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Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics.

ABSTRACT: We engineered light-gated channelrhodopsins (ChRs) whose current strength and light sensitivity enable minimally invasive neuronal circuit interrogation. Current ChR tools applied to the mammalian brain require intracranial surgery for transgene delivery and implantation of fiber-optic cables to produce light-dependent activation of a small volume of tissue. To facilitate expansive optogenetics without the need for invasive implants, our engineering approach leverages the substantial literature of ChR variants to train statistical models for the design of high-performance ChRs. With Gaussian process models trained on a limited experimental set of 102 functionally characterized ChRs, we designed high-photocurrent ChRs with high light sensitivity. Three of these, ChRger1-3, enable optogenetic activation of the nervous system via systemic transgene delivery. ChRger2 enables light-induced neuronal excitation without fiber-optic implantation; that is, this opsin enables transcranial optogenetics.

SUBMITTER: Bedbrook CN

PROVIDER: S-EPMC6858556 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics.

Bedbrook Claire N CN Yang Kevin K KK Robinson J Elliott JE Mackey Elisha D ED Gradinaru Viviana V Arnold Frances H FH

Nature methods 20191014 11

We engineered light-gated channelrhodopsins (ChRs) whose current strength and light sensitivity enable minimally invasive neuronal circuit interrogation. Current ChR tools applied to the mammalian brain require intracranial surgery for transgene delivery and implantation of fiber-optic cables to produce light-dependent activation of a small volume of tissue. To facilitate expansive optogenetics without the need for invasive implants, our engineering approach leverages the substantial literature ...[more]

PMID: 31611694

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Project description:The structure-guided design of chloride-conducting channelrhodopsins has illuminated mechanisms underlying ion selectivity of this remarkable family of light-activated ion channels. The first generation of chloride-conducting channelrhodopsins, guided in part by development of a structure-informed electrostatic model for pore selectivity, included both the introduction of amino acids with positively charged side chains into the ion conduction pathway and the removal of residues hypothesized to support negatively charged binding sites for cations. Engineered channels indeed became chloride selective, reversing near -65 mV and enabling a new kind of optogenetic inhibition; however, these first-generation chloride-conducting channels displayed small photocurrents and were not tested for optogenetic inhibition of behavior. Here we report the validation and further development of the channelrhodopsin pore model via crystal structure-guided engineering of next-generation light-activated chloride channels (iC++) and a bistable variant (SwiChR++) with net photocurrents increased more than 15-fold under physiological conditions, reversal potential further decreased by another ∼ 15 mV, inhibition of spiking faithfully tracking chloride gradients and intrinsic cell properties, strong expression in vivo, and the initial microbial opsin channel-inhibitor-based control of freely moving behavior. We further show that inhibition by light-gated chloride channels is mediated mainly by shunting effects, which exert optogenetic control much more efficiently than the hyperpolarization induced by light-activated chloride pumps. The design and functional features of these next-generation chloride-conducting channelrhodopsins provide both chronic and acute timescale tools for reversible optogenetic inhibition, confirm fundamental predictions of the ion selectivity model, and further elucidate electrostatic and steric structure-function relationships of the light-gated pore.

Dataset Information

Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics.

Publications

Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics.

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