title: Swimming upstream: identifying proteomic signals that drive transcriptional changes using the interactome and multiple "-omics" datasets.
abstract: Signaling and transcription are tightly integrated processes that underlie many cellular responses to the environment. A network of signaling events, often mediated by post-translational modification on proteins, can lead to long-term changes in cellular behavior by altering the activity of specific transcriptional regulators and consequently the expression level of their downstream targets. As many high-throughput, "-omics" methods are now available that can simultaneously measure changes in hundreds of proteins and thousands of transcripts, it should be possible to systematically reconstruct cellular responses to perturbations in order to discover previously unrecognized signaling pathways. This chapter describes a computational method for discovering such pathways that aims to compensate for the varying levels of noise present in these diverse data sources. Based on the concept of constraint optimization on networks, the method seeks to achieve two conflicting aims: (1) to link together many of the signaling proteins and differentially expressed transcripts identified in the experiments "constraints" using previously reported protein-protein and protein-DNA interactions, while (2) keeping the resulting network small and ensuring it is composed of the highest confidence interactions "optimization". A further distinctive feature of this approach is the use of transcriptional data as evidence of upstream signaling events that drive changes in gene expression, rather than as proxies for downstream changes in the levels of the encoded proteins. We recently demonstrated that by applying this method to phosphoproteomic and transcriptional data from the pheromone response in yeast, we were able to recover functionally coherent pathways and to reveal many components of the cellular response that are not readily apparent in the original data. Here, we provide a more detailed description of the method, explore the robustness of the solution to the noise level of input data and discuss the effect of parameter values.
authors.affiliation: Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
authors.forename: Shao-shan Carol
authors.initials: SS
authors.lastname: Huang
authors.forename: Ernest
authors.initials: E
authors.lastname: Fraenkel
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D030562:Databases, Protein; D005656:Fungal Proteins / Q000235:genetics* / Q000378:metabolism; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D008969:Molecular Sequence Data; D010675:Pheromones / Q000235:genetics / Q000378:metabolism; D010750:Phosphoproteins / Q000235:genetics / Q000378:metabolism; D040901:Proteomics / Q000379:methods*; D012333:RNA, Messenger / Q000096:biosynthesis* / Q000235:genetics; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction / Q000235:genetics; D059629:Signal-To-Noise Ratio; D014157:Transcription Factors / Q000235:genetics / Q000378:metabolism; D014158:Transcription, Genetic
chemical_list: D005656:Fungal Proteins; D010675:Pheromones; D010750:Phosphoproteins; D012333:RNA, Messenger; D014157:Transcription Factors