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Title: Towards Systematic Discovery of Signaling Networks in Budding Yeast Filamentous Growth Stress Response Using Interventional Phosphorylation Data}
Article:
Abstract:
Reversible phosphorylation is one of the major mechanisms of signal transduction, and signaling networks are critical regulators of cell growth and development. However, few of these networks have been delineated completely. Towards this end, quantitative phosphoproteomics is emerging as a useful tool enabling large-scale determination of relative phosphorylation levels. However, phosphoproteomics differs from classical proteomics by a more extensive sampling limitation due to the limited number of detectable sites per protein. Here, we propose a comprehensive quantitative analysis pipeline customized for phosphoproteome data from interventional experiments for identifying key proteins in specific pathways, discovering the protein-protein interactions and inferring the signaling network. We also made an effort to partially compensate for the missing value problem, a chronic issue for proteomics studies. The dataset used for this study was generated using SILAC( Stable Isotope Labeling with Amino acids in Cell culture) technique with interventional experiments( kinase-dead mutations). The major components of the pipeline include phosphopeptide meta-analysis, correlation network analysis and causal relationship discovery. We have successfully applied our pipeline to interventional experiments identifying phosphorylation events underlying the transition to a filamentous growth form in Saccharomyces cerevisiae. We identified 5 high-confidence proteins from meta-analysis, and 19 hub proteins from correlation analysis( Pbi2p and Hsp42p were identified by both analyses). All these proteins are involved in stress responses. Nine of them have direct or indirect evidence of involvement in filamentous growth. In addition, we tested four of our predicted proteins, Nth1p, Pbi2p, Pdr12p and Rcn2p, by interventional phenotypic experiments and all of them present differential invasive growth, providing prospective validation of our approach. This comprehensive pipeline presents a systematic way for discovering signaling networks using interventional phosphoproteome data and can suggest candidate proteins for further investigation. We anticipate the methodology to be applicable as well to other interventional studies via different experimental platforms.
Introduction:
Cells exchange and receive information from the environment through signaling pathways, which are crucial for cells to maintain normal functions and properly respond to stress and stimuli. Dysregulation of these processes is a major factor in the emergence of many diseases, including cancer, diabetes, and cardiovascular disease. Reversible phosphorylation is one of the major forms of signal transduction and can affect protein function and gene expression[1]–[7]. Investigations into phosphorylation provide insight into signaling pathways by providing the target sites of phosphorylation and the quantitative changes in phosphorylation level in response to genetic or environmental perturbations. Effective, sensitive identification of candidate proteins for further studies remains a challenge in the face of experimental limitations of current technologies which have a high cost component, provide incomplete coverage of the phosphoproteome, and have sampling limitations which affect replicate runs. Large-scale phosphoproteomics studies on a number of organisms have been carried out using mass spectrometry( MS)-based approaches( reviewed in[8]–[10]). These include two recent global phosphoproteomic studies of the budding yeast( Saccharomyces cerevisiae)[5],[6]. In the study carried out by Bodenmiller at al.[5], protein kinases and phosphatases were systematically perturbed through gene deletions. The system-wide responses to the perturbations were measured by label-free MS-based quantification, and the results evaluated to determine their contributions to understanding the relationships between these signal transduction proteins and cell pathways. Another global interaction study focused on kinase and phosphatase interactions[6] by capturing protein-protein interactions by affinity capture-immunoblot and identifying the isolated protein complexes by mass spectrometry. These two global studies both adopted label-free, cost-effective quantitative approaches. However, label-free methods typically increase variance relative to isotope enrichment methods[11]. For the purpose of this study, we have used isotope labeled SILAC( Stable Isotope Labeling with Amino acids in Cell culture) method[12],[13] to increase sensitivity to change. The general scope of this manuscript encompasses a comprehensive pipeline, incorporating statistical and mathematical methods for investigating and evaluating quantitative phosphoproteomic data, the elucidation of candidate proteins, and the identification of processes to be pursued in subsequent molecular biology and genetic studies. The phosphoproteome data utilized in this analysis was obtained from interventional experiments of a subset of yeast kinases involved in filamentous growth. Filamentous growth is a developmental transition observed in S. cerevisiae where yeast cells form elongated