Autoosa Salari

Molecular Interactions between Tarantula Toxins and Low-Voltage-Activated Calcium Channels

Few gating-modifier toxins have been reported to target low-voltage-activated (LVA) calcium channels, and the structural basis of toxin sensitivity remains incompletely understood. Studies of voltage-gated potassium (Kv) channels have identified the S3b–S4 “paddle motif,” which moves at the protein-lipid interface to drive channel opening, as the target for these amphipathic neurotoxins. Voltage-gated calcium (Cav) channels contain four homologous voltage sensor domains, suggesting multiple toxin binding sites. We show here that the S3–S4 segments within Cav3.1 can be transplanted into Kv2.1 to examine their individual contributions to voltage sensing and pharmacology. With these results, we now have a more complete picture of the conserved nature of the paddle motif in all three major voltage-gated ion channel types (Kv, Nav, and Cav). When screened with tarantula toxins, the four paddle sequences display distinct toxin binding properties, demonstrating that gating-modifier toxins can bind to Cav channels in a domain specific fashion. Domain III was the most commonly and strongly targeted, and mutagenesis revealed an acidic residue that is important for toxin binding. We also measured the lipid partitioning strength of all toxins tested and observed a positive correlation with their inhibition of Cav3.1, suggesting a key role for membrane partitioning.

Estimating kinetic mechanisms with prior knowledge I: Linear parameter constraints

To understand how ion channels and other proteins function at the molecular and cellular levels, one must decrypt their kinetic mechanisms. Sophisticated algorithms have been developed that can be used to extract kinetic parameters from a variety of experimental data types. However, formulating models that not only explain new data, but are also consistent with existing knowledge, remains a challenge. Here, we present a two-part study describing a mathematical and computational formalism that can be used to enforce prior knowledge into the model using constraints. In this first part, we focus on constraints that enforce explicit linear relationships involving rate constants or other model parameters. We develop a simple, linear algebra–based transformation that can be applied to enforce many types of model properties and assumptions, such as microscopic reversibility, allosteric gating, and equality and inequality parameter relationships. This transformation converts the set of linearly interdependent model parameters into a reduced set of independent parameters, which can be passed to an automated search engine for model optimization. In the companion article, we introduce a complementary method that can be used to enforce arbitrary parameter relationships and any constraints that quantify the behavior of the model under certain conditions. The procedures described in this study can, in principle, be coupled to any of the existing methods for solving molecular kinetics for ion channels or other proteins. These concepts can be used not only to enforce existing knowledge but also to formulate and test new hypotheses.

Estimating kinetic mechanisms with prior knowledge II: Behavioral constraints and numerical tests

Kinetic mechanisms predict how ion channels and other proteins function at the molecular and cellular levels. Ideally, a kinetic model should explain new data but also be consistent with existing knowledge. In this two-part study, we present a mathematical and computational formalism that can be used to enforce prior knowledge into kinetic models using constraints. Here, we focus on constraints that quantify the behavior of the model under certain conditions, and on constraints that enforce arbitrary parameter relationships. The penalty-based optimization mechanism described here can be used to enforce virtually any model property or behavior, including those that cannot be easily expressed through mathematical relationships. Examples include maximum open probability, use-dependent availability, and nonlinear parameter relationships. We use a simple kinetic mechanism to test multiple sets of constraints that implement linear parameter relationships and arbitrary model properties and behaviors, and we provide numerical examples. This work complements and extends the companion article, where we show how to enforce explicit linear parameter relationships. By incorporating more knowledge into the parameter estimation procedure, it is possible to obtain more realistic and robust models with greater predictive power.

The Drosophila Gr28bD product is a non-specific cation channel that can be used as a novel thermogenetic tool

Extrinsic control of single neurons and neuronal populations is a powerful approach for understanding how neural circuits function. Adding new thermogenetic tools to existing optogenetic and other forms of intervention will increase the complexity of questions that can be addressed. A good candidate for developing new thermogenetic tools is the Drosophila gustatory receptor family, which has been implicated in high-temperature avoidance behavior. We examined the five members of the Gr28b gene cluster for temperature-dependent properties via three approaches: biophysical characterization in Xenopus oocytes, functional calcium imaging in Drosophila motor neurons, and behavioral assays in adult Drosophila. Our results show that Gr28bD expression in Xenopus oocytes produces a non-specific cationic current that is activated by elevated temperatures. This current is non-inactivating and non-voltage dependent. When expressed in Drosophila motor neurons, Gr28bD can be used to change the firing pattern of individual cells in a temperature-dependent fashion. Finally, we show that pan-neuronal or motor neuron expression of Gr28bD can be used to alter fruit fly behavior with elevated temperatures. Together, these results validate the potential of the Gr28bD gene as a founding member of a new class of thermogenetic tools.

A biphasic locomotor response to acute unsignaled high temperature exposure in Drosophila

Unsignaled stress can have profound effects on animal behavior. While most investigation of stress-effects on behavior follows chronic exposures, less is understood about acute exposures and potential after-effects. We examined walking activity in Drosophila following acute exposure to high temperature or electric shock. Compared to initial walking activity, flies first increase walking with exposure to high temperatures then have a strong reduction in activity. These effects are related to the intensity of the high temperature and number of exposures. The reduction in walking activity following high temperature and electric shock exposures survives context changes and lasts at least five hours. Reduction in the function of the biogenic amines octopamine / tyramine and serotonin both strongly blunt the increase in locomotor activity with high temperature exposure. However, neither set of biogenic amines alter the long lasting depression in walking activity after exposure.

Modeling Ion Channel Kinetics with Parameter Constraints

Ion channel gating mechanisms can be complex and difficult to extract from experimental data. A solution is to apply parameter constraints, which reflect prior knowledge or tested hypotheses and reduce model complexity and speed up computation. Soft constraints balance the existing knowledge with the new experimental data and limit the parameter search engine to a smaller space of more acceptable values. In contrast, hard constraints enforce a mathematical relationship involving one or more parameters of the model. These constraints can be formulated as an invertible transformation between a set of model parameters and a set of “free” parameters. Each constraint reduces the number of free parameters by one. Linear constraints, such as microscopic reversibility or scaling between sequential transitions, can be conveniently obtained with the singular value decomposition. Here, we show how this method can be generalized to implement arbitrary linear constraints. We also show how to make these constraints depend on arbitrary model parameters. This can be applied, for example, to enforce allosteric constraints where the allosteric factor itself is a free parameter. Furthermore, we explore some useful ways for implementing soft constraints.