Conditions-specific quantitative network rewiring of colon cancer-associated KRAS mutations in Caco-2 cell line

Ras is a key switch controlling cell behaviour. In the active, GTP-bound form, Ras can interact with numerous effector proteins in a mutually exclusive manner, suggesting that individual Ras-effector complexes engage in larger cellular (sub)complexes. A detailed characterisation of these (sub)complexes and how they are altered in specific contexts is not fully understood. Focusing on KRAS, we performed AP-MS experiments of exogenous expressed FLAG-KRAS wild-type and three oncogenic mutants in the human Caco-2 cell line exposed to 16 growth contexts – mimicking conditions relevant in the colon and colorectal cancer. We identified four effectors present in complex with KRAS in almost all conditions (“condition-general”) and nine effectors that only form complexes in some contexts (“condition-specific”). We computationally predicted earlier that the latter effectors need additional domains to be recruited to the plasma membrane for efficient recruitment to KRAS. Analysing all interactors in complex with KRAS per condition, we find that the different growth conditions had a larger impact on complex rewiring than the mutation status of KRAS. We also reconstructed effector-mediated (sub)complexes and linked changes in (sub)complex compositions in the different conditions to phenotypic changes. Altogether, our work shows the impact of environmental contexts on network rewiring, which provides insides into tissue-specific signalling mechanisms. Our work also sheds light on why individual KRAS oncogenic mutants may be causing cancer only in specific tissues – despite KRAS being expressed in most cells and tissues

Reconstruction and analysis of a large-scale binary Ras-effector signaling network

Background: Ras is a key cellular signaling hub that controls numerous cell fates via multiple downstream effector pathways. While pathways downstream of effectors such as Raf, PI3K and RalGDS are extensively described in the literature, how other effectors signal downstream of Ras is often still enigmatic. Methods: A comprehensive and unbiased Ras-effector network was reconstructed downstream of 43 effector proteins (converging onto 12 effector classes) using public pathway and protein-protein interaction (PPI) databases. The output is an oriented graph of pairwise interactions defining a 3-layer signaling network downstream of Ras. The 2290 proteins comprising the network were studied for their implication in signaling crosstalk and feedbacks, their subcellular localizations, and their cellular functions. Results: The final Ras-effector network consists of 2290 proteins that are connected via 19,080 binary PPIs, increasingly distributed across the downstream layers, with 441 PPIs in layer 1, 1660 in layer 2, and 16,979 in layer 3. We identified a high level of crosstalk among proteins of the 12 effector classes. A class-specific Ras sub-network was generated in CellDesigner (.xml file) and a functional enrichment analysis thereof shows that 58% of the processes have previously been associated to a respective effector pathway, with the remaining providing insights into novel and unexplored functions of specific effector pathways. Conclusions: Our large-scale and cell general Ras-effector network is a crucial steppingstone towards defining the network boundaries. It constitutes a 'reference interactome' and can be contextualized for specific conditions, e.g. different cell types or biopsy material obtained from cancer patients. Further, it can serve as a basis for elucidating systems properties, such as input-output relationships, crosstalk, and pathway redundancy. Video Abstract

Analysis of Ras-effector interaction competition in large intestine and colorectal cancer context

Cancer is the second leading cause of death globally, and colorectal cancer (CRC) is among the five most common cancers. The small GTPase KRAS is an oncogene that is mutated in ~30% of all CRCs. Pharmacological treatments of CRC are currently unsatisfactory, but much hope rests on network-centric approaches to drug development and cancer treatment. These approaches, however, require a better understanding of how networks downstream of Ras oncoproteins are connected in a particular tissue context–here colon and CRC. Previously we have shown that competition for binding to a ‘hub’ protein, such as Ras, can induce a rewiring of signal transduction networks. In this study, we analysed 56 established and predicted effectors that contain a structural domain with the potential ability to bind to Ras oncoproteins and their link to pathways coordinating intestinal homoeostasis and barrier function. Using protein concentrations in colon tissue and Ras-effector binding affinities, a computational network model was generated that predicted how effectors differentially and competitively bind to Ras in colon context. The model also predicted both qualitative and quantitative changes in Ras-effector complex formations with increased levels of active Ras–to simulate its upregulation in cancer–simply as an emergent property of competition for the same binding interface on the surface of Ras. We also considered how the number of Ras-effector complexes at the membrane can be increased by additional domains present in some effectors that are recruited to the membrane in response to specific conditions (inputs/stimuli/growth factors) in colon context and CRC.Science Foundation Irelan

Analysis of context-specific KRAS-effector (sub)complexes in Caco-2 cells

Ras is a key switch controlling cell behavior. In the GTP-bound form, Ras interacts with numerous effectors in a mutually ex-clusive manner, where individual Ras-effectors are likely part of larger cellular (sub)complexes. The molecular details of these (sub)complexes and their alteration in specific contexts are not understood. Focusing on KRAS, we performed affinity puri-fication (AP)-mass spectrometry (MS) experiments of exoge-nously expressed FLAG-KRAS WT and three oncogenic mutants ("genetic contexts") in the human Caco-2 cell line, each exposed to 11 different culture media ("culture contexts") that mimic conditions relevant in the colon and colorectal cancer. We identified four effectors present in complex with KRAS in all genetic and growth contexts ("context-general effectors"). Seven effectors are found in KRAS complexes in only some contexts ("context-specific effectors"). Analyzing all interactors in complex with KRAS per condition, we find that the culture contexts had a larger impact on interaction rewiring than genetic contexts. We investigated how changes in the interactome impact functional outcomes and created a Shiny app for interactive visualization. We validated some of the functional differences in metabolism and proliferation. Finally, we used networks to evaluate how KRAS-effectors are involved in the modulation of functions by random walk analyses of effector-mediated (sub)complexes. Altogether, our work shows the impact of environmental contexts on network rewiring, which provides insights into tissue-specific signaling mechanisms. This may also explain why KRAS oncogenic mutants may be causing cancer only in specific tissues despite KRAS being expressed in most cells and tissues

Whole-cell energy modeling reveals quantitative changes of predicted energy flows in RAS mutant cancer cell lines

Summary: Cellular utilization of available energy flows to drive a multitude of forms of cellular “work” is a major biological constraint. Cells steer metabolism to address changing phenotypic states but little is known as to how bioenergetics couples to the richness of processes in a cell as a whole. Here, we outline a whole-cell energy framework that is informed by proteomic analysis and an energetics-based gene ontology. We separate analysis of metabolic supply and the capacity to generate high-energy phosphates from a representation of demand that is built on the relative abundance of ATPases and GTPases that deliver cellular work. We employed mouse embryonic fibroblast cell lines that express wild-type KRAS or oncogenic mutations and with distinct phenotypes. We observe shifts between energy-requiring processes. Calibrating against Seahorse analysis, we have created a whole-cell energy budget with apparent predictive power, for instance in relation to protein synthesis