Our group studies human cell signalling with the aim to understand what controls different cell responses in different environments, tissues and conditions. We analyse and integrate diverse 'omics' datasets (with emphasis on (phospho)proteomics and gene essentiality data), to extract and compare context-specific signalling networks.
The long-term aim is to understand the principles of human cell signalling regulation and create predictive and conditional whole-cell signalling models. We will use these models to gain insights into basic cell functions and disease mechanisms, which can aid the design of precise therapeutic approaches and the discovery of reliable biomarkers.
We are interested in taking advantage of the phosphoproteome layer of cell function regulation and integrating it with data from other layers and prior knowledge towards addressing the following questions:
a) How does rewiring human cell signalling networks result in different cell phenotypes?
b) Which are the regulators of this rewiring? What are the molecular mechanisms?
c) How do differences in the genome, or transcriptome layer of cell regulation affect the cell’s signalling networks?
d) What happens to the signalling networks and the cell phenotype when we perturb the network?
e) What are the minimal data points across the layers of cell regulation that we need to measure to be able to predict the cell signalling state and its phenotype?
We are a computational lab, heavily collaborating with experimental groups, and we aim to use any approach that will contribute to addressing our questions. We perform both fundamental research and more translational work through collaborations with Open Targets (e.g. drug synergy prediction and others).
The current knowledge in signalling pathways is context agnostic and highly biased towards well-studied proteins. To address this, we use largely data-driven strategies, which include network inference, propagation, and modelling, so as to expand our knowledge into the under-studied, or “dark space” of human cell signalling and study its architecture at a whole-cell level.
In addition, we take advantage of large-scale gene essentiality screens to study the principles of signalling network rewiring in a context-specific way.
Our long-term goal is to create a flexible and modular framework that can bring together our community to create an accurate, predictive and accessible whole-cell signalling model that can be used for predicting a cell’s signalling state and phenotype given a specific omics dataset or profile.
Sharma*#, Dincer*, Weidemueller, Wright, Petsalaki# (2020) CEN-tools: An integrative platform to identify the ‘contexts’ of essential genes bioRxiv
Invergo*, Peturson*... Cutillas#,Petsalaki#,Beltrao# (2020) Prediction of Signed Protein Kinase Regulatory Circuits. Cell systems 10(5):384-396.e9.
Fernandez-Alonso R, et al. Phosphoproteomics identifies a bimodal EPHA2 receptor switch that promotes embryonic stem cell differentiation. Nat Commun 11:1357
Mueller ... Petsalaki#, Rocks# (2020) Systems analysis of RhoGEF and RhoGAP regulatory proteins reveals spatially organized RAC1 signalling from integrin adhesions. Nat. Cell Biology 22(4):498-511
Betts MJ, Wichmann O, Utz M, Andre T, Petsalaki E et al. (2017) Systematic identification of phosphorylation-mediated protein interaction switches. PLoS Comput Biol. 13(3):e1005462
Yachie N*, Petsalaki E*, et al. (2016) Pooled-matrix protein interaction screens using Barcode Fusion Genetics. Mol. Sys.Biol 12:863
Petsalaki E, et al. (2015) SELPHI: correlation-based identification of kinase-associated networks from global phospho-proteomics data sets. Nucleic Acids Res. 43:W276-W282
Nott TJ, et al. (2015) Phase transition of a disordered nuage protein generates environmentally responsive membraneless organelles. Mol. Cell 57:936