The control structure of the nematode Caenorhabditis elegans: Neuro-sensory integration and proprioceptive feedback

Alteration of mitochondrial homeostasis is an early event in a C. elegans model of human tauopathy

Tauopathies are a group of progressive neurodegenerative disorders characterized by the presence of insoluble intracellular tau filaments in the brain. Evidence suggests that there is a tight connection between mitochondrial dysfunction and tauopathies, including Alzheimer’s disease. However, whether mitochondrial dysfunction occurs prior to the detection of tau aggregates in tauopathies remains elusive. Here, we utilized transgenic nematodes expressing the full length of wild type tau in neuronal cells and monitored mitochondrial morphology alterations over time. Although tau-expressing nematodes did not accumulate detectable levels of tau aggregates during larval stages, they displayed increased mitochondrial damage and locomotion defects compared to the control worms. Chelating calcium restored mitochondrial activity and improved motility in the tau-expressing larvae suggesting a link between mitochondrial damage, calcium homeostasis and neuronal impairment in these animals. Our findings suggest that defective mitochondrial function is an early pathogenic event of tauopathies, taking place before tau aggregation and undermining neuronal homeostasis and organismal fitness. Understanding the molecular mechanisms causing mitochondrial dysfunction early in tauopathy will be of significant clinical and therapeutic value and merits further investigation.

Proprioception, the regulator of motor function

In animals, proper locomotion is crucial to find mates and foods and avoid predators or dangers. Multiple sensory systems detect external and internal cues and integrate them to modulate motor outputs. Proprioception is the internal sense of body position, and proprioceptive control of locomotion is essential to generate and maintain precise patterns of movement or gaits. This proprioceptive feedback system is conserved in many animal species and is mediated by stretch-sensitive receptors called proprioceptors. Recent studies have identified multiple proprioceptive neurons and proprioceptors and their roles in the locomotion of various model organisms. In this review we describe molecular and neuronal mechanisms underlying proprioceptive feedback systems in C. elegans, Drosophila, and mice.

Forward and backward locomotion patterns in C. elegans generated by a connectome-based model simulation

Caenorhabditis elegans (C. elegans) can produce various motion patterns despite having only 69 motor neurons and 95 muscle cells. Previous studies successfully elucidate the connectome and role of the respective motor neuron classes related to movement. However, these models have not analyzed the distribution of the synaptic and gap connection weights. In this study, we examined whether a motor neuron and muscle network can generate oscillations for both forward and backward movement and analyzed the distribution of the trained synaptic and gap connection weights through a machine learning approach. This paper presents a connectome-based neural network model consisting of motor neurons of classes A, B, D, AS, and muscle, considering both synaptic and gap connections. A supervised learning method called backpropagation through time was adapted to train the connection parameters by feeding teacher data composed of the command neuron input and muscle cell activation. Simulation results confirmed that the motor neuron circuit could generate oscillations with different phase patterns corresponding to forward and backward movement, and could be switched at arbitrary times according to the binary inputs simulating the output of command neurons. Subsequently, we confirmed that the trained synaptic and gap connection weights followed a Boltzmann-type distribution. It should be noted that the proposed model can be trained to reproduce the activity patterns measured for an animal (HRB4 strain). Therefore, the supervised learning approach adopted in this study may allow further analysis of complex activity patterns associated with movements.

A Neuromechanical Model of Multiple Network Rhythmic Pattern Generators for Forward Locomotion in C. elegans

Multiple mechanisms contribute to the generation, propagation, and coordination of the rhythmic patterns necessary for locomotion in Caenorhabditis elegans. Current experiments have focused on two possibilities: pacemaker neurons and stretch-receptor feedback. Here, we focus on whether it is possible that a chain of multiple network rhythmic pattern generators in the ventral nerve cord also contribute to locomotion. We use a simulation model to search for parameters of the anatomically constrained ventral nerve cord circuit that, when embodied and situated, can drive forward locomotion on agar, in the absence of pacemaker neurons or stretch-receptor feedback. Systematic exploration of the space of possible solutions reveals that there are multiple configurations that result in locomotion that is consistent with certain aspects of the kinematics of worm locomotion on agar. Analysis of the best solutions reveals that gap junctions between different classes of motorneurons in the ventral nerve cord can play key roles in coordinating the multiple rhythmic pattern generators.

