic processes including phenotypic switching and filamentous growth [32–34]. Deletion of C. albicans EFG1 gene decreases the ability of the cell to adhere to oral epithelial cells in vitro .
C. albicans Cph1 is an ortholog of S. cerevisiae Ste12. In S. cerevisiae, the cells mate by responding to pheromones via the functions of mitogen-activated protein kinase (MAPK) cascade and its downstream TF, Ste12. C. albicans Cph1 is not only required for mating , but is also important for hyphal formation . Finally, efg1/efg1 cph1/cph1 double mutant cannot form hyphae and is also defective in biofilm formation [37, 38].
Rap1 and Tec1: Rap1 is a transcription factor and telomere binding protein that is essential for cell viability in S. cerevisiae. Studies from C. albicans RAP1-deletion mutant shows that Rap1 is required for efficient repression of pseudohyphal growth under yeast-favoring conditions but is not essential for viability of C. albicans .
Fgr15, Gcn4, Skn7, Mcm1 and Adr1: Fgr15 is a putative transcription factor with zinc finger DNA-binding motif. Transposon mutation of FGR15 affects filamentous growth . Gcn4, like its ortholog in S. cerevisiae, activates the transcription of amino acid biosynthetic genes. In addition, C. albicans Gcn4 interacts with the Ras-cAMP pathway to promote filamentous growth in response to amino acid starvation . C. albicans GCN4-deletion mutant reduces biofilm biomass, indicating that Gcn4 is required for normal biofilm growth .
Other TFs identified: Of the 23 TFs indentified, as described above, 10 of them have been shown to relate to various processes of biofilm formation (e.g. filamentation and cell adhesion) or biofilm formation per se. Therefore, the remaining 13 TFs provide good candidates for further experiments to determine their regulatory roles in biofilm formation.
Statistical measurements of the performance
Among total 220 TFs selected for screening, 23 potential biofilm-related TFs with significant RVs were identified (Table 2). Of the other 197 TFs, we also check literature evidences to see if they are validated by experiments as biofilm-related TFs. Twenty-six out of 197 TFs which do not have significant RV were annotated with GO terms such as biofilm formation, cell adhesion, or filamentous growth. The sensitivity, specificity, positive predictive value, and negative predictive value of the proposed screening method were evaluated (see Additional file 2 for details). The proposed approach can identify potential C. albicans biofilm-related TFs with a low sensitivity of 27.78% and a high specificity of 92.93%. Moreover, our method is effective on determining the TFs that are not biofilm-related as the negative predictive value is 86.80%. The positive predictive value is 43.48%, enriching by 2.7-fold the likelihood of screening TFs that are biofilm-related since the biofilm-related prevalence among total 220 TFs is 16.36%. It is noteworthy that these statistics are evaluated based on the published literature evidences and GO annotations, suggesting that if more C. albicans biofilm-related TFs are validated by experiments, the statistics should be improved.
The architecture of C. albicans biofilms and the correlation between biofilm and infection have been analyzed, but our understanding of the gene regulations that are responsible for the biofilm formation is still limited. Since transcription factors play an important role in gene regulatory networks, here, we develop a computational framework via network comparison to screen for C. albicans TFs that may be important for biofilm formation. The original idea is derived from the concept of comparative biology which commonly utilizes comparative approaches in the analysis of genomic sequences to reveal the functional similarities and differences among different species . We extend the concept and compare the gene regulatory networks to explore what makes the difference between biofilm and planktonic cells in C. albicans. The advantage of the proposed screening method lies in the convenience and systematicity. Compared with the time- and labor-consuming experiments, we provide an efficient and rapid way for screening TFs by comparing two gene regulatory networks from the systematic point of view. Richard et al. used a collection of insertion mutations in 197 C. albicans ORFs to screen those mutants that are defective in biofilm formation; however, only 4 such genes are identified. In this study, our computational method has a positive predictive value of 43.48% which is much higher than that shown by Richard et al. (~2.03%). Consequently, the proposed screening method can be useful for providing potential target genes for biologists to perform further experiments. It can be considered as a pre-experiment screening. In addition, our approach is not only capable of studying biofilm and planktonic cells, but can also be used to compare two physiological conditions as long as the adequate data are available. For example, this method can be used to screen TFs possibly involved in the cancer development process by comparing the normal cell and cancer cell and the TFs screened could serve as a starting point for therapeutic intervention .
