Research ArticleCell Biology

Short-term cellular memory tunes the signaling responses of the chemokine receptor CXCR4

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Science Signaling  09 Jul 2019:
Vol. 12, Issue 589, eaaw4204
DOI: 10.1126/scisignal.aaw4204
  • Fig. 1 Conditioning cells with a growth stimulus potentiates subsequent CXCR4 signaling.

    (A) CXCL12 binds to CXCR4 and elicits downstream Akt and ERK activation. Separate kinase translocation reporters (KTRs) for Akt (Aquamarine) and ERK (mCitrine) were stably expressed in breast cancer cells. Phosphorylation and dephosphorylation of the kinase substrate drive the reporter into the cytoplasm or nucleus, respectively. (B) Cells were conditioned for 4 hours with or without growth stimuli or kinase inhibitors. Single-cell, time-lapse imaging was performed for 10 min before and 50 min after addition of CXCL12 (10 ng/ml). Single-cell time tracks (control conditioning, n = 347 cells; FBS conditioning, n = 312 cells) show CXCR4-dependent activation of Akt and ERK in MDA-MB-231 breast cancer cells quantified as log2 of cytoplasmic-to-nuclear ratio (C/N) of fluorescence intensities for each KTR in individual cells and displayed on a pseudocolor scale. (C) Quantification of Akt and ERK activation in cells conditioned with FBS before CXCL12 stimulation (control conditioning, n = 347 cells; FBS conditioning, n = 312 cells). Strong activation was defined by ≥1 increase in log2(C/N) unit. (D) Quantification of Akt and ERK activation in cells conditioned with FBS for 4 (n = 312 cells) or 7 (n = 367 cells) hours compared to control conditioning (n = 347 cells) before CXCL12 stimulation. (E) Quantification of Akt and ERK activation in cells that were conditioned with various concentrations of epidermal growth factor (EGF) for 4 hours before CXCL12 stimulation [control conditioning, n = 347 cells; EGF conditioning (1 ng/ml), 354 cells; EGF conditioning (10 ng/ml), 370 cells; EGF conditioning (30 ng/ml), 358 cells].

  • Fig. 2 The computational conditional signaling model (CSM) predicts CXCR4-mediated Akt and ERK signaling responses, establishing a framework for understanding the range of heterogeneous signaling data.

    (A) The CXCL12-CXCR4 interaction elicits G protein signaling to activate Akt and ERK but can be restrained by negative feedback and cross-talk mechanisms. mTORC1 functions as a central regulator of signaling because it can inhibit the activation of both Akt and ERK. Extrinsic noise in phosphatidylinositol 3-kinase (PI3K), Ras, and mTORC1 promotes activation of Akt and/or ERK in the absence of CXCR4-mediated signaling. Signaling kinetics cover a range of time scales with thicker arrows and lines qualitatively indicating faster reaction rates. A complete list of differential equations, initial conditions, and parameters is available in tables S1 to S5. (B) To encompass heterogeneous signaling responses of single cells in both Akt and ERK, we varied extrinsic noise parameters for PI3K, Ras, and mTORC1 in the CSM. By running combinations of these three parameters, we generated a model library of >12,000 predicted paired Akt and ERK responses. We performed a least-square fit of experimentally determined Akt and ERK responses from the KTRs to predicted responses to derive the PI3K, Ras, and mTORC1 extrinsic noise parameters that best describe each single cell in the experiments.

  • Fig. 3 The CSM captures heterogeneous single-cell signaling responses seen in experiments and reveals the conditional signaling states controlling responsiveness to CXCR4 signaling.

    (A) Responses from the model library match experimentally determined control and FBS conditioned single-cell (control conditioning, n = 347 cells; FBS conditioning, n = 312 cells) CXCR4 signaling to Akt and ERK. Greater than 95% of cells fit the matching criteria. The gray dashed line identifies when CXCL12 was added (10-min time point). Images were taken every 2 min for 60 min. (B) Akt or ERK responses through CXCR4 as affected by different combinations of extrinsic noise parameters for PI3K (orange), Ras (green), and mTORC1 (blue) during simulation. Plots show a 2D plane of the entire 3D signaling landscape generated by varying three parameters (kPI3K, kRas, and kmTORC1) combinatorially. Green and orange dashed lines denote the value on the third axis.