and connected multicellular filaments; these filaments resemble hyphae but lack the parallel-sided walls and structure of true hyphal tubes. This pseudohyphal growth transition is induced in response to several cell stresses, including nitrogen stress, growth in the presence of short-chain alcohols, and glucose stress[14]–[17]. The filamentous growth form presumably represents a foraging mechanism enabling non-motile yeast to better survive cell stress[14]. During pseudohyphal growth, yeast cells elongate due to a delay in the G2/M transition, exhibit an altered budding pattern, and remain connected after cytokinesis[18],[19]. The resulting pseudohyphal filaments extend superficially from a colony over an agar substrate and invasively downward into the solid substrate below the colony. In liquid culture under inducing conditions, a filamentous strain of yeast exhibits elongated cells and multicellular filaments encompassing typically 3–4 cells. It is important to note that most laboratory strains of S. cerevisiae are non-filamentous and that studies of filamentous growth are typically performed in the ∑1278b strain, which we employ here. The molecular basis of filamentous growth in S. cerevisiae is broad in scope. Classic studies have identified key kinase-based signaling networks that regulate the filamentous growth transition. In particular, yeast filamentous growth is regulated by mitogen-activated protein kinase( MAPK) and protein kinase A( PKA) pathways[15],[20],[21] as well as being impacted by other signaling pathways. MAPK pathways are evolutionarily conserved across phyla and consist of three-kinase cascades serving central roles in signal transduction in eukaryotic cells[22]; the yeast filamentous growth MAPK cascade terminates in the MAPK Kss1p. In S. cerevisiae, PKA consists of the regulatory subunit Bcy1p and one of three catalytic subunits Tpk1p, Tpk2p, or Tpk3p; Tpk2p is known to be required for filamentous growth[23]–[25]. It should be noted that the Kss1p MAPK pathway is required for pseudohyphal growth induced by both nitrogen stress and butanol, while the genes GPR1, MEP2, and GPA2, acting upstream of PKA, are not required for butanol-induced filamentous growth[16]. In our experiments, we treated cells with 1%( vol/vol) butanol to induce filamentous growth[26]. A graphical illustration of currently recognized budding yeast filamentous growth pathways, integrating information from authoritative pathway databases and reviews, is shown in Figure 1. While these core signaling units are well defined, the downstream scope of their signaling networks is unclear. We have generated phosphoproteomic datasets indicating kinase-dependent phosphorylation events underlying the filamentous growth transition. Specifically, we generated kinase-dead mutations( also called kinase-inactivating mutations) for a set of eight kinases that we have identified as components of the yeast filamentous growth response: Ksp1p, Kss1p, Sks1p, Ste20p, Snf1p, Tpk2p, Elm1p and Fus3p[20],[26]–[28]. Each of these kinases exhibits a filamentous growth deletion phenotype, with the deletion of KSP1, KSS1, SKS1, STE20, SNF1, and TPK2 yielding a loss of filamentous growth and the deletion of ELM1 and FUS3 yielding enhanced filamentation. The kinase-dead alleles of these proteins were constructed by site-directed mutagenesis. The system-wide phosphorylation responses of the mutant strains were determined using SILAC approach, and we used the Mascot search engine[29] followed by MaxQuant software[30] to identify and quantify peptides and proteins. We obtained phosphorylation level changes from the MaxQuant analysis for mutants versus wild type control for the comprehensive quantitative analyses. The broad focus of the filamentous growth kinase networks in particular has made it difficult to tease out important kinase targets( direct or indirect). Bioinformatics methods provide a promising avenue with which local kinase signaling relationships can be identified. While traditional cluster analyses associated with functional enrichment analysis are useful tools, their performance might be affected by the missing value issue. We need to deal with it in order to obtain reliable clusters and enriched functions. Furthermore, a more integrative and extensive analysis is necessary to find new components of the pathways, uncover relationships between the pathway components, and to elaborate the signaling network structure. Thus we propose this comprehensive quantitative analysis pipeline customized for SILAC data, and partially compensate the missing value issue. The major building blocks include phosphopeptide meta-analysis, correlation network analysis, causal relationship discovery, and validation by literature mining. We have successfully applied the pipeline to analyze our current yeast data. Candidate proteins predicted to contribute to the filamentous growth response were selected by phosphopeptide meta-analysis and correlation network analysis. Causal relationship discovery was performed on candidate proteins identified from our analysis and validated proteins from the literature. The inferred causal relationships, along with the interactions inferred from phosphorylation changes in response to individual mutants, have suggested potential proteins that can be further intervened and studied in the future.