Nonlinear Control in the Nematode C. elegans

Recent whole-brain calcium imaging recordings of the nematode C. elegans have demonstrated that the neural activity associated with behavior is dominated by dynamics on a low-dimensional manifold that can be clustered according to behavioral states. Previous models of C. elegans dynamics have either been linear models, which cannot support the existence of multiple fixed points in the system, or Markov-switching models, which do not describe how control signals in C. elegans neural dynamics can produce switches between stable states. It remains unclear how a network of neurons can produce fast and slow timescale dynamics that control transitions between stable states in a single model. We propose a global, nonlinear control model which is minimally parameterized and captures the state transitions described by Markov-switching models with a single dynamical system. The model is fit by reproducing the timeseries of the dominant PCA mode in the calcium imaging data. Long and short time-scale changes in transition statistics can be characterized via changes in a single parameter in the control model. Some of these macro-scale transitions have experimental correlates to single neuro-modulators that seem to act as biological controls, allowing this model to generate testable hypotheses about the effect of these neuro-modulators on the global dynamics. The theory provides an elegant characterization of control in the neuron population dynamics in C. elegans. Moreover, the mathematical structure of the nonlinear control framework provides a paradigm that can be generalized to more complex systems with an arbitrary number of behavioral states.

Unsupervised learning of control signals and their encodings in Caenorhabditis elegans whole-brain recordings

A major goal of computational neuroscience is to understand the relationship between synapse-level structure and network-level functionality. Caenorhabditis elegans is a model organism to probe this relationship due to the historic availability of the synaptic structure (connectome) and recent advances in whole brain calcium imaging techniques. Recent work has applied the concept of network controllability to neuronal networks, discovering some neurons that are able to drive the network to a certain state. However, previous work uses a linear model of the network dynamics, and it is unclear if the real neuronal network conforms to this assumption. Here, we propose a method to build a global, low-dimensional model of the dynamics, whereby an underlying global linear dynamical system is actuated by temporally sparse control signals. A key novelty of this method is discovering candidate control signals that the network uses to control itself. We analyse these control signals in two ways, showing they are interpretable and biologically plausible. First, these control signals are associated with transitions between behaviours, which were previously annotated via expert-generated features. Second, these signals can be predicted both from neurons previously implicated in behavioural transitions but also additional neurons previously unassociated with these behaviours. The proposed mathematical framework is generic and can be generalized to other neurosensory systems, potentially revealing transitions and their encodings in a completely unsupervised way.

Adapting techniques for calcium imaging in muscles of adult Brugia malayi

Brugia malayi is a human filarial nematode parasite that causes lymphatic filariasis or 'elephantiasis' a disfiguring neglected tropical disease. This parasite is a more tractable nematode parasite for the experimental study of anthelmintic drugs and has been studied with patch-clamp and RNAi techniques. Unlike in C. elegans however, calcium signaling in B. malayi or other nematode parasites has not been achieved, limiting the studies of the mode of action of anthelmintic drugs. We describe here the development of calcium imaging methods that allow us to characterize changes in cellular calcium in the muscles of B. malayi. This is a powerful technique that can help in elucidating the mode of action of selected anthelmintics. We developed two approaches that allow the recording of calcium signals in the muscles of adult B. malayi: (a) soaking the muscles with Fluo-3AM, promoting large-scale imaging of multiple cells simultaneously and, (b) direct insertion of Fluo-3 using microinjection, providing the possibility of performing dual calcium and electrophysiological recordings. Here, we describe the techniques used to optimize dye entry into the muscle cells and demonstrate that detectable increases in Fluo-3 fluorescence to elevated calcium concentrations can be achieved in B. malayi using both techniques.

Three-dimensional simulation of the Caenorhabditis elegans body and muscle cells in liquid and gel environments for behavioural analysis

To better understand how a nervous system controls the movements of an organism, we have created a three-dimensional computational biomechanical model of the Caenorhabditis elegans body based on real anatomical structure. The body model is created with a particle system-based simulation engine known as Sibernetic, which implements the smoothed particle-hydrodynamics algorithm. The model includes an elastic body-wall cuticle subject to hydrostatic pressure. This cuticle is then driven by body-wall muscle cells that contract and relax, whose positions and shape are mapped from C. elegans anatomy, and determined from light microscopy and electron micrograph data. We show that by using different muscle activation patterns, this model is capable of producing C. elegans-like behaviours, including crawling and swimming locomotion in environments with different viscosities, while fitting multiple additional known biomechanical properties of the animal. This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

Signatures of proprioceptive control in Caenorhabditis elegans locomotion

Animal neuromechanics describes the coordinated self-propelled movement of a body, subject to the combined effects of internal neural control and mechanical forces. Here we use a computational model to identify effects of neural and mechanical modulation on undulatory forward locomotion of Caenorhabditis elegans, with a focus on proprioceptively driven neural control. We reveal a fundamental relationship between body elasticity and environmental drag in determining the dynamics of the body and demonstrate the manifestation of this relationship in the context of proprioceptively driven control. By considering characteristics unique to proprioceptive neurons, we predict the signatures of internal gait modulation that contrast with the known signatures of externally or biomechanically modulated gait. We further show that proprioceptive feedback can suppress neuromechanical phase lags during undulatory locomotion, contrasting with well studied advancing phase lags that have long been a signature of centrally generated, feed-forward control.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.