  • Fig. 4 FBS conditioning shifted the conditional signaling state of MDA-MB-231 breast cancer cells to a region more permissive to CXCL12-CXCR4 signaling to Akt.

    (A to C) Occupancy maps (normalized to contain 1 × 106 cells) illustrate combinations of extrinsic noise parameters (conditional signaling state) corresponding to regions of 3D signaling landscape (CSM output) where experimental cells most frequently match. Contour lines display numbers of cells out of 1 × 106. Occupancies were summed in the third dimension for purposes of viewing the map in two dimensions. Cyan and yellow underlays illustrate regions of responsiveness for Akt and ERK, respectively. Green and orange lines illustrate the specific Ras and PI3K planes, respectively, corresponding to the responsiveness underlays. Occupancy maps are shown for MDA-MB-231 cells that received control conditioning (A) or FBS conditioning (B) before CXCR4 stimulation. (C) Quantification of cells with each signaling response. We defined responses in the computational model as >5 nM increases in kinase activity for Akt or ERK.

  • Fig. 5 Genetic mutations set the subset of conditional states available to cells, but these states can be tuned to further edit signaling behavior.

    (A) Occupancy map shown as contour lines is a 3D histogram, which (normalized to contain 1 × 106 cells) illustrates combinations of extrinsic noise parameters (conditional signaling state) corresponding to regions of the 3D signaling landscape (CSM output) where experimental cells most frequently match. Cyan and yellow underlays illustrate regions of responsiveness for Akt and ERK, respectively. Green and orange lines illustrate the specific Ras and PI3K planes, respectively, corresponding to the responsiveness underlays. Occupancy map shown for patient-derived Vari-068 cells (which have an inactivating mutation in PTEN) that received control conditioning before CXCR4 stimulation. (B) Occupancy map shown as contour lines (normalized to contain 1 × 106 cells) illustrates combinations of extrinsic noise parameters corresponding to regions of the 3D signaling landscape where experimental cells most frequently match. Cyan and yellow underlays illustrate regions of responsiveness for Akt and ERK, respectively. Green and orange lines illustrate the specific Ras and PI3K planes, respectively, corresponding to the responsiveness underlays. Occupancy maps are shown for patient-derived Vari-068 cells that received FBS conditioning before CXCR4 stimulation. (C) Quantification of cells with each signaling response. We defined responses from the computational model as >5 nM increases in kinase activity for Akt or ERK.

  • Fig. 6 SUM-159 cells occupy conditional signaling states with ERK responsiveness.

    (A) Occupancy map shows where experimental cells most frequently match model predictions. Cyan and yellow underlays illustrate regions of responsiveness for Akt and ERK, respectively. Green and orange lines illustrate the specific Ras and PI3K planes, respectively, corresponding to the responsiveness underlays. Occupancy map is shown for SUM-159 cells (which have activating mutations in both ERK and Akt) that received control conditioning. (B) Summary of the percentage of cells with each signaling response. We defined responses from the computational model as >5 nM increases in kinase activity for Akt or ERK. (C) A summary of how genetic mutations and growth factor conditioning interact to tune cellular responsiveness in Akt and ERK by shifting the conditional signaling state at the single-cell scale. CA, constitutive activation; lof, loss of function.

  • Fig. 7 Computational modeling correctly predicts that trametinib conditioning potentiates subsequent CXCR4-mediated Akt signaling in a subset of MDA-MB-231 cells.

    (A) The CSM predicts Akt (left) and ERK (right) signaling dynamics at simulated concentrations of trametinib conditioning that inhibit 50 or 90% of MEK activity relative to control before CXCR4 stimulation. The predicted signaling dynamics denoted by the red and blue dots correspond to different conditional signaling states and correspond to the dots in (C). (B) The CSM accurately predicts single-cell experimental CXCR4-mediated Akt and ERK signaling dynamics for trametinib conditioning (n = 447 cells) on MDA-MB-231 cells (control conditioning, n = 429 cells). Trametinib conditioning was modeled as a 50% decrease in the rate of MEK-mediated phosphorylation of ERK, consistent with noncompetitive inhibition kinetics. (C) Difference maps show the CSM-predicted change in peak Akt activation between the control conditioned and trametinib-conditioned cells at each conditional signaling state. Shaded gray surface contours show regions in signaling landscape with conditional signaling states that position trametinib-conditioned cells for the listed increases in peak Akt activation (nM). Contour lines display numbers of cells out of 1 million occupying conditional signaling states after matching experimental trametinib-conditioned cells to the CSM. Green and orange lines illustrate the specific Ras and PI3K planes, respectively, of the CSM corresponding to the responsiveness underlays. Red dots indicate conditional signaling states that were permissive to Akt signaling under control conditioning. Blue dots indicate conditional states that were not permissive to Akt signaling under control conditioning. The red and blue dots from (C) correspond with those in (A).