Discussion:
In this study, we demonstrate that interventional phosphoproteome studies can provide new insight into signaling pathways involved in biological processes such as yeast filamentous growth. In order to increase sensitivity to smaller changes in phosphorylation relative to previous yeast global phosphoproteome studies[5],[6], we used SILAC, an isotope labeling approach. Isotope labeling approaches are generally more precise relative to label-free approaches[11], but require greater resources to implement, resulting in trade-offs between precision and missing data due to sampling limitations inherent to current instruments. We proposed and developed a comprehensive computational and statistical analysis pipeline for the post-identification studies of phosphoproteome data. The analyses are aimed at discovering candidate components of significant pathways involved in filamentous growth as well as the potential targets of the pathways, and to provide more information on the signaling network structure by monitoring changes in phosphorylation in response to mutational interventions. We applied the pipeline to analyze our interim high mass accuracy yeast phosphoproteome datasets and a total of 882 unique phosphopeptides representing 486 proteins were identified as significantly influenced by at least one out of 8 kinase-dead mutants. Twenty-eight unique phosphopeptides having globally significant phosphorylation were identified from the whole dataset among which 5 peptides representing 5 proteins, Nth1p, Pbi2p, Rcn2p, Pdr12p and Hsp42p, were identified as high-confidence candidates. Nineteen candidate proteins with relatively high degrees of connectivity were selected as hub proteins in the stringent correlation network( Pbi2p and Hsp42p were identified as hub proteins too). Among the high-confidence candidate proteins, 3 proteins have been previously reported to be directly involved in filamentous growth and another 6 proteins were also supported, in terms of genetic and physical interactions with known components involved in filamentous growth. The remaining proteins have been implicated in various other stress responses and may play roles in filamentous growth or may be secondary stress responders. In particular, we validated four candidate proteins, Nth1p, Pbi2p, Pdr12p and Rcn2p as impacting invasive growth. Causal relationship discovery was further performed on the candidates and validated proteins. The inferred causal relationships, along with the interactions inferred from phosphorylation changes in response to individual mutants, form phosphoprotein interaction networks, which suggested potential proteins to be intervened in future studies. Each of the kinases mutated in this study had previously been implicated in filamentous growth. Many of these kinases are known to also affect pathways that are not involved directly in filamentous growth. However, the proteins which change phosphorylation level in response to multiple mutants are reasonable candidates involved in filamentous growth. The sensitivity of such detection is constrained by the degree of overlap between pathways, the coverage of pathways by the mutants, and the extent of missing data. Upstream components of isolated pathways may be missed, while downstream core components are more likely to be identified. A remaining challenge for quantitative phosphoproteome analysis arises from the sampling limitations and resolution of current mass spectrometers[11]. This feature of tandem mass spectra of complex mixtures results in poor overlap of peptides identified across samples unless a relatively large number of replicate experiments are carried out( which is time consuming and often economically impractical for large-scale projects). For this reason, a significant number of missing values exist in these datasets which can obscure potential candidates for further validation studies. This is likely to be alleviated to some extent in the future as mass spectrometry technologies continue to improve, but we have developed methods to partially compensate for the missing data issue. In the phosphopeptide meta-analysis, an extension of Fisher's combined probability test was made to relax the restrictions of complete measurements. The causal network modeling component was also developed to allow missing values without excluding the incomplete measurements. We also performed cluster analysis of phosphopeptides. Instead of adopting traditional clustering methods, we directly identified the most stable clusters using missing value-imputed data. Our approach was able to pick out significantly enriched functions, and identify a number of reliable candidate proteins for further validation of which four were validated. This analysis pipeline has been developed to study yeast filamentous growth pathways; however, the methodology is not limited to yeast or this biological process. It can be applied to other complex organisms to facilitate investigation into various biological processes. We anticipate the methodology to be applicable as well to other interventional studies via different experiment platforms.
Signal transduction is a ubiquitous and essential mechanism regulating cellular functions , including responses to environmental stress . Dysfunction of signaling pathways results in a variety of diseases , including cancer , diabetes , and cardiovascular disease . Phosphorylation regulates the activity of signaling and target proteins at different cellular locations and controls activation and inactivation of signal pathways . Here , we provide an analysis of phosphoproteome datasets from yeast , utilizing kinase mutants versus wild type strains . In order to provide an objective approach to identify candidate proteins involved in the transition to a filamentous growth form , we proposed and applied a comprehensive pipeline incorporating statistical and mathematical methods to investigate the phosphoproteome data from multiple perspectives . This included phosphorylation variation in response to a single mutant , phosphorylation variation patterns over multiple mutants , and the relationships represented by these patterns . We make an effort to discover the components and targets of the signaling network , infer the network structure , and to find the relationships of changes of protein phosphorylation to cellular functions , specifically in response to stress in the context of filamentous growth .