Analysis of Biased Agonism

Probing phosphorylation events in biological membranes: The transducer function

The extracellular environment is sensed by receptors in the plasma membrane. Some of these receptors initiate cytoplasmic signaling cascades involving phosphorylation: the addition of a phosphate group to a specific amino acid, such as tyrosine, in a protein. Receptor Tyrosine Kinases (RTKs) are one large class of membrane receptors that can directly initiate signaling cascades through their intracellular kinase domains, which both catalyze tyrosine phosphorylation and get phosphorylated. In the first step of signaling, the ligands stabilize phosphorylation-competent RTK dimers and oligomers, which leads to the phosphorylation of specific tyrosine residues in the activation loop of the kinases. Here we discuss quantitative measurements of tyrosine phosphorylation efficiencies for RTKs, described by the "transducer function". The transducer function links the phosphorylation (the response) and the binding of the activating ligand to the receptor (the stimulus). We overview a methodology that allows such measurements in direct response to ligand binding. We discuss experiments which demonstrate that EGF is a partial agonist, and that two tyrosines in the intracellular domain of EGFR, Y1068 and Y1173, are differentially phosphorylated in the EGF-bound EGFR dimers.

Ligand bias underlies differential signaling of multiple FGFs via FGFR1

The differential signaling of multiple FGF ligands through a single fibroblast growth factor (FGF) receptor (FGFR) plays an important role in embryonic development. Here, we use quantitative biophysical tools to uncover the mechanism behind differences in FGFR1c signaling in response to FGF4, FGF8, and FGF9, a process which is relevant for limb bud outgrowth. We find that FGF8 preferentially induces FRS2 phosphorylation and extracellular matrix loss, while FGF4 and FGF9 preferentially induce FGFR1c phosphorylation and cell growth arrest. Thus, we demonstrate that FGF8 is a biased FGFR1c ligand, as compared to FGF4 and FGF9. Förster resonance energy transfer experiments reveal a correlation between biased signaling and the conformation of the FGFR1c transmembrane domain dimer. Our findings expand the mechanistic understanding of FGF signaling during development and bring the poorly understood concept of receptor tyrosine kinase ligand bias into the spotlight.

Dynamics of receptor activation by agonists

How do agonists turn on receptors? The model system we have used to address this question is the adult-type skeletal muscle nicotinic acetylcholine receptor. This ligand-gated ion channel has two orthosteric sites (for neurotransmitters) in the extracellular domain linked to an allosteric site (a gate) in the transmembrane domain. The goal of this perspective is to summarize how measurements of agonist binding energy reveal the dynamics of the neurotransmitter sites and the fundamental link between binding and gating.

Quantitative receptor model for responses that are left- or right-shifted versus occupancy (are more or less concentration sensitive): the SABRE approach

Simple one-to three-parameter models routinely used to fit typical dose-response curves and calculate EC50 values using the Hill or Clark equation cannot provide the full picture connecting measured response to receptor occupancy, which can be quite complex due to the interplay between partial agonism and (pathway-dependent) signal amplification. The recently introduced SABRE quantitative receptor model is the first one that explicitly includes a parameter for signal amplification (γ) in addition to those for binding affinity (K d), receptor-activation efficacy (ε), constitutive activity (ε R0), and steepness of response (Hill slope, n). It can provide a unified framework to fit complex cases, where fractional response and occupancy do not match, as well as simple ones, where parameters constrained to specific values can be used (e.g., ε R0 = 0, γ = 1, or n = 1). Here, it is shown for the first time that SABRE can fit not only typical cases where response curves are left-shifted compared to occupancy (κ = K d/EC50 > 1) due to signal amplification (γ > 1), but also less common ones where they are right-shifted (i.e., less concentration-sensitive; κ = K d/EC50 < 1) by modeling them as apparent signal attenuation/loss (γ < 1). Illustrations are provided with μ-opioid receptor (MOPr) data from three different experiments with one left- and one right-shifted response (G protein activation and β-arrestin2 recruitment, respectively; EC50,Gprt < K d < EC50,βArr). For such cases of diverging pathways with differently shifted responses, partial agonists can cause very weak responses in the less concentration-sensitive pathway without having to be biased ligands due to the combination of low ligand efficacy and signal attenuation/loss-an illustration with SABRE-fitted oliceridine data is included.

