Dataset Information

Extensive Rewiring of the the Epidermal Growth Factor Receptor EGFR Networks in Colorectal Cancer Cells Expressing Transforming Levels of Oncogenic KRASG13D

ABSTRACT: Protein-protein-interaction networks (PPINs) organize fundamental biological processes, but how oncogenic mutations impact these interactions and their functions at a network-level scale is poorly understood. Here, we analyze how a common oncogenic KRAS mutation (KRASG13D) affects PPIN structure and function of the Epidermal Growth Factor Receptor (EGFR) network in colorectal cancer (CRC) cells. Mapping >6,000 PPIs shows that this network is extensively rewired in cells expressing transforming levels of KRASG13D (mtKRAS). The factors driving PPIN rewiring are multifactorial including changes in protein expression and phosphorylation. Mathematical modelling also suggests that the binding dynamics of low and high affinity KRAS interactors contribute to rewiring. PPIN rewiring substantially alters the composition of protein complexes, signal flow, transcriptional regulation, and cellular phenotype. These changes are validated by targeted and global experimental analysis. Importantly, genetic alterations in the most extensively rewired PPIN nodes occur frequently in CRC and are prognostic of poor patient outcomes.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Cell

DISEASE(S): Colon Cancer

SUBMITTER: Mohamed Ali Jarboui

LAB HEAD: Mohamed Ali Jarboui

PROVIDER: PXD016431 | Pride | 2019-12-04

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	170302_o2_05_so170123_Hu_phospho_HCT116-2.raw	Raw
	170302_o2_06_so170123_Hu_phospho_HKE3-2.raw	Raw
	170302_o2_08_so170123_Hu_phospho_HCT116-3.raw	Raw
	170302_o2_09_so170123_Hu_phospho_HKE3-3.raw	Raw
	170302_o2_11_so170123_Hu_phospho_HCT116-4.raw	Raw

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Publications

Extensive rewiring of the EGFR network in colorectal cancer cells expressing transforming levels of KRAS<sup>G13D</sup>.

Kennedy Susan A SA Jarboui Mohamed-Ali MA Srihari Sriganesh S Raso Cinzia C Bryan Kenneth K Dernayka Layal L Charitou Theodosia T Bernal-Llinares Manuel M Herrera-Montavez Carlos C Krstic Aleksandar A Matallanas David D Kotlyar Max M Jurisica Igor I Curak Jasna J Wong Victoria V Stagljar Igor I LeBihan Thierry T Imrie Lisa L Pillai Priyanka P Lynn Miriam A MA Fasterius Erik E Al-Khalili Szigyarto Cristina C Breen James J Kiel Christina C Serrano Luis L Rauch Nora N Rukhlenko Oleksii O Kholodenko Boris N BN Iglesias-Martinez Luis F LF Ryan Colm J CJ Pilkington Ruth R Cammareri Patrizia P Sansom Owen O Shave Steven S Auer Manfred M Horn Nicola N Klose Franziska F Ueffing Marius M Boldt Karsten K Lynn David J DJ Kolch Walter W

Nature communications 20200124 1

Protein-protein-interaction networks (PPINs) organize fundamental biological processes, but how oncogenic mutations impact these interactions and their functions at a network-level scale is poorly understood. Here, we analyze how a common oncogenic KRAS mutation (KRAS<sup>G13D</sup>) affects PPIN structure and function of the Epidermal Growth Factor Receptor (EGFR) network in colorectal cancer (CRC) cells. Mapping >6000 PPIs shows that this network is extensively rewired in cells expressing tran ...[more]

PMID: 31980649

Similar Datasets

Project description:This dataset contains transcription profiles of TOP10 E.coli transformed with 85 rewired network plasmids first described in: Evolvability and hierarchy in rewired bacterial gene networks. Nature 452:840-5 (2008). The data are described and analysed in an accompanying paper Baumstark et al.,The propagation of perturbations in shuffled bacterial gene networks (Submitted, 2015). 260 raw data microarrays are provided in total, representing biological triplicates (a,b and c; independent colonies grown from the same transformation, under standard growth conditions; LB, 16h). This is done for each of 255 promoter-ORF constructs (e.g. appY-crp, etc.) and 5 control construct microarrays (empty plasmid; Co). Co controls are provided for comparison, to show the relative effect of the rewiring genetic perturbation. The final processed data compares the number of perturbed genes, comparing between the average expression values of each rewired construct and Co. Methods: The 85 rewiring plasmids (Isalan et al, 2008) were transformed into E. coli TOP10 cells and grown under standardised conditions: bacteria were freshly plated onto LB Agar plates (with 100 μg/ml Ampicillin and 50 μg/ml Streptomycin) and incubated overnight at 37oC to form colonies. Single colonies (<3 days old) were used to inoculate 2 ml of LB in 14 ml culture tubes, containing 100 μg/ml Ampicillin and 50 μg/ml Streptomycin. Constructs were grown for 16h at 37oC, at 220 rpm in an orbital shaker. 10 μg of extracted total bacterial RNA (integrity number > 7.0) was used with Affymetrix GeneChip E. coli Genome 2.0 Arrays. Key to names: RAW DATA samples 1-260 _Co_1a.CEL = Control Co, colony a _Co_1b.CEL = Control Co, colony b etc. appY_O_30a.CEL = appY-promoter only, colony a appY_O_30b.CEL = appY-promoter only, colony b etc. appY_crp_34a.CEL = rewired construct, appY-promoter expressing crp ORF, colony a appY_crp_34b.CEL = rewired construct, appY-promoter expressing crp ORF, colony b appY_crp_34b.CEL = rewired construct, appY-promoter expressing crp ORF, colony c etc. PROCESSED DATA sample 261: probe_set_expression_value_norm_all - normalised data for all samples, for all E. coli K12 genes sample 262: probe_set_expression_value_norm_MG1655 - normalised data for all samples, for E. coli MG1655 subset of genes