Stochastic dynamics of Type-I interferon responses.

Area of Science:

• Immunology
• Systems Biology
• Molecular Biology

Background:

• Interferon (IFN) triggers hundreds of IFN-stimulated genes (ISGs) for antiviral defense.
• Cell-to-cell variability in ISG induction is known but its origins and impact are unclear.
• This heterogeneity is often attributed to molecular noise in the JAK-STAT signaling pathway.

Purpose of the Study:

• To investigate the sources of variability in the interferon-alpha response.
• To analyze the induction of MxA and IFIT1 as indicators of IFN response.
• To understand the robustness of the JAK-STAT pathway in transmitting signals.

Main Methods:

• Integration of time-resolved flow cytometry data.
• Stochastic modeling of the JAK-STAT signaling pathway.
• Fitting probability distributions to flow cytometry data to capture response complexity.

Main Results:

• Both experimental data and simulations showed graded responses for MxA and IFIT1 induction.
• Stochastic effects were significant during early JAK-STAT pathway steps (ligand-receptor activation, ISGF3 complex formation).
• Intrinsic variability was negligible for the final induction of the studied ISGs.

Conclusions:

• The JAK-STAT signaling pathway exhibits robustness.
• The pathway effectively transmits information even in the presence of stochasticity.
• The study clarifies the sources and impact of variability in interferon-stimulated gene induction.