Determinants of Ligand Specificity and Functional Plasticity in Type I Interferon Signaling.

The Type I Interferon family of cytokines all act through the same cell surface receptor and induce phosphorylation of the same subset of response regulators of the STAT family. Despite their shared receptor, different Type I Interferons have different functions during immune response to infection. In particular, they differ in the potency of their induced anti-viral and anti-proliferative responses in target cells. It remains not fully understood how these functional differences can arise in a ligand-specific manner both at the level of STAT phosphorylation and the downstream function. We use a minimal computational model of Type I Interferon signaling, focusing on Interferon-α and Interferon-ß. We validate the model with quantitative experimental data to identify the key determinants of specificity and functional plasticity in Type I Interferon signaling. We investigate different mechanisms of signal discrimination, and how multiple system components such as binding affinity, receptor expression levels and their variability, receptor internalization, short-term negative feedback by SOCS1 protein, and differential receptor expression play together to ensure ligand specificity on the level of STAT phosphorylation. Based on these results, we propose phenomenological functional mappings from STAT activation to downstream anti-viral and anti-proliferative activity to investigate differential signal processing steps downstream of STAT phosphorylation. We find that the negative feedback by the protein USP18, which enhances differences in signaling between Interferons via ligand-dependent refractoriness, can give rise to functional plasticity in Interferon-α and Interferon-ß signaling, and explore other factors that control functional plasticity. Beyond Type I Interferon signaling, our results have a broad applicability to questions of signaling specificity and functional plasticity in signaling systems with multiple ligands acting through a bottleneck of a small number of shared receptors.

Absolute Ligand Discrimination by Dimeric Signaling Receptors.

Many signaling pathways act through shared components, where different ligand molecules bind the same receptors or activate overlapping sets of response regulators downstream. Nevertheless, different ligands acting through cross-wired pathways often lead to different outcomes in terms of the target cell behavior and function. Although a number of mechanisms have been proposed, it still largely remains unclear how cells can reliably discriminate different molecular ligands under such circumstances. Here we show that signaling via ligand-induced receptor dimerization-a very common motif in cellular signaling-naturally incorporates a mechanism for the discrimination of ligands acting through the same receptor.

Protein translocation without specific quality control in a computational model of the Tat system.

The twin-arginine translocation (Tat) system transports folded proteins of various sizes across both bacterial and plant thylakoid membranes. The membrane-associated TatA protein is an essential component of the Tat translocon, and a broad distribution of different sized TatA-clusters is observed in bacterial membranes. We assume that the size dynamics of TatA clusters are affected by substrate binding, unbinding, and translocation to associated TatBC clusters, where clusters with bound translocation substrates favour growth and those without associated substrates favour shrinkage. With a stochastic model of substrate binding and cluster dynamics, we numerically determine the TatA cluster size distribution. We include a proportion of targeted but non-translocatable (NT) substrates, with the simplifying hypothesis that the substrate translocatability does not directly affect cluster dynamical rate constants or substrate binding or unbinding rates. This amounts to a translocation model without specific quality control. Nevertheless, NT substrates will remain associated with TatA clusters until unbound and so will affect cluster sizes and translocation rates. We find that the number of larger TatA clusters depends on the NT fraction f. The translocation rate can be optimized by tuning the rate of spontaneous substrate unbinding, [Formula: see text]. We present an analytically solvable three-state model of substrate translocation without cluster size dynamics that follows our computed translocation rates, and that is consistent with in vitro Tat-translocation data in the presence of NT substrates.

Quantification of fluorophore copy number from intrinsic fluctuations during fluorescence photobleaching.

We present a theoretical technique for quantifying the cellular copy-number of fluorophores that relies on the random nature of the photobleaching process. Our approach does not require single-molecule sensitivity, and therefore can be used with commonly used epifluorescence microscopes. Fluctuations arising from photobleaching can be used to estimate the proportionality between fluorescence intensity and copy-number, which can then be used with subsequent intensity measurements to estimate copy-number. We calculate the statistical errors of our approach and verify them with stochastic simulations. By using fluctuations over the entire photobleaching process, we obtain significantly smaller errors than previous approaches that have used fluctuations arising from cytoplasmic proteins partitioning during cellular division. From the time-dependence of the fluctuations as photobleaching proceeds, we can discriminate between desired photobleach fluctuations and background noise or photon shot noise. Our approach does not require cellular division and the photobleaching rate sets a timescale that is adjustable with respect to cellular processes. We hope that our approach will now be applied experimentally.