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Operating regimes in a single enzymatic cascade at ensemble-level.

Akshay Parundekar1, Girija Kalantre1, Akshada Khadpekar1

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Cell-to-cell variability in kinase signaling, like the MAPK cascade, influences operating regimes. This study shows that analyzing protein distribution data (pMEK and pERK) can reveal these regimes without dose-response curves, aiding cellular signaling research.

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Area of Science:

  • Systems Biology
  • Cellular Signaling Networks
  • Biophysics

Background:

  • Enzymatic cascades, such as the MAPK cascade, are crucial in cellular signaling, mediating transitions between protein inactive and active states via phosphorylation-dephosphorylation cycles.
  • The information processing capabilities of these cascades are characterized by four distinct operating regimes (hyperbolic, signal-transducing, threshold-hyperbolic, ultrasensitive), defined by their dose-response curves.
  • Previous regime identification relied on deterministic models and dose-response curves, neglecting the impact of cell-to-cell variability.

Purpose of the Study:

  • To demonstrate that experimentally acquired single-cell distribution data of upstream kinase (pMEK) and response protein (pERK) can directly infer the operating regime of a MAPK cascade.
  • To establish a method for regime identification independent of traditional dose-response curves, incorporating cell-to-cell variability.
  • To investigate the influence of cell-to-cell variability on regime dynamics and transitions.

Main Methods:

  • Utilized reporter-based single-cell experimentation to obtain snapshot distributions of pMEK and pERK in Jurkat-T cells stimulated with PMA.
  • Developed a method based on the monotonic relationship between experimental observables: ratio of inter-quartile range (RIQR) and ratio of medians (RM) of pMEK and pERK distributions.
  • Employed a quasi-steady state approximated model, incorporating input gamma distribution constrained by stimulus-specific pMEK distributions, to analyze regime behavior and predict response distributions.

Main Results:

  • Established a direct link between distribution pair statistics (RIQR and RM) and the operating regime of the MAPK cascade, enabling regime inference from snapshot data.
  • Showed that cell-to-cell variability in the upstream kinase can predict bimodal pERK distributions within the signal-transducing (ST) regime.
  • Observed that low-dosage PMA stimulation maintains the hyperbolic (H) regime, while high-dosage stimulation induces a transition from the H to the ST regime in Jurkat-T cells.

Conclusions:

  • Single-cell distribution data provides a powerful, direct approach to inferring cellular signaling cascade operating regimes, even without dose-response curves.
  • Cell-to-cell variability plays a significant role in shaping signaling dynamics and can lead to complex response patterns like bimodality.
  • The study reveals regime plasticity in response to stimulus intensity, with the MAPK cascade transitioning between regimes under varying PMA concentrations.