Although our approach is shown to be useful, some drawbacks or improvements are still need to be taken in consideration. One assumption of the stochastic dynamic model in equation (1) is that the time delay of transcriptional regulation of the TF to the target gene is only one time unit (about seven minutes in this study), which is not always the case. Previous studies have shown from gene expression profiles that different time delays are required for different TFs to exert regulatory effects on their target genes [21, 50, 51]. However, since the time delays cannot be experimentally measurable for all the TFs and its potential target genes and the computationally predicted time delays are not completely reliable, the time delays are all set to one time unit when reconstructing the gene regulatory networks. In addition to the time-delay assumption, one important consideration is data accuracy from public domains. For example, based on the orthology information between C. albicans and S. cerevisiae, we adopt the information of regulatory associations between TFs and genes from S. cerevisiae to the study of C. albicans. The orthology mappings were performed at CGD using InParanoid software, which basically employs the computed sequence similarity to determine orthologs . If the orthology mapping data is not perfectly accurate, it can result in the misinterpretation of regulatory associations between TFs and genes in C. albicans. To overcome the problem, it is better to acquire the TF-gene regulatory associations directly from the experiments (e.g. genome-wide ChIP-chip) using C. albicans. Recently, genome-wide location analysis by ChIP (chromatin immunoprecipitation)-chip has been developed for the study of C. albicans[52, 53]. However, similar studies for biofilm-related TFs are still not available. Another shortage of the information from public domains is the lack of information related to S. cerevisiae TF-gene association in YEASTRACT and ChIP-chip data from Harbison et al., although orthologs of the TF and target genes do exist in C. albicans. Consequently, it will not be able to reconstruct the corresponding gene regulatory network, thus the particular TF is being excluded from the TF pool. One can also solve this problem by performing C. albicans ChIP-chip experiments. Once the reliable C. albicans TF-gene regulatory associations are obtained, the performance of the proposed screening method can be improved and the reliable gene regulatory networks can be reconstructed.
Numerous factors can affect C. albicans biofilm formation, including supporting substrate, growth medium, and C. albicans strains [6, 9]. Given the complex conditions that affect the kinetics of biofilm formation process and the huge amounts of data generated by post-genomic approaches under different experimental conditions, we can now investigate the most significant TFs that are responsible for the biofilm formation. The screening of biofilm-related TFs is the initial step to elucidate the whole gene regulatory network that governs biofilm formation. Lu and Collins  have successfully demonstrated that synthetic biology techniques are feasible to engineer bacteriophage to express DspB, an enzyme that hydrolyzes the crucial biofilm formation adhesin (β-1,6-N-acetyl-D-glucosamine) encoded by genes pgaABCD in E. coli[55, 56], therefore reducing bacterial biofilms. As a result, by combining the systems biology approaches to gain more insight into the molecular mechanisms for biofilm formation with the synthetic biology techniques to engineer the enzyme needed, we may develop new therapeutic strategies to combat the recalcitrant infections caused by C. albicans and other microbial pathogens.
Biofilm formation is a major virulence factor in C. albicans pathogenesis and is related to antidrug resistance of this organism. However, little is known about the molecular mechanisms that regulate biofilm formation. In this study, we developed an efficient computational framework for global screening of potential transcription factors controlling C. albicans biofilm formation. S. cerevisiae information was used to infer the possible TF-gene regulatory associations in C. albicans. Gene regulatory networks of C. albicans biofilm and planktonic cells were compared to identify the transcription factors involved in biofilm formation and maintenance. A total of twenty-three TFs are identified; ten of them are previously reported to be involved in biofilm formation. Literature evidences indicate that our approach can be useful to reveal TFs significant in biofilm formation and importantly, provide new targets for further studies to understand the regulatory mechanisms in biofilm formation and the fundamental difference between biofilm and planktonic cells, which can serve as the starting point for therapeutic intervention of C. albicans infections.
The work was supported by the National Science Council of Taiwan under grants NSC 97-2627-B-007-001, NSC 98-2627-B-007-014 (to B.-S. Chen) and NSC 97-2627-B-007-002, NSC 98-2627-B-007-015 (to C.-Y. Lan).
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