Quantification of ligand and mutation-induced bias in EGFR phosphorylation in direct response to ligand binding

Signaling bias is the ability of a receptor to differentially activate downstream signaling pathways in response to different ligands. Bias investigations have been hindered by inconsistent results in different cellular contexts. Here we introduce a methodology to identify and quantify bias in signal transduction across the plasma membrane without contributions from feedback loops and system bias. We apply the methodology to quantify phosphorylation efficiencies and determine absolute bias coefficients. We show that the signaling of epidermal growth factor receptor (EGFR) to EGF and TGFα is biased towards Y1068 and against Y1173 phosphorylation, but has no bias for epiregulin. We further show that the L834R mutation found in non-small-cell lung cancer induces signaling bias as it switches the preferences to Y1173 phosphorylation. The knowledge gained here challenges the current understanding of EGFR signaling in health and disease and opens avenues for the exploration of biased inhibitors as anti-cancer therapies.

Quantitative assessment of ligand bias from bias plots: The bias coefficient "kappa"

The current methods for quantifying ligand bias involve the construction of bias plots and the calculations of bias coefficients that can be compared using statistical methods. However, widely used bias coefficients can diverge in their abilities to identify ligand bias and can give false positives. As the empirical bias plots are considered the most reliable tools in bias identification, here we develop an analytical description of bias plot trajectories and introduce a bias coefficient, kappa, which is calculated from these trajectories. The new bias coefficient complements the tool-set in ligand bias identification in cell signaling research.

Agonist efficiency links binding and gating in a nicotinic receptor

Receptors signal by switching between resting (C) and active (O) shapes ('gating') under the influence of agonists. The receptor's maximum response depends on the difference in agonist binding energy, O minus C. In nicotinic receptors, efficiency (η) represents the fraction of agonist binding energy applied to a local rearrangement (an induced fit) that initiates gating. In this receptor, free energy changes in gating and binding can be interchanged by the conversion factor η. Efficiencies estimated from concentration-response curves (23 agonists, 53 mutations) sort into five discrete classes (%): 0.56 (17), 0.51(32), 0.45(13), 0.41(26), and 0.31(12), implying that there are 5 C versus O binding site structural pairs. Within each class efficacy and affinity are corelated linearly, but multiple classes hide this relationship. η unites agonist binding with receptor gating and calibrates one link in a chain of coupled domain rearrangements that comprises the allosteric transition of the protein.

The biophysical basis of receptor tyrosine kinase ligand functional selectivity: Trk-B case study

Tropomyosin receptor kinase B (Trk-B) belongs to the second largest family of membrane receptors, Receptor Tyrosine Kinases (RTKs). Trk-B is known to interact with three different neurotrophins: Brain-Derived Neurotrophic Factor (BDNF), Neurotrophin-4 (NT-4), and Neurotrophin-3 (NT-3). All three neurotrophins are involved in survival and proliferation of neuronal cells, but each induces distinct signaling through Trk-B. We hypothesize that the different biological effects correlate with differences in the interactions between the Trk-B receptors, when bound to different ligands, in the plasma membrane. To test this hypothesis, we use quantitative FRET to characterize Trk-B dimerization in response to NT-3 and NT-4 in live cells, and compare it to the previously published data for Trk-B in the absence and presence of BDNF. Our study reveals that the distinct Trk-B signaling outcomes are underpinned by both different configurations and different stabilities of the three ligand-bound Trk-B dimers in the plasma membrane.

