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 
. 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 
). These include two recent global phosphoproteomic studies of the budding yeast (Saccharomyces cerevisiae
. In the study carried out by Bodenmiller at al.
, 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 
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 
. For the purpose of this study, we have used isotope labeled SILAC (Stable Isotope Labeling with Amino acids in Cell culture) method 
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 
. The filamentous growth form presumably represents a foraging mechanism enabling non-motile yeast to better survive cell stress 
. 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 
. 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 
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 
; 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 
. 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
, and GPA2
, acting upstream of PKA, are not required for butanol-induced filamentous growth 
. In our experiments, we treated cells with 1% (vol/vol) butanol to induce filamentous growth 
. A graphical illustration of currently recognized budding yeast filamentous growth pathways, integrating information from authoritative pathway databases and reviews, is shown in . While these core signaling units are well defined, the downstream scope of their signaling networks is unclear.
Graphical illustration of the filamentous growth pathway in budding yeast from previous studies.
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 
. Each of these kinases exhibits a filamentous growth deletion phenotype, with the deletion of KSP1
, and TPK2
yielding a loss of filamentous growth and the deletion of ELM1
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 
followed by MaxQuant software 
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.