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Conservation of protein abundance patterns reveals the regulatory architecture of the EGFR-MAPK pathway

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Science Signaling  12 Jul 2016:
Vol. 9, Issue 436, pp. rs6
DOI: 10.1126/scisignal.aaf0891
  • Fig. 1 Identification of core and primary feedback regulators of the EGFR-MAPK pathway.

    (A) Schematic of the “omics” assays (yellow) and analysis (red and blue). HMECs were perturbed with EGF (10 ng/ml), 10 μM U0126, or 225 mAb (10 μg/ml) overnight to identify genes whose expression was significantly altered or with EGF or 225 mAb to assess changes in protein phosphorylation. From the results (see table S1), significantly altered genes or proteins that interact with core MAPK pathway proteins and altered pathway activity were classified as feedback regulators. (B) Map of the EGFR-MAPK interaction network. Core proteins are in red, positive feedback regulators are in green, and negative feedback regulators are in blue. Activating interactions are shown as arrows, inhibiting interactions are shown as blue “T” lines, and protein-protein interactions are shown as dotted lines. Red arrow indicates unknown biochemical mechanisms. (C) HMEC 184A1 cells analyzed by global RNA-Seq and shotgun proteomics. Genes were then ranked by the sum of their mapped reads. Spectral counts of the corresponding genes were then averaged in bins of N = 500. The percent of genes in each bin for which spectral counts were recorded is indicated with filled circles. Arrows indicate ranking of gene expression of either core EGFR-MAPK pathway proteins (red) or feedback regulators (blue). Error bars are the SD of the mean of the spectral counts per bin. Data are listed in table S2.

  • Fig. 2 Relative abundance of proteins of the EGFR-MAPK signaling pathway as assessed by deep proteomics surveys.

    (A) Reported abundances of proteins in the EGFR-MAPK pathway from the study of Geiger et al. (47) with n = 11 different cell lines. Proteins are grouped into either core components or feedback regulators as described in the text. Within groups, proteins are listed alphabetically. Data from the label-free quantification intensity values for both core and regulated components of the EGFR-MAPK pathway are plotted. The box encloses the upper and lower quartiles, the midline is the median value, and the whiskers show the data range. Numbers below each protein group indicates the number of cell types in which that protein was detected. Green arrows indicate proteins that were observed in less than half of the surveyed cell types. Asterisks (*) indicate proteins that were not detected or reported. (B) Same as in (A), except the study was that of Lawrence et al. (14). In this survey, the iBAQ (intensity-based absolute quantification) label-free method was used for protein quantification, using n = 20 different cell types (n = 2 replicates each) and n = 4 tumors.

  • Fig. 3 Abundance variance of highly conserved proteins and MAPK pathway proteins appears different depending on the approach used for protein quantification.

    Blue distribution is log2 sample variance SILAC (stable isotope labeling with amino acids in cell culture) data from Khan et al. (48), using n = 5 biological replicates per species and n = 3 species (N = 15 total samples). Red distribution is iTRAQ data from the current study (n = 7 cell types), and green curve is data from the study of Lawrence et al. (14) (n = 24 samples). Data were sorted into 50 equal bins of between 15 and 40 protein variance values each. Red arrows represent the variance values of MAPK pathway proteins found in our data set for comparison, whereas the green arrows are data on the same proteins in the Lawrence et al. (14) data set.

  • Fig. 4 Variability of mRNA and protein abundance of EGFR-MAPK pathway components across cell lines using RNA-Seq and targeted proteomics.

    (A) Top: the expression of mRNA for the species indicated on the x axis was determined by RNA-Seq and normalized to reads per kilobase per million mapped reads (RPKM). Symbols correspond to values from the indicated cell lines (n = 8). Boxes represent the statistics of each species as described in the legend of Fig. 2A. Bottom: absolute quantification of the indicated proteins by targeted SRM (n = 8 cell types, each representing n = 4 samples), corrected for cell number and normalized to measured EGFR abundance as described in Materials and Methods. (B) Relationship between relative mRNA versus protein abundances of selected EGFR-MAPK pathway components across all cell lines shown in (A). The log2 value of the mRNA of each cell line (pooled from n = 4 samples) divided by the average of all lines was plotted against the comparable protein value. Error bars are SD from n = 4 samples. The lines are linear regression of the values with Pearson’s correlation coefficient (cc).

  • Fig. 5 Median abundance and variability of proteins in the EGFR-MAPK pathway in a panel of cell lines.

    (A) Plot of log2 variance of mean mRNA and protein abundance of EGFR-MAPK pathway proteins across a panel of cell lines. Red symbols are core components. Black squares are feedback regulators. Line is linear regression of all values. Dotted box is the median variance of highly conserved proteins +1 SD, derived as described in Materials and Methods. Proteins falling outside of the dotted box are individually labeled. (B) Size of each node is directly proportional to median protein abundance with a minimum node size of 7 and a maximum node size of 390. Node color reflects the calculated percent coefficient of variation of the protein (n = 7 cell lines, each value being the average of n = 4 samples). Edges are as described in the legend of Fig. 1B.

  • Fig. 6 Maximal phosphorylation of MAPK in a panel of responsive cell lines occurs well below maximal receptor occupancy.