Ligand bias in receptor tyrosine kinase signaling

Ligand bias is the ability of ligands to differentially activate certain receptor signaling responses compared with others. It reflects differences in the responses of a receptor to specific ligands and has implications for the development of highly specific therapeutics. Whereas ligand bias has been studied primarily for G protein-coupled receptors (GPCRs), there are also reports of ligand bias for receptor tyrosine kinases (RTKs). However, the understanding of RTK ligand bias is lagging behind the knowledge of GPCR ligand bias. In this review, we highlight how protocols that were developed to study GPCR signaling can be used to identify and quantify RTK ligand bias. We also introduce an operational model that can provide insights into the biophysical basis of RTK activation and ligand bias. Finally, we discuss possible mechanisms underpinning RTK ligand bias. Thus, this review serves as a primer for researchers interested in investigating ligand bias in RTK signaling.

A single unified model for fitting simple to complex receptor response data

The fitting of complex receptor-response data where fractional response and occupancy do not match is challenging. They encompass important cases including (a) the presence of "receptor reserve" and/or partial agonism, (b) multiple responses assessed at different vantage points along a pathway, (c) responses that are different along diverging downstream pathways (biased agonism), and (d) constitutive activity. For these, simple models such as the well-known Clark or Hill equations cannot be used. Those that can, such as the operational (Black&Leff) model, do not provide a unified approach, have multiple nonintuitive parameters that are challenging to fit in well-defined manner, have difficulties incorporating binding data, and cannot be reduced or connected to simpler forms. We have recently introduced a quantitative receptor model (SABRE) that includes parameters for Signal Amplification (γ), Binding affinity (Kd), Receptor activation Efficacy (ε), and constitutive activity (εR0). It provides a single equation to fit complex cases within a full two-state framework with the possibility of incorporating receptor occupancy data (i.e., experimental Kds). Simpler cases can be fit by using consecutively reduced forms obtained by constraining parameters to specific values, e.g., εR0 = 0: no constitutive activity, γ = 1: no amplification (Emax-type fitting), and ε = 1: no partial agonism (Clark equation). Here, a Hill-type extension is introduced (n ≠ 1), and simulated and experimental receptor-response data from simple to increasingly complex cases are fitted within the unified framework of SABRE with differently constrained parameters.

A Receptor Model With Binding Affinity, Activation Efficacy, and Signal Amplification Parameters for Complex Fractional Response Versus Occupancy Data

In quantitative pharmacology, multi-parameter receptor models are needed to account for the complex nonlinear relationship between fractional occupancy and response that can occur due to the intermixing of the effects of partial receptor activation and post-receptor signal amplification. Here, a general two-state receptor model and corresponding quantitative forms are proposed that unify three distinct processes, each characterized with its own parameter: 1) receptor binding, characterized by K_d, the equilibrium dissociation constant used for binding affinity; 2) receptor activation, characterized by an (intrinsic) efficacy parameter ε; and 3) post-activation signal transduction (amplification), characterized by a gain parameter γ. Constitutive activity is accommodated via an additional ε_R0 parameter quantifying the activation of the ligand-free receptor. Receptors can be active or inactive in both their ligand-free and ligand-bound states (two-state receptor theory), but ligand binding alters the likelihood of activation (induced fit). Because structural data now confirm that for most receptors in their active conformation, the small-molecule ligand-binding site is buried inside, straightforward binding to the active form (direct conformational selection) is unlikely. The proposed general equation has parameters that are more intuitive and better suited for optimization by nonlinear regression than those of the operational (Black and Leff) or del Castillo–Katz model. The model provides a unified framework for fitting complex data including a) fractional responses that do not match independently measured fractional occupancies, b) responses measured after partial irreversible inactivation of the “receptor reserve” (Furchgott method), c) fractional responses that are different along distinct downstream pathways (biased agonism), and d) responses with constitutive receptor activity. Furthermore, unlike previous models, the present one can be reduced back for special cases of its parameters to consecutively nested simplified forms that can be used on their own when adequate (e.g., ε_R0 = 0, no constitutive activity; γ = 1: E_max model for partial agonism; ε = 1: Clark equation).