  • Fig. 8 Computational modeling correctly predicts that conditioning with the mTORC1 inhibitor ridaforolimus potentiates subsequent CXCR4-mediated Akt and ERK signaling in MDA-MB-231 cells.

    (A) The CSM predicts Akt (left) and ERK (right) signaling dynamics at simulated concentrations of ridaforolimus conditioning that inhibit mTORC1 activity by 50 or 90% relative to control before CXCR4 stimulation. The predicted signaling dynamics denoted by the red and blue dots correspond to cells able to respond and not respond in Akt under control conditioning, respectively. (B) Difference maps show the CSM-predicted change in peak Akt (left) and peak ERK (right) activation between the control conditioned and ridaforolimus-conditioned cells at each conditional signaling state. Shaded gray surface contours show regions in signaling landscape with conditional signaling states that position ridaforolimus-conditioned cells for the listed increases in peak activation (nM). Contour lines display numbers of cells out of 1 million occupying conditional signaling states after matching experimental ridaforolimus-conditioned cells to the CSM. Green and orange lines illustrate the specific Ras and PI3K planes, respectively, of the CSM corresponding to the responsiveness underlays. The red and blue dots from (B) correspond to those in (A). The CSM-predicted signaling dynamics are from 50% inhibition of mTORC1.

Supplementary Materials

  • stke.sciencemag.org/cgi/content/full/12/589/eaaw4204/DC1

    Fig. S1. KTRs report concentration- and time-dependent CXCR4 signaling in response to FBS.

    Fig. S2. There is a trend between Akt responsiveness and ERK responsiveness, but initial Akt and ERK activity are poorly correlated with responsiveness in each respective kinase.

    Fig. S3. CSM for CXCR4 signaling to ERK and Akt.

    Fig. S4. Differential equations for the three species containing extrinsic noise terms in the computational model.

    Fig. S5. Extrinsic noise parameters for PI3K, Ras, and mTORC1 produce a highly differentiated signaling landscape of basal activity and CXCR4 responsiveness to ERK and Akt.

    Fig. S6. Conditional states of cells shift in the context of different conditioning times, stimuli, and genetic mutations.

    Fig. S7. Computational modeling of responses to the MEK inhibitor trametinib and the mTORC1 inhibitor ridaforolimus shows concentration-dependent, context-specific effects on the activation of Akt and ERK by CXCR4 in MDA-MB-231 cells.

    Table S1. CSM species descriptions and initial conditions.

    Table S2. CSM rate equations.

    Table S3. CSM differential equations.

    Table S4. CSM parameter values.

    Table S5. CSM parameters for modeling kinase inhibition.

    References (6575)

  • This PDF file includes:

    • Fig. S1. KTRs report concentration- and time-dependent CXCR4 signaling in response to FBS.
    • Fig. S2. There is a trend between Akt responsiveness and ERK responsiveness, but initial Akt and ERK activity are poorly correlated with responsiveness in each respective kinase.
    • Fig. S3. CSM for CXCR4 signaling to ERK and Akt.
    • Fig. S4. Differential equations for the three species containing extrinsic noise terms in the computational model.
    • Fig. S5. Extrinsic noise parameters for PI3K, Ras, and mTORC1 produce a highly differentiated signaling landscape of basal activity and CXCR4 responsiveness to ERK and Akt.
    • Fig. S6. Conditional states of cells shift in the context of different conditioning times, stimuli, and genetic mutations.
    • Fig. S7. Computational modeling of responses to the MEK inhibitor trametinib and the mTORC1 inhibitor ridaforolimus shows concentration-dependent, context-specific effects on the activation of Akt and ERK by CXCR4 in MDA-MB-231 cells.
    • Table S1. CSM species descriptions and initial conditions.
    • Table S2. CSM rate equations.
    • Table S3. CSM differential equations.
    • Table S4. CSM parameter values.
    • Table S5. CSM parameters for modeling kinase inhibition.
    • References (6575)

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