    (A) Plot of the amounts of occupied EGFR and activated (phosphorylated, “p”) MAPK in the indicated cell lines. Data are the mean response of n = 5 independent experiments normalized to a scale of 0 to 1 ± SEM as a function of occupied receptors at 10 min. Sigmoidal curves were fit to data from SKBR3 (red), HS578T (dashed), and MDA-MB231 (blue) cell lines. Range marker corresponds to the abundance range of SOS1 + SOS2 in evaluated cell lines. Results from MCF7 cells were not included because of their lack of significant response. (B) Abundance of MAPK1 or doubly phosphorylated MAPK1 in cells treated with and without EGF (10 ng/ml) for 10 min assayed by quantitative SRM-based proteomics (55). Results are from n = 3 samples with technical replicates expressed as percent of total MAPK1 ± SD. Open circles are from MCF7 cells, whereas other symbols are the same as in (A). (C) HMEC 184A1 treated with EGF (10 ng/ml) for 5 min and occupied EGFR calculated as described in Materials and Methods. The amounts of phosphorylated MAPK (blue squares) and phosphorylated EGFR (red circles) were measured using an enzyme-linked immunosorbent assay; RAS activity was measured by a pull-down assay (42). Data are the mean response of n = 4 independent experiments normalized to a scale of 0 to 1 ± SEM and fit to a sigmoid function.

  • Table 1 Expression of mRNA encoding autocrine ligands in multiple cell lines.

    Libraries from each cell line were prepared as described in Materials and Methods, sequenced, and mapped against the reference human genome. Reads mapping to the indicated genes were converted to RPKM using Avadis NGS. Values in the top 10,000 ranking of gene expression are in boldface.

    Gene
    symbol
    Cell type
    BT20HMECsMCF10AMCF7MB231NHDFSKBR3HS578T
    AREG1.0142.484.626.431.71.61.40.1
    BTC0.71.11.10.81.10.21.70.2
    EGF0.50.20.40.61.40.43.60.5
    EPGN0.211.728.50.20.50.32.00.0
    EREG0.553.21.70.42.70.50.30.0
    HBEGF0.24.21.20.611.10.40.31.8
    TGFA7.934.03.32.622.50.50.60.1

Supplementary Materials

  • www.sciencesignaling.org/cgi/content/full/9/436/rs6/DC1

    Fig. S1. Relative abundance of proteins of the EGFR-MAPK signaling pathway as assessed by deep proteomics surveys.

    Fig. S2. Comparison of log2 variance distribution of conserved versus nonconserved proteins and mRNA.

    Fig. S3. Relationship between mRNA expression and protein abundances of EGFR-MAPK pathway components across all cell lines.

    Fig. S4. Kinetic response of cells to EGF as a function of ligand dose.

    Fig. S5. Comparison between simulated and measured EGF binding to cells.

    Fig. S6. Two-component mixture model of the copy number gain frequencies for breast and all cancers.

    Table S1. EGFR-MAPK pathway–associated genes regulated by pathway perturbation.

    Table S2. RNA expression and protein abundance of HMECs ranked by RNA-Seq reads.

    Table S3. iTRAQ and transcriptome analysis of proteins with conserved abundances.

    Table S4. Transcriptomics analysis of EGFR-MAPK pathway components across seven cell lines.

    Table S5. SRM analysis of all of the EGFR-MAPK pathway proteins.

    Table S6. Variance analysis of signaling proteins in the EGFR-MAPK pathway.

    Table S7. Copy number gain or loss frequency for genes in the EGFR pathway.

  • Supplementary Materials for:

    Conservation of protein abundance patterns reveals the regulatory architecture of the EGFR-MAPK pathway

    Tujin Shi, Mario Niepel, Jason E. McDermott, Yuqian Gao, Carrie D. Nicora, William B. Chrisler, Lye M. Markillie, Vladislav A. Petyuk, Richard D. Smith, Karin D. Rodland, Peter K. Sorger, Wei-Jun Qian, H. Steven Wiley*

    *Corresponding author. Email: steven.wiley{at}pnnl.gov

    This PDF file includes:

    • Fig. S1. Relative abundance of proteins of the EGFR-MAPK signaling pathway as assessed by deep proteomics surveys.
    • Fig. S2. Comparison of log2 variance distribution of conserved versus nonconserved proteins and mRNA.
    • Fig. S3. Relationship between mRNA expression and protein abundances of EGFRMAPK pathway components across all cell lines.
    • Fig. S4. Kinetic response of cells to EGF as a function of ligand dose.
    • Fig. S5. Comparison between simulated and measured EGF binding to cells.
    • Fig. S6. Two-component mixture model of the copy number gain frequencies for breast and all cancers.
    • Legends for tables S1 to S7

    [Download PDF]

    Technical Details

    Format: Adobe Acrobat PDF

    Size: 1 MB

    Other Supplementary Material for this manuscript includes the following:

    • Table S1 (Microsoft Excel format). EGFR-MAPK pathway–associated genes regulated by pathway perturbation.
    • Table S2 (Microsoft Excel format). RNA expression and protein abundance of HMECs ranked by RNA-Seq reads.
    • Table S3 (Microsoft Excel format). iTRAQ and transcriptome analysis of proteins with conserved abundances.
    • Table S4 (Microsoft Excel format). Transcriptomics analysis of EGFR-MAPK pathway components across seven cell lines.
    • Table S5 (Microsoft Excel format). SRM analysis of all of the EGFR-MAPK pathway proteins.
    • Table S6 (Microsoft Excel format). Variance analysis of signaling proteins in the EGFR-MAPK pathway.
    • Table S7 (Microsoft Excel format). Copy number gain or loss frequency for genes in the EGFR pathway.

    [Download Tables S1 to S7]


    Citation: T. Shi, M. Niepel, J.E. McDermott, Y. Gao, C. D. Nicora, W. B. Chrisler, L. M. Markillie, V. A. Petyuk, R. D. Smith, K. D. Rodland, P. K. Sorger, W.-J. Qian, H. S. Wiley, Conservation of protein abundance patterns reveals the regulatory architecture of the EGFR-MAPK pathway. Sci. Signal. 9, rs6 (2016).

    © 2016 American Association for the Advancement of Science

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