Melanopsin Variants as Intrinsic Optogenetic On and Off Switches for Transient versus Sustained Activation of G Protein Pathways

Abstract

G-protein-coupled receptors (GPCRs) represent the major protein family for cellular modulation in mammals. Therefore, various strategies have been developed to analyze the function of GPCRs involving pharmaco- and optogenetic approaches [1, 2]. However, a tool that combines precise control of the activation and deactivation of GPCR pathways and/or neuronal firing with limited phototoxicity is still missing. We compared the biophysical properties and optogenetic application of a human and a mouse melanopsin variant (hOpn4L and mOpn4L) on the control of Gi/o and Gq pathways in heterologous expression systems and mouse brain. We found that GPCR pathways can be switched on/off by blue/yellow light. The proteins differ in their kinetics and wavelength dependence to activate and deactivate G protein pathways. Whereas mOpn4L is maximally activated by very short light pulses, leading to sustained G protein activation, G protein responses of hOpn4L need longer light pulses to be activated and decline in amplitude. Based on the different biophysical properties, brief light activation of mOpn4L is sufficient to induce sustained neuronal firing in cerebellar Purkinje cells (PC), whereas brief light activation of hOpn4L induces AP firing, which declines in frequency over time. Most importantly, mOpn4L-induced sustained firing can be switched off by yellow light. Based on the biophysical properties, hOpn4L and mOpn4L represent the first GPCR optogenetic tools, which can be used to switch GPCR pathways/neuronal firing on an off with temporal precision and limited phototoxicity. We suggest to name these tools moMo and huMo for future optogenetic applications.

Ectopic Expression of Mouse Melanopsin in Drosophila Photoreceptors Reveals Fast Response Kinetics and Persistent Dark Excitation

“…This claim is consistent with the heterologous expression of melanopsin-producing responses that are similar across cell types, and differing as one would expect for different melanopsins (26,27). The melanopsin-containing photoreceptors of amphioxus produce fast light responses (28).…”

Understanding melanopsin using bayesian generative models − an Introduction

“…While it is possible to use non-parametric basis-functions (e.g. splines) to estimate a non-linear form of the function, in our case there is reasonable evidence (Emanuel and Do, 2015;Spoida et al, 2016), that the activation function follows a Gaussian tuning function. We incorporate this in our model (Figure 8 A) and decided to use a Gaussian with three unknown parameters: a mean, a variance (those two parameters regulate at which wavelengths the cell get de/activated) and a normalization parameter which regulates the strength of the de/activation (Figure 8 B).…”

“…In contrast to other opsins, melanopsins' activation is tonic, once activated it stays activated for several seconds to minutes (Spoida et al, 2016). Melanopsin presumably occurs in two states, the M (active) and R (resting, inactive) states (for a review see (Schmidt and Kofuji, 2009), but see (Emanuel and Do, 2015)).…”

“…A cell containing melanopsin will begin to fire if photons of a certain wavelength activate the protein. Melanopsin is activated using blue light (470 nm) and can subsequently deactivated using green-yellow light (560 nm).In contrast to other opsins, melanopsins' activation is tonic, once activated it stays activated for several seconds to minutes (Spoida et al, 2016). Melanopsin presumably occurs in two states, the M (active) and R (resting, inactive) states (for a review see (Schmidt and Kofuji, 2009), but see (Emanuel and Do, 2015)).…”

“…Concurrently, a constant transition-probability from R to M and M to R, exists that leads the cell to an equilibrium distribution of melanopsin in M and R state configurations. Here, we use data from melanopsin patch clamp recordings (Spoida et al, 2016), where cells at resting state are activated using blue light and subsequently deactivated with red light (Figure 1) In this paper we develop a Bayesian mechanistic model of melanopsin and discuss the implementation of the model, the inverse fit, model checks, the interpretation of the parameters and how we can exchange parts of the model in a modular way to improve our understanding and design new experiments.. …”